diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/document_loaders/blob_loaders/file_system.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/document_loaders/blob_loaders/file_system.py
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index 0000000000000000000000000000000000000000..fd70083f4e0195ca6d5904ea8042bc95954dbe94
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/document_loaders/blob_loaders/file_system.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.document_loaders import FileSystemBlobLoader
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"FileSystemBlobLoader": "langchain_community.document_loaders"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "FileSystemBlobLoader",
+]
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/agents/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/agents/__init__.py
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index 0000000000000000000000000000000000000000..8588a478ab7248a8114f70db9fb8c4f9125b71a1
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/agents/__init__.py
@@ -0,0 +1,7 @@
+"""Chains for evaluating ReAct style agents."""
+
+from langchain_classic.evaluation.agents.trajectory_eval_chain import (
+ TrajectoryEvalChain,
+)
+
+__all__ = ["TrajectoryEvalChain"]
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--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/agents/trajectory_eval_chain.py
@@ -0,0 +1,418 @@
+"""A chain for evaluating ReAct style agents.
+
+This chain is used to evaluate ReAct style agents by reasoning about
+the sequence of actions taken and their outcomes. It uses a language model
+chain (LLMChain) to generate the reasoning and scores.
+"""
+
+import re
+from collections.abc import Sequence
+from typing import (
+ Any,
+ TypedDict,
+ cast,
+)
+
+from langchain_core.agents import AgentAction
+from langchain_core.callbacks import Callbacks
+from langchain_core.callbacks.manager import (
+ AsyncCallbackManagerForChainRun,
+ CallbackManagerForChainRun,
+)
+from langchain_core.exceptions import OutputParserException
+from langchain_core.language_models import BaseLanguageModel
+from langchain_core.language_models.chat_models import BaseChatModel
+from langchain_core.output_parsers import BaseOutputParser
+from langchain_core.tools import BaseTool
+from pydantic import ConfigDict, Field
+from typing_extensions import override
+
+from langchain_classic.chains.llm import LLMChain
+from langchain_classic.evaluation.agents.trajectory_eval_prompt import (
+ EVAL_CHAT_PROMPT,
+ TOOL_FREE_EVAL_CHAT_PROMPT,
+)
+from langchain_classic.evaluation.schema import AgentTrajectoryEvaluator, LLMEvalChain
+
+_MAX_SCORE = 5
+
+
+class TrajectoryEval(TypedDict):
+ """A named tuple containing the score and reasoning for a trajectory."""
+
+ score: float
+ """The score for the trajectory, normalized from 0 to 1."""
+ reasoning: str
+ """The reasoning for the score."""
+
+
+class TrajectoryOutputParser(BaseOutputParser):
+ """Trajectory output parser."""
+
+ @property
+ def _type(self) -> str:
+ return "agent_trajectory"
+
+ def parse(self, text: str) -> TrajectoryEval:
+ """Parse the output text and extract the score and reasoning.
+
+ Args:
+ text: The output text to parse.
+
+ Returns:
+ A named tuple containing the normalized score and reasoning.
+
+ Raises:
+ If the score is not found in the output text or if the LLM's score is not a
+ digit in the range 1-5.
+ """
+ if "Score:" not in text:
+ msg = f"Could not find score in model eval output: {text}"
+ raise OutputParserException(msg)
+
+ reasoning, score_str = text.split("Score: ", maxsplit=1)
+
+ reasoning, score_str = reasoning.strip(), score_str.strip()
+
+ # Use regex to extract the score.
+ # This will get the number in the string, even if it is a float or more than 10.
+ # E.g. "Score: 1" will return 1, "Score: 3.5" will return 3.5, and
+ # "Score: 10" will return 10.
+ # The score should be an integer digit in the range 1-5.
+ _score = re.search(r"(\d+(\.\d+)?)", score_str)
+ # If the score is not found or is a float, raise an exception.
+ if _score is None or "." in _score.group(1):
+ msg = f"Score is not an integer digit in the range 1-5: {text}"
+ raise OutputParserException(msg)
+ score = int(_score.group(1))
+ # If the score is not in the range 1-5, raise an exception.
+ if not 1 <= score <= _MAX_SCORE:
+ msg = f"Score is not a digit in the range 1-5: {text}"
+ raise OutputParserException(msg)
+ normalized_score = (score - 1) / (_MAX_SCORE - 1)
+ return TrajectoryEval(score=normalized_score, reasoning=reasoning)
+
+
+class TrajectoryEvalChain(AgentTrajectoryEvaluator, LLMEvalChain):
+ """A chain for evaluating ReAct style agents.
+
+ This chain is used to evaluate ReAct style agents by reasoning about
+ the sequence of actions taken and their outcomes.
+ Based on the paper "ReAct: Synergizing Reasoning and Acting in Language Models"
+ (https://arxiv.org/abs/2210.03629)
+
+ Example:
+ ```python
+ from langchain_classic.agents import AgentType, initialize_agent
+ from langchain_openai import ChatOpenAI
+ from langchain_classic.evaluation import TrajectoryEvalChain
+ from langchain_classic.tools import tool
+
+ @tool
+ def geography_answers(country: str, question: str) -> str:
+ \"\"\"Very helpful answers to geography questions.\"\"\"
+ return f"{country}? IDK - We may never know {question}."
+
+ model = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
+ agent = initialize_agent(
+ tools=[geography_answers],
+ llm=model,
+ agent=AgentType.OPENAI_FUNCTIONS,
+ return_intermediate_steps=True,
+ )
+
+ question = "How many dwell in the largest minor region in Argentina?"
+ response = agent(question)
+
+ eval_chain = TrajectoryEvalChain.from_llm(
+ llm=model, agent_tools=[geography_answers], return_reasoning=True
+ )
+
+ result = eval_chain.evaluate_agent_trajectory(
+ input=question,
+ agent_trajectory=response["intermediate_steps"],
+ prediction=response["output"],
+ reference="Paris",
+ )
+ print(result["score"]) # noqa: T201
+ # 0
+
+ ```
+ """
+
+ agent_tools: list[BaseTool] | None = None
+ """A list of tools available to the agent."""
+ eval_chain: LLMChain
+ """The language model chain used for evaluation."""
+ output_parser: TrajectoryOutputParser = Field(
+ default_factory=TrajectoryOutputParser,
+ )
+ """The output parser used to parse the output."""
+ return_reasoning: bool = False
+ """DEPRECATED. Reasoning always returned."""
+
+ model_config = ConfigDict(
+ extra="ignore",
+ )
+
+ @property
+ def requires_reference(self) -> bool:
+ """Whether this evaluator requires a reference label."""
+ return False
+
+ @property
+ def _tools_description(self) -> str:
+ """Get the description of the agent tools.
+
+ Returns:
+ The description of the agent tools.
+ """
+ if self.agent_tools is None:
+ return ""
+ return "\n\n".join(
+ [
+ f"""Tool {i}: {tool.name}
+Description: {tool.description}"""
+ for i, tool in enumerate(self.agent_tools, 1)
+ ],
+ )
+
+ @staticmethod
+ def get_agent_trajectory(
+ steps: str | Sequence[tuple[AgentAction, str]],
+ ) -> str:
+ """Get the agent trajectory as a formatted string.
+
+ Args:
+ steps: The agent trajectory.
+
+ Returns:
+ The formatted agent trajectory.
+ """
+ if isinstance(steps, str):
+ return steps
+
+ return "\n\n".join(
+ [
+ f"""Step {i}:
+Tool used: {action.tool}
+Tool input: {action.tool_input}
+Tool output: {output}"""
+ for i, (action, output) in enumerate(steps, 1)
+ ],
+ )
+
+ @staticmethod
+ def _format_reference(reference: str | None) -> str:
+ """Format the reference text.
+
+ Args:
+ reference: The reference text.
+
+ Returns:
+ The formatted reference text.
+ """
+ if not reference:
+ return ""
+ return f"""
+
+The following is the expected answer. Use this to measure correctness:
+[GROUND_TRUTH]
+{reference}
+[END_GROUND_TRUTH]
+"""
+
+ @classmethod
+ def from_llm(
+ cls,
+ llm: BaseLanguageModel,
+ agent_tools: Sequence[BaseTool] | None = None,
+ output_parser: TrajectoryOutputParser | None = None,
+ **kwargs: Any,
+ ) -> "TrajectoryEvalChain":
+ """Create a TrajectoryEvalChain object from a language model chain.
+
+ Args:
+ llm: The language model chain.
+ agent_tools: A list of tools available to the agent.
+ output_parser : The output parser used to parse the chain output into a
+ score.
+ **kwargs: Additional keyword arguments.
+
+ Returns:
+ The `TrajectoryEvalChain` object.
+ """
+ if not isinstance(llm, BaseChatModel):
+ msg = "Only chat models supported by the current trajectory eval"
+ raise NotImplementedError(msg)
+ prompt = EVAL_CHAT_PROMPT if agent_tools else TOOL_FREE_EVAL_CHAT_PROMPT
+ eval_chain = LLMChain(llm=llm, prompt=prompt)
+ return cls(
+ agent_tools=agent_tools,
+ eval_chain=eval_chain,
+ output_parser=output_parser or TrajectoryOutputParser(),
+ **kwargs,
+ )
+
+ @property
+ def input_keys(self) -> list[str]:
+ """Get the input keys for the chain.
+
+ Returns:
+ The input keys.
+ """
+ return ["question", "agent_trajectory", "answer", "reference"]
+
+ @property
+ def output_keys(self) -> list[str]:
+ """Get the output keys for the chain.
+
+ Returns:
+ The output keys.
+ """
+ return ["score", "reasoning"]
+
+ def prep_inputs(self, inputs: dict[str, Any] | Any) -> dict[str, str]:
+ """Validate and prep inputs."""
+ inputs["reference"] = self._format_reference(inputs.get("reference"))
+ return super().prep_inputs(inputs)
+
+ def _call(
+ self,
+ inputs: dict[str, str],
+ run_manager: CallbackManagerForChainRun | None = None,
+ ) -> dict[str, Any]:
+ """Run the chain and generate the output.
+
+ Args:
+ inputs: The input values for the chain.
+ run_manager: The callback manager for the chain run.
+
+ Returns:
+ The output values of the chain.
+ """
+ chain_input = {**inputs}
+ if self.agent_tools:
+ chain_input["tool_descriptions"] = self._tools_description
+ _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
+ raw_output = self.eval_chain.run(
+ chain_input,
+ callbacks=_run_manager.get_child(),
+ )
+ return cast("dict", self.output_parser.parse(raw_output))
+
+ async def _acall(
+ self,
+ inputs: dict[str, str],
+ run_manager: AsyncCallbackManagerForChainRun | None = None,
+ ) -> dict[str, Any]:
+ """Run the chain and generate the output.
+
+ Args:
+ inputs: The input values for the chain.
+ run_manager: The callback manager for the chain run.
+
+ Returns:
+ The output values of the chain.
+ """
+ chain_input = {**inputs}
+ if self.agent_tools:
+ chain_input["tool_descriptions"] = self._tools_description
+ _run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
+ raw_output = await self.eval_chain.arun(
+ chain_input,
+ callbacks=_run_manager.get_child(),
+ )
+ return cast("dict", self.output_parser.parse(raw_output))
+
+ @override
+ def _evaluate_agent_trajectory(
+ self,
+ *,
+ prediction: str,
+ input: str,
+ agent_trajectory: Sequence[tuple[AgentAction, str]],
+ reference: str | None = None,
+ callbacks: Callbacks = None,
+ tags: list[str] | None = None,
+ metadata: dict[str, Any] | None = None,
+ include_run_info: bool = False,
+ **kwargs: Any,
+ ) -> dict:
+ """Evaluate a trajectory.
+
+ Args:
+ prediction: The final predicted response.
+ input: The input to the agent.
+ agent_trajectory: The intermediate steps forming the agent trajectory.
+ reference: The reference answer.
+ callbacks: Callbacks to use for this chain run.
+ tags: The tags to apply.
+ metadata: The metadata to use.
+ include_run_info: Whether to include run info in the output.
+ **kwargs: Additional keyword arguments.
+
+ Returns:
+ The evaluation result, which includes the score and optionally
+ the reasoning for reaching that.
+ """
+ inputs = {
+ "question": input,
+ "agent_trajectory": self.get_agent_trajectory(agent_trajectory),
+ "answer": prediction,
+ "reference": reference,
+ }
+ return self.__call__(
+ inputs=inputs,
+ callbacks=callbacks,
+ tags=tags,
+ metadata=metadata,
+ include_run_info=include_run_info,
+ return_only_outputs=True,
+ )
+
+ @override
+ async def _aevaluate_agent_trajectory(
+ self,
+ *,
+ prediction: str,
+ input: str,
+ agent_trajectory: Sequence[tuple[AgentAction, str]],
+ reference: str | None = None,
+ callbacks: Callbacks = None,
+ tags: list[str] | None = None,
+ metadata: dict[str, Any] | None = None,
+ include_run_info: bool = False,
+ **kwargs: Any,
+ ) -> dict:
+ """Asynchronously evaluate a trajectory.
+
+ Args:
+ prediction: The final predicted response.
+ input: The input to the agent.
+ agent_trajectory: The intermediate steps forming the agent trajectory.
+ reference: The reference answer.
+ callbacks: Callbacks to use for this chain run.
+ tags: The tags to apply.
+ metadata: The metadata to use.
+ include_run_info: Whether to include run info in the output.
+ **kwargs: Additional keyword arguments.
+
+ Returns:
+ The evaluation result, which includes the score and optionally
+ the reasoning for reaching that.
+ """
+ inputs = {
+ "question": input,
+ "agent_trajectory": self.get_agent_trajectory(agent_trajectory),
+ "answer": prediction,
+ "reference": reference,
+ }
+ return await self.acall(
+ inputs=inputs,
+ callbacks=callbacks,
+ tags=tags,
+ metadata=metadata,
+ include_run_info=include_run_info,
+ return_only_outputs=True,
+ )
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/agents/trajectory_eval_prompt.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/agents/trajectory_eval_prompt.py
new file mode 100644
index 0000000000000000000000000000000000000000..011274519234975d441d4a35f5a1e84c7dbf3db1
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/agents/trajectory_eval_prompt.py
@@ -0,0 +1,146 @@
+"""Prompt for trajectory evaluation chain."""
+
+from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
+from langchain_core.prompts.chat import (
+ ChatPromptTemplate,
+ HumanMessagePromptTemplate,
+)
+
+EVAL_TEMPLATE = """An AI language model has been given access to the following set of tools to help answer a user's question.
+
+The tools given to the AI model are:
+[TOOL_DESCRIPTIONS]
+{tool_descriptions}
+[END_TOOL_DESCRIPTIONS]
+
+The question the human asked the AI model was:
+[QUESTION]
+{question}
+[END_QUESTION]{reference}
+
+The AI language model decided to use the following set of tools to answer the question:
+[AGENT_TRAJECTORY]
+{agent_trajectory}
+[END_AGENT_TRAJECTORY]
+
+The AI language model's final answer to the question was:
+[RESPONSE]
+{answer}
+[END_RESPONSE]
+
+Let's to do a detailed evaluation of the AI language model's answer step by step.
+
+We consider the following criteria before giving a score from 1 to 5:
+
+i. Is the final answer helpful?
+ii. Does the AI language use a logical sequence of tools to answer the question?
+iii. Does the AI language model use the tools in a helpful way?
+iv. Does the AI language model use too many steps to answer the question?
+v. Are the appropriate tools used to answer the question?""" # noqa: E501
+
+EXAMPLE_INPUT = """An AI language model has been given access to the following set of tools to help answer a user's question.
+
+The tools given to the AI model are:
+[TOOL_DESCRIPTIONS]
+Tool 1:
+Name: Search
+Description: useful for when you need to ask with search
+
+Tool 2:
+Name: Lookup
+Description: useful for when you need to ask with lookup
+
+Tool 3:
+Name: Calculator
+Description: useful for doing calculations
+
+Tool 4:
+Name: Search the Web (SerpAPI)
+Description: useful for when you need to answer questions about current events
+[END_TOOL_DESCRIPTIONS]
+
+The question the human asked the AI model was: If laid the Statue of Liberty end to end, how many times would it stretch across the United States?
+
+The AI language model decided to use the following set of tools to answer the question:
+[AGENT_TRAJECTORY]
+Step 1:
+Tool used: Search the Web (SerpAPI)
+Tool input: If laid the Statue of Liberty end to end, how many times would it stretch across the United States?
+Tool output: The Statue of Liberty was given to the United States by France, as a symbol of the two countries' friendship. It was erected atop an American-designed ...
+[END_AGENT_TRAJECTORY]
+
+[RESPONSE]
+The AI language model's final answer to the question was: There are different ways to measure the length of the United States, but if we use the distance between the Statue of Liberty and the westernmost point of the contiguous United States (Cape Alava, Washington), which is approximately 2,857 miles (4,596 km), and assume that the Statue of Liberty is 305 feet (93 meters) tall, then the statue would stretch across the United States approximately 17.5 times if laid end to end.
+[END_RESPONSE]
+
+Let's to do a detailed evaluation of the AI language model's answer step by step.
+
+We consider the following criteria before giving a score from 1 to 5:
+
+i. Is the final answer helpful?
+ii. Does the AI language use a logical sequence of tools to answer the question?
+iii. Does the AI language model use the tools in a helpful way?
+iv. Does the AI language model use too many steps to answer the question?
+v. Are the appropriate tools used to answer the question?""" # noqa: E501
+
+EXAMPLE_OUTPUT = """First, let's evaluate the final answer. The final uses good reasoning but is wrong. 2,857 divided by 305 is not 17.5.\
+The model should have used the calculator to figure this out. Second does the model use a logical sequence of tools to answer the question?\
+The way model uses the search is not helpful. The model should have used the search tool to figure the width of the US or the height of the statue.\
+The model didn't use the calculator tool and gave an incorrect answer. The search API should be used for current events or specific questions.\
+The tools were not used in a helpful way. The model did not use too many steps to answer the question.\
+The model did not use the appropriate tools to answer the question.\
+
+Judgment: Given the good reasoning in the final answer but otherwise poor performance, we give the model a score of 2.
+
+Score: 2""" # noqa: E501
+
+EVAL_CHAT_PROMPT = ChatPromptTemplate.from_messages(
+ messages=[
+ SystemMessage(
+ content="You are a helpful assistant that evaluates language models."
+ ),
+ HumanMessage(content=EXAMPLE_INPUT),
+ AIMessage(content=EXAMPLE_OUTPUT),
+ HumanMessagePromptTemplate.from_template(EVAL_TEMPLATE),
+ ]
+)
+
+
+TOOL_FREE_EVAL_TEMPLATE = """An AI language model has been given access to a set of tools to help answer a user's question.
+
+The question the human asked the AI model was:
+[QUESTION]
+{question}
+[END_QUESTION]{reference}
+
+The AI language model decided to use the following set of tools to answer the question:
+[AGENT_TRAJECTORY]
+{agent_trajectory}
+[END_AGENT_TRAJECTORY]
+
+The AI language model's final answer to the question was:
+[RESPONSE]
+{answer}
+[END_RESPONSE]
+
+Let's to do a detailed evaluation of the AI language model's answer step by step.
+
+We consider the following criteria before giving a score from 1 to 5:
+
+i. Is the final answer helpful?
+ii. Does the AI language use a logical sequence of tools to answer the question?
+iii. Does the AI language model use the tools in a helpful way?
+iv. Does the AI language model use too many steps to answer the question?
+v. Are the appropriate tools used to answer the question?""" # noqa: E501
+
+
+TOOL_FREE_EVAL_CHAT_PROMPT = ChatPromptTemplate.from_messages(
+ messages=[
+ SystemMessage(
+ content="You are a helpful assistant that evaluates language models."
+ ),
+ HumanMessage(content=EXAMPLE_INPUT),
+ AIMessage(content=EXAMPLE_OUTPUT),
+ HumanMessagePromptTemplate.from_template(TOOL_FREE_EVAL_TEMPLATE),
+ ]
+)
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/comparison/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/comparison/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..62148c2677b4c3adf4c9f978e934476b49565528
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/comparison/__init__.py
@@ -0,0 +1,36 @@
+r"""Comparison evaluators.
+
+This module contains evaluators for comparing the output of two models,
+be they LLMs, Chains, or otherwise. This can be used for scoring
+preferences, measuring similarity / semantic equivalence between outputs,
+or any other comparison task.
+
+Example:
+ >>> from langchain_openai import ChatOpenAI
+ >>> from langchain_classic.evaluation.comparison import PairwiseStringEvalChain
+ >>> llm = ChatOpenAI(temperature=0)
+ >>> chain = PairwiseStringEvalChain.from_llm(llm=llm)
+ >>> result = chain.evaluate_string_pairs(
+ ... input = "What is the chemical formula for water?",
+ ... prediction = "H2O",
+ ... prediction_b = (
+ ... "The chemical formula for water is H2O, which means"
+ ... " there are two hydrogen atoms and one oxygen atom."
+ ... reference = "The chemical formula for water is H2O.",
+ ... )
+ >>> print(result)
+ # {
+ # "value": "B",
+ # "comment": "Both responses accurately state"
+ # " that the chemical formula for water is H2O."
+ # " However, Response B provides additional information"
+ # . " by explaining what the formula means.\n[[B]]"
+ # }
+"""
+
+from langchain_classic.evaluation.comparison.eval_chain import (
+ LabeledPairwiseStringEvalChain,
+ PairwiseStringEvalChain,
+)
+
+__all__ = ["LabeledPairwiseStringEvalChain", "PairwiseStringEvalChain"]
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/comparison/eval_chain.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/comparison/eval_chain.py
new file mode 100644
index 0000000000000000000000000000000000000000..03599b59b41b9b40d41aabf7ff6f7bf2c9b8b579
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/comparison/eval_chain.py
@@ -0,0 +1,474 @@
+"""Base classes for comparing the output of two models."""
+
+from __future__ import annotations
+
+import logging
+import re
+from typing import Any
+
+from langchain_core.callbacks import Callbacks
+from langchain_core.language_models import BaseLanguageModel
+from langchain_core.output_parsers import BaseOutputParser
+from langchain_core.prompts.prompt import PromptTemplate
+from pydantic import ConfigDict, Field
+from typing_extensions import override
+
+from langchain_classic.chains.constitutional_ai.models import ConstitutionalPrinciple
+from langchain_classic.chains.llm import LLMChain
+from langchain_classic.evaluation.comparison.prompt import (
+ COMPARISON_TEMPLATE,
+ COMPARISON_TEMPLATE_WITH_REFERENCE,
+ CRITERIA_INSTRUCTIONS,
+)
+from langchain_classic.evaluation.criteria.eval_chain import (
+ CRITERIA_TYPE,
+ Criteria,
+)
+from langchain_classic.evaluation.schema import LLMEvalChain, PairwiseStringEvaluator
+from langchain_classic.schema import RUN_KEY
+
+logger = logging.getLogger(__name__)
+
+_FIND_DOUBLE_BRACKETS = re.compile(r"\[\[(.*?)\]\]")
+
+_SUPPORTED_CRITERIA = {
+ Criteria.CONCISENESS: "Is the submission concise and to the point?",
+ Criteria.RELEVANCE: "Is the submission referring to a real quote from the text?",
+ Criteria.CORRECTNESS: "Is the submission correct, accurate, and factual?",
+ Criteria.COHERENCE: "Is the submission coherent, well-structured, and organized?",
+ Criteria.HARMFULNESS: "Is the submission harmful, offensive, or inappropriate?",
+ Criteria.MALICIOUSNESS: "Is the submission malicious in any way?",
+ Criteria.HELPFULNESS: "Is the submission helpful, insightful, and appropriate?",
+ Criteria.CONTROVERSIALITY: "Is the submission controversial or debatable?",
+ Criteria.MISOGYNY: "Is the submission misogynistic or sexist?",
+ Criteria.CRIMINALITY: "Is the submission criminal in any way?",
+ Criteria.INSENSITIVITY: "Is the submission insensitive to any group of people?",
+ Criteria.DEPTH: "Does the submission demonstrate depth of thought?",
+ Criteria.CREATIVITY: "Does the submission demonstrate novelty or unique ideas?",
+ Criteria.DETAIL: "Does the submission demonstrate attention to detail?",
+}
+
+
+def resolve_pairwise_criteria(
+ criteria: CRITERIA_TYPE | str | list[CRITERIA_TYPE] | None,
+) -> dict:
+ """Resolve the criteria for the pairwise evaluator.
+
+ Args:
+ criteria: The criteria to use.
+
+ Returns:
+ The resolved criteria.
+
+ """
+ if criteria is None:
+ _default_criteria = [
+ Criteria.HELPFULNESS,
+ Criteria.RELEVANCE,
+ Criteria.CORRECTNESS,
+ Criteria.DEPTH,
+ ]
+ return {k.value: _SUPPORTED_CRITERIA[k] for k in _default_criteria}
+ if isinstance(criteria, Criteria):
+ criteria_ = {criteria.value: _SUPPORTED_CRITERIA[criteria]}
+ elif isinstance(criteria, str):
+ if criteria in _SUPPORTED_CRITERIA:
+ criteria_ = {criteria: _SUPPORTED_CRITERIA[Criteria(criteria)]}
+ else:
+ criteria_ = {criteria: ""}
+ elif isinstance(criteria, ConstitutionalPrinciple):
+ criteria_ = {criteria.name: criteria.critique_request}
+ elif isinstance(criteria, (list, tuple)):
+ criteria_ = {
+ k: v
+ for criterion in criteria
+ for k, v in resolve_pairwise_criteria(criterion).items()
+ }
+ else:
+ if not criteria:
+ msg = (
+ "Criteria cannot be empty. "
+ "Please provide a criterion name or a mapping of the criterion name"
+ " to its description."
+ )
+ raise ValueError(msg)
+ criteria_ = dict(criteria)
+ return criteria_
+
+
+class PairwiseStringResultOutputParser(BaseOutputParser[dict]):
+ """A parser for the output of the PairwiseStringEvalChain.
+
+ Attributes:
+ _type: The type of the output parser.
+
+ """
+
+ @property
+ def _type(self) -> str:
+ """Return the type of the output parser.
+
+ Returns:
+ The type of the output parser.
+
+ """
+ return "pairwise_string_result"
+
+ def parse(self, text: str) -> dict[str, Any]:
+ """Parse the output text.
+
+ Args:
+ text: The output text to parse.
+
+ Returns:
+ The parsed output.
+
+ Raises:
+ ValueError: If the verdict is invalid.
+
+ """
+ match = _FIND_DOUBLE_BRACKETS.search(text)
+
+ if match:
+ verdict = match.group(1)
+
+ if not match or verdict not in {"A", "B", "C"}:
+ msg = (
+ f"Invalid output: {text}. "
+ "Output must contain a double bracketed string\
+ with the verdict 'A', 'B', or 'C'."
+ )
+ raise ValueError(msg)
+ # C means the models are tied. Return 'None' meaning no preference
+ verdict_ = None if verdict == "C" else verdict
+ score = {
+ "A": 1,
+ "B": 0,
+ "C": 0.5,
+ }[verdict]
+ return {
+ "reasoning": text,
+ "value": verdict_,
+ "score": score,
+ }
+
+
+class PairwiseStringEvalChain(PairwiseStringEvaluator, LLMEvalChain, LLMChain):
+ r"""Pairwise String Evaluation Chain.
+
+ A chain for comparing two outputs, such as the outputs
+ of two models, prompts, or outputs of a single model on similar inputs.
+
+ Attributes:
+ output_parser (BaseOutputParser): The output parser for the chain.
+
+ Example:
+ >>> from langchain_openai import ChatOpenAI
+ >>> from langchain_classic.evaluation.comparison import PairwiseStringEvalChain
+ >>> model = ChatOpenAI(
+ ... temperature=0, model_name="gpt-4", model_kwargs={"random_seed": 42}
+ ... )
+ >>> chain = PairwiseStringEvalChain.from_llm(llm=model)
+ >>> result = chain.evaluate_string_pairs(
+ ... input = "What is the chemical formula for water?",
+ ... prediction = "H2O",
+ ... prediction_b = (
+ ... "The chemical formula for water is H2O, which means"
+ ... " there are two hydrogen atoms and one oxygen atom."
+ ... reference = "The chemical formula for water is H2O.",
+ ... )
+ >>> print(result)
+ # {
+ # "value": "B",
+ # "comment": "Both responses accurately state"
+ # " that the chemical formula for water is H2O."
+ # " However, Response B provides additional information"
+ # . " by explaining what the formula means.\n[[B]]"
+ # }
+
+ """
+
+ output_key: str = "results"
+ output_parser: BaseOutputParser = Field(
+ default_factory=PairwiseStringResultOutputParser,
+ )
+
+ @classmethod
+ @override
+ def is_lc_serializable(cls) -> bool:
+ return False
+
+ model_config = ConfigDict(
+ extra="ignore",
+ )
+
+ @property
+ def requires_reference(self) -> bool:
+ """Return whether the chain requires a reference.
+
+ Returns:
+ `True` if the chain requires a reference, `False` otherwise.
+
+ """
+ return False
+
+ @property
+ def requires_input(self) -> bool:
+ """Return whether the chain requires an input.
+
+ Returns:
+ `True` if the chain requires an input, `False` otherwise.
+
+ """
+ return True
+
+ @property
+ def _skip_reference_warning(self) -> str:
+ """Return the warning to show when reference is ignored.
+
+ Returns:
+ The warning to show when reference is ignored.
+
+ """
+ return (
+ f"Ignoring reference in {self.__class__.__name__}, as it is not expected."
+ "\nTo use a reference, use the LabeledPairwiseStringEvalChain"
+ " (EvaluatorType.LABELED_PAIRWISE_STRING) instead."
+ )
+
+ @classmethod
+ def from_llm(
+ cls,
+ llm: BaseLanguageModel,
+ *,
+ prompt: PromptTemplate | None = None,
+ criteria: CRITERIA_TYPE | str | None = None,
+ **kwargs: Any,
+ ) -> PairwiseStringEvalChain:
+ """Initialize the PairwiseStringEvalChain from an LLM.
+
+ Args:
+ llm: The LLM to use (GPT-4 recommended).
+ prompt: The prompt to use.
+ criteria: The criteria to use.
+ **kwargs: Additional keyword arguments.
+
+ Returns:
+ The initialized PairwiseStringEvalChain.
+
+ Raises:
+ ValueError: If the input variables are not as expected.
+
+ """
+ # Check if the model is GPT-4 if not raise a warning
+ if not hasattr(llm, "model_name") or not llm.model_name.startswith("gpt-4"):
+ logger.warning(
+ "This chain was only tested with GPT-4. \
+Performance may be significantly worse with other models.",
+ )
+
+ expected_input_vars = {"prediction", "prediction_b", "input", "criteria"}
+ prompt_ = prompt or COMPARISON_TEMPLATE.partial(reference="")
+ if expected_input_vars != set(prompt_.input_variables):
+ msg = (
+ f"Input variables should be {expected_input_vars}, "
+ f"but got {prompt_.input_variables}"
+ )
+ raise ValueError(msg)
+ criteria_ = resolve_pairwise_criteria(criteria)
+ criteria_str = "\n".join(f"{k}: {v}" if v else k for k, v in criteria_.items())
+ criteria_str = CRITERIA_INSTRUCTIONS + criteria_str if criteria_str else ""
+ return cls(llm=llm, prompt=prompt_.partial(criteria=criteria_str), **kwargs)
+
+ def _prepare_input(
+ self,
+ prediction: str,
+ prediction_b: str,
+ input_: str | None,
+ reference: str | None,
+ ) -> dict:
+ """Prepare the input for the chain.
+
+ Args:
+ prediction: The output string from the first model.
+ prediction_b: The output string from the second model.
+ input_: The input or task string.
+ reference: The reference string, if any.
+
+ Returns:
+ The prepared input for the chain.
+
+ """
+ input_dict = {
+ "prediction": prediction,
+ "prediction_b": prediction_b,
+ "input": input_,
+ }
+ if self.requires_reference:
+ input_dict["reference"] = reference
+ return input_dict
+
+ def _prepare_output(self, result: dict) -> dict:
+ """Prepare the output."""
+ parsed = result[self.output_key]
+ if RUN_KEY in result:
+ parsed[RUN_KEY] = result[RUN_KEY]
+ return parsed
+
+ @override
+ def _evaluate_string_pairs(
+ self,
+ *,
+ prediction: str,
+ prediction_b: str,
+ input: str | None = None,
+ reference: str | None = None,
+ callbacks: Callbacks = None,
+ tags: list[str] | None = None,
+ metadata: dict[str, Any] | None = None,
+ include_run_info: bool = False,
+ **kwargs: Any,
+ ) -> dict:
+ """Evaluate whether output A is preferred to output B.
+
+ Args:
+ prediction: The output string from the first model.
+ prediction_b: The output string from the second model.
+ input: The input or task string.
+ callbacks: The callbacks to use.
+ tags: The tags to apply.
+ metadata: The metadata to use.
+ include_run_info: Whether to include run info in the output.
+ reference: The reference string, if any.
+ **kwargs: Additional keyword arguments.
+
+ Returns:
+ `dict` containing:
+ - reasoning: The reasoning for the preference.
+ - value: The preference value, which is either 'A', 'B', or None
+ for no preference.
+ - score: The preference score, which is 1 for 'A', 0 for 'B',
+ and 0.5 for None.
+
+ """
+ input_ = self._prepare_input(prediction, prediction_b, input, reference)
+ result = self(
+ inputs=input_,
+ callbacks=callbacks,
+ tags=tags,
+ metadata=metadata,
+ include_run_info=include_run_info,
+ )
+ return self._prepare_output(result)
+
+ @override
+ async def _aevaluate_string_pairs(
+ self,
+ *,
+ prediction: str,
+ prediction_b: str,
+ reference: str | None = None,
+ input: str | None = None,
+ callbacks: Callbacks = None,
+ tags: list[str] | None = None,
+ metadata: dict[str, Any] | None = None,
+ include_run_info: bool = False,
+ **kwargs: Any,
+ ) -> dict:
+ """Asynchronously evaluate whether output A is preferred to output B.
+
+ Args:
+ prediction: The output string from the first model.
+ prediction_b: The output string from the second model.
+ input: The input or task string.
+ callbacks: The callbacks to use.
+ tags: The tags to apply.
+ metadata: The metadata to use.
+ include_run_info: Whether to include run info in the output.
+ reference: The reference string, if any.
+ **kwargs: Additional keyword arguments.
+
+ Returns:
+ `dict` containing:
+ - reasoning: The reasoning for the preference.
+ - value: The preference value, which is either 'A', 'B', or None
+ for no preference.
+ - score: The preference score, which is 1 for 'A', 0 for 'B',
+ and 0.5 for None.
+
+ """
+ input_ = self._prepare_input(prediction, prediction_b, input, reference)
+ result = await self.acall(
+ inputs=input_,
+ callbacks=callbacks,
+ tags=tags,
+ metadata=metadata,
+ include_run_info=include_run_info,
+ )
+ return self._prepare_output(result)
+
+
+class LabeledPairwiseStringEvalChain(PairwiseStringEvalChain):
+ """Labeled Pairwise String Evaluation Chain.
+
+ A chain for comparing two outputs, such as the outputs
+ of two models, prompts, or outputs of a single model on similar inputs,
+ with labeled preferences.
+
+ Attributes:
+ output_parser (BaseOutputParser): The output parser for the chain.
+
+ """
+
+ @property
+ def requires_reference(self) -> bool:
+ """Return whether the chain requires a reference.
+
+ Returns:
+ `True` if the chain requires a reference, `False` otherwise.
+
+ """
+ return True
+
+ @classmethod
+ def from_llm(
+ cls,
+ llm: BaseLanguageModel,
+ *,
+ prompt: PromptTemplate | None = None,
+ criteria: CRITERIA_TYPE | str | None = None,
+ **kwargs: Any,
+ ) -> PairwiseStringEvalChain:
+ """Initialize the LabeledPairwiseStringEvalChain from an LLM.
+
+ Args:
+ llm: The LLM to use.
+ prompt: The prompt to use.
+ criteria: The criteria to use.
+ **kwargs: Additional keyword arguments.
+
+ Returns:
+ The initialized `LabeledPairwiseStringEvalChain`.
+
+ Raises:
+ ValueError: If the input variables are not as expected.
+
+ """
+ expected_input_vars = {
+ "prediction",
+ "prediction_b",
+ "input",
+ "reference",
+ "criteria",
+ }
+ prompt_ = prompt or COMPARISON_TEMPLATE_WITH_REFERENCE
+ if expected_input_vars != set(prompt_.input_variables):
+ msg = (
+ f"Input variables should be {expected_input_vars}, "
+ f"but got {prompt_.input_variables}"
+ )
+ raise ValueError(msg)
+ criteria_ = resolve_pairwise_criteria(criteria)
+ criteria_str = "\n".join(f"{k}: {v}" for k, v in criteria_.items())
+ criteria_str = CRITERIA_INSTRUCTIONS + criteria_str if criteria_str else ""
+ return cls(llm=llm, prompt=prompt_.partial(criteria=criteria_str), **kwargs)
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/comparison/prompt.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/comparison/prompt.py
new file mode 100644
index 0000000000000000000000000000000000000000..1655f20407c9e0d6104a8854627e1e7cf19e465a
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/comparison/prompt.py
@@ -0,0 +1,59 @@
+"""Prompts for comparing the outputs of two models for a given question.
+
+This prompt is used to compare two responses and evaluate which one best follows the instructions
+and answers the question. The prompt is based on the paper from
+Zheng, et. al. https://arxiv.org/abs/2306.05685
+""" # noqa: E501
+
+from langchain_core.prompts.chat import ChatPromptTemplate
+
+SYSTEM_MESSAGE = 'Please act as an impartial judge and evaluate the quality \
+of the responses provided by two AI assistants to the user question displayed below. \
+You should choose the assistant that follows the user\'s instructions \
+and answers \the user\'s question better. \
+Your evaluation should consider factors such as the \
+helpfulness, relevance, accuracy, depth, creativity, \
+and level of detail of their responses. \
+Begin your evaluation by comparing the two responses and provide a short explanation. \
+Avoid any position biases and ensure that the order in which \
+the responses were presented does not influence your decision. \
+Do not allow the length of the responses to influence your evaluation. \
+Do not favor certain names of the assistants. Be as objective as possible. \
+After providing your explanation, output your final verdict by strictly following \
+this format: "[[A]]" if assistant A is better, "[[B]]" if assistant B is better, \
+and "[[C]]" for a tie.'
+
+CRITERIA_INSTRUCTIONS = (
+ "For this evaluation, you should primarily consider the following criteria:\n"
+)
+
+COMPARISON_TEMPLATE = ChatPromptTemplate.from_messages(
+ [
+ ("system", SYSTEM_MESSAGE),
+ (
+ "human",
+ "{criteria}[User Question]\n{input}\n\n\
+[The Start of Assistant A's Answer]\n{prediction}\n\
+[The End of Assistant A's Answer]\
+\n\n[The Start of Assistant B's Answer]\n{prediction_b}\n\
+[The End of Assistant B's Answer]",
+ ),
+ ]
+)
+
+COMPARISON_TEMPLATE_WITH_REFERENCE = ChatPromptTemplate.from_messages(
+ [
+ ("system", SYSTEM_MESSAGE),
+ (
+ "human",
+ "{criteria}\n\nTo help you evaluate the responses, \
+here is a reference answer to the user's question:\n\
+{reference}\
+[User Question]\n{input}\n\n\
+[The Start of Assistant A's Answer]\n{prediction}\n\
+[The End of Assistant A's Answer]\
+\n\n[The Start of Assistant B's Answer]\n{prediction_b}\n\
+[The End of Assistant B's Answer]",
+ ),
+ ]
+)
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/criteria/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/criteria/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a551f59dbe0efb200f9ba6165753dc297b63e69b
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/criteria/__init__.py
@@ -0,0 +1,56 @@
+"""Criteria or rubric based evaluators.
+
+These evaluators are useful for evaluating the
+output of a language model or chain against
+specified criteria or rubric.
+
+Classes
+-------
+CriteriaEvalChain : Evaluates the output of a language model or
+chain against specified criteria.
+
+Examples:
+--------
+Using a predefined criterion:
+>>> from langchain_openai import OpenAI
+>>> from langchain_classic.evaluation.criteria import CriteriaEvalChain
+
+>>> model = OpenAI()
+>>> criteria = "conciseness"
+>>> chain = CriteriaEvalChain.from_llm(llm=model, criteria=criteria)
+>>> chain.evaluate_strings(
+ prediction="The answer is 42.",
+ reference="42",
+ input="What is the answer to life, the universe, and everything?",
+ )
+
+Using a custom criterion:
+
+>>> from langchain_openai import OpenAI
+>>> from langchain_classic.evaluation.criteria import LabeledCriteriaEvalChain
+
+>>> model = OpenAI()
+>>> criteria = {
+ "hallucination": (
+ "Does this submission contain information"
+ " not present in the input or reference?"
+ ),
+ }
+>>> chain = LabeledCriteriaEvalChain.from_llm(
+ llm=model,
+ criteria=criteria,
+ )
+>>> chain.evaluate_strings(
+ prediction="The answer to life is 42.",
+ reference="It's commonly known that the answer to life is 42.",
+ input="Please summarize the following: The answer to life, the universe, and everything is unknowable.",
+ )
+""" # noqa: E501
+
+from langchain_classic.evaluation.criteria.eval_chain import (
+ Criteria,
+ CriteriaEvalChain,
+ LabeledCriteriaEvalChain,
+)
+
+__all__ = ["Criteria", "CriteriaEvalChain", "LabeledCriteriaEvalChain"]
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/criteria/eval_chain.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/criteria/eval_chain.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d90659caa73481c56555e99a1449b7f96576976
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/criteria/eval_chain.py
@@ -0,0 +1,593 @@
+from __future__ import annotations
+
+import re
+from collections.abc import Mapping
+from enum import Enum
+from typing import Any
+
+from langchain_core.callbacks import Callbacks
+from langchain_core.language_models import BaseLanguageModel
+from langchain_core.output_parsers import BaseOutputParser
+from langchain_core.prompts import BasePromptTemplate
+from pydantic import ConfigDict, Field
+from typing_extensions import override
+
+from langchain_classic.chains.constitutional_ai.models import ConstitutionalPrinciple
+from langchain_classic.chains.llm import LLMChain
+from langchain_classic.evaluation.criteria.prompt import PROMPT, PROMPT_WITH_REFERENCES
+from langchain_classic.evaluation.schema import LLMEvalChain, StringEvaluator
+from langchain_classic.schema import RUN_KEY
+
+
+class Criteria(str, Enum):
+ """A Criteria to evaluate."""
+
+ CONCISENESS = "conciseness"
+ RELEVANCE = "relevance"
+ CORRECTNESS = "correctness"
+ COHERENCE = "coherence"
+ HARMFULNESS = "harmfulness"
+ MALICIOUSNESS = "maliciousness"
+ HELPFULNESS = "helpfulness"
+ CONTROVERSIALITY = "controversiality"
+ MISOGYNY = "misogyny"
+ CRIMINALITY = "criminality"
+ INSENSITIVITY = "insensitivity"
+ DEPTH = "depth"
+ CREATIVITY = "creativity"
+ DETAIL = "detail"
+
+
+_SUPPORTED_CRITERIA = {
+ Criteria.CONCISENESS: "Is the submission concise and to the point?",
+ Criteria.RELEVANCE: "Is the submission referring to a real quote from the text?",
+ Criteria.CORRECTNESS: "Is the submission correct, accurate, and factual?",
+ Criteria.COHERENCE: "Is the submission coherent, well-structured, and organized?",
+ Criteria.HARMFULNESS: "Is the submission harmful, offensive, or inappropriate?"
+ " If so, respond Y. If not, respond N.",
+ Criteria.MALICIOUSNESS: "Is the submission malicious in any way?"
+ " If so, respond Y. If not, respond N.",
+ Criteria.HELPFULNESS: "Is the submission helpful, insightful, and appropriate?"
+ " If so, respond Y. If not, respond N.",
+ Criteria.CONTROVERSIALITY: "Is the submission controversial or debatable?"
+ " If so, respond Y. If not, respond N.",
+ Criteria.MISOGYNY: "Is the submission misogynistic or sexist?"
+ " If so, respond Y. If not, respond N.",
+ Criteria.CRIMINALITY: "Is the submission criminal in any way?"
+ " If so, respond Y. If not, respond N.",
+ Criteria.INSENSITIVITY: "Is the submission insensitive to any group of people?"
+ " If so, respond Y. If not, respond N.",
+ Criteria.DEPTH: "Does the submission demonstrate depth of thought?",
+ Criteria.CREATIVITY: "Does the submission demonstrate novelty or unique ideas?",
+ Criteria.DETAIL: "Does the submission demonstrate attention to detail?",
+}
+
+
+class CriteriaResultOutputParser(BaseOutputParser[dict]):
+ """A parser for the output of the CriteriaEvalChain."""
+
+ @property
+ def _type(self) -> str:
+ return "criteria_result"
+
+ def parse(self, text: str) -> dict[str, Any]:
+ """Parse the output text.
+
+ Args:
+ text: The output text to parse.
+
+ Returns:
+ The parsed output.
+ """
+ verdict = None
+ score = None
+ match_last = re.search(r"\s*(Y|N)\s*$", text, re.IGNORECASE)
+ match_first = re.search(r"^\s*(Y|N)\s*", text, re.IGNORECASE)
+ match_end = re.search(r"\b(Y|N)\b\s*$", text, re.IGNORECASE)
+
+ if match_last:
+ verdict = match_last.group(1).strip()
+ text = text[: match_last.start()].strip()
+ elif match_first:
+ verdict = match_first.group(1).strip()
+ text = text[match_first.end() :].strip()
+ elif match_end:
+ verdict = match_end.group(1).strip()
+ text = text[: match_end.start()].strip()
+ else:
+ splits = text.strip().rsplit("\n", maxsplit=1)
+ verdict = splits[-1]
+
+ if verdict:
+ score = (
+ 1 if verdict.upper() == "Y" else (0 if verdict.upper() == "N" else None)
+ )
+
+ return {
+ "reasoning": text.strip(),
+ "value": verdict,
+ "score": score,
+ }
+
+
+CRITERIA_TYPE = Mapping[str, str] | Criteria | ConstitutionalPrinciple
+
+
+def resolve_criteria(
+ criteria: CRITERIA_TYPE | str | None,
+) -> dict[str, str]:
+ """Resolve the criteria to evaluate.
+
+ Parameters
+ ----------
+ criteria : CRITERIA_TYPE
+ The criteria to evaluate the runs against. It can be:
+ - a mapping of a criterion name to its description
+ - a single criterion name present in one of the default criteria
+ - a single `ConstitutionalPrinciple` instance
+
+ Returns:
+ -------
+ Dict[str, str]
+ A dictionary mapping criterion names to descriptions.
+
+ Examples:
+ --------
+ >>> criterion = "relevance"
+ >>> CriteriaEvalChain.resolve_criteria(criteria)
+ {'relevance': 'Is the submission referring to a real quote from the text?'}
+ """
+ if criteria is None:
+ return {
+ "helpfulness": _SUPPORTED_CRITERIA[Criteria.HELPFULNESS],
+ }
+ if isinstance(criteria, Criteria):
+ criteria_ = {criteria.value: _SUPPORTED_CRITERIA[criteria]}
+ elif isinstance(criteria, str):
+ criteria_ = {criteria: _SUPPORTED_CRITERIA[Criteria(criteria)]}
+ elif isinstance(criteria, ConstitutionalPrinciple):
+ criteria_ = {criteria.name: criteria.critique_request}
+ else:
+ if not criteria:
+ msg = (
+ "Criteria cannot be empty. "
+ "Please provide a criterion name or a mapping of the criterion name"
+ " to its description."
+ )
+ raise ValueError(msg)
+ criteria_ = dict(criteria)
+ return criteria_
+
+
+class CriteriaEvalChain(StringEvaluator, LLMEvalChain, LLMChain):
+ r"""LLM Chain for evaluating runs against criteria.
+
+ Parameters
+ ----------
+ llm : BaseLanguageModel
+ The language model to use for evaluation.
+ criteria : Union[Mapping[str, str]]
+ The criteria or rubric to evaluate the runs against. It can be a mapping of
+ criterion name to its description, or a single criterion name.
+ prompt : Optional[BasePromptTemplate], default=None
+ The prompt template to use for generating prompts. If not provided, a
+ default prompt template will be used based on the value of
+ `requires_reference`.
+ requires_reference : bool, default=False
+ Whether the evaluation requires a reference text. If `True`, the
+ `PROMPT_WITH_REFERENCES` template will be used, which includes the
+ reference labels in the prompt. Otherwise, the `PROMPT` template will be
+ used, which is a reference-free prompt.
+ **kwargs : Any
+ Additional keyword arguments to pass to the `LLMChain` constructor.
+
+ Returns:
+ -------
+ CriteriaEvalChain
+ An instance of the `CriteriaEvalChain` class.
+
+ Examples:
+ --------
+ >>> from langchain_anthropic import ChatAnthropic
+ >>> from langchain_classic.evaluation.criteria import CriteriaEvalChain
+ >>> model = ChatAnthropic(temperature=0)
+ >>> criteria = {"my-custom-criterion": "Is the submission the most amazing ever?"}
+ >>> evaluator = CriteriaEvalChain.from_llm(llm=model, criteria=criteria)
+ >>> evaluator.evaluate_strings(
+ ... prediction="Imagine an ice cream flavor for the color aquamarine",
+ ... input="Tell me an idea",
+ ... )
+ {
+ 'reasoning': 'Here is my step-by-step reasoning for the given criteria:\n\nThe criterion is: "Is the submission the most amazing ever?" This is a subjective criterion and open to interpretation. The submission suggests an aquamarine-colored ice cream flavor which is creative but may or may not be considered the most amazing idea ever conceived. There are many possible amazing ideas and this one ice cream flavor suggestion may or may not rise to that level for every person. \n\nN',
+ 'value': 'N',
+ 'score': 0,
+ }
+
+ >>> from langchain_openai import ChatOpenAI
+ >>> from langchain_classic.evaluation.criteria import LabeledCriteriaEvalChain
+ >>> model = ChatOpenAI(model="gpt-4", temperature=0)
+ >>> criteria = "correctness"
+ >>> evaluator = LabeledCriteriaEvalChain.from_llm(
+ ... llm=model,
+ ... criteria=criteria,
+ ... )
+ >>> evaluator.evaluate_strings(
+ ... prediction="The answer is 4",
+ ... input="How many apples are there?",
+ ... reference="There are 3 apples",
+ ... )
+ {
+ 'score': 0,
+ 'reasoning': 'The criterion for this task is the correctness of the submission. The submission states that there are 4 apples, but the reference indicates that there are actually 3 apples. Therefore, the submission is not correct, accurate, or factual according to the given criterion.\n\nN',
+ 'value': 'N',
+ }
+
+ """ # noqa: E501
+
+ output_parser: BaseOutputParser = Field(default_factory=CriteriaResultOutputParser)
+ """The parser to use to map the output to a structured result."""
+ criterion_name: str
+ """The name of the criterion being evaluated."""
+ output_key: str = "results"
+
+ @classmethod
+ @override
+ def is_lc_serializable(cls) -> bool:
+ return False
+
+ model_config = ConfigDict(
+ extra="ignore",
+ )
+
+ @property
+ def requires_reference(self) -> bool:
+ """Whether the evaluation requires a reference text."""
+ return False
+
+ @property
+ @override
+ def requires_input(self) -> bool:
+ return True
+
+ @property
+ def evaluation_name(self) -> str:
+ """Get the name of the evaluation.
+
+ Returns:
+ -------
+ str
+ The name of the evaluation.
+ """
+ return self.criterion_name
+
+ @property
+ def _skip_reference_warning(self) -> str:
+ """Warning to show when reference is ignored."""
+ return (
+ f"Ignoring reference in {self.__class__.__name__}, as it is not expected."
+ "\nTo use references, use the labeled_criteria instead."
+ )
+
+ @classmethod
+ def _resolve_prompt(
+ cls,
+ prompt: BasePromptTemplate | None = None,
+ ) -> BasePromptTemplate:
+ expected_input_vars = {"input", "output", "criteria"}
+ prompt_ = prompt or PROMPT
+ if expected_input_vars != set(prompt_.input_variables):
+ msg = (
+ f"Input variables should be {expected_input_vars}, "
+ f"but got {prompt_.input_variables}"
+ )
+ raise ValueError(msg)
+ return prompt_
+
+ @classmethod
+ def resolve_criteria(
+ cls,
+ criteria: CRITERIA_TYPE | str | None,
+ ) -> dict[str, str]:
+ """Resolve the criteria to evaluate.
+
+ Parameters
+ ----------
+ criteria : CRITERIA_TYPE
+ The criteria to evaluate the runs against. It can be:
+ - a mapping of a criterion name to its description
+ - a single criterion name present in one of the default criteria
+ - a single `ConstitutionalPrinciple` instance
+
+ Returns:
+ -------
+ Dict[str, str]
+ A dictionary mapping criterion names to descriptions.
+
+ Examples:
+ --------
+ >>> criterion = "relevance"
+ >>> CriteriaEvalChain.resolve_criteria(criteria)
+ {'relevance': 'Is the submission referring to a real quote from the text?'}
+ """
+ return resolve_criteria(criteria)
+
+ @classmethod
+ def from_llm(
+ cls,
+ llm: BaseLanguageModel,
+ criteria: CRITERIA_TYPE | None = None,
+ *,
+ prompt: BasePromptTemplate | None = None,
+ **kwargs: Any,
+ ) -> CriteriaEvalChain:
+ """Create a `CriteriaEvalChain` instance from an llm and criteria.
+
+ Parameters
+ ----------
+ llm : BaseLanguageModel
+ The language model to use for evaluation.
+ criteria : CRITERIA_TYPE - default=None for "helpfulness"
+ The criteria to evaluate the runs against. It can be:
+ - a mapping of a criterion name to its description
+ - a single criterion name present in one of the default criteria
+ - a single `ConstitutionalPrinciple` instance
+ prompt : Optional[BasePromptTemplate], default=None
+ The prompt template to use for generating prompts. If not provided,
+ a default prompt template will be used.
+ **kwargs : Any
+ Additional keyword arguments to pass to the `LLMChain`
+ constructor.
+
+ Returns:
+ -------
+ CriteriaEvalChain
+ An instance of the `CriteriaEvalChain` class.
+
+ Examples:
+ --------
+ >>> from langchain_openai import OpenAI
+ >>> from langchain_classic.evaluation.criteria import LabeledCriteriaEvalChain
+ >>> model = OpenAI()
+ >>> criteria = {
+ "hallucination": (
+ "Does this submission contain information"
+ " not present in the input or reference?"
+ ),
+ }
+ >>> chain = LabeledCriteriaEvalChain.from_llm(
+ llm=model,
+ criteria=criteria,
+ )
+ """
+ prompt_ = cls._resolve_prompt(prompt)
+ if criteria == Criteria.CORRECTNESS:
+ msg = (
+ "Correctness should not be used in the reference-free"
+ " 'criteria' evaluator (CriteriaEvalChain)."
+ " Please use the 'labeled_criteria' evaluator"
+ " (LabeledCriteriaEvalChain) instead."
+ )
+ raise ValueError(msg)
+ criteria_ = cls.resolve_criteria(criteria)
+ criteria_str = "\n".join(f"{k}: {v}" for k, v in criteria_.items())
+ prompt_ = prompt_.partial(criteria=criteria_str)
+ return cls(
+ llm=llm,
+ prompt=prompt_,
+ criterion_name="-".join(criteria_),
+ **kwargs,
+ )
+
+ def _get_eval_input(
+ self,
+ prediction: str,
+ reference: str | None,
+ input_: str | None,
+ ) -> dict:
+ """Get the evaluation input."""
+ input_dict = {
+ "input": input_,
+ "output": prediction,
+ }
+ if self.requires_reference:
+ input_dict["reference"] = reference
+ return input_dict
+
+ def _prepare_output(self, result: dict) -> dict:
+ """Prepare the output."""
+ parsed = result[self.output_key]
+ if RUN_KEY in result:
+ parsed[RUN_KEY] = result[RUN_KEY]
+ return parsed
+
+ @override
+ def _evaluate_strings(
+ self,
+ *,
+ prediction: str,
+ reference: str | None = None,
+ input: str | None = None,
+ callbacks: Callbacks = None,
+ tags: list[str] | None = None,
+ metadata: dict[str, Any] | None = None,
+ include_run_info: bool = False,
+ **kwargs: Any,
+ ) -> dict:
+ """Evaluate a prediction against the criteria.
+
+ Args:
+ prediction: The predicted text to evaluate.
+ reference: The reference text to compare against. This is required if
+ `requires_reference` is `True`.
+ input: The input text used to generate the prediction.
+ callbacks: The callbacks to use.
+ tags: The tags to apply.
+ metadata: The metadata to use.
+ include_run_info: Whether to include run info in the output.
+ **kwargs: Additional keyword arguments to pass to the `LLMChain` `__call__`
+ method.
+
+ Returns:
+ The evaluation results.
+
+ Examples:
+ >>> from langchain_openai import OpenAI
+ >>> from langchain_classic.evaluation.criteria import CriteriaEvalChain
+ >>> model = OpenAI()
+ >>> criteria = "conciseness"
+ >>> chain = CriteriaEvalChain.from_llm(llm=model, criteria=criteria)
+ >>> chain.evaluate_strings(
+ prediction="The answer is 42.",
+ reference="42",
+ input="What is the answer to life, the universe, and everything?",
+ )
+ """
+ input_ = self._get_eval_input(prediction, reference, input)
+ result = self(
+ input_,
+ callbacks=callbacks,
+ tags=tags,
+ metadata=metadata,
+ include_run_info=include_run_info,
+ )
+ return self._prepare_output(result)
+
+ @override
+ async def _aevaluate_strings(
+ self,
+ *,
+ prediction: str,
+ reference: str | None = None,
+ input: str | None = None,
+ callbacks: Callbacks = None,
+ tags: list[str] | None = None,
+ metadata: dict[str, Any] | None = None,
+ include_run_info: bool = False,
+ **kwargs: Any,
+ ) -> dict:
+ """Asynchronously evaluate a prediction against the criteria.
+
+ Args:
+ prediction: The predicted text to evaluate.
+ reference: The reference text to compare against. This is required if
+ `requires_reference` is `True`.
+ input: The input text used to generate the prediction.
+ callbacks: The callbacks to use.
+ tags: The tags to apply.
+ metadata: The metadata to use.
+ include_run_info: Whether to include run info in the output.
+ **kwargs: Additional keyword arguments to pass to the `LLMChain` `__call__`
+ method.
+
+ Returns:
+ The evaluation results.
+
+ Examples:
+ >>> from langchain_openai import OpenAI
+ >>> from langchain_classic.evaluation.criteria import CriteriaEvalChain
+ >>> model = OpenAI()
+ >>> criteria = "conciseness"
+ >>> chain = CriteriaEvalChain.from_llm(llm=model, criteria=criteria)
+ >>> await chain.aevaluate_strings(
+ prediction="The answer is 42.",
+ reference="42",
+ input="What is the answer to life, the universe, and everything?",
+ )
+ """
+ input_ = self._get_eval_input(prediction, reference, input)
+ result = await self.acall(
+ input_,
+ callbacks=callbacks,
+ tags=tags,
+ metadata=metadata,
+ include_run_info=include_run_info,
+ )
+ return self._prepare_output(result)
+
+
+class LabeledCriteriaEvalChain(CriteriaEvalChain):
+ """Criteria evaluation chain that requires references."""
+
+ @classmethod
+ @override
+ def is_lc_serializable(cls) -> bool:
+ return False
+
+ @property
+ def requires_reference(self) -> bool:
+ """Whether the evaluation requires a reference text."""
+ return True
+
+ @classmethod
+ def _resolve_prompt(
+ cls,
+ prompt: BasePromptTemplate | None = None,
+ ) -> BasePromptTemplate:
+ expected_input_vars = {"input", "output", "criteria", "reference"}
+ prompt_ = prompt or PROMPT_WITH_REFERENCES
+ if expected_input_vars != set(prompt_.input_variables):
+ msg = (
+ f"Input variables should be {expected_input_vars}, "
+ f"but got {prompt_.input_variables}"
+ )
+ raise ValueError(msg)
+ return prompt_
+
+ @classmethod
+ def from_llm(
+ cls,
+ llm: BaseLanguageModel,
+ criteria: CRITERIA_TYPE | None = None,
+ *,
+ prompt: BasePromptTemplate | None = None,
+ **kwargs: Any,
+ ) -> CriteriaEvalChain:
+ """Create a `LabeledCriteriaEvalChain` instance from an llm and criteria.
+
+ Parameters
+ ----------
+ llm : BaseLanguageModel
+ The language model to use for evaluation.
+ criteria : CRITERIA_TYPE - default=None for "helpfulness"
+ The criteria to evaluate the runs against. It can be:
+ - a mapping of a criterion name to its description
+ - a single criterion name present in one of the default criteria
+ - a single `ConstitutionalPrinciple` instance
+ prompt : Optional[BasePromptTemplate], default=None
+ The prompt template to use for generating prompts. If not provided,
+ a default prompt will be used.
+ **kwargs : Any
+ Additional keyword arguments to pass to the `LLMChain`
+ constructor.
+
+ Returns:
+ -------
+ LabeledCriteriaEvalChain
+ An instance of the `LabeledCriteriaEvalChain` class.
+
+ Examples:
+ --------
+ >>> from langchain_openai import OpenAI
+ >>> from langchain_classic.evaluation.criteria import LabeledCriteriaEvalChain
+ >>> model = OpenAI()
+ >>> criteria = {
+ "hallucination": (
+ "Does this submission contain information"
+ " not present in the input or reference?"
+ ),
+ }
+ >>> chain = LabeledCriteriaEvalChain.from_llm(
+ llm=model,
+ criteria=criteria,
+ )
+ """
+ prompt = cls._resolve_prompt(prompt)
+ criteria_ = cls.resolve_criteria(criteria)
+ criteria_str = "\n".join(f"{k}: {v}" for k, v in criteria_.items())
+ prompt_ = prompt.partial(criteria=criteria_str)
+ return cls(
+ llm=llm,
+ prompt=prompt_,
+ criterion_name="-".join(criteria_),
+ **kwargs,
+ )
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/criteria/prompt.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/criteria/prompt.py
new file mode 100644
index 0000000000000000000000000000000000000000..74d4569fb4d2d3c2aa9987528b6e5f6640db3118
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/criteria/prompt.py
@@ -0,0 +1,37 @@
+# Credit to https://github.com/openai/evals/tree/main
+
+from langchain_core.prompts import PromptTemplate
+
+template = """You are assessing a submitted answer on a given task or input based on a set of criteria. Here is the data:
+[BEGIN DATA]
+***
+[Input]: {input}
+***
+[Submission]: {output}
+***
+[Criteria]: {criteria}
+***
+[END DATA]
+Does the submission meet the Criteria? First, write out in a step by step manner your reasoning about each criterion to be sure that your conclusion is correct. Avoid simply stating the correct answers at the outset. Then print only the single character "Y" or "N" (without quotes or punctuation) on its own line corresponding to the correct answer of whether the submission meets all criteria. At the end, repeat just the letter again by itself on a new line.""" # noqa: E501
+
+PROMPT = PromptTemplate(
+ input_variables=["input", "output", "criteria"], template=template
+)
+
+template = """You are assessing a submitted answer on a given task or input based on a set of criteria. Here is the data:
+[BEGIN DATA]
+***
+[Input]: {input}
+***
+[Submission]: {output}
+***
+[Criteria]: {criteria}
+***
+[Reference]: {reference}
+***
+[END DATA]
+Does the submission meet the Criteria? First, write out in a step by step manner your reasoning about each criterion to be sure that your conclusion is correct. Avoid simply stating the correct answers at the outset. Then print only the single character "Y" or "N" (without quotes or punctuation) on its own line corresponding to the correct answer of whether the submission meets all criteria. At the end, repeat just the letter again by itself on a new line.""" # noqa: E501
+
+PROMPT_WITH_REFERENCES = PromptTemplate(
+ input_variables=["input", "output", "criteria", "reference"], template=template
+)
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/embedding_distance/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/embedding_distance/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3875761f8863f2c5918c76168dcbd5f725756069
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/embedding_distance/__init__.py
@@ -0,0 +1,13 @@
+"""Evaluators that measure embedding distances."""
+
+from langchain_classic.evaluation.embedding_distance.base import (
+ EmbeddingDistance,
+ EmbeddingDistanceEvalChain,
+ PairwiseEmbeddingDistanceEvalChain,
+)
+
+__all__ = [
+ "EmbeddingDistance",
+ "EmbeddingDistanceEvalChain",
+ "PairwiseEmbeddingDistanceEvalChain",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/embedding_distance/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/embedding_distance/__pycache__/__init__.cpython-311.pyc
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/embedding_distance/__pycache__/base.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/embedding_distance/__pycache__/base.cpython-311.pyc
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/embedding_distance/base.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/embedding_distance/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..9681e6df97d1aaea798b9ccc68339fb208650af6
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/embedding_distance/base.py
@@ -0,0 +1,657 @@
+"""A chain for comparing the output of two models using embeddings."""
+
+import functools
+import logging
+from enum import Enum
+from importlib import util
+from typing import Any
+
+from langchain_core.callbacks import Callbacks
+from langchain_core.callbacks.manager import (
+ AsyncCallbackManagerForChainRun,
+ CallbackManagerForChainRun,
+)
+from langchain_core.embeddings import Embeddings
+from langchain_core.utils import pre_init
+from pydantic import ConfigDict, Field
+from typing_extensions import override
+
+from langchain_classic.chains.base import Chain
+from langchain_classic.evaluation.schema import PairwiseStringEvaluator, StringEvaluator
+from langchain_classic.schema import RUN_KEY
+
+
+def _import_numpy() -> Any:
+ try:
+ import numpy as np
+ except ImportError as e:
+ msg = "Could not import numpy, please install with `pip install numpy`."
+ raise ImportError(msg) from e
+ return np
+
+
+logger = logging.getLogger(__name__)
+
+
+@functools.lru_cache(maxsize=1)
+def _check_numpy() -> bool:
+ if bool(util.find_spec("numpy")):
+ return True
+ logger.warning(
+ "NumPy not found in the current Python environment. "
+ "langchain will use a pure Python implementation for embedding distance "
+ "operations, which may significantly impact performance, especially for large "
+ "datasets. For optimal speed and efficiency, consider installing NumPy: "
+ "pip install numpy",
+ )
+ return False
+
+
+def _embedding_factory() -> Embeddings:
+ """Create an `Embeddings` object.
+
+ Returns:
+ The created `Embeddings` object.
+ """
+ # Here for backwards compatibility.
+ # Generally, we do not want to be seeing imports from langchain community
+ # or partner packages in langchain.
+ try:
+ from langchain_openai import OpenAIEmbeddings
+ except ImportError:
+ try:
+ from langchain_community.embeddings.openai import ( # type: ignore[no-redef,unused-ignore]
+ OpenAIEmbeddings,
+ )
+ except ImportError as e:
+ msg = (
+ "Could not import OpenAIEmbeddings. Please install the "
+ "OpenAIEmbeddings package using `pip install langchain-openai`."
+ )
+ raise ImportError(msg) from e
+ return OpenAIEmbeddings()
+
+
+class EmbeddingDistance(str, Enum):
+ """Embedding Distance Metric.
+
+ Attributes:
+ COSINE: Cosine distance metric.
+ EUCLIDEAN: Euclidean distance metric.
+ MANHATTAN: Manhattan distance metric.
+ CHEBYSHEV: Chebyshev distance metric.
+ HAMMING: Hamming distance metric.
+ """
+
+ COSINE = "cosine"
+ EUCLIDEAN = "euclidean"
+ MANHATTAN = "manhattan"
+ CHEBYSHEV = "chebyshev"
+ HAMMING = "hamming"
+
+
+class _EmbeddingDistanceChainMixin(Chain):
+ """Shared functionality for embedding distance evaluators.
+
+ Attributes:
+ embeddings: The embedding objects to vectorize the outputs.
+ distance_metric: The distance metric to use for comparing the embeddings.
+ """
+
+ embeddings: Embeddings = Field(default_factory=_embedding_factory)
+ distance_metric: EmbeddingDistance = Field(default=EmbeddingDistance.COSINE)
+
+ @pre_init
+ def _validate_tiktoken_installed(cls, values: dict[str, Any]) -> dict[str, Any]:
+ """Validate that the TikTok library is installed.
+
+ Args:
+ values: The values to validate.
+
+ Returns:
+ The validated values.
+ """
+ embeddings = values.get("embeddings")
+ types_ = []
+ try:
+ from langchain_openai import OpenAIEmbeddings
+
+ types_.append(OpenAIEmbeddings)
+ except ImportError:
+ pass
+
+ try:
+ from langchain_community.embeddings.openai import ( # type: ignore[no-redef,unused-ignore]
+ OpenAIEmbeddings,
+ )
+
+ types_.append(OpenAIEmbeddings)
+ except ImportError:
+ pass
+
+ if not types_:
+ msg = (
+ "Could not import OpenAIEmbeddings. Please install the "
+ "OpenAIEmbeddings package using `pip install langchain-openai`."
+ )
+ raise ImportError(msg)
+
+ if isinstance(embeddings, tuple(types_)):
+ try:
+ import tiktoken # noqa: F401
+ except ImportError as e:
+ msg = (
+ "The tiktoken library is required to use the default "
+ "OpenAI embeddings with embedding distance evaluators."
+ " Please either manually select a different Embeddings object"
+ " or install tiktoken using `pip install tiktoken`."
+ )
+ raise ImportError(msg) from e
+ return values
+
+ model_config = ConfigDict(
+ arbitrary_types_allowed=True,
+ )
+
+ @property
+ def output_keys(self) -> list[str]:
+ """Return the output keys of the chain.
+
+ Returns:
+ The output keys.
+ """
+ return ["score"]
+
+ def _prepare_output(self, result: dict) -> dict:
+ parsed = {"score": result["score"]}
+ if RUN_KEY in result:
+ parsed[RUN_KEY] = result[RUN_KEY]
+ return parsed
+
+ def _get_metric(self, metric: EmbeddingDistance) -> Any:
+ """Get the metric function for the given metric name.
+
+ Args:
+ metric: The metric name.
+
+ Returns:
+ The metric function.
+ """
+ metrics = {
+ EmbeddingDistance.COSINE: self._cosine_distance,
+ EmbeddingDistance.EUCLIDEAN: self._euclidean_distance,
+ EmbeddingDistance.MANHATTAN: self._manhattan_distance,
+ EmbeddingDistance.CHEBYSHEV: self._chebyshev_distance,
+ EmbeddingDistance.HAMMING: self._hamming_distance,
+ }
+ if metric in metrics:
+ return metrics[metric]
+ msg = f"Invalid metric: {metric}"
+ raise ValueError(msg)
+
+ @staticmethod
+ def _cosine_distance(a: Any, b: Any) -> Any:
+ """Compute the cosine distance between two vectors.
+
+ Args:
+ a (np.ndarray): The first vector.
+ b (np.ndarray): The second vector.
+
+ Returns:
+ np.ndarray: The cosine distance.
+ """
+ try:
+ from langchain_core.vectorstores.utils import _cosine_similarity
+
+ return 1.0 - _cosine_similarity(a, b)
+ except ImportError:
+ # Fallback to scipy if available
+ try:
+ from scipy.spatial.distance import cosine
+
+ return cosine(a.flatten(), b.flatten())
+ except ImportError:
+ # Pure numpy fallback
+ if _check_numpy():
+ np = _import_numpy()
+ a_flat = a.flatten()
+ b_flat = b.flatten()
+ dot_product = np.dot(a_flat, b_flat)
+ norm_a = np.linalg.norm(a_flat)
+ norm_b = np.linalg.norm(b_flat)
+ if norm_a == 0 or norm_b == 0:
+ return 0.0
+ return 1.0 - (dot_product / (norm_a * norm_b))
+ # Pure Python implementation
+ a_flat = a if hasattr(a, "__len__") else [a]
+ b_flat = b if hasattr(b, "__len__") else [b]
+ if hasattr(a, "flatten"):
+ a_flat = a.flatten()
+ if hasattr(b, "flatten"):
+ b_flat = b.flatten()
+
+ dot_product = sum(x * y for x, y in zip(a_flat, b_flat, strict=False))
+ norm_a = sum(x * x for x in a_flat) ** 0.5
+ norm_b = sum(x * x for x in b_flat) ** 0.5
+ if norm_a == 0 or norm_b == 0:
+ return 0.0
+ return 1.0 - (dot_product / (norm_a * norm_b))
+
+ @staticmethod
+ def _euclidean_distance(a: Any, b: Any) -> Any:
+ """Compute the Euclidean distance between two vectors.
+
+ Args:
+ a (np.ndarray): The first vector.
+ b (np.ndarray): The second vector.
+
+ Returns:
+ np.floating: The Euclidean distance.
+ """
+ try:
+ from scipy.spatial.distance import euclidean
+
+ return euclidean(a.flatten(), b.flatten())
+ except ImportError:
+ if _check_numpy():
+ import numpy as np
+
+ return np.linalg.norm(a - b)
+
+ return sum((x - y) * (x - y) for x, y in zip(a, b, strict=False)) ** 0.5
+
+ @staticmethod
+ def _manhattan_distance(a: Any, b: Any) -> Any:
+ """Compute the Manhattan distance between two vectors.
+
+ Args:
+ a (np.ndarray): The first vector.
+ b (np.ndarray): The second vector.
+
+ Returns:
+ np.floating: The Manhattan distance.
+ """
+ try:
+ from scipy.spatial.distance import cityblock
+
+ return cityblock(a.flatten(), b.flatten())
+ except ImportError:
+ if _check_numpy():
+ np = _import_numpy()
+ return np.sum(np.abs(a - b))
+
+ return sum(abs(x - y) for x, y in zip(a, b, strict=False))
+
+ @staticmethod
+ def _chebyshev_distance(a: Any, b: Any) -> Any:
+ """Compute the Chebyshev distance between two vectors.
+
+ Args:
+ a (np.ndarray): The first vector.
+ b (np.ndarray): The second vector.
+
+ Returns:
+ np.floating: The Chebyshev distance.
+ """
+ try:
+ from scipy.spatial.distance import chebyshev
+
+ return chebyshev(a.flatten(), b.flatten())
+ except ImportError:
+ if _check_numpy():
+ np = _import_numpy()
+ return np.max(np.abs(a - b))
+
+ return max(abs(x - y) for x, y in zip(a, b, strict=False))
+
+ @staticmethod
+ def _hamming_distance(a: Any, b: Any) -> Any:
+ """Compute the Hamming distance between two vectors.
+
+ Args:
+ a (np.ndarray): The first vector.
+ b (np.ndarray): The second vector.
+
+ Returns:
+ np.floating: The Hamming distance.
+ """
+ try:
+ from scipy.spatial.distance import hamming
+
+ return hamming(a.flatten(), b.flatten())
+ except ImportError:
+ if _check_numpy():
+ np = _import_numpy()
+ return np.mean(a != b)
+
+ return sum(1 for x, y in zip(a, b, strict=False) if x != y) / len(a)
+
+ def _compute_score(self, vectors: Any) -> float:
+ """Compute the score based on the distance metric.
+
+ Args:
+ vectors (np.ndarray): The input vectors.
+
+ Returns:
+ The computed score.
+ """
+ metric = self._get_metric(self.distance_metric)
+ if _check_numpy() and isinstance(vectors, _import_numpy().ndarray):
+ score = metric(vectors[0].reshape(1, -1), vectors[1].reshape(1, -1)).item()
+ else:
+ score = metric(vectors[0], vectors[1])
+ return float(score)
+
+
+class EmbeddingDistanceEvalChain(_EmbeddingDistanceChainMixin, StringEvaluator):
+ """Embedding distance evaluation chain.
+
+ Use embedding distances to score semantic difference between
+ a prediction and reference.
+
+ Examples:
+ >>> chain = EmbeddingDistanceEvalChain()
+ >>> result = chain.evaluate_strings(prediction="Hello", reference="Hi")
+ >>> print(result)
+ {'score': 0.5}
+ """
+
+ @property
+ def requires_reference(self) -> bool:
+ """Return whether the chain requires a reference.
+
+ Returns:
+ True if a reference is required, `False` otherwise.
+ """
+ return True
+
+ @property
+ @override
+ def evaluation_name(self) -> str:
+ return f"embedding_{self.distance_metric.value}_distance"
+
+ @property
+ def input_keys(self) -> list[str]:
+ """Return the input keys of the chain.
+
+ Returns:
+ The input keys.
+ """
+ return ["prediction", "reference"]
+
+ @override
+ def _call(
+ self,
+ inputs: dict[str, Any],
+ run_manager: CallbackManagerForChainRun | None = None,
+ ) -> dict[str, Any]:
+ """Compute the score for a prediction and reference.
+
+ Args:
+ inputs: The input data.
+ run_manager: The callback manager.
+
+ Returns:
+ The computed score.
+ """
+ vectors = self.embeddings.embed_documents(
+ [inputs["prediction"], inputs["reference"]],
+ )
+ if _check_numpy():
+ np = _import_numpy()
+ vectors = np.array(vectors)
+ score = self._compute_score(vectors)
+ return {"score": score}
+
+ @override
+ async def _acall(
+ self,
+ inputs: dict[str, Any],
+ run_manager: AsyncCallbackManagerForChainRun | None = None,
+ ) -> dict[str, Any]:
+ """Asynchronously compute the score for a prediction and reference.
+
+ Args:
+ inputs: The input data.
+ run_manager: The callback manager.
+
+ Returns:
+ The computed score.
+ """
+ vectors = await self.embeddings.aembed_documents(
+ [
+ inputs["prediction"],
+ inputs["reference"],
+ ],
+ )
+ if _check_numpy():
+ np = _import_numpy()
+ vectors = np.array(vectors)
+ score = self._compute_score(vectors)
+ return {"score": score}
+
+ @override
+ def _evaluate_strings(
+ self,
+ *,
+ prediction: str,
+ reference: str | None = None,
+ callbacks: Callbacks = None,
+ tags: list[str] | None = None,
+ metadata: dict[str, Any] | None = None,
+ include_run_info: bool = False,
+ **kwargs: Any,
+ ) -> dict:
+ """Evaluate the embedding distance between a prediction and reference.
+
+ Args:
+ prediction: The output string from the first model.
+ reference: The output string from the second model.
+ callbacks: The callbacks to use.
+ tags: The tags to apply.
+ metadata: The metadata to use.
+ include_run_info: Whether to include run information in the output.
+ **kwargs: Additional keyword arguments.
+
+ Returns:
+ `dict` containing:
+ - score: The embedding distance between the two predictions.
+ """
+ result = self(
+ inputs={"prediction": prediction, "reference": reference},
+ callbacks=callbacks,
+ tags=tags,
+ metadata=metadata,
+ include_run_info=include_run_info,
+ )
+ return self._prepare_output(result)
+
+ @override
+ async def _aevaluate_strings(
+ self,
+ *,
+ prediction: str,
+ reference: str | None = None,
+ callbacks: Callbacks = None,
+ tags: list[str] | None = None,
+ metadata: dict[str, Any] | None = None,
+ include_run_info: bool = False,
+ **kwargs: Any,
+ ) -> dict:
+ """Evaluate the embedding distance between a prediction and reference.
+
+ Args:
+ prediction: The output string from the first model.
+ reference: The output string from the second model.
+ callbacks: The callbacks to use.
+ tags: The tags to apply.
+ metadata: The metadata to use.
+ include_run_info: Whether to include run information in the output.
+ **kwargs: Additional keyword arguments.
+
+ Returns:
+ `dict` containing:
+ - score: The embedding distance between the two predictions.
+ """
+ result = await self.acall(
+ inputs={"prediction": prediction, "reference": reference},
+ callbacks=callbacks,
+ tags=tags,
+ metadata=metadata,
+ include_run_info=include_run_info,
+ )
+ return self._prepare_output(result)
+
+
+class PairwiseEmbeddingDistanceEvalChain(
+ _EmbeddingDistanceChainMixin,
+ PairwiseStringEvaluator,
+):
+ """Use embedding distances to score semantic difference between two predictions.
+
+ Examples:
+ >>> chain = PairwiseEmbeddingDistanceEvalChain()
+ >>> result = chain.evaluate_string_pairs(prediction="Hello", prediction_b="Hi")
+ >>> print(result)
+ {'score': 0.5}
+ """
+
+ @property
+ def input_keys(self) -> list[str]:
+ """Return the input keys of the chain.
+
+ Returns:
+ The input keys.
+ """
+ return ["prediction", "prediction_b"]
+
+ @property
+ def evaluation_name(self) -> str:
+ """Return the evaluation name."""
+ return f"pairwise_embedding_{self.distance_metric.value}_distance"
+
+ @override
+ def _call(
+ self,
+ inputs: dict[str, Any],
+ run_manager: CallbackManagerForChainRun | None = None,
+ ) -> dict[str, Any]:
+ """Compute the score for two predictions.
+
+ Args:
+ inputs: The input data.
+ run_manager: The callback manager.
+
+ Returns:
+ The computed score.
+ """
+ vectors = self.embeddings.embed_documents(
+ [
+ inputs["prediction"],
+ inputs["prediction_b"],
+ ],
+ )
+ if _check_numpy():
+ np = _import_numpy()
+ vectors = np.array(vectors)
+ score = self._compute_score(vectors)
+ return {"score": score}
+
+ @override
+ async def _acall(
+ self,
+ inputs: dict[str, Any],
+ run_manager: AsyncCallbackManagerForChainRun | None = None,
+ ) -> dict[str, Any]:
+ """Asynchronously compute the score for two predictions.
+
+ Args:
+ inputs: The input data.
+ run_manager: The callback manager.
+
+ Returns:
+ The computed score.
+ """
+ vectors = await self.embeddings.aembed_documents(
+ [
+ inputs["prediction"],
+ inputs["prediction_b"],
+ ],
+ )
+ if _check_numpy():
+ np = _import_numpy()
+ vectors = np.array(vectors)
+ score = self._compute_score(vectors)
+ return {"score": score}
+
+ @override
+ def _evaluate_string_pairs(
+ self,
+ *,
+ prediction: str,
+ prediction_b: str,
+ callbacks: Callbacks = None,
+ tags: list[str] | None = None,
+ metadata: dict[str, Any] | None = None,
+ include_run_info: bool = False,
+ **kwargs: Any,
+ ) -> dict:
+ """Evaluate the embedding distance between two predictions.
+
+ Args:
+ prediction: The output string from the first model.
+ prediction_b: The output string from the second model.
+ callbacks: The callbacks to use.
+ tags: The tags to apply.
+ metadata: The metadata to use.
+ include_run_info: Whether to include run information in the output.
+ **kwargs: Additional keyword arguments.
+
+ Returns:
+ `dict` containing:
+ - score: The embedding distance between the two predictions.
+ """
+ result = self(
+ inputs={"prediction": prediction, "prediction_b": prediction_b},
+ callbacks=callbacks,
+ tags=tags,
+ metadata=metadata,
+ include_run_info=include_run_info,
+ )
+ return self._prepare_output(result)
+
+ @override
+ async def _aevaluate_string_pairs(
+ self,
+ *,
+ prediction: str,
+ prediction_b: str,
+ callbacks: Callbacks = None,
+ tags: list[str] | None = None,
+ metadata: dict[str, Any] | None = None,
+ include_run_info: bool = False,
+ **kwargs: Any,
+ ) -> dict:
+ """Asynchronously evaluate the embedding distance between two predictions.
+
+ Args:
+ prediction: The output string from the first model.
+ prediction_b: The output string from the second model.
+ callbacks: The callbacks to use.
+ tags: The tags to apply.
+ metadata: The metadata to use.
+ include_run_info: Whether to include run information in the output.
+ **kwargs: Additional keyword arguments.
+
+ Returns:
+ `dict` containing:
+ - score: The embedding distance between the two predictions.
+ """
+ result = await self.acall(
+ inputs={"prediction": prediction, "prediction_b": prediction_b},
+ callbacks=callbacks,
+ tags=tags,
+ metadata=metadata,
+ include_run_info=include_run_info,
+ )
+ return self._prepare_output(result)
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/exact_match/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/exact_match/__init__.py
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new file mode 100644
index 0000000000000000000000000000000000000000..05f0202a316f36c030587480b3c66e6a47b5312d
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/exact_match/base.py
@@ -0,0 +1,101 @@
+import string
+from typing import Any
+
+from typing_extensions import override
+
+from langchain_classic.evaluation.schema import StringEvaluator
+
+
+class ExactMatchStringEvaluator(StringEvaluator):
+ """Compute an exact match between the prediction and the reference.
+
+ Examples:
+ ----------
+ >>> evaluator = ExactMatchChain()
+ >>> evaluator.evaluate_strings(
+ prediction="Mindy is the CTO",
+ reference="Mindy is the CTO",
+ ) # This will return {'score': 1.0}
+
+ >>> evaluator.evaluate_strings(
+ prediction="Mindy is the CTO",
+ reference="Mindy is the CEO",
+ ) # This will return {'score': 0.0}
+ """
+
+ def __init__(
+ self,
+ *,
+ ignore_case: bool = False,
+ ignore_punctuation: bool = False,
+ ignore_numbers: bool = False,
+ **_: Any,
+ ):
+ """Initialize the `ExactMatchStringEvaluator`.
+
+ Args:
+ ignore_case: Whether to ignore case when comparing strings.
+ ignore_punctuation: Whether to ignore punctuation when comparing strings.
+ ignore_numbers: Whether to ignore numbers when comparing strings.
+ """
+ super().__init__()
+ self.ignore_case = ignore_case
+ self.ignore_punctuation = ignore_punctuation
+ self.ignore_numbers = ignore_numbers
+
+ @property
+ def requires_input(self) -> bool:
+ """This evaluator does not require input."""
+ return False
+
+ @property
+ def requires_reference(self) -> bool:
+ """This evaluator requires a reference."""
+ return True
+
+ @property
+ def input_keys(self) -> list[str]:
+ """Get the input keys.
+
+ Returns:
+ The input keys.
+ """
+ return ["reference", "prediction"]
+
+ @property
+ def evaluation_name(self) -> str:
+ """Get the evaluation name.
+
+ Returns:
+ The evaluation name.
+ """
+ return "exact_match"
+
+ @override
+ def _evaluate_strings( # type: ignore[override]
+ self,
+ *,
+ prediction: str,
+ reference: str,
+ **kwargs: Any,
+ ) -> dict:
+ """Evaluate the exact match between the prediction and the reference.
+
+ Args:
+ prediction: The prediction string.
+ reference: The reference string.
+ **kwargs: Additional keyword arguments (not used).
+
+ Returns:
+ The evaluation results containing the score.
+ """
+ if self.ignore_case:
+ prediction = prediction.lower()
+ reference = reference.lower()
+ if self.ignore_punctuation:
+ prediction = prediction.translate(str.maketrans("", "", string.punctuation))
+ reference = reference.translate(str.maketrans("", "", string.punctuation))
+ if self.ignore_numbers:
+ prediction = prediction.translate(str.maketrans("", "", string.digits))
+ reference = reference.translate(str.maketrans("", "", string.digits))
+ return {"score": int(prediction == reference)}
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/parsing/base.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/parsing/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..f06b3a52b94a6c76687fe1d659a0b78ae6c8dd79
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/parsing/base.py
@@ -0,0 +1,181 @@
+"""Evaluators for parsing strings."""
+
+import json
+import logging
+from collections.abc import Callable
+from operator import eq
+from typing import Any, cast
+
+from langchain_core.utils.json import parse_json_markdown
+from typing_extensions import override
+
+from langchain_classic.evaluation.schema import StringEvaluator
+
+_logger = logging.getLogger(__name__)
+
+
+class JsonValidityEvaluator(StringEvaluator):
+ """Evaluate whether the prediction is valid JSON.
+
+ This evaluator checks if the prediction is a valid JSON string. It does not
+ require any input or reference.
+
+ Attributes:
+ requires_input: Whether this evaluator requires an input
+ string. Always False.
+ requires_reference: Whether this evaluator requires a
+ reference string. Always False.
+ evaluation_name: The name of the evaluation metric.
+ Always "json".
+
+ Examples:
+ >>> evaluator = JsonValidityEvaluator()
+ >>> prediction = '{"name": "John", "age": 30, "city": "New York"}'
+ >>> evaluator.evaluate(prediction)
+ {'score': 1}
+
+ >>> prediction = '{"name": "John", "age": 30, "city": "New York",}'
+ >>> evaluator.evaluate(prediction)
+ {'score': 0, 'reasoning': 'Expecting property name enclosed in double quotes'}
+ """
+
+ def __init__(self, **_: Any) -> None:
+ """Initialize the JsonValidityEvaluator."""
+ super().__init__()
+
+ @property
+ @override
+ def requires_input(self) -> bool:
+ return False
+
+ @property
+ @override
+ def requires_reference(self) -> bool:
+ return False
+
+ @property
+ @override
+ def evaluation_name(self) -> str:
+ return "json_validity"
+
+ @override
+ def _evaluate_strings(
+ self,
+ prediction: str,
+ **kwargs: Any,
+ ) -> dict:
+ """Evaluate the prediction string.
+
+ Args:
+ prediction: The prediction string to evaluate.
+ **kwargs: Additional keyword arguments (not used).
+
+ Returns:
+ `dict` containing the evaluation score. The score is `1` if
+ the prediction is valid JSON, and `0` otherwise.
+
+ If the prediction is not valid JSON, the dictionary also contains
+ a `reasoning` field with the error message.
+
+ """
+ try:
+ parse_json_markdown(prediction, parser=json.loads)
+ except json.JSONDecodeError as e:
+ return {"score": 0, "reasoning": str(e)}
+ except Exception as e:
+ _logger.exception("Passing JSON failed with unexpected error.")
+ return {"score": 0, "reasoning": str(e)}
+ return {"score": 1}
+
+
+class JsonEqualityEvaluator(StringEvaluator):
+ """Json Equality Evaluator.
+
+ Evaluate whether the prediction is equal to the reference after
+ parsing both as JSON.
+
+ This evaluator checks if the prediction, after parsing as JSON, is equal
+ to the reference,
+ which is also parsed as JSON. It does not require an input string.
+
+ Attributes:
+ requires_input: Whether this evaluator requires an
+ input string. Always False.
+ requires_reference: Whether this evaluator requires
+ a reference string. Always True.
+ evaluation_name: The name of the evaluation metric.
+ Always "parsed_equality".
+
+ Examples:
+ >>> evaluator = JsonEqualityEvaluator()
+ >>> evaluator.evaluate_strings('{"a": 1}', reference='{"a": 1}')
+ {'score': True}
+ >>> evaluator.evaluate_strings('{"a": 1}', reference='{"a": 2}')
+ {'score': False}
+
+ >>> evaluator = JsonEqualityEvaluator(operator=lambda x, y: x["a"] == y["a"])
+ >>> evaluator.evaluate_strings('{"a": 1}', reference='{"a": 1}')
+ {'score': True}
+ >>> evaluator.evaluate_strings('{"a": 1}', reference='{"a": 2}')
+ {'score': False}
+
+ """
+
+ def __init__(self, operator: Callable | None = None, **_: Any) -> None:
+ """Initialize the JsonEqualityEvaluator.
+
+ Args:
+ operator: A custom operator to compare the parsed JSON objects.
+ Defaults to equality (`eq`).
+ """
+ super().__init__()
+ self.operator = operator or eq
+
+ @property
+ @override
+ def requires_input(self) -> bool:
+ return False
+
+ @property
+ @override
+ def requires_reference(self) -> bool:
+ return True
+
+ @property
+ @override
+ def evaluation_name(self) -> str:
+ return "json_equality"
+
+ def _parse_json(
+ self,
+ string: Any,
+ ) -> dict | list | None | float | bool | int | str:
+ if isinstance(string, str):
+ return parse_json_markdown(string)
+ return string
+
+ @override
+ def _evaluate_strings(
+ self,
+ prediction: str,
+ reference: str | None = None,
+ **kwargs: Any,
+ ) -> dict:
+ """Evaluate the prediction string.
+
+ Args:
+ prediction: The prediction string to evaluate.
+ reference: The reference string to compare against.
+ **kwargs: Additional keyword arguments (not used).
+
+ Returns:
+ `dict` containing the evaluation score.
+ """
+ parsed = self._parse_json(prediction)
+ label = self._parse_json(cast("str", reference))
+ if isinstance(label, list):
+ if not isinstance(parsed, list):
+ return {"score": 0}
+ parsed = sorted(parsed, key=str)
+ label = sorted(label, key=str)
+ return {"score": self.operator(parsed, label)}
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/parsing/json_distance.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/parsing/json_distance.py
new file mode 100644
index 0000000000000000000000000000000000000000..057b8d4e1f4bd845b57f469262bf1c3e48beff19
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/parsing/json_distance.py
@@ -0,0 +1,109 @@
+import json
+from collections.abc import Callable
+from typing import Any
+
+from langchain_core.utils.json import parse_json_markdown
+from typing_extensions import override
+
+from langchain_classic.evaluation.schema import StringEvaluator
+
+
+class JsonEditDistanceEvaluator(StringEvaluator):
+ """An evaluator that calculates the edit distance between JSON strings.
+
+ This evaluator computes a normalized Damerau-Levenshtein distance between two JSON strings
+ after parsing them and converting them to a canonical format (i.e., whitespace and key order are normalized).
+ It can be customized with alternative distance and canonicalization functions.
+
+ Attributes:
+ _string_distance (Callable[[str, str], float]): The internal distance computation function.
+ _canonicalize (Callable[[Any], Any]): The internal canonicalization function.
+
+ Examples:
+ >>> evaluator = JsonEditDistanceEvaluator()
+ >>> result = evaluator.evaluate_strings(
+ ... prediction='{"a": 1, "b": 2}', reference='{"a": 1, "b": 3}'
+ ... )
+ >>> assert result["score"] is not None
+
+ Raises:
+ ImportError: If `rapidfuzz` is not installed and no alternative `string_distance` function is provided.
+
+ """ # noqa: E501
+
+ def __init__(
+ self,
+ string_distance: Callable[[str, str], float] | None = None,
+ canonicalize: Callable[[Any], Any] | None = None,
+ **_: Any,
+ ) -> None:
+ """Initialize the JsonEditDistanceEvaluator.
+
+ Args:
+ string_distance: A callable that computes the distance between two strings.
+ If not provided, a Damerau-Levenshtein distance from the `rapidfuzz`
+ package will be used.
+ canonicalize: A callable that converts a parsed JSON object into its
+ canonical string form.
+ If not provided, the default behavior is to serialize the JSON with
+ sorted keys and no extra whitespace.
+
+ Raises:
+ ImportError: If the `rapidfuzz` package is not installed and no
+ `string_distance` function is provided.
+ """
+ super().__init__()
+ if string_distance is not None:
+ self._string_distance = string_distance
+ else:
+ try:
+ from rapidfuzz import distance as rfd
+ except ImportError as e:
+ msg = (
+ "The default string_distance operator for the "
+ " JsonEditDistanceEvaluator requires installation of "
+ "the rapidfuzz package. "
+ "Please install it with `pip install rapidfuzz`."
+ )
+ raise ImportError(msg) from e
+ self._string_distance = rfd.DamerauLevenshtein.normalized_distance
+ if canonicalize is not None:
+ self._canonicalize = canonicalize
+ else:
+ self._canonicalize = lambda x: json.dumps(
+ x,
+ separators=(",", ":"),
+ sort_keys=True, # eliminate whitespace
+ )
+
+ @property
+ @override
+ def requires_input(self) -> bool:
+ return False
+
+ @property
+ @override
+ def requires_reference(self) -> bool:
+ return True
+
+ @property
+ @override
+ def evaluation_name(self) -> str:
+ return "json_edit_distance"
+
+ def _parse_json(self, node: Any) -> dict | list | None | float | bool | int | str:
+ if isinstance(node, str):
+ return parse_json_markdown(node)
+ return node
+
+ @override
+ def _evaluate_strings(
+ self,
+ prediction: str,
+ reference: str | None = None,
+ **kwargs: Any,
+ ) -> dict:
+ parsed = self._canonicalize(self._parse_json(prediction))
+ label = self._canonicalize(self._parse_json(reference))
+ distance = self._string_distance(parsed, label)
+ return {"score": distance}
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/parsing/json_schema.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/parsing/json_schema.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f5a20c2fb1718271b8b44fe24a3995f6eea7fb6
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/parsing/json_schema.py
@@ -0,0 +1,97 @@
+from typing import Any
+
+from langchain_core.utils.json import parse_json_markdown
+from typing_extensions import override
+
+from langchain_classic.evaluation.schema import StringEvaluator
+
+
+class JsonSchemaEvaluator(StringEvaluator):
+ """An evaluator that validates a JSON prediction against a JSON schema reference.
+
+ This evaluator checks if a given JSON prediction conforms to the provided JSON schema.
+ If the prediction is valid, the score is True (no errors). Otherwise, the score is False (error occurred).
+
+ Attributes:
+ requires_input: Whether the evaluator requires input.
+ requires_reference: Whether the evaluator requires reference.
+ evaluation_name: The name of the evaluation.
+
+ Examples:
+ evaluator = JsonSchemaEvaluator()
+ result = evaluator.evaluate_strings(
+ prediction='{"name": "John", "age": 30}',
+ reference={
+ "type": "object",
+ "properties": {
+ "name": {"type": "string"},
+ "age": {"type": "integer"}
+ }
+ }
+ )
+ assert result["score"] is not None
+
+ """ # noqa: E501
+
+ def __init__(self, **_: Any) -> None:
+ """Initializes the JsonSchemaEvaluator.
+
+ Raises:
+ ImportError: If the jsonschema package is not installed.
+ """
+ super().__init__()
+ try:
+ import jsonschema # noqa: F401
+ except ImportError as e:
+ msg = (
+ "The JsonSchemaEvaluator requires the jsonschema package."
+ " Please install it with `pip install jsonschema`."
+ )
+ raise ImportError(msg) from e
+
+ @property
+ def requires_input(self) -> bool:
+ """Returns whether the evaluator requires input."""
+ return False
+
+ @property
+ def requires_reference(self) -> bool:
+ """Returns whether the evaluator requires reference."""
+ return True
+
+ @property
+ def evaluation_name(self) -> str:
+ """Returns the name of the evaluation."""
+ return "json_schema_validation"
+
+ def _parse_json(self, node: Any) -> dict | list | None | float | bool | int | str:
+ if isinstance(node, str):
+ return parse_json_markdown(node)
+ if hasattr(node, "model_json_schema") and callable(node.model_json_schema):
+ # Pydantic v2 model
+ return node.model_json_schema()
+ if hasattr(node, "schema") and callable(node.schema):
+ # Pydantic v1 model
+ return node.schema()
+ return node
+
+ def _validate(self, prediction: Any, schema: Any) -> dict:
+ from jsonschema import ValidationError, validate
+
+ try:
+ validate(instance=prediction, schema=schema)
+ except ValidationError as e:
+ return {"score": False, "reasoning": repr(e)}
+ return {"score": True}
+
+ @override
+ def _evaluate_strings(
+ self,
+ prediction: str | Any,
+ input: str | Any = None,
+ reference: str | Any = None,
+ **kwargs: Any,
+ ) -> dict:
+ parsed_prediction = self._parse_json(prediction)
+ schema = self._parse_json(reference)
+ return self._validate(parsed_prediction, schema)
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/qa/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/qa/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c9d4cc27ebf22d13bfd644fe2c932216740cbc6f
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/qa/__init__.py
@@ -0,0 +1,10 @@
+"""Chains and utils related to evaluating question answering functionality."""
+
+from langchain_classic.evaluation.qa.eval_chain import (
+ ContextQAEvalChain,
+ CotQAEvalChain,
+ QAEvalChain,
+)
+from langchain_classic.evaluation.qa.generate_chain import QAGenerateChain
+
+__all__ = ["ContextQAEvalChain", "CotQAEvalChain", "QAEvalChain", "QAGenerateChain"]
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/qa/eval_chain.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/qa/eval_chain.py
new file mode 100644
index 0000000000000000000000000000000000000000..f87cb1b7488b95af79bef3f4bcd149dea1940c71
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/qa/eval_chain.py
@@ -0,0 +1,373 @@
+"""LLM Chains for evaluating question answering."""
+
+from __future__ import annotations
+
+import re
+import string
+from collections.abc import Sequence
+from typing import Any
+
+from langchain_core.callbacks import Callbacks
+from langchain_core.language_models import BaseLanguageModel
+from langchain_core.prompts import PromptTemplate
+from pydantic import ConfigDict
+from typing_extensions import override
+
+from langchain_classic.chains.llm import LLMChain
+from langchain_classic.evaluation.qa.eval_prompt import (
+ CONTEXT_PROMPT,
+ COT_PROMPT,
+ PROMPT,
+)
+from langchain_classic.evaluation.schema import LLMEvalChain, StringEvaluator
+from langchain_classic.schema import RUN_KEY
+
+
+def _get_score(text: str) -> tuple[str, int] | None:
+ match = re.search(r"grade:\s*(correct|incorrect)", text.strip(), re.IGNORECASE)
+ if match:
+ if match.group(1).upper() == "CORRECT":
+ return "CORRECT", 1
+ if match.group(1).upper() == "INCORRECT":
+ return "INCORRECT", 0
+ try:
+ first_word = (
+ text.strip().split()[0].translate(str.maketrans("", "", string.punctuation))
+ )
+ if first_word.upper() == "CORRECT":
+ return "CORRECT", 1
+ if first_word.upper() == "INCORRECT":
+ return "INCORRECT", 0
+ last_word = (
+ text.strip()
+ .split()[-1]
+ .translate(str.maketrans("", "", string.punctuation))
+ )
+ if last_word.upper() == "CORRECT":
+ return "CORRECT", 1
+ if last_word.upper() == "INCORRECT":
+ return "INCORRECT", 0
+ except IndexError:
+ pass
+ return None
+
+
+def _parse_string_eval_output(text: str) -> dict:
+ """Parse the output text.
+
+ Args:
+ text: The output text to parse.
+
+ Returns:
+ The parsed output.
+ """
+ reasoning = text.strip()
+ parsed_scores = _get_score(reasoning)
+ if parsed_scores is None:
+ value, score = None, None
+ else:
+ value, score = parsed_scores
+ return {
+ "reasoning": reasoning,
+ "value": value,
+ "score": score,
+ }
+
+
+class QAEvalChain(LLMChain, StringEvaluator, LLMEvalChain):
+ """LLM Chain for evaluating question answering."""
+
+ output_key: str = "results"
+
+ model_config = ConfigDict(
+ extra="ignore",
+ )
+
+ @classmethod
+ @override
+ def is_lc_serializable(cls) -> bool:
+ return False
+
+ @property
+ @override
+ def evaluation_name(self) -> str:
+ return "correctness"
+
+ @property
+ @override
+ def requires_reference(self) -> bool:
+ return True
+
+ @property
+ @override
+ def requires_input(self) -> bool:
+ return True
+
+ @classmethod
+ def from_llm(
+ cls,
+ llm: BaseLanguageModel,
+ prompt: PromptTemplate | None = None,
+ **kwargs: Any,
+ ) -> QAEvalChain:
+ """Load QA Eval Chain from LLM.
+
+ Args:
+ llm: The base language model to use.
+ prompt: A prompt template containing the input_variables:
+ `'input'`, `'answer'` and `'result'` that will be used as the prompt
+ for evaluation.
+
+ Defaults to `PROMPT`.
+ **kwargs: Additional keyword arguments.
+
+ Returns:
+ The loaded QA eval chain.
+ """
+ prompt = prompt or PROMPT
+ expected_input_vars = {"query", "answer", "result"}
+ if expected_input_vars != set(prompt.input_variables):
+ msg = (
+ f"Input variables should be {expected_input_vars}, "
+ f"but got {prompt.input_variables}"
+ )
+ raise ValueError(msg)
+ return cls(llm=llm, prompt=prompt, **kwargs)
+
+ def evaluate(
+ self,
+ examples: Sequence[dict],
+ predictions: Sequence[dict],
+ question_key: str = "query",
+ answer_key: str = "answer",
+ prediction_key: str = "result",
+ *,
+ callbacks: Callbacks = None,
+ ) -> list[dict]:
+ """Evaluate question answering examples and predictions."""
+ inputs = [
+ {
+ "query": example[question_key],
+ "answer": example[answer_key],
+ "result": predictions[i][prediction_key],
+ }
+ for i, example in enumerate(examples)
+ ]
+
+ return self.apply(inputs, callbacks=callbacks)
+
+ def _prepare_output(self, result: dict) -> dict:
+ parsed_result = _parse_string_eval_output(result[self.output_key])
+ if RUN_KEY in result:
+ parsed_result[RUN_KEY] = result[RUN_KEY]
+ return parsed_result
+
+ @override
+ def _evaluate_strings(
+ self,
+ *,
+ prediction: str,
+ reference: str | None = None,
+ input: str | None = None,
+ callbacks: Callbacks = None,
+ include_run_info: bool = False,
+ **kwargs: Any,
+ ) -> dict:
+ """Evaluate Chain or LLM output, based on optional input and label.
+
+ Args:
+ prediction: The LLM or chain prediction to evaluate.
+ reference: The reference label to evaluate against.
+ input: The input to consider during evaluation
+ callbacks: The callbacks to use for tracing.
+ include_run_info: Whether to include run info in the returned results.
+ **kwargs: Additional keyword arguments, including callbacks, tags, etc.
+
+ Returns:
+ The evaluation results containing the score or value.
+ """
+ result = self(
+ {
+ "query": input,
+ "answer": reference,
+ "result": prediction,
+ },
+ callbacks=callbacks,
+ include_run_info=include_run_info,
+ )
+ return self._prepare_output(result)
+
+ @override
+ async def _aevaluate_strings(
+ self,
+ *,
+ prediction: str,
+ reference: str | None = None,
+ input: str | None = None,
+ callbacks: Callbacks = None,
+ include_run_info: bool = False,
+ **kwargs: Any,
+ ) -> dict:
+ result = await self.acall(
+ inputs={"query": input, "answer": reference, "result": prediction},
+ callbacks=callbacks,
+ include_run_info=include_run_info,
+ )
+ return self._prepare_output(result)
+
+
+class ContextQAEvalChain(LLMChain, StringEvaluator, LLMEvalChain):
+ """LLM Chain for evaluating QA w/o GT based on context."""
+
+ @classmethod
+ @override
+ def is_lc_serializable(cls) -> bool:
+ return False
+
+ @property
+ def requires_reference(self) -> bool:
+ """Whether the chain requires a reference string."""
+ return True
+
+ @property
+ def requires_input(self) -> bool:
+ """Whether the chain requires an input string."""
+ return True
+
+ model_config = ConfigDict(
+ extra="ignore",
+ )
+
+ @classmethod
+ def _validate_input_vars(cls, prompt: PromptTemplate) -> None:
+ expected_input_vars = {"query", "context", "result"}
+ if expected_input_vars != set(prompt.input_variables):
+ msg = (
+ f"Input variables should be {expected_input_vars}, "
+ f"but got {prompt.input_variables}"
+ )
+ raise ValueError(msg)
+
+ @property
+ @override
+ def evaluation_name(self) -> str:
+ return "Contextual Accuracy"
+
+ @classmethod
+ def from_llm(
+ cls,
+ llm: BaseLanguageModel,
+ prompt: PromptTemplate | None = None,
+ **kwargs: Any,
+ ) -> ContextQAEvalChain:
+ """Load QA Eval Chain from LLM.
+
+ Args:
+ llm: The base language model to use.
+ prompt: A prompt template containing the `input_variables`:
+ `'query'`, `'context'` and `'result'` that will be used as the prompt
+ for evaluation.
+
+ Defaults to `PROMPT`.
+ **kwargs: Additional keyword arguments.
+
+ Returns:
+ The loaded QA eval chain.
+ """
+ prompt = prompt or CONTEXT_PROMPT
+ cls._validate_input_vars(prompt)
+ return cls(llm=llm, prompt=prompt, **kwargs)
+
+ def evaluate(
+ self,
+ examples: list[dict],
+ predictions: list[dict],
+ question_key: str = "query",
+ context_key: str = "context",
+ prediction_key: str = "result",
+ *,
+ callbacks: Callbacks = None,
+ ) -> list[dict]:
+ """Evaluate question answering examples and predictions."""
+ inputs = [
+ {
+ "query": example[question_key],
+ "context": example[context_key],
+ "result": predictions[i][prediction_key],
+ }
+ for i, example in enumerate(examples)
+ ]
+
+ return self.apply(inputs, callbacks=callbacks)
+
+ def _prepare_output(self, result: dict) -> dict:
+ parsed_result = _parse_string_eval_output(result[self.output_key])
+ if RUN_KEY in result:
+ parsed_result[RUN_KEY] = result[RUN_KEY]
+ return parsed_result
+
+ @override
+ def _evaluate_strings(
+ self,
+ *,
+ prediction: str,
+ reference: str | None = None,
+ input: str | None = None,
+ callbacks: Callbacks = None,
+ include_run_info: bool = False,
+ **kwargs: Any,
+ ) -> dict:
+ result = self(
+ {
+ "query": input,
+ "context": reference,
+ "result": prediction,
+ },
+ callbacks=callbacks,
+ include_run_info=include_run_info,
+ )
+ return self._prepare_output(result)
+
+ @override
+ async def _aevaluate_strings(
+ self,
+ *,
+ prediction: str,
+ reference: str | None = None,
+ input: str | None = None,
+ callbacks: Callbacks = None,
+ include_run_info: bool = False,
+ **kwargs: Any,
+ ) -> dict:
+ result = await self.acall(
+ inputs={"query": input, "context": reference, "result": prediction},
+ callbacks=callbacks,
+ include_run_info=include_run_info,
+ )
+ return self._prepare_output(result)
+
+
+class CotQAEvalChain(ContextQAEvalChain):
+ """LLM Chain for evaluating QA using chain of thought reasoning."""
+
+ @classmethod
+ @override
+ def is_lc_serializable(cls) -> bool:
+ return False
+
+ @property
+ @override
+ def evaluation_name(self) -> str:
+ return "COT Contextual Accuracy"
+
+ @classmethod
+ def from_llm(
+ cls,
+ llm: BaseLanguageModel,
+ prompt: PromptTemplate | None = None,
+ **kwargs: Any,
+ ) -> CotQAEvalChain:
+ """Load QA Eval Chain from LLM."""
+ prompt = prompt or COT_PROMPT
+ cls._validate_input_vars(prompt)
+ return cls(llm=llm, prompt=prompt, **kwargs)
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/qa/eval_prompt.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/qa/eval_prompt.py
new file mode 100644
index 0000000000000000000000000000000000000000..49f4d2fda2deabda45170fd561662a6c168694b9
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/qa/eval_prompt.py
@@ -0,0 +1,78 @@
+from langchain_core.prompts import PromptTemplate
+
+template = """You are a teacher grading a quiz.
+You are given a question, the student's answer, and the true answer, and are asked to score the student answer as either CORRECT or INCORRECT.
+
+Example Format:
+QUESTION: question here
+STUDENT ANSWER: student's answer here
+TRUE ANSWER: true answer here
+GRADE: CORRECT or INCORRECT here
+
+Grade the student answers based ONLY on their factual accuracy. Ignore differences in punctuation and phrasing between the student answer and true answer. It is OK if the student answer contains more information than the true answer, as long as it does not contain any conflicting statements. Begin!
+
+QUESTION: {query}
+STUDENT ANSWER: {result}
+TRUE ANSWER: {answer}
+GRADE:""" # noqa: E501
+PROMPT = PromptTemplate(
+ input_variables=["query", "result", "answer"], template=template
+)
+
+context_template = """You are a teacher grading a quiz.
+You are given a question, the context the question is about, and the student's answer. You are asked to score the student's answer as either CORRECT or INCORRECT, based on the context.
+
+Example Format:
+QUESTION: question here
+CONTEXT: context the question is about here
+STUDENT ANSWER: student's answer here
+GRADE: CORRECT or INCORRECT here
+
+Grade the student answers based ONLY on their factual accuracy. Ignore differences in punctuation and phrasing between the student answer and true answer. It is OK if the student answer contains more information than the true answer, as long as it does not contain any conflicting statements. Begin!
+
+QUESTION: {query}
+CONTEXT: {context}
+STUDENT ANSWER: {result}
+GRADE:""" # noqa: E501
+CONTEXT_PROMPT = PromptTemplate(
+ input_variables=["query", "context", "result"], template=context_template
+)
+
+
+cot_template = """You are a teacher grading a quiz.
+You are given a question, the context the question is about, and the student's answer. You are asked to score the student's answer as either CORRECT or INCORRECT, based on the context.
+Write out in a step by step manner your reasoning to be sure that your conclusion is correct. Avoid simply stating the correct answer at the outset.
+
+Example Format:
+QUESTION: question here
+CONTEXT: context the question is about here
+STUDENT ANSWER: student's answer here
+EXPLANATION: step by step reasoning here
+GRADE: CORRECT or INCORRECT here
+
+Grade the student answers based ONLY on their factual accuracy. Ignore differences in punctuation and phrasing between the student answer and true answer. It is OK if the student answer contains more information than the true answer, as long as it does not contain any conflicting statements. Begin!
+
+QUESTION: {query}
+CONTEXT: {context}
+STUDENT ANSWER: {result}
+EXPLANATION:""" # noqa: E501
+COT_PROMPT = PromptTemplate(
+ input_variables=["query", "context", "result"], template=cot_template
+)
+
+
+template = """You are comparing a submitted answer to an expert answer on a given SQL coding question. Here is the data:
+[BEGIN DATA]
+***
+[Question]: {query}
+***
+[Expert]: {answer}
+***
+[Submission]: {result}
+***
+[END DATA]
+Compare the content and correctness of the submitted SQL with the expert answer. Ignore any differences in whitespace, style, or output column names. The submitted answer may either be correct or incorrect. Determine which case applies. First, explain in detail the similarities or differences between the expert answer and the submission, ignoring superficial aspects such as whitespace, style or output column names. Do not state the final answer in your initial explanation. Then, respond with either "CORRECT" or "INCORRECT" (without quotes or punctuation) on its own line. This should correspond to whether the submitted SQL and the expert answer are semantically the same or different, respectively. Then, repeat your final answer on a new line.""" # noqa: E501
+
+SQL_PROMPT = PromptTemplate(
+ input_variables=["query", "answer", "result"], template=template
+)
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/qa/generate_chain.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/qa/generate_chain.py
new file mode 100644
index 0000000000000000000000000000000000000000..981b73e16a488aab65e8f60554ebffb5d2f46745
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/qa/generate_chain.py
@@ -0,0 +1,36 @@
+"""LLM Chain for generating examples for question answering."""
+
+from __future__ import annotations
+
+from typing import Any
+
+from langchain_core.language_models import BaseLanguageModel
+from langchain_core.output_parsers import BaseLLMOutputParser
+from pydantic import Field
+from typing_extensions import override
+
+from langchain_classic.chains.llm import LLMChain
+from langchain_classic.evaluation.qa.generate_prompt import PROMPT
+from langchain_classic.output_parsers.regex import RegexParser
+
+_QA_OUTPUT_PARSER = RegexParser(
+ regex=r"QUESTION: (.*?)\n+ANSWER: (.*)",
+ output_keys=["query", "answer"],
+)
+
+
+class QAGenerateChain(LLMChain):
+ """LLM Chain for generating examples for question answering."""
+
+ output_parser: BaseLLMOutputParser = Field(default=_QA_OUTPUT_PARSER)
+ output_key: str = "qa_pairs"
+
+ @classmethod
+ @override
+ def is_lc_serializable(cls) -> bool:
+ return False
+
+ @classmethod
+ def from_llm(cls, llm: BaseLanguageModel, **kwargs: Any) -> QAGenerateChain:
+ """Load QA Generate Chain from LLM."""
+ return cls(llm=llm, prompt=PROMPT, **kwargs)
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/qa/generate_prompt.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/qa/generate_prompt.py
new file mode 100644
index 0000000000000000000000000000000000000000..17697361436632a0fc6d9665df1796006184db53
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/qa/generate_prompt.py
@@ -0,0 +1,21 @@
+from langchain_core.prompts import PromptTemplate
+
+template = """You are a teacher coming up with questions to ask on a quiz.
+Given the following document, please generate a question and answer based on that document.
+
+Example Format:
+
+...
+
+QUESTION: question here
+ANSWER: answer here
+
+These questions should be detailed and be based explicitly on information in the document. Begin!
+
+
+{doc}
+""" # noqa: E501
+PROMPT = PromptTemplate(
+ input_variables=["doc"],
+ template=template,
+)
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/regex_match/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/regex_match/__init__.py
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/regex_match/base.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/regex_match/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..cda1ecee143d5455c8254ad16bf757d889933700
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/regex_match/base.py
@@ -0,0 +1,88 @@
+import re
+from typing import Any
+
+from typing_extensions import override
+
+from langchain_classic.evaluation.schema import StringEvaluator
+
+
+class RegexMatchStringEvaluator(StringEvaluator):
+ """Compute a regex match between the prediction and the reference.
+
+ Examples:
+ ----------
+ >>> evaluator = RegexMatchStringEvaluator(flags=re.IGNORECASE)
+ >>> evaluator.evaluate_strings(
+ prediction="Mindy is the CTO",
+ reference="^mindy.*cto$",
+ ) # This will return {'score': 1.0} due to the IGNORECASE flag
+
+ >>> evaluator = RegexMatchStringEvaluator()
+ >>> evaluator.evaluate_strings(
+ prediction="Mindy is the CTO",
+ reference="^Mike.*CEO$",
+ ) # This will return {'score': 0.0}
+
+ >>> evaluator.evaluate_strings(
+ prediction="Mindy is the CTO",
+ reference="^Mike.*CEO$|^Mindy.*CTO$",
+ ) # This will return {'score': 1.0} as the prediction matches the second pattern in the union
+ """ # noqa: E501
+
+ def __init__(self, *, flags: int = 0, **_: Any): # Default is no flags
+ """Initialize the RegexMatchStringEvaluator.
+
+ Args:
+ flags: Flags to use for the regex match. Defaults to no flags.
+ """
+ super().__init__()
+ self.flags = flags
+
+ @property
+ def requires_input(self) -> bool:
+ """This evaluator does not require input."""
+ return False
+
+ @property
+ def requires_reference(self) -> bool:
+ """This evaluator requires a reference."""
+ return True
+
+ @property
+ def input_keys(self) -> list[str]:
+ """Get the input keys.
+
+ Returns:
+ The input keys.
+ """
+ return ["reference", "prediction"]
+
+ @property
+ def evaluation_name(self) -> str:
+ """Get the evaluation name.
+
+ Returns:
+ The evaluation name.
+ """
+ return "regex_match"
+
+ @override
+ def _evaluate_strings( # type: ignore[override]
+ self,
+ *,
+ prediction: str,
+ reference: str,
+ **kwargs: Any,
+ ) -> dict:
+ """Evaluate the regex match between the prediction and the reference.
+
+ Args:
+ prediction: The prediction string.
+ reference: The reference regex pattern.
+ **kwargs: Additional keyword arguments (not used).
+
+ Returns:
+ The evaluation results containing the score.
+ """
+ match = re.match(reference, prediction, flags=self.flags)
+ return {"score": int(bool(match))}
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/scoring/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/scoring/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5527d530e4a9118d95efaa4f1ff88e6b126691ce
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/scoring/__init__.py
@@ -0,0 +1,31 @@
+"""Scoring evaluators.
+
+This module contains evaluators for scoring on a 1-10 the output of models,
+be they LLMs, Chains, or otherwise. This can be based on a variety of
+criteria and or a reference answer.
+
+Example:
+ >>> from langchain_openai import ChatOpenAI
+ >>> from langchain_classic.evaluation.scoring import ScoreStringEvalChain
+ >>> model = ChatOpenAI(temperature=0, model_name="gpt-4")
+ >>> chain = ScoreStringEvalChain.from_llm(llm=model)
+ >>> result = chain.evaluate_strings(
+ ... input="What is the chemical formula for water?",
+ ... prediction="H2O",
+ ... reference="The chemical formula for water is H2O.",
+ ... )
+ >>> print(result)
+ # {
+ # "score": 8,
+ # "comment": "The response accurately states "
+ # "that the chemical formula for water is H2O."
+ # "However, it does not provide an explanation of what the formula means."
+ # }
+"""
+
+from langchain_classic.evaluation.scoring.eval_chain import (
+ LabeledScoreStringEvalChain,
+ ScoreStringEvalChain,
+)
+
+__all__ = ["LabeledScoreStringEvalChain", "ScoreStringEvalChain"]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/scoring/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/scoring/__pycache__/__init__.cpython-311.pyc
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/scoring/eval_chain.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/scoring/eval_chain.py
new file mode 100644
index 0000000000000000000000000000000000000000..f5ac405ac83c6c3f81f8803bd29ddabea91bf5bd
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/scoring/eval_chain.py
@@ -0,0 +1,484 @@
+"""Base classes for scoring the output of a model on a scale of 1-10."""
+
+from __future__ import annotations
+
+import logging
+import re
+from typing import Any
+
+from langchain_core.callbacks import Callbacks
+from langchain_core.language_models import BaseLanguageModel
+from langchain_core.output_parsers import BaseOutputParser
+from langchain_core.prompts.prompt import PromptTemplate
+from pydantic import ConfigDict, Field
+from typing_extensions import override
+
+from langchain_classic.chains.constitutional_ai.models import ConstitutionalPrinciple
+from langchain_classic.chains.llm import LLMChain
+from langchain_classic.evaluation.criteria.eval_chain import (
+ CRITERIA_TYPE,
+ Criteria,
+)
+from langchain_classic.evaluation.schema import LLMEvalChain, StringEvaluator
+from langchain_classic.evaluation.scoring.prompt import (
+ CRITERIA_INSTRUCTIONS,
+ DEFAULT_CRITERIA,
+ SCORING_TEMPLATE,
+ SCORING_TEMPLATE_WITH_REFERENCE,
+)
+from langchain_classic.schema import RUN_KEY
+
+logger = logging.getLogger(__name__)
+
+_FIND_DOUBLE_BRACKETS = re.compile(r"\[\[(.*?)\]\]")
+
+_SUPPORTED_CRITERIA = {
+ Criteria.CONCISENESS: "Is the submission concise and to the point?",
+ Criteria.RELEVANCE: "Is the submission referring to a real quote from the text?",
+ Criteria.CORRECTNESS: "Is the submission correct, accurate, and factual?",
+ Criteria.COHERENCE: "Is the submission coherent, well-structured, and organized?",
+ Criteria.HARMFULNESS: "Is the submission harmful, offensive, or inappropriate?",
+ Criteria.MALICIOUSNESS: "Is the submission malicious in any way?",
+ Criteria.HELPFULNESS: "Is the submission helpful, insightful, and appropriate?",
+ Criteria.CONTROVERSIALITY: "Is the submission controversial or debatable?",
+ Criteria.MISOGYNY: "Is the submission misogynistic or sexist?",
+ Criteria.CRIMINALITY: "Is the submission criminal in any way?",
+ Criteria.INSENSITIVITY: "Is the submission insensitive to any group of people?",
+ Criteria.DEPTH: "Does the submission demonstrate depth of thought?",
+ Criteria.CREATIVITY: "Does the submission demonstrate novelty or unique ideas?",
+ Criteria.DETAIL: "Does the submission demonstrate attention to detail?",
+}
+
+
+def resolve_criteria(
+ criteria: CRITERIA_TYPE | str | list[CRITERIA_TYPE] | None,
+) -> dict:
+ """Resolve the criteria for the pairwise evaluator.
+
+ Args:
+ criteria: The criteria to use.
+
+ Returns:
+ The resolved criteria.
+
+ """
+ if criteria is None:
+ _default_criteria = [
+ Criteria.HELPFULNESS,
+ Criteria.RELEVANCE,
+ Criteria.CORRECTNESS,
+ Criteria.DEPTH,
+ ]
+ return {k.value: _SUPPORTED_CRITERIA[k] for k in _default_criteria}
+ if isinstance(criteria, Criteria):
+ criteria_ = {criteria.value: _SUPPORTED_CRITERIA[criteria]}
+ elif isinstance(criteria, str):
+ if criteria in _SUPPORTED_CRITERIA:
+ criteria_ = {criteria: _SUPPORTED_CRITERIA[Criteria(criteria)]}
+ else:
+ criteria_ = {criteria: ""}
+ elif isinstance(criteria, ConstitutionalPrinciple):
+ criteria_ = {criteria.name: criteria.critique_request}
+ elif isinstance(criteria, (list, tuple)):
+ criteria_ = {
+ k: v
+ for criterion in criteria
+ for k, v in resolve_criteria(criterion).items()
+ }
+ else:
+ if not criteria:
+ msg = (
+ "Criteria cannot be empty. "
+ "Please provide a criterion name or a mapping of the criterion name"
+ " to its description."
+ )
+ raise ValueError(msg)
+ criteria_ = dict(criteria)
+ return criteria_
+
+
+class ScoreStringResultOutputParser(BaseOutputParser[dict]):
+ """A parser for the output of the ScoreStringEvalChain.
+
+ Attributes:
+ _type: The type of the output parser.
+
+ """
+
+ @property
+ def _type(self) -> str:
+ """Return the type of the output parser.
+
+ Returns:
+ The type of the output parser.
+
+ """
+ return "pairwise_string_result"
+
+ def parse(self, text: str) -> dict[str, Any]:
+ """Parse the output text.
+
+ Args:
+ text: The output text to parse.
+
+ Returns:
+ The parsed output.
+
+ Raises:
+ ValueError: If the verdict is invalid.
+
+ """
+ match = _FIND_DOUBLE_BRACKETS.search(text)
+
+ if match:
+ verdict = match.group(1)
+
+ if not match or verdict not in [*list("123456789"), "10"]:
+ msg = (
+ f"Invalid output: {text}. "
+ "Output must contain a double bracketed string\
+ with the verdict between 1 and 10."
+ )
+ raise ValueError(msg)
+
+ return {
+ "reasoning": text,
+ "score": int(verdict),
+ }
+
+
+class ScoreStringEvalChain(StringEvaluator, LLMEvalChain, LLMChain):
+ """A chain for scoring on a scale of 1-10 the output of a model.
+
+ Attributes:
+ output_parser (BaseOutputParser): The output parser for the chain.
+
+ Example:
+ >>> from langchain_openai import ChatOpenAI
+ >>> from langchain_classic.evaluation.scoring import ScoreStringEvalChain
+ >>> model = ChatOpenAI(temperature=0, model_name="gpt-4")
+ >>> chain = ScoreStringEvalChain.from_llm(llm=model)
+ >>> result = chain.evaluate_strings(
+ ... input="What is the chemical formula for water?",
+ ... prediction="H2O",
+ ... reference="The chemical formula for water is H2O.",
+ ... )
+ >>> print(result)
+ # {
+ # "score": 8,
+ # "comment": "The response accurately states "
+ # "that the chemical formula for water is H2O."
+ # "However, it does not provide an explanation of what the formula means."
+ # }
+
+ """
+
+ output_key: str = "results"
+ output_parser: BaseOutputParser = Field(
+ default_factory=ScoreStringResultOutputParser,
+ )
+ normalize_by: float | None = None
+ """The value to normalize the score by, if specified."""
+ criterion_name: str
+ """The name of the criterion being evaluated."""
+
+ model_config = ConfigDict(
+ extra="ignore",
+ )
+
+ @classmethod
+ @override
+ def is_lc_serializable(cls) -> bool:
+ return False
+
+ @property
+ def requires_reference(self) -> bool:
+ """Return whether the chain requires a reference.
+
+ Returns:
+ `True` if the chain requires a reference, `False` otherwise.
+
+ """
+ return False
+
+ @property
+ def requires_input(self) -> bool:
+ """Return whether the chain requires an input.
+
+ Returns:
+ `True` if the chain requires an input, `False` otherwise.
+
+ """
+ return True
+
+ @property
+ def evaluation_name(self) -> str:
+ """Get the name of the evaluation.
+
+ Returns:
+ -------
+ str
+ The name of the evaluation.
+ """
+ return f"score_string:{self.criterion_name}"
+
+ @property
+ def _skip_reference_warning(self) -> str:
+ """Return the warning to show when reference is ignored.
+
+ Returns:
+ The warning to show when reference is ignored.
+
+ """
+ return (
+ f"Ignoring reference in {self.__class__.__name__}, as it is not expected."
+ "\nTo use a reference, use the LabeledScoreStringEvalChain instead."
+ " (EvaluatorType.LABELED_SCORE_STRING) instead."
+ )
+
+ @classmethod
+ def from_llm(
+ cls,
+ llm: BaseLanguageModel,
+ *,
+ prompt: PromptTemplate | None = None,
+ criteria: CRITERIA_TYPE | str | None = None,
+ normalize_by: float | None = None,
+ **kwargs: Any,
+ ) -> ScoreStringEvalChain:
+ """Initialize the ScoreStringEvalChain from an LLM.
+
+ Args:
+ llm: The LLM to use (GPT-4 recommended).
+ prompt: The prompt to use.
+ criteria: The criteria to use.
+ normalize_by: The value to normalize the score by.
+ **kwargs: Additional keyword arguments.
+
+ Returns:
+ The initialized ScoreStringEvalChain.
+
+ Raises:
+ ValueError: If the input variables are not as expected.
+
+ """
+ if not (hasattr(llm, "model_name") and not llm.model_name.startswith("gpt-4")):
+ logger.warning(
+ "This chain was only tested with GPT-4. \
+Performance may be significantly worse with other models.",
+ )
+
+ expected_input_vars = {"prediction", "input", "criteria"}
+ prompt_ = prompt or SCORING_TEMPLATE.partial(reference="")
+ if expected_input_vars != set(prompt_.input_variables):
+ msg = (
+ f"Input variables should be {expected_input_vars}, "
+ f"but got {prompt_.input_variables}"
+ )
+ raise ValueError(msg)
+ criteria_ = resolve_criteria(criteria)
+ criteria_str = "\n".join(
+ f"{k}: {v}" if v else k for k, v in criteria_.items()
+ ).strip()
+ criteria_str = (
+ CRITERIA_INSTRUCTIONS + f"{criteria_str}\n"
+ if criteria_str
+ else DEFAULT_CRITERIA
+ )
+ return cls(
+ llm=llm,
+ prompt=prompt_.partial(criteria=criteria_str),
+ normalize_by=normalize_by,
+ criterion_name="-".join(criteria_),
+ **kwargs,
+ )
+
+ def _prepare_input(
+ self,
+ prediction: str,
+ input_: str | None,
+ reference: str | None,
+ ) -> dict:
+ """Prepare the input for the chain.
+
+ Args:
+ prediction: The output string from the first model.
+ prediction_b: The output string from the second model.
+ input_: The input or task string.
+ reference: The reference string, if any.
+
+ Returns:
+ The prepared input for the chain.
+
+ """
+ input_dict = {
+ "prediction": prediction,
+ "input": input_,
+ }
+ if self.requires_reference:
+ input_dict["reference"] = reference
+ return input_dict
+
+ def _prepare_output(self, result: dict) -> dict:
+ """Prepare the output."""
+ parsed = result[self.output_key]
+ if RUN_KEY in result:
+ parsed[RUN_KEY] = result[RUN_KEY]
+ if "score" in parsed and self.normalize_by is not None:
+ parsed["score"] = parsed["score"] / self.normalize_by
+ return parsed
+
+ @override
+ def _evaluate_strings(
+ self,
+ *,
+ prediction: str,
+ input: str | None = None,
+ reference: str | None = None,
+ callbacks: Callbacks = None,
+ tags: list[str] | None = None,
+ metadata: dict[str, Any] | None = None,
+ include_run_info: bool = False,
+ **kwargs: Any,
+ ) -> dict:
+ """Score the output string.
+
+ Args:
+ prediction: The output string from the first model.
+ input: The input or task string.
+ callbacks: The callbacks to use.
+ tags: Optional tags to use.
+ metadata: Optional metadata to use.
+ include_run_info: Whether to include run information in the output.
+ reference: The reference string, if any.
+ **kwargs: Additional keyword arguments.
+
+ Returns:
+ `dict` containing:
+ - reasoning: The reasoning for the preference.
+ - score: A score between 1 and 10.
+
+ """
+ input_ = self._prepare_input(prediction, input, reference)
+ result = self(
+ inputs=input_,
+ callbacks=callbacks,
+ tags=tags,
+ metadata=metadata,
+ include_run_info=include_run_info,
+ )
+ return self._prepare_output(result)
+
+ @override
+ async def _aevaluate_strings(
+ self,
+ *,
+ prediction: str,
+ reference: str | None = None,
+ input: str | None = None,
+ callbacks: Callbacks = None,
+ tags: list[str] | None = None,
+ metadata: dict[str, Any] | None = None,
+ include_run_info: bool = False,
+ **kwargs: Any,
+ ) -> dict:
+ """Asynchronously score the output string.
+
+ Args:
+ prediction: The output string from the first model.
+ input: The input or task string.
+ callbacks: The callbacks to use.
+ tags: Optional tags to use.
+ metadata: Optional metadata to use.
+ include_run_info: Whether to include run information in the output.
+ reference: The reference string, if any.
+ **kwargs: Additional keyword arguments.
+
+ Returns:
+ `dict` containing:
+ - reasoning: The reasoning for the preference.
+ - score: A score between 1 and 10.
+
+ """
+ input_ = self._prepare_input(prediction, input, reference)
+ result = await self.acall(
+ inputs=input_,
+ callbacks=callbacks,
+ tags=tags,
+ metadata=metadata,
+ include_run_info=include_run_info,
+ )
+ return self._prepare_output(result)
+
+
+class LabeledScoreStringEvalChain(ScoreStringEvalChain):
+ """A chain for scoring the output of a model on a scale of 1-10.
+
+ Attributes:
+ output_parser (BaseOutputParser): The output parser for the chain.
+
+ """
+
+ @property
+ def requires_reference(self) -> bool:
+ """Return whether the chain requires a reference.
+
+ Returns:
+ `True` if the chain requires a reference, `False` otherwise.
+
+ """
+ return True
+
+ @classmethod
+ def from_llm(
+ cls,
+ llm: BaseLanguageModel,
+ *,
+ prompt: PromptTemplate | None = None,
+ criteria: CRITERIA_TYPE | str | None = None,
+ normalize_by: float | None = None,
+ **kwargs: Any,
+ ) -> LabeledScoreStringEvalChain:
+ """Initialize the LabeledScoreStringEvalChain from an LLM.
+
+ Args:
+ llm: The LLM to use.
+ prompt: The prompt to use.
+ criteria: The criteria to use.
+ normalize_by: The value to normalize the score by.
+ **kwargs: Additional keyword arguments.
+
+ Returns:
+ The initialized LabeledScoreStringEvalChain.
+
+ Raises:
+ ValueError: If the input variables are not as expected.
+
+ """
+ expected_input_vars = {
+ "prediction",
+ "input",
+ "reference",
+ "criteria",
+ }
+ prompt_ = prompt or SCORING_TEMPLATE_WITH_REFERENCE
+ if expected_input_vars != set(prompt_.input_variables):
+ msg = (
+ f"Input variables should be {expected_input_vars}, "
+ f"but got {prompt_.input_variables}"
+ )
+ raise ValueError(msg)
+ criteria_ = resolve_criteria(criteria)
+ criteria_str = "\n".join(f"{k}: {v}" for k, v in criteria_.items()).strip()
+ criteria_str = (
+ CRITERIA_INSTRUCTIONS + f"{criteria_str}\n"
+ if criteria_str
+ else DEFAULT_CRITERIA
+ )
+ return cls(
+ llm=llm,
+ prompt=prompt_.partial(criteria=criteria_str),
+ normalize_by=normalize_by,
+ criterion_name="-".join(criteria_),
+ **kwargs,
+ )
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/scoring/prompt.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/scoring/prompt.py
new file mode 100644
index 0000000000000000000000000000000000000000..3aa6c59b6506d01896abf6ca0eea89ee0b4a8e51
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/scoring/prompt.py
@@ -0,0 +1,53 @@
+"""Prompts for scoring the outputs of a models for a given question.
+
+This prompt is used to score the responses and evaluate how it follows the instructions
+and answers the question. The prompt is based on the paper from
+Zheng, et. al. https://arxiv.org/abs/2306.05685
+"""
+
+from langchain_core.prompts.chat import ChatPromptTemplate
+
+SYSTEM_MESSAGE = "You are a helpful assistant."
+
+CRITERIA_INSTRUCTIONS = (
+ "For this evaluation, you should primarily consider the following criteria:\n"
+)
+
+DEFAULT_CRITERIA = " Your evaluation \
+should consider factors such as the helpfulness, relevance, accuracy, \
+depth, creativity, and level of detail of the response."
+
+SCORING_TEMPLATE = ChatPromptTemplate.from_messages(
+ [
+ ("system", SYSTEM_MESSAGE),
+ (
+ "human",
+ '[Instruction]\nPlease act as an impartial judge \
+and evaluate the quality of the response provided by an AI \
+assistant to the user question displayed below. {criteria}Begin your evaluation \
+by providing a short explanation. Be as objective as possible. \
+After providing your explanation, you must rate the response on a scale of 1 to 10 \
+by strictly following this format: "[[rating]]", for example: "Rating: [[5]]".\n\n\
+[Question]\n{input}\n\n[The Start of Assistant\'s Answer]\n{prediction}\n\
+[The End of Assistant\'s Answer]',
+ ),
+ ]
+)
+
+SCORING_TEMPLATE_WITH_REFERENCE = ChatPromptTemplate.from_messages(
+ [
+ ("system", SYSTEM_MESSAGE),
+ (
+ "human",
+ "[Instruction]\nPlease act as an impartial judge \
+and evaluate the quality of the response provided by an AI \
+assistant to the user question displayed below. {criteria}"
+ '[Ground truth]\n{reference}\nBegin your evaluation \
+by providing a short explanation. Be as objective as possible. \
+After providing your explanation, you must rate the response on a scale of 1 to 10 \
+by strictly following this format: "[[rating]]", for example: "Rating: [[5]]".\n\n\
+[Question]\n{input}\n\n[The Start of Assistant\'s Answer]\n{prediction}\n\
+[The End of Assistant\'s Answer]',
+ ),
+ ]
+)
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/string_distance/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/string_distance/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..aac30f53d6d55c1a62506e85d78ed72bfdf692bc
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/string_distance/__init__.py
@@ -0,0 +1,13 @@
+"""String distance evaluators."""
+
+from langchain_classic.evaluation.string_distance.base import (
+ PairwiseStringDistanceEvalChain,
+ StringDistance,
+ StringDistanceEvalChain,
+)
+
+__all__ = [
+ "PairwiseStringDistanceEvalChain",
+ "StringDistance",
+ "StringDistanceEvalChain",
+]
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/string_distance/base.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/string_distance/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..88e91cffefed2ba3b3510dd825eae86f81b29a29
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/evaluation/string_distance/base.py
@@ -0,0 +1,452 @@
+"""String distance evaluators based on the RapidFuzz library."""
+
+from collections.abc import Callable
+from enum import Enum
+from typing import Any
+
+from langchain_core.callbacks import Callbacks
+from langchain_core.callbacks.manager import (
+ AsyncCallbackManagerForChainRun,
+ CallbackManagerForChainRun,
+)
+from langchain_core.utils import pre_init
+from pydantic import Field
+from typing_extensions import override
+
+from langchain_classic.chains.base import Chain
+from langchain_classic.evaluation.schema import PairwiseStringEvaluator, StringEvaluator
+from langchain_classic.schema import RUN_KEY
+
+
+def _load_rapidfuzz() -> Any:
+ """Load the RapidFuzz library.
+
+ Raises:
+ ImportError: If the rapidfuzz library is not installed.
+
+ Returns:
+ The `rapidfuzz.distance` module.
+ """
+ try:
+ import rapidfuzz
+ except ImportError as e:
+ msg = (
+ "Please install the rapidfuzz library to use the FuzzyMatchStringEvaluator."
+ "Please install it with `pip install rapidfuzz`."
+ )
+ raise ImportError(msg) from e
+ return rapidfuzz.distance
+
+
+class StringDistance(str, Enum):
+ """Distance metric to use.
+
+ Attributes:
+ `DAMERAU_LEVENSHTEIN`: The Damerau-Levenshtein distance.
+ `LEVENSHTEIN`: The Levenshtein distance.
+ `JARO`: The Jaro distance.
+ `JARO_WINKLER`: The Jaro-Winkler distance.
+ `HAMMING`: The Hamming distance.
+ `INDEL`: The Indel distance.
+ """
+
+ DAMERAU_LEVENSHTEIN = "damerau_levenshtein"
+ LEVENSHTEIN = "levenshtein"
+ JARO = "jaro"
+ JARO_WINKLER = "jaro_winkler"
+ HAMMING = "hamming"
+ INDEL = "indel"
+
+
+class _RapidFuzzChainMixin(Chain):
+ """Shared methods for the rapidfuzz string distance evaluators."""
+
+ distance: StringDistance = Field(default=StringDistance.JARO_WINKLER)
+ normalize_score: bool = Field(default=True)
+ """Whether to normalize the score to a value between `0` and `1`.
+ Applies only to the Levenshtein and Damerau-Levenshtein distances."""
+
+ @pre_init
+ def validate_dependencies(cls, values: dict[str, Any]) -> dict[str, Any]:
+ """Validate that the rapidfuzz library is installed.
+
+ Args:
+ values: The input values.
+
+ Returns:
+ The validated values.
+ """
+ _load_rapidfuzz()
+ return values
+
+ @property
+ def output_keys(self) -> list[str]:
+ """Get the output keys.
+
+ Returns:
+ The output keys.
+ """
+ return ["score"]
+
+ def _prepare_output(self, result: dict[str, Any]) -> dict[str, Any]:
+ """Prepare the output dictionary.
+
+ Args:
+ result: The evaluation results.
+
+ Returns:
+ The prepared output dictionary.
+ """
+ result = {"score": result["score"]}
+ if RUN_KEY in result:
+ result[RUN_KEY] = result[RUN_KEY].dict()
+ return result
+
+ @staticmethod
+ def _get_metric(distance: str, *, normalize_score: bool = False) -> Callable:
+ """Get the distance metric function based on the distance type.
+
+ Args:
+ distance: The distance type.
+ normalize_score: Whether to normalize the score.
+
+ Returns:
+ The distance metric function.
+
+ Raises:
+ ValueError: If the distance metric is invalid.
+ """
+ from rapidfuzz import distance as rf_distance
+
+ module_map: dict[str, Any] = {
+ StringDistance.DAMERAU_LEVENSHTEIN: rf_distance.DamerauLevenshtein,
+ StringDistance.LEVENSHTEIN: rf_distance.Levenshtein,
+ StringDistance.JARO: rf_distance.Jaro,
+ StringDistance.JARO_WINKLER: rf_distance.JaroWinkler,
+ StringDistance.HAMMING: rf_distance.Hamming,
+ StringDistance.INDEL: rf_distance.Indel,
+ }
+ if distance not in module_map:
+ msg = (
+ f"Invalid distance metric: {distance}"
+ f"\nMust be one of: {list(StringDistance)}"
+ )
+ raise ValueError(msg)
+ module = module_map[distance]
+ if normalize_score:
+ return module.normalized_distance
+ return module.distance
+
+ @property
+ def metric(self) -> Callable:
+ """Get the distance metric function.
+
+ Returns:
+ The distance metric function.
+ """
+ return _RapidFuzzChainMixin._get_metric(
+ self.distance,
+ normalize_score=self.normalize_score,
+ )
+
+ def compute_metric(self, a: str, b: str) -> float:
+ """Compute the distance between two strings.
+
+ Args:
+ a: The first string.
+ b: The second string.
+
+ Returns:
+ The distance between the two strings.
+ """
+ return self.metric(a, b)
+
+
+class StringDistanceEvalChain(StringEvaluator, _RapidFuzzChainMixin):
+ """Compute string distances between the prediction and the reference.
+
+ Examples:
+ ----------
+ >>> from langchain_classic.evaluation import StringDistanceEvalChain
+ >>> evaluator = StringDistanceEvalChain()
+ >>> evaluator.evaluate_strings(
+ prediction="Mindy is the CTO",
+ reference="Mindy is the CEO",
+ )
+
+ Using the `load_evaluator` function:
+
+ >>> from langchain_classic.evaluation import load_evaluator
+ >>> evaluator = load_evaluator("string_distance")
+ >>> evaluator.evaluate_strings(
+ prediction="The answer is three",
+ reference="three",
+ )
+ """
+
+ @property
+ def requires_input(self) -> bool:
+ """This evaluator does not require input."""
+ return False
+
+ @property
+ def requires_reference(self) -> bool:
+ """This evaluator does not require a reference."""
+ return True
+
+ @property
+ def input_keys(self) -> list[str]:
+ """Get the input keys.
+
+ Returns:
+ The input keys.
+ """
+ return ["reference", "prediction"]
+
+ @property
+ def evaluation_name(self) -> str:
+ """Get the evaluation name.
+
+ Returns:
+ The evaluation name.
+ """
+ return f"{self.distance.value}_distance"
+
+ @override
+ def _call(
+ self,
+ inputs: dict[str, Any],
+ run_manager: CallbackManagerForChainRun | None = None,
+ ) -> dict[str, Any]:
+ """Compute the string distance between the prediction and the reference.
+
+ Args:
+ inputs: The input values.
+ run_manager: The callback manager.
+
+ Returns:
+ The evaluation results containing the score.
+ """
+ return {"score": self.compute_metric(inputs["reference"], inputs["prediction"])}
+
+ @override
+ async def _acall(
+ self,
+ inputs: dict[str, Any],
+ run_manager: AsyncCallbackManagerForChainRun | None = None,
+ ) -> dict[str, Any]:
+ """Compute the string distance between the prediction and the reference.
+
+ Args:
+ inputs: The input values.
+ run_manager: The callback manager.
+
+ Returns:
+ The evaluation results containing the score.
+ """
+ return {"score": self.compute_metric(inputs["reference"], inputs["prediction"])}
+
+ @override
+ def _evaluate_strings(
+ self,
+ *,
+ prediction: str,
+ reference: str | None = None,
+ input: str | None = None,
+ callbacks: Callbacks = None,
+ tags: list[str] | None = None,
+ metadata: dict[str, Any] | None = None,
+ include_run_info: bool = False,
+ **kwargs: Any,
+ ) -> dict:
+ """Evaluate the string distance between the prediction and the reference.
+
+ Args:
+ prediction: The prediction string.
+ reference: The reference string.
+ input: The input string.
+ callbacks: The callbacks to use.
+ tags: The tags to apply.
+ metadata: The metadata to use.
+ include_run_info: Whether to include run info in the output.
+ **kwargs: Additional keyword arguments.
+
+ Returns:
+ The evaluation results containing the score.
+ """
+ result = self(
+ inputs={"prediction": prediction, "reference": reference},
+ callbacks=callbacks,
+ tags=tags,
+ metadata=metadata,
+ include_run_info=include_run_info,
+ )
+
+ return self._prepare_output(result)
+
+ @override
+ async def _aevaluate_strings(
+ self,
+ *,
+ prediction: str,
+ reference: str | None = None,
+ input: str | None = None,
+ callbacks: Callbacks = None,
+ tags: list[str] | None = None,
+ metadata: dict[str, Any] | None = None,
+ include_run_info: bool = False,
+ **kwargs: Any,
+ ) -> dict:
+ """Evaluate the string distance between the prediction and the reference.
+
+ Args:
+ prediction: The prediction string.
+ reference: The reference string.
+ input: The input string.
+ callbacks: The callbacks to use.
+ tags: The tags to apply.
+ metadata: The metadata to apply.
+ include_run_info: Whether to include run info in the output.
+ **kwargs: Additional keyword arguments.
+
+ Returns:
+ The evaluation results containing the score.
+ """
+ result = await self.acall(
+ inputs={"prediction": prediction, "reference": reference},
+ callbacks=callbacks,
+ tags=tags,
+ metadata=metadata,
+ include_run_info=include_run_info,
+ )
+ return self._prepare_output(result)
+
+
+class PairwiseStringDistanceEvalChain(PairwiseStringEvaluator, _RapidFuzzChainMixin):
+ """Compute string edit distances between two predictions."""
+
+ @property
+ def input_keys(self) -> list[str]:
+ """Get the input keys.
+
+ Returns:
+ The input keys.
+ """
+ return ["prediction", "prediction_b"]
+
+ @property
+ def evaluation_name(self) -> str:
+ """Get the evaluation name.
+
+ Returns:
+ The evaluation name.
+ """
+ return f"pairwise_{self.distance.value}_distance"
+
+ @override
+ def _call(
+ self,
+ inputs: dict[str, Any],
+ run_manager: CallbackManagerForChainRun | None = None,
+ ) -> dict[str, Any]:
+ """Compute the string distance between two predictions.
+
+ Args:
+ inputs: The input values.
+ run_manager: The callback manager.
+
+ Returns:
+ The evaluation results containing the score.
+ """
+ return {
+ "score": self.compute_metric(inputs["prediction"], inputs["prediction_b"]),
+ }
+
+ @override
+ async def _acall(
+ self,
+ inputs: dict[str, Any],
+ run_manager: AsyncCallbackManagerForChainRun | None = None,
+ ) -> dict[str, Any]:
+ """Asynchronously compute the string distance between two predictions.
+
+ Args:
+ inputs: The input values.
+ run_manager: The callback manager.
+
+ Returns:
+ The evaluation results containing the score.
+ """
+ return {
+ "score": self.compute_metric(inputs["prediction"], inputs["prediction_b"]),
+ }
+
+ @override
+ def _evaluate_string_pairs(
+ self,
+ *,
+ prediction: str,
+ prediction_b: str,
+ callbacks: Callbacks = None,
+ tags: list[str] | None = None,
+ metadata: dict[str, Any] | None = None,
+ include_run_info: bool = False,
+ **kwargs: Any,
+ ) -> dict:
+ """Evaluate the string distance between two predictions.
+
+ Args:
+ prediction: The first prediction string.
+ prediction_b: The second prediction string.
+ callbacks: The callbacks to use.
+ tags: The tags to apply.
+ metadata: The metadata to use.
+ include_run_info: Whether to include run info in the output.
+ **kwargs: Additional keyword arguments.
+
+ Returns:
+ The evaluation results containing the score.
+ """
+ result = self(
+ inputs={"prediction": prediction, "prediction_b": prediction_b},
+ callbacks=callbacks,
+ tags=tags,
+ metadata=metadata,
+ include_run_info=include_run_info,
+ )
+ return self._prepare_output(result)
+
+ @override
+ async def _aevaluate_string_pairs(
+ self,
+ *,
+ prediction: str,
+ prediction_b: str,
+ callbacks: Callbacks = None,
+ tags: list[str] | None = None,
+ metadata: dict[str, Any] | None = None,
+ include_run_info: bool = False,
+ **kwargs: Any,
+ ) -> dict:
+ """Asynchronously evaluate the string distance between two predictions.
+
+ Args:
+ prediction: The first prediction string.
+ prediction_b: The second prediction string.
+ callbacks: The callbacks to use.
+ tags: The tags to apply.
+ metadata: The metadata to use.
+ include_run_info: Whether to include run info in the output.
+ **kwargs: Additional keyword arguments.
+
+ Returns:
+ The evaluation results containing the score.
+ """
+ result = await self.acall(
+ inputs={"prediction": prediction, "prediction_b": prediction_b},
+ callbacks=callbacks,
+ tags=tags,
+ metadata=metadata,
+ include_run_info=include_run_info,
+ )
+ return self._prepare_output(result)
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/indexes/prompts/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/indexes/prompts/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..fd8ff5d357d1177b2c195345a94907f549feab82
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/indexes/prompts/__init__.py
@@ -0,0 +1,13 @@
+"""Relevant prompts for constructing indexes."""
+
+from langchain_core._api import warn_deprecated
+
+warn_deprecated(
+ since="0.1.47",
+ message=(
+ "langchain.indexes.prompts will be removed in the future."
+ "If you're relying on these prompts, please open an issue on "
+ "GitHub to explain your use case."
+ ),
+ pending=True,
+)
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/indexes/prompts/entity_extraction.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/indexes/prompts/entity_extraction.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea959bc28d39593c08f0d063c8a30664605bb6be
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/indexes/prompts/entity_extraction.py
@@ -0,0 +1,39 @@
+from langchain_core.prompts.prompt import PromptTemplate
+
+_DEFAULT_ENTITY_EXTRACTION_TEMPLATE = """You are an AI assistant reading the transcript of a conversation between an AI and a human. Extract all of the proper nouns from the last line of conversation. As a guideline, a proper noun is generally capitalized. You should definitely extract all names and places.
+
+The conversation history is provided just in case of a coreference (e.g. "What do you know about him" where "him" is defined in a previous line) -- ignore items mentioned there that are not in the last line.
+
+Return the output as a single comma-separated list, or NONE if there is nothing of note to return (e.g. the user is just issuing a greeting or having a simple conversation).
+
+EXAMPLE
+Conversation history:
+Person #1: how's it going today?
+AI: "It's going great! How about you?"
+Person #1: good! busy working on Langchain. lots to do.
+AI: "That sounds like a lot of work! What kind of things are you doing to make Langchain better?"
+Last line:
+Person #1: i'm trying to improve Langchain's interfaces, the UX, its integrations with various products the user might want ... a lot of stuff.
+Output: Langchain
+END OF EXAMPLE
+
+EXAMPLE
+Conversation history:
+Person #1: how's it going today?
+AI: "It's going great! How about you?"
+Person #1: good! busy working on Langchain. lots to do.
+AI: "That sounds like a lot of work! What kind of things are you doing to make Langchain better?"
+Last line:
+Person #1: i'm trying to improve Langchain's interfaces, the UX, its integrations with various products the user might want ... a lot of stuff. I'm working with Person #2.
+Output: Langchain, Person #2
+END OF EXAMPLE
+
+Conversation history (for reference only):
+{history}
+Last line of conversation (for extraction):
+Human: {input}
+
+Output:""" # noqa: E501
+ENTITY_EXTRACTION_PROMPT = PromptTemplate(
+ input_variables=["history", "input"], template=_DEFAULT_ENTITY_EXTRACTION_TEMPLATE
+)
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/indexes/prompts/entity_summarization.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/indexes/prompts/entity_summarization.py
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index 0000000000000000000000000000000000000000..89044f2e91c518e898cdec7b52b14d65ad89f186
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/indexes/prompts/entity_summarization.py
@@ -0,0 +1,24 @@
+from langchain_core.prompts.prompt import PromptTemplate
+
+_DEFAULT_ENTITY_SUMMARIZATION_TEMPLATE = """You are an AI assistant helping a human keep track of facts about relevant people, places, and concepts in their life. Update the summary of the provided entity in the "Entity" section based on the last line of your conversation with the human. If you are writing the summary for the first time, return a single sentence.
+The update should only include facts that are relayed in the last line of conversation about the provided entity, and should only contain facts about the provided entity.
+
+If there is no new information about the provided entity or the information is not worth noting (not an important or relevant fact to remember long-term), return the existing summary unchanged.
+
+Full conversation history (for context):
+{history}
+
+Entity to summarize:
+{entity}
+
+Existing summary of {entity}:
+{summary}
+
+Last line of conversation:
+Human: {input}
+Updated summary:""" # noqa: E501
+
+ENTITY_SUMMARIZATION_PROMPT = PromptTemplate(
+ input_variables=["entity", "summary", "history", "input"],
+ template=_DEFAULT_ENTITY_SUMMARIZATION_TEMPLATE,
+)
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/indexes/prompts/knowledge_triplet_extraction.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/indexes/prompts/knowledge_triplet_extraction.py
new file mode 100644
index 0000000000000000000000000000000000000000..b78b9098e227ceee0aea46e85dfa766a89b79bb3
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/indexes/prompts/knowledge_triplet_extraction.py
@@ -0,0 +1,36 @@
+from langchain_core.prompts.prompt import PromptTemplate
+
+KG_TRIPLE_DELIMITER = "<|>"
+
+_DEFAULT_KNOWLEDGE_TRIPLE_EXTRACTION_TEMPLATE = (
+ "You are a networked intelligence helping a human track knowledge triples"
+ " about all relevant people, things, concepts, etc. and integrating"
+ " them with your knowledge stored within your weights"
+ " as well as that stored in a knowledge graph."
+ " Extract all of the knowledge triples from the text."
+ " A knowledge triple is a clause that contains a subject, a predicate,"
+ " and an object. The subject is the entity being described,"
+ " the predicate is the property of the subject that is being"
+ " described, and the object is the value of the property.\n\n"
+ "EXAMPLE\n"
+ "It's a state in the US. It's also the number 1 producer of gold in the US.\n\n"
+ f"Output: (Nevada, is a, state){KG_TRIPLE_DELIMITER}(Nevada, is in, US)"
+ f"{KG_TRIPLE_DELIMITER}(Nevada, is the number 1 producer of, gold)\n"
+ "END OF EXAMPLE\n\n"
+ "EXAMPLE\n"
+ "I'm going to the store.\n\n"
+ "Output: NONE\n"
+ "END OF EXAMPLE\n\n"
+ "EXAMPLE\n"
+ "Oh huh. I know Descartes likes to drive antique scooters and play the mandolin.\n"
+ f"Output: (Descartes, likes to drive, antique scooters){KG_TRIPLE_DELIMITER}(Descartes, plays, mandolin)\n" # noqa: E501
+ "END OF EXAMPLE\n\n"
+ "EXAMPLE\n"
+ "{text}"
+ "Output:"
+)
+
+KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT = PromptTemplate(
+ input_variables=["text"],
+ template=_DEFAULT_KNOWLEDGE_TRIPLE_EXTRACTION_TEMPLATE,
+)
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/llms/grammars/json.gbnf b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/llms/grammars/json.gbnf
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index 0000000000000000000000000000000000000000..61bd2b2e65bf9c2632dc7713a8bed5420bebef28
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/llms/grammars/json.gbnf
@@ -0,0 +1,29 @@
+# Grammar for subset of JSON - doesn't support full string or number syntax
+
+root ::= object
+value ::= object | array | string | number | boolean | "null"
+
+object ::=
+ "{" ws (
+ string ":" ws value
+ ("," ws string ":" ws value)*
+ )? "}"
+
+array ::=
+ "[" ws (
+ value
+ ("," ws value)*
+ )? "]"
+
+string ::=
+ "\"" (
+ [^"\\] |
+ "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes
+ )* "\"" ws
+
+# Only plain integers currently
+number ::= "-"? [0-9]+ ws
+boolean ::= ("true" | "false") ws
+
+# Optional space: by convention, applied in this grammar after literal chars when allowed
+ws ::= ([ \t\n] ws)?
\ No newline at end of file
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/llms/grammars/list.gbnf b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/llms/grammars/list.gbnf
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--- /dev/null
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@@ -0,0 +1,14 @@
+root ::= "[" items "]" EOF
+
+items ::= item ("," ws* item)*
+
+item ::= string
+
+string ::=
+ "\"" word (ws+ word)* "\"" ws*
+
+word ::= [a-zA-Z]+
+
+ws ::= " "
+
+EOF ::= "\n"
\ No newline at end of file
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..27a66cb618e965b2d296817cbc284d9d0aa5ce08
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/__init__.py
@@ -0,0 +1,84 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.chat_message_histories import (
+ AstraDBChatMessageHistory,
+ CassandraChatMessageHistory,
+ ChatMessageHistory,
+ CosmosDBChatMessageHistory,
+ DynamoDBChatMessageHistory,
+ ElasticsearchChatMessageHistory,
+ FileChatMessageHistory,
+ FirestoreChatMessageHistory,
+ MomentoChatMessageHistory,
+ MongoDBChatMessageHistory,
+ Neo4jChatMessageHistory,
+ PostgresChatMessageHistory,
+ RedisChatMessageHistory,
+ RocksetChatMessageHistory,
+ SingleStoreDBChatMessageHistory,
+ SQLChatMessageHistory,
+ StreamlitChatMessageHistory,
+ UpstashRedisChatMessageHistory,
+ XataChatMessageHistory,
+ ZepChatMessageHistory,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "AstraDBChatMessageHistory": "langchain_community.chat_message_histories",
+ "CassandraChatMessageHistory": "langchain_community.chat_message_histories",
+ "ChatMessageHistory": "langchain_community.chat_message_histories",
+ "CosmosDBChatMessageHistory": "langchain_community.chat_message_histories",
+ "DynamoDBChatMessageHistory": "langchain_community.chat_message_histories",
+ "ElasticsearchChatMessageHistory": "langchain_community.chat_message_histories",
+ "FileChatMessageHistory": "langchain_community.chat_message_histories",
+ "FirestoreChatMessageHistory": "langchain_community.chat_message_histories",
+ "MomentoChatMessageHistory": "langchain_community.chat_message_histories",
+ "MongoDBChatMessageHistory": "langchain_community.chat_message_histories",
+ "Neo4jChatMessageHistory": "langchain_community.chat_message_histories",
+ "PostgresChatMessageHistory": "langchain_community.chat_message_histories",
+ "RedisChatMessageHistory": "langchain_community.chat_message_histories",
+ "RocksetChatMessageHistory": "langchain_community.chat_message_histories",
+ "SQLChatMessageHistory": "langchain_community.chat_message_histories",
+ "SingleStoreDBChatMessageHistory": "langchain_community.chat_message_histories",
+ "StreamlitChatMessageHistory": "langchain_community.chat_message_histories",
+ "UpstashRedisChatMessageHistory": "langchain_community.chat_message_histories",
+ "XataChatMessageHistory": "langchain_community.chat_message_histories",
+ "ZepChatMessageHistory": "langchain_community.chat_message_histories",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "AstraDBChatMessageHistory",
+ "CassandraChatMessageHistory",
+ "ChatMessageHistory",
+ "CosmosDBChatMessageHistory",
+ "DynamoDBChatMessageHistory",
+ "ElasticsearchChatMessageHistory",
+ "FileChatMessageHistory",
+ "FirestoreChatMessageHistory",
+ "MomentoChatMessageHistory",
+ "MongoDBChatMessageHistory",
+ "Neo4jChatMessageHistory",
+ "PostgresChatMessageHistory",
+ "RedisChatMessageHistory",
+ "RocksetChatMessageHistory",
+ "SQLChatMessageHistory",
+ "SingleStoreDBChatMessageHistory",
+ "StreamlitChatMessageHistory",
+ "UpstashRedisChatMessageHistory",
+ "XataChatMessageHistory",
+ "ZepChatMessageHistory",
+]
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/astradb.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/astradb.py
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index 0000000000000000000000000000000000000000..bba6a3f75d3d9d70debab54658ceeb482b53f569
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/astradb.py
@@ -0,0 +1,25 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.chat_message_histories import AstraDBChatMessageHistory
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "AstraDBChatMessageHistory": "langchain_community.chat_message_histories",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "AstraDBChatMessageHistory",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/cassandra.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/cassandra.py
new file mode 100644
index 0000000000000000000000000000000000000000..60bd212b5a410b9c7790ccb4786a74812342224a
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/cassandra.py
@@ -0,0 +1,25 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.chat_message_histories import CassandraChatMessageHistory
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "CassandraChatMessageHistory": "langchain_community.chat_message_histories",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "CassandraChatMessageHistory",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/cosmos_db.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/cosmos_db.py
new file mode 100644
index 0000000000000000000000000000000000000000..3356e8bda6def6964164ef5868c7bcbd914c1968
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/cosmos_db.py
@@ -0,0 +1,25 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.chat_message_histories import CosmosDBChatMessageHistory
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "CosmosDBChatMessageHistory": "langchain_community.chat_message_histories",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "CosmosDBChatMessageHistory",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/dynamodb.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/dynamodb.py
new file mode 100644
index 0000000000000000000000000000000000000000..ee70bdd2b702c68d0a9fe95572d50f7b65bbc20a
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/dynamodb.py
@@ -0,0 +1,25 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.chat_message_histories import DynamoDBChatMessageHistory
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "DynamoDBChatMessageHistory": "langchain_community.chat_message_histories",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "DynamoDBChatMessageHistory",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/elasticsearch.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/elasticsearch.py
new file mode 100644
index 0000000000000000000000000000000000000000..4481efa7d180c02692d8a64ea09c8989b6eeb1b9
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/elasticsearch.py
@@ -0,0 +1,27 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.chat_message_histories import (
+ ElasticsearchChatMessageHistory,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "ElasticsearchChatMessageHistory": "langchain_community.chat_message_histories",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "ElasticsearchChatMessageHistory",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/file.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/file.py
new file mode 100644
index 0000000000000000000000000000000000000000..d3bd1d5c6b06e1b8b6127fc8fa4fd43cc0b496ff
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/file.py
@@ -0,0 +1,25 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.chat_message_histories import FileChatMessageHistory
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "FileChatMessageHistory": "langchain_community.chat_message_histories",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "FileChatMessageHistory",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/firestore.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/firestore.py
new file mode 100644
index 0000000000000000000000000000000000000000..d2d1d840418af9553a8f1c04a1e1810b84e4e8f6
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/firestore.py
@@ -0,0 +1,25 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.chat_message_histories import FirestoreChatMessageHistory
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "FirestoreChatMessageHistory": "langchain_community.chat_message_histories",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "FirestoreChatMessageHistory",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/in_memory.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/in_memory.py
new file mode 100644
index 0000000000000000000000000000000000000000..679c9ce665e7cbea0f946d853b26bdcb392da1bd
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/in_memory.py
@@ -0,0 +1,5 @@
+from langchain_core.chat_history import InMemoryChatMessageHistory as ChatMessageHistory
+
+__all__ = [
+ "ChatMessageHistory",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/momento.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/momento.py
new file mode 100644
index 0000000000000000000000000000000000000000..83dff6ac7d46952f563f8fb35c7c5af9cc682d29
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/momento.py
@@ -0,0 +1,25 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.chat_message_histories import MomentoChatMessageHistory
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "MomentoChatMessageHistory": "langchain_community.chat_message_histories",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "MomentoChatMessageHistory",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/mongodb.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/mongodb.py
new file mode 100644
index 0000000000000000000000000000000000000000..a323971f12388272cdb5ebca27edcd5bd5b8bdb9
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/mongodb.py
@@ -0,0 +1,25 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.chat_message_histories import MongoDBChatMessageHistory
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "MongoDBChatMessageHistory": "langchain_community.chat_message_histories",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "MongoDBChatMessageHistory",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/neo4j.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/neo4j.py
new file mode 100644
index 0000000000000000000000000000000000000000..78b2f0a15e8ac588b3e4eb14a53df16ee9b8b792
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/neo4j.py
@@ -0,0 +1,25 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.chat_message_histories import Neo4jChatMessageHistory
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "Neo4jChatMessageHistory": "langchain_community.chat_message_histories",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "Neo4jChatMessageHistory",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/postgres.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/postgres.py
new file mode 100644
index 0000000000000000000000000000000000000000..63111f833dd05d8b8382f191066295d5be2c2993
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/postgres.py
@@ -0,0 +1,25 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.chat_message_histories import PostgresChatMessageHistory
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "PostgresChatMessageHistory": "langchain_community.chat_message_histories",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "PostgresChatMessageHistory",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/redis.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/redis.py
new file mode 100644
index 0000000000000000000000000000000000000000..d3600cc914472c2c9aac98b3f2ce6867b8a348ed
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/redis.py
@@ -0,0 +1,25 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.chat_message_histories import RedisChatMessageHistory
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "RedisChatMessageHistory": "langchain_community.chat_message_histories",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "RedisChatMessageHistory",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/rocksetdb.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/rocksetdb.py
new file mode 100644
index 0000000000000000000000000000000000000000..92683877a4d895f999a6a39876fd8fbe7455ee0d
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/rocksetdb.py
@@ -0,0 +1,25 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.chat_message_histories import RocksetChatMessageHistory
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "RocksetChatMessageHistory": "langchain_community.chat_message_histories",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "RocksetChatMessageHistory",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/singlestoredb.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/singlestoredb.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f027f62aff0799be8eb809f8a5790bbb27b04c6
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/singlestoredb.py
@@ -0,0 +1,27 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.chat_message_histories import (
+ SingleStoreDBChatMessageHistory,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "SingleStoreDBChatMessageHistory": "langchain_community.chat_message_histories",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "SingleStoreDBChatMessageHistory",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/sql.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/sql.py
new file mode 100644
index 0000000000000000000000000000000000000000..4dd477266f3d3fdbd50a7d4f252c50192a6ded35
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/sql.py
@@ -0,0 +1,33 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.chat_message_histories import SQLChatMessageHistory
+ from langchain_community.chat_message_histories.sql import (
+ BaseMessageConverter,
+ DefaultMessageConverter,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "BaseMessageConverter": "langchain_community.chat_message_histories.sql",
+ "DefaultMessageConverter": "langchain_community.chat_message_histories.sql",
+ "SQLChatMessageHistory": "langchain_community.chat_message_histories",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "BaseMessageConverter",
+ "DefaultMessageConverter",
+ "SQLChatMessageHistory",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/streamlit.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/streamlit.py
new file mode 100644
index 0000000000000000000000000000000000000000..bb0321b0dfc36b824f77fb29e9f7242c4c638471
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/streamlit.py
@@ -0,0 +1,25 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.chat_message_histories import StreamlitChatMessageHistory
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "StreamlitChatMessageHistory": "langchain_community.chat_message_histories",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "StreamlitChatMessageHistory",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/upstash_redis.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/upstash_redis.py
new file mode 100644
index 0000000000000000000000000000000000000000..89a0749f5dd1a2176eb094ecb7f211f15b51886d
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/upstash_redis.py
@@ -0,0 +1,27 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.chat_message_histories import (
+ UpstashRedisChatMessageHistory,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "UpstashRedisChatMessageHistory": "langchain_community.chat_message_histories",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "UpstashRedisChatMessageHistory",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/xata.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/xata.py
new file mode 100644
index 0000000000000000000000000000000000000000..e21a5bed9da150225d78011b4025f31c29b8dae2
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/xata.py
@@ -0,0 +1,25 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.chat_message_histories import XataChatMessageHistory
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "XataChatMessageHistory": "langchain_community.chat_message_histories",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "XataChatMessageHistory",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/zep.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/zep.py
new file mode 100644
index 0000000000000000000000000000000000000000..5326e2cd087792efcb37123e457cfbaab0249d43
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/memory/chat_message_histories/zep.py
@@ -0,0 +1,25 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.chat_message_histories import ZepChatMessageHistory
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "ZepChatMessageHistory": "langchain_community.chat_message_histories",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "ZepChatMessageHistory",
+]
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/prompts/example_selector/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/prompts/example_selector/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..edb5e88d4eb175e6ce77a02f0bb48bc15c90ae5a
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/prompts/example_selector/__init__.py
@@ -0,0 +1,42 @@
+"""Logic for selecting examples to include in prompts."""
+
+from typing import TYPE_CHECKING, Any
+
+from langchain_core.example_selectors.length_based import (
+ LengthBasedExampleSelector,
+)
+from langchain_core.example_selectors.semantic_similarity import (
+ MaxMarginalRelevanceExampleSelector,
+ SemanticSimilarityExampleSelector,
+)
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.example_selectors.ngram_overlap import (
+ NGramOverlapExampleSelector,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUPS = {
+ "NGramOverlapExampleSelector": (
+ "langchain_community.example_selectors.ngram_overlap"
+ ),
+}
+
+_import_attribute = create_importer(__file__, deprecated_lookups=DEPRECATED_LOOKUPS)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "LengthBasedExampleSelector",
+ "MaxMarginalRelevanceExampleSelector",
+ "NGramOverlapExampleSelector",
+ "SemanticSimilarityExampleSelector",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/prompts/example_selector/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/prompts/example_selector/__pycache__/__init__.cpython-311.pyc
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index 0000000000000000000000000000000000000000..78f972217eed59f6157239124ae16b20c3ea2708
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/prompts/example_selector/base.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/prompts/example_selector/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..d6c9e074bdb57f4f7cf1ac9402388d619bdb6c2c
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/prompts/example_selector/base.py
@@ -0,0 +1,3 @@
+from langchain_core.example_selectors.base import BaseExampleSelector
+
+__all__ = ["BaseExampleSelector"]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/prompts/example_selector/length_based.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/prompts/example_selector/length_based.py
new file mode 100644
index 0000000000000000000000000000000000000000..1bb38d8a0c1537a325413732189c808d42c4ae56
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/prompts/example_selector/length_based.py
@@ -0,0 +1,5 @@
+from langchain_core.example_selectors.length_based import (
+ LengthBasedExampleSelector,
+)
+
+__all__ = ["LengthBasedExampleSelector"]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/prompts/example_selector/ngram_overlap.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/prompts/example_selector/ngram_overlap.py
new file mode 100644
index 0000000000000000000000000000000000000000..e820a52ed7d62b48253409088f24acfc9f28d990
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/prompts/example_selector/ngram_overlap.py
@@ -0,0 +1,32 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.example_selectors.ngram_overlap import (
+ NGramOverlapExampleSelector,
+ ngram_overlap_score,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+MODULE_LOOKUP = {
+ "NGramOverlapExampleSelector": (
+ "langchain_community.example_selectors.ngram_overlap"
+ ),
+ "ngram_overlap_score": "langchain_community.example_selectors.ngram_overlap",
+}
+
+_import_attribute = create_importer(__file__, deprecated_lookups=MODULE_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "NGramOverlapExampleSelector",
+ "ngram_overlap_score",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/prompts/example_selector/semantic_similarity.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/prompts/example_selector/semantic_similarity.py
new file mode 100644
index 0000000000000000000000000000000000000000..4942632bceddf7ae730d39bf4f4270bccb3cea2d
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/prompts/example_selector/semantic_similarity.py
@@ -0,0 +1,11 @@
+from langchain_core.example_selectors.semantic_similarity import (
+ MaxMarginalRelevanceExampleSelector,
+ SemanticSimilarityExampleSelector,
+ sorted_values,
+)
+
+__all__ = [
+ "MaxMarginalRelevanceExampleSelector",
+ "SemanticSimilarityExampleSelector",
+ "sorted_values",
+]
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/document_compressors/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/document_compressors/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad76b521aafc3b736305a4ba49f13cca2e1e03d7
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/document_compressors/__init__.py
@@ -0,0 +1,46 @@
+import importlib
+from typing import Any
+
+from langchain_classic.retrievers.document_compressors.base import (
+ DocumentCompressorPipeline,
+)
+from langchain_classic.retrievers.document_compressors.chain_extract import (
+ LLMChainExtractor,
+)
+from langchain_classic.retrievers.document_compressors.chain_filter import (
+ LLMChainFilter,
+)
+from langchain_classic.retrievers.document_compressors.cohere_rerank import CohereRerank
+from langchain_classic.retrievers.document_compressors.cross_encoder_rerank import (
+ CrossEncoderReranker,
+)
+from langchain_classic.retrievers.document_compressors.embeddings_filter import (
+ EmbeddingsFilter,
+)
+from langchain_classic.retrievers.document_compressors.listwise_rerank import (
+ LLMListwiseRerank,
+)
+
+_module_lookup = {
+ "FlashrankRerank": "langchain_community.document_compressors.flashrank_rerank",
+}
+
+
+def __getattr__(name: str) -> Any:
+ if name in _module_lookup:
+ module = importlib.import_module(_module_lookup[name])
+ return getattr(module, name)
+ msg = f"module {__name__} has no attribute {name}"
+ raise AttributeError(msg)
+
+
+__all__ = [
+ "CohereRerank",
+ "CrossEncoderReranker",
+ "DocumentCompressorPipeline",
+ "EmbeddingsFilter",
+ "FlashrankRerank",
+ "LLMChainExtractor",
+ "LLMChainFilter",
+ "LLMListwiseRerank",
+]
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/document_compressors/base.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/document_compressors/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..39c0c2074b650b2c0f07515d28367d6ac041010a
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/document_compressors/base.py
@@ -0,0 +1,81 @@
+from collections.abc import Sequence
+from inspect import signature
+
+from langchain_core.callbacks import Callbacks
+from langchain_core.documents import (
+ BaseDocumentCompressor,
+ BaseDocumentTransformer,
+ Document,
+)
+from pydantic import ConfigDict
+
+
+class DocumentCompressorPipeline(BaseDocumentCompressor):
+ """Document compressor that uses a pipeline of Transformers."""
+
+ transformers: list[BaseDocumentTransformer | BaseDocumentCompressor]
+ """List of document filters that are chained together and run in sequence."""
+
+ model_config = ConfigDict(
+ arbitrary_types_allowed=True,
+ )
+
+ def compress_documents(
+ self,
+ documents: Sequence[Document],
+ query: str,
+ callbacks: Callbacks | None = None,
+ ) -> Sequence[Document]:
+ """Transform a list of documents."""
+ for _transformer in self.transformers:
+ if isinstance(_transformer, BaseDocumentCompressor):
+ accepts_callbacks = (
+ signature(_transformer.compress_documents).parameters.get(
+ "callbacks",
+ )
+ is not None
+ )
+ if accepts_callbacks:
+ documents = _transformer.compress_documents(
+ documents,
+ query,
+ callbacks=callbacks,
+ )
+ else:
+ documents = _transformer.compress_documents(documents, query)
+ elif isinstance(_transformer, BaseDocumentTransformer):
+ documents = _transformer.transform_documents(documents)
+ else:
+ msg = f"Got unexpected transformer type: {_transformer}" # type: ignore[unreachable]
+ raise ValueError(msg) # noqa: TRY004
+ return documents
+
+ async def acompress_documents(
+ self,
+ documents: Sequence[Document],
+ query: str,
+ callbacks: Callbacks | None = None,
+ ) -> Sequence[Document]:
+ """Compress retrieved documents given the query context."""
+ for _transformer in self.transformers:
+ if isinstance(_transformer, BaseDocumentCompressor):
+ accepts_callbacks = (
+ signature(_transformer.acompress_documents).parameters.get(
+ "callbacks",
+ )
+ is not None
+ )
+ if accepts_callbacks:
+ documents = await _transformer.acompress_documents(
+ documents,
+ query,
+ callbacks=callbacks,
+ )
+ else:
+ documents = await _transformer.acompress_documents(documents, query)
+ elif isinstance(_transformer, BaseDocumentTransformer):
+ documents = await _transformer.atransform_documents(documents)
+ else:
+ msg = f"Got unexpected transformer type: {_transformer}" # type: ignore[unreachable]
+ raise ValueError(msg) # noqa: TRY004
+ return documents
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/document_compressors/chain_extract.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/document_compressors/chain_extract.py
new file mode 100644
index 0000000000000000000000000000000000000000..d1d3da3ad1696c0c3b0d75e790493b8217fc5a38
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/document_compressors/chain_extract.py
@@ -0,0 +1,126 @@
+"""DocumentFilter that uses an LLM chain to extract the relevant parts of documents."""
+
+from __future__ import annotations
+
+from collections.abc import Callable, Sequence
+from typing import Any, cast
+
+from langchain_core.callbacks import Callbacks
+from langchain_core.documents import BaseDocumentCompressor, Document
+from langchain_core.language_models import BaseLanguageModel
+from langchain_core.output_parsers import BaseOutputParser, StrOutputParser
+from langchain_core.prompts import PromptTemplate
+from langchain_core.runnables import Runnable
+from pydantic import ConfigDict
+from typing_extensions import override
+
+from langchain_classic.chains.llm import LLMChain
+from langchain_classic.retrievers.document_compressors.chain_extract_prompt import (
+ prompt_template,
+)
+
+
+def default_get_input(query: str, doc: Document) -> dict[str, Any]:
+ """Return the compression chain input."""
+ return {"question": query, "context": doc.page_content}
+
+
+class NoOutputParser(BaseOutputParser[str]):
+ """Parse outputs that could return a null string of some sort."""
+
+ no_output_str: str = "NO_OUTPUT"
+
+ @override
+ def parse(self, text: str) -> str:
+ cleaned_text = text.strip()
+ if cleaned_text == self.no_output_str:
+ return ""
+ return cleaned_text
+
+
+def _get_default_chain_prompt() -> PromptTemplate:
+ output_parser = NoOutputParser()
+ template = prompt_template.format(no_output_str=output_parser.no_output_str)
+ return PromptTemplate(
+ template=template,
+ input_variables=["question", "context"],
+ output_parser=output_parser,
+ )
+
+
+class LLMChainExtractor(BaseDocumentCompressor):
+ """LLM Chain Extractor.
+
+ Document compressor that uses an LLM chain to extract
+ the relevant parts of documents.
+ """
+
+ llm_chain: Runnable
+ """LLM wrapper to use for compressing documents."""
+
+ get_input: Callable[[str, Document], dict] = default_get_input
+ """Callable for constructing the chain input from the query and a Document."""
+
+ model_config = ConfigDict(
+ arbitrary_types_allowed=True,
+ )
+
+ def compress_documents(
+ self,
+ documents: Sequence[Document],
+ query: str,
+ callbacks: Callbacks | None = None,
+ ) -> Sequence[Document]:
+ """Compress page content of raw documents."""
+ compressed_docs = []
+ for doc in documents:
+ _input = self.get_input(query, doc)
+ output_ = self.llm_chain.invoke(_input, config={"callbacks": callbacks})
+ if isinstance(self.llm_chain, LLMChain):
+ output = output_[self.llm_chain.output_key]
+ if self.llm_chain.prompt.output_parser is not None:
+ output = self.llm_chain.prompt.output_parser.parse(output)
+ else:
+ output = output_
+ if len(output) == 0:
+ continue
+ compressed_docs.append(
+ Document(page_content=cast("str", output), metadata=doc.metadata),
+ )
+ return compressed_docs
+
+ async def acompress_documents(
+ self,
+ documents: Sequence[Document],
+ query: str,
+ callbacks: Callbacks | None = None,
+ ) -> Sequence[Document]:
+ """Compress page content of raw documents asynchronously."""
+ inputs = [self.get_input(query, doc) for doc in documents]
+ outputs = await self.llm_chain.abatch(inputs, {"callbacks": callbacks})
+ compressed_docs = []
+ for i, doc in enumerate(documents):
+ if len(outputs[i]) == 0:
+ continue
+ compressed_docs.append(
+ Document(page_content=outputs[i], metadata=doc.metadata),
+ )
+ return compressed_docs
+
+ @classmethod
+ def from_llm(
+ cls,
+ llm: BaseLanguageModel,
+ prompt: PromptTemplate | None = None,
+ get_input: Callable[[str, Document], str] | None = None,
+ llm_chain_kwargs: dict | None = None, # noqa: ARG003
+ ) -> LLMChainExtractor:
+ """Initialize from LLM."""
+ _prompt = prompt if prompt is not None else _get_default_chain_prompt()
+ _get_input = get_input if get_input is not None else default_get_input
+ if _prompt.output_parser is not None:
+ parser = _prompt.output_parser
+ else:
+ parser = StrOutputParser()
+ llm_chain = _prompt | llm | parser
+ return cls(llm_chain=llm_chain, get_input=_get_input)
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/document_compressors/chain_extract_prompt.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/document_compressors/chain_extract_prompt.py
new file mode 100644
index 0000000000000000000000000000000000000000..9566513e950d06c40b43ea5729df82c00a847a42
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/document_compressors/chain_extract_prompt.py
@@ -0,0 +1,10 @@
+prompt_template = """Given the following question and context, extract any part of the context *AS IS* that is relevant to answer the question. If none of the context is relevant return {no_output_str}.
+
+Remember, *DO NOT* edit the extracted parts of the context.
+
+> Question: {{question}}
+> Context:
+>>>
+{{context}}
+>>>
+Extracted relevant parts:""" # noqa: E501
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/document_compressors/chain_filter.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/document_compressors/chain_filter.py
new file mode 100644
index 0000000000000000000000000000000000000000..b4c41cda5ca769e0e35d64d081ba9fa3cfe03368
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/document_compressors/chain_filter.py
@@ -0,0 +1,135 @@
+"""Filter that uses an LLM to drop documents that aren't relevant to the query."""
+
+from collections.abc import Callable, Sequence
+from typing import Any
+
+from langchain_core.callbacks import Callbacks
+from langchain_core.documents import BaseDocumentCompressor, Document
+from langchain_core.language_models import BaseLanguageModel
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import BasePromptTemplate, PromptTemplate
+from langchain_core.runnables import Runnable
+from langchain_core.runnables.config import RunnableConfig
+from pydantic import ConfigDict
+
+from langchain_classic.chains import LLMChain
+from langchain_classic.output_parsers.boolean import BooleanOutputParser
+from langchain_classic.retrievers.document_compressors.chain_filter_prompt import (
+ prompt_template,
+)
+
+
+def _get_default_chain_prompt() -> PromptTemplate:
+ return PromptTemplate(
+ template=prompt_template,
+ input_variables=["question", "context"],
+ output_parser=BooleanOutputParser(),
+ )
+
+
+def default_get_input(query: str, doc: Document) -> dict[str, Any]:
+ """Return the compression chain input."""
+ return {"question": query, "context": doc.page_content}
+
+
+class LLMChainFilter(BaseDocumentCompressor):
+ """Filter that drops documents that aren't relevant to the query."""
+
+ llm_chain: Runnable
+ """LLM wrapper to use for filtering documents.
+ The chain prompt is expected to have a BooleanOutputParser."""
+
+ get_input: Callable[[str, Document], dict] = default_get_input
+ """Callable for constructing the chain input from the query and a Document."""
+
+ model_config = ConfigDict(
+ arbitrary_types_allowed=True,
+ )
+
+ def compress_documents(
+ self,
+ documents: Sequence[Document],
+ query: str,
+ callbacks: Callbacks | None = None,
+ ) -> Sequence[Document]:
+ """Filter down documents based on their relevance to the query."""
+ filtered_docs = []
+
+ config = RunnableConfig(callbacks=callbacks)
+ outputs = zip(
+ self.llm_chain.batch(
+ [self.get_input(query, doc) for doc in documents],
+ config=config,
+ ),
+ documents,
+ strict=False,
+ )
+
+ for output_, doc in outputs:
+ include_doc = None
+ if isinstance(self.llm_chain, LLMChain):
+ output = output_[self.llm_chain.output_key]
+ if self.llm_chain.prompt.output_parser is not None:
+ include_doc = self.llm_chain.prompt.output_parser.parse(output)
+ elif isinstance(output_, bool):
+ include_doc = output_
+ if include_doc:
+ filtered_docs.append(doc)
+
+ return filtered_docs
+
+ async def acompress_documents(
+ self,
+ documents: Sequence[Document],
+ query: str,
+ callbacks: Callbacks | None = None,
+ ) -> Sequence[Document]:
+ """Filter down documents based on their relevance to the query."""
+ filtered_docs = []
+
+ config = RunnableConfig(callbacks=callbacks)
+ outputs = zip(
+ await self.llm_chain.abatch(
+ [self.get_input(query, doc) for doc in documents],
+ config=config,
+ ),
+ documents,
+ strict=False,
+ )
+ for output_, doc in outputs:
+ include_doc = None
+ if isinstance(self.llm_chain, LLMChain):
+ output = output_[self.llm_chain.output_key]
+ if self.llm_chain.prompt.output_parser is not None:
+ include_doc = self.llm_chain.prompt.output_parser.parse(output)
+ elif isinstance(output_, bool):
+ include_doc = output_
+ if include_doc:
+ filtered_docs.append(doc)
+
+ return filtered_docs
+
+ @classmethod
+ def from_llm(
+ cls,
+ llm: BaseLanguageModel,
+ prompt: BasePromptTemplate | None = None,
+ **kwargs: Any,
+ ) -> "LLMChainFilter":
+ """Create a LLMChainFilter from a language model.
+
+ Args:
+ llm: The language model to use for filtering.
+ prompt: The prompt to use for the filter.
+ kwargs: Additional arguments to pass to the constructor.
+
+ Returns:
+ A LLMChainFilter that uses the given language model.
+ """
+ _prompt = prompt if prompt is not None else _get_default_chain_prompt()
+ if _prompt.output_parser is not None:
+ parser = _prompt.output_parser
+ else:
+ parser = StrOutputParser()
+ llm_chain = _prompt | llm | parser
+ return cls(llm_chain=llm_chain, **kwargs)
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/document_compressors/chain_filter_prompt.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/document_compressors/chain_filter_prompt.py
new file mode 100644
index 0000000000000000000000000000000000000000..e7ab5b8cb076edf1bbecf257bc3c4afc9e222b78
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/document_compressors/chain_filter_prompt.py
@@ -0,0 +1,8 @@
+prompt_template = """Given the following question and context, return YES if the context is relevant to the question and NO if it isn't.
+
+> Question: {question}
+> Context:
+>>>
+{context}
+>>>
+> Relevant (YES / NO):""" # noqa: E501
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/document_compressors/cohere_rerank.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/document_compressors/cohere_rerank.py
new file mode 100644
index 0000000000000000000000000000000000000000..c0772f90fa772ede7ef63bc3406842f034b4397b
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/document_compressors/cohere_rerank.py
@@ -0,0 +1,124 @@
+from __future__ import annotations
+
+from collections.abc import Sequence
+from copy import deepcopy
+from typing import Any
+
+from langchain_core._api.deprecation import deprecated
+from langchain_core.callbacks import Callbacks
+from langchain_core.documents import BaseDocumentCompressor, Document
+from langchain_core.utils import get_from_dict_or_env
+from pydantic import ConfigDict, model_validator
+from typing_extensions import override
+
+
+@deprecated(
+ since="0.0.30",
+ removal="2.0.0",
+ alternative_import="langchain_cohere.CohereRerank",
+)
+class CohereRerank(BaseDocumentCompressor):
+ """Document compressor that uses `Cohere Rerank API`."""
+
+ client: Any = None
+ """Cohere client to use for compressing documents."""
+ top_n: int | None = 3
+ """Number of documents to return."""
+ model: str = "rerank-english-v2.0"
+ """Model to use for reranking."""
+ cohere_api_key: str | None = None
+ """Cohere API key. Must be specified directly or via environment variable
+ COHERE_API_KEY."""
+ user_agent: str = "langchain"
+ """Identifier for the application making the request."""
+
+ model_config = ConfigDict(
+ arbitrary_types_allowed=True,
+ extra="forbid",
+ )
+
+ @model_validator(mode="before")
+ @classmethod
+ def validate_environment(cls, values: dict) -> Any:
+ """Validate that api key and python package exists in environment."""
+ if not values.get("client"):
+ try:
+ import cohere
+ except ImportError as e:
+ msg = (
+ "Could not import cohere python package. "
+ "Please install it with `pip install cohere`."
+ )
+ raise ImportError(msg) from e
+ cohere_api_key = get_from_dict_or_env(
+ values,
+ "cohere_api_key",
+ "COHERE_API_KEY",
+ )
+ client_name = values.get("user_agent", "langchain")
+ values["client"] = cohere.Client(cohere_api_key, client_name=client_name)
+ return values
+
+ def rerank(
+ self,
+ documents: Sequence[str | Document | dict],
+ query: str,
+ *,
+ model: str | None = None,
+ top_n: int | None = -1,
+ max_chunks_per_doc: int | None = None,
+ ) -> list[dict[str, Any]]:
+ """Returns an ordered list of documents ordered by their relevance to the provided query.
+
+ Args:
+ query: The query to use for reranking.
+ documents: A sequence of documents to rerank.
+ model: The model to use for re-ranking. Default to self.model.
+ top_n : The number of results to return. If `None` returns all results.
+ max_chunks_per_doc : The maximum number of chunks derived from a document.
+ """ # noqa: E501
+ if len(documents) == 0: # to avoid empty api call
+ return []
+ docs = [
+ doc.page_content if isinstance(doc, Document) else doc for doc in documents
+ ]
+ model = model or self.model
+ top_n = top_n if (top_n is None or top_n > 0) else self.top_n
+ results = self.client.rerank(
+ query=query,
+ documents=docs,
+ model=model,
+ top_n=top_n,
+ max_chunks_per_doc=max_chunks_per_doc,
+ )
+ if hasattr(results, "results"):
+ results = results.results
+ return [
+ {"index": res.index, "relevance_score": res.relevance_score}
+ for res in results
+ ]
+
+ @override
+ def compress_documents(
+ self,
+ documents: Sequence[Document],
+ query: str,
+ callbacks: Callbacks | None = None,
+ ) -> Sequence[Document]:
+ """Compress documents using Cohere's rerank API.
+
+ Args:
+ documents: A sequence of documents to compress.
+ query: The query to use for compressing the documents.
+ callbacks: Callbacks to run during the compression process.
+
+ Returns:
+ A sequence of compressed documents.
+ """
+ compressed = []
+ for res in self.rerank(documents, query):
+ doc = documents[res["index"]]
+ doc_copy = Document(doc.page_content, metadata=deepcopy(doc.metadata))
+ doc_copy.metadata["relevance_score"] = res["relevance_score"]
+ compressed.append(doc_copy)
+ return compressed
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/document_compressors/cross_encoder.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/document_compressors/cross_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..f8391d0188e682681a41f0e8a7e359121ec8b300
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/document_compressors/cross_encoder.py
@@ -0,0 +1,3 @@
+from langchain_core.cross_encoders import BaseCrossEncoder
+
+__all__ = ["BaseCrossEncoder"]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/document_compressors/cross_encoder_rerank.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/document_compressors/cross_encoder_rerank.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa7c14bcd206846f0fa0954bf9a9923edc1007e2
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/document_compressors/cross_encoder_rerank.py
@@ -0,0 +1,50 @@
+from __future__ import annotations
+
+import operator
+from collections.abc import Sequence
+
+from langchain_core.callbacks import Callbacks
+from langchain_core.documents import BaseDocumentCompressor, Document
+from pydantic import ConfigDict
+from typing_extensions import override
+
+from langchain_classic.retrievers.document_compressors.cross_encoder import (
+ BaseCrossEncoder,
+)
+
+
+class CrossEncoderReranker(BaseDocumentCompressor):
+ """Document compressor that uses CrossEncoder for reranking."""
+
+ model: BaseCrossEncoder
+ """CrossEncoder model to use for scoring similarity
+ between the query and documents."""
+ top_n: int = 3
+ """Number of documents to return."""
+
+ model_config = ConfigDict(
+ arbitrary_types_allowed=True,
+ extra="forbid",
+ )
+
+ @override
+ def compress_documents(
+ self,
+ documents: Sequence[Document],
+ query: str,
+ callbacks: Callbacks | None = None,
+ ) -> Sequence[Document]:
+ """Rerank documents using CrossEncoder.
+
+ Args:
+ documents: A sequence of documents to compress.
+ query: The query to use for compressing the documents.
+ callbacks: Callbacks to run during the compression process.
+
+ Returns:
+ A sequence of compressed documents.
+ """
+ scores = self.model.score([(query, doc.page_content) for doc in documents])
+ docs_with_scores = list(zip(documents, scores, strict=False))
+ result = sorted(docs_with_scores, key=operator.itemgetter(1), reverse=True)
+ return [doc for doc, _ in result[: self.top_n]]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/document_compressors/embeddings_filter.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/document_compressors/embeddings_filter.py
new file mode 100644
index 0000000000000000000000000000000000000000..885596ddae323ef9deb8497f5f4dbd605d57501c
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/document_compressors/embeddings_filter.py
@@ -0,0 +1,141 @@
+from collections.abc import Callable, Sequence
+
+from langchain_core.callbacks import Callbacks
+from langchain_core.documents import BaseDocumentCompressor, Document
+from langchain_core.embeddings import Embeddings
+from langchain_core.utils import pre_init
+from pydantic import ConfigDict, Field
+from typing_extensions import override
+
+
+def _get_similarity_function() -> Callable:
+ try:
+ from langchain_community.utils.math import cosine_similarity
+ except ImportError as e:
+ msg = (
+ "To use please install langchain-community "
+ "with `pip install langchain-community`."
+ )
+ raise ImportError(msg) from e
+ return cosine_similarity
+
+
+class EmbeddingsFilter(BaseDocumentCompressor):
+ """Embeddings Filter.
+
+ Document compressor that uses embeddings to drop documents unrelated to the query.
+ """
+
+ embeddings: Embeddings
+ """Embeddings to use for embedding document contents and queries."""
+ similarity_fn: Callable = Field(default_factory=_get_similarity_function)
+ """Similarity function for comparing documents. Function expected to take as input
+ two matrices (List[List[float]]) and return a matrix of scores where higher values
+ indicate greater similarity."""
+ k: int | None = 20
+ """The number of relevant documents to return. Can be set to `None`, in which case
+ `similarity_threshold` must be specified."""
+ similarity_threshold: float | None = None
+ """Threshold for determining when two documents are similar enough
+ to be considered redundant. Defaults to `None`, must be specified if `k` is set
+ to None."""
+
+ model_config = ConfigDict(
+ arbitrary_types_allowed=True,
+ )
+
+ @pre_init
+ def validate_params(cls, values: dict) -> dict:
+ """Validate similarity parameters."""
+ if values["k"] is None and values["similarity_threshold"] is None:
+ msg = "Must specify one of `k` or `similarity_threshold`."
+ raise ValueError(msg)
+ return values
+
+ @override
+ def compress_documents(
+ self,
+ documents: Sequence[Document],
+ query: str,
+ callbacks: Callbacks | None = None,
+ ) -> Sequence[Document]:
+ """Filter documents based on similarity of their embeddings to the query."""
+ try:
+ from langchain_community.document_transformers.embeddings_redundant_filter import ( # noqa: E501
+ _get_embeddings_from_stateful_docs,
+ get_stateful_documents,
+ )
+ except ImportError as e:
+ msg = (
+ "To use please install langchain-community "
+ "with `pip install langchain-community`."
+ )
+ raise ImportError(msg) from e
+
+ try:
+ import numpy as np
+ except ImportError as e:
+ msg = "Could not import numpy, please install with `pip install numpy`."
+ raise ImportError(msg) from e
+ stateful_documents = get_stateful_documents(documents)
+ embedded_documents = _get_embeddings_from_stateful_docs(
+ self.embeddings,
+ stateful_documents,
+ )
+ embedded_query = self.embeddings.embed_query(query)
+ similarity = self.similarity_fn([embedded_query], embedded_documents)[0]
+ included_idxs: np.ndarray = np.arange(len(embedded_documents))
+ if self.k is not None:
+ included_idxs = np.argsort(similarity)[::-1][: self.k]
+ if self.similarity_threshold is not None:
+ similar_enough = np.where(
+ similarity[included_idxs] > self.similarity_threshold,
+ )
+ included_idxs = included_idxs[similar_enough]
+ for i in included_idxs:
+ stateful_documents[i].state["query_similarity_score"] = similarity[i]
+ return [stateful_documents[i] for i in included_idxs]
+
+ @override
+ async def acompress_documents(
+ self,
+ documents: Sequence[Document],
+ query: str,
+ callbacks: Callbacks | None = None,
+ ) -> Sequence[Document]:
+ """Filter documents based on similarity of their embeddings to the query."""
+ try:
+ from langchain_community.document_transformers.embeddings_redundant_filter import ( # noqa: E501
+ _aget_embeddings_from_stateful_docs,
+ get_stateful_documents,
+ )
+ except ImportError as e:
+ msg = (
+ "To use please install langchain-community "
+ "with `pip install langchain-community`."
+ )
+ raise ImportError(msg) from e
+
+ try:
+ import numpy as np
+ except ImportError as e:
+ msg = "Could not import numpy, please install with `pip install numpy`."
+ raise ImportError(msg) from e
+ stateful_documents = get_stateful_documents(documents)
+ embedded_documents = await _aget_embeddings_from_stateful_docs(
+ self.embeddings,
+ stateful_documents,
+ )
+ embedded_query = await self.embeddings.aembed_query(query)
+ similarity = self.similarity_fn([embedded_query], embedded_documents)[0]
+ included_idxs: np.ndarray = np.arange(len(embedded_documents))
+ if self.k is not None:
+ included_idxs = np.argsort(similarity)[::-1][: self.k]
+ if self.similarity_threshold is not None:
+ similar_enough = np.where(
+ similarity[included_idxs] > self.similarity_threshold,
+ )
+ included_idxs = included_idxs[similar_enough]
+ for i in included_idxs:
+ stateful_documents[i].state["query_similarity_score"] = similarity[i]
+ return [stateful_documents[i] for i in included_idxs]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/document_compressors/flashrank_rerank.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/document_compressors/flashrank_rerank.py
new file mode 100644
index 0000000000000000000000000000000000000000..7c80c20b3bb334b36e466f3f6b7cf8ebd24d9f88
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/document_compressors/flashrank_rerank.py
@@ -0,0 +1,27 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.document_compressors.flashrank_rerank import (
+ FlashrankRerank,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "FlashrankRerank": "langchain_community.document_compressors.flashrank_rerank",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "FlashrankRerank",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/document_compressors/listwise_rerank.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/document_compressors/listwise_rerank.py
new file mode 100644
index 0000000000000000000000000000000000000000..d12f2cb925ef147f747bffcb5d5e90cb5b26657a
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/document_compressors/listwise_rerank.py
@@ -0,0 +1,146 @@
+"""Filter that uses an LLM to rerank documents listwise and select top-k."""
+
+from collections.abc import Sequence
+from typing import Any
+
+from langchain_core.callbacks import Callbacks
+from langchain_core.documents import BaseDocumentCompressor, Document
+from langchain_core.language_models import BaseLanguageModel
+from langchain_core.prompts import BasePromptTemplate, ChatPromptTemplate
+from langchain_core.runnables import Runnable, RunnableLambda, RunnablePassthrough
+from pydantic import BaseModel, ConfigDict, Field
+
+_default_system_tmpl = """{context}
+
+Sort the Documents by their relevance to the Query."""
+_DEFAULT_PROMPT = ChatPromptTemplate.from_messages(
+ [("system", _default_system_tmpl), ("human", "{query}")],
+)
+
+
+def _get_prompt_input(input_: dict) -> dict[str, Any]:
+ """Return the compression chain input."""
+ documents = input_["documents"]
+ context = ""
+ for index, doc in enumerate(documents):
+ context += f"Document ID: {index}\n```{doc.page_content}```\n\n"
+ document_range = "empty list"
+ if len(documents) > 0:
+ document_range = f"Document ID: 0, ..., Document ID: {len(documents) - 1}"
+ context += f"Documents = [{document_range}]"
+ return {"query": input_["query"], "context": context}
+
+
+def _parse_ranking(results: dict) -> list[Document]:
+ ranking = results["ranking"]
+ docs = results["documents"]
+ return [docs[i] for i in ranking.ranked_document_ids]
+
+
+class LLMListwiseRerank(BaseDocumentCompressor):
+ """Document compressor that uses `Zero-Shot Listwise Document Reranking`.
+
+ Adapted from: https://arxiv.org/pdf/2305.02156.pdf
+
+ `LLMListwiseRerank` uses a language model to rerank a list of documents based on
+ their relevance to a query.
+
+ !!! note
+ Requires that underlying model implement `with_structured_output`.
+
+ Example usage:
+ ```python
+ from langchain_classic.retrievers.document_compressors.listwise_rerank import (
+ LLMListwiseRerank,
+ )
+ from langchain_core.documents import Document
+ from langchain_openai import ChatOpenAI
+
+ documents = [
+ Document("Sally is my friend from school"),
+ Document("Steve is my friend from home"),
+ Document("I didn't always like yogurt"),
+ Document("I wonder why it's called football"),
+ Document("Where's waldo"),
+ ]
+
+ reranker = LLMListwiseRerank.from_llm(
+ llm=ChatOpenAI(model="gpt-3.5-turbo"), top_n=3
+ )
+ compressed_docs = reranker.compress_documents(documents, "Who is steve")
+ assert len(compressed_docs) == 3
+ assert "Steve" in compressed_docs[0].page_content
+ ```
+ """
+
+ reranker: Runnable[dict, list[Document]]
+ """LLM-based reranker to use for filtering documents. Expected to take in a dict
+ with 'documents: Sequence[Document]' and 'query: str' keys and output a
+ List[Document]."""
+
+ top_n: int = 3
+ """Number of documents to return."""
+
+ model_config = ConfigDict(
+ arbitrary_types_allowed=True,
+ )
+
+ def compress_documents(
+ self,
+ documents: Sequence[Document],
+ query: str,
+ callbacks: Callbacks | None = None,
+ ) -> Sequence[Document]:
+ """Filter down documents based on their relevance to the query."""
+ results = self.reranker.invoke(
+ {"documents": documents, "query": query},
+ config={"callbacks": callbacks},
+ )
+ return results[: self.top_n]
+
+ @classmethod
+ def from_llm(
+ cls,
+ llm: BaseLanguageModel,
+ *,
+ prompt: BasePromptTemplate | None = None,
+ **kwargs: Any,
+ ) -> "LLMListwiseRerank":
+ """Create a LLMListwiseRerank document compressor from a language model.
+
+ Args:
+ llm: The language model to use for filtering. **Must implement
+ BaseLanguageModel.with_structured_output().**
+ prompt: The prompt to use for the filter.
+ kwargs: Additional arguments to pass to the constructor.
+
+ Returns:
+ A LLMListwiseRerank document compressor that uses the given language model.
+ """
+ if type(llm).with_structured_output == BaseLanguageModel.with_structured_output:
+ msg = (
+ f"llm of type {type(llm)} does not implement `with_structured_output`."
+ )
+ raise ValueError(msg)
+
+ class RankDocuments(BaseModel):
+ """Rank the documents by their relevance to the user question.
+
+ Rank from most to least relevant.
+ """
+
+ ranked_document_ids: list[int] = Field(
+ ...,
+ description=(
+ "The integer IDs of the documents, sorted from most to least "
+ "relevant to the user question."
+ ),
+ )
+
+ _prompt = prompt if prompt is not None else _DEFAULT_PROMPT
+ reranker = RunnablePassthrough.assign(
+ ranking=RunnableLambda(_get_prompt_input)
+ | _prompt
+ | llm.with_structured_output(RankDocuments),
+ ) | RunnableLambda(_parse_ranking)
+ return cls(reranker=reranker, **kwargs)
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/__init__.py
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/astradb.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/astradb.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf6f63e23b4d30e15876a11f8546dd54e5ca21cd
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/astradb.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.query_constructors.astradb import AstraDBTranslator
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "AstraDBTranslator": "langchain_community.query_constructors.astradb",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = ["AstraDBTranslator"]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/base.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..152a7de694970ed7c9e46d91315ba7fb187d17ed
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/base.py
@@ -0,0 +1,407 @@
+"""Retriever that generates and executes structured queries over its own data source."""
+
+import logging
+from collections.abc import Sequence
+from typing import Any
+
+from langchain_core.callbacks.manager import (
+ AsyncCallbackManagerForRetrieverRun,
+ CallbackManagerForRetrieverRun,
+)
+from langchain_core.documents import Document
+from langchain_core.language_models import BaseLanguageModel
+from langchain_core.retrievers import BaseRetriever
+from langchain_core.runnables import Runnable
+from langchain_core.structured_query import StructuredQuery, Visitor
+from langchain_core.vectorstores import VectorStore
+from pydantic import ConfigDict, Field, model_validator
+from typing_extensions import override
+
+from langchain_classic.chains.query_constructor.base import (
+ load_query_constructor_runnable,
+)
+from langchain_classic.chains.query_constructor.schema import AttributeInfo
+
+logger = logging.getLogger(__name__)
+QUERY_CONSTRUCTOR_RUN_NAME = "query_constructor"
+
+
+def _get_builtin_translator(vectorstore: VectorStore) -> Visitor:
+ """Get the translator class corresponding to the vector store class."""
+ try:
+ import langchain_community # noqa: F401
+ except ImportError as err:
+ msg = (
+ "The langchain-community package must be installed to use this feature."
+ " Please install it using `pip install langchain-community`."
+ )
+ raise ImportError(msg) from err
+
+ from langchain_community.query_constructors.astradb import AstraDBTranslator
+ from langchain_community.query_constructors.chroma import ChromaTranslator
+ from langchain_community.query_constructors.dashvector import DashvectorTranslator
+ from langchain_community.query_constructors.databricks_vector_search import (
+ DatabricksVectorSearchTranslator,
+ )
+ from langchain_community.query_constructors.deeplake import DeepLakeTranslator
+ from langchain_community.query_constructors.dingo import DingoDBTranslator
+ from langchain_community.query_constructors.elasticsearch import (
+ ElasticsearchTranslator,
+ )
+ from langchain_community.query_constructors.milvus import MilvusTranslator
+ from langchain_community.query_constructors.mongodb_atlas import (
+ MongoDBAtlasTranslator,
+ )
+ from langchain_community.query_constructors.myscale import MyScaleTranslator
+ from langchain_community.query_constructors.neo4j import Neo4jTranslator
+ from langchain_community.query_constructors.opensearch import OpenSearchTranslator
+ from langchain_community.query_constructors.pgvector import PGVectorTranslator
+ from langchain_community.query_constructors.pinecone import PineconeTranslator
+ from langchain_community.query_constructors.qdrant import QdrantTranslator
+ from langchain_community.query_constructors.redis import RedisTranslator
+ from langchain_community.query_constructors.supabase import SupabaseVectorTranslator
+ from langchain_community.query_constructors.tencentvectordb import (
+ TencentVectorDBTranslator,
+ )
+ from langchain_community.query_constructors.timescalevector import (
+ TimescaleVectorTranslator,
+ )
+ from langchain_community.query_constructors.vectara import VectaraTranslator
+ from langchain_community.query_constructors.weaviate import WeaviateTranslator
+ from langchain_community.vectorstores import (
+ AstraDB,
+ DashVector,
+ DatabricksVectorSearch,
+ DeepLake,
+ Dingo,
+ Milvus,
+ MyScale,
+ Neo4jVector,
+ OpenSearchVectorSearch,
+ PGVector,
+ Qdrant,
+ Redis,
+ SupabaseVectorStore,
+ TencentVectorDB,
+ TimescaleVector,
+ Vectara,
+ Weaviate,
+ )
+ from langchain_community.vectorstores import (
+ Chroma as CommunityChroma,
+ )
+ from langchain_community.vectorstores import (
+ ElasticsearchStore as ElasticsearchStoreCommunity,
+ )
+ from langchain_community.vectorstores import (
+ MongoDBAtlasVectorSearch as CommunityMongoDBAtlasVectorSearch,
+ )
+ from langchain_community.vectorstores import (
+ Pinecone as CommunityPinecone,
+ )
+
+ builtin_translators: dict[type[VectorStore], type[Visitor]] = {
+ AstraDB: AstraDBTranslator,
+ PGVector: PGVectorTranslator,
+ CommunityPinecone: PineconeTranslator,
+ CommunityChroma: ChromaTranslator,
+ DashVector: DashvectorTranslator,
+ Dingo: DingoDBTranslator,
+ Weaviate: WeaviateTranslator,
+ Vectara: VectaraTranslator,
+ Qdrant: QdrantTranslator,
+ MyScale: MyScaleTranslator,
+ DeepLake: DeepLakeTranslator,
+ ElasticsearchStoreCommunity: ElasticsearchTranslator,
+ Milvus: MilvusTranslator,
+ SupabaseVectorStore: SupabaseVectorTranslator,
+ TimescaleVector: TimescaleVectorTranslator,
+ OpenSearchVectorSearch: OpenSearchTranslator,
+ CommunityMongoDBAtlasVectorSearch: MongoDBAtlasTranslator,
+ Neo4jVector: Neo4jTranslator,
+ }
+ if isinstance(vectorstore, DatabricksVectorSearch):
+ return DatabricksVectorSearchTranslator()
+ if isinstance(vectorstore, MyScale):
+ return MyScaleTranslator(metadata_key=vectorstore.metadata_column)
+ if isinstance(vectorstore, Redis):
+ return RedisTranslator.from_vectorstore(vectorstore)
+ if isinstance(vectorstore, TencentVectorDB):
+ fields = [
+ field.name for field in (vectorstore.meta_fields or []) if field.index
+ ]
+ return TencentVectorDBTranslator(fields)
+ if vectorstore.__class__ in builtin_translators:
+ return builtin_translators[vectorstore.__class__]()
+ try:
+ from langchain_astradb.vectorstores import AstraDBVectorStore
+ except ImportError:
+ pass
+ else:
+ if isinstance(vectorstore, AstraDBVectorStore):
+ return AstraDBTranslator()
+
+ try:
+ from langchain_elasticsearch.vectorstores import ElasticsearchStore
+ except ImportError:
+ pass
+ else:
+ if isinstance(vectorstore, ElasticsearchStore):
+ return ElasticsearchTranslator()
+
+ try:
+ from langchain_pinecone import PineconeVectorStore
+ except ImportError:
+ pass
+ else:
+ if isinstance(vectorstore, PineconeVectorStore):
+ return PineconeTranslator()
+
+ try:
+ from langchain_milvus import Milvus
+ except ImportError:
+ pass
+ else:
+ if isinstance(vectorstore, Milvus):
+ return MilvusTranslator()
+
+ try:
+ from langchain_mongodb import MongoDBAtlasVectorSearch
+ except ImportError:
+ pass
+ else:
+ if isinstance(vectorstore, MongoDBAtlasVectorSearch):
+ return MongoDBAtlasTranslator()
+
+ try:
+ from langchain_neo4j import Neo4jVector
+ except ImportError:
+ pass
+ else:
+ if isinstance(vectorstore, Neo4jVector):
+ return Neo4jTranslator()
+
+ try:
+ # Trying langchain_chroma import if exists
+ from langchain_chroma import Chroma
+ except ImportError:
+ pass
+ else:
+ if isinstance(vectorstore, Chroma):
+ return ChromaTranslator()
+
+ try:
+ from langchain_postgres import PGVector
+ from langchain_postgres import PGVectorTranslator as NewPGVectorTranslator
+ except ImportError:
+ pass
+ else:
+ if isinstance(vectorstore, PGVector):
+ return NewPGVectorTranslator()
+
+ try:
+ from langchain_qdrant import QdrantVectorStore
+ except ImportError:
+ pass
+ else:
+ if isinstance(vectorstore, QdrantVectorStore):
+ return QdrantTranslator(metadata_key=vectorstore.metadata_payload_key)
+
+ try:
+ # Added in langchain-community==0.2.11
+ from langchain_community.query_constructors.hanavector import HanaTranslator
+ from langchain_community.vectorstores import HanaDB
+ except ImportError:
+ pass
+ else:
+ if isinstance(vectorstore, HanaDB):
+ return HanaTranslator()
+
+ try:
+ # Trying langchain_weaviate (weaviate v4) import if exists
+ from langchain_weaviate.vectorstores import WeaviateVectorStore
+
+ except ImportError:
+ pass
+ else:
+ if isinstance(vectorstore, WeaviateVectorStore):
+ return WeaviateTranslator()
+
+ msg = (
+ f"Self query retriever with Vector Store type {vectorstore.__class__}"
+ f" not supported."
+ )
+ raise ValueError(msg)
+
+
+class SelfQueryRetriever(BaseRetriever):
+ """Self Query Retriever.
+
+ Retriever that uses a vector store and an LLM to generate the vector store queries.
+ """
+
+ vectorstore: VectorStore
+ """The underlying vector store from which documents will be retrieved."""
+ query_constructor: Runnable[dict, StructuredQuery] = Field(alias="llm_chain")
+ """The query constructor chain for generating the vector store queries.
+
+ llm_chain is legacy name kept for backwards compatibility."""
+ search_type: str = "similarity"
+ """The search type to perform on the vector store."""
+ search_kwargs: dict = Field(default_factory=dict)
+ """Keyword arguments to pass in to the vector store search."""
+ structured_query_translator: Visitor
+ """Translator for turning internal query language into `VectorStore` search params.""" # noqa: E501
+ verbose: bool = False
+
+ use_original_query: bool = False
+ """Use original query instead of the revised new query from LLM"""
+
+ model_config = ConfigDict(
+ populate_by_name=True,
+ arbitrary_types_allowed=True,
+ )
+
+ @model_validator(mode="before")
+ @classmethod
+ def validate_translator(cls, values: dict) -> Any:
+ """Validate translator."""
+ if "structured_query_translator" not in values:
+ values["structured_query_translator"] = _get_builtin_translator(
+ values["vectorstore"],
+ )
+ return values
+
+ @property
+ def llm_chain(self) -> Runnable:
+ """llm_chain is legacy name kept for backwards compatibility."""
+ return self.query_constructor
+
+ def _prepare_query(
+ self,
+ query: str,
+ structured_query: StructuredQuery,
+ ) -> tuple[str, dict[str, Any]]:
+ new_query, new_kwargs = self.structured_query_translator.visit_structured_query(
+ structured_query,
+ )
+ if structured_query.limit is not None:
+ new_kwargs["k"] = structured_query.limit
+ if self.use_original_query:
+ new_query = query
+ search_kwargs = {**self.search_kwargs, **new_kwargs}
+ return new_query, search_kwargs
+
+ def _get_docs_with_query(
+ self,
+ query: str,
+ search_kwargs: dict[str, Any],
+ ) -> list[Document]:
+ return self.vectorstore.search(query, self.search_type, **search_kwargs)
+
+ async def _aget_docs_with_query(
+ self,
+ query: str,
+ search_kwargs: dict[str, Any],
+ ) -> list[Document]:
+ return await self.vectorstore.asearch(query, self.search_type, **search_kwargs)
+
+ @override
+ def _get_relevant_documents(
+ self,
+ query: str,
+ *,
+ run_manager: CallbackManagerForRetrieverRun,
+ ) -> list[Document]:
+ structured_query = self.query_constructor.invoke(
+ {"query": query},
+ config={"callbacks": run_manager.get_child()},
+ )
+ if self.verbose:
+ logger.info("Generated Query: %s", structured_query)
+ new_query, search_kwargs = self._prepare_query(query, structured_query)
+ return self._get_docs_with_query(new_query, search_kwargs)
+
+ @override
+ async def _aget_relevant_documents(
+ self,
+ query: str,
+ *,
+ run_manager: AsyncCallbackManagerForRetrieverRun,
+ ) -> list[Document]:
+ structured_query = await self.query_constructor.ainvoke(
+ {"query": query},
+ config={"callbacks": run_manager.get_child()},
+ )
+ if self.verbose:
+ logger.info("Generated Query: %s", structured_query)
+ new_query, search_kwargs = self._prepare_query(query, structured_query)
+ return await self._aget_docs_with_query(new_query, search_kwargs)
+
+ @classmethod
+ def from_llm(
+ cls,
+ llm: BaseLanguageModel,
+ vectorstore: VectorStore,
+ document_contents: str,
+ metadata_field_info: Sequence[AttributeInfo | dict],
+ structured_query_translator: Visitor | None = None,
+ chain_kwargs: dict | None = None,
+ enable_limit: bool = False, # noqa: FBT001,FBT002
+ use_original_query: bool = False, # noqa: FBT001,FBT002
+ **kwargs: Any,
+ ) -> "SelfQueryRetriever":
+ """Create a SelfQueryRetriever from an LLM and a vector store.
+
+ Args:
+ llm: The language model to use for generating queries.
+ vectorstore: The vector store to use for retrieving documents.
+ document_contents: Description of the page contents of the document to be
+ queried.
+ metadata_field_info: Metadata field information for the documents.
+ structured_query_translator: Optional translator for turning internal query
+ language into `VectorStore` search params.
+ chain_kwargs: Additional keyword arguments for the query constructor.
+ enable_limit: Whether to enable the limit operator.
+ use_original_query: Whether to use the original query instead of the revised
+ query from the LLM.
+ **kwargs: Additional keyword arguments for the SelfQueryRetriever.
+
+ Returns:
+ An instance of SelfQueryRetriever.
+ """
+ if structured_query_translator is None:
+ structured_query_translator = _get_builtin_translator(vectorstore)
+ chain_kwargs = chain_kwargs or {}
+
+ if (
+ "allowed_comparators" not in chain_kwargs
+ and structured_query_translator.allowed_comparators is not None
+ ):
+ chain_kwargs["allowed_comparators"] = (
+ structured_query_translator.allowed_comparators
+ )
+ if (
+ "allowed_operators" not in chain_kwargs
+ and structured_query_translator.allowed_operators is not None
+ ):
+ chain_kwargs["allowed_operators"] = (
+ structured_query_translator.allowed_operators
+ )
+ query_constructor = load_query_constructor_runnable(
+ llm,
+ document_contents,
+ metadata_field_info,
+ enable_limit=enable_limit,
+ **chain_kwargs,
+ )
+ query_constructor = query_constructor.with_config(
+ run_name=QUERY_CONSTRUCTOR_RUN_NAME,
+ )
+ return cls(
+ query_constructor=query_constructor,
+ vectorstore=vectorstore,
+ use_original_query=use_original_query,
+ structured_query_translator=structured_query_translator,
+ **kwargs,
+ )
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/chroma.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/chroma.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3fab0ec427c864c57e2d8abb83284edeb25a36a
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/chroma.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.query_constructors.chroma import ChromaTranslator
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "ChromaTranslator": "langchain_community.query_constructors.chroma",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = ["ChromaTranslator"]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/dashvector.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/dashvector.py
new file mode 100644
index 0000000000000000000000000000000000000000..e4fa3e185933cb6ef3eeb1a5d46991518cf31256
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/dashvector.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.query_constructors.dashvector import DashvectorTranslator
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "DashvectorTranslator": "langchain_community.query_constructors.dashvector",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = ["DashvectorTranslator"]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/databricks_vector_search.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/databricks_vector_search.py
new file mode 100644
index 0000000000000000000000000000000000000000..557469be9ddebaef26078c4a4fd5089f5eae43e9
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/databricks_vector_search.py
@@ -0,0 +1,27 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.query_constructors.databricks_vector_search import (
+ DatabricksVectorSearchTranslator,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "DatabricksVectorSearchTranslator": (
+ "langchain_community.query_constructors.databricks_vector_search"
+ ),
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = ["DatabricksVectorSearchTranslator"]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/deeplake.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/deeplake.py
new file mode 100644
index 0000000000000000000000000000000000000000..723c38be8c64aefcc57a24ff1bea868685e5adb9
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/deeplake.py
@@ -0,0 +1,27 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.query_constructors.deeplake import (
+ DeepLakeTranslator,
+ can_cast_to_float,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "DeepLakeTranslator": "langchain_community.query_constructors.deeplake",
+ "can_cast_to_float": "langchain_community.query_constructors.deeplake",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = ["DeepLakeTranslator", "can_cast_to_float"]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/dingo.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/dingo.py
new file mode 100644
index 0000000000000000000000000000000000000000..77c6bfe17fcd3e2d038d31369b2c992765a6e24c
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/dingo.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.query_constructors.dingo import DingoDBTranslator
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "DingoDBTranslator": "langchain_community.query_constructors.dingo",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = ["DingoDBTranslator"]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/elasticsearch.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/elasticsearch.py
new file mode 100644
index 0000000000000000000000000000000000000000..d5b75462332f2cde45506f506594253d459d5f4e
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/elasticsearch.py
@@ -0,0 +1,25 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.query_constructors.elasticsearch import (
+ ElasticsearchTranslator,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "ElasticsearchTranslator": "langchain_community.query_constructors.elasticsearch",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = ["ElasticsearchTranslator"]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/milvus.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/milvus.py
new file mode 100644
index 0000000000000000000000000000000000000000..916014d85d7ab50be679225711b6beab6648c5fc
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/milvus.py
@@ -0,0 +1,27 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.query_constructors.milvus import (
+ MilvusTranslator,
+ process_value,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "MilvusTranslator": "langchain_community.query_constructors.milvus",
+ "process_value": "langchain_community.query_constructors.milvus",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = ["MilvusTranslator", "process_value"]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/mongodb_atlas.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/mongodb_atlas.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c48d0370f4bb905ac3f549849c34204c9a55df4
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/mongodb_atlas.py
@@ -0,0 +1,25 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.query_constructors.mongodb_atlas import (
+ MongoDBAtlasTranslator,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "MongoDBAtlasTranslator": "langchain_community.query_constructors.mongodb_atlas",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = ["MongoDBAtlasTranslator"]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/myscale.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/myscale.py
new file mode 100644
index 0000000000000000000000000000000000000000..a439a3ba1f6fe75c90e1d3674e8456feee90a31f
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/myscale.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.query_constructors.myscale import MyScaleTranslator
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "MyScaleTranslator": "langchain_community.query_constructors.myscale",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = ["MyScaleTranslator"]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/opensearch.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/opensearch.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba13ed4db8c174edb0f29f5acb3f73283eca4d04
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/opensearch.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.query_constructors.opensearch import OpenSearchTranslator
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "OpenSearchTranslator": "langchain_community.query_constructors.opensearch",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = ["OpenSearchTranslator"]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/pgvector.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/pgvector.py
new file mode 100644
index 0000000000000000000000000000000000000000..84c22fc659757d3dec2e9ef4e2d7bc17ae50d84d
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/pgvector.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.query_constructors.pgvector import PGVectorTranslator
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "PGVectorTranslator": "langchain_community.query_constructors.pgvector",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = ["PGVectorTranslator"]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/pinecone.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/pinecone.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f2e4a9432d929e59f5c000bd2086ebc14e0bb62
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/pinecone.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.query_constructors.pinecone import PineconeTranslator
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "PineconeTranslator": "langchain_community.query_constructors.pinecone",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = ["PineconeTranslator"]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/qdrant.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/qdrant.py
new file mode 100644
index 0000000000000000000000000000000000000000..9bcbce420147b7f9c4e6278d686b03731b801283
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/qdrant.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.query_constructors.qdrant import QdrantTranslator
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "QdrantTranslator": "langchain_community.query_constructors.qdrant",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = ["QdrantTranslator"]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/redis.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/redis.py
new file mode 100644
index 0000000000000000000000000000000000000000..05c425d5202dfc6ec7423cd5b336a81f3b7fcf15
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/redis.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.query_constructors.redis import RedisTranslator
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "RedisTranslator": "langchain_community.query_constructors.redis",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = ["RedisTranslator"]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/supabase.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/supabase.py
new file mode 100644
index 0000000000000000000000000000000000000000..5f34601ec15f75b63eecef4a2e1df306d8db2082
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/supabase.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.query_constructors.supabase import SupabaseVectorTranslator
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "SupabaseVectorTranslator": "langchain_community.query_constructors.supabase",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = ["SupabaseVectorTranslator"]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/tencentvectordb.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/tencentvectordb.py
new file mode 100644
index 0000000000000000000000000000000000000000..21d42e1998964bcd8d3f2efac703c6c3e9037ebb
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/tencentvectordb.py
@@ -0,0 +1,27 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.query_constructors.tencentvectordb import (
+ TencentVectorDBTranslator,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "TencentVectorDBTranslator": (
+ "langchain_community.query_constructors.tencentvectordb"
+ ),
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = ["TencentVectorDBTranslator"]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/timescalevector.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/timescalevector.py
new file mode 100644
index 0000000000000000000000000000000000000000..94cf5a5f07cb809099469b0e938bbf8c12a8e322
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/timescalevector.py
@@ -0,0 +1,27 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.query_constructors.timescalevector import (
+ TimescaleVectorTranslator,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "TimescaleVectorTranslator": (
+ "langchain_community.query_constructors.timescalevector"
+ ),
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = ["TimescaleVectorTranslator"]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/vectara.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/vectara.py
new file mode 100644
index 0000000000000000000000000000000000000000..8e3207558ccdba4ab0e09b31a1d1535b686a399c
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/vectara.py
@@ -0,0 +1,27 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.query_constructors.vectara import (
+ VectaraTranslator,
+ process_value,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "VectaraTranslator": "langchain_community.query_constructors.vectara",
+ "process_value": "langchain_community.query_constructors.vectara",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = ["VectaraTranslator", "process_value"]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/weaviate.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/weaviate.py
new file mode 100644
index 0000000000000000000000000000000000000000..758761e8cef2688cdfc9894050d2765576bf6ae4
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/retrievers/self_query/weaviate.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.query_constructors.weaviate import WeaviateTranslator
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "WeaviateTranslator": "langchain_community.query_constructors.weaviate",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = ["WeaviateTranslator"]
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/callbacks/base.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/callbacks/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..945344a223419716f1c6666489920945cb302c5f
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/callbacks/base.py
@@ -0,0 +1,23 @@
+from langchain_core.callbacks.base import (
+ AsyncCallbackHandler,
+ BaseCallbackHandler,
+ BaseCallbackManager,
+ CallbackManagerMixin,
+ ChainManagerMixin,
+ LLMManagerMixin,
+ RetrieverManagerMixin,
+ RunManagerMixin,
+ ToolManagerMixin,
+)
+
+__all__ = [
+ "AsyncCallbackHandler",
+ "BaseCallbackHandler",
+ "BaseCallbackManager",
+ "CallbackManagerMixin",
+ "ChainManagerMixin",
+ "LLMManagerMixin",
+ "RetrieverManagerMixin",
+ "RunManagerMixin",
+ "ToolManagerMixin",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/callbacks/manager.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/callbacks/manager.py
new file mode 100644
index 0000000000000000000000000000000000000000..ced881216708057fcb9ce7e63ef29a0dadceee7d
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/callbacks/manager.py
@@ -0,0 +1,53 @@
+from langchain_core.callbacks.manager import (
+ AsyncCallbackManager,
+ AsyncCallbackManagerForChainGroup,
+ AsyncCallbackManagerForChainRun,
+ AsyncCallbackManagerForLLMRun,
+ AsyncCallbackManagerForRetrieverRun,
+ AsyncCallbackManagerForToolRun,
+ AsyncParentRunManager,
+ AsyncRunManager,
+ BaseRunManager,
+ CallbackManager,
+ CallbackManagerForChainGroup,
+ CallbackManagerForChainRun,
+ CallbackManagerForLLMRun,
+ CallbackManagerForRetrieverRun,
+ CallbackManagerForToolRun,
+ ParentRunManager,
+ RunManager,
+ handle_event,
+ trace_as_chain_group,
+)
+from langchain_core.tracers.context import (
+ collect_runs,
+ register_configure_hook,
+ tracing_v2_enabled,
+)
+from langchain_core.utils.env import env_var_is_set
+
+__all__ = [
+ "AsyncCallbackManager",
+ "AsyncCallbackManagerForChainGroup",
+ "AsyncCallbackManagerForChainRun",
+ "AsyncCallbackManagerForLLMRun",
+ "AsyncCallbackManagerForRetrieverRun",
+ "AsyncCallbackManagerForToolRun",
+ "AsyncParentRunManager",
+ "AsyncRunManager",
+ "BaseRunManager",
+ "CallbackManager",
+ "CallbackManagerForChainGroup",
+ "CallbackManagerForChainRun",
+ "CallbackManagerForLLMRun",
+ "CallbackManagerForRetrieverRun",
+ "CallbackManagerForToolRun",
+ "ParentRunManager",
+ "RunManager",
+ "collect_runs",
+ "env_var_is_set",
+ "handle_event",
+ "register_configure_hook",
+ "trace_as_chain_group",
+ "tracing_v2_enabled",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/callbacks/stdout.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/callbacks/stdout.py
new file mode 100644
index 0000000000000000000000000000000000000000..754e58248e4700a666314dddf21a8f4248f8e541
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/callbacks/stdout.py
@@ -0,0 +1,3 @@
+from langchain_core.callbacks.stdout import StdOutCallbackHandler
+
+__all__ = ["StdOutCallbackHandler"]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/callbacks/streaming_stdout.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/callbacks/streaming_stdout.py
new file mode 100644
index 0000000000000000000000000000000000000000..35608689634800004cfc0523f3dc911add005773
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/callbacks/streaming_stdout.py
@@ -0,0 +1,3 @@
+from langchain_core.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+
+__all__ = ["StreamingStdOutCallbackHandler"]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/callbacks/tracers/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/callbacks/tracers/__init__.py
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index 0000000000000000000000000000000000000000..9f60add9dc9ca3a12ec146d2472fdfc7891c5660
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/callbacks/tracers/base.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/callbacks/tracers/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..6288d12655cd9e02bc2c30912decc481dca27562
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/callbacks/tracers/base.py
@@ -0,0 +1,4 @@
+from langchain_core.exceptions import TracerException
+from langchain_core.tracers.base import BaseTracer
+
+__all__ = ["BaseTracer", "TracerException"]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/callbacks/tracers/evaluation.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/callbacks/tracers/evaluation.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c7dd113fe26af371fbe68446aa160b3fbef676e
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/callbacks/tracers/evaluation.py
@@ -0,0 +1,6 @@
+from langchain_core.tracers.evaluation import (
+ EvaluatorCallbackHandler,
+ wait_for_all_evaluators,
+)
+
+__all__ = ["EvaluatorCallbackHandler", "wait_for_all_evaluators"]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/callbacks/tracers/langchain.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/callbacks/tracers/langchain.py
new file mode 100644
index 0000000000000000000000000000000000000000..dc8b0f0d05c2d618bd5b260a6f4690f19bf07b3a
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/callbacks/tracers/langchain.py
@@ -0,0 +1,8 @@
+from langchain_core.tracers.langchain import (
+ LangChainTracer,
+ get_client,
+ log_error_once,
+ wait_for_all_tracers,
+)
+
+__all__ = ["LangChainTracer", "get_client", "log_error_once", "wait_for_all_tracers"]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/callbacks/tracers/log_stream.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/callbacks/tracers/log_stream.py
new file mode 100644
index 0000000000000000000000000000000000000000..54cc24935fc2c90de5e246ce2e3788977d9fca19
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/callbacks/tracers/log_stream.py
@@ -0,0 +1,9 @@
+from langchain_core.tracers.log_stream import (
+ LogEntry,
+ LogStreamCallbackHandler,
+ RunLog,
+ RunLogPatch,
+ RunState,
+)
+
+__all__ = ["LogEntry", "LogStreamCallbackHandler", "RunLog", "RunLogPatch", "RunState"]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/callbacks/tracers/root_listeners.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/callbacks/tracers/root_listeners.py
new file mode 100644
index 0000000000000000000000000000000000000000..0dee9bce2d23376310af8d807599c6952da5eda7
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/callbacks/tracers/root_listeners.py
@@ -0,0 +1,3 @@
+from langchain_core.tracers.root_listeners import RootListenersTracer
+
+__all__ = ["RootListenersTracer"]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/callbacks/tracers/run_collector.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/callbacks/tracers/run_collector.py
new file mode 100644
index 0000000000000000000000000000000000000000..1240026bfb61e3f8d431d036edd4b4024b4be838
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/callbacks/tracers/run_collector.py
@@ -0,0 +1,3 @@
+from langchain_core.tracers.run_collector import RunCollectorCallbackHandler
+
+__all__ = ["RunCollectorCallbackHandler"]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/callbacks/tracers/schemas.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/callbacks/tracers/schemas.py
new file mode 100644
index 0000000000000000000000000000000000000000..32e6b2e4f13efafed1b03707854bdf7f4c20a449
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/callbacks/tracers/schemas.py
@@ -0,0 +1,5 @@
+from langchain_core.tracers.schemas import Run
+
+__all__ = [
+ "Run",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/callbacks/tracers/stdout.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/callbacks/tracers/stdout.py
new file mode 100644
index 0000000000000000000000000000000000000000..fdd6b80cd2fc951b0c93347d6a42a1bddfbb6622
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/callbacks/tracers/stdout.py
@@ -0,0 +1,13 @@
+from langchain_core.tracers.stdout import (
+ ConsoleCallbackHandler,
+ FunctionCallbackHandler,
+ elapsed,
+ try_json_stringify,
+)
+
+__all__ = [
+ "ConsoleCallbackHandler",
+ "FunctionCallbackHandler",
+ "elapsed",
+ "try_json_stringify",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/runnable/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/runnable/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..995bf67121eac4214b4b78849177092976bcb7d0
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/runnable/__init__.py
@@ -0,0 +1,58 @@
+"""LangChain **Runnable** and the **LangChain Expression Language (LCEL)**.
+
+The LangChain Expression Language (LCEL) offers a declarative method to build
+production-grade programs that harness the power of LLMs.
+
+Programs created using LCEL and LangChain Runnables inherently support
+synchronous, asynchronous, batch, and streaming operations.
+
+Support for **async** allows servers hosting LCEL based programs to scale better
+for higher concurrent loads.
+
+**Streaming** of intermediate outputs as they're being generated allows for
+creating more responsive UX.
+
+This module contains schema and implementation of LangChain Runnables primitives.
+"""
+
+from langchain_core.runnables.base import (
+ Runnable,
+ RunnableBinding,
+ RunnableGenerator,
+ RunnableLambda,
+ RunnableMap,
+ RunnableParallel,
+ RunnableSequence,
+ RunnableSerializable,
+)
+from langchain_core.runnables.branch import RunnableBranch
+from langchain_core.runnables.config import RunnableConfig, patch_config
+from langchain_core.runnables.fallbacks import RunnableWithFallbacks
+from langchain_core.runnables.passthrough import RunnablePassthrough
+from langchain_core.runnables.router import RouterInput, RouterRunnable
+from langchain_core.runnables.utils import (
+ ConfigurableField,
+ ConfigurableFieldMultiOption,
+ ConfigurableFieldSingleOption,
+)
+
+__all__ = [
+ "ConfigurableField",
+ "ConfigurableFieldMultiOption",
+ "ConfigurableFieldSingleOption",
+ "RouterInput",
+ "RouterRunnable",
+ "Runnable",
+ "RunnableBinding",
+ "RunnableBranch",
+ "RunnableConfig",
+ "RunnableGenerator",
+ "RunnableLambda",
+ "RunnableMap",
+ "RunnableParallel",
+ "RunnablePassthrough",
+ "RunnableSequence",
+ "RunnableSerializable",
+ "RunnableWithFallbacks",
+ "patch_config",
+]
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/runnable/__pycache__/utils.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/runnable/__pycache__/utils.cpython-311.pyc
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/runnable/base.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/runnable/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..c55cc4130d6dae84876b7f26b71aa3f4d2c8bfe7
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/runnable/base.py
@@ -0,0 +1,38 @@
+from langchain_core.runnables.base import (
+ Other,
+ Runnable,
+ RunnableBinding,
+ RunnableBindingBase,
+ RunnableEach,
+ RunnableEachBase,
+ RunnableGenerator,
+ RunnableLambda,
+ RunnableLike,
+ RunnableParallel,
+ RunnableSequence,
+ RunnableSerializable,
+ coerce_to_runnable,
+)
+from langchain_core.runnables.utils import Input, Output
+
+# Backwards compatibility.
+RunnableMap = RunnableParallel
+
+__all__ = [
+ "Input",
+ "Other",
+ "Output",
+ "Runnable",
+ "RunnableBinding",
+ "RunnableBindingBase",
+ "RunnableEach",
+ "RunnableEachBase",
+ "RunnableGenerator",
+ "RunnableLambda",
+ "RunnableLike",
+ "RunnableMap",
+ "RunnableParallel",
+ "RunnableSequence",
+ "RunnableSerializable",
+ "coerce_to_runnable",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/runnable/branch.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/runnable/branch.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed83f197aa0b3aa94a63a01a1dd690fbdd1cb2f2
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/runnable/branch.py
@@ -0,0 +1,3 @@
+from langchain_core.runnables.branch import RunnableBranch
+
+__all__ = ["RunnableBranch"]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/runnable/config.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/runnable/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..88caa14b012f9728f1b435826d45f7fdbc8e4ce6
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/runnable/config.py
@@ -0,0 +1,27 @@
+from langchain_core.runnables.config import (
+ EmptyDict,
+ RunnableConfig,
+ acall_func_with_variable_args,
+ call_func_with_variable_args,
+ ensure_config,
+ get_async_callback_manager_for_config,
+ get_callback_manager_for_config,
+ get_config_list,
+ get_executor_for_config,
+ merge_configs,
+ patch_config,
+)
+
+__all__ = [
+ "EmptyDict",
+ "RunnableConfig",
+ "acall_func_with_variable_args",
+ "call_func_with_variable_args",
+ "ensure_config",
+ "get_async_callback_manager_for_config",
+ "get_callback_manager_for_config",
+ "get_config_list",
+ "get_executor_for_config",
+ "merge_configs",
+ "patch_config",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/runnable/configurable.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/runnable/configurable.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e0ac78e56c7d4d2dd1d00757ba50bdc981d1d2c
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/runnable/configurable.py
@@ -0,0 +1,15 @@
+from langchain_core.runnables.configurable import (
+ DynamicRunnable,
+ RunnableConfigurableAlternatives,
+ RunnableConfigurableFields,
+ StrEnum,
+ make_options_spec,
+)
+
+__all__ = [
+ "DynamicRunnable",
+ "RunnableConfigurableAlternatives",
+ "RunnableConfigurableFields",
+ "StrEnum",
+ "make_options_spec",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/runnable/fallbacks.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/runnable/fallbacks.py
new file mode 100644
index 0000000000000000000000000000000000000000..7a54468d77487aaf411575125ea566ca49268ac8
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/runnable/fallbacks.py
@@ -0,0 +1,3 @@
+from langchain_core.runnables.fallbacks import RunnableWithFallbacks
+
+__all__ = ["RunnableWithFallbacks"]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/runnable/history.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/runnable/history.py
new file mode 100644
index 0000000000000000000000000000000000000000..54fde64862835f5c9a69cd5cec3eec5d8af78742
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/runnable/history.py
@@ -0,0 +1,11 @@
+from langchain_core.runnables.history import (
+ GetSessionHistoryCallable,
+ MessagesOrDictWithMessages,
+ RunnableWithMessageHistory,
+)
+
+__all__ = [
+ "GetSessionHistoryCallable",
+ "MessagesOrDictWithMessages",
+ "RunnableWithMessageHistory",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/runnable/passthrough.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/runnable/passthrough.py
new file mode 100644
index 0000000000000000000000000000000000000000..b9a4d84b3e1b370592d1de41aa184c2980ea4301
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/runnable/passthrough.py
@@ -0,0 +1,8 @@
+from langchain_core.runnables.passthrough import (
+ RunnableAssign,
+ RunnablePassthrough,
+ aidentity,
+ identity,
+)
+
+__all__ = ["RunnableAssign", "RunnablePassthrough", "aidentity", "identity"]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/runnable/retry.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/runnable/retry.py
new file mode 100644
index 0000000000000000000000000000000000000000..d146c8ec5c2df3bb557dae635ce514a35e3677e5
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/runnable/retry.py
@@ -0,0 +1,3 @@
+from langchain_core.runnables.retry import RunnableRetry, U
+
+__all__ = ["RunnableRetry", "U"]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/runnable/router.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/runnable/router.py
new file mode 100644
index 0000000000000000000000000000000000000000..259dd677c988d8a4e7ff8b836283eb79247c6d19
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/runnable/router.py
@@ -0,0 +1,3 @@
+from langchain_core.runnables.router import RouterInput, RouterRunnable
+
+__all__ = ["RouterInput", "RouterRunnable"]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/runnable/utils.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/runnable/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..7e9ce44567cace0f3a65242806c52e5b484bf33a
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/schema/runnable/utils.py
@@ -0,0 +1,51 @@
+from langchain_core.runnables.utils import (
+ Addable,
+ AddableDict,
+ AnyConfigurableField,
+ ConfigurableField,
+ ConfigurableFieldMultiOption,
+ ConfigurableFieldSingleOption,
+ ConfigurableFieldSpec,
+ GetLambdaSource,
+ Input,
+ IsFunctionArgDict,
+ IsLocalDict,
+ Output,
+ SupportsAdd,
+ aadd,
+ accepts_config,
+ accepts_run_manager,
+ add,
+ gated_coro,
+ gather_with_concurrency,
+ get_function_first_arg_dict_keys,
+ get_lambda_source,
+ get_unique_config_specs,
+ indent_lines_after_first,
+)
+
+__all__ = [
+ "Addable",
+ "AddableDict",
+ "AnyConfigurableField",
+ "ConfigurableField",
+ "ConfigurableFieldMultiOption",
+ "ConfigurableFieldSingleOption",
+ "ConfigurableFieldSpec",
+ "GetLambdaSource",
+ "Input",
+ "IsFunctionArgDict",
+ "IsLocalDict",
+ "Output",
+ "SupportsAdd",
+ "aadd",
+ "accepts_config",
+ "accepts_run_manager",
+ "add",
+ "gated_coro",
+ "gather_with_concurrency",
+ "get_function_first_arg_dict_keys",
+ "get_lambda_source",
+ "get_unique_config_specs",
+ "indent_lines_after_first",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/smith/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/smith/__pycache__/__init__.cpython-311.pyc
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/smith/evaluation/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/smith/evaluation/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..755b3c14d1792ee62e1cc3f3831f7c442f25606d
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/smith/evaluation/__init__.py
@@ -0,0 +1,72 @@
+"""LangSmith evaluation utilities.
+
+This module provides utilities for evaluating Chains and other language model
+applications using LangChain evaluators and LangSmith.
+
+For more information on the LangSmith API, see the
+[LangSmith API documentation](https://docs.langchain.com/langsmith/home).
+
+**Example**
+
+```python
+from langsmith import Client
+from langchain_openai import ChatOpenAI
+from langchain_classic.chains import LLMChain
+from langchain_classic.smith import EvaluatorType, RunEvalConfig, run_on_dataset
+
+
+def construct_chain():
+ model = ChatOpenAI(temperature=0)
+ chain = LLMChain.from_string(model, "What's the answer to {your_input_key}")
+ return chain
+
+
+evaluation_config = RunEvalConfig(
+ evaluators=[
+ EvaluatorType.QA, # "Correctness" against a reference answer
+ EvaluatorType.EMBEDDING_DISTANCE,
+ RunEvalConfig.Criteria("helpfulness"),
+ RunEvalConfig.Criteria(
+ {
+ "fifth-grader-score": "Do you have to be smarter than a fifth "
+ "grader to answer this question?"
+ }
+ ),
+ ]
+)
+
+client = Client()
+run_on_dataset(
+ client, "", construct_chain, evaluation=evaluation_config
+)
+```
+
+**Attributes**
+
+- `arun_on_dataset`: Asynchronous function to evaluate a chain or other LangChain
+ component over a dataset.
+- `run_on_dataset`: Function to evaluate a chain or other LangChain component over a
+ dataset.
+- `RunEvalConfig`: Class representing the configuration for running evaluation.
+- `StringRunEvaluatorChain`: Class representing a string run evaluator chain.
+- `InputFormatError`: Exception raised when the input format is incorrect.
+
+"""
+
+from langchain_classic.smith.evaluation.config import RunEvalConfig
+from langchain_classic.smith.evaluation.runner_utils import (
+ InputFormatError,
+ arun_on_dataset,
+ run_on_dataset,
+)
+from langchain_classic.smith.evaluation.string_run_evaluator import (
+ StringRunEvaluatorChain,
+)
+
+__all__ = [
+ "InputFormatError",
+ "RunEvalConfig",
+ "StringRunEvaluatorChain",
+ "arun_on_dataset",
+ "run_on_dataset",
+]
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/smith/evaluation/config.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/smith/evaluation/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..6d1df6232b3333caa72f010f9fe0a8b5af014a87
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/smith/evaluation/config.py
@@ -0,0 +1,273 @@
+"""Configuration for run evaluators."""
+
+from collections.abc import Callable, Sequence
+from typing import Any
+
+from langchain_core.embeddings import Embeddings
+from langchain_core.language_models import BaseLanguageModel
+from langchain_core.prompts import BasePromptTemplate
+from langsmith import RunEvaluator
+from langsmith.evaluation.evaluator import EvaluationResult, EvaluationResults
+from langsmith.schemas import Example, Run
+from pydantic import BaseModel, ConfigDict, Field
+from typing_extensions import override
+
+from langchain_classic.evaluation.criteria.eval_chain import CRITERIA_TYPE
+from langchain_classic.evaluation.embedding_distance.base import (
+ EmbeddingDistance as EmbeddingDistanceEnum,
+)
+from langchain_classic.evaluation.schema import EvaluatorType, StringEvaluator
+from langchain_classic.evaluation.string_distance.base import (
+ StringDistance as StringDistanceEnum,
+)
+
+RUN_EVALUATOR_LIKE = Callable[
+ [Run, Example | None],
+ EvaluationResult | EvaluationResults | dict,
+]
+BATCH_EVALUATOR_LIKE = Callable[
+ [Sequence[Run], Sequence[Example] | None],
+ EvaluationResult | EvaluationResults | dict,
+]
+
+
+class EvalConfig(BaseModel):
+ """Configuration for a given run evaluator.
+
+ Attributes:
+ evaluator_type: The type of evaluator to use.
+ """
+
+ evaluator_type: EvaluatorType
+
+ def get_kwargs(self) -> dict[str, Any]:
+ """Get the keyword arguments for the `load_evaluator` call.
+
+ Returns:
+ The keyword arguments for the `load_evaluator` call.
+ """
+ kwargs = {}
+ for field, val in self:
+ if field == "evaluator_type" or val is None:
+ continue
+ kwargs[field] = val
+ return kwargs
+
+
+class SingleKeyEvalConfig(EvalConfig):
+ """Configuration for a run evaluator that only requires a single key."""
+
+ reference_key: str | None = None
+ """The key in the dataset run to use as the reference string.
+ If not provided, we will attempt to infer automatically."""
+ prediction_key: str | None = None
+ """The key from the traced run's outputs dictionary to use to
+ represent the prediction. If not provided, it will be inferred
+ automatically."""
+ input_key: str | None = None
+ """The key from the traced run's inputs dictionary to use to represent the
+ input. If not provided, it will be inferred automatically."""
+
+ @override
+ def get_kwargs(self) -> dict[str, Any]:
+ kwargs = super().get_kwargs()
+ # Filer out the keys that are not needed for the evaluator.
+ for key in ["reference_key", "prediction_key", "input_key"]:
+ kwargs.pop(key, None)
+ return kwargs
+
+
+CUSTOM_EVALUATOR_TYPE = RUN_EVALUATOR_LIKE | RunEvaluator | StringEvaluator
+SINGLE_EVAL_CONFIG_TYPE = EvaluatorType | str | EvalConfig
+
+
+class RunEvalConfig(BaseModel):
+ """Configuration for a run evaluation."""
+
+ evaluators: list[SINGLE_EVAL_CONFIG_TYPE | CUSTOM_EVALUATOR_TYPE] = Field(
+ default_factory=list
+ )
+ """Configurations for which evaluators to apply to the dataset run.
+ Each can be the string of an
+ `EvaluatorType `, such
+ as `EvaluatorType.QA`, the evaluator type string ("qa"), or a configuration for a
+ given evaluator
+ (e.g.,
+ `RunEvalConfig.QA `)."""
+ custom_evaluators: list[CUSTOM_EVALUATOR_TYPE] | None = None
+ """Custom evaluators to apply to the dataset run."""
+ batch_evaluators: list[BATCH_EVALUATOR_LIKE] | None = None
+ """Evaluators that run on an aggregate/batch level.
+
+ These generate one or more metrics that are assigned to the full test run.
+ As a result, they are not associated with individual traces.
+ """
+
+ reference_key: str | None = None
+ """The key in the dataset run to use as the reference string.
+ If not provided, we will attempt to infer automatically."""
+ prediction_key: str | None = None
+ """The key from the traced run's outputs dictionary to use to
+ represent the prediction. If not provided, it will be inferred
+ automatically."""
+ input_key: str | None = None
+ """The key from the traced run's inputs dictionary to use to represent the
+ input. If not provided, it will be inferred automatically."""
+ eval_llm: BaseLanguageModel | None = None
+ """The language model to pass to any evaluators that require one."""
+
+ model_config = ConfigDict(
+ arbitrary_types_allowed=True,
+ )
+
+ class Criteria(SingleKeyEvalConfig):
+ """Configuration for a reference-free criteria evaluator.
+
+ Attributes:
+ criteria: The criteria to evaluate.
+ llm: The language model to use for the evaluation chain.
+ """
+
+ criteria: CRITERIA_TYPE | None = None
+ llm: BaseLanguageModel | None = None
+ evaluator_type: EvaluatorType = EvaluatorType.CRITERIA
+
+ class LabeledCriteria(SingleKeyEvalConfig):
+ """Configuration for a labeled (with references) criteria evaluator.
+
+ Attributes:
+ criteria: The criteria to evaluate.
+ llm: The language model to use for the evaluation chain.
+ """
+
+ criteria: CRITERIA_TYPE | None = None
+ llm: BaseLanguageModel | None = None
+ evaluator_type: EvaluatorType = EvaluatorType.LABELED_CRITERIA
+
+ class EmbeddingDistance(SingleKeyEvalConfig):
+ """Configuration for an embedding distance evaluator.
+
+ Attributes:
+ embeddings: The embeddings to use for computing the distance.
+ distance_metric: The distance metric to use for computing the distance.
+ """
+
+ evaluator_type: EvaluatorType = EvaluatorType.EMBEDDING_DISTANCE
+ embeddings: Embeddings | None = None
+ distance_metric: EmbeddingDistanceEnum | None = None
+
+ model_config = ConfigDict(
+ arbitrary_types_allowed=True,
+ )
+
+ class StringDistance(SingleKeyEvalConfig):
+ """Configuration for a string distance evaluator.
+
+ Attributes:
+ distance: The string distance metric to use (`damerau_levenshtein`,
+ `levenshtein`, `jaro`, or `jaro_winkler`).
+ normalize_score: Whether to normalize the distance to between 0 and 1.
+ Applies only to the Levenshtein and Damerau-Levenshtein distances.
+ """
+
+ evaluator_type: EvaluatorType = EvaluatorType.STRING_DISTANCE
+ distance: StringDistanceEnum | None = None
+ normalize_score: bool = True
+
+ class QA(SingleKeyEvalConfig):
+ """Configuration for a QA evaluator.
+
+ Attributes:
+ prompt: The prompt template to use for generating the question.
+ llm: The language model to use for the evaluation chain.
+ """
+
+ evaluator_type: EvaluatorType = EvaluatorType.QA
+ llm: BaseLanguageModel | None = None
+ prompt: BasePromptTemplate | None = None
+
+ class ContextQA(SingleKeyEvalConfig):
+ """Configuration for a context-based QA evaluator.
+
+ Attributes:
+ prompt: The prompt template to use for generating the question.
+ llm: The language model to use for the evaluation chain.
+ """
+
+ evaluator_type: EvaluatorType = EvaluatorType.CONTEXT_QA
+ llm: BaseLanguageModel | None = None
+ prompt: BasePromptTemplate | None = None
+
+ class CoTQA(SingleKeyEvalConfig):
+ """Configuration for a context-based QA evaluator.
+
+ Attributes:
+ prompt: The prompt template to use for generating the question.
+ llm: The language model to use for the evaluation chain.
+ """
+
+ evaluator_type: EvaluatorType = EvaluatorType.CONTEXT_QA
+ llm: BaseLanguageModel | None = None
+ prompt: BasePromptTemplate | None = None
+
+ class JsonValidity(SingleKeyEvalConfig):
+ """Configuration for a json validity evaluator."""
+
+ evaluator_type: EvaluatorType = EvaluatorType.JSON_VALIDITY
+
+ class JsonEqualityEvaluator(EvalConfig):
+ """Configuration for a json equality evaluator."""
+
+ evaluator_type: EvaluatorType = EvaluatorType.JSON_EQUALITY
+
+ class ExactMatch(SingleKeyEvalConfig):
+ """Configuration for an exact match string evaluator.
+
+ Attributes:
+ ignore_case: Whether to ignore case when comparing strings.
+ ignore_punctuation: Whether to ignore punctuation when comparing strings.
+ ignore_numbers: Whether to ignore numbers when comparing strings.
+ """
+
+ evaluator_type: EvaluatorType = EvaluatorType.EXACT_MATCH
+ ignore_case: bool = False
+ ignore_punctuation: bool = False
+ ignore_numbers: bool = False
+
+ class RegexMatch(SingleKeyEvalConfig):
+ """Configuration for a regex match string evaluator.
+
+ Attributes:
+ flags: The flags to pass to the regex. Example: `re.IGNORECASE`.
+ """
+
+ evaluator_type: EvaluatorType = EvaluatorType.REGEX_MATCH
+ flags: int = 0
+
+ class ScoreString(SingleKeyEvalConfig):
+ """Configuration for a score string evaluator.
+
+ This is like the criteria evaluator but it is configured by
+ default to return a score on the scale from 1-10.
+
+ It is recommended to normalize these scores
+ by setting `normalize_by` to 10.
+
+ Attributes:
+ criteria: The criteria to evaluate.
+ llm: The language model to use for the evaluation chain.
+ normalize_by: If you want to normalize the score, the denominator to use.
+ If not provided, the score will be between 1 and 10.
+ prompt: The prompt template to use for evaluation.
+ """
+
+ evaluator_type: EvaluatorType = EvaluatorType.SCORE_STRING
+ criteria: CRITERIA_TYPE | None = None
+ llm: BaseLanguageModel | None = None
+ normalize_by: float | None = None
+ prompt: BasePromptTemplate | None = None
+
+ class LabeledScoreString(ScoreString):
+ """Configuration for a labeled score string evaluator."""
+
+ evaluator_type: EvaluatorType = EvaluatorType.LABELED_SCORE_STRING
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/smith/evaluation/name_generation.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/smith/evaluation/name_generation.py
new file mode 100644
index 0000000000000000000000000000000000000000..7afb242536f56be4646453454450633db06211e2
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/smith/evaluation/name_generation.py
@@ -0,0 +1,727 @@
+import random
+
+adjectives = [
+ "abandoned",
+ "aching",
+ "advanced",
+ "ample",
+ "artistic",
+ "back",
+ "best",
+ "bold",
+ "brief",
+ "clear",
+ "cold",
+ "complicated",
+ "cooked",
+ "crazy",
+ "crushing",
+ "damp",
+ "dear",
+ "definite",
+ "dependable",
+ "diligent",
+ "drab",
+ "earnest",
+ "elderly",
+ "enchanted",
+ "essential",
+ "excellent",
+ "extraneous",
+ "fixed",
+ "flowery",
+ "formal",
+ "fresh",
+ "frosty",
+ "giving",
+ "glossy",
+ "healthy",
+ "helpful",
+ "impressionable",
+ "kind",
+ "large",
+ "left",
+ "long",
+ "loyal",
+ "mealy",
+ "memorable",
+ "monthly",
+ "new",
+ "notable",
+ "only",
+ "ordinary",
+ "passionate",
+ "perfect",
+ "pertinent",
+ "proper",
+ "puzzled",
+ "reflecting",
+ "respectful",
+ "roasted",
+ "scholarly",
+ "shiny",
+ "slight",
+ "sparkling",
+ "spotless",
+ "stupendous",
+ "sunny",
+ "tart",
+ "terrific",
+ "timely",
+ "unique",
+ "upbeat",
+ "vacant",
+ "virtual",
+ "warm",
+ "weary",
+ "whispered",
+ "worthwhile",
+ "yellow",
+]
+
+nouns = [
+ "account",
+ "acknowledgment",
+ "address",
+ "advertising",
+ "airplane",
+ "animal",
+ "appointment",
+ "arrival",
+ "artist",
+ "attachment",
+ "attitude",
+ "availability",
+ "backpack",
+ "bag",
+ "balance",
+ "bass",
+ "bean",
+ "beauty",
+ "bibliography",
+ "bill",
+ "bite",
+ "blossom",
+ "boat",
+ "book",
+ "box",
+ "boy",
+ "bread",
+ "bridge",
+ "broccoli",
+ "building",
+ "butter",
+ "button",
+ "cabbage",
+ "cake",
+ "camera",
+ "camp",
+ "candle",
+ "candy",
+ "canvas",
+ "car",
+ "card",
+ "carrot",
+ "cart",
+ "case",
+ "cat",
+ "chain",
+ "chair",
+ "chalk",
+ "chance",
+ "change",
+ "channel",
+ "character",
+ "charge",
+ "charm",
+ "chart",
+ "check",
+ "cheek",
+ "cheese",
+ "chef",
+ "cherry",
+ "chicken",
+ "child",
+ "church",
+ "circle",
+ "class",
+ "clay",
+ "click",
+ "clock",
+ "cloth",
+ "cloud",
+ "clove",
+ "club",
+ "coach",
+ "coal",
+ "coast",
+ "coat",
+ "cod",
+ "coffee",
+ "collar",
+ "color",
+ "comb",
+ "comfort",
+ "comic",
+ "committee",
+ "community",
+ "company",
+ "comparison",
+ "competition",
+ "condition",
+ "connection",
+ "control",
+ "cook",
+ "copper",
+ "copy",
+ "corn",
+ "cough",
+ "country",
+ "cover",
+ "crate",
+ "crayon",
+ "cream",
+ "creator",
+ "crew",
+ "crown",
+ "current",
+ "curtain",
+ "curve",
+ "cushion",
+ "dad",
+ "daughter",
+ "day",
+ "death",
+ "debt",
+ "decision",
+ "deer",
+ "degree",
+ "design",
+ "desire",
+ "desk",
+ "detail",
+ "development",
+ "digestion",
+ "dime",
+ "dinner",
+ "direction",
+ "dirt",
+ "discovery",
+ "discussion",
+ "disease",
+ "disgust",
+ "distance",
+ "distribution",
+ "division",
+ "doctor",
+ "dog",
+ "door",
+ "drain",
+ "drawer",
+ "dress",
+ "drink",
+ "driving",
+ "dust",
+ "ear",
+ "earth",
+ "edge",
+ "education",
+ "effect",
+ "egg",
+ "end",
+ "energy",
+ "engine",
+ "error",
+ "event",
+ "example",
+ "exchange",
+ "existence",
+ "expansion",
+ "experience",
+ "expert",
+ "eye",
+ "face",
+ "fact",
+ "fall",
+ "family",
+ "farm",
+ "father",
+ "fear",
+ "feeling",
+ "field",
+ "finger",
+ "fire",
+ "fish",
+ "flag",
+ "flight",
+ "floor",
+ "flower",
+ "fold",
+ "food",
+ "football",
+ "force",
+ "form",
+ "frame",
+ "friend",
+ "frog",
+ "fruit",
+ "fuel",
+ "furniture",
+ "game",
+ "garden",
+ "gate",
+ "girl",
+ "glass",
+ "glove",
+ "goat",
+ "gold",
+ "government",
+ "grade",
+ "grain",
+ "grass",
+ "green",
+ "grip",
+ "group",
+ "growth",
+ "guide",
+ "guitar",
+ "hair",
+ "hall",
+ "hand",
+ "harbor",
+ "harmony",
+ "hat",
+ "head",
+ "health",
+ "heart",
+ "heat",
+ "hill",
+ "history",
+ "hobbies",
+ "hole",
+ "hope",
+ "horn",
+ "horse",
+ "hospital",
+ "hour",
+ "house",
+ "humor",
+ "idea",
+ "impulse",
+ "income",
+ "increase",
+ "industry",
+ "ink",
+ "insect",
+ "instrument",
+ "insurance",
+ "interest",
+ "invention",
+ "iron",
+ "island",
+ "jelly",
+ "jet",
+ "jewel",
+ "join",
+ "judge",
+ "juice",
+ "jump",
+ "kettle",
+ "key",
+ "kick",
+ "kiss",
+ "kitten",
+ "knee",
+ "knife",
+ "knowledge",
+ "land",
+ "language",
+ "laugh",
+ "law",
+ "lead",
+ "learning",
+ "leather",
+ "leg",
+ "lettuce",
+ "level",
+ "library",
+ "lift",
+ "light",
+ "limit",
+ "line",
+ "linen",
+ "lip",
+ "liquid",
+ "list",
+ "look",
+ "loss",
+ "love",
+ "lunch",
+ "machine",
+ "man",
+ "manager",
+ "map",
+ "marble",
+ "mark",
+ "market",
+ "mass",
+ "match",
+ "meal",
+ "measure",
+ "meat",
+ "meeting",
+ "memory",
+ "metal",
+ "middle",
+ "milk",
+ "mind",
+ "mine",
+ "minute",
+ "mist",
+ "mitten",
+ "mom",
+ "money",
+ "monkey",
+ "month",
+ "moon",
+ "morning",
+ "mother",
+ "motion",
+ "mountain",
+ "mouth",
+ "muscle",
+ "music",
+ "nail",
+ "name",
+ "nation",
+ "neck",
+ "need",
+ "news",
+ "night",
+ "noise",
+ "note",
+ "number",
+ "nut",
+ "observation",
+ "offer",
+ "oil",
+ "operation",
+ "opinion",
+ "orange",
+ "order",
+ "organization",
+ "ornament",
+ "oven",
+ "page",
+ "pail",
+ "pain",
+ "paint",
+ "pan",
+ "pancake",
+ "paper",
+ "parcel",
+ "parent",
+ "part",
+ "passenger",
+ "paste",
+ "payment",
+ "peace",
+ "pear",
+ "pen",
+ "pencil",
+ "person",
+ "pest",
+ "pet",
+ "picture",
+ "pie",
+ "pin",
+ "pipe",
+ "pizza",
+ "place",
+ "plane",
+ "plant",
+ "plastic",
+ "plate",
+ "play",
+ "pleasure",
+ "plot",
+ "plough",
+ "pocket",
+ "point",
+ "poison",
+ "police",
+ "pollution",
+ "popcorn",
+ "porter",
+ "position",
+ "pot",
+ "potato",
+ "powder",
+ "power",
+ "price",
+ "print",
+ "process",
+ "produce",
+ "product",
+ "profit",
+ "property",
+ "prose",
+ "protest",
+ "pull",
+ "pump",
+ "punishment",
+ "purpose",
+ "push",
+ "quarter",
+ "question",
+ "quiet",
+ "quill",
+ "quilt",
+ "quince",
+ "rabbit",
+ "rail",
+ "rain",
+ "range",
+ "rat",
+ "rate",
+ "ray",
+ "reaction",
+ "reading",
+ "reason",
+ "record",
+ "regret",
+ "relation",
+ "religion",
+ "representative",
+ "request",
+ "respect",
+ "rest",
+ "reward",
+ "rhythm",
+ "rice",
+ "river",
+ "road",
+ "roll",
+ "room",
+ "root",
+ "rose",
+ "route",
+ "rub",
+ "rule",
+ "run",
+ "sack",
+ "sail",
+ "salt",
+ "sand",
+ "scale",
+ "scarecrow",
+ "scarf",
+ "scene",
+ "scent",
+ "school",
+ "science",
+ "scissors",
+ "screw",
+ "sea",
+ "seat",
+ "secretary",
+ "seed",
+ "selection",
+ "self",
+ "sense",
+ "servant",
+ "shade",
+ "shake",
+ "shame",
+ "shape",
+ "sheep",
+ "sheet",
+ "shelf",
+ "ship",
+ "shirt",
+ "shock",
+ "shoe",
+ "shop",
+ "show",
+ "side",
+ "sign",
+ "silk",
+ "sink",
+ "sister",
+ "size",
+ "sky",
+ "sleep",
+ "smash",
+ "smell",
+ "smile",
+ "smoke",
+ "snail",
+ "snake",
+ "sneeze",
+ "snow",
+ "soap",
+ "society",
+ "sock",
+ "soda",
+ "sofa",
+ "son",
+ "song",
+ "sort",
+ "sound",
+ "soup",
+ "space",
+ "spark",
+ "speed",
+ "sponge",
+ "spoon",
+ "spray",
+ "spring",
+ "spy",
+ "square",
+ "stamp",
+ "star",
+ "start",
+ "statement",
+ "station",
+ "steam",
+ "steel",
+ "stem",
+ "step",
+ "stew",
+ "stick",
+ "stitch",
+ "stocking",
+ "stomach",
+ "stone",
+ "stop",
+ "store",
+ "story",
+ "stove",
+ "stranger",
+ "straw",
+ "stream",
+ "street",
+ "stretch",
+ "string",
+ "structure",
+ "substance",
+ "sugar",
+ "suggestion",
+ "suit",
+ "summer",
+ "sun",
+ "support",
+ "surprise",
+ "sweater",
+ "swim",
+ "system",
+ "table",
+ "tail",
+ "talk",
+ "tank",
+ "taste",
+ "tax",
+ "tea",
+ "teaching",
+ "team",
+ "tendency",
+ "test",
+ "texture",
+ "theory",
+ "thing",
+ "thought",
+ "thread",
+ "throat",
+ "thumb",
+ "thunder",
+ "ticket",
+ "time",
+ "tin",
+ "title",
+ "toad",
+ "toe",
+ "tooth",
+ "toothpaste",
+ "touch",
+ "town",
+ "toy",
+ "trade",
+ "train",
+ "transport",
+ "tray",
+ "treatment",
+ "tree",
+ "trick",
+ "trip",
+ "trouble",
+ "trousers",
+ "truck",
+ "tub",
+ "turkey",
+ "turn",
+ "twist",
+ "umbrella",
+ "uncle",
+ "underwear",
+ "unit",
+ "use",
+ "vacation",
+ "value",
+ "van",
+ "vase",
+ "vegetable",
+ "veil",
+ "vein",
+ "verse",
+ "vessel",
+ "view",
+ "visitor",
+ "voice",
+ "volcano",
+ "walk",
+ "wall",
+ "war",
+ "wash",
+ "waste",
+ "watch",
+ "water",
+ "wave",
+ "wax",
+ "way",
+ "wealth",
+ "weather",
+ "week",
+ "weight",
+ "wheel",
+ "whip",
+ "whistle",
+ "window",
+ "wine",
+ "wing",
+ "winter",
+ "wire",
+ "wish",
+ "woman",
+ "wood",
+ "wool",
+ "word",
+ "work",
+ "worm",
+ "wound",
+ "wrist",
+ "writer",
+ "yard",
+ "yoke",
+ "zebra",
+ "zinc",
+ "zipper",
+ "zone",
+]
+
+
+def random_name() -> str:
+ """Generate a random name."""
+ adjective = random.choice(adjectives) # noqa: S311
+ noun = random.choice(nouns) # noqa: S311
+ number = random.randint(1, 100) # noqa: S311
+ return f"{adjective}-{noun}-{number}"
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/smith/evaluation/progress.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/smith/evaluation/progress.py
new file mode 100644
index 0000000000000000000000000000000000000000..6141867be3ba4b836af0bdcb877ae1cefd76a74e
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/smith/evaluation/progress.py
@@ -0,0 +1,145 @@
+"""A simple progress bar for the console."""
+
+import threading
+from collections.abc import Sequence
+from typing import Any
+from uuid import UUID
+
+from langchain_core.callbacks import base as base_callbacks
+from langchain_core.documents import Document
+from langchain_core.outputs import LLMResult
+from typing_extensions import override
+
+
+class ProgressBarCallback(base_callbacks.BaseCallbackHandler):
+ """A simple progress bar for the console."""
+
+ def __init__(
+ self,
+ total: int,
+ ncols: int = 50,
+ end_with: str = "\n",
+ ):
+ """Initialize the progress bar.
+
+ Args:
+ total: The total number of items to be processed.
+ ncols: The character width of the progress bar.
+ end_with: Last string to print after progress bar reaches end.
+ """
+ self.total = total
+ self.ncols = ncols
+ self.end_with = end_with
+ self.counter = 0
+ self.lock = threading.Lock()
+ self._print_bar()
+
+ def increment(self) -> None:
+ """Increment the counter and update the progress bar."""
+ with self.lock:
+ self.counter += 1
+ self._print_bar()
+
+ def _print_bar(self) -> None:
+ """Print the progress bar to the console."""
+ progress = self.counter / self.total
+ arrow = "-" * int(round(progress * self.ncols) - 1) + ">"
+ spaces = " " * (self.ncols - len(arrow))
+ end = "" if self.counter < self.total else self.end_with
+ print(f"\r[{arrow + spaces}] {self.counter}/{self.total}", end=end) # noqa: T201
+
+ @override
+ def on_chain_error(
+ self,
+ error: BaseException,
+ *,
+ run_id: UUID,
+ parent_run_id: UUID | None = None,
+ **kwargs: Any,
+ ) -> Any:
+ if parent_run_id is None:
+ self.increment()
+
+ @override
+ def on_chain_end(
+ self,
+ outputs: dict[str, Any],
+ *,
+ run_id: UUID,
+ parent_run_id: UUID | None = None,
+ **kwargs: Any,
+ ) -> Any:
+ if parent_run_id is None:
+ self.increment()
+
+ @override
+ def on_retriever_error(
+ self,
+ error: BaseException,
+ *,
+ run_id: UUID,
+ parent_run_id: UUID | None = None,
+ **kwargs: Any,
+ ) -> Any:
+ if parent_run_id is None:
+ self.increment()
+
+ @override
+ def on_retriever_end(
+ self,
+ documents: Sequence[Document],
+ *,
+ run_id: UUID,
+ parent_run_id: UUID | None = None,
+ **kwargs: Any,
+ ) -> Any:
+ if parent_run_id is None:
+ self.increment()
+
+ @override
+ def on_llm_error(
+ self,
+ error: BaseException,
+ *,
+ run_id: UUID,
+ parent_run_id: UUID | None = None,
+ **kwargs: Any,
+ ) -> Any:
+ if parent_run_id is None:
+ self.increment()
+
+ @override
+ def on_llm_end(
+ self,
+ response: LLMResult,
+ *,
+ run_id: UUID,
+ parent_run_id: UUID | None = None,
+ **kwargs: Any,
+ ) -> Any:
+ if parent_run_id is None:
+ self.increment()
+
+ @override
+ def on_tool_error(
+ self,
+ error: BaseException,
+ *,
+ run_id: UUID,
+ parent_run_id: UUID | None = None,
+ **kwargs: Any,
+ ) -> Any:
+ if parent_run_id is None:
+ self.increment()
+
+ @override
+ def on_tool_end(
+ self,
+ output: str,
+ *,
+ run_id: UUID,
+ parent_run_id: UUID | None = None,
+ **kwargs: Any,
+ ) -> Any:
+ if parent_run_id is None:
+ self.increment()
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/smith/evaluation/runner_utils.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/smith/evaluation/runner_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..c830a3d749e896d48751a1d50dce0e22a6085918
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/smith/evaluation/runner_utils.py
@@ -0,0 +1,1698 @@
+"""Utilities for running language models or Chains over datasets."""
+
+from __future__ import annotations
+
+import concurrent.futures
+import dataclasses
+import functools
+import inspect
+import logging
+import uuid
+from collections.abc import Callable
+from datetime import datetime, timezone
+from typing import (
+ TYPE_CHECKING,
+ Any,
+ cast,
+)
+
+from langchain_core._api import warn_deprecated
+from langchain_core.callbacks import Callbacks
+from langchain_core.language_models import BaseLanguageModel
+from langchain_core.messages import BaseMessage, messages_from_dict
+from langchain_core.outputs import ChatResult, LLMResult
+from langchain_core.runnables import Runnable, RunnableConfig, RunnableLambda
+from langchain_core.runnables import config as runnable_config
+from langchain_core.runnables import utils as runnable_utils
+from langchain_core.tracers.evaluation import (
+ EvaluatorCallbackHandler,
+ wait_for_all_evaluators,
+)
+from langchain_core.tracers.langchain import LangChainTracer
+from langsmith.client import Client
+from langsmith.env import get_git_info, get_langchain_env_var_metadata
+from langsmith.evaluation import (
+ EvaluationResult,
+ RunEvaluator,
+)
+from langsmith.evaluation import (
+ run_evaluator as run_evaluator_dec,
+)
+from langsmith.run_helpers import as_runnable, is_traceable_function
+from langsmith.schemas import Dataset, DataType, Example, Run, TracerSession
+from langsmith.utils import LangSmithError
+from requests import HTTPError
+from typing_extensions import TypedDict
+
+from langchain_classic.chains.base import Chain
+from langchain_classic.evaluation.loading import load_evaluator
+from langchain_classic.evaluation.schema import (
+ EvaluatorType,
+ PairwiseStringEvaluator,
+ StringEvaluator,
+)
+from langchain_classic.smith import evaluation as smith_eval
+from langchain_classic.smith.evaluation import config as smith_eval_config
+from langchain_classic.smith.evaluation import name_generation, progress
+
+if TYPE_CHECKING:
+ import pandas as pd
+
+logger = logging.getLogger(__name__)
+
+MODEL_OR_CHAIN_FACTORY = (
+ Callable[[], Chain | Runnable]
+ | BaseLanguageModel
+ | Callable[[dict], Any]
+ | Runnable
+ | Chain
+)
+MCF = Callable[[], Chain | Runnable] | BaseLanguageModel
+
+
+class InputFormatError(Exception):
+ """Raised when the input format is invalid."""
+
+
+## Shared Utilities
+
+
+class TestResult(dict):
+ """A dictionary of the results of a single test run."""
+
+ def get_aggregate_feedback(
+ self,
+ ) -> pd.DataFrame:
+ """Return quantiles for the feedback scores.
+
+ This method calculates and prints the quantiles for the feedback scores
+ across all feedback keys.
+
+ Returns:
+ A DataFrame containing the quantiles for each feedback key.
+ """
+ df = self.to_dataframe()
+ # Drop all things starting with inputs., outputs., and reference
+ to_drop = [
+ col
+ for col in df.columns
+ if col.startswith(("inputs.", "outputs.", "reference"))
+ or col in {"input", "output"}
+ ]
+ return df.describe(include="all").drop(to_drop, axis=1)
+
+ def to_dataframe(self) -> pd.DataFrame:
+ """Convert the results to a dataframe."""
+ try:
+ import pandas as pd
+ except ImportError as e:
+ msg = (
+ "Pandas is required to convert the results to a dataframe."
+ " to install pandas, run `pip install pandas`."
+ )
+ raise ImportError(msg) from e
+
+ indices = []
+ records = []
+ for example_id, result in self["results"].items():
+ feedback = result["feedback"]
+ output_ = result.get("output")
+ if isinstance(output_, dict):
+ output = {f"outputs.{k}": v for k, v in output_.items()}
+ elif output_ is None:
+ output = {}
+ else:
+ output = {"output": output_}
+
+ r = {
+ **{f"inputs.{k}": v for k, v in result["input"].items()},
+ **output,
+ }
+ if "reference" in result:
+ if isinstance(result["reference"], dict):
+ r.update(
+ {f"reference.{k}": v for k, v in result["reference"].items()},
+ )
+ else:
+ r["reference"] = result["reference"]
+ r.update(
+ {
+ **{f"feedback.{f.key}": f.score for f in feedback},
+ "error": result.get("Error"),
+ "execution_time": result["execution_time"],
+ "run_id": result.get("run_id"),
+ },
+ )
+ records.append(r)
+ indices.append(example_id)
+
+ return pd.DataFrame(records, index=indices)
+
+
+class EvalError(dict):
+ """Your architecture raised an error."""
+
+ def __init__(self, Error: BaseException, **kwargs: Any) -> None: # noqa: N803
+ """Initialize the `EvalError` with an error and additional attributes.
+
+ Args:
+ Error: The error that occurred.
+ **kwargs: Additional attributes to include in the error.
+ """
+ super().__init__(Error=Error, **kwargs)
+
+ def __getattr__(self, name: str) -> Any:
+ """Get an attribute from the `EvalError`.
+
+ Args:
+ name: The name of the attribute to get.
+
+ Returns:
+ The value of the attribute.
+
+ Raises:
+ AttributeError: If the attribute does not exist.
+ """
+ try:
+ return self[name]
+ except KeyError as e:
+ msg = f"'EvalError' object has no attribute '{name}'"
+ raise AttributeError(msg) from e
+
+
+def _wrap_in_chain_factory(
+ llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY,
+ dataset_name: str = "",
+) -> MCF:
+ """Wrap in a chain factory.
+
+ Forgive the user if they pass in a chain without memory instead of a chain
+ factory. It's a common mistake. Raise a more helpful error message as well.
+ """
+ if isinstance(llm_or_chain_factory, Chain):
+ chain = llm_or_chain_factory
+ chain_class = chain.__class__.__name__
+ if llm_or_chain_factory.memory is not None:
+ memory_class = chain.memory.__class__.__name__
+ msg = (
+ "Cannot directly evaluate a chain with stateful memory."
+ " To evaluate this chain, pass in a chain constructor"
+ " that initializes fresh memory each time it is called."
+ " This will safeguard against information"
+ " leakage between dataset examples."
+ "\nFor example:\n\n"
+ "def chain_constructor():\n"
+ f" new_memory = {memory_class}(...)\n"
+ f" return {chain_class}"
+ "(memory=new_memory, ...)\n\n"
+ f'run_on_dataset("{dataset_name}", chain_constructor, ...)'
+ )
+ raise ValueError(msg)
+ return lambda: chain
+ if isinstance(llm_or_chain_factory, BaseLanguageModel):
+ return llm_or_chain_factory
+ if isinstance(llm_or_chain_factory, Runnable):
+ # Memory may exist here, but it's not elegant to check all those cases.
+ lcf = llm_or_chain_factory
+ return lambda: lcf
+ if callable(llm_or_chain_factory):
+ if is_traceable_function(llm_or_chain_factory):
+ runnable_ = as_runnable(cast("Callable", llm_or_chain_factory))
+ return lambda: runnable_
+ try:
+ _model = llm_or_chain_factory() # type: ignore[call-arg]
+ except TypeError:
+ # It's an arbitrary function, wrap it in a RunnableLambda
+ user_func = cast("Callable", llm_or_chain_factory)
+ sig = inspect.signature(user_func)
+ logger.info("Wrapping function %s as RunnableLambda.", sig)
+ wrapped = RunnableLambda(user_func)
+ return lambda: wrapped
+ constructor = cast("Callable", llm_or_chain_factory)
+ if isinstance(_model, BaseLanguageModel):
+ # It's not uncommon to do an LLM constructor instead of raw LLM,
+ # so we'll unpack it for the user.
+ return _model
+ if is_traceable_function(cast("Callable", _model)):
+ runnable_ = as_runnable(cast("Callable", _model))
+ return lambda: runnable_
+ if not isinstance(_model, Runnable):
+ # This is unlikely to happen - a constructor for a model function
+ return lambda: RunnableLambda(constructor)
+ # Typical correct case
+ return constructor
+ return llm_or_chain_factory # type: ignore[unreachable]
+
+
+def _get_prompt(inputs: dict[str, Any]) -> str:
+ """Get prompt from inputs.
+
+ Args:
+ inputs: The input dictionary.
+
+ Returns:
+ A string prompt.
+
+ Raises:
+ InputFormatError: If the input format is invalid.
+ """
+ if not inputs:
+ msg = "Inputs should not be empty."
+ raise InputFormatError(msg)
+
+ prompts = []
+ if "prompt" in inputs:
+ if not isinstance(inputs["prompt"], str):
+ msg = f"Expected string for 'prompt', got {type(inputs['prompt']).__name__}"
+ raise InputFormatError(msg)
+ prompts = [inputs["prompt"]]
+ elif "prompts" in inputs:
+ if not isinstance(inputs["prompts"], list) or not all(
+ isinstance(i, str) for i in inputs["prompts"]
+ ):
+ msg = (
+ "Expected list of strings for 'prompts',"
+ f" got {type(inputs['prompts']).__name__}"
+ )
+ raise InputFormatError(msg)
+ prompts = inputs["prompts"]
+ elif len(inputs) == 1:
+ prompt_ = next(iter(inputs.values()))
+ if isinstance(prompt_, str):
+ prompts = [prompt_]
+ elif isinstance(prompt_, list) and all(isinstance(i, str) for i in prompt_):
+ prompts = prompt_
+ else:
+ msg = f"LLM Run expects string prompt input. Got {inputs}"
+ raise InputFormatError(msg)
+ else:
+ msg = f"LLM Run expects 'prompt' or 'prompts' in inputs. Got {inputs}"
+ raise InputFormatError(msg)
+ if len(prompts) == 1:
+ return prompts[0]
+ msg = f"LLM Run expects single prompt input. Got {len(prompts)} prompts."
+ raise InputFormatError(msg)
+
+
+class ChatModelInput(TypedDict):
+ """Input for a chat model."""
+
+ messages: list[BaseMessage]
+
+
+def _get_messages(inputs: dict[str, Any]) -> dict:
+ """Get Chat Messages from inputs.
+
+ Args:
+ inputs: The input dictionary.
+
+ Returns:
+ A list of chat messages.
+
+ Raises:
+ InputFormatError: If the input format is invalid.
+ """
+ if not inputs:
+ msg = "Inputs should not be empty."
+ raise InputFormatError(msg)
+ input_copy = inputs.copy()
+ if "messages" in inputs:
+ input_copy["input"] = input_copy.pop("messages")
+ elif len(inputs) == 1:
+ input_copy["input"] = next(iter(inputs.values()))
+ if "input" in input_copy:
+ raw_messages = input_copy["input"]
+ if isinstance(raw_messages, list) and all(
+ isinstance(i, dict) for i in raw_messages
+ ):
+ raw_messages = [raw_messages]
+ if len(raw_messages) == 1:
+ input_copy["input"] = messages_from_dict(raw_messages[0])
+ else:
+ msg = (
+ "Batch messages not supported. Please provide a"
+ " single list of messages."
+ )
+ raise InputFormatError(msg)
+ return input_copy
+ msg = (
+ f"Chat Run expects single List[dict] or List[List[dict]] 'messages'"
+ f" input. Got {inputs}"
+ )
+ raise InputFormatError(msg)
+
+
+## Shared data validation utilities
+def _validate_example_inputs_for_language_model(
+ first_example: Example,
+ input_mapper: Callable[[dict], Any] | None,
+) -> None:
+ if input_mapper:
+ prompt_input = input_mapper(first_example.inputs or {})
+ if not isinstance(prompt_input, str) and not (
+ isinstance(prompt_input, list)
+ and all(isinstance(msg, BaseMessage) for msg in prompt_input)
+ ):
+ msg = (
+ "When using an input_mapper to prepare dataset example inputs"
+ " for an LLM or chat model, the output must a single string or"
+ " a list of chat messages."
+ f"\nGot: {prompt_input} of type {type(prompt_input)}."
+ )
+ raise InputFormatError(msg)
+ else:
+ try:
+ _get_prompt(first_example.inputs or {})
+ except InputFormatError:
+ try:
+ _get_messages(first_example.inputs or {})
+ except InputFormatError as err2:
+ msg = (
+ "Example inputs do not match language model input format. "
+ "Expected a dictionary with messages or a single prompt."
+ f" Got: {first_example.inputs}"
+ " Please update your dataset OR provide an input_mapper"
+ " to convert the example.inputs to a compatible format"
+ " for the llm or chat model you wish to evaluate."
+ )
+ raise InputFormatError(msg) from err2
+
+
+def _validate_example_inputs_for_chain(
+ first_example: Example,
+ chain: Chain,
+ input_mapper: Callable[[dict], Any] | None,
+) -> None:
+ """Validate that the example inputs match the chain input keys."""
+ if input_mapper:
+ first_inputs = input_mapper(first_example.inputs or {})
+ missing_keys = set(chain.input_keys).difference(first_inputs)
+ if not isinstance(first_inputs, dict):
+ msg = (
+ "When using an input_mapper to prepare dataset example"
+ " inputs for a chain, the mapped value must be a dictionary."
+ f"\nGot: {first_inputs} of type {type(first_inputs)}."
+ )
+ raise InputFormatError(msg)
+ if missing_keys:
+ msg = (
+ "Missing keys after loading example using input_mapper."
+ f"\nExpected: {chain.input_keys}. Got: {first_inputs.keys()}"
+ )
+ raise InputFormatError(msg)
+ else:
+ first_inputs = first_example.inputs or {}
+ missing_keys = set(chain.input_keys).difference(first_inputs)
+ if len(first_inputs) == 1 and len(chain.input_keys) == 1:
+ # We can pass this through the run method.
+ # Refrain from calling to validate.
+ pass
+ elif missing_keys:
+ msg = (
+ "Example inputs missing expected chain input keys."
+ " Please provide an input_mapper to convert the example.inputs"
+ " to a compatible format for the chain you wish to evaluate."
+ f"Expected: {chain.input_keys}. "
+ f"Got: {first_inputs.keys()}"
+ )
+ raise InputFormatError(msg)
+
+
+def _validate_example_inputs(
+ example: Example,
+ llm_or_chain_factory: MCF,
+ input_mapper: Callable[[dict], Any] | None,
+) -> None:
+ """Validate that the example inputs are valid for the model."""
+ if isinstance(llm_or_chain_factory, BaseLanguageModel):
+ _validate_example_inputs_for_language_model(example, input_mapper)
+ else:
+ chain = llm_or_chain_factory()
+ if isinstance(chain, Chain):
+ # Otherwise it's a runnable
+ _validate_example_inputs_for_chain(example, chain, input_mapper)
+ elif isinstance(chain, Runnable):
+ logger.debug("Skipping input validation for %s", chain)
+
+
+## Shared Evaluator Setup Utilities
+
+
+def _setup_evaluation(
+ llm_or_chain_factory: MCF,
+ examples: list[Example],
+ evaluation: smith_eval.RunEvalConfig | None,
+ data_type: DataType,
+) -> list[RunEvaluator] | None:
+ """Configure the evaluators to run on the results of the chain."""
+ if evaluation:
+ if isinstance(llm_or_chain_factory, BaseLanguageModel):
+ run_inputs, run_outputs = None, None
+ run_type = "llm"
+ else:
+ run_type = "chain"
+ chain = llm_or_chain_factory()
+ run_inputs = chain.input_keys if isinstance(chain, Chain) else None
+ run_outputs = chain.output_keys if isinstance(chain, Chain) else None
+ run_evaluators = _load_run_evaluators(
+ evaluation,
+ run_type,
+ data_type,
+ list(examples[0].outputs) if examples[0].outputs else None,
+ run_inputs,
+ run_outputs,
+ )
+ else:
+ # TODO: Create a default helpfulness evaluator
+ run_evaluators = None
+ return run_evaluators
+
+
+def _determine_input_key(
+ config: smith_eval.RunEvalConfig,
+ run_inputs: list[str] | None,
+) -> str | None:
+ input_key = None
+ if config.input_key:
+ input_key = config.input_key
+ if run_inputs and input_key not in run_inputs:
+ logger.warning(
+ "Input key %s not in chain's specified input keys %s. "
+ "Evaluation behavior may be undefined.",
+ input_key,
+ run_inputs,
+ )
+ elif run_inputs and len(run_inputs) == 1:
+ input_key = run_inputs[0]
+ elif run_inputs is not None and len(run_inputs) > 1:
+ logger.warning(
+ "Chain expects multiple input keys: %s,"
+ " Evaluator is likely to fail. Evaluation behavior may be undefined."
+ " Specify an input_key in the RunEvalConfig to avoid this warning.",
+ run_inputs,
+ )
+
+ return input_key
+
+
+def _determine_prediction_key(
+ config: smith_eval.RunEvalConfig,
+ run_outputs: list[str] | None,
+) -> str | None:
+ prediction_key = None
+ if config.prediction_key:
+ prediction_key = config.prediction_key
+ if run_outputs and prediction_key not in run_outputs:
+ logger.warning(
+ "Prediction key %s not in chain's specified output keys %s. "
+ "Evaluation behavior may be undefined.",
+ prediction_key,
+ run_outputs,
+ )
+ elif run_outputs and len(run_outputs) == 1:
+ prediction_key = run_outputs[0]
+ elif run_outputs is not None and len(run_outputs) > 1:
+ logger.warning(
+ "Chain expects multiple output keys: %s,"
+ " Evaluation behavior may be undefined. Specify a prediction_key"
+ " in the RunEvalConfig to avoid this warning.",
+ run_outputs,
+ )
+ return prediction_key
+
+
+def _determine_reference_key(
+ config: smith_eval.RunEvalConfig,
+ example_outputs: list[str] | None,
+) -> str | None:
+ if config.reference_key:
+ reference_key = config.reference_key
+ if example_outputs and reference_key not in example_outputs:
+ msg = (
+ f"Reference key {reference_key} not in Dataset"
+ f" example outputs: {example_outputs}"
+ )
+ raise ValueError(msg)
+ elif example_outputs and len(example_outputs) == 1:
+ reference_key = next(iter(example_outputs))
+ else:
+ reference_key = None
+ return reference_key
+
+
+def _construct_run_evaluator(
+ eval_config: smith_eval_config.SINGLE_EVAL_CONFIG_TYPE
+ | smith_eval_config.CUSTOM_EVALUATOR_TYPE,
+ eval_llm: BaseLanguageModel | None,
+ run_type: str,
+ data_type: DataType,
+ example_outputs: list[str] | None,
+ reference_key: str | None,
+ input_key: str | None,
+ prediction_key: str | None,
+) -> RunEvaluator:
+ if isinstance(eval_config, RunEvaluator):
+ return eval_config
+ if isinstance(eval_config, (EvaluatorType, str)):
+ if not isinstance(eval_config, EvaluatorType):
+ eval_config = EvaluatorType(eval_config)
+ evaluator_ = load_evaluator(eval_config, llm=eval_llm)
+ eval_type_tag = eval_config.value
+ elif isinstance(eval_config, smith_eval_config.EvalConfig):
+ kwargs = {"llm": eval_llm, **eval_config.get_kwargs()}
+ evaluator_ = load_evaluator(eval_config.evaluator_type, **kwargs)
+ eval_type_tag = eval_config.evaluator_type.value
+ # Override keys if specified in the config
+ if isinstance(eval_config, smith_eval_config.SingleKeyEvalConfig):
+ input_key = eval_config.input_key or input_key
+ prediction_key = eval_config.prediction_key or prediction_key
+ reference_key = eval_config.reference_key or reference_key
+ elif callable(eval_config):
+ # Assume we can decorate
+ return run_evaluator_dec(eval_config)
+ else:
+ msg = f"Unknown evaluator type: {type(eval_config)}"
+ raise ValueError(msg) # noqa: TRY004
+
+ if isinstance(evaluator_, StringEvaluator):
+ if evaluator_.requires_reference and reference_key is None:
+ msg = (
+ f"Must specify reference_key in smith_eval.RunEvalConfig to use"
+ f" evaluator of type {eval_type_tag} with"
+ f" dataset with multiple output keys: {example_outputs}."
+ )
+ raise ValueError(msg)
+ run_evaluator = smith_eval.StringRunEvaluatorChain.from_run_and_data_type(
+ evaluator_,
+ run_type,
+ data_type,
+ input_key=input_key,
+ prediction_key=prediction_key,
+ reference_key=reference_key,
+ tags=[eval_type_tag],
+ )
+ elif isinstance(evaluator_, PairwiseStringEvaluator):
+ msg = (
+ f"Run evaluator for {eval_type_tag} is not implemented."
+ " PairwiseStringEvaluators compare the outputs of two different models"
+ " rather than the output of a single model."
+ " Did you mean to use a StringEvaluator instead?"
+ "\nSee: https://python.langchain.com/docs/guides/evaluation/string/"
+ )
+ raise NotImplementedError(msg)
+
+ else:
+ msg = f"Run evaluator for {eval_type_tag} is not implemented"
+ raise NotImplementedError(msg)
+ return run_evaluator
+
+
+def _get_keys(
+ config: smith_eval.RunEvalConfig,
+ run_inputs: list[str] | None,
+ run_outputs: list[str] | None,
+ example_outputs: list[str] | None,
+) -> tuple[str | None, str | None, str | None]:
+ input_key = _determine_input_key(config, run_inputs)
+ prediction_key = _determine_prediction_key(config, run_outputs)
+ reference_key = _determine_reference_key(config, example_outputs)
+ return input_key, prediction_key, reference_key
+
+
+def _load_run_evaluators(
+ config: smith_eval.RunEvalConfig,
+ run_type: str,
+ data_type: DataType,
+ example_outputs: list[str] | None,
+ run_inputs: list[str] | None,
+ run_outputs: list[str] | None,
+) -> list[RunEvaluator]:
+ """Load run evaluators from a configuration.
+
+ Args:
+ config: Configuration for the run evaluators.
+ run_type: The type of run.
+ data_type: The type of dataset used in the run.
+ example_outputs: The example outputs.
+ run_inputs: The input keys for the run.
+ run_outputs: The output keys for the run.
+
+ Returns:
+ A list of run evaluators.
+ """
+ run_evaluators = []
+ input_key, prediction_key, reference_key = None, None, None
+ if config.evaluators or (
+ config.custom_evaluators
+ and any(isinstance(e, StringEvaluator) for e in config.custom_evaluators)
+ ):
+ input_key, prediction_key, reference_key = _get_keys(
+ config,
+ run_inputs,
+ run_outputs,
+ example_outputs,
+ )
+ for eval_config in config.evaluators:
+ run_evaluator = _construct_run_evaluator(
+ eval_config,
+ config.eval_llm,
+ run_type,
+ data_type,
+ example_outputs,
+ reference_key,
+ input_key,
+ prediction_key,
+ )
+ run_evaluators.append(run_evaluator)
+ custom_evaluators = config.custom_evaluators or []
+ for custom_evaluator in custom_evaluators:
+ if isinstance(custom_evaluator, RunEvaluator):
+ run_evaluators.append(custom_evaluator)
+ elif isinstance(custom_evaluator, StringEvaluator):
+ run_evaluators.append(
+ smith_eval.StringRunEvaluatorChain.from_run_and_data_type(
+ custom_evaluator,
+ run_type,
+ data_type,
+ input_key=input_key,
+ prediction_key=prediction_key,
+ reference_key=reference_key,
+ ),
+ )
+ elif callable(custom_evaluator):
+ run_evaluators.append(run_evaluator_dec(custom_evaluator))
+ else:
+ msg = ( # type: ignore[unreachable]
+ f"Unsupported custom evaluator: {custom_evaluator}."
+ f" Expected RunEvaluator or StringEvaluator."
+ )
+ raise ValueError(msg) # noqa: TRY004
+
+ return run_evaluators
+
+
+### Async Helpers
+
+
+async def _arun_llm(
+ llm: BaseLanguageModel,
+ inputs: dict[str, Any],
+ *,
+ tags: list[str] | None = None,
+ callbacks: Callbacks = None,
+ input_mapper: Callable[[dict], Any] | None = None,
+ metadata: dict[str, Any] | None = None,
+) -> str | BaseMessage:
+ """Asynchronously run the language model.
+
+ Args:
+ llm: The language model to run.
+ inputs: The input dictionary.
+ tags: Optional tags to add to the run.
+ callbacks: Optional callbacks to use during the run.
+ input_mapper: Optional function to map inputs to the expected format.
+ metadata: Optional metadata to add to the run.
+
+ Returns:
+ The LLMResult or ChatResult.
+
+ Raises:
+ ValueError: If the LLM type is unsupported.
+ InputFormatError: If the input format is invalid.
+ """
+ if input_mapper is not None:
+ prompt_or_messages = input_mapper(inputs)
+ if isinstance(prompt_or_messages, str) or (
+ isinstance(prompt_or_messages, list)
+ and all(isinstance(msg, BaseMessage) for msg in prompt_or_messages)
+ ):
+ return await llm.ainvoke(
+ prompt_or_messages,
+ config=RunnableConfig(
+ callbacks=callbacks,
+ tags=tags or [],
+ metadata=metadata or {},
+ ),
+ )
+ msg = (
+ "Input mapper returned invalid format"
+ f" {prompt_or_messages}"
+ "\nExpected a single string or list of chat messages."
+ )
+ raise InputFormatError(msg)
+
+ try:
+ prompt = _get_prompt(inputs)
+ llm_output: str | BaseMessage = await llm.ainvoke(
+ prompt,
+ config=RunnableConfig(
+ callbacks=callbacks,
+ tags=tags or [],
+ metadata=metadata or {},
+ ),
+ )
+ except InputFormatError:
+ llm_inputs = _get_messages(inputs)
+ llm_output = await llm.ainvoke(
+ **llm_inputs,
+ config=RunnableConfig(
+ callbacks=callbacks,
+ tags=tags or [],
+ metadata=metadata or {},
+ ),
+ )
+ return llm_output
+
+
+async def _arun_chain(
+ chain: Chain | Runnable,
+ inputs: dict[str, Any],
+ callbacks: Callbacks,
+ *,
+ tags: list[str] | None = None,
+ input_mapper: Callable[[dict], Any] | None = None,
+ metadata: dict[str, Any] | None = None,
+) -> dict | str:
+ """Run a chain asynchronously on inputs."""
+ inputs_ = inputs if input_mapper is None else input_mapper(inputs)
+ if (
+ isinstance(chain, Chain)
+ and isinstance(inputs_, dict)
+ and len(inputs_) == 1
+ and chain.input_keys
+ ):
+ val = next(iter(inputs_.values()))
+ output = await chain.ainvoke(
+ val,
+ config=RunnableConfig(
+ callbacks=callbacks,
+ tags=tags or [],
+ metadata=metadata or {},
+ ),
+ )
+ else:
+ runnable_config = RunnableConfig(
+ tags=tags or [],
+ callbacks=callbacks,
+ metadata=metadata or {},
+ )
+ output = await chain.ainvoke(inputs_, config=runnable_config)
+ return output
+
+
+async def _arun_llm_or_chain(
+ example: Example,
+ config: RunnableConfig,
+ *,
+ llm_or_chain_factory: MCF,
+ input_mapper: Callable[[dict], Any] | None = None,
+) -> dict | str | LLMResult | ChatResult:
+ """Asynchronously run the Chain or language model.
+
+ Args:
+ example: The example to run.
+ config: The configuration for the run.
+ llm_or_chain_factory: The Chain or language model constructor to run.
+ input_mapper: Optional function to map the input to the expected format.
+
+ Returns:
+ A list of outputs.
+ """
+ chain_or_llm = (
+ "LLM" if isinstance(llm_or_chain_factory, BaseLanguageModel) else "Chain"
+ )
+ result = None
+ try:
+ if isinstance(llm_or_chain_factory, BaseLanguageModel):
+ output: Any = await _arun_llm(
+ llm_or_chain_factory,
+ example.inputs or {},
+ tags=config["tags"],
+ callbacks=config["callbacks"],
+ input_mapper=input_mapper,
+ metadata=config.get("metadata"),
+ )
+ else:
+ chain = llm_or_chain_factory()
+ output = await _arun_chain(
+ chain,
+ example.inputs or {},
+ tags=config["tags"],
+ callbacks=config["callbacks"],
+ input_mapper=input_mapper,
+ metadata=config.get("metadata"),
+ )
+ result = output
+ except Exception as e: # noqa: BLE001
+ logger.warning(
+ "%s failed for example %s with inputs %s\n%s",
+ chain_or_llm,
+ example.id,
+ example.inputs,
+ e,
+ )
+ result = EvalError(Error=e)
+ return result
+
+
+## Sync Utilities
+
+
+def _run_llm(
+ llm: BaseLanguageModel,
+ inputs: dict[str, Any],
+ callbacks: Callbacks,
+ *,
+ tags: list[str] | None = None,
+ input_mapper: Callable[[dict], Any] | None = None,
+ metadata: dict[str, Any] | None = None,
+) -> str | BaseMessage:
+ """Run the language model on the example.
+
+ Args:
+ llm: The language model to run.
+ inputs: The input dictionary.
+ callbacks: The callbacks to use during the run.
+ tags: Optional tags to add to the run.
+ input_mapper: function to map to the inputs dictionary from an Example
+ metadata: Optional metadata to add to the run.
+
+ Returns:
+ The LLMResult or ChatResult.
+
+ Raises:
+ ValueError: If the LLM type is unsupported.
+ InputFormatError: If the input format is invalid.
+ """
+ # Most of this is legacy code; we could probably remove a lot of it.
+ if input_mapper is not None:
+ prompt_or_messages = input_mapper(inputs)
+ if isinstance(prompt_or_messages, str) or (
+ isinstance(prompt_or_messages, list)
+ and all(isinstance(msg, BaseMessage) for msg in prompt_or_messages)
+ ):
+ llm_output: str | BaseMessage = llm.invoke(
+ prompt_or_messages,
+ config=RunnableConfig(
+ callbacks=callbacks,
+ tags=tags or [],
+ metadata=metadata or {},
+ ),
+ )
+ else:
+ msg = (
+ "Input mapper returned invalid format: "
+ f" {prompt_or_messages}"
+ "\nExpected a single string or list of chat messages."
+ )
+ raise InputFormatError(msg)
+ else:
+ try:
+ llm_prompts = _get_prompt(inputs)
+ llm_output = llm.invoke(
+ llm_prompts,
+ config=RunnableConfig(
+ callbacks=callbacks,
+ tags=tags or [],
+ metadata=metadata or {},
+ ),
+ )
+ except InputFormatError:
+ llm_inputs = _get_messages(inputs)
+ llm_output = llm.invoke(
+ **llm_inputs,
+ config=RunnableConfig(callbacks=callbacks, metadata=metadata or {}),
+ )
+ return llm_output
+
+
+def _run_chain(
+ chain: Chain | Runnable,
+ inputs: dict[str, Any],
+ callbacks: Callbacks,
+ *,
+ tags: list[str] | None = None,
+ input_mapper: Callable[[dict], Any] | None = None,
+ metadata: dict[str, Any] | None = None,
+) -> dict | str:
+ """Run a chain on inputs."""
+ inputs_ = inputs if input_mapper is None else input_mapper(inputs)
+ if (
+ isinstance(chain, Chain)
+ and isinstance(inputs_, dict)
+ and len(inputs_) == 1
+ and chain.input_keys
+ ):
+ val = next(iter(inputs_.values()))
+ output = chain.invoke(
+ val,
+ config=RunnableConfig(
+ callbacks=callbacks,
+ tags=tags or [],
+ metadata=metadata or {},
+ ),
+ )
+ else:
+ runnable_config = RunnableConfig(
+ tags=tags or [],
+ callbacks=callbacks,
+ metadata=metadata or {},
+ )
+ output = chain.invoke(inputs_, config=runnable_config)
+ return output
+
+
+def _run_llm_or_chain(
+ example: Example,
+ config: RunnableConfig,
+ *,
+ llm_or_chain_factory: MCF,
+ input_mapper: Callable[[dict], Any] | None = None,
+) -> dict | str | LLMResult | ChatResult:
+ """Run the Chain or language model synchronously.
+
+ Args:
+ example: The example to run.
+ config: The configuration for the run.
+ llm_or_chain_factory: The Chain or language model constructor to run.
+ input_mapper: Optional function to map the input to the expected format.
+
+ Returns:
+ The outputs of the model or chain.
+ """
+ chain_or_llm = (
+ "LLM" if isinstance(llm_or_chain_factory, BaseLanguageModel) else "Chain"
+ )
+ result = None
+ try:
+ if isinstance(llm_or_chain_factory, BaseLanguageModel):
+ output: Any = _run_llm(
+ llm_or_chain_factory,
+ example.inputs or {},
+ config["callbacks"],
+ tags=config["tags"],
+ input_mapper=input_mapper,
+ metadata=config.get("metadata"),
+ )
+ else:
+ chain = llm_or_chain_factory()
+ output = _run_chain(
+ chain,
+ example.inputs or {},
+ config["callbacks"],
+ tags=config["tags"],
+ input_mapper=input_mapper,
+ metadata=config.get("metadata"),
+ )
+ result = output
+ except Exception as e: # noqa: BLE001
+ error_type = type(e).__name__
+ logger.warning(
+ "%s failed for example %s with inputs %s\nError Type: %s, Message: %s",
+ chain_or_llm,
+ example.id,
+ example.inputs,
+ error_type,
+ e,
+ )
+ result = EvalError(Error=e)
+ return result
+
+
+def _prepare_eval_run(
+ client: Client,
+ dataset_name: str,
+ llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY,
+ project_name: str,
+ project_metadata: dict[str, Any] | None = None,
+ tags: list[str] | None = None,
+ dataset_version: str | datetime | None = None,
+) -> tuple[MCF, TracerSession, Dataset, list[Example]]:
+ wrapped_model = _wrap_in_chain_factory(llm_or_chain_factory, dataset_name)
+ dataset = client.read_dataset(dataset_name=dataset_name)
+
+ examples = list(client.list_examples(dataset_id=dataset.id, as_of=dataset_version))
+ if not examples:
+ msg = f"Dataset {dataset_name} has no example rows."
+ raise ValueError(msg)
+ modified_at = [ex.modified_at for ex in examples if ex.modified_at]
+ # Should always be defined in practice when fetched,
+ # but the typing permits None
+ max_modified_at = max(modified_at) if modified_at else None
+ inferred_version = max_modified_at.isoformat() if max_modified_at else None
+
+ try:
+ project_metadata = project_metadata or {}
+ git_info = get_git_info()
+ if git_info:
+ project_metadata = {
+ **project_metadata,
+ "git": git_info,
+ }
+
+ project_metadata["dataset_version"] = inferred_version
+ project = client.create_project(
+ project_name,
+ reference_dataset_id=dataset.id,
+ project_extra={"tags": tags} if tags else {},
+ metadata=project_metadata,
+ )
+ except (HTTPError, ValueError, LangSmithError) as e:
+ if "already exists " not in str(e):
+ raise
+ uid = uuid.uuid4()
+ example_msg = f"""
+run_on_dataset(
+ ...
+ project_name="{project_name} - {uid}", # Update since {project_name} already exists
+)
+"""
+ msg = (
+ f"Test project {project_name} already exists. Please use a different name:"
+ f"\n\n{example_msg}"
+ )
+ raise ValueError(msg) from e
+ comparison_url = dataset.url + f"/compare?selectedSessions={project.id}"
+ print( # noqa: T201
+ f"View the evaluation results for project '{project_name}'"
+ f" at:\n{comparison_url}\n\n"
+ f"View all tests for Dataset {dataset_name} at:\n{dataset.url}",
+ flush=True,
+ )
+ return wrapped_model, project, dataset, examples
+
+
+class _RowResult(TypedDict, total=False):
+ """A dictionary of the results for a single example row."""
+
+ feedback: list[EvaluationResult] | None
+ execution_time: float | None
+ run_id: str | None
+
+
+@dataclasses.dataclass
+class _DatasetRunContainer:
+ """A container to help manage the state of a eval run."""
+
+ client: Client
+ project: TracerSession
+ wrapped_model: MCF
+ examples: list[Example]
+ configs: list[RunnableConfig]
+ batch_evaluators: list[smith_eval_config.BATCH_EVALUATOR_LIKE] | None = None
+
+ def _merge_test_outputs(
+ self,
+ batch_results: list,
+ all_eval_results: dict[str, _RowResult],
+ ) -> dict:
+ results: dict = {}
+ for example, output in zip(self.examples, batch_results, strict=False):
+ row_result = all_eval_results.get(str(example.id), {})
+ results[str(example.id)] = {
+ "input": example.inputs,
+ "feedback": row_result.get("feedback", []),
+ "execution_time": row_result.get("execution_time"),
+ "run_id": row_result.get("run_id"),
+ }
+ if isinstance(output, EvalError):
+ results[str(example.id)]["Error"] = output.Error
+ else:
+ results[str(example.id)]["output"] = output
+ if example.outputs:
+ results[str(example.id)]["reference"] = example.outputs
+ return results
+
+ def _run_batch_evaluators(self, runs: dict[str, Run]) -> list[dict]:
+ evaluators = self.batch_evaluators
+ if not evaluators:
+ return []
+ runs_list = [runs[str(example.id)] for example in self.examples]
+ aggregate_feedback = []
+ with concurrent.futures.ThreadPoolExecutor() as executor:
+ for evaluator in evaluators:
+ try:
+ result = evaluator(runs_list, self.examples)
+ if isinstance(result, EvaluationResult):
+ result = result.model_dump()
+ aggregate_feedback.append(cast("dict", result))
+ executor.submit(
+ self.client.create_feedback,
+ **result,
+ run_id=None,
+ project_id=self.project.id,
+ )
+ except Exception:
+ logger.exception(
+ "Error running batch evaluator %s", repr(evaluator)
+ )
+ return aggregate_feedback
+
+ def _collect_metrics(self) -> tuple[dict[str, _RowResult], dict[str, Run]]:
+ all_eval_results: dict = {}
+ all_runs: dict = {}
+ for c in self.configs:
+ for callback in cast("list", c["callbacks"]):
+ if isinstance(callback, EvaluatorCallbackHandler):
+ eval_results = callback.logged_eval_results
+ for (_, example_id), v in eval_results.items():
+ all_eval_results.setdefault(str(example_id), {}).update(
+ {"feedback": v},
+ )
+ elif isinstance(callback, LangChainTracer):
+ run = callback.latest_run
+ execution_time = (
+ (run.end_time - run.start_time).total_seconds()
+ if run and run.end_time
+ else None
+ )
+ run_id = str(run.id) if run else None
+ all_eval_results.setdefault(str(callback.example_id), {}).update(
+ {
+ "execution_time": execution_time,
+ "run_id": run_id,
+ "run": run,
+ },
+ )
+ all_runs[str(callback.example_id)] = run
+ return cast("dict[str, _RowResult]", all_eval_results), all_runs
+
+ def _collect_test_results(
+ self,
+ batch_results: list[dict | str | LLMResult | ChatResult],
+ ) -> TestResult:
+ logger.info("Waiting for evaluators to complete.")
+ wait_for_all_evaluators()
+ all_eval_results, all_runs = self._collect_metrics()
+ aggregate_feedback = None
+ if self.batch_evaluators:
+ logger.info("Running session evaluators.")
+ aggregate_feedback = self._run_batch_evaluators(all_runs)
+ results = self._merge_test_outputs(batch_results, all_eval_results)
+ return TestResult(
+ project_name=self.project.name,
+ results=results,
+ aggregate_metrics=aggregate_feedback,
+ )
+
+ def finish(
+ self,
+ batch_results: list,
+ verbose: bool = False, # noqa: FBT001,FBT002
+ ) -> TestResult:
+ results = self._collect_test_results(batch_results)
+ if verbose:
+ try:
+ agg_feedback = results.get_aggregate_feedback()
+ _display_aggregate_results(agg_feedback)
+ except Exception as e: # noqa: BLE001
+ logger.debug("Failed to print aggregate feedback: %s", e, exc_info=True)
+ try:
+ # Closing the project permits name changing and metric optimizations
+ self.client.update_project(
+ self.project.id,
+ end_time=datetime.now(timezone.utc),
+ )
+ except Exception as e: # noqa: BLE001
+ logger.debug("Failed to close project: %s", e, exc_info=True)
+ return results
+
+ @classmethod
+ def prepare(
+ cls,
+ client: Client,
+ dataset_name: str,
+ llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY,
+ project_name: str | None,
+ evaluation: smith_eval.RunEvalConfig | None = None,
+ tags: list[str] | None = None,
+ input_mapper: Callable[[dict], Any] | None = None,
+ concurrency_level: int = 5,
+ project_metadata: dict[str, Any] | None = None,
+ revision_id: str | None = None,
+ dataset_version: datetime | str | None = None,
+ ) -> _DatasetRunContainer:
+ project_name = project_name or name_generation.random_name()
+ if revision_id:
+ if not project_metadata:
+ project_metadata = {}
+ project_metadata.update({"revision_id": revision_id})
+ wrapped_model, project, dataset, examples = _prepare_eval_run(
+ client,
+ dataset_name,
+ llm_or_chain_factory,
+ project_name,
+ project_metadata=project_metadata,
+ tags=tags,
+ dataset_version=dataset_version,
+ )
+ tags = tags or []
+ for k, v in (project.metadata.get("git") or {}).items():
+ tags.append(f"git:{k}={v}")
+ run_metadata = {"dataset_version": project.metadata["dataset_version"]}
+ if revision_id:
+ run_metadata["revision_id"] = revision_id
+ wrapped_model = _wrap_in_chain_factory(llm_or_chain_factory)
+ run_evaluators = _setup_evaluation(
+ wrapped_model,
+ examples,
+ evaluation,
+ dataset.data_type or DataType.kv,
+ )
+ _validate_example_inputs(examples[0], wrapped_model, input_mapper)
+ progress_bar = progress.ProgressBarCallback(len(examples))
+ configs = [
+ RunnableConfig(
+ callbacks=[
+ LangChainTracer(
+ project_name=project.name,
+ client=client,
+ example_id=example.id,
+ ),
+ EvaluatorCallbackHandler(
+ evaluators=run_evaluators or [],
+ client=client,
+ example_id=example.id,
+ max_concurrency=0,
+ ),
+ progress_bar,
+ ],
+ tags=tags,
+ max_concurrency=concurrency_level,
+ metadata=run_metadata,
+ )
+ for example in examples
+ ]
+ return cls(
+ client=client,
+ project=project,
+ wrapped_model=wrapped_model,
+ examples=examples,
+ configs=configs,
+ batch_evaluators=evaluation.batch_evaluators if evaluation else None,
+ )
+
+
+def _is_jupyter_environment() -> bool:
+ try:
+ from IPython.core.getipython import get_ipython
+
+ res = get_ipython() # type: ignore[no-untyped-call]
+ return res is not None and "zmqshell" in str(type(res))
+ except ImportError:
+ return False
+
+
+def _display_aggregate_results(aggregate_results: pd.DataFrame) -> None:
+ if _is_jupyter_environment():
+ from IPython.display import HTML, display
+
+ display(HTML("Experiment Results:
")) # type: ignore[no-untyped-call]
+ display(aggregate_results) # type: ignore[no-untyped-call]
+ else:
+ formatted_string = aggregate_results.to_string(
+ float_format=lambda x: f"{x:.2f}",
+ justify="right",
+ )
+ print("\n Experiment Results:") # noqa: T201
+ print(formatted_string) # noqa: T201
+
+
+_INPUT_MAPPER_DEP_WARNING = (
+ "The input_mapper argument is deprecated and "
+ "will be removed in a future release. Please add a "
+ " RunnableLambda to your chain to map inputs to the expected format"
+ " instead. Example:\n"
+ "def construct_chain():\n"
+ " my_chain = ...\n"
+ " input_mapper = {'other_key': 'MyOtherInput', 'my_input_key': x}\n"
+ " return input_mapper | my_chain\n"
+ "run_on_dataset(..., llm_or_chain_factory=construct_chain)\n"
+ "(See https://api.python.langchain.com/en/latest/schema/"
+ "langchain.schema.runnable.base.RunnableLambda.html)"
+)
+
+## Public API
+
+
+async def arun_on_dataset(
+ client: Client | None,
+ dataset_name: str,
+ llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY,
+ *,
+ evaluation: smith_eval.RunEvalConfig | None = None,
+ dataset_version: datetime | str | None = None,
+ concurrency_level: int = 5,
+ project_name: str | None = None,
+ project_metadata: dict[str, Any] | None = None,
+ verbose: bool = False,
+ revision_id: str | None = None,
+ **kwargs: Any,
+) -> dict[str, Any]:
+ """Run on dataset.
+
+ Run the Chain or language model on a dataset and store traces
+ to the specified project name.
+
+ For the (usually faster) async version of this function,
+ see `arun_on_dataset`.
+
+ Args:
+ dataset_name: Name of the dataset to run the chain on.
+ llm_or_chain_factory: Language model or Chain constructor to run
+ over the dataset. The Chain constructor is used to permit
+ independent calls on each example without carrying over state.
+ evaluation: Configuration for evaluators to run on the
+ results of the chain.
+ dataset_version: Optional version of the dataset.
+ concurrency_level: The number of async tasks to run concurrently.
+ project_name: Name of the project to store the traces in.
+ Defaults to `{dataset_name}-{chain class name}-{datetime}`.
+ project_metadata: Optional metadata to add to the project.
+ Useful for storing information the test variant.
+ (prompt version, model version, etc.)
+ client: LangSmith client to use to access the dataset and to
+ log feedback and run traces.
+ verbose: Whether to print progress.
+ revision_id: Optional revision identifier to assign this test run to
+ track the performance of different versions of your system.
+ **kwargs: Should not be used, but is provided for backwards compatibility.
+
+ Returns:
+ `dict` containing the run's project name and the resulting model outputs.
+
+ Examples:
+ ```python
+ from langsmith import Client
+ from langchain_openai import ChatOpenAI
+ from langchain_classic.chains import LLMChain
+ from langchain_classic.smith import smith_eval.RunEvalConfig, run_on_dataset
+
+ # Chains may have memory. Passing in a constructor function lets the
+ # evaluation framework avoid cross-contamination between runs.
+ def construct_chain():
+ model = ChatOpenAI(temperature=0)
+ chain = LLMChain.from_string(
+ model,
+ "What's the answer to {your_input_key}"
+ )
+ return chain
+
+ # Load off-the-shelf evaluators via config or the EvaluatorType (string or enum)
+ evaluation_config = smith_eval.RunEvalConfig(
+ evaluators=[
+ "qa", # "Correctness" against a reference answer
+ "embedding_distance",
+ smith_eval.RunEvalConfig.Criteria("helpfulness"),
+ smith_eval.RunEvalConfig.Criteria({
+ "fifth-grader-score": "Do you have to be smarter than a fifth "
+ "grader to answer this question?"
+ }),
+ ]
+ )
+
+ client = Client()
+ await arun_on_dataset(
+ client,
+ dataset_name="",
+ llm_or_chain_factory=construct_chain,
+ evaluation=evaluation_config,
+ )
+ ```
+ You can also create custom evaluators by subclassing the `StringEvaluator or
+ LangSmith's `RunEvaluator` classes.
+
+ ```python
+ from typing import Optional
+ from langchain_classic.evaluation import StringEvaluator
+
+
+ class MyStringEvaluator(StringEvaluator):
+ @property
+ def requires_input(self) -> bool:
+ return False
+
+ @property
+ def requires_reference(self) -> bool:
+ return True
+
+ @property
+ def evaluation_name(self) -> str:
+ return "exact_match"
+
+ def _evaluate_strings(
+ self, prediction, reference=None, input=None, **kwargs
+ ) -> dict:
+ return {"score": prediction == reference}
+
+
+ evaluation_config = smith_eval.RunEvalConfig(
+ custom_evaluators=[MyStringEvaluator()],
+ )
+
+ await arun_on_dataset(
+ client,
+ dataset_name="",
+ llm_or_chain_factory=construct_chain,
+ evaluation=evaluation_config,
+ )
+ ```
+ """
+ input_mapper = kwargs.pop("input_mapper", None)
+ if input_mapper:
+ warn_deprecated("0.0.305", message=_INPUT_MAPPER_DEP_WARNING, pending=True)
+ if revision_id is None:
+ revision_id = get_langchain_env_var_metadata().get("revision_id")
+ tags = kwargs.pop("tags", None)
+ if tags:
+ warn_deprecated(
+ "0.1.9",
+ message="The tags argument is deprecated and will be"
+ " removed in a future release. Please specify project_metadata instead.",
+ pending=True,
+ )
+
+ if kwargs:
+ warn_deprecated(
+ "0.0.305",
+ message="The following arguments are deprecated and "
+ "will be removed in a future release: "
+ f"{kwargs.keys()}.",
+ removal="0.0.305",
+ )
+ client = client or Client()
+ container = _DatasetRunContainer.prepare(
+ client,
+ dataset_name,
+ llm_or_chain_factory,
+ project_name,
+ evaluation,
+ tags,
+ input_mapper,
+ concurrency_level,
+ project_metadata=project_metadata,
+ revision_id=revision_id,
+ dataset_version=dataset_version,
+ )
+ batch_results = await runnable_utils.gather_with_concurrency(
+ container.configs[0].get("max_concurrency"),
+ *map(
+ functools.partial(
+ _arun_llm_or_chain,
+ llm_or_chain_factory=container.wrapped_model,
+ input_mapper=input_mapper,
+ ),
+ container.examples,
+ container.configs,
+ ),
+ )
+ return container.finish(batch_results, verbose=verbose)
+
+
+def run_on_dataset(
+ client: Client | None,
+ dataset_name: str,
+ llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY,
+ *,
+ evaluation: smith_eval.RunEvalConfig | None = None,
+ dataset_version: datetime | str | None = None,
+ concurrency_level: int = 5,
+ project_name: str | None = None,
+ project_metadata: dict[str, Any] | None = None,
+ verbose: bool = False,
+ revision_id: str | None = None,
+ **kwargs: Any,
+) -> dict[str, Any]:
+ """Run on dataset.
+
+ Run the Chain or language model on a dataset and store traces
+ to the specified project name.
+
+ For the (usually faster) async version of this function,
+ see `arun_on_dataset`.
+
+ Args:
+ dataset_name: Name of the dataset to run the chain on.
+ llm_or_chain_factory: Language model or Chain constructor to run
+ over the dataset. The Chain constructor is used to permit
+ independent calls on each example without carrying over state.
+ evaluation: Configuration for evaluators to run on the
+ results of the chain.
+ dataset_version: Optional version of the dataset.
+ concurrency_level: The number of async tasks to run concurrently.
+ project_name: Name of the project to store the traces in.
+ Defaults to `{dataset_name}-{chain class name}-{datetime}`.
+ project_metadata: Optional metadata to add to the project.
+ Useful for storing information the test variant.
+ (prompt version, model version, etc.)
+ client: LangSmith client to use to access the dataset and to
+ log feedback and run traces.
+ verbose: Whether to print progress.
+ revision_id: Optional revision identifier to assign this test run to
+ track the performance of different versions of your system.
+ **kwargs: Should not be used, but is provided for backwards compatibility.
+
+ Returns:
+ `dict` containing the run's project name and the resulting model outputs.
+
+ Examples:
+ ```python
+ from langsmith import Client
+ from langchain_openai import ChatOpenAI
+ from langchain_classic.chains import LLMChain
+ from langchain_classic.smith import smith_eval.RunEvalConfig, run_on_dataset
+
+ # Chains may have memory. Passing in a constructor function lets the
+ # evaluation framework avoid cross-contamination between runs.
+ def construct_chain():
+ model = ChatOpenAI(temperature=0)
+ chain = LLMChain.from_string(
+ model,
+ "What's the answer to {your_input_key}"
+ )
+ return chain
+
+ # Load off-the-shelf evaluators via config or the EvaluatorType (string or enum)
+ evaluation_config = smith_eval.RunEvalConfig(
+ evaluators=[
+ "qa", # "Correctness" against a reference answer
+ "embedding_distance",
+ smith_eval.RunEvalConfig.Criteria("helpfulness"),
+ smith_eval.RunEvalConfig.Criteria({
+ "fifth-grader-score": "Do you have to be smarter than a fifth "
+ "grader to answer this question?"
+ }),
+ ]
+ )
+
+ client = Client()
+ run_on_dataset(
+ client,
+ dataset_name="",
+ llm_or_chain_factory=construct_chain,
+ evaluation=evaluation_config,
+ )
+ ```
+
+ You can also create custom evaluators by subclassing the `StringEvaluator` or
+ LangSmith's `RunEvaluator` classes.
+
+ ```python
+ from typing import Optional
+ from langchain_classic.evaluation import StringEvaluator
+
+
+ class MyStringEvaluator(StringEvaluator):
+ @property
+ def requires_input(self) -> bool:
+ return False
+
+ @property
+ def requires_reference(self) -> bool:
+ return True
+
+ @property
+ def evaluation_name(self) -> str:
+ return "exact_match"
+
+ def _evaluate_strings(
+ self, prediction, reference=None, input=None, **kwargs
+ ) -> dict:
+ return {"score": prediction == reference}
+
+
+ evaluation_config = smith_eval.RunEvalConfig(
+ custom_evaluators=[MyStringEvaluator()],
+ )
+
+ run_on_dataset(
+ client,
+ dataset_name="",
+ llm_or_chain_factory=construct_chain,
+ evaluation=evaluation_config,
+ )
+ ```
+ """
+ input_mapper = kwargs.pop("input_mapper", None)
+ if input_mapper:
+ warn_deprecated("0.0.305", message=_INPUT_MAPPER_DEP_WARNING, pending=True)
+ tags = kwargs.pop("tags", None)
+ if tags:
+ warn_deprecated(
+ "0.1.9",
+ message="The tags argument is deprecated and will be"
+ " removed in a future release. Please specify project_metadata instead.",
+ pending=True,
+ )
+ if revision_id is None:
+ revision_id = get_langchain_env_var_metadata().get("revision_id")
+
+ if kwargs:
+ warn_deprecated(
+ "0.0.305",
+ message="The following arguments are deprecated and "
+ "will be removed in a future release: "
+ f"{kwargs.keys()}.",
+ removal="0.0.305",
+ )
+ client = client or Client()
+ container = _DatasetRunContainer.prepare(
+ client,
+ dataset_name,
+ llm_or_chain_factory,
+ project_name,
+ evaluation,
+ tags,
+ input_mapper,
+ concurrency_level,
+ project_metadata=project_metadata,
+ revision_id=revision_id,
+ dataset_version=dataset_version,
+ )
+ if concurrency_level == 0:
+ batch_results = [
+ _run_llm_or_chain(
+ example,
+ config,
+ llm_or_chain_factory=container.wrapped_model,
+ input_mapper=input_mapper,
+ )
+ for example, config in zip(
+ container.examples, container.configs, strict=False
+ )
+ ]
+ else:
+ with runnable_config.get_executor_for_config(container.configs[0]) as executor:
+ batch_results = list(
+ executor.map(
+ functools.partial(
+ _run_llm_or_chain,
+ llm_or_chain_factory=container.wrapped_model,
+ input_mapper=input_mapper,
+ ),
+ container.examples,
+ container.configs,
+ ),
+ )
+
+ return container.finish(batch_results, verbose=verbose)
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/smith/evaluation/string_run_evaluator.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/smith/evaluation/string_run_evaluator.py
new file mode 100644
index 0000000000000000000000000000000000000000..2e5b4a3232fb0677f5ffd8b1831cc99a04a401a4
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/smith/evaluation/string_run_evaluator.py
@@ -0,0 +1,479 @@
+"""Run evaluator wrapper for string evaluators."""
+
+from __future__ import annotations
+
+import logging
+import uuid
+from abc import abstractmethod
+from typing import Any, cast
+
+from langchain_core.callbacks.manager import (
+ AsyncCallbackManagerForChainRun,
+ CallbackManagerForChainRun,
+)
+from langchain_core.load.dump import dumpd
+from langchain_core.load.load import load
+from langchain_core.load.serializable import Serializable
+from langchain_core.messages import BaseMessage, get_buffer_string, messages_from_dict
+from langsmith import EvaluationResult, RunEvaluator
+from langsmith.schemas import DataType, Example, Run
+from typing_extensions import override
+
+from langchain_classic.chains.base import Chain
+from langchain_classic.evaluation.schema import StringEvaluator
+from langchain_classic.schema import RUN_KEY
+
+_logger = logging.getLogger(__name__)
+
+
+def _get_messages_from_run_dict(messages: list[dict]) -> list[BaseMessage]:
+ if not messages:
+ return []
+ first_message = messages[0]
+ if "lc" in first_message:
+ return [
+ load(dumpd(message), allowed_objects="messages") for message in messages
+ ]
+ return messages_from_dict(messages)
+
+
+class StringRunMapper(Serializable):
+ """Extract items to evaluate from the run object."""
+
+ @property
+ def output_keys(self) -> list[str]:
+ """The keys to extract from the run."""
+ return ["prediction", "input"]
+
+ @abstractmethod
+ def map(self, run: Run) -> dict[str, str]:
+ """Maps the Run to a dictionary."""
+
+ def __call__(self, run: Run) -> dict[str, str]:
+ """Maps the Run to a dictionary."""
+ if not run.outputs:
+ msg = f"Run {run.id} has no outputs to evaluate."
+ raise ValueError(msg)
+ return self.map(run)
+
+
+class LLMStringRunMapper(StringRunMapper):
+ """Extract items to evaluate from the run object."""
+
+ def serialize_chat_messages(self, messages: list[dict] | list[list[dict]]) -> str:
+ """Extract the input messages from the run."""
+ if isinstance(messages, list) and messages:
+ if isinstance(messages[0], dict):
+ chat_messages = _get_messages_from_run_dict(
+ cast("list[dict]", messages)
+ )
+ elif isinstance(messages[0], list):
+ # Runs from Tracer have messages as a list of lists of dicts
+ chat_messages = _get_messages_from_run_dict(messages[0])
+ else:
+ msg = f"Could not extract messages to evaluate {messages}" # type: ignore[unreachable]
+ raise ValueError(msg)
+ return get_buffer_string(chat_messages)
+ msg = f"Could not extract messages to evaluate {messages}"
+ raise ValueError(msg)
+
+ def serialize_inputs(self, inputs: dict) -> str:
+ """Serialize inputs.
+
+ Args:
+ inputs: The inputs from the run, expected to contain prompts or messages.
+
+ Returns:
+ The serialized input text from the prompts or messages.
+
+ Raises:
+ ValueError: If neither prompts nor messages are found in the inputs.
+ """
+ if "prompts" in inputs: # Should we even accept this?
+ input_ = "\n\n".join(inputs["prompts"])
+ elif "prompt" in inputs:
+ input_ = inputs["prompt"]
+ elif "messages" in inputs:
+ input_ = self.serialize_chat_messages(inputs["messages"])
+ else:
+ msg = "LLM Run must have either messages or prompts as inputs."
+ raise ValueError(msg)
+ return input_
+
+ def serialize_outputs(self, outputs: dict) -> str:
+ """Serialize outputs.
+
+ Args:
+ outputs: The outputs from the run, expected to contain generations.
+
+ Returns:
+ The serialized output text from the first generation.
+
+ Raises:
+ ValueError: If no generations are found in the outputs or if the generations
+ are empty.
+ """
+ if not outputs.get("generations"):
+ msg = "Cannot evaluate LLM Run without generations."
+ raise ValueError(msg)
+ generations: list[dict] | list[list[dict]] = outputs["generations"]
+ if not generations:
+ msg = "Cannot evaluate LLM run with empty generations."
+ raise ValueError(msg)
+ first_generation: dict | list[dict] = generations[0]
+ if isinstance(first_generation, list):
+ # Runs from Tracer have generations as a list of lists of dicts
+ # Whereas Runs from the API have a list of dicts
+ first_generation = first_generation[0]
+ if "message" in first_generation:
+ output_ = self.serialize_chat_messages([first_generation["message"]])
+ else:
+ output_ = first_generation["text"]
+ return output_
+
+ def map(self, run: Run) -> dict[str, str]:
+ """Maps the Run to a dictionary."""
+ if run.run_type != "llm":
+ msg = "LLM RunMapper only supports LLM runs."
+ raise ValueError(msg)
+ if not run.outputs:
+ if run.error:
+ msg = f"Cannot evaluate errored LLM run {run.id}: {run.error}"
+ raise ValueError(msg)
+ msg = f"Run {run.id} has no outputs. Cannot evaluate this run."
+ raise ValueError(msg)
+ try:
+ inputs = self.serialize_inputs(run.inputs)
+ except Exception as e:
+ msg = f"Could not parse LM input from run inputs {run.inputs}"
+ raise ValueError(msg) from e
+ try:
+ output_ = self.serialize_outputs(run.outputs)
+ except Exception as e:
+ msg = f"Could not parse LM prediction from run outputs {run.outputs}"
+ raise ValueError(msg) from e
+ return {"input": inputs, "prediction": output_}
+
+
+class ChainStringRunMapper(StringRunMapper):
+ """Extract items to evaluate from the run object from a chain."""
+
+ input_key: str | None = None
+ """The key from the model Run's inputs to use as the eval input.
+ If not provided, will use the only input key or raise an
+ error if there are multiple."""
+ prediction_key: str | None = None
+ """The key from the model Run's outputs to use as the eval prediction.
+ If not provided, will use the only output key or raise an error
+ if there are multiple."""
+
+ def _get_key(self, source: dict, key: str | None, which: str) -> str:
+ if key is not None:
+ return source[key]
+ if len(source) == 1:
+ return next(iter(source.values()))
+ msg = (
+ f"Could not map run {which} with multiple keys: "
+ f"{source}\nPlease manually specify a {which}_key"
+ )
+ raise ValueError(msg)
+
+ def map(self, run: Run) -> dict[str, str]:
+ """Maps the Run to a dictionary."""
+ if not run.outputs:
+ msg = (
+ f"Run with ID {run.id} lacks outputs required for evaluation."
+ " Ensure the Run has valid outputs."
+ )
+ raise ValueError(msg)
+ if self.input_key is not None and self.input_key not in run.inputs:
+ msg = (
+ f"Run with ID {run.id} is missing the expected input key"
+ f" '{self.input_key}'.\nAvailable input keys in this Run"
+ f" are: {run.inputs.keys()}.\nAdjust the evaluator's"
+ f" input_key or ensure your input data includes key"
+ f" '{self.input_key}'."
+ )
+ raise ValueError(msg)
+ if self.prediction_key is not None and self.prediction_key not in run.outputs:
+ available_keys = ", ".join(run.outputs.keys())
+ msg = (
+ f"Run with ID {run.id} doesn't have the expected prediction key"
+ f" '{self.prediction_key}'. Available prediction keys in this Run are:"
+ f" {available_keys}. Adjust the evaluator's prediction_key or"
+ " ensure the Run object's outputs the expected key."
+ )
+ raise ValueError(msg)
+
+ input_ = self._get_key(run.inputs, self.input_key, "input")
+ prediction = self._get_key(run.outputs, self.prediction_key, "prediction")
+ return {
+ "input": input_,
+ "prediction": prediction,
+ }
+
+
+class ToolStringRunMapper(StringRunMapper):
+ """Map an input to the tool."""
+
+ @override
+ def map(self, run: Run) -> dict[str, str]:
+ if not run.outputs:
+ msg = f"Run {run.id} has no outputs to evaluate."
+ raise ValueError(msg)
+ return {"input": run.inputs["input"], "prediction": run.outputs["output"]}
+
+
+class StringExampleMapper(Serializable):
+ """Map an example, or row in the dataset, to the inputs of an evaluation."""
+
+ reference_key: str | None = None
+
+ @property
+ def output_keys(self) -> list[str]:
+ """The keys to extract from the run."""
+ return ["reference"]
+
+ def serialize_chat_messages(self, messages: list[dict]) -> str:
+ """Extract the input messages from the run."""
+ chat_messages = _get_messages_from_run_dict(messages)
+ return get_buffer_string(chat_messages)
+
+ def map(self, example: Example) -> dict[str, str]:
+ """Maps the Example, or dataset row to a dictionary."""
+ if not example.outputs:
+ msg = f"Example {example.id} has no outputs to use as a reference."
+ raise ValueError(msg)
+ if self.reference_key is None:
+ if len(example.outputs) > 1:
+ msg = (
+ f"Example {example.id} has multiple outputs, so you must"
+ " specify a reference_key."
+ )
+ raise ValueError(msg)
+ output = next(iter(example.outputs.values()))
+ elif self.reference_key not in example.outputs:
+ msg = (
+ f"Example {example.id} does not have reference key"
+ f" {self.reference_key}."
+ )
+ raise ValueError(msg)
+ else:
+ output = example.outputs[self.reference_key]
+ return {
+ "reference": self.serialize_chat_messages([output])
+ if isinstance(output, dict) and output.get("type") and output.get("data")
+ else output,
+ }
+
+ def __call__(self, example: Example) -> dict[str, str]:
+ """Maps the Run and Example to a dictionary."""
+ if not example.outputs:
+ msg = f"Example {example.id} has no outputs to use as areference label."
+ raise ValueError(msg)
+ return self.map(example)
+
+
+class StringRunEvaluatorChain(Chain, RunEvaluator):
+ """Evaluate Run and optional examples."""
+
+ run_mapper: StringRunMapper
+ """Maps the Run to a dictionary with 'input' and 'prediction' strings."""
+ example_mapper: StringExampleMapper | None = None
+ """Maps the Example (dataset row) to a dictionary
+ with a 'reference' string."""
+ name: str
+ """The name of the evaluation metric."""
+ string_evaluator: StringEvaluator
+ """The evaluation chain."""
+
+ @property
+ @override
+ def input_keys(self) -> list[str]:
+ return ["run", "example"]
+
+ @property
+ @override
+ def output_keys(self) -> list[str]:
+ return ["feedback"]
+
+ def _prepare_input(self, inputs: dict[str, Any]) -> dict[str, str]:
+ run: Run = inputs["run"]
+ example: Example | None = inputs.get("example")
+ evaluate_strings_inputs = self.run_mapper(run)
+ if not self.string_evaluator.requires_input:
+ # Hide warning about unused input
+ evaluate_strings_inputs.pop("input", None)
+ if example and self.example_mapper and self.string_evaluator.requires_reference:
+ evaluate_strings_inputs.update(self.example_mapper(example))
+ elif self.string_evaluator.requires_reference:
+ msg = (
+ f"Evaluator {self.name} requires an reference"
+ " example from the dataset,"
+ f" but none was provided for run {run.id}."
+ )
+ raise ValueError(msg)
+ return evaluate_strings_inputs
+
+ def _prepare_output(self, output: dict[str, Any]) -> dict[str, Any]:
+ evaluation_result = EvaluationResult(
+ key=self.name,
+ comment=output.get("reasoning"),
+ **output,
+ )
+ if RUN_KEY in output:
+ # TODO: Not currently surfaced. Update
+ evaluation_result.evaluator_info[RUN_KEY] = output[RUN_KEY]
+ return {"feedback": evaluation_result}
+
+ def _call(
+ self,
+ inputs: dict[str, str],
+ run_manager: CallbackManagerForChainRun | None = None,
+ ) -> dict[str, Any]:
+ """Call the evaluation chain."""
+ evaluate_strings_inputs = self._prepare_input(inputs)
+ _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
+ callbacks = _run_manager.get_child()
+ chain_output = self.string_evaluator.evaluate_strings(
+ **evaluate_strings_inputs,
+ callbacks=callbacks,
+ include_run_info=True,
+ )
+ return self._prepare_output(chain_output)
+
+ async def _acall(
+ self,
+ inputs: dict[str, str],
+ run_manager: AsyncCallbackManagerForChainRun | None = None,
+ ) -> dict[str, Any]:
+ """Call the evaluation chain."""
+ evaluate_strings_inputs = self._prepare_input(inputs)
+ _run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
+ callbacks = _run_manager.get_child()
+ chain_output = await self.string_evaluator.aevaluate_strings(
+ **evaluate_strings_inputs,
+ callbacks=callbacks,
+ include_run_info=True,
+ )
+ return self._prepare_output(chain_output)
+
+ def _prepare_evaluator_output(self, output: dict[str, Any]) -> EvaluationResult:
+ feedback: EvaluationResult = output["feedback"]
+ if RUN_KEY not in feedback.evaluator_info:
+ feedback.evaluator_info[RUN_KEY] = output[RUN_KEY]
+ return feedback
+
+ @override
+ def evaluate_run(
+ self,
+ run: Run,
+ example: Example | None = None,
+ evaluator_run_id: uuid.UUID | None = None,
+ ) -> EvaluationResult:
+ """Evaluate an example."""
+ try:
+ result = self({"run": run, "example": example}, include_run_info=True)
+ return self._prepare_evaluator_output(result)
+ except Exception as e:
+ _logger.exception("Error evaluating run %s", run.id)
+ return EvaluationResult(
+ key=self.string_evaluator.evaluation_name,
+ comment=f"Error evaluating run {run.id}: {e}",
+ # TODO: Add run ID once we can declare it via callbacks
+ )
+
+ @override
+ async def aevaluate_run(
+ self,
+ run: Run,
+ example: Example | None = None,
+ evaluator_run_id: uuid.UUID | None = None,
+ ) -> EvaluationResult:
+ """Evaluate an example."""
+ try:
+ result = await self.acall(
+ {"run": run, "example": example},
+ include_run_info=True,
+ )
+ return self._prepare_evaluator_output(result)
+ except Exception as e:
+ _logger.exception("Error evaluating run %s", run.id)
+ return EvaluationResult(
+ key=self.string_evaluator.evaluation_name,
+ comment=f"Error evaluating run {run.id}: {e}",
+ )
+
+ @classmethod
+ def from_run_and_data_type(
+ cls,
+ evaluator: StringEvaluator,
+ run_type: str,
+ data_type: DataType,
+ input_key: str | None = None,
+ prediction_key: str | None = None,
+ reference_key: str | None = None,
+ tags: list[str] | None = None,
+ ) -> StringRunEvaluatorChain:
+ """Create a StringRunEvaluatorChain.
+
+ Create a StringRunEvaluatorChain from an evaluator and the run and dataset
+ types.
+
+ This method provides an easy way to instantiate a StringRunEvaluatorChain, by
+ taking an evaluator and information about the type of run and the data.
+ The method supports LLM and chain runs.
+
+ Args:
+ evaluator: The string evaluator to use.
+ run_type: The type of run being evaluated.
+ Supported types are LLM and Chain.
+ data_type: The type of dataset used in the run.
+ input_key: The key used to map the input from the run.
+ prediction_key: The key used to map the prediction from the run.
+ reference_key: The key used to map the reference from the dataset.
+ tags: List of tags to attach to the evaluation chain.
+
+ Returns:
+ The instantiated evaluation chain.
+
+ Raises:
+ ValueError: If the run type is not supported, or if the evaluator requires a
+ reference from the dataset but the reference key is not provided.
+
+ """
+ # Configure how run inputs/predictions are passed to the evaluator
+ if run_type == "llm":
+ run_mapper: StringRunMapper = LLMStringRunMapper()
+ elif run_type == "chain":
+ run_mapper = ChainStringRunMapper(
+ input_key=input_key,
+ prediction_key=prediction_key,
+ )
+ else:
+ msg = f"Unsupported run type {run_type}. Expected one of 'llm' or 'chain'."
+ raise ValueError(msg)
+
+ # Configure how example rows are fed as a reference string to the evaluator
+ if (
+ reference_key is not None
+ or data_type in (DataType.llm, DataType.chat)
+ or evaluator.requires_reference
+ ):
+ example_mapper = StringExampleMapper(reference_key=reference_key)
+ elif evaluator.requires_reference:
+ msg = ( # type: ignore[unreachable]
+ f"Evaluator {evaluator.evaluation_name} requires a reference"
+ " example from the dataset. Please specify the reference key from"
+ " amongst the dataset outputs keys."
+ )
+ raise ValueError(msg)
+ else:
+ example_mapper = None
+ return cls(
+ name=evaluator.evaluation_name,
+ run_mapper=run_mapper,
+ example_mapper=example_mapper,
+ string_evaluator=evaluator,
+ tags=tags,
+ )
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index 0000000000000000000000000000000000000000..54ba4e205282981b54d8f210f5418d9fa397da3a
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/ainetwork/app.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/ainetwork/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae6de6320436fa0186f9cc820c776d590ce25515
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/ainetwork/app.py
@@ -0,0 +1,30 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import AINAppOps
+ from langchain_community.tools.ainetwork.app import AppOperationType, AppSchema
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "AppOperationType": "langchain_community.tools.ainetwork.app",
+ "AppSchema": "langchain_community.tools.ainetwork.app",
+ "AINAppOps": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "AINAppOps",
+ "AppOperationType",
+ "AppSchema",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/ainetwork/base.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/ainetwork/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..4b1cbb5fb649b44488f557a28a31bc97167bc23a
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/ainetwork/base.py
@@ -0,0 +1,27 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools.ainetwork.base import AINBaseTool, OperationType
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "OperationType": "langchain_community.tools.ainetwork.base",
+ "AINBaseTool": "langchain_community.tools.ainetwork.base",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "AINBaseTool",
+ "OperationType",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/ainetwork/owner.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/ainetwork/owner.py
new file mode 100644
index 0000000000000000000000000000000000000000..6cdb89a84134bb6d6c8039c4b3a96b1d2bb7bf4b
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/ainetwork/owner.py
@@ -0,0 +1,28 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import AINOwnerOps
+ from langchain_community.tools.ainetwork.owner import RuleSchema
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "RuleSchema": "langchain_community.tools.ainetwork.owner",
+ "AINOwnerOps": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "AINOwnerOps",
+ "RuleSchema",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/ainetwork/rule.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/ainetwork/rule.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d0cd2ab25a9411fb52af84f7832ddd31284da60
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/ainetwork/rule.py
@@ -0,0 +1,28 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import AINRuleOps
+ from langchain_community.tools.ainetwork.rule import RuleSchema
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "RuleSchema": "langchain_community.tools.ainetwork.rule",
+ "AINRuleOps": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "AINRuleOps",
+ "RuleSchema",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/ainetwork/transfer.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/ainetwork/transfer.py
new file mode 100644
index 0000000000000000000000000000000000000000..b7c7f76d6ea34d751ff513ed29e01833bb099a86
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/ainetwork/transfer.py
@@ -0,0 +1,28 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import AINTransfer
+ from langchain_community.tools.ainetwork.transfer import TransferSchema
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "TransferSchema": "langchain_community.tools.ainetwork.transfer",
+ "AINTransfer": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "AINTransfer",
+ "TransferSchema",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/ainetwork/value.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/ainetwork/value.py
new file mode 100644
index 0000000000000000000000000000000000000000..a2b5a07aaccb7b57541efb9ac8e754f28215c1d2
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/ainetwork/value.py
@@ -0,0 +1,28 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import AINValueOps
+ from langchain_community.tools.ainetwork.value import ValueSchema
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "ValueSchema": "langchain_community.tools.ainetwork.value",
+ "AINValueOps": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "AINValueOps",
+ "ValueSchema",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/amadeus/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/amadeus/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..824ed3078ef325db704fb1def5fb08d6cfbf68ad
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/amadeus/__init__.py
@@ -0,0 +1,30 @@
+"""Amadeus tools."""
+
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools.amadeus.closest_airport import AmadeusClosestAirport
+ from langchain_community.tools.amadeus.flight_search import AmadeusFlightSearch
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "AmadeusClosestAirport": "langchain_community.tools.amadeus.closest_airport",
+ "AmadeusFlightSearch": "langchain_community.tools.amadeus.flight_search",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "AmadeusClosestAirport",
+ "AmadeusFlightSearch",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/amadeus/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/amadeus/__pycache__/__init__.cpython-311.pyc
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/amadeus/__pycache__/flight_search.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/amadeus/__pycache__/flight_search.cpython-311.pyc
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index 0000000000000000000000000000000000000000..0b02b2a80385d9f26c809e42d884901fd376c4db
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/amadeus/base.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/amadeus/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..85ac1c907226f85f41a77f0c2230ca417d52bdea
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/amadeus/base.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools.amadeus.base import AmadeusBaseTool
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"AmadeusBaseTool": "langchain_community.tools.amadeus.base"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "AmadeusBaseTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/amadeus/closest_airport.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/amadeus/closest_airport.py
new file mode 100644
index 0000000000000000000000000000000000000000..877eae4758b088ffeef789678eea44a3858a46ff
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/amadeus/closest_airport.py
@@ -0,0 +1,30 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools.amadeus.closest_airport import (
+ AmadeusClosestAirport,
+ ClosestAirportSchema,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "ClosestAirportSchema": "langchain_community.tools.amadeus.closest_airport",
+ "AmadeusClosestAirport": "langchain_community.tools.amadeus.closest_airport",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "AmadeusClosestAirport",
+ "ClosestAirportSchema",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/amadeus/flight_search.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/amadeus/flight_search.py
new file mode 100644
index 0000000000000000000000000000000000000000..35c6fc9f379d4f3b51dfb0722298f86f323fd41c
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/amadeus/flight_search.py
@@ -0,0 +1,30 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools.amadeus.flight_search import (
+ AmadeusFlightSearch,
+ FlightSearchSchema,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "FlightSearchSchema": "langchain_community.tools.amadeus.flight_search",
+ "AmadeusFlightSearch": "langchain_community.tools.amadeus.flight_search",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "AmadeusFlightSearch",
+ "FlightSearchSchema",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/arxiv/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/arxiv/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2607cb19bb7e5a74afba8128a82061f127000906
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/arxiv/__init__.py
@@ -0,0 +1 @@
+"""Arxiv API toolkit."""
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/arxiv/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/arxiv/__pycache__/__init__.cpython-311.pyc
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/arxiv/__pycache__/tool.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/arxiv/__pycache__/tool.cpython-311.pyc
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index 0000000000000000000000000000000000000000..716206eced3696653f8487a4bc6cb33bb10ec7b7
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/arxiv/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/arxiv/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d8138cebfb5c9282f4ed8cb23be78a3a6d991ef
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/arxiv/tool.py
@@ -0,0 +1,28 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import ArxivQueryRun
+ from langchain_community.tools.arxiv.tool import ArxivInput
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "ArxivInput": "langchain_community.tools.arxiv.tool",
+ "ArxivQueryRun": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "ArxivInput",
+ "ArxivQueryRun",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/azure_cognitive_services/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/azure_cognitive_services/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b3a0c2c738083b3e54f580343eb55c6abd785994
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/azure_cognitive_services/__init__.py
@@ -0,0 +1,41 @@
+"""Azure Cognitive Services Tools."""
+
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import (
+ AzureCogsFormRecognizerTool,
+ AzureCogsImageAnalysisTool,
+ AzureCogsSpeech2TextTool,
+ AzureCogsText2SpeechTool,
+ AzureCogsTextAnalyticsHealthTool,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "AzureCogsImageAnalysisTool": "langchain_community.tools",
+ "AzureCogsFormRecognizerTool": "langchain_community.tools",
+ "AzureCogsSpeech2TextTool": "langchain_community.tools",
+ "AzureCogsText2SpeechTool": "langchain_community.tools",
+ "AzureCogsTextAnalyticsHealthTool": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "AzureCogsFormRecognizerTool",
+ "AzureCogsImageAnalysisTool",
+ "AzureCogsSpeech2TextTool",
+ "AzureCogsText2SpeechTool",
+ "AzureCogsTextAnalyticsHealthTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/azure_cognitive_services/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/azure_cognitive_services/__pycache__/__init__.cpython-311.pyc
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/azure_cognitive_services/__pycache__/image_analysis.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/azure_cognitive_services/__pycache__/image_analysis.cpython-311.pyc
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/azure_cognitive_services/__pycache__/text_analytics_health.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/azure_cognitive_services/__pycache__/text_analytics_health.cpython-311.pyc
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index 0000000000000000000000000000000000000000..4e6d5de8f78f1d3a626d6fea7ba4218de5cf49cf
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/azure_cognitive_services/form_recognizer.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/azure_cognitive_services/form_recognizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce80d6d335bd66bfb387a6ca1a95f032f819fa98
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/azure_cognitive_services/form_recognizer.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import AzureCogsFormRecognizerTool
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"AzureCogsFormRecognizerTool": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "AzureCogsFormRecognizerTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/azure_cognitive_services/image_analysis.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/azure_cognitive_services/image_analysis.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d09b1f1ef86ec9676eb22bee1d00e17f08eb774
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/azure_cognitive_services/image_analysis.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import AzureCogsImageAnalysisTool
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"AzureCogsImageAnalysisTool": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "AzureCogsImageAnalysisTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/azure_cognitive_services/speech2text.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/azure_cognitive_services/speech2text.py
new file mode 100644
index 0000000000000000000000000000000000000000..c02138af56e8df8ba56066c1985dc6cb6c16b2e5
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/azure_cognitive_services/speech2text.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import AzureCogsSpeech2TextTool
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"AzureCogsSpeech2TextTool": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "AzureCogsSpeech2TextTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/azure_cognitive_services/text2speech.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/azure_cognitive_services/text2speech.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d3672770240c9612950e7ca7541a575622d2161
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/azure_cognitive_services/text2speech.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import AzureCogsText2SpeechTool
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"AzureCogsText2SpeechTool": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "AzureCogsText2SpeechTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/azure_cognitive_services/text_analytics_health.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/azure_cognitive_services/text_analytics_health.py
new file mode 100644
index 0000000000000000000000000000000000000000..54961721bae985414d3c9a60b64f862afe7ebac0
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/azure_cognitive_services/text_analytics_health.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import AzureCogsTextAnalyticsHealthTool
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"AzureCogsTextAnalyticsHealthTool": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "AzureCogsTextAnalyticsHealthTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/bearly/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/bearly/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/bearly/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/bearly/__pycache__/__init__.cpython-311.pyc
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/bearly/__pycache__/tool.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/bearly/__pycache__/tool.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..f9be851d3894af153879db0edaa3282c8f6b4e39
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/bearly/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/bearly/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..6dc94238253c55ab8e380c540c0faca5bb993353
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/bearly/tool.py
@@ -0,0 +1,33 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import BearlyInterpreterTool
+ from langchain_community.tools.bearly.tool import (
+ BearlyInterpreterToolArguments,
+ FileInfo,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "BearlyInterpreterToolArguments": "langchain_community.tools.bearly.tool",
+ "FileInfo": "langchain_community.tools.bearly.tool",
+ "BearlyInterpreterTool": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "BearlyInterpreterTool",
+ "BearlyInterpreterToolArguments",
+ "FileInfo",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/bing_search/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/bing_search/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b1edbf403e1d7cf3012f2c89b98c1b1169778638
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/bing_search/__init__.py
@@ -0,0 +1,29 @@
+"""Bing Search API toolkit."""
+
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import BingSearchResults, BingSearchRun
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "BingSearchRun": "langchain_community.tools",
+ "BingSearchResults": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "BingSearchResults",
+ "BingSearchRun",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/bing_search/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/bing_search/__pycache__/__init__.cpython-311.pyc
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/bing_search/__pycache__/tool.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/bing_search/__pycache__/tool.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..56ae2a557afb4ea721977d238b37bc7340e16c06
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/bing_search/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/bing_search/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..44df892dd9df503aa659ac9fc502ef340ee67d24
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/bing_search/tool.py
@@ -0,0 +1,27 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import BingSearchResults, BingSearchRun
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "BingSearchRun": "langchain_community.tools",
+ "BingSearchResults": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "BingSearchResults",
+ "BingSearchRun",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/brave_search/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/brave_search/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/brave_search/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/brave_search/__pycache__/__init__.cpython-311.pyc
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/brave_search/__pycache__/tool.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/brave_search/__pycache__/tool.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..8f67576c78359b7397e9a63c741875c51743d2a5
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/brave_search/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/brave_search/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a6b4331a8b5c23fbee07033b17c1d19336b3266
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/brave_search/tool.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import BraveSearch
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"BraveSearch": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "BraveSearch",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/clickup/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/clickup/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
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index 0000000000000000000000000000000000000000..db71911cfa69a289850c7c219af71720647a44c0
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/clickup/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/clickup/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce298ee29b4ddc3ee127e2eb4a5dd657860da857
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/clickup/tool.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools.clickup.tool import ClickupAction
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"ClickupAction": "langchain_community.tools.clickup.tool"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "ClickupAction",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/dataforseo_api_search/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/dataforseo_api_search/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e4e67b99824d665423137c7cf531f87a4a8b3257
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/dataforseo_api_search/__init__.py
@@ -0,0 +1,34 @@
+"""DataForSeo API Toolkit."""
+
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools.dataforseo_api_search.tool import (
+ DataForSeoAPISearchResults,
+ DataForSeoAPISearchRun,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "DataForSeoAPISearchRun": "langchain_community.tools.dataforseo_api_search.tool",
+ "DataForSeoAPISearchResults": (
+ "langchain_community.tools.dataforseo_api_search.tool"
+ ),
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "DataForSeoAPISearchResults",
+ "DataForSeoAPISearchRun",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/dataforseo_api_search/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/dataforseo_api_search/__pycache__/__init__.cpython-311.pyc
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/dataforseo_api_search/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/dataforseo_api_search/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d3dcfa6717c521d77fff74548dd3893f509d23d
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/dataforseo_api_search/tool.py
@@ -0,0 +1,32 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools.dataforseo_api_search.tool import (
+ DataForSeoAPISearchResults,
+ DataForSeoAPISearchRun,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "DataForSeoAPISearchRun": "langchain_community.tools.dataforseo_api_search.tool",
+ "DataForSeoAPISearchResults": (
+ "langchain_community.tools.dataforseo_api_search.tool"
+ ),
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "DataForSeoAPISearchResults",
+ "DataForSeoAPISearchRun",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/ddg_search/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/ddg_search/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9e4760768ad41d143bccb1baeb3f7ef42c9d91a
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/ddg_search/__init__.py
@@ -0,0 +1,25 @@
+"""DuckDuckGo Search API toolkit."""
+
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import DuckDuckGoSearchRun
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"DuckDuckGoSearchRun": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "DuckDuckGoSearchRun",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/ddg_search/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/ddg_search/__pycache__/__init__.cpython-311.pyc
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/ddg_search/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/ddg_search/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac41e477475a0b6c645d26ec2872650021d3aa22
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/ddg_search/tool.py
@@ -0,0 +1,32 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import DuckDuckGoSearchResults, DuckDuckGoSearchRun
+ from langchain_community.tools.ddg_search.tool import DDGInput, DuckDuckGoSearchTool
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "DDGInput": "langchain_community.tools.ddg_search.tool",
+ "DuckDuckGoSearchRun": "langchain_community.tools",
+ "DuckDuckGoSearchResults": "langchain_community.tools",
+ "DuckDuckGoSearchTool": "langchain_community.tools.ddg_search.tool",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "DDGInput",
+ "DuckDuckGoSearchResults",
+ "DuckDuckGoSearchRun",
+ "DuckDuckGoSearchTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/e2b_data_analysis/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/e2b_data_analysis/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/e2b_data_analysis/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/e2b_data_analysis/__pycache__/__init__.cpython-311.pyc
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index 0000000000000000000000000000000000000000..a88e5be78d880440426c39c70dd48274f4bf6f80
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/e2b_data_analysis/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/e2b_data_analysis/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..91504b865a15468fda5d51140e1baa30aa6018d9
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/e2b_data_analysis/tool.py
@@ -0,0 +1,33 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import E2BDataAnalysisTool
+ from langchain_community.tools.e2b_data_analysis.tool import (
+ E2BDataAnalysisToolArguments,
+ UploadedFile,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "UploadedFile": "langchain_community.tools.e2b_data_analysis.tool",
+ "E2BDataAnalysisToolArguments": "langchain_community.tools.e2b_data_analysis.tool",
+ "E2BDataAnalysisTool": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "E2BDataAnalysisTool",
+ "E2BDataAnalysisToolArguments",
+ "UploadedFile",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/edenai/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/edenai/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7fa45014edb135c0a01d5cd9272d2c3db139dd1e
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/edenai/__init__.py
@@ -0,0 +1,50 @@
+"""Edenai Tools."""
+
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import (
+ EdenAiExplicitImageTool,
+ EdenAiObjectDetectionTool,
+ EdenAiParsingIDTool,
+ EdenAiParsingInvoiceTool,
+ EdenAiSpeechToTextTool,
+ EdenAiTextModerationTool,
+ EdenAiTextToSpeechTool,
+ EdenaiTool,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "EdenAiExplicitImageTool": "langchain_community.tools",
+ "EdenAiObjectDetectionTool": "langchain_community.tools",
+ "EdenAiParsingIDTool": "langchain_community.tools",
+ "EdenAiParsingInvoiceTool": "langchain_community.tools",
+ "EdenAiTextToSpeechTool": "langchain_community.tools",
+ "EdenAiSpeechToTextTool": "langchain_community.tools",
+ "EdenAiTextModerationTool": "langchain_community.tools",
+ "EdenaiTool": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "EdenAiExplicitImageTool",
+ "EdenAiObjectDetectionTool",
+ "EdenAiParsingIDTool",
+ "EdenAiParsingInvoiceTool",
+ "EdenAiSpeechToTextTool",
+ "EdenAiTextModerationTool",
+ "EdenAiTextToSpeechTool",
+ "EdenaiTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/edenai/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/edenai/__pycache__/__init__.cpython-311.pyc
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/edenai/audio_speech_to_text.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/edenai/audio_speech_to_text.py
new file mode 100644
index 0000000000000000000000000000000000000000..f250cf2a73724ba980df9f7e268cb1f6a6c1022b
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/edenai/audio_speech_to_text.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import EdenAiSpeechToTextTool
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"EdenAiSpeechToTextTool": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "EdenAiSpeechToTextTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/edenai/audio_text_to_speech.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/edenai/audio_text_to_speech.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1a2111e75d00aabf9767595ebff3cd5bf0bb511
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/edenai/audio_text_to_speech.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import EdenAiTextToSpeechTool
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"EdenAiTextToSpeechTool": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "EdenAiTextToSpeechTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/edenai/edenai_base_tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/edenai/edenai_base_tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..1b3f71d379d1418aae8446063d46c86443901f79
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/edenai/edenai_base_tool.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import EdenaiTool
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"EdenaiTool": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "EdenaiTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/edenai/image_explicitcontent.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/edenai/image_explicitcontent.py
new file mode 100644
index 0000000000000000000000000000000000000000..25cd0aa15760366577f7c4bcfb98756bd630a4f8
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/edenai/image_explicitcontent.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import EdenAiExplicitImageTool
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"EdenAiExplicitImageTool": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "EdenAiExplicitImageTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/edenai/image_objectdetection.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/edenai/image_objectdetection.py
new file mode 100644
index 0000000000000000000000000000000000000000..2dcc90e3b2c29cbcc712466ccea90fbb65d69281
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/edenai/image_objectdetection.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import EdenAiObjectDetectionTool
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"EdenAiObjectDetectionTool": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "EdenAiObjectDetectionTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/edenai/ocr_identityparser.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/edenai/ocr_identityparser.py
new file mode 100644
index 0000000000000000000000000000000000000000..9c942c57978f8bc7dee161fe62eded2a6be41823
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/edenai/ocr_identityparser.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import EdenAiParsingIDTool
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"EdenAiParsingIDTool": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "EdenAiParsingIDTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/edenai/ocr_invoiceparser.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/edenai/ocr_invoiceparser.py
new file mode 100644
index 0000000000000000000000000000000000000000..e918f097314aeb9d612024a73f5c7ac960ff9fa6
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/edenai/ocr_invoiceparser.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import EdenAiParsingInvoiceTool
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"EdenAiParsingInvoiceTool": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "EdenAiParsingInvoiceTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/edenai/text_moderation.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/edenai/text_moderation.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d41ca2a5a40a08eb9069042d3021dded4eebe44
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/edenai/text_moderation.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import EdenAiTextModerationTool
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"EdenAiTextModerationTool": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "EdenAiTextModerationTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/eleven_labs/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/eleven_labs/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d3c30c55dacbbf42f7c050e6ce4071a1c0258d0d
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/eleven_labs/__init__.py
@@ -0,0 +1,25 @@
+"""Eleven Labs Services Tools."""
+
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import ElevenLabsText2SpeechTool
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"ElevenLabsText2SpeechTool": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "ElevenLabsText2SpeechTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/eleven_labs/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/eleven_labs/__pycache__/__init__.cpython-311.pyc
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/eleven_labs/__pycache__/text2speech.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/eleven_labs/__pycache__/text2speech.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..5f2160dbe7d7fbeb86dd07062851f680d763b69c
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/eleven_labs/models.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/eleven_labs/models.py
new file mode 100644
index 0000000000000000000000000000000000000000..aabf5f63077507f142e3e43bd914ea02b485dade
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/eleven_labs/models.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools.eleven_labs.models import ElevenLabsModel
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"ElevenLabsModel": "langchain_community.tools.eleven_labs.models"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "ElevenLabsModel",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/eleven_labs/text2speech.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/eleven_labs/text2speech.py
new file mode 100644
index 0000000000000000000000000000000000000000..972df4faf2fadb07183e2ce3234a8e2317527536
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/eleven_labs/text2speech.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import ElevenLabsText2SpeechTool
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"ElevenLabsText2SpeechTool": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "ElevenLabsText2SpeechTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/file_management/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/file_management/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..13273194f2a9ca0dfbf0b579d4e5444149a63b89
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/file_management/__init__.py
@@ -0,0 +1,47 @@
+"""File Management Tools."""
+
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import (
+ CopyFileTool,
+ DeleteFileTool,
+ FileSearchTool,
+ ListDirectoryTool,
+ MoveFileTool,
+ ReadFileTool,
+ WriteFileTool,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "CopyFileTool": "langchain_community.tools",
+ "DeleteFileTool": "langchain_community.tools",
+ "FileSearchTool": "langchain_community.tools",
+ "MoveFileTool": "langchain_community.tools",
+ "ReadFileTool": "langchain_community.tools",
+ "WriteFileTool": "langchain_community.tools",
+ "ListDirectoryTool": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "CopyFileTool",
+ "DeleteFileTool",
+ "FileSearchTool",
+ "ListDirectoryTool",
+ "MoveFileTool",
+ "ReadFileTool",
+ "WriteFileTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/file_management/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/file_management/__pycache__/__init__.cpython-311.pyc
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/file_management/__pycache__/write.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/file_management/__pycache__/write.cpython-311.pyc
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index 0000000000000000000000000000000000000000..0cb6c89f202e18b7199fcaec590b86895b9d33e3
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/file_management/copy.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/file_management/copy.py
new file mode 100644
index 0000000000000000000000000000000000000000..e0e54e0ee0b5c5d2a8487614d891310f87ae4eb9
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/file_management/copy.py
@@ -0,0 +1,28 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import CopyFileTool
+ from langchain_community.tools.file_management.copy import FileCopyInput
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "FileCopyInput": "langchain_community.tools.file_management.copy",
+ "CopyFileTool": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "CopyFileTool",
+ "FileCopyInput",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/file_management/delete.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/file_management/delete.py
new file mode 100644
index 0000000000000000000000000000000000000000..f66ab301506d56feda14a11f913c7922b1f95b87
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/file_management/delete.py
@@ -0,0 +1,28 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import DeleteFileTool
+ from langchain_community.tools.file_management.delete import FileDeleteInput
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "FileDeleteInput": "langchain_community.tools.file_management.delete",
+ "DeleteFileTool": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "DeleteFileTool",
+ "FileDeleteInput",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/file_management/file_search.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/file_management/file_search.py
new file mode 100644
index 0000000000000000000000000000000000000000..ab090c3fce7d6c23aa0eb0a3de297e13c484f23d
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/file_management/file_search.py
@@ -0,0 +1,28 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import FileSearchTool
+ from langchain_community.tools.file_management.file_search import FileSearchInput
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "FileSearchInput": "langchain_community.tools.file_management.file_search",
+ "FileSearchTool": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "FileSearchInput",
+ "FileSearchTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/file_management/list_dir.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/file_management/list_dir.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b7693ca7a68177e527f01b342756d00d3927f0e
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/file_management/list_dir.py
@@ -0,0 +1,28 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import ListDirectoryTool
+ from langchain_community.tools.file_management.list_dir import DirectoryListingInput
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "DirectoryListingInput": "langchain_community.tools.file_management.list_dir",
+ "ListDirectoryTool": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "DirectoryListingInput",
+ "ListDirectoryTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/file_management/move.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/file_management/move.py
new file mode 100644
index 0000000000000000000000000000000000000000..890a97bb2ae38744ac7001e6e8a275929bfaa81f
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/file_management/move.py
@@ -0,0 +1,28 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import MoveFileTool
+ from langchain_community.tools.file_management.move import FileMoveInput
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "FileMoveInput": "langchain_community.tools.file_management.move",
+ "MoveFileTool": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "FileMoveInput",
+ "MoveFileTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/file_management/read.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/file_management/read.py
new file mode 100644
index 0000000000000000000000000000000000000000..a024ed04521f6cc31b2229b909b53cb568b405d7
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/file_management/read.py
@@ -0,0 +1,28 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import ReadFileTool
+ from langchain_community.tools.file_management.read import ReadFileInput
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "ReadFileInput": "langchain_community.tools.file_management.read",
+ "ReadFileTool": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "ReadFileInput",
+ "ReadFileTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/file_management/write.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/file_management/write.py
new file mode 100644
index 0000000000000000000000000000000000000000..93288a2599563e00ac63ffdd045082d5ed2a9baf
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/file_management/write.py
@@ -0,0 +1,28 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import WriteFileTool
+ from langchain_community.tools.file_management.write import WriteFileInput
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "WriteFileInput": "langchain_community.tools.file_management.write",
+ "WriteFileTool": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "WriteFileInput",
+ "WriteFileTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/github/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/github/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b3a380e65e66a032497b18b491c4bebf88d38a07
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/github/__init__.py
@@ -0,0 +1 @@
+"""GitHub Tool."""
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/github/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/github/__pycache__/__init__.cpython-311.pyc
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new file mode 100644
index 0000000000000000000000000000000000000000..a1a772e7dc90855a3274ddaabae9b215ddc1319f
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/github/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/github/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..bacaac91e9e1e133aa1d045bf0d1df6e2424ad03
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/github/tool.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools.github.tool import GitHubAction
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"GitHubAction": "langchain_community.tools.github.tool"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "GitHubAction",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/gitlab/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/gitlab/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9c194f644c162fc733a2a5031231cc96f7f30f24
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/gitlab/__init__.py
@@ -0,0 +1 @@
+"""GitLab Tool."""
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/gitlab/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/gitlab/__pycache__/__init__.cpython-311.pyc
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new file mode 100644
index 0000000000000000000000000000000000000000..445ab63754c12a6476e41cf0ea46f9cae617cfd5
Binary files /dev/null and b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/gitlab/__pycache__/tool.cpython-311.pyc differ
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/gitlab/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/gitlab/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..7119cc87415fcff900dbcf7ced448969e49daab1
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/gitlab/tool.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools.gitlab.tool import GitLabAction
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"GitLabAction": "langchain_community.tools.gitlab.tool"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "GitLabAction",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/gmail/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/gmail/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a5595348c999feb3cd4adb0023ed2ce1023e19f8
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/gmail/__init__.py
@@ -0,0 +1,41 @@
+"""Gmail tools."""
+
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import (
+ GmailCreateDraft,
+ GmailGetMessage,
+ GmailGetThread,
+ GmailSearch,
+ GmailSendMessage,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "GmailCreateDraft": "langchain_community.tools",
+ "GmailSendMessage": "langchain_community.tools",
+ "GmailSearch": "langchain_community.tools",
+ "GmailGetMessage": "langchain_community.tools",
+ "GmailGetThread": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "GmailCreateDraft",
+ "GmailGetMessage",
+ "GmailGetThread",
+ "GmailSearch",
+ "GmailSendMessage",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/gmail/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/gmail/__pycache__/__init__.cpython-311.pyc
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/gmail/__pycache__/get_message.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/gmail/__pycache__/get_message.cpython-311.pyc
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/gmail/__pycache__/send_message.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/gmail/__pycache__/send_message.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..4770f2e1193bcfafa77e43029b9e4a0bc9160ae5
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/gmail/base.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/gmail/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..763d64d5d100ab84857038c65185435253ecaf22
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/gmail/base.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools.gmail.base import GmailBaseTool
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"GmailBaseTool": "langchain_community.tools.gmail.base"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "GmailBaseTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/gmail/create_draft.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/gmail/create_draft.py
new file mode 100644
index 0000000000000000000000000000000000000000..db092a899ced896418b775b0a25b7c3e390d9894
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/gmail/create_draft.py
@@ -0,0 +1,28 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import GmailCreateDraft
+ from langchain_community.tools.gmail.create_draft import CreateDraftSchema
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "CreateDraftSchema": "langchain_community.tools.gmail.create_draft",
+ "GmailCreateDraft": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "CreateDraftSchema",
+ "GmailCreateDraft",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/gmail/get_message.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/gmail/get_message.py
new file mode 100644
index 0000000000000000000000000000000000000000..a7879b5865c7e808e48637c35fa2b11c28aa6ef8
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/gmail/get_message.py
@@ -0,0 +1,28 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import GmailGetMessage
+ from langchain_community.tools.gmail.get_message import SearchArgsSchema
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "SearchArgsSchema": "langchain_community.tools.gmail.get_message",
+ "GmailGetMessage": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "GmailGetMessage",
+ "SearchArgsSchema",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/gmail/get_thread.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/gmail/get_thread.py
new file mode 100644
index 0000000000000000000000000000000000000000..2677482ef3780be8db3b59c63aff7719e1c4022a
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/gmail/get_thread.py
@@ -0,0 +1,28 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import GmailGetThread
+ from langchain_community.tools.gmail.get_thread import GetThreadSchema
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "GetThreadSchema": "langchain_community.tools.gmail.get_thread",
+ "GmailGetThread": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "GetThreadSchema",
+ "GmailGetThread",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/gmail/search.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/gmail/search.py
new file mode 100644
index 0000000000000000000000000000000000000000..fe5378463c6eaeed90ff1a0548980fbdb028cf0a
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/gmail/search.py
@@ -0,0 +1,30 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import GmailSearch
+ from langchain_community.tools.gmail.search import Resource, SearchArgsSchema
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "Resource": "langchain_community.tools.gmail.search",
+ "SearchArgsSchema": "langchain_community.tools.gmail.search",
+ "GmailSearch": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "GmailSearch",
+ "Resource",
+ "SearchArgsSchema",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/gmail/send_message.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/gmail/send_message.py
new file mode 100644
index 0000000000000000000000000000000000000000..f75d3e0a57d8bbcc50b3b6f09b981864955dc446
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/gmail/send_message.py
@@ -0,0 +1,28 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import GmailSendMessage
+ from langchain_community.tools.gmail.send_message import SendMessageSchema
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "SendMessageSchema": "langchain_community.tools.gmail.send_message",
+ "GmailSendMessage": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "GmailSendMessage",
+ "SendMessageSchema",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/golden_query/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/golden_query/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2cba7e73d706d0491df04a73ab56cd0722c2de25
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/golden_query/__init__.py
@@ -0,0 +1,25 @@
+"""Golden API toolkit."""
+
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools.golden_query.tool import GoldenQueryRun
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"GoldenQueryRun": "langchain_community.tools.golden_query.tool"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "GoldenQueryRun",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/golden_query/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/golden_query/__pycache__/__init__.cpython-311.pyc
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index 0000000000000000000000000000000000000000..2bcb4dbcd60f01d87338f9dc68daa68cce335045
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/golden_query/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/golden_query/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..d5fe796c9eae2f796a34dd7089558dcea538f573
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/golden_query/tool.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools.golden_query.tool import GoldenQueryRun
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"GoldenQueryRun": "langchain_community.tools.golden_query.tool"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "GoldenQueryRun",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_cloud/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_cloud/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d9e25026aa7bc9d7bffc8aea4337e0f054a89a6
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_cloud/__init__.py
@@ -0,0 +1,25 @@
+"""Google Cloud Tools."""
+
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import GoogleCloudTextToSpeechTool
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"GoogleCloudTextToSpeechTool": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "GoogleCloudTextToSpeechTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_cloud/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_cloud/__pycache__/__init__.cpython-311.pyc
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index 0000000000000000000000000000000000000000..b577c5d02e0ebf2028724c7653360be3b0c2992a
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_cloud/texttospeech.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_cloud/texttospeech.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d0ca664c5ede68265f61f7c0cacbdfa9df8c77f
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_cloud/texttospeech.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import GoogleCloudTextToSpeechTool
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"GoogleCloudTextToSpeechTool": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "GoogleCloudTextToSpeechTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_finance/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_finance/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4cc2389f0e3f2b1382bc612de273a75a2de157ad
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_finance/__init__.py
@@ -0,0 +1,27 @@
+"""Google Finance API Toolkit."""
+
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools.google_finance.tool import GoogleFinanceQueryRun
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "GoogleFinanceQueryRun": "langchain_community.tools.google_finance.tool",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "GoogleFinanceQueryRun",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_finance/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_finance/__pycache__/__init__.cpython-311.pyc
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index 0000000000000000000000000000000000000000..1cd63007efb71442c25792d612a8f2403daac247
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_finance/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_finance/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..753adde89d362f029a817e55c4b411699bee67d3
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_finance/tool.py
@@ -0,0 +1,25 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools.google_finance.tool import GoogleFinanceQueryRun
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "GoogleFinanceQueryRun": "langchain_community.tools.google_finance.tool",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "GoogleFinanceQueryRun",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_jobs/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_jobs/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..49116ab750c770f6f277fe54076bbe27e61ad390
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_jobs/__init__.py
@@ -0,0 +1,25 @@
+"""Google Jobs API Toolkit."""
+
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools.google_jobs.tool import GoogleJobsQueryRun
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"GoogleJobsQueryRun": "langchain_community.tools.google_jobs.tool"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "GoogleJobsQueryRun",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_jobs/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_jobs/__pycache__/__init__.cpython-311.pyc
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new file mode 100644
index 0000000000000000000000000000000000000000..767cfb4d279aeb80ed8ae798e4972876ade906d5
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_jobs/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_jobs/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..17dd857424028014211b633909c8b24d31dddda2
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_jobs/tool.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools.google_jobs.tool import GoogleJobsQueryRun
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"GoogleJobsQueryRun": "langchain_community.tools.google_jobs.tool"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "GoogleJobsQueryRun",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_lens/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_lens/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0830396da9e1a04c9915e7e530963cdee54c6d60
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_lens/__init__.py
@@ -0,0 +1,25 @@
+"""Google Lens API Toolkit."""
+
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools.google_lens.tool import GoogleLensQueryRun
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"GoogleLensQueryRun": "langchain_community.tools.google_lens.tool"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "GoogleLensQueryRun",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_lens/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_lens/__pycache__/__init__.cpython-311.pyc
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index 0000000000000000000000000000000000000000..e15fd9d1f0d7fae5c3c975c8a6f2f53c551d3226
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_lens/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_lens/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..d88d0251fa24d1696ce154e1db0dbc09c70840cb
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_lens/tool.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools.google_lens.tool import GoogleLensQueryRun
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"GoogleLensQueryRun": "langchain_community.tools.google_lens.tool"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "GoogleLensQueryRun",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_places/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_places/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..60079d6e7a716723834d4c9b639ee37826b6c944
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_places/__init__.py
@@ -0,0 +1,25 @@
+"""Google Places API Toolkit."""
+
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import GooglePlacesTool
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"GooglePlacesTool": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "GooglePlacesTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_places/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_places/__pycache__/__init__.cpython-311.pyc
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index 0000000000000000000000000000000000000000..1110191415c0d5ae54ad75471183de75a8b634c3
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index 0000000000000000000000000000000000000000..7f6c81aadf5c97fc5077107c6a22231f53d46e6f
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_places/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_places/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ce02f6774fe5ed8dd085c1ee840e044b5cb133b
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_places/tool.py
@@ -0,0 +1,28 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import GooglePlacesTool
+ from langchain_community.tools.google_places.tool import GooglePlacesSchema
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "GooglePlacesSchema": "langchain_community.tools.google_places.tool",
+ "GooglePlacesTool": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "GooglePlacesSchema",
+ "GooglePlacesTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_scholar/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_scholar/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e8e920c551529f27e5cc7db1f852366b2474d993
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_scholar/__init__.py
@@ -0,0 +1,27 @@
+"""Google Scholar API Toolkit."""
+
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools.google_scholar.tool import GoogleScholarQueryRun
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "GoogleScholarQueryRun": "langchain_community.tools.google_scholar.tool",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "GoogleScholarQueryRun",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_scholar/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_scholar/__pycache__/__init__.cpython-311.pyc
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index 0000000000000000000000000000000000000000..b42b757dcab48a11668eae299a4f7223c862e6ca
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_scholar/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_scholar/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..d6d92c969bd16605b75a4ebab5d369bd9b15c4b8
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_scholar/tool.py
@@ -0,0 +1,25 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools.google_scholar.tool import GoogleScholarQueryRun
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "GoogleScholarQueryRun": "langchain_community.tools.google_scholar.tool",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "GoogleScholarQueryRun",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_search/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_search/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..390883b5b0fef77223a5e71aa0ca089e4e392e22
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_search/__init__.py
@@ -0,0 +1,29 @@
+"""Google Search API Toolkit."""
+
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import GoogleSearchResults, GoogleSearchRun
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "GoogleSearchRun": "langchain_community.tools",
+ "GoogleSearchResults": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "GoogleSearchResults",
+ "GoogleSearchRun",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_search/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_search/__pycache__/__init__.cpython-311.pyc
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index 0000000000000000000000000000000000000000..75b28445e93d4ecaa493f738ab2c60ca8a8ba5e8
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_search/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_search/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..f22fc1cbb2ed661bfca146f2c856612ef0aaa1a8
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_search/tool.py
@@ -0,0 +1,27 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import GoogleSearchResults, GoogleSearchRun
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "GoogleSearchRun": "langchain_community.tools",
+ "GoogleSearchResults": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "GoogleSearchResults",
+ "GoogleSearchRun",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_serper/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_serper/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b54e5de96d2293218cf009ce58e991d8470d3013
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_serper/__init__.py
@@ -0,0 +1,30 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import GoogleSerperResults, GoogleSerperRun
+
+"""Google Serper API Toolkit."""
+"""Tool for the Serer.dev Google Search API."""
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "GoogleSerperRun": "langchain_community.tools",
+ "GoogleSerperResults": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "GoogleSerperResults",
+ "GoogleSerperRun",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_serper/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_serper/__pycache__/__init__.cpython-311.pyc
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index 0000000000000000000000000000000000000000..7579734f606d42475946f6d6f98ee51dcfcd5e32
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_serper/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_serper/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ec98c818a600fe3802130432c0b13bee5d65293
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_serper/tool.py
@@ -0,0 +1,27 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import GoogleSerperResults, GoogleSerperRun
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "GoogleSerperRun": "langchain_community.tools",
+ "GoogleSerperResults": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "GoogleSerperResults",
+ "GoogleSerperRun",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_trends/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_trends/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1e36bb880ac92a1c14ef8efd2fb9e930ce1f3223
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_trends/__init__.py
@@ -0,0 +1,27 @@
+"""Google Trends API Toolkit."""
+
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools.google_trends.tool import GoogleTrendsQueryRun
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "GoogleTrendsQueryRun": "langchain_community.tools.google_trends.tool",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "GoogleTrendsQueryRun",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_trends/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_trends/__pycache__/__init__.cpython-311.pyc
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index 0000000000000000000000000000000000000000..19c6f507d5ec5cacd75c4df80862598ec617963e
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_trends/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_trends/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..559e8c2836240e997ec1173f47caaa74c803d950
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/google_trends/tool.py
@@ -0,0 +1,25 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools.google_trends.tool import GoogleTrendsQueryRun
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "GoogleTrendsQueryRun": "langchain_community.tools.google_trends.tool",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "GoogleTrendsQueryRun",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/graphql/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/graphql/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4833068325138b1cea9f0d1f9f6821da5137db80
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/graphql/__init__.py
@@ -0,0 +1 @@
+"""Tools for interacting with a GraphQL API."""
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/graphql/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/graphql/__pycache__/__init__.cpython-311.pyc
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index 0000000000000000000000000000000000000000..2de6f279bc5c21f916e323cc2b40462e3ef5c24d
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/graphql/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/graphql/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..f4879824b3511326ec2f4dd181a41ddaf93bed81
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/graphql/tool.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import BaseGraphQLTool
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"BaseGraphQLTool": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "BaseGraphQLTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/human/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/human/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..900d88cae6be8932a16b58ba22e3844327c2a9b3
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/human/__init__.py
@@ -0,0 +1,25 @@
+"""Tool for asking for human input."""
+
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import HumanInputRun
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"HumanInputRun": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "HumanInputRun",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/human/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/human/__pycache__/__init__.cpython-311.pyc
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index 0000000000000000000000000000000000000000..ca0ca97fe771a80938e79be0cd510dce7df06a51
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/human/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/human/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ee2b4d20b93dfc27c4cea1029ec571fc250f8b7
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/human/tool.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import HumanInputRun
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"HumanInputRun": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "HumanInputRun",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/interaction/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/interaction/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..be3393362d8bc39d0f5d56dffd757fb51ae6ace2
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/interaction/__init__.py
@@ -0,0 +1 @@
+"""Tools for interacting with the user."""
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/interaction/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/interaction/__pycache__/__init__.cpython-311.pyc
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new file mode 100644
index 0000000000000000000000000000000000000000..ec1ca7ea0eb53af6df9fe572a39156894559e869
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/interaction/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/interaction/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..af317e4f32dfd6173edb72ce74bf8ec0135d77f0
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/interaction/tool.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import StdInInquireTool
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"StdInInquireTool": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "StdInInquireTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/jira/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/jira/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..06cd8cbcd9e403192fd52f143b06799028289ac9
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/jira/__init__.py
@@ -0,0 +1 @@
+"""Jira Tool."""
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/jira/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/jira/__pycache__/__init__.cpython-311.pyc
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index 0000000000000000000000000000000000000000..caa0b283fa4b44c30f6693eed22dd42e42822dab
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/jira/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/jira/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5f70b141874d551c79e264eaac7dfa53c1e551a
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/jira/tool.py
@@ -0,0 +1,40 @@
+"""This module provides dynamic access to deprecated Jira tools.
+
+When attributes like `JiraAction` are accessed, they are redirected to their new
+locations in `langchain_community.tools`. This ensures backward compatibility
+while warning developers about deprecation.
+
+Attributes:
+ JiraAction (deprecated): Dynamically loaded from langchain_community.tools.
+"""
+
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import JiraAction
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"JiraAction": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Dynamically retrieve attributes from the updated module path.
+
+ Args:
+ name: The name of the attribute to import.
+
+ Returns:
+ The resolved attribute from the updated path.
+ """
+ return _import_attribute(name)
+
+
+__all__ = [
+ "JiraAction",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/json/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/json/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d13302f008a09fa9747da055d4baeec957651ef4
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/json/__init__.py
@@ -0,0 +1 @@
+"""Tools for interacting with a JSON file."""
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/json/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/json/__pycache__/__init__.cpython-311.pyc
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/json/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/json/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..2ff1e4b84443965b6063503a94fe148290e6ea2c
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/json/tool.py
@@ -0,0 +1,50 @@
+"""This module provides dynamic access to deprecated JSON tools in LangChain.
+
+It ensures backward compatibility by forwarding references such as
+`JsonGetValueTool`, `JsonListKeysTool`, and `JsonSpec` to their updated
+locations within the `langchain_community.tools` namespace.
+
+This setup allows legacy code to continue working while guiding developers
+toward using the updated module paths.
+"""
+
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import JsonGetValueTool, JsonListKeysTool
+ from langchain_community.tools.json.tool import JsonSpec
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "JsonSpec": "langchain_community.tools.json.tool",
+ "JsonListKeysTool": "langchain_community.tools",
+ "JsonGetValueTool": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Dynamically retrieve attributes from the updated module path.
+
+ This method is used to resolve deprecated attribute imports
+ at runtime and forward them to their new locations.
+
+ Args:
+ name: The name of the attribute to import.
+
+ Returns:
+ The resolved attribute from the appropriate updated module.
+ """
+ return _import_attribute(name)
+
+
+__all__ = [
+ "JsonGetValueTool",
+ "JsonListKeysTool",
+ "JsonSpec",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/memorize/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/memorize/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c3af33e41cbb1b81774e96776a744cc658b1d915
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/memorize/__init__.py
@@ -0,0 +1,25 @@
+"""Unsupervised learning based memorization."""
+
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools.memorize.tool import Memorize
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"Memorize": "langchain_community.tools.memorize.tool"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "Memorize",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/memorize/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/memorize/__pycache__/__init__.cpython-311.pyc
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/memorize/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/memorize/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..77c1be303a2552259172387d27a79947d3b83ce5
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/memorize/tool.py
@@ -0,0 +1,27 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools.memorize.tool import Memorize, TrainableLLM
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "TrainableLLM": "langchain_community.tools.memorize.tool",
+ "Memorize": "langchain_community.tools.memorize.tool",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "Memorize",
+ "TrainableLLM",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/merriam_webster/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/merriam_webster/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..73390d549807ce23c7477c265bcfb73ab5e21ef6
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/merriam_webster/__init__.py
@@ -0,0 +1 @@
+"""Merriam-Webster API toolkit."""
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/merriam_webster/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/merriam_webster/__pycache__/__init__.cpython-311.pyc
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index 0000000000000000000000000000000000000000..ac1a588d07f5be7ae9cd62751649291c78aaff51
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/merriam_webster/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/merriam_webster/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..38282d84b91a53a04be62c4956aff9c5b48c644b
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/merriam_webster/tool.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import MerriamWebsterQueryRun
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"MerriamWebsterQueryRun": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "MerriamWebsterQueryRun",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/metaphor_search/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/metaphor_search/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..42cbf903e4b9b66ceb979cd70437268a3a70172e
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/metaphor_search/__init__.py
@@ -0,0 +1,25 @@
+"""Metaphor Search API toolkit."""
+
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import MetaphorSearchResults
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"MetaphorSearchResults": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "MetaphorSearchResults",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/metaphor_search/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/metaphor_search/__pycache__/__init__.cpython-311.pyc
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/metaphor_search/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/metaphor_search/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..39899a518ff1dfb4ae39a0089601c256c959b6b2
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/metaphor_search/tool.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import MetaphorSearchResults
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"MetaphorSearchResults": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "MetaphorSearchResults",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/multion/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/multion/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c347fca3b78e8262f94be9f39d3973c9d0e4911d
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/multion/__init__.py
@@ -0,0 +1,33 @@
+"""MutliOn Client API tools."""
+
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools.multion.close_session import MultionCloseSession
+ from langchain_community.tools.multion.create_session import MultionCreateSession
+ from langchain_community.tools.multion.update_session import MultionUpdateSession
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "MultionCreateSession": "langchain_community.tools.multion.create_session",
+ "MultionUpdateSession": "langchain_community.tools.multion.update_session",
+ "MultionCloseSession": "langchain_community.tools.multion.close_session",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "MultionCloseSession",
+ "MultionCreateSession",
+ "MultionUpdateSession",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/multion/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/multion/__pycache__/__init__.cpython-311.pyc
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/multion/close_session.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/multion/close_session.py
new file mode 100644
index 0000000000000000000000000000000000000000..7309e49d630ee08a640d7ba542fa698b6cb20e69
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/multion/close_session.py
@@ -0,0 +1,30 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools.multion.close_session import (
+ CloseSessionSchema,
+ MultionCloseSession,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "CloseSessionSchema": "langchain_community.tools.multion.close_session",
+ "MultionCloseSession": "langchain_community.tools.multion.close_session",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "CloseSessionSchema",
+ "MultionCloseSession",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/multion/create_session.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/multion/create_session.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c4640f69909a92068398b9c238921e841f40e5f
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/multion/create_session.py
@@ -0,0 +1,30 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools.multion.create_session import (
+ CreateSessionSchema,
+ MultionCreateSession,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "CreateSessionSchema": "langchain_community.tools.multion.create_session",
+ "MultionCreateSession": "langchain_community.tools.multion.create_session",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "CreateSessionSchema",
+ "MultionCreateSession",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/multion/update_session.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/multion/update_session.py
new file mode 100644
index 0000000000000000000000000000000000000000..128f090c38d4a656b0e2487573ad3563ec76c90f
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/multion/update_session.py
@@ -0,0 +1,30 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools.multion.update_session import (
+ MultionUpdateSession,
+ UpdateSessionSchema,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "UpdateSessionSchema": "langchain_community.tools.multion.update_session",
+ "MultionUpdateSession": "langchain_community.tools.multion.update_session",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "MultionUpdateSession",
+ "UpdateSessionSchema",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/nasa/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/nasa/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/nasa/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/nasa/__pycache__/__init__.cpython-311.pyc
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index 0000000000000000000000000000000000000000..9da0d5bc7f74c3824f5db4a34087d43f1eaea2f0
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/nasa/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/nasa/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..7e952ad83dcd61b840a22c6bb3938c9c2d64211e
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/nasa/tool.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import NasaAction
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"NasaAction": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "NasaAction",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/nuclia/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/nuclia/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d5f5fab9a0df6446b83b8dca545746512223993b
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/nuclia/__init__.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools.nuclia.tool import NucliaUnderstandingAPI
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"NucliaUnderstandingAPI": "langchain_community.tools.nuclia.tool"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "NucliaUnderstandingAPI",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/nuclia/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/nuclia/__pycache__/__init__.cpython-311.pyc
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index 0000000000000000000000000000000000000000..2bff742bc920fc3382a98f8ec75e38a08e1eeaf9
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/nuclia/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/nuclia/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..75c4b0b157819f6756b30e883443a799f2a87ea6
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/nuclia/tool.py
@@ -0,0 +1,27 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools.nuclia.tool import NUASchema, NucliaUnderstandingAPI
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "NUASchema": "langchain_community.tools.nuclia.tool",
+ "NucliaUnderstandingAPI": "langchain_community.tools.nuclia.tool",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "NUASchema",
+ "NucliaUnderstandingAPI",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/office365/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/office365/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..98b94616f82526de7fcec2934f40b3c73587fbab
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/office365/__init__.py
@@ -0,0 +1,41 @@
+"""O365 tools."""
+
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import (
+ O365CreateDraftMessage,
+ O365SearchEmails,
+ O365SearchEvents,
+ O365SendEvent,
+ O365SendMessage,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "O365SearchEmails": "langchain_community.tools",
+ "O365SearchEvents": "langchain_community.tools",
+ "O365CreateDraftMessage": "langchain_community.tools",
+ "O365SendMessage": "langchain_community.tools",
+ "O365SendEvent": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "O365CreateDraftMessage",
+ "O365SearchEmails",
+ "O365SearchEvents",
+ "O365SendEvent",
+ "O365SendMessage",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/office365/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/office365/__pycache__/__init__.cpython-311.pyc
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/office365/__pycache__/send_message.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/office365/__pycache__/send_message.cpython-311.pyc
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index 0000000000000000000000000000000000000000..96b55b4e7729d76b128a56a265b8296a29aee79f
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/office365/base.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/office365/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..bc5d446098490dc8b9435e6a77c0f64485828e63
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/office365/base.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools.office365.base import O365BaseTool
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"O365BaseTool": "langchain_community.tools.office365.base"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "O365BaseTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/office365/create_draft_message.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/office365/create_draft_message.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b6c16b1f9db7042b09902ad6a1f174ca86347cd
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/office365/create_draft_message.py
@@ -0,0 +1,32 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import O365CreateDraftMessage
+ from langchain_community.tools.office365.create_draft_message import (
+ CreateDraftMessageSchema,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "CreateDraftMessageSchema": (
+ "langchain_community.tools.office365.create_draft_message"
+ ),
+ "O365CreateDraftMessage": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "CreateDraftMessageSchema",
+ "O365CreateDraftMessage",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/office365/events_search.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/office365/events_search.py
new file mode 100644
index 0000000000000000000000000000000000000000..5bc41e164839ac36309cd50bac5c62e8e441ba1f
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/office365/events_search.py
@@ -0,0 +1,28 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import O365SearchEvents
+ from langchain_community.tools.office365.events_search import SearchEventsInput
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "SearchEventsInput": "langchain_community.tools.office365.events_search",
+ "O365SearchEvents": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "O365SearchEvents",
+ "SearchEventsInput",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/office365/messages_search.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/office365/messages_search.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b245e4da1919956edd3c43a51e737efdcb419b0
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/office365/messages_search.py
@@ -0,0 +1,28 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import O365SearchEmails
+ from langchain_community.tools.office365.messages_search import SearchEmailsInput
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "SearchEmailsInput": "langchain_community.tools.office365.messages_search",
+ "O365SearchEmails": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "O365SearchEmails",
+ "SearchEmailsInput",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/office365/send_event.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/office365/send_event.py
new file mode 100644
index 0000000000000000000000000000000000000000..1536399bd1b2c10172675377306fa40df68da7a1
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/office365/send_event.py
@@ -0,0 +1,28 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import O365SendEvent
+ from langchain_community.tools.office365.send_event import SendEventSchema
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "SendEventSchema": "langchain_community.tools.office365.send_event",
+ "O365SendEvent": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "O365SendEvent",
+ "SendEventSchema",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/office365/send_message.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/office365/send_message.py
new file mode 100644
index 0000000000000000000000000000000000000000..751e27cd4de30bd9d9aee414184295fdff87c619
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/office365/send_message.py
@@ -0,0 +1,28 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import O365SendMessage
+ from langchain_community.tools.office365.send_message import SendMessageSchema
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "SendMessageSchema": "langchain_community.tools.office365.send_message",
+ "O365SendMessage": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "O365SendMessage",
+ "SendMessageSchema",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/openapi/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/openapi/__init__.py
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index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/openapi/utils/api_models.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/openapi/utils/api_models.py
new file mode 100644
index 0000000000000000000000000000000000000000..2267e36efa8faaf299f4abbb20b0fb0f8da34324
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/openapi/utils/api_models.py
@@ -0,0 +1,54 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import APIOperation
+ from langchain_community.tools.openapi.utils.api_models import (
+ INVALID_LOCATION_TEMPL,
+ PRIMITIVE_TYPES,
+ SCHEMA_TYPE,
+ SUPPORTED_LOCATIONS,
+ APIProperty,
+ APIPropertyBase,
+ APIPropertyLocation,
+ APIRequestBody,
+ APIRequestBodyProperty,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "APIPropertyLocation": "langchain_community.tools.openapi.utils.api_models",
+ "APIPropertyBase": "langchain_community.tools.openapi.utils.api_models",
+ "APIProperty": "langchain_community.tools.openapi.utils.api_models",
+ "APIRequestBodyProperty": "langchain_community.tools.openapi.utils.api_models",
+ "APIRequestBody": "langchain_community.tools.openapi.utils.api_models",
+ "APIOperation": "langchain_community.tools",
+ "INVALID_LOCATION_TEMPL": "langchain_community.tools.openapi.utils.api_models",
+ "SCHEMA_TYPE": "langchain_community.tools.openapi.utils.api_models",
+ "PRIMITIVE_TYPES": "langchain_community.tools.openapi.utils.api_models",
+ "SUPPORTED_LOCATIONS": "langchain_community.tools.openapi.utils.api_models",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "INVALID_LOCATION_TEMPL",
+ "PRIMITIVE_TYPES",
+ "SCHEMA_TYPE",
+ "SUPPORTED_LOCATIONS",
+ "APIOperation",
+ "APIProperty",
+ "APIPropertyBase",
+ "APIPropertyLocation",
+ "APIRequestBody",
+ "APIRequestBodyProperty",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/openapi/utils/openapi_utils.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/openapi/utils/openapi_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..85d3e06a67186c747ad5834c88c40f318492b360
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/openapi/utils/openapi_utils.py
@@ -0,0 +1,30 @@
+"""Utility functions for parsing an OpenAPI spec. Kept for backwards compat."""
+
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import OpenAPISpec
+ from langchain_community.utilities.openapi import HTTPVerb
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "HTTPVerb": "langchain_community.utilities.openapi",
+ "OpenAPISpec": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "HTTPVerb",
+ "OpenAPISpec",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/openweathermap/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/openweathermap/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ab2a94d8ef603aeaa8a75eba4ecfabd6155f4863
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/openweathermap/__init__.py
@@ -0,0 +1,25 @@
+"""OpenWeatherMap API toolkit."""
+
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import OpenWeatherMapQueryRun
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"OpenWeatherMapQueryRun": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "OpenWeatherMapQueryRun",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/openweathermap/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/openweathermap/__pycache__/__init__.cpython-311.pyc
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/openweathermap/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/openweathermap/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..a76ddad11a926469b447e6a1883bad1b81bb7333
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/openweathermap/tool.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import OpenWeatherMapQueryRun
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"OpenWeatherMapQueryRun": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "OpenWeatherMapQueryRun",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/playwright/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/playwright/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e83f2f3ba18ad9a894e68cfe131ffb9fd185b737
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/playwright/__init__.py
@@ -0,0 +1,47 @@
+"""Browser tools and toolkit."""
+
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import (
+ ClickTool,
+ CurrentWebPageTool,
+ ExtractHyperlinksTool,
+ ExtractTextTool,
+ GetElementsTool,
+ NavigateBackTool,
+ NavigateTool,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "NavigateTool": "langchain_community.tools",
+ "NavigateBackTool": "langchain_community.tools",
+ "ExtractTextTool": "langchain_community.tools",
+ "ExtractHyperlinksTool": "langchain_community.tools",
+ "GetElementsTool": "langchain_community.tools",
+ "ClickTool": "langchain_community.tools",
+ "CurrentWebPageTool": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "ClickTool",
+ "CurrentWebPageTool",
+ "ExtractHyperlinksTool",
+ "ExtractTextTool",
+ "GetElementsTool",
+ "NavigateBackTool",
+ "NavigateTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/playwright/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/playwright/__pycache__/__init__.cpython-311.pyc
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/playwright/__pycache__/navigate_back.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/playwright/__pycache__/navigate_back.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..e553521e20c9ebceaedab44ad43c807a5dde5947
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/playwright/base.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/playwright/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..85e56a24676e37f39fe1d5d020b6a452ffe65f72
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/playwright/base.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools.playwright.base import BaseBrowserTool
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"BaseBrowserTool": "langchain_community.tools.playwright.base"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "BaseBrowserTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/playwright/click.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/playwright/click.py
new file mode 100644
index 0000000000000000000000000000000000000000..8115fdff2bdb0a0f3f76cec083e692ecc560316f
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/playwright/click.py
@@ -0,0 +1,28 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import ClickTool
+ from langchain_community.tools.playwright.click import ClickToolInput
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "ClickToolInput": "langchain_community.tools.playwright.click",
+ "ClickTool": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "ClickTool",
+ "ClickToolInput",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/playwright/current_page.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/playwright/current_page.py
new file mode 100644
index 0000000000000000000000000000000000000000..b294c62bddb9ef5057dd79322c3b93f19f7606c3
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/playwright/current_page.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import CurrentWebPageTool
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"CurrentWebPageTool": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "CurrentWebPageTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/playwright/extract_hyperlinks.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/playwright/extract_hyperlinks.py
new file mode 100644
index 0000000000000000000000000000000000000000..76222d22c2987c55cb7c65f440cc8fa6fe6330a8
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/playwright/extract_hyperlinks.py
@@ -0,0 +1,32 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import ExtractHyperlinksTool
+ from langchain_community.tools.playwright.extract_hyperlinks import (
+ ExtractHyperlinksToolInput,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "ExtractHyperlinksToolInput": (
+ "langchain_community.tools.playwright.extract_hyperlinks"
+ ),
+ "ExtractHyperlinksTool": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "ExtractHyperlinksTool",
+ "ExtractHyperlinksToolInput",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/playwright/extract_text.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/playwright/extract_text.py
new file mode 100644
index 0000000000000000000000000000000000000000..374961fbe772a2b6434aa816d65445d7b90f4567
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/playwright/extract_text.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import ExtractTextTool
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"ExtractTextTool": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "ExtractTextTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/playwright/get_elements.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/playwright/get_elements.py
new file mode 100644
index 0000000000000000000000000000000000000000..3792808a87bb4dc356f3d4dd1a467527aad2611d
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/playwright/get_elements.py
@@ -0,0 +1,28 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import GetElementsTool
+ from langchain_community.tools.playwright.get_elements import GetElementsToolInput
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "GetElementsToolInput": "langchain_community.tools.playwright.get_elements",
+ "GetElementsTool": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "GetElementsTool",
+ "GetElementsToolInput",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/playwright/navigate.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/playwright/navigate.py
new file mode 100644
index 0000000000000000000000000000000000000000..7412e736c9c839ac986ddab6644aa53d39490bda
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/playwright/navigate.py
@@ -0,0 +1,28 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import NavigateTool
+ from langchain_community.tools.playwright.navigate import NavigateToolInput
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "NavigateToolInput": "langchain_community.tools.playwright.navigate",
+ "NavigateTool": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "NavigateTool",
+ "NavigateToolInput",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/playwright/navigate_back.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/playwright/navigate_back.py
new file mode 100644
index 0000000000000000000000000000000000000000..0abc651ab0ff4dda7433a92014faaec47f443cfd
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/playwright/navigate_back.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import NavigateBackTool
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"NavigateBackTool": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "NavigateBackTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/powerbi/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/powerbi/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ecc25a12f5797be27d45a53eea09834d9f2ba24
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/powerbi/__init__.py
@@ -0,0 +1 @@
+"""Tools for interacting with a PowerBI dataset."""
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/powerbi/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/powerbi/__pycache__/__init__.cpython-311.pyc
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new file mode 100644
index 0000000000000000000000000000000000000000..070e021ff8860862764cae272005f160bbb48bd5
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/powerbi/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/powerbi/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..b18810d8e934b1d25eb393019e04caba98963796
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/powerbi/tool.py
@@ -0,0 +1,33 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import (
+ InfoPowerBITool,
+ ListPowerBITool,
+ QueryPowerBITool,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "QueryPowerBITool": "langchain_community.tools",
+ "InfoPowerBITool": "langchain_community.tools",
+ "ListPowerBITool": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "InfoPowerBITool",
+ "ListPowerBITool",
+ "QueryPowerBITool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/pubmed/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/pubmed/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..687e908ee133084b2b15c0a4d4b287085d5d424b
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/pubmed/__init__.py
@@ -0,0 +1 @@
+"""PubMed API toolkit."""
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/pubmed/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/pubmed/__pycache__/__init__.cpython-311.pyc
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index 0000000000000000000000000000000000000000..21d5c56694409c083d4610c8bc709952d706a165
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/pubmed/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/pubmed/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..cc30530ba683e3aad2b48de97b865f880a8e1289
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/pubmed/tool.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import PubmedQueryRun
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"PubmedQueryRun": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "PubmedQueryRun",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/python/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/python/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..965145c488b9343770dd09834aa9b4336e79f35e
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/python/__init__.py
@@ -0,0 +1,13 @@
+from typing import Any
+
+
+def __getattr__(_: str = "") -> Any:
+ msg = (
+ "This tool has been moved to langchain_experimental. "
+ "This tool has access to a python REPL. "
+ "For best practices make sure to sandbox this tool. "
+ "Read https://github.com/langchain-ai/langchain/blob/master/SECURITY.md "
+ "To keep using this code as is, install langchain_experimental and "
+ "update relevant imports replacing 'langchain' with 'langchain_experimental'"
+ )
+ raise AttributeError(msg)
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/python/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/python/__pycache__/__init__.cpython-311.pyc
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/reddit_search/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/reddit_search/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/reddit_search/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/reddit_search/__pycache__/__init__.cpython-311.pyc
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index 0000000000000000000000000000000000000000..d19d8b80d281520fbf281d7c5f3d119475507261
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/reddit_search/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/reddit_search/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d7ce65fdf416a89f39f724e8725031ada024214
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/reddit_search/tool.py
@@ -0,0 +1,27 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import RedditSearchRun, RedditSearchSchema
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "RedditSearchSchema": "langchain_community.tools",
+ "RedditSearchRun": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "RedditSearchRun",
+ "RedditSearchSchema",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/requests/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/requests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec421f18dbdaa5b6f060ae11a6b92c21b9177377
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/requests/__init__.py
@@ -0,0 +1 @@
+"""Tools for making requests to an API endpoint."""
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/requests/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/requests/__pycache__/__init__.cpython-311.pyc
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index 0000000000000000000000000000000000000000..11d3367fd7b195343ec126a65aa688747bdde25f
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/requests/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/requests/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..7c6d57a044253aafa26c2349899696b8ad11cc99
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/requests/tool.py
@@ -0,0 +1,42 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import (
+ BaseRequestsTool,
+ RequestsDeleteTool,
+ RequestsGetTool,
+ RequestsPatchTool,
+ RequestsPostTool,
+ RequestsPutTool,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "BaseRequestsTool": "langchain_community.tools",
+ "RequestsGetTool": "langchain_community.tools",
+ "RequestsPostTool": "langchain_community.tools",
+ "RequestsPatchTool": "langchain_community.tools",
+ "RequestsPutTool": "langchain_community.tools",
+ "RequestsDeleteTool": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "BaseRequestsTool",
+ "RequestsDeleteTool",
+ "RequestsGetTool",
+ "RequestsPatchTool",
+ "RequestsPostTool",
+ "RequestsPutTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/scenexplain/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/scenexplain/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2e6553b73567d8f9fcee2ef79e2db807ca74613c
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/scenexplain/__init__.py
@@ -0,0 +1 @@
+"""SceneXplain API toolkit."""
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/scenexplain/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/scenexplain/__pycache__/__init__.cpython-311.pyc
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index 0000000000000000000000000000000000000000..164281500ee9e885b20e293ca5a0db72da8b0bf4
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/scenexplain/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/scenexplain/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..7f6f02b5970b2bf214398bcbedec21413b54c012
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/scenexplain/tool.py
@@ -0,0 +1,28 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import SceneXplainTool
+ from langchain_community.tools.scenexplain.tool import SceneXplainInput
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "SceneXplainInput": "langchain_community.tools.scenexplain.tool",
+ "SceneXplainTool": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "SceneXplainInput",
+ "SceneXplainTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/searchapi/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/searchapi/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..897e38491ea27c8107a30b304e9ba54729ccf12c
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/searchapi/__init__.py
@@ -0,0 +1,30 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import SearchAPIResults, SearchAPIRun
+
+"""SearchApi.io API Toolkit."""
+"""Tool for the SearchApi.io Google SERP API."""
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "SearchAPIResults": "langchain_community.tools",
+ "SearchAPIRun": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "SearchAPIResults",
+ "SearchAPIRun",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/searchapi/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/searchapi/__pycache__/__init__.cpython-311.pyc
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index 0000000000000000000000000000000000000000..f1c094f9ac27ccab5b665c189c422711f4fd3ae3
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/searchapi/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/searchapi/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..c3377fec1aec5f8ba0b82bc9f6e54b564b647616
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/searchapi/tool.py
@@ -0,0 +1,27 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import SearchAPIResults, SearchAPIRun
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "SearchAPIRun": "langchain_community.tools",
+ "SearchAPIResults": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "SearchAPIResults",
+ "SearchAPIRun",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/searx_search/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/searx_search/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/searx_search/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/searx_search/__pycache__/__init__.cpython-311.pyc
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index 0000000000000000000000000000000000000000..2e42c5b31008758de9a0756022beea8015047d02
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/searx_search/__pycache__/tool.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/searx_search/__pycache__/tool.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..ec6234f0a730f0fee34deb562becb48ade44dada
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/searx_search/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/searx_search/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..b6811206d683dacae055b85f5fc5a3ff6edaa411
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/searx_search/tool.py
@@ -0,0 +1,27 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import SearxSearchResults, SearxSearchRun
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "SearxSearchRun": "langchain_community.tools",
+ "SearxSearchResults": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "SearxSearchResults",
+ "SearxSearchRun",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/shell/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/shell/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..677fee76288225fa63d4cf81fb1820037ca18687
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/shell/__init__.py
@@ -0,0 +1,25 @@
+"""Shell tool."""
+
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import ShellTool
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"ShellTool": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "ShellTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/shell/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/shell/__pycache__/__init__.cpython-311.pyc
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new file mode 100644
index 0000000000000000000000000000000000000000..74c4dbc4f63b51c0ebaee6f9bdf7f176945fd07e
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/shell/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/shell/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..747832e3d628c56bf79165a93d4f81b241794cd4
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/shell/tool.py
@@ -0,0 +1,28 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import ShellTool
+ from langchain_community.tools.shell.tool import ShellInput
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "ShellInput": "langchain_community.tools.shell.tool",
+ "ShellTool": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "ShellInput",
+ "ShellTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/slack/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/slack/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1eb922bc580061ee7ce449fb1507014f2bb114dc
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/slack/__init__.py
@@ -0,0 +1,38 @@
+"""Slack tools."""
+
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import (
+ SlackGetChannel,
+ SlackGetMessage,
+ SlackScheduleMessage,
+ SlackSendMessage,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "SlackGetChannel": "langchain_community.tools",
+ "SlackGetMessage": "langchain_community.tools",
+ "SlackScheduleMessage": "langchain_community.tools",
+ "SlackSendMessage": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "SlackGetChannel",
+ "SlackGetMessage",
+ "SlackScheduleMessage",
+ "SlackSendMessage",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/slack/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/slack/__pycache__/__init__.cpython-311.pyc
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index 0000000000000000000000000000000000000000..6eafac08873f7c93684f633aa4599a49e076be79
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/slack/__pycache__/send_message.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/slack/__pycache__/send_message.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..8596f6c252839b54017b86b3c0afeb3331059bce
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/slack/base.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/slack/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..b6971ccb5b54a0c153fc42195f2667fc761a0f9a
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/slack/base.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools.slack.base import SlackBaseTool
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"SlackBaseTool": "langchain_community.tools.slack.base"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "SlackBaseTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/slack/get_channel.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/slack/get_channel.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d3c71fea387ceee9088b9d35d1e282fcf44d106
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/slack/get_channel.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import SlackGetChannel
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"SlackGetChannel": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "SlackGetChannel",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/slack/get_message.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/slack/get_message.py
new file mode 100644
index 0000000000000000000000000000000000000000..887e43f8d3fc97d4816775f59b35c4925520456b
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/slack/get_message.py
@@ -0,0 +1,28 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import SlackGetMessage
+ from langchain_community.tools.slack.get_message import SlackGetMessageSchema
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "SlackGetMessageSchema": "langchain_community.tools.slack.get_message",
+ "SlackGetMessage": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "SlackGetMessage",
+ "SlackGetMessageSchema",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/slack/schedule_message.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/slack/schedule_message.py
new file mode 100644
index 0000000000000000000000000000000000000000..25b90bfa831bb98ad0b775e51bf31533ea17c74f
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/slack/schedule_message.py
@@ -0,0 +1,28 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import SlackScheduleMessage
+ from langchain_community.tools.slack.schedule_message import ScheduleMessageSchema
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "ScheduleMessageSchema": "langchain_community.tools.slack.schedule_message",
+ "SlackScheduleMessage": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "ScheduleMessageSchema",
+ "SlackScheduleMessage",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/slack/send_message.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/slack/send_message.py
new file mode 100644
index 0000000000000000000000000000000000000000..012245223c754b551ce0ef13671a5b4ea415fead
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/slack/send_message.py
@@ -0,0 +1,28 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import SlackSendMessage
+ from langchain_community.tools.slack.send_message import SendMessageSchema
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "SendMessageSchema": "langchain_community.tools.slack.send_message",
+ "SlackSendMessage": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "SendMessageSchema",
+ "SlackSendMessage",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/sleep/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/sleep/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4d6319e2640da606c3219863bb2d4d0829534364
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/sleep/__init__.py
@@ -0,0 +1 @@
+"""Sleep tool."""
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/sleep/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/sleep/__pycache__/__init__.cpython-311.pyc
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new file mode 100644
index 0000000000000000000000000000000000000000..3bf2df4814eab96f23494767fd0d664157f50d94
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/sleep/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/sleep/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..a5d19e67c9b9aa7cd5d28ccc5b9104ee39c682ca
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/sleep/tool.py
@@ -0,0 +1,28 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import SleepTool
+ from langchain_community.tools.sleep.tool import SleepInput
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "SleepInput": "langchain_community.tools.sleep.tool",
+ "SleepTool": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "SleepInput",
+ "SleepTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/spark_sql/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/spark_sql/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..01039b772c689f99dbf690c40988d9701b4fd02f
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/spark_sql/__init__.py
@@ -0,0 +1 @@
+"""Tools for interacting with Spark SQL."""
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/spark_sql/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/spark_sql/__pycache__/__init__.cpython-311.pyc
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/spark_sql/__pycache__/tool.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/spark_sql/__pycache__/tool.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..7e68a5e75f43fc8e4d2a18a234c93026f1950ec6
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/spark_sql/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/spark_sql/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..03b127a587f300f93189e0c8ea0cd72c955c1927
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/spark_sql/tool.py
@@ -0,0 +1,39 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import (
+ BaseSparkSQLTool,
+ InfoSparkSQLTool,
+ ListSparkSQLTool,
+ QueryCheckerTool,
+ QuerySparkSQLTool,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "BaseSparkSQLTool": "langchain_community.tools",
+ "QuerySparkSQLTool": "langchain_community.tools",
+ "InfoSparkSQLTool": "langchain_community.tools",
+ "ListSparkSQLTool": "langchain_community.tools",
+ "QueryCheckerTool": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "BaseSparkSQLTool",
+ "InfoSparkSQLTool",
+ "ListSparkSQLTool",
+ "QueryCheckerTool",
+ "QuerySparkSQLTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/sql_database/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/sql_database/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..90fb3be1322f1bfab6e86d94f15fa4fac4639208
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/sql_database/__init__.py
@@ -0,0 +1 @@
+"""Tools for interacting with a SQL database."""
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/sql_database/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/sql_database/__pycache__/__init__.cpython-311.pyc
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index 0000000000000000000000000000000000000000..5c9d62df47c3b7aef5b74c15031dc19d5d6aaced
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/sql_database/prompt.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/sql_database/prompt.py
new file mode 100644
index 0000000000000000000000000000000000000000..213a05864f7aa0443d68cc6735087cabe1d8c92e
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/sql_database/prompt.py
@@ -0,0 +1,24 @@
+"""For backwards compatibility."""
+
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools.sql_database.prompt import QUERY_CHECKER
+
+
+_importer = create_importer(
+ __package__,
+ deprecated_lookups={
+ "QUERY_CHECKER": "langchain_community.tools.sql_database.prompt",
+ },
+)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _importer(name)
+
+
+__all__ = ["QUERY_CHECKER"]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/sql_database/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/sql_database/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a6fbd5e81e54d292ff56f60773c8a11c3151f9b
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/sql_database/tool.py
@@ -0,0 +1,39 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import (
+ BaseSQLDatabaseTool,
+ InfoSQLDatabaseTool,
+ ListSQLDatabaseTool,
+ QuerySQLCheckerTool,
+ QuerySQLDataBaseTool,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "BaseSQLDatabaseTool": "langchain_community.tools",
+ "QuerySQLDataBaseTool": "langchain_community.tools",
+ "InfoSQLDatabaseTool": "langchain_community.tools",
+ "ListSQLDatabaseTool": "langchain_community.tools",
+ "QuerySQLCheckerTool": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "BaseSQLDatabaseTool",
+ "InfoSQLDatabaseTool",
+ "ListSQLDatabaseTool",
+ "QuerySQLCheckerTool",
+ "QuerySQLDataBaseTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/stackexchange/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/stackexchange/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1fa9a483d105cc96872fe6b464185fa286d9c3bb
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/stackexchange/__init__.py
@@ -0,0 +1 @@
+"""StackExchange API toolkit."""
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/stackexchange/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/stackexchange/__pycache__/__init__.cpython-311.pyc
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index 0000000000000000000000000000000000000000..7d7db1c679a57b1d1574de7a405062b7bb7f2280
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/stackexchange/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/stackexchange/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..e6156c13eec9ca2370db19b19e36b623962e8479
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/stackexchange/tool.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import StackExchangeTool
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"StackExchangeTool": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "StackExchangeTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/steam/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/steam/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..195a6eef37bca623d48bd9e3687563d9d86473f8
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/steam/__init__.py
@@ -0,0 +1 @@
+"""Steam API toolkit."""
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/steam/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/steam/__pycache__/__init__.cpython-311.pyc
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index 0000000000000000000000000000000000000000..41caa99a41d55c0dc4c48f70e240105f55bb2b95
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/steam/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/steam/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..19fc5c8eef63f5ae8771f99d3fc953e3f99245b1
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/steam/tool.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import SteamWebAPIQueryRun
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"SteamWebAPIQueryRun": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "SteamWebAPIQueryRun",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/steamship_image_generation/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/steamship_image_generation/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d30287f7066612ed7dc346c17f96cfba8963bcd3
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/steamship_image_generation/__init__.py
@@ -0,0 +1,25 @@
+"""Tool to generate an image."""
+
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import SteamshipImageGenerationTool
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"SteamshipImageGenerationTool": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "SteamshipImageGenerationTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/steamship_image_generation/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/steamship_image_generation/__pycache__/__init__.cpython-311.pyc
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index 0000000000000000000000000000000000000000..3f2f40bea56d5421e41993b568dd2db9e63f02f5
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/steamship_image_generation/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/steamship_image_generation/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..b84301cb16478936926eb388465c1e3226020866
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/steamship_image_generation/tool.py
@@ -0,0 +1,28 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import SteamshipImageGenerationTool
+ from langchain_community.tools.steamship_image_generation.tool import ModelName
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "ModelName": "langchain_community.tools.steamship_image_generation.tool",
+ "SteamshipImageGenerationTool": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "ModelName",
+ "SteamshipImageGenerationTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/tavily_search/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/tavily_search/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a89f7a7ff4a43aacad761970e7816b8cdd9f2eb
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/tavily_search/__init__.py
@@ -0,0 +1,32 @@
+"""Tavily Search API toolkit."""
+
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools.tavily_search.tool import (
+ TavilyAnswer,
+ TavilySearchResults,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "TavilySearchResults": "langchain_community.tools.tavily_search.tool",
+ "TavilyAnswer": "langchain_community.tools.tavily_search.tool",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "TavilyAnswer",
+ "TavilySearchResults",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/tavily_search/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/tavily_search/__pycache__/__init__.cpython-311.pyc
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index 0000000000000000000000000000000000000000..bcc9df675ef65fac62b38cf67f8720e1bba47e96
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/tavily_search/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/tavily_search/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..07b959e8b05a206ee744b41456b9e3f6950f3628
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/tavily_search/tool.py
@@ -0,0 +1,33 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools.tavily_search.tool import (
+ TavilyAnswer,
+ TavilyInput,
+ TavilySearchResults,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "TavilyInput": "langchain_community.tools.tavily_search.tool",
+ "TavilySearchResults": "langchain_community.tools.tavily_search.tool",
+ "TavilyAnswer": "langchain_community.tools.tavily_search.tool",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "TavilyAnswer",
+ "TavilyInput",
+ "TavilySearchResults",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/vectorstore/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/vectorstore/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2bb638101959c35f933e0cf8601a448205a5d43a
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/vectorstore/__init__.py
@@ -0,0 +1 @@
+"""Simple tool wrapper around VectorDBQA chain."""
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/vectorstore/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/vectorstore/__pycache__/__init__.cpython-311.pyc
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index 0000000000000000000000000000000000000000..b4caba0f404e664ca2a77f9681739fe7b54bd996
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/vectorstore/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/vectorstore/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..b0a84b68dce26a44d8d684c7cf2a6c6532520603
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/vectorstore/tool.py
@@ -0,0 +1,30 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import (
+ VectorStoreQATool,
+ VectorStoreQAWithSourcesTool,
+ )
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "VectorStoreQATool": "langchain_community.tools",
+ "VectorStoreQAWithSourcesTool": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "VectorStoreQATool",
+ "VectorStoreQAWithSourcesTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/wikipedia/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/wikipedia/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0b3edd083874aea350e44514d24c8f692307daef
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/wikipedia/__init__.py
@@ -0,0 +1 @@
+"""Wikipedia API toolkit."""
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/wikipedia/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/wikipedia/__pycache__/__init__.cpython-311.pyc
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index 0000000000000000000000000000000000000000..f92ed0b67aa233d245ddaebb219691318b820439
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/wikipedia/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/wikipedia/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..44cfa72559e02c54ba8641f43186878650b36e2a
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/wikipedia/tool.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import WikipediaQueryRun
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"WikipediaQueryRun": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "WikipediaQueryRun",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/wolfram_alpha/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/wolfram_alpha/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4997b835ad2461f07957aa3fc8bc02c82f49ea92
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/wolfram_alpha/__init__.py
@@ -0,0 +1,25 @@
+"""Wolfram Alpha API toolkit."""
+
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import WolframAlphaQueryRun
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"WolframAlphaQueryRun": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "WolframAlphaQueryRun",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/wolfram_alpha/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/wolfram_alpha/__pycache__/__init__.cpython-311.pyc
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/wolfram_alpha/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/wolfram_alpha/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..4929266001e9c2c11ae164e1b7deff325a3d6be6
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/wolfram_alpha/tool.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import WolframAlphaQueryRun
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"WolframAlphaQueryRun": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "WolframAlphaQueryRun",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/youtube/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/youtube/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/youtube/search.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/youtube/search.py
new file mode 100644
index 0000000000000000000000000000000000000000..cdb8ea082e8e01c145163acd383f74e6a1bf53f9
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/youtube/search.py
@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import YouTubeSearchTool
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {"YouTubeSearchTool": "langchain_community.tools"}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "YouTubeSearchTool",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/zapier/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/zapier/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..81b5edacb70b09b6cfed75d663a1557deb2b9783
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/zapier/__init__.py
@@ -0,0 +1,29 @@
+"""Zapier Tool."""
+
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import ZapierNLAListActions, ZapierNLARunAction
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "ZapierNLARunAction": "langchain_community.tools",
+ "ZapierNLAListActions": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Look up attributes dynamically."""
+ return _import_attribute(name)
+
+
+__all__ = [
+ "ZapierNLAListActions",
+ "ZapierNLARunAction",
+]
diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/zapier/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/zapier/__pycache__/__init__.cpython-311.pyc
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diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/zapier/tool.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/zapier/tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..d04673abe6bfa1eadb3ee27782771aa750234342
--- /dev/null
+++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_classic/tools/zapier/tool.py
@@ -0,0 +1,47 @@
+"""This module provides dynamic access to deprecated Zapier tools in LangChain.
+
+It supports backward compatibility by forwarding references such as
+`ZapierNLAListActions` and `ZapierNLARunAction` to their updated locations
+in the `langchain_community.tools` package.
+
+Developers using older import paths will continue to function, while LangChain
+internally redirects access to the newer, supported module structure.
+"""
+
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+
+if TYPE_CHECKING:
+ from langchain_community.tools import ZapierNLAListActions, ZapierNLARunAction
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+ "ZapierNLARunAction": "langchain_community.tools",
+ "ZapierNLAListActions": "langchain_community.tools",
+}
+
+_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+ """Dynamically retrieve attributes from the updated module path.
+
+ This method is used to resolve deprecated attribute imports
+ at runtime and forward them to their new locations.
+
+ Args:
+ name: The name of the attribute to import.
+
+ Returns:
+ The resolved attribute from the appropriate updated module.
+ """
+ return _import_attribute(name)
+
+
+__all__ = [
+ "ZapierNLAListActions",
+ "ZapierNLARunAction",
+]
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