diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a498372ede407411f9477dcb91ea00d70c1c6749 Binary files /dev/null and b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/__pycache__/__init__.cpython-311.pyc differ diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/__pycache__/llm_requests.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/__pycache__/llm_requests.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..247913bec9937d593013bfd4cc0cf76faaec18b6 Binary files /dev/null and b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/__pycache__/llm_requests.cpython-311.pyc differ diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/ernie_functions/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/ernie_functions/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..28e91d12dccd217aa48cf3f7163e91da3f78549e --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/ernie_functions/__init__.py @@ -0,0 +1,17 @@ +from langchain_classic.chains.ernie_functions.base import ( + convert_to_ernie_function, + create_ernie_fn_chain, + create_ernie_fn_runnable, + create_structured_output_chain, + create_structured_output_runnable, + get_ernie_output_parser, +) + +__all__ = [ + "convert_to_ernie_function", + "create_structured_output_chain", + "create_ernie_fn_chain", + "create_structured_output_runnable", + "create_ernie_fn_runnable", + "get_ernie_output_parser", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/ernie_functions/base.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/ernie_functions/base.py new file mode 100644 index 0000000000000000000000000000000000000000..e80b70268763f99d13aa128ddede0203453f7e80 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/ernie_functions/base.py @@ -0,0 +1,553 @@ +"""Methods for creating chains that use Ernie function-calling APIs.""" + +import inspect +from typing import ( + Any, + Callable, + Dict, + List, + Optional, + Sequence, + Tuple, + Type, + Union, + cast, +) + +from langchain_classic.chains import LLMChain +from langchain_core.language_models import BaseLanguageModel +from langchain_core.output_parsers import ( + BaseGenerationOutputParser, + BaseLLMOutputParser, + BaseOutputParser, +) +from langchain_core.prompts import BasePromptTemplate +from langchain_core.runnables import Runnable +from langchain_core.utils.pydantic import is_basemodel_subclass +from pydantic import BaseModel + +from langchain_community.output_parsers.ernie_functions import ( + JsonOutputFunctionsParser, + PydanticAttrOutputFunctionsParser, + PydanticOutputFunctionsParser, +) +from langchain_community.utils.ernie_functions import convert_pydantic_to_ernie_function + +PYTHON_TO_JSON_TYPES = { + "str": "string", + "int": "number", + "float": "number", + "bool": "boolean", +} + + +def _get_python_function_name(function: Callable) -> str: + """Get the name of a Python function.""" + return function.__name__ + + +def _parse_python_function_docstring(function: Callable) -> Tuple[str, dict]: + """Parse the function and argument descriptions from the docstring of a function. + + Assumes the function docstring follows Google Python style guide. + """ + docstring = inspect.getdoc(function) + if docstring: + docstring_blocks = docstring.split("\n\n") + descriptors = [] + args_block = None + past_descriptors = False + for block in docstring_blocks: + if block.startswith("Args:"): + args_block = block + break + elif block.startswith("Returns:") or block.startswith("Example:"): + # Don't break in case Args come after + past_descriptors = True + elif not past_descriptors: + descriptors.append(block) + else: + continue + description = " ".join(descriptors) + else: + description = "" + args_block = None + arg_descriptions = {} + if args_block: + arg = None + for line in args_block.split("\n")[1:]: + if ":" in line: + arg, desc = line.split(":") + arg_descriptions[arg.strip()] = desc.strip() + elif arg: + arg_descriptions[arg.strip()] += " " + line.strip() + return description, arg_descriptions + + +def _get_python_function_arguments(function: Callable, arg_descriptions: dict) -> dict: + """Get JsonSchema describing a Python functions arguments. + + Assumes all function arguments are of primitive types (int, float, str, bool) or + are subclasses of pydantic.BaseModel. + """ + properties = {} + annotations = inspect.getfullargspec(function).annotations + for arg, arg_type in annotations.items(): + if arg == "return": + continue + if isinstance(arg_type, type) and is_basemodel_subclass(arg_type): + # Mypy error: + # "type" has no attribute "schema" + properties[arg] = arg_type.schema() # type: ignore[attr-defined] + elif arg_type.__name__ in PYTHON_TO_JSON_TYPES: + properties[arg] = {"type": PYTHON_TO_JSON_TYPES[arg_type.__name__]} + if arg in arg_descriptions: + if arg not in properties: + properties[arg] = {} + properties[arg]["description"] = arg_descriptions[arg] + return properties + + +def _get_python_function_required_args(function: Callable) -> List[str]: + """Get the required arguments for a Python function.""" + spec = inspect.getfullargspec(function) + required = spec.args[: -len(spec.defaults)] if spec.defaults else spec.args + required += [k for k in spec.kwonlyargs if k not in (spec.kwonlydefaults or {})] + + is_class = type(function) is type + if is_class and required[0] == "self": + required = required[1:] + return required + + +def convert_python_function_to_ernie_function( + function: Callable, +) -> Dict[str, Any]: + """Convert a Python function to an Ernie function-calling API compatible dict. + + Assumes the Python function has type hints and a docstring with a description. If + the docstring has Google Python style argument descriptions, these will be + included as well. + """ + description, arg_descriptions = _parse_python_function_docstring(function) + return { + "name": _get_python_function_name(function), + "description": description, + "parameters": { + "type": "object", + "properties": _get_python_function_arguments(function, arg_descriptions), + "required": _get_python_function_required_args(function), + }, + } + + +def convert_to_ernie_function( + function: Union[Dict[str, Any], Type[BaseModel], Callable], +) -> Dict[str, Any]: + """Convert a raw function/class to an Ernie function. + + Args: + function: Either a dictionary, a pydantic.BaseModel class, or a Python function. + If a dictionary is passed in, it is assumed to already be a valid Ernie + function. + + Returns: + A dict version of the passed in function which is compatible with the + Ernie function-calling API. + """ + if isinstance(function, dict): + return function + elif isinstance(function, type) and is_basemodel_subclass(function): + return cast(Dict, convert_pydantic_to_ernie_function(function)) + elif callable(function): + return convert_python_function_to_ernie_function(function) + + else: + raise ValueError( + f"Unsupported function type {type(function)}. Functions must be passed in" + f" as Dict, pydantic.BaseModel, or Callable." + ) + + +def get_ernie_output_parser( + functions: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable]], +) -> Union[BaseOutputParser, BaseGenerationOutputParser]: + """Get the appropriate function output parser given the user functions. + + Args: + functions: Sequence where element is a dictionary, a pydantic.BaseModel class, + or a Python function. If a dictionary is passed in, it is assumed to + already be a valid Ernie function. + + Returns: + A PydanticOutputFunctionsParser if functions are Pydantic classes, otherwise + a JsonOutputFunctionsParser. If there's only one function and it is + not a Pydantic class, then the output parser will automatically extract + only the function arguments and not the function name. + """ + function_names = [convert_to_ernie_function(f)["name"] for f in functions] + if isinstance(functions[0], type) and is_basemodel_subclass(functions[0]): + if len(functions) > 1: + pydantic_schema: Union[Dict, Type[BaseModel]] = { + name: fn for name, fn in zip(function_names, functions) + } + else: + pydantic_schema = functions[0] + output_parser: Union[BaseOutputParser, BaseGenerationOutputParser] = ( + PydanticOutputFunctionsParser(pydantic_schema=pydantic_schema) + ) + else: + output_parser = JsonOutputFunctionsParser(args_only=len(functions) <= 1) + return output_parser + + +def create_ernie_fn_runnable( + functions: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable]], + llm: Runnable, + prompt: BasePromptTemplate, + *, + output_parser: Optional[Union[BaseOutputParser, BaseGenerationOutputParser]] = None, + **kwargs: Any, +) -> Runnable: + """Create a runnable sequence that uses Ernie functions. + + Args: + functions: A sequence of either dictionaries, pydantic.BaseModels classes, or + Python functions. If dictionaries are passed in, they are assumed to + already be a valid Ernie functions. If only a single + function is passed in, then it will be enforced that the model use that + function. pydantic.BaseModels and Python functions should have docstrings + describing what the function does. For best results, pydantic.BaseModels + should have descriptions of the parameters and Python functions should have + Google Python style args descriptions in the docstring. Additionally, + Python functions should only use primitive types (str, int, float, bool) or + pydantic.BaseModels for arguments. + llm: Language model to use, assumed to support the Ernie function-calling API. + prompt: BasePromptTemplate to pass to the model. + output_parser: BaseLLMOutputParser to use for parsing model outputs. By default + will be inferred from the function types. If pydantic.BaseModels are passed + in, then the OutputParser will try to parse outputs using those. Otherwise + model outputs will simply be parsed as JSON. If multiple functions are + passed in and they are not pydantic.BaseModels, the chain output will + include both the name of the function that was returned and the arguments + to pass to the function. + + Returns: + A runnable sequence that will pass in the given functions to the model when run. + + Example: + .. code-block:: python + + from typing import Optional + + from langchain_classic.chains.ernie_functions import create_ernie_fn_chain + from langchain_community.chat_models import ErnieBotChat + from langchain_core.prompts import ChatPromptTemplate + from pydantic import BaseModel, Field + + + class RecordPerson(BaseModel): + \"\"\"Record some identifying information about a person.\"\"\" + + name: str = Field(..., description="The person's name") + age: int = Field(..., description="The person's age") + fav_food: Optional[str] = Field(None, description="The person's favorite food") + + + class RecordDog(BaseModel): + \"\"\"Record some identifying information about a dog.\"\"\" + + name: str = Field(..., description="The dog's name") + color: str = Field(..., description="The dog's color") + fav_food: Optional[str] = Field(None, description="The dog's favorite food") + + + llm = ErnieBotChat(model_name="ERNIE-Bot-4") + prompt = ChatPromptTemplate.from_messages( + [ + ("user", "Make calls to the relevant function to record the entities in the following input: {input}"), + ("assistant", "OK!"), + ("user", "Tip: Make sure to answer in the correct format"), + ] + ) + chain = create_ernie_fn_runnable([RecordPerson, RecordDog], llm, prompt) + chain.invoke({"input": "Harry was a chubby brown beagle who loved chicken"}) + # -> RecordDog(name="Harry", color="brown", fav_food="chicken") + """ # noqa: E501 + if not functions: + raise ValueError("Need to pass in at least one function. Received zero.") + ernie_functions = [convert_to_ernie_function(f) for f in functions] + llm_kwargs: Dict[str, Any] = {"functions": ernie_functions, **kwargs} + if len(ernie_functions) == 1: + llm_kwargs["function_call"] = {"name": ernie_functions[0]["name"]} + output_parser = output_parser or get_ernie_output_parser(functions) + return prompt | llm.bind(**llm_kwargs) | output_parser + + +def create_structured_output_runnable( + output_schema: Union[Dict[str, Any], Type[BaseModel]], + llm: Runnable, + prompt: BasePromptTemplate, + *, + output_parser: Optional[Union[BaseOutputParser, BaseGenerationOutputParser]] = None, + **kwargs: Any, +) -> Runnable: + """Create a runnable that uses an Ernie function to get a structured output. + + Args: + output_schema: Either a dictionary or pydantic.BaseModel class. If a dictionary + is passed in, it's assumed to already be a valid JsonSchema. + For best results, pydantic.BaseModels should have docstrings describing what + the schema represents and descriptions for the parameters. + llm: Language model to use, assumed to support the Ernie function-calling API. + prompt: BasePromptTemplate to pass to the model. + output_parser: BaseLLMOutputParser to use for parsing model outputs. By default + will be inferred from the function types. If pydantic.BaseModels are passed + in, then the OutputParser will try to parse outputs using those. Otherwise + model outputs will simply be parsed as JSON. + + Returns: + A runnable sequence that will pass the given function to the model when run. + + Example: + .. code-block:: python + + from typing import Optional + + from langchain_classic.chains.ernie_functions import create_structured_output_chain + from langchain_community.chat_models import ErnieBotChat + from langchain_core.prompts import ChatPromptTemplate + from pydantic import BaseModel, Field + + class Dog(BaseModel): + \"\"\"Identifying information about a dog.\"\"\" + + name: str = Field(..., description="The dog's name") + color: str = Field(..., description="The dog's color") + fav_food: Optional[str] = Field(None, description="The dog's favorite food") + + llm = ErnieBotChat(model_name="ERNIE-Bot-4") + prompt = ChatPromptTemplate.from_messages( + [ + ("user", "Use the given format to extract information from the following input: {input}"), + ("assistant", "OK!"), + ("user", "Tip: Make sure to answer in the correct format"), + ] + ) + chain = create_structured_output_chain(Dog, llm, prompt) + chain.invoke({"input": "Harry was a chubby brown beagle who loved chicken"}) + # -> Dog(name="Harry", color="brown", fav_food="chicken") + """ # noqa: E501 + if isinstance(output_schema, dict): + function: Any = { + "name": "output_formatter", + "description": ( + "Output formatter. Should always be used to format your response to the" + " user." + ), + "parameters": output_schema, + } + else: + + class _OutputFormatter(BaseModel): + """Output formatter. Should always be used to format your response to the user.""" # noqa: E501 + + output: output_schema # type: ignore[valid-type] + + function = _OutputFormatter + output_parser = output_parser or PydanticAttrOutputFunctionsParser( + pydantic_schema=_OutputFormatter, attr_name="output" + ) + return create_ernie_fn_runnable( + [function], + llm, + prompt, + output_parser=output_parser, + **kwargs, + ) + + +""" --- Legacy --- """ + + +def create_ernie_fn_chain( + functions: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable]], + llm: BaseLanguageModel, + prompt: BasePromptTemplate, + *, + output_key: str = "function", + output_parser: Optional[BaseLLMOutputParser] = None, + **kwargs: Any, +) -> LLMChain: + """[Legacy] Create an LLM chain that uses Ernie functions. + + Args: + functions: A sequence of either dictionaries, pydantic.BaseModels classes, or + Python functions. If dictionaries are passed in, they are assumed to + already be a valid Ernie functions. If only a single + function is passed in, then it will be enforced that the model use that + function. pydantic.BaseModels and Python functions should have docstrings + describing what the function does. For best results, pydantic.BaseModels + should have descriptions of the parameters and Python functions should have + Google Python style args descriptions in the docstring. Additionally, + Python functions should only use primitive types (str, int, float, bool) or + pydantic.BaseModels for arguments. + llm: Language model to use, assumed to support the Ernie function-calling API. + prompt: BasePromptTemplate to pass to the model. + output_key: The key to use when returning the output in LLMChain.__call__. + output_parser: BaseLLMOutputParser to use for parsing model outputs. By default + will be inferred from the function types. If pydantic.BaseModels are passed + in, then the OutputParser will try to parse outputs using those. Otherwise + model outputs will simply be parsed as JSON. If multiple functions are + passed in and they are not pydantic.BaseModels, the chain output will + include both the name of the function that was returned and the arguments + to pass to the function. + + Returns: + An LLMChain that will pass in the given functions to the model when run. + + Example: + .. code-block:: python + + from typing import Optional + + from langchain_classic.chains.ernie_functions import create_ernie_fn_chain + from langchain_community.chat_models import ErnieBotChat + from langchain_core.prompts import ChatPromptTemplate + + from pydantic import BaseModel, Field + + + class RecordPerson(BaseModel): + \"\"\"Record some identifying information about a person.\"\"\" + + name: str = Field(..., description="The person's name") + age: int = Field(..., description="The person's age") + fav_food: Optional[str] = Field(None, description="The person's favorite food") + + + class RecordDog(BaseModel): + \"\"\"Record some identifying information about a dog.\"\"\" + + name: str = Field(..., description="The dog's name") + color: str = Field(..., description="The dog's color") + fav_food: Optional[str] = Field(None, description="The dog's favorite food") + + + llm = ErnieBotChat(model_name="ERNIE-Bot-4") + prompt = ChatPromptTemplate.from_messages( + [ + ("user", "Make calls to the relevant function to record the entities in the following input: {input}"), + ("assistant", "OK!"), + ("user", "Tip: Make sure to answer in the correct format"), + ] + ) + chain = create_ernie_fn_chain([RecordPerson, RecordDog], llm, prompt) + chain.run("Harry was a chubby brown beagle who loved chicken") + # -> RecordDog(name="Harry", color="brown", fav_food="chicken") + """ # noqa: E501 + if not functions: + raise ValueError("Need to pass in at least one function. Received zero.") + ernie_functions = [convert_to_ernie_function(f) for f in functions] + output_parser = output_parser or get_ernie_output_parser(functions) + llm_kwargs: Dict[str, Any] = { + "functions": ernie_functions, + } + if len(ernie_functions) == 1: + llm_kwargs["function_call"] = {"name": ernie_functions[0]["name"]} + llm_chain = LLMChain( + llm=llm, + prompt=prompt, + output_parser=output_parser, + llm_kwargs=llm_kwargs, + output_key=output_key, + **kwargs, + ) + return llm_chain + + +def create_structured_output_chain( + output_schema: Union[Dict[str, Any], Type[BaseModel]], + llm: BaseLanguageModel, + prompt: BasePromptTemplate, + *, + output_key: str = "function", + output_parser: Optional[BaseLLMOutputParser] = None, + **kwargs: Any, +) -> LLMChain: + """[Legacy] Create an LLMChain that uses an Ernie function to get a structured output. + + Args: + output_schema: Either a dictionary or pydantic.BaseModel class. If a dictionary + is passed in, it's assumed to already be a valid JsonSchema. + For best results, pydantic.BaseModels should have docstrings describing what + the schema represents and descriptions for the parameters. + llm: Language model to use, assumed to support the Ernie function-calling API. + prompt: BasePromptTemplate to pass to the model. + output_key: The key to use when returning the output in LLMChain.__call__. + output_parser: BaseLLMOutputParser to use for parsing model outputs. By default + will be inferred from the function types. If pydantic.BaseModels are passed + in, then the OutputParser will try to parse outputs using those. Otherwise + model outputs will simply be parsed as JSON. + + Returns: + An LLMChain that will pass the given function to the model. + + Example: + .. code-block:: python + + from typing import Optional + + from langchain_classic.chains.ernie_functions import create_structured_output_chain + from langchain_community.chat_models import ErnieBotChat + from langchain_core.prompts import ChatPromptTemplate + + from pydantic import BaseModel, Field + + class Dog(BaseModel): + \"\"\"Identifying information about a dog.\"\"\" + + name: str = Field(..., description="The dog's name") + color: str = Field(..., description="The dog's color") + fav_food: Optional[str] = Field(None, description="The dog's favorite food") + + llm = ErnieBotChat(model_name="ERNIE-Bot-4") + prompt = ChatPromptTemplate.from_messages( + [ + ("user", "Use the given format to extract information from the following input: {input}"), + ("assistant", "OK!"), + ("user", "Tip: Make sure to answer in the correct format"), + ] + ) + chain = create_structured_output_chain(Dog, llm, prompt) + chain.run("Harry was a chubby brown beagle who loved chicken") + # -> Dog(name="Harry", color="brown", fav_food="chicken") + """ # noqa: E501 + if isinstance(output_schema, dict): + function: Any = { + "name": "output_formatter", + "description": ( + "Output formatter. Should always be used to format your response to the" + " user." + ), + "parameters": output_schema, + } + else: + + class _OutputFormatter(BaseModel): + """Output formatter. Should always be used to format your response to the user.""" # noqa: E501 + + output: output_schema # type: ignore[valid-type] + + function = _OutputFormatter + output_parser = output_parser or PydanticAttrOutputFunctionsParser( + pydantic_schema=_OutputFormatter, attr_name="output" + ) + return create_ernie_fn_chain( + [function], + llm, + prompt, + output_key=output_key, + output_parser=output_parser, + **kwargs, + ) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f3bc55efbca8efd011ea4a5aa5fbd25bb5b4c457 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/__init__.py @@ -0,0 +1 @@ +"""Question answering over a knowledge graph.""" diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/arangodb.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/arangodb.py new file mode 100644 index 0000000000000000000000000000000000000000..42a3d362b840a083e731d0c49ed4c0e226403739 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/arangodb.py @@ -0,0 +1,273 @@ +"""Question answering over a graph.""" + +from __future__ import annotations + +import re +from typing import Any, Dict, List, Optional + +from langchain_classic.chains.base import Chain +from langchain_classic.chains.llm import LLMChain +from langchain_core.callbacks import CallbackManagerForChainRun +from langchain_core.language_models import BaseLanguageModel +from langchain_core.prompts import BasePromptTemplate +from pydantic import Field + +from langchain_community.chains.graph_qa.prompts import ( + AQL_FIX_PROMPT, + AQL_GENERATION_PROMPT, + AQL_QA_PROMPT, +) +from langchain_community.graphs.arangodb_graph import ArangoGraph + + +class ArangoGraphQAChain(Chain): + """Chain for question-answering against a graph by generating AQL statements. + + *Security note*: Make sure that the database connection uses credentials + that are narrowly-scoped to only include necessary permissions. + Failure to do so may result in data corruption or loss, since the calling + code may attempt commands that would result in deletion, mutation + of data if appropriately prompted or reading sensitive data if such + data is present in the database. + The best way to guard against such negative outcomes is to (as appropriate) + limit the permissions granted to the credentials used with this tool. + + See https://python.langchain.com/docs/security for more information. + """ + + graph: ArangoGraph = Field(exclude=True) + aql_generation_chain: LLMChain + aql_fix_chain: LLMChain + qa_chain: LLMChain + input_key: str = "query" #: :meta private: + output_key: str = "result" #: :meta private: + + # Specifies the maximum number of AQL Query Results to return + top_k: int = 10 + + # Specifies the set of AQL Query Examples that promote few-shot-learning + aql_examples: str = "" + + # Specify whether to return the AQL Query in the output dictionary + return_aql_query: bool = False + + # Specify whether to return the AQL JSON Result in the output dictionary + return_aql_result: bool = False + + # Specify the maximum amount of AQL Generation attempts that should be made + max_aql_generation_attempts: int = 3 + + allow_dangerous_requests: bool = False + """Forced user opt-in to acknowledge that the chain can make dangerous requests. + + *Security note*: Make sure that the database connection uses credentials + that are narrowly-scoped to only include necessary permissions. + Failure to do so may result in data corruption or loss, since the calling + code may attempt commands that would result in deletion, mutation + of data if appropriately prompted or reading sensitive data if such + data is present in the database. + The best way to guard against such negative outcomes is to (as appropriate) + limit the permissions granted to the credentials used with this tool. + + See https://python.langchain.com/docs/security for more information. + """ + + def __init__(self, **kwargs: Any) -> None: + """Initialize the chain.""" + super().__init__(**kwargs) + if self.allow_dangerous_requests is not True: + raise ValueError( + "In order to use this chain, you must acknowledge that it can make " + "dangerous requests by setting `allow_dangerous_requests` to `True`." + "You must narrowly scope the permissions of the database connection " + "to only include necessary permissions. Failure to do so may result " + "in data corruption or loss or reading sensitive data if such data is " + "present in the database." + "Only use this chain if you understand the risks and have taken the " + "necessary precautions. " + "See https://python.langchain.com/docs/security for more information." + ) + + @property + def input_keys(self) -> List[str]: + return [self.input_key] + + @property + def output_keys(self) -> List[str]: + return [self.output_key] + + @property + def _chain_type(self) -> str: + return "graph_aql_chain" + + @classmethod + def from_llm( + cls, + llm: BaseLanguageModel, + *, + qa_prompt: BasePromptTemplate = AQL_QA_PROMPT, + aql_generation_prompt: BasePromptTemplate = AQL_GENERATION_PROMPT, + aql_fix_prompt: BasePromptTemplate = AQL_FIX_PROMPT, + **kwargs: Any, + ) -> ArangoGraphQAChain: + """Initialize from LLM.""" + qa_chain = LLMChain(llm=llm, prompt=qa_prompt) + aql_generation_chain = LLMChain(llm=llm, prompt=aql_generation_prompt) + aql_fix_chain = LLMChain(llm=llm, prompt=aql_fix_prompt) + + return cls( + qa_chain=qa_chain, + aql_generation_chain=aql_generation_chain, + aql_fix_chain=aql_fix_chain, + **kwargs, + ) + + def _call( + self, + inputs: Dict[str, Any], + run_manager: Optional[CallbackManagerForChainRun] = None, + ) -> Dict[str, Any]: + """ + Generate an AQL statement from user input, use it retrieve a response + from an ArangoDB Database instance, and respond to the user input + in natural language. + + Users can modify the following ArangoGraphQAChain Class Variables: + + :var top_k: The maximum number of AQL Query Results to return + :type top_k: int + + :var aql_examples: A set of AQL Query Examples that are passed to + the AQL Generation Prompt Template to promote few-shot-learning. + Defaults to an empty string. + :type aql_examples: str + + :var return_aql_query: Whether to return the AQL Query in the + output dictionary. Defaults to False. + :type return_aql_query: bool + + :var return_aql_result: Whether to return the AQL Query in the + output dictionary. Defaults to False + :type return_aql_result: bool + + :var max_aql_generation_attempts: The maximum amount of AQL + Generation attempts to be made prior to raising the last + AQL Query Execution Error. Defaults to 3. + :type max_aql_generation_attempts: int + """ + _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() + callbacks = _run_manager.get_child() + user_input = inputs[self.input_key] + + ######################### + # Generate AQL Query # + aql_generation_output = self.aql_generation_chain.run( + { + "adb_schema": self.graph.schema, + "aql_examples": self.aql_examples, + "user_input": user_input, + }, + callbacks=callbacks, + ) + ######################### + + aql_query = "" + aql_error = "" + aql_result = None + aql_generation_attempt = 1 + + while ( + aql_result is None + and aql_generation_attempt < self.max_aql_generation_attempts + 1 + ): + ##################### + # Extract AQL Query # + pattern = r"```(?i:aql)?(.*?)```" + matches = re.findall(pattern, aql_generation_output, re.DOTALL) + if not matches: + _run_manager.on_text( + "Invalid Response: ", end="\n", verbose=self.verbose + ) + _run_manager.on_text( + aql_generation_output, color="red", end="\n", verbose=self.verbose + ) + raise ValueError(f"Response is Invalid: {aql_generation_output}") + + aql_query = matches[0] + ##################### + + _run_manager.on_text( + f"AQL Query ({aql_generation_attempt}):", verbose=self.verbose + ) + _run_manager.on_text( + aql_query, color="green", end="\n", verbose=self.verbose + ) + + ##################### + # Execute AQL Query # + from arango import AQLQueryExecuteError + + try: + aql_result = self.graph.query(aql_query, self.top_k) + except AQLQueryExecuteError as e: + aql_error = e.error_message + + _run_manager.on_text( + "AQL Query Execution Error: ", end="\n", verbose=self.verbose + ) + _run_manager.on_text( + aql_error, color="yellow", end="\n\n", verbose=self.verbose + ) + + ######################## + # Retry AQL Generation # + aql_generation_output = self.aql_fix_chain.run( + { + "adb_schema": self.graph.schema, + "aql_query": aql_query, + "aql_error": aql_error, + }, + callbacks=callbacks, + ) + ######################## + + ##################### + + aql_generation_attempt += 1 + + if aql_result is None: + m = f""" + Maximum amount of AQL Query Generation attempts reached. + Unable to execute the AQL Query due to the following error: + {aql_error} + """ + raise ValueError(m) + + _run_manager.on_text("AQL Result:", end="\n", verbose=self.verbose) + _run_manager.on_text( + str(aql_result), color="green", end="\n", verbose=self.verbose + ) + + ######################## + # Interpret AQL Result # + result = self.qa_chain( + { + "adb_schema": self.graph.schema, + "user_input": user_input, + "aql_query": aql_query, + "aql_result": aql_result, + }, + callbacks=callbacks, + ) + ######################## + + # Return results # + result = {self.output_key: result[self.qa_chain.output_key]} + + if self.return_aql_query: + result["aql_query"] = aql_query + + if self.return_aql_result: + result["aql_result"] = aql_result + + return result diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/base.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/base.py new file mode 100644 index 0000000000000000000000000000000000000000..f3e31af96a896b1545065a8d108c89c7e9d2f840 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/base.py @@ -0,0 +1,104 @@ +"""Question answering over a graph.""" + +from __future__ import annotations + +from typing import Any, Dict, List, Optional + +from langchain_classic.chains.base import Chain +from langchain_classic.chains.llm import LLMChain +from langchain_core.callbacks.manager import CallbackManagerForChainRun +from langchain_core.language_models import BaseLanguageModel +from langchain_core.prompts import BasePromptTemplate +from pydantic import Field + +from langchain_community.chains.graph_qa.prompts import ( + ENTITY_EXTRACTION_PROMPT, + GRAPH_QA_PROMPT, +) +from langchain_community.graphs.networkx_graph import NetworkxEntityGraph, get_entities + + +class GraphQAChain(Chain): + """Chain for question-answering against a graph. + + *Security note*: Make sure that the database connection uses credentials + that are narrowly-scoped to only include necessary permissions. + Failure to do so may result in data corruption or loss, since the calling + code may attempt commands that would result in deletion, mutation + of data if appropriately prompted or reading sensitive data if such + data is present in the database. + The best way to guard against such negative outcomes is to (as appropriate) + limit the permissions granted to the credentials used with this tool. + + See https://python.langchain.com/docs/security for more information. + """ + + graph: NetworkxEntityGraph = Field(exclude=True) + entity_extraction_chain: LLMChain + qa_chain: LLMChain + input_key: str = "query" #: :meta private: + output_key: str = "result" #: :meta private: + + @property + def input_keys(self) -> List[str]: + """Input keys. + + :meta private: + """ + return [self.input_key] + + @property + def output_keys(self) -> List[str]: + """Output keys. + + :meta private: + """ + _output_keys = [self.output_key] + return _output_keys + + @classmethod + def from_llm( + cls, + llm: BaseLanguageModel, + qa_prompt: BasePromptTemplate = GRAPH_QA_PROMPT, + entity_prompt: BasePromptTemplate = ENTITY_EXTRACTION_PROMPT, + **kwargs: Any, + ) -> GraphQAChain: + """Initialize from LLM.""" + qa_chain = LLMChain(llm=llm, prompt=qa_prompt) + entity_chain = LLMChain(llm=llm, prompt=entity_prompt) + + return cls( + qa_chain=qa_chain, + entity_extraction_chain=entity_chain, + **kwargs, + ) + + def _call( + self, + inputs: Dict[str, Any], + run_manager: Optional[CallbackManagerForChainRun] = None, + ) -> Dict[str, str]: + """Extract entities, look up info and answer question.""" + _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() + question = inputs[self.input_key] + + entity_string = self.entity_extraction_chain.run(question) + + _run_manager.on_text("Entities Extracted:", end="\n", verbose=self.verbose) + _run_manager.on_text( + entity_string, color="green", end="\n", verbose=self.verbose + ) + entities = get_entities(entity_string) + context = "" + all_triplets = [] + for entity in entities: + all_triplets.extend(self.graph.get_entity_knowledge(entity)) + context = "\n".join(all_triplets) + _run_manager.on_text("Full Context:", end="\n", verbose=self.verbose) + _run_manager.on_text(context, color="green", end="\n", verbose=self.verbose) + result = self.qa_chain( + {"question": question, "context": context}, + callbacks=_run_manager.get_child(), + ) + return {self.output_key: result[self.qa_chain.output_key]} diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/cypher.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/cypher.py new file mode 100644 index 0000000000000000000000000000000000000000..925be36d5480dc29afc9774765edbe78a66207d9 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/cypher.py @@ -0,0 +1,421 @@ +"""Question answering over a graph.""" + +from __future__ import annotations + +import re +from typing import Any, Dict, List, Optional, Union + +from langchain_classic.chains.base import Chain +from langchain_classic.chains.llm import LLMChain +from langchain_core._api.deprecation import deprecated +from langchain_core.callbacks import CallbackManagerForChainRun +from langchain_core.language_models import BaseLanguageModel +from langchain_core.messages import ( + AIMessage, + BaseMessage, + SystemMessage, + ToolMessage, +) +from langchain_core.output_parsers import StrOutputParser +from langchain_core.prompts import ( + BasePromptTemplate, + ChatPromptTemplate, + HumanMessagePromptTemplate, + MessagesPlaceholder, +) +from langchain_core.runnables import Runnable +from pydantic import Field + +from langchain_community.chains.graph_qa.cypher_utils import ( + CypherQueryCorrector, + Schema, +) +from langchain_community.chains.graph_qa.prompts import ( + CYPHER_GENERATION_PROMPT, + CYPHER_QA_PROMPT, +) +from langchain_community.graphs.graph_store import GraphStore + +INTERMEDIATE_STEPS_KEY = "intermediate_steps" + +FUNCTION_RESPONSE_SYSTEM = """You are an assistant that helps to form nice and human +understandable answers based on the provided information from tools. +Do not add any other information that wasn't present in the tools, and use +very concise style in interpreting results! +""" + + +@deprecated( + since="0.3.8", + removal="1.0", + alternative_import="langchain_neo4j.chains.graph_qa.cypher.extract_cypher", +) +def extract_cypher(text: str) -> str: + """Extract Cypher code from a text. + + Args: + text: Text to extract Cypher code from. + + Returns: + Cypher code extracted from the text. + """ + # The pattern to find Cypher code enclosed in triple backticks + pattern = r"```(.*?)```" + + # Find all matches in the input text + matches = re.findall(pattern, text, re.DOTALL) + + return matches[0] if matches else text + + +@deprecated( + since="0.3.8", + removal="1.0", + alternative_import="langchain_neo4j.chains.graph_qa.cypher.construct_schema", +) +def construct_schema( + structured_schema: Dict[str, Any], + include_types: List[str], + exclude_types: List[str], +) -> str: + """Filter the schema based on included or excluded types""" + + def filter_func(x: str) -> bool: + return x in include_types if include_types else x not in exclude_types + + filtered_schema: Dict[str, Any] = { + "node_props": { + k: v + for k, v in structured_schema.get("node_props", {}).items() + if filter_func(k) + }, + "rel_props": { + k: v + for k, v in structured_schema.get("rel_props", {}).items() + if filter_func(k) + }, + "relationships": [ + r + for r in structured_schema.get("relationships", []) + if all(filter_func(r[t]) for t in ["start", "end", "type"]) + ], + } + + # Format node properties + formatted_node_props = [] + for label, properties in filtered_schema["node_props"].items(): + props_str = ", ".join( + [f"{prop['property']}: {prop['type']}" for prop in properties] + ) + formatted_node_props.append(f"{label} {{{props_str}}}") + + # Format relationship properties + formatted_rel_props = [] + for rel_type, properties in filtered_schema["rel_props"].items(): + props_str = ", ".join( + [f"{prop['property']}: {prop['type']}" for prop in properties] + ) + formatted_rel_props.append(f"{rel_type} {{{props_str}}}") + + # Format relationships + formatted_rels = [ + f"(:{el['start']})-[:{el['type']}]->(:{el['end']})" + for el in filtered_schema["relationships"] + ] + + return "\n".join( + [ + "Node properties are the following:", + ",".join(formatted_node_props), + "Relationship properties are the following:", + ",".join(formatted_rel_props), + "The relationships are the following:", + ",".join(formatted_rels), + ] + ) + + +@deprecated( + since="0.3.8", + removal="1.0", + alternative_import="langchain_neo4j.chains.graph_qa.cypher.get_function_response", +) +def get_function_response( + question: str, context: List[Dict[str, Any]] +) -> List[BaseMessage]: + TOOL_ID = "call_H7fABDuzEau48T10Qn0Lsh0D" + messages = [ + AIMessage( + content="", + additional_kwargs={ + "tool_calls": [ + { + "id": TOOL_ID, + "function": { + "arguments": '{"question":"' + question + '"}', + "name": "GetInformation", + }, + "type": "function", + } + ] + }, + ), + ToolMessage(content=str(context), tool_call_id=TOOL_ID), + ] + return messages + + +@deprecated( + since="0.3.8", + removal="1.0", + alternative_import="langchain_neo4j.GraphCypherQAChain", +) +class GraphCypherQAChain(Chain): + """Chain for question-answering against a graph by generating Cypher statements. + + *Security note*: Make sure that the database connection uses credentials + that are narrowly-scoped to only include necessary permissions. + Failure to do so may result in data corruption or loss, since the calling + code may attempt commands that would result in deletion, mutation + of data if appropriately prompted or reading sensitive data if such + data is present in the database. + The best way to guard against such negative outcomes is to (as appropriate) + limit the permissions granted to the credentials used with this tool. + + See https://python.langchain.com/docs/security for more information. + """ + + graph: GraphStore = Field(exclude=True) + cypher_generation_chain: LLMChain + qa_chain: Union[LLMChain, Runnable] + graph_schema: str + input_key: str = "query" #: :meta private: + output_key: str = "result" #: :meta private: + top_k: int = 10 + """Number of results to return from the query""" + return_intermediate_steps: bool = False + """Whether or not to return the intermediate steps along with the final answer.""" + return_direct: bool = False + """Whether or not to return the result of querying the graph directly.""" + cypher_query_corrector: Optional[CypherQueryCorrector] = None + """Optional cypher validation tool""" + use_function_response: bool = False + """Whether to wrap the database context as tool/function response""" + allow_dangerous_requests: bool = False + """Forced user opt-in to acknowledge that the chain can make dangerous requests. + + *Security note*: Make sure that the database connection uses credentials + that are narrowly-scoped to only include necessary permissions. + Failure to do so may result in data corruption or loss, since the calling + code may attempt commands that would result in deletion, mutation + of data if appropriately prompted or reading sensitive data if such + data is present in the database. + The best way to guard against such negative outcomes is to (as appropriate) + limit the permissions granted to the credentials used with this tool. + + See https://python.langchain.com/docs/security for more information. + """ + + def __init__(self, **kwargs: Any) -> None: + """Initialize the chain.""" + super().__init__(**kwargs) + if self.allow_dangerous_requests is not True: + raise ValueError( + "In order to use this chain, you must acknowledge that it can make " + "dangerous requests by setting `allow_dangerous_requests` to `True`." + "You must narrowly scope the permissions of the database connection " + "to only include necessary permissions. Failure to do so may result " + "in data corruption or loss or reading sensitive data if such data is " + "present in the database." + "Only use this chain if you understand the risks and have taken the " + "necessary precautions. " + "See https://python.langchain.com/docs/security for more information." + ) + + @property + def input_keys(self) -> List[str]: + """Return the input keys. + + :meta private: + """ + return [self.input_key] + + @property + def output_keys(self) -> List[str]: + """Return the output keys. + + :meta private: + """ + _output_keys = [self.output_key] + return _output_keys + + @property + def _chain_type(self) -> str: + return "graph_cypher_chain" + + @classmethod + def from_llm( + cls, + llm: Optional[BaseLanguageModel] = None, + *, + qa_prompt: Optional[BasePromptTemplate] = None, + cypher_prompt: Optional[BasePromptTemplate] = None, + cypher_llm: Optional[BaseLanguageModel] = None, + qa_llm: Optional[Union[BaseLanguageModel, Any]] = None, + exclude_types: List[str] = [], + include_types: List[str] = [], + validate_cypher: bool = False, + qa_llm_kwargs: Optional[Dict[str, Any]] = None, + cypher_llm_kwargs: Optional[Dict[str, Any]] = None, + use_function_response: bool = False, + function_response_system: str = FUNCTION_RESPONSE_SYSTEM, + **kwargs: Any, + ) -> GraphCypherQAChain: + """Initialize from LLM.""" + + if not cypher_llm and not llm: + raise ValueError("Either `llm` or `cypher_llm` parameters must be provided") + if not qa_llm and not llm: + raise ValueError("Either `llm` or `qa_llm` parameters must be provided") + if cypher_llm and qa_llm and llm: + raise ValueError( + "You can specify up to two of 'cypher_llm', 'qa_llm'" + ", and 'llm', but not all three simultaneously." + ) + if cypher_prompt and cypher_llm_kwargs: + raise ValueError( + "Specifying cypher_prompt and cypher_llm_kwargs together is" + " not allowed. Please pass prompt via cypher_llm_kwargs." + ) + if qa_prompt and qa_llm_kwargs: + raise ValueError( + "Specifying qa_prompt and qa_llm_kwargs together is" + " not allowed. Please pass prompt via qa_llm_kwargs." + ) + use_qa_llm_kwargs = qa_llm_kwargs if qa_llm_kwargs is not None else {} + use_cypher_llm_kwargs = ( + cypher_llm_kwargs if cypher_llm_kwargs is not None else {} + ) + if "prompt" not in use_qa_llm_kwargs: + use_qa_llm_kwargs["prompt"] = ( + qa_prompt if qa_prompt is not None else CYPHER_QA_PROMPT + ) + if "prompt" not in use_cypher_llm_kwargs: + use_cypher_llm_kwargs["prompt"] = ( + cypher_prompt if cypher_prompt is not None else CYPHER_GENERATION_PROMPT + ) + + qa_llm = qa_llm or llm + if use_function_response: + try: + qa_llm.bind_tools({}) # type: ignore[union-attr] + response_prompt = ChatPromptTemplate.from_messages( + [ + SystemMessage(content=function_response_system), + HumanMessagePromptTemplate.from_template("{question}"), + MessagesPlaceholder(variable_name="function_response"), + ] + ) + qa_chain = response_prompt | qa_llm | StrOutputParser() # type: ignore[operator] + except (NotImplementedError, AttributeError): + raise ValueError("Provided LLM does not support native tools/functions") + else: + qa_chain = LLMChain(llm=qa_llm, **use_qa_llm_kwargs) # type: ignore[arg-type] + + cypher_generation_chain = LLMChain( + llm=cypher_llm or llm, # type: ignore[arg-type] + **use_cypher_llm_kwargs, + ) + + if exclude_types and include_types: + raise ValueError( + "Either `exclude_types` or `include_types` " + "can be provided, but not both" + ) + graph_schema = construct_schema( + kwargs["graph"].get_structured_schema, include_types, exclude_types + ) + + cypher_query_corrector = None + if validate_cypher: + corrector_schema = [ + Schema(el["start"], el["type"], el["end"]) + for el in kwargs["graph"].structured_schema.get("relationships") + ] + cypher_query_corrector = CypherQueryCorrector(corrector_schema) + + return cls( + graph_schema=graph_schema, + qa_chain=qa_chain, + cypher_generation_chain=cypher_generation_chain, + cypher_query_corrector=cypher_query_corrector, + use_function_response=use_function_response, + **kwargs, + ) + + def _call( + self, + inputs: Dict[str, Any], + run_manager: Optional[CallbackManagerForChainRun] = None, + ) -> Dict[str, Any]: + """Generate Cypher statement, use it to look up in db and answer question.""" + _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() + callbacks = _run_manager.get_child() + question = inputs[self.input_key] + args = { + "question": question, + "schema": self.graph_schema, + } + args.update(inputs) + + intermediate_steps: List = [] + + generated_cypher = self.cypher_generation_chain.run(args, callbacks=callbacks) + + # Extract Cypher code if it is wrapped in backticks + generated_cypher = extract_cypher(generated_cypher) + + # Correct Cypher query if enabled + if self.cypher_query_corrector: + generated_cypher = self.cypher_query_corrector(generated_cypher) + + _run_manager.on_text("Generated Cypher:", end="\n", verbose=self.verbose) + _run_manager.on_text( + generated_cypher, color="green", end="\n", verbose=self.verbose + ) + + intermediate_steps.append({"query": generated_cypher}) + + # Retrieve and limit the number of results + # Generated Cypher be null if query corrector identifies invalid schema + if generated_cypher: + context = self.graph.query(generated_cypher)[: self.top_k] + else: + context = [] + + if self.return_direct: + final_result = context + else: + _run_manager.on_text("Full Context:", end="\n", verbose=self.verbose) + _run_manager.on_text( + str(context), color="green", end="\n", verbose=self.verbose + ) + + intermediate_steps.append({"context": context}) + if self.use_function_response: + function_response = get_function_response(question, context) + final_result = self.qa_chain.invoke( # type: ignore[assignment] + {"question": question, "function_response": function_response}, + ) + else: + result = self.qa_chain.invoke( + {"question": question, "context": context}, + callbacks=callbacks, + ) + final_result = result[self.qa_chain.output_key] # type: ignore[union-attr] + + chain_result: Dict[str, Any] = {self.output_key: final_result} + if self.return_intermediate_steps: + chain_result[INTERMEDIATE_STEPS_KEY] = intermediate_steps + + return chain_result diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/cypher_utils.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/cypher_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..4d8c7c45572fb7a0645cf613fd3c58d5bed809a4 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/cypher_utils.py @@ -0,0 +1,267 @@ +import re +from collections import namedtuple +from typing import Any, Dict, List, Optional, Tuple + +from langchain_core._api.deprecation import deprecated + +Schema = namedtuple("Schema", ["left_node", "relation", "right_node"]) + + +@deprecated( + since="0.3.8", + removal="1.0", + alternative_import="langchain_neo4j.chains.graph_qa.cypher_utils.CypherQueryCorrector", +) +class CypherQueryCorrector: + """ + Used to correct relationship direction in generated Cypher statements. + This code is copied from the winner's submission to the Cypher competition: + https://github.com/sakusaku-rich/cypher-direction-competition + """ + + property_pattern = re.compile(r"\{.+?\}") + node_pattern = re.compile(r"\(.+?\)") + path_pattern = re.compile( + r"(\([^\,\(\)]*?(\{.+\})?[^\,\(\)]*?\))(?)(\([^\,\(\)]*?(\{.+\})?[^\,\(\)]*?\))" + ) + node_relation_node_pattern = re.compile( + r"(\()+(?P[^()]*?)\)(?P.*?)\((?P[^()]*?)(\))+" + ) + relation_type_pattern = re.compile(r":(?P.+?)?(\{.+\})?]") + + def __init__(self, schemas: List[Schema]): + """ + Args: + schemas: list of schemas + """ + self.schemas = schemas + + def clean_node(self, node: str) -> str: + """ + Args: + node: node in string format + + """ + node = re.sub(self.property_pattern, "", node) + node = node.replace("(", "") + node = node.replace(")", "") + node = node.strip() + return node + + def detect_node_variables(self, query: str) -> Dict[str, List[str]]: + """ + Args: + query: cypher query + """ + nodes = re.findall(self.node_pattern, query) + nodes = [self.clean_node(node) for node in nodes] + res: Dict[str, Any] = {} + for node in nodes: + parts = node.split(":") + if parts == "": + continue + variable = parts[0] + if variable not in res: + res[variable] = [] + res[variable] += parts[1:] + return res + + def extract_paths(self, query: str) -> "List[str]": + """ + Args: + query: cypher query + """ + paths = [] + idx = 0 + while matched := self.path_pattern.findall(query[idx:]): + matched = matched[0] + matched = [ + m for i, m in enumerate(matched) if i not in [1, len(matched) - 1] + ] + path = "".join(matched) + idx = query.find(path) + len(path) - len(matched[-1]) + paths.append(path) + return paths + + def judge_direction(self, relation: str) -> str: + """ + Args: + relation: relation in string format + """ + direction = "BIDIRECTIONAL" + if relation[0] == "<": + direction = "INCOMING" + if relation[-1] == ">": + direction = "OUTGOING" + return direction + + def extract_node_variable(self, part: str) -> Optional[str]: + """ + Args: + part: node in string format + """ + part = part.lstrip("(").rstrip(")") + idx = part.find(":") + if idx != -1: + part = part[:idx] + return None if part == "" else part + + def detect_labels( + self, str_node: str, node_variable_dict: Dict[str, Any] + ) -> List[str]: + """ + Args: + str_node: node in string format + node_variable_dict: dictionary of node variables + """ + splitted_node = str_node.split(":") + variable = splitted_node[0] + labels = [] + if variable in node_variable_dict: + labels = node_variable_dict[variable] + elif variable == "" and len(splitted_node) > 1: + labels = splitted_node[1:] + return labels + + def verify_schema( + self, + from_node_labels: List[str], + relation_types: List[str], + to_node_labels: List[str], + ) -> bool: + """ + Args: + from_node_labels: labels of the from node + relation_type: type of the relation + to_node_labels: labels of the to node + """ + valid_schemas = self.schemas + if from_node_labels != []: + from_node_labels = [label.strip("`") for label in from_node_labels] + valid_schemas = [ + schema for schema in valid_schemas if schema[0] in from_node_labels + ] + if to_node_labels != []: + to_node_labels = [label.strip("`") for label in to_node_labels] + valid_schemas = [ + schema for schema in valid_schemas if schema[2] in to_node_labels + ] + if relation_types != []: + relation_types = [type.strip("`") for type in relation_types] + valid_schemas = [ + schema for schema in valid_schemas if schema[1] in relation_types + ] + return valid_schemas != [] + + def detect_relation_types(self, str_relation: str) -> Tuple[str, List[str]]: + """ + Args: + str_relation: relation in string format + """ + relation_direction = self.judge_direction(str_relation) + relation_type = self.relation_type_pattern.search(str_relation) + if relation_type is None or relation_type.group("relation_type") is None: + return relation_direction, [] + relation_types = [ + t.strip().strip("!") + for t in relation_type.group("relation_type").split("|") + ] + return relation_direction, relation_types + + def correct_query(self, query: str) -> str: + """ + Args: + query: cypher query + """ + node_variable_dict = self.detect_node_variables(query) + paths = self.extract_paths(query) + for path in paths: + original_path = path + start_idx = 0 + while start_idx < len(path): + match_res = re.match(self.node_relation_node_pattern, path[start_idx:]) + if match_res is None: + break + start_idx += match_res.start() + match_dict = match_res.groupdict() + left_node_labels = self.detect_labels( + match_dict["left_node"], node_variable_dict + ) + right_node_labels = self.detect_labels( + match_dict["right_node"], node_variable_dict + ) + end_idx = ( + start_idx + + 4 + + len(match_dict["left_node"]) + + len(match_dict["relation"]) + + len(match_dict["right_node"]) + ) + original_partial_path = original_path[start_idx : end_idx + 1] + relation_direction, relation_types = self.detect_relation_types( + match_dict["relation"] + ) + + if relation_types != [] and "".join(relation_types).find("*") != -1: + start_idx += ( + len(match_dict["left_node"]) + len(match_dict["relation"]) + 2 + ) + continue + + if relation_direction == "OUTGOING": + is_legal = self.verify_schema( + left_node_labels, relation_types, right_node_labels + ) + if not is_legal: + is_legal = self.verify_schema( + right_node_labels, relation_types, left_node_labels + ) + if is_legal: + corrected_relation = "<" + match_dict["relation"][:-1] + corrected_partial_path = original_partial_path.replace( + match_dict["relation"], corrected_relation + ) + query = query.replace( + original_partial_path, corrected_partial_path + ) + else: + return "" + elif relation_direction == "INCOMING": + is_legal = self.verify_schema( + right_node_labels, relation_types, left_node_labels + ) + if not is_legal: + is_legal = self.verify_schema( + left_node_labels, relation_types, right_node_labels + ) + if is_legal: + corrected_relation = match_dict["relation"][1:] + ">" + corrected_partial_path = original_partial_path.replace( + match_dict["relation"], corrected_relation + ) + query = query.replace( + original_partial_path, corrected_partial_path + ) + else: + return "" + else: + is_legal = self.verify_schema( + left_node_labels, relation_types, right_node_labels + ) + is_legal |= self.verify_schema( + right_node_labels, relation_types, left_node_labels + ) + if not is_legal: + return "" + + start_idx += ( + len(match_dict["left_node"]) + len(match_dict["relation"]) + 2 + ) + return query + + def __call__(self, query: str) -> str: + """Correct the query to make it valid. If + Args: + query: cypher query + """ + return self.correct_query(query) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/falkordb.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/falkordb.py new file mode 100644 index 0000000000000000000000000000000000000000..1f47fb561665576751bfc83feb1a34d96aafcb0a --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/falkordb.py @@ -0,0 +1,189 @@ +"""Question answering over a graph.""" + +from __future__ import annotations + +import re +from typing import Any, Dict, List, Optional + +from langchain_classic.chains.base import Chain +from langchain_classic.chains.llm import LLMChain +from langchain_core.callbacks import CallbackManagerForChainRun +from langchain_core.language_models import BaseLanguageModel +from langchain_core.prompts import BasePromptTemplate +from pydantic import Field + +from langchain_community.chains.graph_qa.prompts import ( + CYPHER_GENERATION_PROMPT, + CYPHER_QA_PROMPT, +) +from langchain_community.graphs import FalkorDBGraph + +INTERMEDIATE_STEPS_KEY = "intermediate_steps" + + +def extract_cypher(text: str) -> str: + """ + Extract Cypher code from a text. + Args: + text: Text to extract Cypher code from. + + Returns: + Cypher code extracted from the text. + """ + # The pattern to find Cypher code enclosed in triple backticks + pattern = r"```(.*?)```" + + # Find all matches in the input text + matches = re.findall(pattern, text, re.DOTALL) + + return matches[0] if matches else text + + +class FalkorDBQAChain(Chain): + """Chain for question-answering against a graph by generating Cypher statements. + + *Security note*: Make sure that the database connection uses credentials + that are narrowly-scoped to only include necessary permissions. + Failure to do so may result in data corruption or loss, since the calling + code may attempt commands that would result in deletion, mutation + of data if appropriately prompted or reading sensitive data if such + data is present in the database. + The best way to guard against such negative outcomes is to (as appropriate) + limit the permissions granted to the credentials used with this tool. + + See https://python.langchain.com/docs/security for more information. + """ + + graph: FalkorDBGraph = Field(exclude=True) + cypher_generation_chain: LLMChain + qa_chain: LLMChain + input_key: str = "query" #: :meta private: + output_key: str = "result" #: :meta private: + top_k: int = 10 + """Number of results to return from the query""" + return_intermediate_steps: bool = False + """Whether or not to return the intermediate steps along with the final answer.""" + return_direct: bool = False + """Whether or not to return the result of querying the graph directly.""" + + allow_dangerous_requests: bool = False + """Forced user opt-in to acknowledge that the chain can make dangerous requests. + + *Security note*: Make sure that the database connection uses credentials + that are narrowly-scoped to only include necessary permissions. + Failure to do so may result in data corruption or loss, since the calling + code may attempt commands that would result in deletion, mutation + of data if appropriately prompted or reading sensitive data if such + data is present in the database. + The best way to guard against such negative outcomes is to (as appropriate) + limit the permissions granted to the credentials used with this tool. + + See https://python.langchain.com/docs/security for more information. + """ + + def __init__(self, **kwargs: Any) -> None: + """Initialize the chain.""" + super().__init__(**kwargs) + if self.allow_dangerous_requests is not True: + raise ValueError( + "In order to use this chain, you must acknowledge that it can make " + "dangerous requests by setting `allow_dangerous_requests` to `True`." + "You must narrowly scope the permissions of the database connection " + "to only include necessary permissions. Failure to do so may result " + "in data corruption or loss or reading sensitive data if such data is " + "present in the database." + "Only use this chain if you understand the risks and have taken the " + "necessary precautions. " + "See https://python.langchain.com/docs/security for more information." + ) + + @property + def input_keys(self) -> List[str]: + """Return the input keys. + + :meta private: + """ + return [self.input_key] + + @property + def output_keys(self) -> List[str]: + """Return the output keys. + + :meta private: + """ + _output_keys = [self.output_key] + return _output_keys + + @property + def _chain_type(self) -> str: + return "graph_cypher_chain" + + @classmethod + def from_llm( + cls, + llm: BaseLanguageModel, + *, + qa_prompt: BasePromptTemplate = CYPHER_QA_PROMPT, + cypher_prompt: BasePromptTemplate = CYPHER_GENERATION_PROMPT, + **kwargs: Any, + ) -> FalkorDBQAChain: + """Initialize from LLM.""" + qa_chain = LLMChain(llm=llm, prompt=qa_prompt) + cypher_generation_chain = LLMChain(llm=llm, prompt=cypher_prompt) + + return cls( + qa_chain=qa_chain, + cypher_generation_chain=cypher_generation_chain, + **kwargs, + ) + + def _call( + self, + inputs: Dict[str, Any], + run_manager: Optional[CallbackManagerForChainRun] = None, + ) -> Dict[str, Any]: + """Generate Cypher statement, use it to look up in db and answer question.""" + _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() + callbacks = _run_manager.get_child() + question = inputs[self.input_key] + + intermediate_steps: List = [] + + generated_cypher = self.cypher_generation_chain.run( + {"question": question, "schema": self.graph.schema}, callbacks=callbacks + ) + + # Extract Cypher code if it is wrapped in backticks + generated_cypher = extract_cypher(generated_cypher) + + _run_manager.on_text("Generated Cypher:", end="\n", verbose=self.verbose) + _run_manager.on_text( + generated_cypher, color="green", end="\n", verbose=self.verbose + ) + + intermediate_steps.append({"query": generated_cypher}) + + # Retrieve and limit the number of results + context = self.graph.query(generated_cypher)[: self.top_k] + + if self.return_direct: + final_result = context + else: + _run_manager.on_text("Full Context:", end="\n", verbose=self.verbose) + _run_manager.on_text( + str(context), color="green", end="\n", verbose=self.verbose + ) + + intermediate_steps.append({"context": context}) + + result = self.qa_chain( + {"question": question, "context": context}, + callbacks=callbacks, + ) + final_result = result[self.qa_chain.output_key] + + chain_result: Dict[str, Any] = {self.output_key: final_result} + if self.return_intermediate_steps: + chain_result[INTERMEDIATE_STEPS_KEY] = intermediate_steps + + return chain_result diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/gremlin.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/gremlin.py new file mode 100644 index 0000000000000000000000000000000000000000..1680bd8f4c16f3e2984544f692d0df9bb6cfc7e1 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/gremlin.py @@ -0,0 +1,253 @@ +"""Question answering over a graph.""" + +from __future__ import annotations + +from typing import Any, Dict, List, Optional + +from langchain_classic.chains.base import Chain +from langchain_classic.chains.llm import LLMChain +from langchain_core.callbacks.manager import CallbackManager, CallbackManagerForChainRun +from langchain_core.language_models import BaseLanguageModel +from langchain_core.prompts import BasePromptTemplate +from langchain_core.prompts.prompt import PromptTemplate +from pydantic import Field + +from langchain_community.chains.graph_qa.prompts import ( + CYPHER_QA_PROMPT, + GRAPHDB_SPARQL_FIX_TEMPLATE, + GREMLIN_GENERATION_PROMPT, +) +from langchain_community.graphs import GremlinGraph + +INTERMEDIATE_STEPS_KEY = "intermediate_steps" + + +def extract_gremlin(text: str) -> str: + """Extract Gremlin code from a text. + + Args: + text: Text to extract Gremlin code from. + + Returns: + Gremlin code extracted from the text. + """ + text = text.replace("`", "") + if text.startswith("gremlin"): + text = text[len("gremlin") :] + return text.replace("\n", "") + + +class GremlinQAChain(Chain): + """Chain for question-answering against a graph by generating gremlin statements. + + *Security note*: Make sure that the database connection uses credentials + that are narrowly-scoped to only include necessary permissions. + Failure to do so may result in data corruption or loss, since the calling + code may attempt commands that would result in deletion, mutation + of data if appropriately prompted or reading sensitive data if such + data is present in the database. + The best way to guard against such negative outcomes is to (as appropriate) + limit the permissions granted to the credentials used with this tool. + + See https://python.langchain.com/docs/security for more information. + """ + + graph: GremlinGraph = Field(exclude=True) + gremlin_generation_chain: LLMChain + qa_chain: LLMChain + gremlin_fix_chain: LLMChain + max_fix_retries: int = 3 + input_key: str = "query" #: :meta private: + output_key: str = "result" #: :meta private: + top_k: int = 100 + return_direct: bool = False + return_intermediate_steps: bool = False + + allow_dangerous_requests: bool = False + """Forced user opt-in to acknowledge that the chain can make dangerous requests. + + *Security note*: Make sure that the database connection uses credentials + that are narrowly-scoped to only include necessary permissions. + Failure to do so may result in data corruption or loss, since the calling + code may attempt commands that would result in deletion, mutation + of data if appropriately prompted or reading sensitive data if such + data is present in the database. + The best way to guard against such negative outcomes is to (as appropriate) + limit the permissions granted to the credentials used with this tool. + + See https://python.langchain.com/docs/security for more information. + """ + + def __init__(self, **kwargs: Any) -> None: + """Initialize the chain.""" + super().__init__(**kwargs) + if self.allow_dangerous_requests is not True: + raise ValueError( + "In order to use this chain, you must acknowledge that it can make " + "dangerous requests by setting `allow_dangerous_requests` to `True`." + "You must narrowly scope the permissions of the database connection " + "to only include necessary permissions. Failure to do so may result " + "in data corruption or loss or reading sensitive data if such data is " + "present in the database." + "Only use this chain if you understand the risks and have taken the " + "necessary precautions. " + "See https://python.langchain.com/docs/security for more information." + ) + + @property + def input_keys(self) -> List[str]: + """Input keys. + + :meta private: + """ + return [self.input_key] + + @property + def output_keys(self) -> List[str]: + """Output keys. + + :meta private: + """ + _output_keys = [self.output_key] + return _output_keys + + @classmethod + def from_llm( + cls, + llm: BaseLanguageModel, + *, + gremlin_fix_prompt: BasePromptTemplate = PromptTemplate( + input_variables=["error_message", "generated_sparql", "schema"], + template=GRAPHDB_SPARQL_FIX_TEMPLATE.replace("SPARQL", "Gremlin").replace( + "in Turtle format", "" + ), + ), + qa_prompt: BasePromptTemplate = CYPHER_QA_PROMPT, + gremlin_prompt: BasePromptTemplate = GREMLIN_GENERATION_PROMPT, + **kwargs: Any, + ) -> GremlinQAChain: + """Initialize from LLM.""" + qa_chain = LLMChain(llm=llm, prompt=qa_prompt) + gremlin_generation_chain = LLMChain(llm=llm, prompt=gremlin_prompt) + gremlinl_fix_chain = LLMChain(llm=llm, prompt=gremlin_fix_prompt) + return cls( + qa_chain=qa_chain, + gremlin_generation_chain=gremlin_generation_chain, + gremlin_fix_chain=gremlinl_fix_chain, + **kwargs, + ) + + def _call( + self, + inputs: Dict[str, Any], + run_manager: Optional[CallbackManagerForChainRun] = None, + ) -> Dict[str, str]: + """Generate gremlin statement, use it to look up in db and answer question.""" + _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() + callbacks = _run_manager.get_child() + question = inputs[self.input_key] + + intermediate_steps: List = [] + + chain_response = self.gremlin_generation_chain.invoke( + {"question": question, "schema": self.graph.get_schema}, callbacks=callbacks + ) + + generated_gremlin = extract_gremlin( + chain_response[self.gremlin_generation_chain.output_key] + ) + + _run_manager.on_text("Generated gremlin:", end="\n", verbose=self.verbose) + _run_manager.on_text( + generated_gremlin, color="green", end="\n", verbose=self.verbose + ) + + intermediate_steps.append({"query": generated_gremlin}) + + if generated_gremlin: + context = self.execute_with_retry( + _run_manager, callbacks, generated_gremlin + )[: self.top_k] + else: + context = [] + + if self.return_direct: + final_result = context + else: + _run_manager.on_text("Full Context:", end="\n", verbose=self.verbose) + _run_manager.on_text( + str(context), color="green", end="\n", verbose=self.verbose + ) + + intermediate_steps.append({"context": context}) + + result = self.qa_chain.invoke( + {"question": question, "context": context}, + callbacks=callbacks, + ) + final_result = result[self.qa_chain.output_key] + + chain_result: Dict[str, Any] = {self.output_key: final_result} + if self.return_intermediate_steps: + chain_result[INTERMEDIATE_STEPS_KEY] = intermediate_steps + + return chain_result + + def execute_query(self, query: str) -> List[Any]: + try: + return self.graph.query(query) + except Exception as e: + if hasattr(e, "status_message"): + raise ValueError(e.status_message) + else: + raise ValueError(str(e)) + + def execute_with_retry( + self, + _run_manager: CallbackManagerForChainRun, + callbacks: CallbackManager, + generated_gremlin: str, + ) -> List[Any]: + try: + return self.execute_query(generated_gremlin) + except Exception as e: + retries = 0 + error_message = str(e) + self.log_invalid_query(_run_manager, generated_gremlin, error_message) + + while retries < self.max_fix_retries: + try: + fix_chain_result = self.gremlin_fix_chain.invoke( + { + "error_message": error_message, + # we are borrowing template from sparql + "generated_sparql": generated_gremlin, + "schema": self.schema, + }, + callbacks=callbacks, + ) + fixed_gremlin = fix_chain_result[self.gremlin_fix_chain.output_key] + return self.execute_query(fixed_gremlin) + except Exception as e: + retries += 1 + parse_exception = str(e) + self.log_invalid_query(_run_manager, fixed_gremlin, parse_exception) + + raise ValueError("The generated Gremlin query is invalid.") + + def log_invalid_query( + self, + _run_manager: CallbackManagerForChainRun, + generated_query: str, + error_message: str, + ) -> None: + _run_manager.on_text("Invalid Gremlin query: ", end="\n", verbose=self.verbose) + _run_manager.on_text( + generated_query, color="red", end="\n", verbose=self.verbose + ) + _run_manager.on_text( + "Gremlin Query Parse Error: ", end="\n", verbose=self.verbose + ) + _run_manager.on_text( + error_message, color="red", end="\n\n", verbose=self.verbose + ) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/hugegraph.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/hugegraph.py new file mode 100644 index 0000000000000000000000000000000000000000..206e26df3836f3254239996f5b66ce08a57ce000 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/hugegraph.py @@ -0,0 +1,138 @@ +"""Question answering over a graph.""" + +from __future__ import annotations + +from typing import Any, Dict, List, Optional + +from langchain_classic.chains.base import Chain +from langchain_classic.chains.llm import LLMChain +from langchain_core.callbacks import CallbackManagerForChainRun +from langchain_core.language_models import BaseLanguageModel +from langchain_core.prompts import BasePromptTemplate +from pydantic import Field + +from langchain_community.chains.graph_qa.prompts import ( + CYPHER_QA_PROMPT, + GREMLIN_GENERATION_PROMPT, +) +from langchain_community.graphs.hugegraph import HugeGraph + + +class HugeGraphQAChain(Chain): + """Chain for question-answering against a graph by generating gremlin statements. + + *Security note*: Make sure that the database connection uses credentials + that are narrowly-scoped to only include necessary permissions. + Failure to do so may result in data corruption or loss, since the calling + code may attempt commands that would result in deletion, mutation + of data if appropriately prompted or reading sensitive data if such + data is present in the database. + The best way to guard against such negative outcomes is to (as appropriate) + limit the permissions granted to the credentials used with this tool. + + See https://python.langchain.com/docs/security for more information. + """ + + graph: HugeGraph = Field(exclude=True) + gremlin_generation_chain: LLMChain + qa_chain: LLMChain + input_key: str = "query" #: :meta private: + output_key: str = "result" #: :meta private: + + allow_dangerous_requests: bool = False + """Forced user opt-in to acknowledge that the chain can make dangerous requests. + + *Security note*: Make sure that the database connection uses credentials + that are narrowly-scoped to only include necessary permissions. + Failure to do so may result in data corruption or loss, since the calling + code may attempt commands that would result in deletion, mutation + of data if appropriately prompted or reading sensitive data if such + data is present in the database. + The best way to guard against such negative outcomes is to (as appropriate) + limit the permissions granted to the credentials used with this tool. + + See https://python.langchain.com/docs/security for more information. + """ + + def __init__(self, **kwargs: Any) -> None: + """Initialize the chain.""" + super().__init__(**kwargs) + if self.allow_dangerous_requests is not True: + raise ValueError( + "In order to use this chain, you must acknowledge that it can make " + "dangerous requests by setting `allow_dangerous_requests` to `True`." + "You must narrowly scope the permissions of the database connection " + "to only include necessary permissions. Failure to do so may result " + "in data corruption or loss or reading sensitive data if such data is " + "present in the database." + "Only use this chain if you understand the risks and have taken the " + "necessary precautions. " + "See https://python.langchain.com/docs/security for more information." + ) + + @property + def input_keys(self) -> List[str]: + """Input keys. + + :meta private: + """ + return [self.input_key] + + @property + def output_keys(self) -> List[str]: + """Output keys. + + :meta private: + """ + _output_keys = [self.output_key] + return _output_keys + + @classmethod + def from_llm( + cls, + llm: BaseLanguageModel, + *, + qa_prompt: BasePromptTemplate = CYPHER_QA_PROMPT, + gremlin_prompt: BasePromptTemplate = GREMLIN_GENERATION_PROMPT, + **kwargs: Any, + ) -> HugeGraphQAChain: + """Initialize from LLM.""" + qa_chain = LLMChain(llm=llm, prompt=qa_prompt) + gremlin_generation_chain = LLMChain(llm=llm, prompt=gremlin_prompt) + + return cls( + qa_chain=qa_chain, + gremlin_generation_chain=gremlin_generation_chain, + **kwargs, + ) + + def _call( + self, + inputs: Dict[str, Any], + run_manager: Optional[CallbackManagerForChainRun] = None, + ) -> Dict[str, str]: + """Generate gremlin statement, use it to look up in db and answer question.""" + _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() + callbacks = _run_manager.get_child() + question = inputs[self.input_key] + + generated_gremlin = self.gremlin_generation_chain.run( + {"question": question, "schema": self.graph.get_schema}, callbacks=callbacks + ) + + _run_manager.on_text("Generated gremlin:", end="\n", verbose=self.verbose) + _run_manager.on_text( + generated_gremlin, color="green", end="\n", verbose=self.verbose + ) + context = self.graph.query(generated_gremlin) + + _run_manager.on_text("Full Context:", end="\n", verbose=self.verbose) + _run_manager.on_text( + str(context), color="green", end="\n", verbose=self.verbose + ) + + result = self.qa_chain( + {"question": question, "context": context}, + callbacks=callbacks, + ) + return {self.output_key: result[self.qa_chain.output_key]} diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/kuzu.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/kuzu.py new file mode 100644 index 0000000000000000000000000000000000000000..c4da7b5dcdd29f9104b030ebb203f264b27ed7f5 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/kuzu.py @@ -0,0 +1,196 @@ +"""Question answering over a graph.""" + +from __future__ import annotations + +import re +from typing import Any, Dict, List, Optional + +from langchain_classic.chains.base import Chain +from langchain_classic.chains.llm import LLMChain +from langchain_core.callbacks import CallbackManagerForChainRun +from langchain_core.language_models import BaseLanguageModel +from langchain_core.prompts import BasePromptTemplate +from pydantic import Field + +from langchain_community.chains.graph_qa.prompts import ( + CYPHER_QA_PROMPT, + KUZU_GENERATION_PROMPT, +) +from langchain_community.graphs.kuzu_graph import KuzuGraph + + +def remove_prefix(text: str, prefix: str) -> str: + """Remove a prefix from a text. + + Args: + text: Text to remove the prefix from. + prefix: Prefix to remove from the text. + + Returns: + Text with the prefix removed. + """ + if text.startswith(prefix): + return text[len(prefix) :] + return text + + +def extract_cypher(text: str) -> str: + """Extract Cypher code from a text. + + Args: + text: Text to extract Cypher code from. + + Returns: + Cypher code extracted from the text. + """ + # The pattern to find Cypher code enclosed in triple backticks + pattern = r"```(.*?)```" + + # Find all matches in the input text + matches = re.findall(pattern, text, re.DOTALL) + + return matches[0] if matches else text + + +class KuzuQAChain(Chain): + """Question-answering against a graph by generating Cypher statements for Kùzu. + + *Security note*: Make sure that the database connection uses credentials + that are narrowly-scoped to only include necessary permissions. + Failure to do so may result in data corruption or loss, since the calling + code may attempt commands that would result in deletion, mutation + of data if appropriately prompted or reading sensitive data if such + data is present in the database. + The best way to guard against such negative outcomes is to (as appropriate) + limit the permissions granted to the credentials used with this tool. + + See https://python.langchain.com/docs/security for more information. + """ + + graph: KuzuGraph = Field(exclude=True) + cypher_generation_chain: LLMChain + qa_chain: LLMChain + input_key: str = "query" #: :meta private: + output_key: str = "result" #: :meta private: + + allow_dangerous_requests: bool = False + """Forced user opt-in to acknowledge that the chain can make dangerous requests. + + *Security note*: Make sure that the database connection uses credentials + that are narrowly-scoped to only include necessary permissions. + Failure to do so may result in data corruption or loss, since the calling + code may attempt commands that would result in deletion, mutation + of data if appropriately prompted or reading sensitive data if such + data is present in the database. + The best way to guard against such negative outcomes is to (as appropriate) + limit the permissions granted to the credentials used with this tool. + + See https://python.langchain.com/docs/security for more information. + """ + + def __init__(self, **kwargs: Any) -> None: + """Initialize the chain.""" + super().__init__(**kwargs) + if self.allow_dangerous_requests is not True: + raise ValueError( + "In order to use this chain, you must acknowledge that it can make " + "dangerous requests by setting `allow_dangerous_requests` to `True`." + "You must narrowly scope the permissions of the database connection " + "to only include necessary permissions. Failure to do so may result " + "in data corruption or loss or reading sensitive data if such data is " + "present in the database." + "Only use this chain if you understand the risks and have taken the " + "necessary precautions. " + "See https://python.langchain.com/docs/security for more information." + ) + + @property + def input_keys(self) -> List[str]: + """Return the input keys. + + :meta private: + """ + return [self.input_key] + + @property + def output_keys(self) -> List[str]: + """Return the output keys. + + :meta private: + """ + _output_keys = [self.output_key] + return _output_keys + + @classmethod + def from_llm( + cls, + llm: Optional[BaseLanguageModel] = None, + *, + qa_prompt: BasePromptTemplate = CYPHER_QA_PROMPT, + cypher_prompt: BasePromptTemplate = KUZU_GENERATION_PROMPT, + cypher_llm: Optional[BaseLanguageModel] = None, + qa_llm: Optional[BaseLanguageModel] = None, + **kwargs: Any, + ) -> KuzuQAChain: + """Initialize from LLM.""" + if not cypher_llm and not llm: + raise ValueError("Either `llm` or `cypher_llm` parameters must be provided") + if not qa_llm and not llm: + raise ValueError( + "Either `llm` or `qa_llm` parameters must be provided along with" + " `cypher_llm`" + ) + if cypher_llm and qa_llm and llm: + raise ValueError( + "You can specify up to two of 'cypher_llm', 'qa_llm'" + ", and 'llm', but not all three simultaneously." + ) + + qa_chain = LLMChain( + llm=qa_llm or llm, # type: ignore[arg-type] + prompt=qa_prompt, + ) + cypher_generation_chain = LLMChain( + llm=cypher_llm or llm, # type: ignore[arg-type] + prompt=cypher_prompt, + ) + + return cls( + qa_chain=qa_chain, + cypher_generation_chain=cypher_generation_chain, + **kwargs, + ) + + def _call( + self, + inputs: Dict[str, Any], + run_manager: Optional[CallbackManagerForChainRun] = None, + ) -> Dict[str, str]: + """Generate Cypher statement, use it to look up in db and answer question.""" + _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() + callbacks = _run_manager.get_child() + question = inputs[self.input_key] + + generated_cypher = self.cypher_generation_chain.run( + {"question": question, "schema": self.graph.get_schema}, callbacks=callbacks + ) + # Extract Cypher code if it is wrapped in triple backticks + # with the language marker "cypher" + generated_cypher = remove_prefix(extract_cypher(generated_cypher), "cypher") + + _run_manager.on_text("Generated Cypher:", end="\n", verbose=self.verbose) + _run_manager.on_text( + generated_cypher, color="green", end="\n", verbose=self.verbose + ) + context = self.graph.query(generated_cypher) + + _run_manager.on_text("Full Context:", end="\n", verbose=self.verbose) + _run_manager.on_text( + str(context), color="green", end="\n", verbose=self.verbose + ) + + result = self.qa_chain( + {"question": question, "context": context}, + callbacks=callbacks, + ) + return {self.output_key: result[self.qa_chain.output_key]} diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/memgraph.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/memgraph.py new file mode 100644 index 0000000000000000000000000000000000000000..349bdf67d2c45a208268918df03e3be0fedeb279 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/memgraph.py @@ -0,0 +1,316 @@ +"""Question answering over a graph.""" + +from __future__ import annotations + +import re +from typing import Any, Dict, List, Optional, Union + +from langchain_classic.chains.base import Chain +from langchain_core.callbacks import CallbackManagerForChainRun +from langchain_core.language_models import BaseLanguageModel +from langchain_core.messages import ( + AIMessage, + BaseMessage, + SystemMessage, + ToolMessage, +) +from langchain_core.output_parsers import StrOutputParser +from langchain_core.prompts import ( + BasePromptTemplate, + ChatPromptTemplate, + HumanMessagePromptTemplate, + MessagesPlaceholder, +) +from langchain_core.runnables import Runnable +from pydantic import Field + +from langchain_community.chains.graph_qa.prompts import ( + MEMGRAPH_GENERATION_PROMPT, + MEMGRAPH_QA_PROMPT, +) +from langchain_community.graphs.memgraph_graph import MemgraphGraph + +INTERMEDIATE_STEPS_KEY = "intermediate_steps" + +FUNCTION_RESPONSE_SYSTEM = """You are an assistant that helps to form nice and human +understandable answers based on the provided information from tools. +Do not add any other information that wasn't present in the tools, and use +very concise style in interpreting results! +""" + + +def extract_cypher(text: str) -> str: + """Extract Cypher code from a text. + + Args: + text: Text to extract Cypher code from. + + Returns: + Cypher code extracted from the text. + """ + # The pattern to find Cypher code enclosed in triple backticks + pattern = r"```(.*?)```" + + # Find all matches in the input text + matches = re.findall(pattern, text, re.DOTALL) + + return matches[0] if matches else text + + +def get_function_response( + question: str, context: List[Dict[str, Any]] +) -> List[BaseMessage]: + TOOL_ID = "call_H7fABDuzEau48T10Qn0Lsh0D" + messages = [ + AIMessage( + content="", + additional_kwargs={ + "tool_calls": [ + { + "id": TOOL_ID, + "function": { + "arguments": '{"question":"' + question + '"}', + "name": "GetInformation", + }, + "type": "function", + } + ] + }, + ), + ToolMessage(content=str(context), tool_call_id=TOOL_ID), + ] + return messages + + +class MemgraphQAChain(Chain): + """Chain for question-answering against a graph by generating Cypher statements. + + *Security note*: Make sure that the database connection uses credentials + that are narrowly-scoped to only include necessary permissions. + Failure to do so may result in data corruption or loss, since the calling + code may attempt commands that would result in deletion, mutation + of data if appropriately prompted or reading sensitive data if such + data is present in the database. + The best way to guard against such negative outcomes is to (as appropriate) + limit the permissions granted to the credentials used with this tool. + + See https://python.langchain.com/docs/security for more information. + """ + + graph: MemgraphGraph = Field(exclude=True) + cypher_generation_chain: Runnable + qa_chain: Runnable + graph_schema: str + input_key: str = "query" #: :meta private: + output_key: str = "result" #: :meta private: + top_k: int = 10 + """Number of results to return from the query""" + return_intermediate_steps: bool = False + """Whether or not to return the intermediate steps along with the final answer.""" + return_direct: bool = False + """Optional cypher validation tool""" + use_function_response: bool = False + """Whether to wrap the database context as tool/function response""" + allow_dangerous_requests: bool = False + """Forced user opt-in to acknowledge that the chain can make dangerous requests. + + *Security note*: Make sure that the database connection uses credentials + that are narrowly-scoped to only include necessary permissions. + Failure to do so may result in data corruption or loss, since the calling + code may attempt commands that would result in deletion, mutation + of data if appropriately prompted or reading sensitive data if such + data is present in the database. + The best way to guard against such negative outcomes is to (as appropriate) + limit the permissions granted to the credentials used with this tool. + + See https://python.langchain.com/docs/security for more information. + """ + + def __init__(self, **kwargs: Any) -> None: + """Initialize the chain.""" + super().__init__(**kwargs) + if self.allow_dangerous_requests is not True: + raise ValueError( + "In order to use this chain, you must acknowledge that it can make " + "dangerous requests by setting `allow_dangerous_requests` to `True`." + "You must narrowly scope the permissions of the database connection " + "to only include necessary permissions. Failure to do so may result " + "in data corruption or loss or reading sensitive data if such data is " + "present in the database." + "Only use this chain if you understand the risks and have taken the " + "necessary precautions. " + "See https://python.langchain.com/docs/security for more information." + ) + + @property + def input_keys(self) -> List[str]: + """Return the input keys. + + :meta private: + """ + return [self.input_key] + + @property + def output_keys(self) -> List[str]: + """Return the output keys. + + :meta private: + """ + _output_keys = [self.output_key] + return _output_keys + + @property + def _chain_type(self) -> str: + return "graph_cypher_chain" + + @classmethod + def from_llm( + cls, + llm: Optional[BaseLanguageModel] = None, + *, + qa_prompt: Optional[BasePromptTemplate] = None, + cypher_prompt: Optional[BasePromptTemplate] = None, + cypher_llm: Optional[BaseLanguageModel] = None, + qa_llm: Optional[Union[BaseLanguageModel, Any]] = None, + qa_llm_kwargs: Optional[Dict[str, Any]] = None, + cypher_llm_kwargs: Optional[Dict[str, Any]] = None, + use_function_response: bool = False, + function_response_system: str = FUNCTION_RESPONSE_SYSTEM, + **kwargs: Any, + ) -> MemgraphQAChain: + """Initialize from LLM.""" + + if not cypher_llm and not llm: + raise ValueError("Either `llm` or `cypher_llm` parameters must be provided") + if not qa_llm and not llm: + raise ValueError("Either `llm` or `qa_llm` parameters must be provided") + if cypher_llm and qa_llm and llm: + raise ValueError( + "You can specify up to two of 'cypher_llm', 'qa_llm'" + ", and 'llm', but not all three simultaneously." + ) + if cypher_prompt and cypher_llm_kwargs: + raise ValueError( + "Specifying cypher_prompt and cypher_llm_kwargs together is" + " not allowed. Please pass prompt via cypher_llm_kwargs." + ) + if qa_prompt and qa_llm_kwargs: + raise ValueError( + "Specifying qa_prompt and qa_llm_kwargs together is" + " not allowed. Please pass prompt via qa_llm_kwargs." + ) + use_qa_llm_kwargs = qa_llm_kwargs if qa_llm_kwargs is not None else {} + use_cypher_llm_kwargs = ( + cypher_llm_kwargs if cypher_llm_kwargs is not None else {} + ) + if "prompt" not in use_qa_llm_kwargs: + use_qa_llm_kwargs["prompt"] = ( + qa_prompt if qa_prompt is not None else MEMGRAPH_QA_PROMPT + ) + if "prompt" not in use_cypher_llm_kwargs: + use_cypher_llm_kwargs["prompt"] = ( + cypher_prompt + if cypher_prompt is not None + else MEMGRAPH_GENERATION_PROMPT + ) + + qa_llm = qa_llm or llm + if use_function_response: + try: + qa_llm.bind_tools({}) # type: ignore[union-attr] + response_prompt = ChatPromptTemplate.from_messages( + [ + SystemMessage(content=function_response_system), + HumanMessagePromptTemplate.from_template("{question}"), + MessagesPlaceholder(variable_name="function_response"), + ] + ) + qa_chain = response_prompt | qa_llm | StrOutputParser() # type: ignore[operator] + except (NotImplementedError, AttributeError): + raise ValueError("Provided LLM does not support native tools/functions") + else: + qa_chain = use_qa_llm_kwargs["prompt"] | qa_llm | StrOutputParser() + + prompt = use_cypher_llm_kwargs["prompt"] + llm_to_use = cypher_llm if cypher_llm is not None else llm + + if prompt is not None and llm_to_use is not None: + cypher_generation_chain = prompt | llm_to_use | StrOutputParser() + else: + raise ValueError( + "Missing required components for the cypher generation chain: " + "'prompt' or 'llm'" + ) + + graph_schema = kwargs["graph"].get_schema + + return cls( + graph_schema=graph_schema, + qa_chain=qa_chain, + cypher_generation_chain=cypher_generation_chain, + use_function_response=use_function_response, + **kwargs, + ) + + def _call( + self, + inputs: Dict[str, Any], + run_manager: Optional[CallbackManagerForChainRun] = None, + ) -> Dict[str, Any]: + """Generate Cypher statement, use it to look up in db and answer question.""" + _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() + callbacks = _run_manager.get_child() + question = inputs[self.input_key] + args = { + "question": question, + "schema": self.graph_schema, + } + args.update(inputs) + + intermediate_steps: List = [] + + generated_cypher = self.cypher_generation_chain.invoke( + args, callbacks=callbacks + ) + # Extract Cypher code if it is wrapped in backticks + generated_cypher = extract_cypher(generated_cypher) + + _run_manager.on_text("Generated Cypher:", end="\n", verbose=self.verbose) + _run_manager.on_text( + generated_cypher, color="green", end="\n", verbose=self.verbose + ) + + intermediate_steps.append({"query": generated_cypher}) + + # Retrieve and limit the number of results + # Generated Cypher be null if query corrector identifies invalid schema + if generated_cypher: + context = self.graph.query(generated_cypher)[: self.top_k] + else: + context = [] + + if self.return_direct: + result = context + else: + _run_manager.on_text("Full Context:", end="\n", verbose=self.verbose) + _run_manager.on_text( + str(context), color="green", end="\n", verbose=self.verbose + ) + + intermediate_steps.append({"context": context}) + if self.use_function_response: + function_response = get_function_response(question, context) + result = self.qa_chain.invoke( + {"question": question, "function_response": function_response}, + ) + else: + result = self.qa_chain.invoke( + {"question": question, "context": context}, + callbacks=callbacks, + ) + + chain_result: Dict[str, Any] = {"result": result} + if self.return_intermediate_steps: + chain_result[INTERMEDIATE_STEPS_KEY] = intermediate_steps + + return chain_result diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/nebulagraph.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/nebulagraph.py new file mode 100644 index 0000000000000000000000000000000000000000..48326b508d65d19a280f2f5759c9f1c5603c43e3 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/nebulagraph.py @@ -0,0 +1,138 @@ +"""Question answering over a graph.""" + +from __future__ import annotations + +from typing import Any, Dict, List, Optional + +from langchain_classic.chains.base import Chain +from langchain_classic.chains.llm import LLMChain +from langchain_core.callbacks import CallbackManagerForChainRun +from langchain_core.language_models import BaseLanguageModel +from langchain_core.prompts import BasePromptTemplate +from pydantic import Field + +from langchain_community.chains.graph_qa.prompts import ( + CYPHER_QA_PROMPT, + NGQL_GENERATION_PROMPT, +) +from langchain_community.graphs.nebula_graph import NebulaGraph + + +class NebulaGraphQAChain(Chain): + """Chain for question-answering against a graph by generating nGQL statements. + + *Security note*: Make sure that the database connection uses credentials + that are narrowly-scoped to only include necessary permissions. + Failure to do so may result in data corruption or loss, since the calling + code may attempt commands that would result in deletion, mutation + of data if appropriately prompted or reading sensitive data if such + data is present in the database. + The best way to guard against such negative outcomes is to (as appropriate) + limit the permissions granted to the credentials used with this tool. + + See https://python.langchain.com/docs/security for more information. + """ + + graph: NebulaGraph = Field(exclude=True) + ngql_generation_chain: LLMChain + qa_chain: LLMChain + input_key: str = "query" #: :meta private: + output_key: str = "result" #: :meta private: + + allow_dangerous_requests: bool = False + """Forced user opt-in to acknowledge that the chain can make dangerous requests. + + *Security note*: Make sure that the database connection uses credentials + that are narrowly-scoped to only include necessary permissions. + Failure to do so may result in data corruption or loss, since the calling + code may attempt commands that would result in deletion, mutation + of data if appropriately prompted or reading sensitive data if such + data is present in the database. + The best way to guard against such negative outcomes is to (as appropriate) + limit the permissions granted to the credentials used with this tool. + + See https://python.langchain.com/docs/security for more information. + """ + + def __init__(self, **kwargs: Any) -> None: + """Initialize the chain.""" + super().__init__(**kwargs) + if self.allow_dangerous_requests is not True: + raise ValueError( + "In order to use this chain, you must acknowledge that it can make " + "dangerous requests by setting `allow_dangerous_requests` to `True`." + "You must narrowly scope the permissions of the database connection " + "to only include necessary permissions. Failure to do so may result " + "in data corruption or loss or reading sensitive data if such data is " + "present in the database." + "Only use this chain if you understand the risks and have taken the " + "necessary precautions. " + "See https://python.langchain.com/docs/security for more information." + ) + + @property + def input_keys(self) -> List[str]: + """Return the input keys. + + :meta private: + """ + return [self.input_key] + + @property + def output_keys(self) -> List[str]: + """Return the output keys. + + :meta private: + """ + _output_keys = [self.output_key] + return _output_keys + + @classmethod + def from_llm( + cls, + llm: BaseLanguageModel, + *, + qa_prompt: BasePromptTemplate = CYPHER_QA_PROMPT, + ngql_prompt: BasePromptTemplate = NGQL_GENERATION_PROMPT, + **kwargs: Any, + ) -> NebulaGraphQAChain: + """Initialize from LLM.""" + qa_chain = LLMChain(llm=llm, prompt=qa_prompt) + ngql_generation_chain = LLMChain(llm=llm, prompt=ngql_prompt) + + return cls( + qa_chain=qa_chain, + ngql_generation_chain=ngql_generation_chain, + **kwargs, + ) + + def _call( + self, + inputs: Dict[str, Any], + run_manager: Optional[CallbackManagerForChainRun] = None, + ) -> Dict[str, str]: + """Generate nGQL statement, use it to look up in db and answer question.""" + _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() + callbacks = _run_manager.get_child() + question = inputs[self.input_key] + + generated_ngql = self.ngql_generation_chain.run( + {"question": question, "schema": self.graph.get_schema}, callbacks=callbacks + ) + + _run_manager.on_text("Generated nGQL:", end="\n", verbose=self.verbose) + _run_manager.on_text( + generated_ngql, color="green", end="\n", verbose=self.verbose + ) + context = self.graph.query(generated_ngql) + + _run_manager.on_text("Full Context:", end="\n", verbose=self.verbose) + _run_manager.on_text( + str(context), color="green", end="\n", verbose=self.verbose + ) + + result = self.qa_chain( + {"question": question, "context": context}, + callbacks=callbacks, + ) + return {self.output_key: result[self.qa_chain.output_key]} diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/neptune_cypher.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/neptune_cypher.py new file mode 100644 index 0000000000000000000000000000000000000000..7318962b620c297764a36d879b8689daead07898 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/neptune_cypher.py @@ -0,0 +1,254 @@ +from __future__ import annotations + +import re +from typing import Any, Dict, List, Optional + +from langchain_classic.chains.base import Chain +from langchain_classic.chains.llm import LLMChain +from langchain_classic.chains.prompt_selector import ConditionalPromptSelector +from langchain_core._api.deprecation import deprecated +from langchain_core.callbacks import CallbackManagerForChainRun +from langchain_core.language_models import BaseLanguageModel +from langchain_core.prompts.base import BasePromptTemplate +from pydantic import Field + +from langchain_community.chains.graph_qa.prompts import ( + CYPHER_QA_PROMPT, + NEPTUNE_OPENCYPHER_GENERATION_PROMPT, + NEPTUNE_OPENCYPHER_GENERATION_SIMPLE_PROMPT, +) +from langchain_community.graphs import BaseNeptuneGraph + +INTERMEDIATE_STEPS_KEY = "intermediate_steps" + + +def trim_query(query: str) -> str: + """Trim the query to only include Cypher keywords.""" + keywords = ( + "CALL", + "CREATE", + "DELETE", + "DETACH", + "LIMIT", + "MATCH", + "MERGE", + "OPTIONAL", + "ORDER", + "REMOVE", + "RETURN", + "SET", + "SKIP", + "UNWIND", + "WITH", + "WHERE", + "//", + ) + + lines = query.split("\n") + new_query = "" + + for line in lines: + if line.strip().upper().startswith(keywords): + new_query += line + "\n" + + return new_query + + +def extract_cypher(text: str) -> str: + """Extract Cypher code from text using Regex.""" + # The pattern to find Cypher code enclosed in triple backticks + pattern = r"```(.*?)```" + + # Find all matches in the input text + matches = re.findall(pattern, text, re.DOTALL) + + return matches[0] if matches else text + + +def use_simple_prompt(llm: BaseLanguageModel) -> bool: + """Decides whether to use the simple prompt""" + if llm._llm_type and "anthropic" in llm._llm_type: # type: ignore[attr-defined] + return True + + # Bedrock anthropic + if hasattr(llm, "model_id") and "anthropic" in llm.model_id: + return True + + return False + + +PROMPT_SELECTOR = ConditionalPromptSelector( + default_prompt=NEPTUNE_OPENCYPHER_GENERATION_PROMPT, + conditionals=[(use_simple_prompt, NEPTUNE_OPENCYPHER_GENERATION_SIMPLE_PROMPT)], +) + + +@deprecated( + since="0.3.15", + removal="1.0", + alternative_import="langchain_aws.create_neptune_opencypher_qa_chain", +) +class NeptuneOpenCypherQAChain(Chain): + """Chain for question-answering against a Neptune graph + by generating openCypher statements. + + *Security note*: Make sure that the database connection uses credentials + that are narrowly-scoped to only include necessary permissions. + Failure to do so may result in data corruption or loss, since the calling + code may attempt commands that would result in deletion, mutation + of data if appropriately prompted or reading sensitive data if such + data is present in the database. + The best way to guard against such negative outcomes is to (as appropriate) + limit the permissions granted to the credentials used with this tool. + + See https://python.langchain.com/docs/security for more information. + + Example: + .. code-block:: python + + chain = NeptuneOpenCypherQAChain.from_llm( + llm=llm, + graph=graph + ) + response = chain.run(query) + """ + + graph: BaseNeptuneGraph = Field(exclude=True) + cypher_generation_chain: LLMChain + qa_chain: LLMChain + input_key: str = "query" #: :meta private: + output_key: str = "result" #: :meta private: + top_k: int = 10 + return_intermediate_steps: bool = False + """Whether or not to return the intermediate steps along with the final answer.""" + return_direct: bool = False + """Whether or not to return the result of querying the graph directly.""" + extra_instructions: Optional[str] = None + """Extra instructions by the appended to the query generation prompt.""" + + allow_dangerous_requests: bool = False + """Forced user opt-in to acknowledge that the chain can make dangerous requests. + + *Security note*: Make sure that the database connection uses credentials + that are narrowly-scoped to only include necessary permissions. + Failure to do so may result in data corruption or loss, since the calling + code may attempt commands that would result in deletion, mutation + of data if appropriately prompted or reading sensitive data if such + data is present in the database. + The best way to guard against such negative outcomes is to (as appropriate) + limit the permissions granted to the credentials used with this tool. + + See https://python.langchain.com/docs/security for more information. + """ + + def __init__(self, **kwargs: Any) -> None: + """Initialize the chain.""" + super().__init__(**kwargs) + if self.allow_dangerous_requests is not True: + raise ValueError( + "In order to use this chain, you must acknowledge that it can make " + "dangerous requests by setting `allow_dangerous_requests` to `True`." + "You must narrowly scope the permissions of the database connection " + "to only include necessary permissions. Failure to do so may result " + "in data corruption or loss or reading sensitive data if such data is " + "present in the database." + "Only use this chain if you understand the risks and have taken the " + "necessary precautions. " + "See https://python.langchain.com/docs/security for more information." + ) + + @property + def input_keys(self) -> List[str]: + """Return the input keys. + + :meta private: + """ + return [self.input_key] + + @property + def output_keys(self) -> List[str]: + """Return the output keys. + + :meta private: + """ + _output_keys = [self.output_key] + return _output_keys + + @classmethod + def from_llm( + cls, + llm: BaseLanguageModel, + *, + qa_prompt: BasePromptTemplate = CYPHER_QA_PROMPT, + cypher_prompt: Optional[BasePromptTemplate] = None, + extra_instructions: Optional[str] = None, + **kwargs: Any, + ) -> NeptuneOpenCypherQAChain: + """Initialize from LLM.""" + qa_chain = LLMChain(llm=llm, prompt=qa_prompt) + + _cypher_prompt = cypher_prompt or PROMPT_SELECTOR.get_prompt(llm) + cypher_generation_chain = LLMChain(llm=llm, prompt=_cypher_prompt) + + return cls( + qa_chain=qa_chain, + cypher_generation_chain=cypher_generation_chain, + extra_instructions=extra_instructions, + **kwargs, + ) + + def _call( + self, + inputs: Dict[str, Any], + run_manager: Optional[CallbackManagerForChainRun] = None, + ) -> Dict[str, Any]: + """Generate Cypher statement, use it to look up in db and answer question.""" + _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() + callbacks = _run_manager.get_child() + question = inputs[self.input_key] + + intermediate_steps: List = [] + + generated_cypher = self.cypher_generation_chain.run( + { + "question": question, + "schema": self.graph.get_schema, + "extra_instructions": self.extra_instructions or "", + }, + callbacks=callbacks, + ) + + # Extract Cypher code if it is wrapped in backticks + generated_cypher = extract_cypher(generated_cypher) + generated_cypher = trim_query(generated_cypher) + + _run_manager.on_text("Generated Cypher:", end="\n", verbose=self.verbose) + _run_manager.on_text( + generated_cypher, color="green", end="\n", verbose=self.verbose + ) + + intermediate_steps.append({"query": generated_cypher}) + + context = self.graph.query(generated_cypher) + + if self.return_direct: + final_result = context + else: + _run_manager.on_text("Full Context:", end="\n", verbose=self.verbose) + _run_manager.on_text( + str(context), color="green", end="\n", verbose=self.verbose + ) + + intermediate_steps.append({"context": context}) + + result = self.qa_chain( + {"question": question, "context": context}, + callbacks=callbacks, + ) + final_result = result[self.qa_chain.output_key] + + chain_result: Dict[str, Any] = {self.output_key: final_result} + if self.return_intermediate_steps: + chain_result[INTERMEDIATE_STEPS_KEY] = intermediate_steps + + return chain_result diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/neptune_sparql.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/neptune_sparql.py new file mode 100644 index 0000000000000000000000000000000000000000..60a35eab284aa90aee2703537ed77418c6f633d5 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/neptune_sparql.py @@ -0,0 +1,242 @@ +""" +Question answering over an RDF or OWL graph using SPARQL. +""" + +from __future__ import annotations + +from typing import Any, Dict, List, Optional + +from langchain_classic.chains.base import Chain +from langchain_classic.chains.llm import LLMChain +from langchain_core._api.deprecation import deprecated +from langchain_core.callbacks.manager import CallbackManagerForChainRun +from langchain_core.language_models import BaseLanguageModel +from langchain_core.prompts.base import BasePromptTemplate +from langchain_core.prompts.prompt import PromptTemplate +from pydantic import Field + +from langchain_community.chains.graph_qa.prompts import SPARQL_QA_PROMPT +from langchain_community.graphs import NeptuneRdfGraph + +INTERMEDIATE_STEPS_KEY = "intermediate_steps" + +SPARQL_GENERATION_TEMPLATE = """ +Task: Generate a SPARQL SELECT statement for querying a graph database. +For instance, to find all email addresses of John Doe, the following +query in backticks would be suitable: +``` +PREFIX foaf: +SELECT ?email +WHERE {{ + ?person foaf:name "John Doe" . + ?person foaf:mbox ?email . +}} +``` +Instructions: +Use only the node types and properties provided in the schema. +Do not use any node types and properties that are not explicitly provided. +Include all necessary prefixes. + +Examples: + +Schema: +{schema} +Note: Be as concise as possible. +Do not include any explanations or apologies in your responses. +Do not respond to any questions that ask for anything else than +for you to construct a SPARQL query. +Do not include any text except the SPARQL query generated. + +The question is: +{prompt}""" + +SPARQL_GENERATION_PROMPT = PromptTemplate( + input_variables=["schema", "prompt"], template=SPARQL_GENERATION_TEMPLATE +) + + +def extract_sparql(query: str) -> str: + """Extract SPARQL code from a text. + + Args: + query: Text to extract SPARQL code from. + + Returns: + SPARQL code extracted from the text. + """ + query = query.strip() + querytoks = query.split("```") + if len(querytoks) == 3: + query = querytoks[1] + + if query.startswith("sparql"): + query = query[6:] + elif query.startswith("") and query.endswith(""): + query = query[8:-9] + return query + + +@deprecated( + since="0.3.15", + removal="1.0", + alternative_import="langchain_aws.create_neptune_sparql_qa_chain", +) +class NeptuneSparqlQAChain(Chain): + """Chain for question-answering against a Neptune graph + by generating SPARQL statements. + + *Security note*: Make sure that the database connection uses credentials + that are narrowly-scoped to only include necessary permissions. + Failure to do so may result in data corruption or loss, since the calling + code may attempt commands that would result in deletion, mutation + of data if appropriately prompted or reading sensitive data if such + data is present in the database. + The best way to guard against such negative outcomes is to (as appropriate) + limit the permissions granted to the credentials used with this tool. + + See https://python.langchain.com/docs/security for more information. + + Example: + .. code-block:: python + + chain = NeptuneSparqlQAChain.from_llm( + llm=llm, + graph=graph + ) + response = chain.invoke(query) + """ + + graph: NeptuneRdfGraph = Field(exclude=True) + sparql_generation_chain: LLMChain + qa_chain: LLMChain + input_key: str = "query" #: :meta private: + output_key: str = "result" #: :meta private: + top_k: int = 10 + return_intermediate_steps: bool = False + """Whether or not to return the intermediate steps along with the final answer.""" + return_direct: bool = False + """Whether or not to return the result of querying the graph directly.""" + extra_instructions: Optional[str] = None + """Extra instructions by the appended to the query generation prompt.""" + + allow_dangerous_requests: bool = False + """Forced user opt-in to acknowledge that the chain can make dangerous requests. + + *Security note*: Make sure that the database connection uses credentials + that are narrowly-scoped to only include necessary permissions. + Failure to do so may result in data corruption or loss, since the calling + code may attempt commands that would result in deletion, mutation + of data if appropriately prompted or reading sensitive data if such + data is present in the database. + The best way to guard against such negative outcomes is to (as appropriate) + limit the permissions granted to the credentials used with this tool. + + See https://python.langchain.com/docs/security for more information. + """ + + def __init__(self, **kwargs: Any) -> None: + """Initialize the chain.""" + super().__init__(**kwargs) + if self.allow_dangerous_requests is not True: + raise ValueError( + "In order to use this chain, you must acknowledge that it can make " + "dangerous requests by setting `allow_dangerous_requests` to `True`." + "You must narrowly scope the permissions of the database connection " + "to only include necessary permissions. Failure to do so may result " + "in data corruption or loss or reading sensitive data if such data is " + "present in the database." + "Only use this chain if you understand the risks and have taken the " + "necessary precautions. " + "See https://python.langchain.com/docs/security for more information." + ) + + @property + def input_keys(self) -> List[str]: + return [self.input_key] + + @property + def output_keys(self) -> List[str]: + _output_keys = [self.output_key] + return _output_keys + + @classmethod + def from_llm( + cls, + llm: BaseLanguageModel, + *, + qa_prompt: BasePromptTemplate = SPARQL_QA_PROMPT, + sparql_prompt: BasePromptTemplate = SPARQL_GENERATION_PROMPT, + examples: Optional[str] = None, + **kwargs: Any, + ) -> NeptuneSparqlQAChain: + """Initialize from LLM.""" + qa_chain = LLMChain(llm=llm, prompt=qa_prompt) + template_to_use = SPARQL_GENERATION_TEMPLATE + if examples: + template_to_use = template_to_use.replace( + "Examples:", "Examples: " + examples + ) + sparql_prompt = PromptTemplate( + input_variables=["schema", "prompt"], template=template_to_use + ) + sparql_generation_chain = LLMChain(llm=llm, prompt=sparql_prompt) + + return cls( + qa_chain=qa_chain, + sparql_generation_chain=sparql_generation_chain, + examples=examples, + **kwargs, + ) + + def _call( + self, + inputs: Dict[str, Any], + run_manager: Optional[CallbackManagerForChainRun] = None, + ) -> Dict[str, str]: + """ + Generate SPARQL query, use it to retrieve a response from the gdb and answer + the question. + """ + _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() + callbacks = _run_manager.get_child() + prompt = inputs[self.input_key] + + intermediate_steps: List = [] + + generated_sparql = self.sparql_generation_chain.run( + {"prompt": prompt, "schema": self.graph.get_schema}, callbacks=callbacks + ) + + # Extract SPARQL + generated_sparql = extract_sparql(generated_sparql) + + _run_manager.on_text("Generated SPARQL:", end="\n", verbose=self.verbose) + _run_manager.on_text( + generated_sparql, color="green", end="\n", verbose=self.verbose + ) + + intermediate_steps.append({"query": generated_sparql}) + + context = self.graph.query(generated_sparql) + + if self.return_direct: + final_result = context + else: + _run_manager.on_text("Full Context:", end="\n", verbose=self.verbose) + _run_manager.on_text( + str(context), color="green", end="\n", verbose=self.verbose + ) + + intermediate_steps.append({"context": context}) + + result = self.qa_chain( + {"prompt": prompt, "context": context}, + callbacks=callbacks, + ) + final_result = result[self.qa_chain.output_key] + + chain_result: Dict[str, Any] = {self.output_key: final_result} + if self.return_intermediate_steps: + chain_result[INTERMEDIATE_STEPS_KEY] = intermediate_steps + + return chain_result diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/ontotext_graphdb.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/ontotext_graphdb.py new file mode 100644 index 0000000000000000000000000000000000000000..613100a33ba265415bb3dd6985dda4e6ee638166 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/ontotext_graphdb.py @@ -0,0 +1,222 @@ +"""Question answering over a graph.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, Any, Dict, List, Optional + +if TYPE_CHECKING: + import rdflib + +from langchain_classic.chains.base import Chain +from langchain_classic.chains.llm import LLMChain +from langchain_core.callbacks.manager import CallbackManager, CallbackManagerForChainRun +from langchain_core.language_models import BaseLanguageModel +from langchain_core.prompts.base import BasePromptTemplate +from pydantic import Field + +from langchain_community.chains.graph_qa.prompts import ( + GRAPHDB_QA_PROMPT, + GRAPHDB_SPARQL_FIX_PROMPT, + GRAPHDB_SPARQL_GENERATION_PROMPT, +) +from langchain_community.graphs import OntotextGraphDBGraph + + +class OntotextGraphDBQAChain(Chain): + """Question-answering against Ontotext GraphDB + https://graphdb.ontotext.com/ by generating SPARQL queries. + + *Security note*: Make sure that the database connection uses credentials + that are narrowly-scoped to only include necessary permissions. + Failure to do so may result in data corruption or loss, since the calling + code may attempt commands that would result in deletion, mutation + of data if appropriately prompted or reading sensitive data if such + data is present in the database. + The best way to guard against such negative outcomes is to (as appropriate) + limit the permissions granted to the credentials used with this tool. + + See https://python.langchain.com/docs/security for more information. + """ + + graph: OntotextGraphDBGraph = Field(exclude=True) + sparql_generation_chain: LLMChain + sparql_fix_chain: LLMChain + max_fix_retries: int + qa_chain: LLMChain + input_key: str = "query" #: :meta private: + output_key: str = "result" #: :meta private: + + allow_dangerous_requests: bool = False + """Forced user opt-in to acknowledge that the chain can make dangerous requests. + + *Security note*: Make sure that the database connection uses credentials + that are narrowly-scoped to only include necessary permissions. + Failure to do so may result in data corruption or loss, since the calling + code may attempt commands that would result in deletion, mutation + of data if appropriately prompted or reading sensitive data if such + data is present in the database. + The best way to guard against such negative outcomes is to (as appropriate) + limit the permissions granted to the credentials used with this tool. + + See https://python.langchain.com/docs/security for more information. + """ + + def __init__(self, **kwargs: Any) -> None: + """Initialize the chain.""" + super().__init__(**kwargs) + if self.allow_dangerous_requests is not True: + raise ValueError( + "In order to use this chain, you must acknowledge that it can make " + "dangerous requests by setting `allow_dangerous_requests` to `True`." + "You must narrowly scope the permissions of the database connection " + "to only include necessary permissions. Failure to do so may result " + "in data corruption or loss or reading sensitive data if such data is " + "present in the database." + "Only use this chain if you understand the risks and have taken the " + "necessary precautions. " + "See https://python.langchain.com/docs/security for more information." + ) + + @property + def input_keys(self) -> List[str]: + return [self.input_key] + + @property + def output_keys(self) -> List[str]: + _output_keys = [self.output_key] + return _output_keys + + @classmethod + def from_llm( + cls, + llm: BaseLanguageModel, + *, + sparql_generation_prompt: BasePromptTemplate = GRAPHDB_SPARQL_GENERATION_PROMPT, + sparql_fix_prompt: BasePromptTemplate = GRAPHDB_SPARQL_FIX_PROMPT, + max_fix_retries: int = 5, + qa_prompt: BasePromptTemplate = GRAPHDB_QA_PROMPT, + **kwargs: Any, + ) -> OntotextGraphDBQAChain: + """Initialize from LLM.""" + sparql_generation_chain = LLMChain(llm=llm, prompt=sparql_generation_prompt) + sparql_fix_chain = LLMChain(llm=llm, prompt=sparql_fix_prompt) + max_fix_retries = max_fix_retries + qa_chain = LLMChain(llm=llm, prompt=qa_prompt) + return cls( + qa_chain=qa_chain, + sparql_generation_chain=sparql_generation_chain, + sparql_fix_chain=sparql_fix_chain, + max_fix_retries=max_fix_retries, + **kwargs, + ) + + def _call( + self, + inputs: Dict[str, Any], + run_manager: Optional[CallbackManagerForChainRun] = None, + ) -> Dict[str, str]: + """ + Generate a SPARQL query, use it to retrieve a response from GraphDB and answer + the question. + """ + _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() + callbacks = _run_manager.get_child() + prompt = inputs[self.input_key] + ontology_schema = self.graph.get_schema + + sparql_generation_chain_result = self.sparql_generation_chain.invoke( + {"prompt": prompt, "schema": ontology_schema}, callbacks=callbacks + ) + generated_sparql = sparql_generation_chain_result[ + self.sparql_generation_chain.output_key + ] + + generated_sparql = self._get_prepared_sparql_query( + _run_manager, callbacks, generated_sparql, ontology_schema + ) + query_results = self._execute_query(generated_sparql) + + qa_chain_result = self.qa_chain.invoke( + {"prompt": prompt, "context": query_results}, callbacks=callbacks + ) + result = qa_chain_result[self.qa_chain.output_key] + return {self.output_key: result} + + def _get_prepared_sparql_query( + self, + _run_manager: CallbackManagerForChainRun, + callbacks: CallbackManager, + generated_sparql: str, + ontology_schema: str, + ) -> str: + try: + return self._prepare_sparql_query(_run_manager, generated_sparql) + except Exception as e: + retries = 0 + error_message = str(e) + self._log_invalid_sparql_query( + _run_manager, generated_sparql, error_message + ) + + while retries < self.max_fix_retries: + try: + sparql_fix_chain_result = self.sparql_fix_chain.invoke( + { + "error_message": error_message, + "generated_sparql": generated_sparql, + "schema": ontology_schema, + }, + callbacks=callbacks, + ) + generated_sparql = sparql_fix_chain_result[ + self.sparql_fix_chain.output_key + ] + return self._prepare_sparql_query(_run_manager, generated_sparql) + except Exception as e: + retries += 1 + parse_exception = str(e) + self._log_invalid_sparql_query( + _run_manager, generated_sparql, parse_exception + ) + + raise ValueError("The generated SPARQL query is invalid.") + + def _prepare_sparql_query( + self, _run_manager: CallbackManagerForChainRun, generated_sparql: str + ) -> str: + from rdflib.plugins.sparql import prepareQuery + + prepareQuery(generated_sparql) + self._log_prepared_sparql_query(_run_manager, generated_sparql) + return generated_sparql + + def _log_prepared_sparql_query( + self, _run_manager: CallbackManagerForChainRun, generated_query: str + ) -> None: + _run_manager.on_text("Generated SPARQL:", end="\n", verbose=self.verbose) + _run_manager.on_text( + generated_query, color="green", end="\n", verbose=self.verbose + ) + + def _log_invalid_sparql_query( + self, + _run_manager: CallbackManagerForChainRun, + generated_query: str, + error_message: str, + ) -> None: + _run_manager.on_text("Invalid SPARQL query: ", end="\n", verbose=self.verbose) + _run_manager.on_text( + generated_query, color="red", end="\n", verbose=self.verbose + ) + _run_manager.on_text( + "SPARQL Query Parse Error: ", end="\n", verbose=self.verbose + ) + _run_manager.on_text( + error_message, color="red", end="\n\n", verbose=self.verbose + ) + + def _execute_query(self, query: str) -> List[rdflib.query.ResultRow]: + try: + return self.graph.query(query) + except Exception: + raise ValueError("Failed to execute the generated SPARQL query.") diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/prompts.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/prompts.py new file mode 100644 index 0000000000000000000000000000000000000000..9077da3e00e3dc38dfce2a27bec4013756d9264f --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/prompts.py @@ -0,0 +1,468 @@ +# flake8: noqa +from langchain_core.prompts.prompt import PromptTemplate + +_DEFAULT_ENTITY_EXTRACTION_TEMPLATE = """Extract all entities from the following text. As a guideline, a proper noun is generally capitalized. You should definitely extract all names and places. + +Return the output as a single comma-separated list, or NONE if there is nothing of note to return. + +EXAMPLE +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 +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 Sam. +Output: Langchain, Sam +END OF EXAMPLE + +Begin! + +{input} +Output:""" +ENTITY_EXTRACTION_PROMPT = PromptTemplate( + input_variables=["input"], template=_DEFAULT_ENTITY_EXTRACTION_TEMPLATE +) + +_DEFAULT_GRAPH_QA_TEMPLATE = """Use the following knowledge triplets to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer. + +{context} + +Question: {question} +Helpful Answer:""" +GRAPH_QA_PROMPT = PromptTemplate( + template=_DEFAULT_GRAPH_QA_TEMPLATE, input_variables=["context", "question"] +) + +CYPHER_GENERATION_TEMPLATE = """Task:Generate Cypher statement to query a graph database. +Instructions: +Use only the provided relationship types and properties in the schema. +Do not use any other relationship types or properties that are not provided. +Schema: +{schema} +Note: Do not include any explanations or apologies in your responses. +Do not respond to any questions that might ask anything else than for you to construct a Cypher statement. +Do not include any text except the generated Cypher statement. + +The question is: +{question}""" +CYPHER_GENERATION_PROMPT = PromptTemplate( + input_variables=["schema", "question"], template=CYPHER_GENERATION_TEMPLATE +) + +NEBULAGRAPH_EXTRA_INSTRUCTIONS = """ +Instructions: + +First, generate cypher then convert it to NebulaGraph Cypher dialect(rather than standard): +1. it requires explicit label specification only when referring to node properties: v.`Foo`.name +2. note explicit label specification is not needed for edge properties, so it's e.name instead of e.`Bar`.name +3. it uses double equals sign for comparison: `==` rather than `=` +For instance: +```diff +< MATCH (p:person)-[e:directed]->(m:movie) WHERE m.name = 'The Godfather II' +< RETURN p.name, e.year, m.name; +--- +> MATCH (p:`person`)-[e:directed]->(m:`movie`) WHERE m.`movie`.`name` == 'The Godfather II' +> RETURN p.`person`.`name`, e.year, m.`movie`.`name`; +```\n""" + +NGQL_GENERATION_TEMPLATE = CYPHER_GENERATION_TEMPLATE.replace( + "Generate Cypher", "Generate NebulaGraph Cypher" +).replace("Instructions:", NEBULAGRAPH_EXTRA_INSTRUCTIONS) + +NGQL_GENERATION_PROMPT = PromptTemplate( + input_variables=["schema", "question"], template=NGQL_GENERATION_TEMPLATE +) + +KUZU_EXTRA_INSTRUCTIONS = """ +Instructions: +Generate the Kùzu dialect of Cypher with the following rules in mind: +1. Do not omit the relationship pattern. Always use `()-[]->()` instead of `()->()`. +2. Do not include triple backticks ``` in your response. Return only Cypher. +3. Do not return any notes or comments in your response. +\n""" + +KUZU_GENERATION_TEMPLATE = CYPHER_GENERATION_TEMPLATE.replace( + "Generate Cypher", "Generate Kùzu Cypher" +).replace("Instructions:", KUZU_EXTRA_INSTRUCTIONS) + +KUZU_GENERATION_PROMPT = PromptTemplate( + input_variables=["schema", "question"], template=KUZU_GENERATION_TEMPLATE +) + +GREMLIN_GENERATION_TEMPLATE = CYPHER_GENERATION_TEMPLATE.replace("Cypher", "Gremlin") + +GREMLIN_GENERATION_PROMPT = PromptTemplate( + input_variables=["schema", "question"], template=GREMLIN_GENERATION_TEMPLATE +) + +CYPHER_QA_TEMPLATE = """You are an assistant that helps to form nice and human understandable answers. +The information part contains the provided information that you must use to construct an answer. +The provided information is authoritative, you must never doubt it or try to use your internal knowledge to correct it. +Make the answer sound as a response to the question. Do not mention that you based the result on the given information. +Here is an example: + +Question: Which managers own Neo4j stocks? +Context:[manager:CTL LLC, manager:JANE STREET GROUP LLC] +Helpful Answer: CTL LLC, JANE STREET GROUP LLC owns Neo4j stocks. + +Follow this example when generating answers. +If the provided information is empty, say that you don't know the answer. +Information: +{context} + +Question: {question} +Helpful Answer:""" +CYPHER_QA_PROMPT = PromptTemplate( + input_variables=["context", "question"], template=CYPHER_QA_TEMPLATE +) + +SPARQL_INTENT_TEMPLATE = """Task: Identify the intent of a prompt and return the appropriate SPARQL query type. +You are an assistant that distinguishes different types of prompts and returns the corresponding SPARQL query types. +Consider only the following query types: +* SELECT: this query type corresponds to questions +* UPDATE: this query type corresponds to all requests for deleting, inserting, or changing triples +Note: Be as concise as possible. +Do not include any explanations or apologies in your responses. +Do not respond to any questions that ask for anything else than for you to identify a SPARQL query type. +Do not include any unnecessary whitespaces or any text except the query type, i.e., either return 'SELECT' or 'UPDATE'. + +The prompt is: +{prompt} +Helpful Answer:""" +SPARQL_INTENT_PROMPT = PromptTemplate( + input_variables=["prompt"], template=SPARQL_INTENT_TEMPLATE +) + +SPARQL_GENERATION_SELECT_TEMPLATE = """Task: Generate a SPARQL SELECT statement for querying a graph database. +For instance, to find all email addresses of John Doe, the following query in backticks would be suitable: +``` +PREFIX foaf: +SELECT ?email +WHERE {{ + ?person foaf:name "John Doe" . + ?person foaf:mbox ?email . +}} +``` +Instructions: +Use only the node types and properties provided in the schema. +Do not use any node types and properties that are not explicitly provided. +Include all necessary prefixes. +Schema: +{schema} +Note: Be as concise as possible. +Do not include any explanations or apologies in your responses. +Do not respond to any questions that ask for anything else than for you to construct a SPARQL query. +Do not include any text except the SPARQL query generated. + +The question is: +{prompt}""" +SPARQL_GENERATION_SELECT_PROMPT = PromptTemplate( + input_variables=["schema", "prompt"], template=SPARQL_GENERATION_SELECT_TEMPLATE +) + +SPARQL_GENERATION_UPDATE_TEMPLATE = """Task: Generate a SPARQL UPDATE statement for updating a graph database. +For instance, to add 'jane.doe@foo.bar' as a new email address for Jane Doe, the following query in backticks would be suitable: +``` +PREFIX foaf: +INSERT {{ + ?person foaf:mbox . +}} +WHERE {{ + ?person foaf:name "Jane Doe" . +}} +``` +Instructions: +Make the query as short as possible and avoid adding unnecessary triples. +Use only the node types and properties provided in the schema. +Do not use any node types and properties that are not explicitly provided. +Include all necessary prefixes. +Schema: +{schema} +Note: Be as concise as possible. +Do not include any explanations or apologies in your responses. +Do not respond to any questions that ask for anything else than for you to construct a SPARQL query. +Return only the generated SPARQL query, nothing else. + +The information to be inserted is: +{prompt}""" +SPARQL_GENERATION_UPDATE_PROMPT = PromptTemplate( + input_variables=["schema", "prompt"], template=SPARQL_GENERATION_UPDATE_TEMPLATE +) + +SPARQL_QA_TEMPLATE = """Task: Generate a natural language response from the results of a SPARQL query. +You are an assistant that creates well-written and human understandable answers. +The information part contains the information provided, which you can use to construct an answer. +The information provided is authoritative, you must never doubt it or try to use your internal knowledge to correct it. +Make your response sound like the information is coming from an AI assistant, but don't add any information. +Information: +{context} + +Question: {prompt} +Helpful Answer:""" +SPARQL_QA_PROMPT = PromptTemplate( + input_variables=["context", "prompt"], template=SPARQL_QA_TEMPLATE +) + +GRAPHDB_SPARQL_GENERATION_TEMPLATE = """ +Write a SPARQL SELECT query for querying a graph database. +The ontology schema delimited by triple backticks in Turtle format is: +``` +{schema} +``` +Use only the classes and properties provided in the schema to construct the SPARQL query. +Do not use any classes or properties that are not explicitly provided in the SPARQL query. +Include all necessary prefixes. +Do not include any explanations or apologies in your responses. +Do not wrap the query in backticks. +Do not include any text except the SPARQL query generated. +The question delimited by triple backticks is: +``` +{prompt} +``` +""" +GRAPHDB_SPARQL_GENERATION_PROMPT = PromptTemplate( + input_variables=["schema", "prompt"], + template=GRAPHDB_SPARQL_GENERATION_TEMPLATE, +) + +GRAPHDB_SPARQL_FIX_TEMPLATE = """ +This following SPARQL query delimited by triple backticks +``` +{generated_sparql} +``` +is not valid. +The error delimited by triple backticks is +``` +{error_message} +``` +Give me a correct version of the SPARQL query. +Do not change the logic of the query. +Do not include any explanations or apologies in your responses. +Do not wrap the query in backticks. +Do not include any text except the SPARQL query generated. +The ontology schema delimited by triple backticks in Turtle format is: +``` +{schema} +``` +""" + +GRAPHDB_SPARQL_FIX_PROMPT = PromptTemplate( + input_variables=["error_message", "generated_sparql", "schema"], + template=GRAPHDB_SPARQL_FIX_TEMPLATE, +) + +GRAPHDB_QA_TEMPLATE = """Task: Generate a natural language response from the results of a SPARQL query. +You are an assistant that creates well-written and human understandable answers. +The information part contains the information provided, which you can use to construct an answer. +The information provided is authoritative, you must never doubt it or try to use your internal knowledge to correct it. +Make your response sound like the information is coming from an AI assistant, but don't add any information. +Don't use internal knowledge to answer the question, just say you don't know if no information is available. +Information: +{context} + +Question: {prompt} +Helpful Answer:""" +GRAPHDB_QA_PROMPT = PromptTemplate( + input_variables=["context", "prompt"], template=GRAPHDB_QA_TEMPLATE +) + +AQL_GENERATION_TEMPLATE = """Task: Generate an ArangoDB Query Language (AQL) query from a User Input. + +You are an ArangoDB Query Language (AQL) expert responsible for translating a `User Input` into an ArangoDB Query Language (AQL) query. + +You are given an `ArangoDB Schema`. It is a JSON Object containing: +1. `Graph Schema`: Lists all Graphs within the ArangoDB Database Instance, along with their Edge Relationships. +2. `Collection Schema`: Lists all Collections within the ArangoDB Database Instance, along with their document/edge properties and a document/edge example. + +You may also be given a set of `AQL Query Examples` to help you create the `AQL Query`. If provided, the `AQL Query Examples` should be used as a reference, similar to how `ArangoDB Schema` should be used. + +Things you should do: +- Think step by step. +- Rely on `ArangoDB Schema` and `AQL Query Examples` (if provided) to generate the query. +- Begin the `AQL Query` by the `WITH` AQL keyword to specify all of the ArangoDB Collections required. +- Return the `AQL Query` wrapped in 3 backticks (```). +- Use only the provided relationship types and properties in the `ArangoDB Schema` and any `AQL Query Examples` queries. +- Only answer to requests related to generating an AQL Query. +- If a request is unrelated to generating AQL Query, say that you cannot help the user. + +Things you should not do: +- Do not use any properties/relationships that can't be inferred from the `ArangoDB Schema` or the `AQL Query Examples`. +- Do not include any text except the generated AQL Query. +- Do not provide explanations or apologies in your responses. +- Do not generate an AQL Query that removes or deletes any data. + +Under no circumstance should you generate an AQL Query that deletes any data whatsoever. + +ArangoDB Schema: +{adb_schema} + +AQL Query Examples (Optional): +{aql_examples} + +User Input: +{user_input} + +AQL Query: +""" + +AQL_GENERATION_PROMPT = PromptTemplate( + input_variables=["adb_schema", "aql_examples", "user_input"], + template=AQL_GENERATION_TEMPLATE, +) + +AQL_FIX_TEMPLATE = """Task: Address the ArangoDB Query Language (AQL) error message of an ArangoDB Query Language query. + +You are an ArangoDB Query Language (AQL) expert responsible for correcting the provided `AQL Query` based on the provided `AQL Error`. + +The `AQL Error` explains why the `AQL Query` could not be executed in the database. +The `AQL Error` may also contain the position of the error relative to the total number of lines of the `AQL Query`. +For example, 'error X at position 2:5' denotes that the error X occurs on line 2, column 5 of the `AQL Query`. + +You are also given the `ArangoDB Schema`. It is a JSON Object containing: +1. `Graph Schema`: Lists all Graphs within the ArangoDB Database Instance, along with their Edge Relationships. +2. `Collection Schema`: Lists all Collections within the ArangoDB Database Instance, along with their document/edge properties and a document/edge example. + +You will output the `Corrected AQL Query` wrapped in 3 backticks (```). Do not include any text except the Corrected AQL Query. + +Remember to think step by step. + +ArangoDB Schema: +{adb_schema} + +AQL Query: +{aql_query} + +AQL Error: +{aql_error} + +Corrected AQL Query: +""" + +AQL_FIX_PROMPT = PromptTemplate( + input_variables=[ + "adb_schema", + "aql_query", + "aql_error", + ], + template=AQL_FIX_TEMPLATE, +) + +AQL_QA_TEMPLATE = """Task: Generate a natural language `Summary` from the results of an ArangoDB Query Language query. + +You are an ArangoDB Query Language (AQL) expert responsible for creating a well-written `Summary` from the `User Input` and associated `AQL Result`. + +A user has executed an ArangoDB Query Language query, which has returned the AQL Result in JSON format. +You are responsible for creating an `Summary` based on the AQL Result. + +You are given the following information: +- `ArangoDB Schema`: contains a schema representation of the user's ArangoDB Database. +- `User Input`: the original question/request of the user, which has been translated into an AQL Query. +- `AQL Query`: the AQL equivalent of the `User Input`, translated by another AI Model. Should you deem it to be incorrect, suggest a different AQL Query. +- `AQL Result`: the JSON output returned by executing the `AQL Query` within the ArangoDB Database. + +Remember to think step by step. + +Your `Summary` should sound like it is a response to the `User Input`. +Your `Summary` should not include any mention of the `AQL Query` or the `AQL Result`. + +ArangoDB Schema: +{adb_schema} + +User Input: +{user_input} + +AQL Query: +{aql_query} + +AQL Result: +{aql_result} +""" +AQL_QA_PROMPT = PromptTemplate( + input_variables=["adb_schema", "user_input", "aql_query", "aql_result"], + template=AQL_QA_TEMPLATE, +) + + +NEPTUNE_OPENCYPHER_EXTRA_INSTRUCTIONS = """ +Instructions: +Generate the query in openCypher format and follow these rules: +Do not use `NONE`, `ALL` or `ANY` predicate functions, rather use list comprehensions. +Do not use `REDUCE` function. Rather use a combination of list comprehension and the `UNWIND` clause to achieve similar results. +Do not use `FOREACH` clause. Rather use a combination of `WITH` and `UNWIND` clauses to achieve similar results.{extra_instructions} +\n""" + +NEPTUNE_OPENCYPHER_GENERATION_TEMPLATE = CYPHER_GENERATION_TEMPLATE.replace( + "Instructions:", NEPTUNE_OPENCYPHER_EXTRA_INSTRUCTIONS +) + +NEPTUNE_OPENCYPHER_GENERATION_PROMPT = PromptTemplate( + input_variables=["schema", "question", "extra_instructions"], + template=NEPTUNE_OPENCYPHER_GENERATION_TEMPLATE, +) + +NEPTUNE_OPENCYPHER_GENERATION_SIMPLE_TEMPLATE = """ +Write an openCypher query to answer the following question. Do not explain the answer. Only return the query.{extra_instructions} +Question: "{question}". +Here is the property graph schema: +{schema} +\n""" + +NEPTUNE_OPENCYPHER_GENERATION_SIMPLE_PROMPT = PromptTemplate( + input_variables=["schema", "question", "extra_instructions"], + template=NEPTUNE_OPENCYPHER_GENERATION_SIMPLE_TEMPLATE, +) + +MEMGRAPH_GENERATION_TEMPLATE = """Your task is to directly translate natural language inquiry into precise and executable Cypher query for Memgraph database. +You will utilize a provided database schema to understand the structure, nodes and relationships within the Memgraph database. +Instructions: +- Use provided node and relationship labels and property names from the +schema which describes the database's structure. Upon receiving a user +question, synthesize the schema to craft a precise Cypher query that +directly corresponds to the user's intent. +- Generate valid executable Cypher queries on top of Memgraph database. +Any explanation, context, or additional information that is not a part +of the Cypher query syntax should be omitted entirely. +- Use Memgraph MAGE procedures instead of Neo4j APOC procedures. +- Do not include any explanations or apologies in your responses. +- Do not include any text except the generated Cypher statement. +- For queries that ask for information or functionalities outside the direct +generation of Cypher queries, use the Cypher query format to communicate +limitations or capabilities. For example: RETURN "I am designed to generate +Cypher queries based on the provided schema only." +Schema: +{schema} + +With all the above information and instructions, generate Cypher query for the +user question. + +The question is: +{question}""" + +MEMGRAPH_GENERATION_PROMPT = PromptTemplate( + input_variables=["schema", "question"], template=MEMGRAPH_GENERATION_TEMPLATE +) + + +MEMGRAPH_QA_TEMPLATE = """Your task is to form nice and human +understandable answers. The information part contains the provided +information that you must use to construct an answer. +The provided information is authoritative, you must never doubt it or try to +use your internal knowledge to correct it. Make the answer sound as a +response to the question. Do not mention that you based the result on the +given information. Here is an example: + +Question: Which managers own Neo4j stocks? +Context:[manager:CTL LLC, manager:JANE STREET GROUP LLC] +Helpful Answer: CTL LLC, JANE STREET GROUP LLC owns Neo4j stocks. + +Follow this example when generating answers. If the provided information is +empty, say that you don't know the answer. + +Information: +{context} + +Question: {question} +Helpful Answer:""" +MEMGRAPH_QA_PROMPT = PromptTemplate( + input_variables=["context", "question"], template=MEMGRAPH_QA_TEMPLATE +) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/sparql.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/sparql.py new file mode 100644 index 0000000000000000000000000000000000000000..56ed5cc9b57364c6891f9b61d41b0a8c2a471c59 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/graph_qa/sparql.py @@ -0,0 +1,184 @@ +""" +Question answering over an RDF or OWL graph using SPARQL. +""" + +from __future__ import annotations + +from typing import Any, Dict, List, Optional + +from langchain_classic.chains.base import Chain +from langchain_classic.chains.llm import LLMChain +from langchain_core.callbacks import CallbackManagerForChainRun +from langchain_core.language_models import BaseLanguageModel +from langchain_core.prompts.base import BasePromptTemplate +from pydantic import Field + +from langchain_community.chains.graph_qa.prompts import ( + SPARQL_GENERATION_SELECT_PROMPT, + SPARQL_GENERATION_UPDATE_PROMPT, + SPARQL_INTENT_PROMPT, + SPARQL_QA_PROMPT, +) +from langchain_community.graphs.rdf_graph import RdfGraph + + +class GraphSparqlQAChain(Chain): + """Question-answering against an RDF or OWL graph by generating SPARQL statements. + + *Security note*: Make sure that the database connection uses credentials + that are narrowly-scoped to only include necessary permissions. + Failure to do so may result in data corruption or loss, since the calling + code may attempt commands that would result in deletion, mutation + of data if appropriately prompted or reading sensitive data if such + data is present in the database. + The best way to guard against such negative outcomes is to (as appropriate) + limit the permissions granted to the credentials used with this tool. + + See https://python.langchain.com/docs/security for more information. + """ + + graph: RdfGraph = Field(exclude=True) + sparql_generation_select_chain: LLMChain + sparql_generation_update_chain: LLMChain + sparql_intent_chain: LLMChain + qa_chain: LLMChain + return_sparql_query: bool = False + input_key: str = "query" #: :meta private: + output_key: str = "result" #: :meta private: + sparql_query_key: str = "sparql_query" #: :meta private: + + allow_dangerous_requests: bool = False + """Forced user opt-in to acknowledge that the chain can make dangerous requests. + + *Security note*: Make sure that the database connection uses credentials + that are narrowly-scoped to only include necessary permissions. + Failure to do so may result in data corruption or loss, since the calling + code may attempt commands that would result in deletion, mutation + of data if appropriately prompted or reading sensitive data if such + data is present in the database. + The best way to guard against such negative outcomes is to (as appropriate) + limit the permissions granted to the credentials used with this tool. + + See https://python.langchain.com/docs/security for more information. + """ + + def __init__(self, **kwargs: Any) -> None: + """Initialize the chain.""" + super().__init__(**kwargs) + if self.allow_dangerous_requests is not True: + raise ValueError( + "In order to use this chain, you must acknowledge that it can make " + "dangerous requests by setting `allow_dangerous_requests` to `True`." + "You must narrowly scope the permissions of the database connection " + "to only include necessary permissions. Failure to do so may result " + "in data corruption or loss or reading sensitive data if such data is " + "present in the database." + "Only use this chain if you understand the risks and have taken the " + "necessary precautions. " + "See https://python.langchain.com/docs/security for more information." + ) + + @property + def input_keys(self) -> List[str]: + """Return the input keys. + + :meta private: + """ + return [self.input_key] + + @property + def output_keys(self) -> List[str]: + """Return the output keys. + + :meta private: + """ + _output_keys = [self.output_key] + return _output_keys + + @classmethod + def from_llm( + cls, + llm: BaseLanguageModel, + *, + qa_prompt: BasePromptTemplate = SPARQL_QA_PROMPT, + sparql_select_prompt: BasePromptTemplate = SPARQL_GENERATION_SELECT_PROMPT, + sparql_update_prompt: BasePromptTemplate = SPARQL_GENERATION_UPDATE_PROMPT, + sparql_intent_prompt: BasePromptTemplate = SPARQL_INTENT_PROMPT, + **kwargs: Any, + ) -> GraphSparqlQAChain: + """Initialize from LLM.""" + qa_chain = LLMChain(llm=llm, prompt=qa_prompt) + sparql_generation_select_chain = LLMChain(llm=llm, prompt=sparql_select_prompt) + sparql_generation_update_chain = LLMChain(llm=llm, prompt=sparql_update_prompt) + sparql_intent_chain = LLMChain(llm=llm, prompt=sparql_intent_prompt) + + return cls( + qa_chain=qa_chain, + sparql_generation_select_chain=sparql_generation_select_chain, + sparql_generation_update_chain=sparql_generation_update_chain, + sparql_intent_chain=sparql_intent_chain, + **kwargs, + ) + + def _call( + self, + inputs: Dict[str, Any], + run_manager: Optional[CallbackManagerForChainRun] = None, + ) -> Dict[str, str]: + """ + Generate SPARQL query, use it to retrieve a response from the gdb and answer + the question. + """ + _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() + callbacks = _run_manager.get_child() + prompt = inputs[self.input_key] + + _intent = self.sparql_intent_chain.run({"prompt": prompt}, callbacks=callbacks) + intent = _intent.strip() + + if "SELECT" in intent and "UPDATE" not in intent: + sparql_generation_chain = self.sparql_generation_select_chain + intent = "SELECT" + elif "UPDATE" in intent and "SELECT" not in intent: + sparql_generation_chain = self.sparql_generation_update_chain + intent = "UPDATE" + else: + raise ValueError( + "I am sorry, but this prompt seems to fit none of the currently " + "supported SPARQL query types, i.e., SELECT and UPDATE." + ) + + _run_manager.on_text("Identified intent:", end="\n", verbose=self.verbose) + _run_manager.on_text(intent, color="green", end="\n", verbose=self.verbose) + + generated_sparql = sparql_generation_chain.run( + {"prompt": prompt, "schema": self.graph.get_schema}, callbacks=callbacks + ) + + _run_manager.on_text("Generated SPARQL:", end="\n", verbose=self.verbose) + _run_manager.on_text( + generated_sparql, color="green", end="\n", verbose=self.verbose + ) + + if intent == "SELECT": + context = self.graph.query(generated_sparql) + + _run_manager.on_text("Full Context:", end="\n", verbose=self.verbose) + _run_manager.on_text( + str(context), color="green", end="\n", verbose=self.verbose + ) + result = self.qa_chain( + {"prompt": prompt, "context": context}, + callbacks=callbacks, + ) + res = result[self.qa_chain.output_key] + elif intent == "UPDATE": + self.graph.update(generated_sparql) + res = "Successfully inserted triples into the graph." + else: + raise ValueError("Unsupported SPARQL query type.") + + chain_result: Dict[str, Any] = {self.output_key: res} + if self.return_sparql_query: + chain_result[self.sparql_query_key] = generated_sparql + return chain_result diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/natbot/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/natbot/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..aeec86f8bf21976881339ffa2546a4b88ea538e8 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/natbot/__init__.py @@ -0,0 +1,8 @@ +"""Implement a GPT-3 driven browser. + +Heavily influenced from https://github.com/nat/natbot +""" + +from langchain_community.chains.natbot.base import NatBotChain + +__all__ = ["NatBotChain"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/natbot/base.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/natbot/base.py new file mode 100644 index 0000000000000000000000000000000000000000..609d5c281032a1f9edf5a61aebe53fe7f8d47b1c --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/natbot/base.py @@ -0,0 +1,3 @@ +from langchain_classic.chains import NatBotChain + +__all__ = ["NatBotChain"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/natbot/crawler.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/natbot/crawler.py new file mode 100644 index 0000000000000000000000000000000000000000..794ea0f69f922e6db5f652acb2686b150da397f9 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/natbot/crawler.py @@ -0,0 +1,7 @@ +from langchain_classic.chains.natbot.crawler import ( + Crawler, + ElementInViewPort, + black_listed_elements, +) + +__all__ = ["ElementInViewPort", "Crawler", "black_listed_elements"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/natbot/prompt.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/natbot/prompt.py new file mode 100644 index 0000000000000000000000000000000000000000..0147ee81f138ef2755713fba03ca0288afa02d5f --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/natbot/prompt.py @@ -0,0 +1,3 @@ +from langchain_classic.chains.natbot.prompt import PROMPT + +__all__ = ["PROMPT"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/openapi/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/openapi/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/openapi/chain.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/openapi/chain.py new file mode 100644 index 0000000000000000000000000000000000000000..3b9ebe9a3678f13f3fd126cadf245e6187448a71 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/openapi/chain.py @@ -0,0 +1,230 @@ +"""Chain that makes API calls and summarizes the responses to answer a question.""" + +from __future__ import annotations + +import json +from typing import Any, Dict, List, NamedTuple, Optional, cast + +from langchain_classic.chains.api.openapi.requests_chain import APIRequesterChain +from langchain_classic.chains.api.openapi.response_chain import APIResponderChain +from langchain_classic.chains.base import Chain +from langchain_classic.chains.llm import LLMChain +from langchain_core.callbacks import CallbackManagerForChainRun, Callbacks +from langchain_core.language_models import BaseLanguageModel +from pydantic import BaseModel, Field +from requests import Response + +from langchain_community.tools.openapi.utils.api_models import APIOperation +from langchain_community.utilities.requests import Requests + + +class _ParamMapping(NamedTuple): + """Mapping from parameter name to parameter value.""" + + query_params: List[str] + body_params: List[str] + path_params: List[str] + + +class OpenAPIEndpointChain(Chain, BaseModel): + """Chain interacts with an OpenAPI endpoint using natural language.""" + + api_request_chain: LLMChain + api_response_chain: Optional[LLMChain] = None + api_operation: APIOperation + requests: Requests = Field(exclude=True, default_factory=Requests) + param_mapping: _ParamMapping = Field(alias="param_mapping") + return_intermediate_steps: bool = False + instructions_key: str = "instructions" #: :meta private: + output_key: str = "output" #: :meta private: + max_text_length: Optional[int] = Field(ge=0) #: :meta private: + + @property + def input_keys(self) -> List[str]: + """Expect input key. + + :meta private: + """ + return [self.instructions_key] + + @property + def output_keys(self) -> List[str]: + """Expect output key. + + :meta private: + """ + if not self.return_intermediate_steps: + return [self.output_key] + else: + return [self.output_key, "intermediate_steps"] + + def _construct_path(self, args: Dict[str, str]) -> str: + """Construct the path from the deserialized input.""" + path = self.api_operation.base_url + self.api_operation.path + for param in self.param_mapping.path_params: + path = path.replace(f"{{{param}}}", str(args.pop(param, ""))) + return path + + def _extract_query_params(self, args: Dict[str, str]) -> Dict[str, str]: + """Extract the query params from the deserialized input.""" + query_params = {} + for param in self.param_mapping.query_params: + if param in args: + query_params[param] = args.pop(param) + return query_params + + def _extract_body_params(self, args: Dict[str, str]) -> Optional[Dict[str, str]]: + """Extract the request body params from the deserialized input.""" + body_params = None + if self.param_mapping.body_params: + body_params = {} + for param in self.param_mapping.body_params: + if param in args: + body_params[param] = args.pop(param) + return body_params + + def deserialize_json_input(self, serialized_args: str) -> dict: + """Use the serialized typescript dictionary. + + Resolve the path, query params dict, and optional requestBody dict. + """ + args: dict = json.loads(serialized_args) + path = self._construct_path(args) + body_params = self._extract_body_params(args) + query_params = self._extract_query_params(args) + return { + "url": path, + "data": body_params, + "params": query_params, + } + + def _get_output(self, output: str, intermediate_steps: dict) -> dict: + """Return the output from the API call.""" + if self.return_intermediate_steps: + return { + self.output_key: output, + "intermediate_steps": intermediate_steps, + } + else: + return {self.output_key: output} + + def _call( + self, + inputs: Dict[str, Any], + run_manager: Optional[CallbackManagerForChainRun] = None, + ) -> Dict[str, str]: + _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() + intermediate_steps = {} + instructions = inputs[self.instructions_key] + instructions = instructions[: self.max_text_length] + _api_arguments = self.api_request_chain.predict_and_parse( + instructions=instructions, callbacks=_run_manager.get_child() + ) + api_arguments = cast(str, _api_arguments) + intermediate_steps["request_args"] = api_arguments + _run_manager.on_text( + api_arguments, color="green", end="\n", verbose=self.verbose + ) + if api_arguments.startswith("ERROR"): + return self._get_output(api_arguments, intermediate_steps) + elif api_arguments.startswith("MESSAGE:"): + return self._get_output( + api_arguments[len("MESSAGE:") :], intermediate_steps + ) + try: + request_args = self.deserialize_json_input(api_arguments) + method = getattr(self.requests, self.api_operation.method.value) + api_response: Response = method(**request_args) + if api_response.status_code != 200: + method_str = str(self.api_operation.method.value) + response_text = ( + f"{api_response.status_code}: {api_response.reason}" + + f"\nFor {method_str.upper()} {request_args['url']}\n" + + f"Called with args: {request_args['params']}" + ) + else: + response_text = api_response.text + except Exception as e: + response_text = f"Error with message {str(e)}" + response_text = response_text[: self.max_text_length] + intermediate_steps["response_text"] = response_text + _run_manager.on_text( + response_text, color="blue", end="\n", verbose=self.verbose + ) + if self.api_response_chain is not None: + _answer = self.api_response_chain.predict_and_parse( + response=response_text, + instructions=instructions, + callbacks=_run_manager.get_child(), + ) + answer = cast(str, _answer) + _run_manager.on_text(answer, color="yellow", end="\n", verbose=self.verbose) + return self._get_output(answer, intermediate_steps) + else: + return self._get_output(response_text, intermediate_steps) + + @classmethod + def from_url_and_method( + cls, + spec_url: str, + path: str, + method: str, + llm: BaseLanguageModel, + requests: Optional[Requests] = None, + return_intermediate_steps: bool = False, + **kwargs: Any, + # TODO: Handle async + ) -> "OpenAPIEndpointChain": + """Create an OpenAPIEndpoint from a spec at the specified url.""" + operation = APIOperation.from_openapi_url(spec_url, path, method) + return cls.from_api_operation( + operation, + requests=requests, + llm=llm, + return_intermediate_steps=return_intermediate_steps, + **kwargs, + ) + + @classmethod + def from_api_operation( + cls, + operation: APIOperation, + llm: BaseLanguageModel, + requests: Optional[Requests] = None, + verbose: bool = False, + return_intermediate_steps: bool = False, + raw_response: bool = False, + callbacks: Callbacks = None, + **kwargs: Any, + # TODO: Handle async + ) -> "OpenAPIEndpointChain": + """Create an OpenAPIEndpointChain from an operation and a spec.""" + param_mapping = _ParamMapping( + query_params=operation.query_params, + body_params=operation.body_params, + path_params=operation.path_params, + ) + requests_chain = APIRequesterChain.from_llm_and_typescript( + llm, + typescript_definition=operation.to_typescript(), + verbose=verbose, + callbacks=callbacks, + ) + if raw_response: + response_chain = None + else: + response_chain = APIResponderChain.from_llm( + llm, verbose=verbose, callbacks=callbacks + ) + _requests = requests or Requests() + return cls( + api_request_chain=requests_chain, + api_response_chain=response_chain, + api_operation=operation, + requests=_requests, + param_mapping=param_mapping, + verbose=verbose, + return_intermediate_steps=return_intermediate_steps, + callbacks=callbacks, + **kwargs, + ) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/openapi/prompts.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/openapi/prompts.py new file mode 100644 index 0000000000000000000000000000000000000000..84e5a2baee986bf3dc69d708bebe5c7e8a522ceb --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/openapi/prompts.py @@ -0,0 +1,57 @@ +# flake8: noqa +REQUEST_TEMPLATE = """You are a helpful AI Assistant. Please provide JSON arguments to agentFunc() based on the user's instructions. + +API_SCHEMA: ```typescript +{schema} +``` + +USER_INSTRUCTIONS: "{instructions}" + +Your arguments must be plain json provided in a markdown block: + +ARGS: ```json +{{valid json conforming to API_SCHEMA}} +``` + +Example +----- + +ARGS: ```json +{{"foo": "bar", "baz": {{"qux": "quux"}}}} +``` + +The block must be no more than 1 line long, and all arguments must be valid JSON. All string arguments must be wrapped in double quotes. +You MUST strictly comply to the types indicated by the provided schema, including all required args. + +If you don't have sufficient information to call the function due to things like requiring specific uuid's, you can reply with the following message: + +Message: ```text +Concise response requesting the additional information that would make calling the function successful. +``` + +Begin +----- +ARGS: +""" +RESPONSE_TEMPLATE = """You are a helpful AI assistant trained to answer user queries from API responses. +You attempted to call an API, which resulted in: +API_RESPONSE: {response} + +USER_COMMENT: "{instructions}" + + +If the API_RESPONSE can answer the USER_COMMENT respond with the following markdown json block: +Response: ```json +{{"response": "Human-understandable synthesis of the API_RESPONSE"}} +``` + +Otherwise respond with the following markdown json block: +Response Error: ```json +{{"response": "What you did and a concise statement of the resulting error. If it can be easily fixed, provide a suggestion."}} +``` + +You MUST respond as a markdown json code block. The person you are responding to CANNOT see the API_RESPONSE, so if there is any relevant information there you must include it in your response. + +Begin: +--- +""" diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/openapi/requests_chain.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/openapi/requests_chain.py new file mode 100644 index 0000000000000000000000000000000000000000..f6102f180f97e0744ccc7f8ae8fd4b7e8c2127d6 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/openapi/requests_chain.py @@ -0,0 +1,62 @@ +"""request parser.""" + +import json +import re +from typing import Any + +from langchain_classic.chains.api.openapi.prompts import REQUEST_TEMPLATE +from langchain_classic.chains.llm import LLMChain +from langchain_core.language_models import BaseLanguageModel +from langchain_core.output_parsers import BaseOutputParser +from langchain_core.prompts.prompt import PromptTemplate + + +class APIRequesterOutputParser(BaseOutputParser): + """Parse the request and error tags.""" + + def _load_json_block(self, serialized_block: str) -> str: + try: + return json.dumps(json.loads(serialized_block, strict=False)) + except json.JSONDecodeError: + return "ERROR serializing request." + + def parse(self, llm_output: str) -> str: + """Parse the request and error tags.""" + + json_match = re.search(r"```json(.*?)```", llm_output, re.DOTALL) + if json_match: + return self._load_json_block(json_match.group(1).strip()) + message_match = re.search(r"```text(.*?)```", llm_output, re.DOTALL) + if message_match: + return f"MESSAGE: {message_match.group(1).strip()}" + return "ERROR making request" + + @property + def _type(self) -> str: + return "api_requester" + + +class APIRequesterChain(LLMChain): + """Get the request parser.""" + + @classmethod + def is_lc_serializable(cls) -> bool: + return False + + @classmethod + def from_llm_and_typescript( + cls, + llm: BaseLanguageModel, + typescript_definition: str, + verbose: bool = True, + **kwargs: Any, + ) -> LLMChain: + """Get the request parser.""" + output_parser = APIRequesterOutputParser() + prompt = PromptTemplate( + template=REQUEST_TEMPLATE, + output_parser=output_parser, + partial_variables={"schema": typescript_definition}, + input_variables=["instructions"], + ) + return cls(prompt=prompt, llm=llm, verbose=verbose, **kwargs) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/openapi/response_chain.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/openapi/response_chain.py new file mode 100644 index 0000000000000000000000000000000000000000..3c3ec0ac9ce8ae404e0c3ae22ee34636b9b44527 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/openapi/response_chain.py @@ -0,0 +1,57 @@ +"""Response parser.""" + +import json +import re +from typing import Any + +from langchain_classic.chains.api.openapi.prompts import RESPONSE_TEMPLATE +from langchain_classic.chains.llm import LLMChain +from langchain_core.language_models import BaseLanguageModel +from langchain_core.output_parsers import BaseOutputParser +from langchain_core.prompts.prompt import PromptTemplate + + +class APIResponderOutputParser(BaseOutputParser): + """Parse the response and error tags.""" + + def _load_json_block(self, serialized_block: str) -> str: + try: + response_content = json.loads(serialized_block, strict=False) + return response_content.get("response", "ERROR parsing response.") + except json.JSONDecodeError: + return "ERROR parsing response." + except: + raise + + def parse(self, llm_output: str) -> str: + """Parse the response and error tags.""" + json_match = re.search(r"```json(.*?)```", llm_output, re.DOTALL) + if json_match: + return self._load_json_block(json_match.group(1).strip()) + else: + raise ValueError(f"No response found in output: {llm_output}.") + + @property + def _type(self) -> str: + return "api_responder" + + +class APIResponderChain(LLMChain): + """Get the response parser.""" + + @classmethod + def is_lc_serializable(cls) -> bool: + return False + + @classmethod + def from_llm( + cls, llm: BaseLanguageModel, verbose: bool = True, **kwargs: Any + ) -> LLMChain: + """Get the response parser.""" + output_parser = APIResponderOutputParser() + prompt = PromptTemplate( + template=RESPONSE_TEMPLATE, + output_parser=output_parser, + input_variables=["response", "instructions"], + ) + return cls(prompt=prompt, llm=llm, verbose=verbose, **kwargs) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/pebblo_retrieval/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/pebblo_retrieval/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git 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+from langchain_classic.chains.base import Chain +from langchain_classic.chains.combine_documents.base import BaseCombineDocumentsChain +from langchain_core.callbacks import ( + AsyncCallbackManagerForChainRun, + CallbackManagerForChainRun, +) +from langchain_core.documents import Document +from langchain_core.language_models import BaseLanguageModel +from langchain_core.vectorstores import VectorStoreRetriever +from pydantic import ConfigDict, Field, validator + +from langchain_community.chains.pebblo_retrieval.enforcement_filters import ( + SUPPORTED_VECTORSTORES, + set_enforcement_filters, +) +from langchain_community.chains.pebblo_retrieval.models import ( + App, + AuthContext, + ChainInfo, + Framework, + Model, + SemanticContext, + VectorDB, +) +from langchain_community.chains.pebblo_retrieval.utilities import ( + PLUGIN_VERSION, + PebbloRetrievalAPIWrapper, + get_runtime, +) + +logger = logging.getLogger(__name__) + + +class PebbloRetrievalQA(Chain): + """ + Retrieval Chain with Identity & Semantic Enforcement for question-answering + against a vector database. + """ + + combine_documents_chain: BaseCombineDocumentsChain + """Chain to use to combine the documents.""" + input_key: str = "query" #: :meta private: + output_key: str = "result" #: :meta private: + return_source_documents: bool = False + """Return the source documents or not.""" + + retriever: VectorStoreRetriever = Field(exclude=True) + """VectorStore to use for retrieval.""" + auth_context_key: str = "auth_context" #: :meta private: + """Authentication context for identity enforcement.""" + semantic_context_key: str = "semantic_context" #: :meta private: + """Semantic context for semantic enforcement.""" + app_name: str #: :meta private: + """App name.""" + owner: str #: :meta private: + """Owner of app.""" + description: str #: :meta private: + """Description of app.""" + api_key: Optional[str] = None #: :meta private: + """Pebblo cloud API key for app.""" + classifier_url: Optional[str] = None #: :meta private: + """Classifier endpoint.""" + classifier_location: str = "local" #: :meta private: + """Classifier location. It could be either of 'local' or 'pebblo-cloud'.""" + _discover_sent: bool = False #: :meta private: + """Flag to check if discover payload has been sent.""" + enable_prompt_gov: bool = True #: :meta private: + """Flag to check if prompt governance is enabled or not""" + pb_client: PebbloRetrievalAPIWrapper = Field( + default_factory=PebbloRetrievalAPIWrapper + ) + """Pebblo Retrieval API client""" + + def _call( + self, + inputs: Dict[str, Any], + run_manager: Optional[CallbackManagerForChainRun] = None, + ) -> Dict[str, Any]: + """Run get_relevant_text and llm on input query. + + If chain has 'return_source_documents' as 'True', returns + the retrieved documents as well under the key 'source_documents'. + + Example: + .. code-block:: python + + res = indexqa({'query': 'This is my query'}) + answer, docs = res['result'], res['source_documents'] + """ + prompt_time = datetime.datetime.now().isoformat() + _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() + question = inputs[self.input_key] + auth_context = inputs.get(self.auth_context_key) + semantic_context = inputs.get(self.semantic_context_key) + _, prompt_entities = self.pb_client.check_prompt_validity(question) + + accepts_run_manager = ( + "run_manager" in inspect.signature(self._get_docs).parameters + ) + if accepts_run_manager: + docs = self._get_docs( + question, auth_context, semantic_context, run_manager=_run_manager + ) + else: + docs = self._get_docs(question, auth_context, semantic_context) # type: ignore[call-arg] + answer = self.combine_documents_chain.run( + input_documents=docs, question=question, callbacks=_run_manager.get_child() + ) + + self.pb_client.send_prompt( + self.app_name, + self.retriever, + question, + answer, + auth_context, + docs, + prompt_entities, + prompt_time, + self.enable_prompt_gov, + ) + + if self.return_source_documents: + return {self.output_key: answer, "source_documents": docs} + else: + return {self.output_key: answer} + + async def _acall( + self, + inputs: Dict[str, Any], + run_manager: Optional[AsyncCallbackManagerForChainRun] = None, + ) -> Dict[str, Any]: + """Run get_relevant_text and llm on input query. + + If chain has 'return_source_documents' as 'True', returns + the retrieved documents as well under the key 'source_documents'. + + Example: + .. code-block:: python + + res = indexqa({'query': 'This is my query'}) + answer, docs = res['result'], res['source_documents'] + """ + prompt_time = datetime.datetime.now().isoformat() + _run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager() + question = inputs[self.input_key] + auth_context = inputs.get(self.auth_context_key) + semantic_context = inputs.get(self.semantic_context_key) + accepts_run_manager = ( + "run_manager" in inspect.signature(self._aget_docs).parameters + ) + + _, prompt_entities = await self.pb_client.acheck_prompt_validity(question) + + if accepts_run_manager: + docs = await self._aget_docs( + question, auth_context, semantic_context, run_manager=_run_manager + ) + else: + docs = await self._aget_docs(question, auth_context, semantic_context) # type: ignore[call-arg] + answer = await self.combine_documents_chain.arun( + input_documents=docs, question=question, callbacks=_run_manager.get_child() + ) + + await self.pb_client.asend_prompt( + self.app_name, + self.retriever, + question, + answer, + auth_context, + docs, + prompt_entities, + prompt_time, + self.enable_prompt_gov, + ) + + if self.return_source_documents: + return {self.output_key: answer, "source_documents": docs} + else: + return {self.output_key: answer} + + model_config = ConfigDict( + populate_by_name=True, + arbitrary_types_allowed=True, + extra="forbid", + ) + + @property + def input_keys(self) -> List[str]: + """Input keys. + + :meta private: + """ + return [self.input_key, self.auth_context_key, self.semantic_context_key] + + @property + def output_keys(self) -> List[str]: + """Output keys. + + :meta private: + """ + _output_keys = [self.output_key] + if self.return_source_documents: + _output_keys += ["source_documents"] + return _output_keys + + @property + def _chain_type(self) -> str: + """Return the chain type.""" + return "pebblo_retrieval_qa" + + @classmethod + def from_chain_type( + cls, + llm: BaseLanguageModel, + app_name: str, + description: str, + owner: str, + chain_type: str = "stuff", + chain_type_kwargs: Optional[dict] = None, + api_key: Optional[str] = None, + classifier_url: Optional[str] = None, + classifier_location: str = "local", + **kwargs: Any, + ) -> "PebbloRetrievalQA": + """Load chain from chain type.""" + from langchain_classic.chains.question_answering import load_qa_chain + + _chain_type_kwargs = chain_type_kwargs or {} + combine_documents_chain = load_qa_chain( + llm, chain_type=chain_type, **_chain_type_kwargs + ) + + # generate app + app: App = PebbloRetrievalQA._get_app_details( + app_name=app_name, + description=description, + owner=owner, + llm=llm, + **kwargs, + ) + # initialize Pebblo API client + pb_client = PebbloRetrievalAPIWrapper( + api_key=api_key, + classifier_location=classifier_location, + classifier_url=classifier_url, + ) + # send app discovery request + pb_client.send_app_discover(app) + return cls( + combine_documents_chain=combine_documents_chain, + app_name=app_name, + owner=owner, + description=description, + api_key=api_key, + classifier_url=classifier_url, + classifier_location=classifier_location, + pb_client=pb_client, + **kwargs, + ) + + @validator("retriever", pre=True, always=True) + def validate_vectorstore( + cls, retriever: VectorStoreRetriever + ) -> VectorStoreRetriever: + """ + Validate that the vectorstore of the retriever is supported vectorstores. + """ + if retriever.vectorstore.__class__.__name__ not in SUPPORTED_VECTORSTORES: + raise ValueError( + f"Vectorstore must be an instance of one of the supported " + f"vectorstores: {SUPPORTED_VECTORSTORES}. " + f"Got '{retriever.vectorstore.__class__.__name__}' instead." + ) + return retriever + + def _get_docs( + self, + question: str, + auth_context: Optional[AuthContext], + semantic_context: Optional[SemanticContext], + *, + run_manager: CallbackManagerForChainRun, + ) -> List[Document]: + """Get docs.""" + set_enforcement_filters(self.retriever, auth_context, semantic_context) + return self.retriever.invoke( + question, config={"callbacks": run_manager.get_child()} + ) + + async def _aget_docs( + self, + question: str, + auth_context: Optional[AuthContext], + semantic_context: Optional[SemanticContext], + *, + run_manager: AsyncCallbackManagerForChainRun, + ) -> List[Document]: + """Get docs.""" + set_enforcement_filters(self.retriever, auth_context, semantic_context) + return await self.retriever.ainvoke( + question, config={"callbacks": run_manager.get_child()} + ) + + @staticmethod + def _get_app_details( + app_name: str, + owner: str, + description: str, + llm: BaseLanguageModel, + **kwargs: Any, + ) -> App: + """Fetch app details. Internal method. + Returns: + App: App details. + """ + framework, runtime = get_runtime() + chains = PebbloRetrievalQA.get_chain_details(llm, **kwargs) + app = App( + name=app_name, + owner=owner, + description=description, + runtime=runtime, + framework=framework, + chains=chains, + plugin_version=PLUGIN_VERSION, + client_version=Framework( + name="langchain_community", + version=version("langchain_community"), + ), + ) + return app + + @classmethod + def set_discover_sent(cls) -> None: + cls._discover_sent = True + + @classmethod + def get_chain_details( + cls, llm: BaseLanguageModel, **kwargs: Any + ) -> List[ChainInfo]: + """ + Get chain details. + + Args: + llm (BaseLanguageModel): Language model instance. + **kwargs: Additional keyword arguments. + + Returns: + List[ChainInfo]: Chain details. + """ + llm_dict = llm.__dict__ + chains = [ + ChainInfo( + name=cls.__name__, + model=Model( + name=llm_dict.get("model_name", llm_dict.get("model")), + vendor=llm.__class__.__name__, + ), + vector_dbs=[ + VectorDB( + name=kwargs["retriever"].vectorstore.__class__.__name__, + embedding_model=str( + kwargs["retriever"].vectorstore._embeddings.model + ) + if hasattr(kwargs["retriever"].vectorstore, "_embeddings") + else ( + str(kwargs["retriever"].vectorstore._embedding.model) + if hasattr(kwargs["retriever"].vectorstore, "_embedding") + else None + ), + ) + ], + ), + ] + return chains diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/pebblo_retrieval/enforcement_filters.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/pebblo_retrieval/enforcement_filters.py new file mode 100644 index 0000000000000000000000000000000000000000..579b86acb0ebc15c2bf6e2f0ee291d341379e5b6 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/pebblo_retrieval/enforcement_filters.py @@ -0,0 +1,532 @@ +""" +Identity & Semantic Enforcement filters for PebbloRetrievalQA chain: + +This module contains methods for applying Identity and Semantic Enforcement filters +in the PebbloRetrievalQA chain. +These filters are used to control the retrieval of documents based on authorization and +semantic context. +The Identity Enforcement filter ensures that only authorized identities can access +certain documents, while the Semantic Enforcement filter controls document retrieval +based on semantic context. + +The methods in this module are designed to work with different types of vector stores. +""" + +import logging +from typing import Any, List, Optional, Union + +from langchain_core.vectorstores import VectorStoreRetriever + +from langchain_community.chains.pebblo_retrieval.models import ( + AuthContext, + SemanticContext, +) + +logger = logging.getLogger(__name__) + +PINECONE = "Pinecone" +QDRANT = "Qdrant" +PGVECTOR = "PGVector" +PINECONE_VECTOR_STORE = "PineconeVectorStore" + +SUPPORTED_VECTORSTORES = {PINECONE, QDRANT, PGVECTOR, PINECONE_VECTOR_STORE} + + +def clear_enforcement_filters(retriever: VectorStoreRetriever) -> None: + """ + Clear the identity and semantic enforcement filters in the retriever search_kwargs. + """ + if retriever.vectorstore.__class__.__name__ == PGVECTOR: + search_kwargs = retriever.search_kwargs + if "filter" in search_kwargs: + filters = search_kwargs["filter"] + _pgvector_clear_pebblo_filters( + search_kwargs, filters, "authorized_identities" + ) + _pgvector_clear_pebblo_filters( + search_kwargs, filters, "pebblo_semantic_topics" + ) + _pgvector_clear_pebblo_filters( + search_kwargs, filters, "pebblo_semantic_entities" + ) + + +def set_enforcement_filters( + retriever: VectorStoreRetriever, + auth_context: Optional[AuthContext], + semantic_context: Optional[SemanticContext], +) -> None: + """ + Set identity and semantic enforcement filters in the retriever. + """ + # Clear existing enforcement filters + clear_enforcement_filters(retriever) + if auth_context is not None: + _set_identity_enforcement_filter(retriever, auth_context) + if semantic_context is not None: + _set_semantic_enforcement_filter(retriever, semantic_context) + + +def _apply_qdrant_semantic_filter( + search_kwargs: dict, semantic_context: Optional[SemanticContext] +) -> None: + """ + Set semantic enforcement filter in search_kwargs for Qdrant vectorstore. + """ + try: + from qdrant_client.http import models as rest + except ImportError as e: + raise ValueError( + "Could not import `qdrant-client.http` python package. " + "Please install it with `pip install qdrant-client`." + ) from e + + # Create a semantic enforcement filter condition + semantic_filters: List[ + Union[ + rest.FieldCondition, + rest.IsEmptyCondition, + rest.IsNullCondition, + rest.HasIdCondition, + rest.NestedCondition, + rest.Filter, + ] + ] = [] + + if ( + semantic_context is not None + and semantic_context.pebblo_semantic_topics is not None + ): + semantic_topics_filter = rest.FieldCondition( + key="metadata.pebblo_semantic_topics", + match=rest.MatchAny(any=semantic_context.pebblo_semantic_topics.deny), + ) + semantic_filters.append(semantic_topics_filter) + if ( + semantic_context is not None + and semantic_context.pebblo_semantic_entities is not None + ): + semantic_entities_filter = rest.FieldCondition( + key="metadata.pebblo_semantic_entities", + match=rest.MatchAny(any=semantic_context.pebblo_semantic_entities.deny), + ) + semantic_filters.append(semantic_entities_filter) + + # If 'filter' already exists in search_kwargs + if "filter" in search_kwargs: + existing_filter: rest.Filter = search_kwargs["filter"] + + # Check if existing_filter is a qdrant-client filter + if isinstance(existing_filter, rest.Filter): + # If 'must_not' condition exists in the existing filter + if isinstance(existing_filter.must_not, list): + # Warn if 'pebblo_semantic_topics' or 'pebblo_semantic_entities' + # filter is overridden + new_must_not_conditions: List[ + Union[ + rest.FieldCondition, + rest.IsEmptyCondition, + rest.IsNullCondition, + rest.HasIdCondition, + rest.NestedCondition, + rest.Filter, + ] + ] = [] + # Drop semantic filter conditions if already present + for condition in existing_filter.must_not: + if hasattr(condition, "key"): + if condition.key == "metadata.pebblo_semantic_topics": + continue + if condition.key == "metadata.pebblo_semantic_entities": + continue + new_must_not_conditions.append(condition) + # Add semantic enforcement filters to 'must_not' conditions + existing_filter.must_not = new_must_not_conditions + existing_filter.must_not.extend(semantic_filters) + else: + # Set 'must_not' condition with semantic enforcement filters + existing_filter.must_not = semantic_filters + else: + raise TypeError( + "Using dict as a `filter` is deprecated. " + "Please use qdrant-client filters directly: " + "https://qdrant.tech/documentation/concepts/filtering/" + ) + else: + # If 'filter' does not exist in search_kwargs, create it + search_kwargs["filter"] = rest.Filter(must_not=semantic_filters) + + +def _apply_qdrant_authorization_filter( + search_kwargs: dict, auth_context: Optional[AuthContext] +) -> None: + """ + Set identity enforcement filter in search_kwargs for Qdrant vectorstore. + """ + try: + from qdrant_client.http import models as rest + except ImportError as e: + raise ValueError( + "Could not import `qdrant-client.http` python package. " + "Please install it with `pip install qdrant-client`." + ) from e + + if auth_context is not None: + # Create a identity enforcement filter condition + identity_enforcement_filter = rest.FieldCondition( + key="metadata.authorized_identities", + match=rest.MatchAny(any=auth_context.user_auth), + ) + else: + return + + # If 'filter' already exists in search_kwargs + if "filter" in search_kwargs: + existing_filter: rest.Filter = search_kwargs["filter"] + + # Check if existing_filter is a qdrant-client filter + if isinstance(existing_filter, rest.Filter): + # If 'must' exists in the existing filter + if existing_filter.must: + new_must_conditions: List[ + Union[ + rest.FieldCondition, + rest.IsEmptyCondition, + rest.IsNullCondition, + rest.HasIdCondition, + rest.NestedCondition, + rest.Filter, + ] + ] = [] + # Drop 'authorized_identities' filter condition if already present + for condition in existing_filter.must: + if ( + hasattr(condition, "key") + and condition.key == "metadata.authorized_identities" + ): + continue + new_must_conditions.append(condition) + + # Add identity enforcement filter to 'must' conditions + existing_filter.must = new_must_conditions + existing_filter.must.append(identity_enforcement_filter) + else: + # Set 'must' condition with identity enforcement filter + existing_filter.must = [identity_enforcement_filter] + else: + raise TypeError( + "Using dict as a `filter` is deprecated. " + "Please use qdrant-client filters directly: " + "https://qdrant.tech/documentation/concepts/filtering/" + ) + else: + # If 'filter' does not exist in search_kwargs, create it + search_kwargs["filter"] = rest.Filter(must=[identity_enforcement_filter]) + + +def _apply_pinecone_semantic_filter( + search_kwargs: dict, semantic_context: Optional[SemanticContext] +) -> None: + """ + Set semantic enforcement filter in search_kwargs for Pinecone vectorstore. + """ + # Check if semantic_context is provided + semantic_context = semantic_context + if semantic_context is not None: + if semantic_context.pebblo_semantic_topics is not None: + # Add pebblo_semantic_topics filter to search_kwargs + search_kwargs.setdefault("filter", {})["pebblo_semantic_topics"] = { + "$nin": semantic_context.pebblo_semantic_topics.deny + } + + if semantic_context.pebblo_semantic_entities is not None: + # Add pebblo_semantic_entities filter to search_kwargs + search_kwargs.setdefault("filter", {})["pebblo_semantic_entities"] = { + "$nin": semantic_context.pebblo_semantic_entities.deny + } + + +def _apply_pinecone_authorization_filter( + search_kwargs: dict, auth_context: Optional[AuthContext] +) -> None: + """ + Set identity enforcement filter in search_kwargs for Pinecone vectorstore. + """ + if auth_context is not None: + search_kwargs.setdefault("filter", {})["authorized_identities"] = { + "$in": auth_context.user_auth + } + + +def _apply_pgvector_filter( + search_kwargs: dict, filters: Optional[Any], pebblo_filter: dict +) -> None: + """ + Apply pebblo filters in the search_kwargs filters. + """ + if isinstance(filters, dict): + if len(filters) == 1: + # The only operators allowed at the top level are $and, $or, and $not + # First check if an operator or a field + key, value = list(filters.items())[0] + if key.startswith("$"): + # Then it's an operator + if key.lower() not in ["$and", "$or", "$not"]: + raise ValueError( + f"Invalid filter condition. Expected $and, $or or $not " + f"but got: {key}" + ) + if not isinstance(value, list): + raise ValueError( + f"Expected a list, but got {type(value)} for value: {value}" + ) + + # Here we handle the $and, $or, and $not operators(Semantic filters) + if key.lower() == "$and": + # Add pebblo_filter to the $and list as it is + value.append(pebblo_filter) + elif key.lower() == "$not": + # Check if pebblo_filter is an operator or a field + _key, _value = list(pebblo_filter.items())[0] + if _key.startswith("$"): + # Then it's a operator + if _key.lower() == "$not": + # It's Semantic filter, add it's value to filters + value.append(_value) + logger.warning( + "Adding $not operator to the existing $not operator" + ) + return + else: + # Only $not operator is supported in pebblo_filter + raise ValueError( + f"Invalid filter key. Expected '$not' but got: {_key}" + ) + else: + # Then it's a field(Auth filter), move filters into $and + search_kwargs["filter"] = {"$and": [filters, pebblo_filter]} + return + elif key.lower() == "$or": + search_kwargs["filter"] = {"$and": [filters, pebblo_filter]} + else: + # Then it's a field and we can check pebblo_filter now + # Check if pebblo_filter is an operator or a field + _key, _ = list(pebblo_filter.items())[0] + if _key.startswith("$"): + # Then it's a operator + if _key.lower() == "$not": + # It's a $not operator(Semantic filter), move filters into $and + search_kwargs["filter"] = {"$and": [filters, pebblo_filter]} + return + else: + # Only $not operator is allowed in pebblo_filter + raise ValueError( + f"Invalid filter key. Expected '$not' but got: {_key}" + ) + else: + # Then it's a field(This handles Auth filter) + filters.update(pebblo_filter) + return + elif len(filters) > 1: + # Then all keys have to be fields (they cannot be operators) + for key in filters.keys(): + if key.startswith("$"): + raise ValueError( + f"Invalid filter condition. Expected a field but got: {key}" + ) + # filters should all be fields and we can check pebblo_filter now + # Check if pebblo_filter is an operator or a field + _key, _ = list(pebblo_filter.items())[0] + if _key.startswith("$"): + # Then it's a operator + if _key.lower() == "$not": + # It's a $not operator(Semantic filter), move filters into '$and' + search_kwargs["filter"] = {"$and": [filters, pebblo_filter]} + return + else: + # Only $not operator is supported in pebblo_filter + raise ValueError( + f"Invalid filter key. Expected '$not' but got: {_key}" + ) + else: + # Then it's a field(This handles Auth filter) + filters.update(pebblo_filter) + return + else: + # Got an empty dictionary for filters, set pebblo_filter in filter + search_kwargs.setdefault("filter", {}).update(pebblo_filter) + elif filters is None: + # If filters is None, set pebblo_filter as a new filter + search_kwargs.setdefault("filter", {}).update(pebblo_filter) + else: + raise ValueError( + f"Invalid filter. Expected a dictionary/None but got type: {type(filters)}" + ) + + +def _pgvector_clear_pebblo_filters( + search_kwargs: dict, filters: dict, pebblo_filter_key: str +) -> None: + """ + Remove pebblo filters from the search_kwargs filters. + """ + if isinstance(filters, dict): + if len(filters) == 1: + # The only operators allowed at the top level are $and, $or, and $not + # First check if an operator or a field + key, value = list(filters.items())[0] + if key.startswith("$"): + # Then it's an operator + # Validate the operator's key and value type + if key.lower() not in ["$and", "$or", "$not"]: + raise ValueError( + f"Invalid filter condition. Expected $and, $or or $not " + f"but got: {key}" + ) + elif not isinstance(value, list): + raise ValueError( + f"Expected a list, but got {type(value)} for value: {value}" + ) + + # Here we handle the $and, $or, and $not operators + if key.lower() == "$and": + # Remove the pebblo filter from the $and list + for i, _filter in enumerate(value): + if pebblo_filter_key in _filter: + # This handles Auth filter + value.pop(i) + break + # Check for $not operator with Semantic filter + if "$not" in _filter: + sem_filter_found = False + # This handles Semantic filter + for j, nested_filter in enumerate(_filter["$not"]): + if pebblo_filter_key in nested_filter: + if len(_filter["$not"]) == 1: + # If only one filter is left, + # then remove the $not operator + value.pop(i) + else: + value[i]["$not"].pop(j) + sem_filter_found = True + break + if sem_filter_found: + break + if len(value) == 1: + # If only one filter is left, then remove the $and operator + search_kwargs["filter"] = value[0] + elif key.lower() == "$not": + # Remove the pebblo filter from the $not list + for i, _filter in enumerate(value): + if pebblo_filter_key in _filter: + # This removes Semantic filter + value.pop(i) + break + if len(value) == 0: + # If no filter is left, then unset the filter + search_kwargs["filter"] = {} + elif key.lower() == "$or": + # If $or, pebblo filter will not be present + return + else: + # Then it's a field, check if it's a pebblo filter + if key == pebblo_filter_key: + filters.pop(key) + return + elif len(filters) > 1: + # Then all keys have to be fields (they cannot be operators) + if pebblo_filter_key in filters: + # This handles Auth filter + filters.pop(pebblo_filter_key) + return + else: + # Got an empty dictionary for filters, ignore the filter + return + elif filters is None: + # If filters is None, ignore the filter + return + else: + raise ValueError( + f"Invalid filter. Expected a dictionary/None but got type: {type(filters)}" + ) + + +def _apply_pgvector_semantic_filter( + search_kwargs: dict, semantic_context: Optional[SemanticContext] +) -> None: + """ + Set semantic enforcement filter in search_kwargs for PGVector vectorstore. + """ + # Check if semantic_context is provided + if semantic_context is not None: + _semantic_filters = [] + filters = search_kwargs.get("filter") + if semantic_context.pebblo_semantic_topics is not None: + # Add pebblo_semantic_topics filter to search_kwargs + topic_filter: dict = { + "pebblo_semantic_topics": { + "$eq": semantic_context.pebblo_semantic_topics.deny + } + } + _semantic_filters.append(topic_filter) + + if semantic_context.pebblo_semantic_entities is not None: + # Add pebblo_semantic_entities filter to search_kwargs + entity_filter: dict = { + "pebblo_semantic_entities": { + "$eq": semantic_context.pebblo_semantic_entities.deny + } + } + _semantic_filters.append(entity_filter) + + if len(_semantic_filters) > 0: + semantic_filter: dict = {"$not": _semantic_filters} + _apply_pgvector_filter(search_kwargs, filters, semantic_filter) + + +def _apply_pgvector_authorization_filter( + search_kwargs: dict, auth_context: Optional[AuthContext] +) -> None: + """ + Set identity enforcement filter in search_kwargs for PGVector vectorstore. + """ + if auth_context is not None: + auth_filter: dict = {"authorized_identities": {"$eq": auth_context.user_auth}} + filters = search_kwargs.get("filter") + _apply_pgvector_filter(search_kwargs, filters, auth_filter) + + +def _set_identity_enforcement_filter( + retriever: VectorStoreRetriever, auth_context: Optional[AuthContext] +) -> None: + """ + Set identity enforcement filter in search_kwargs. + + This method sets the identity enforcement filter in the search_kwargs + of the retriever based on the type of the vectorstore. + """ + search_kwargs = retriever.search_kwargs + if retriever.vectorstore.__class__.__name__ in [PINECONE, PINECONE_VECTOR_STORE]: + _apply_pinecone_authorization_filter(search_kwargs, auth_context) + elif retriever.vectorstore.__class__.__name__ == QDRANT: + _apply_qdrant_authorization_filter(search_kwargs, auth_context) + elif retriever.vectorstore.__class__.__name__ == PGVECTOR: + _apply_pgvector_authorization_filter(search_kwargs, auth_context) + + +def _set_semantic_enforcement_filter( + retriever: VectorStoreRetriever, semantic_context: Optional[SemanticContext] +) -> None: + """ + Set semantic enforcement filter in search_kwargs. + + This method sets the semantic enforcement filter in the search_kwargs + of the retriever based on the type of the vectorstore. + """ + search_kwargs = retriever.search_kwargs + if retriever.vectorstore.__class__.__name__ == PINECONE: + _apply_pinecone_semantic_filter(search_kwargs, semantic_context) + elif retriever.vectorstore.__class__.__name__ == QDRANT: + _apply_qdrant_semantic_filter(search_kwargs, semantic_context) + elif retriever.vectorstore.__class__.__name__ == PGVECTOR: + _apply_pgvector_semantic_filter(search_kwargs, semantic_context) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/pebblo_retrieval/models.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/pebblo_retrieval/models.py new file mode 100644 index 0000000000000000000000000000000000000000..97e29769ced6f65034ecbc7f0896d2fa77fd482b --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/pebblo_retrieval/models.py @@ -0,0 +1,151 @@ +"""Models for the PebbloRetrievalQA chain.""" + +from typing import Any, List, Optional, Union + +from pydantic import BaseModel + + +class AuthContext(BaseModel): + """Class for an authorization context.""" + + name: Optional[str] = None + user_id: str + user_auth: List[str] + """List of user authorizations, which may include their User ID and + the groups they are part of""" + + +class SemanticEntities(BaseModel): + """Class for a semantic entity filter.""" + + deny: List[str] + + +class SemanticTopics(BaseModel): + """Class for a semantic topic filter.""" + + deny: List[str] + + +class SemanticContext(BaseModel): + """Class for a semantic context.""" + + pebblo_semantic_entities: Optional[SemanticEntities] = None + pebblo_semantic_topics: Optional[SemanticTopics] = None + + def __init__(self, **data: Any) -> None: + super().__init__(**data) + + # Validate semantic_context + if ( + self.pebblo_semantic_entities is None + and self.pebblo_semantic_topics is None + ): + raise ValueError( + "semantic_context must contain 'pebblo_semantic_entities' or " + "'pebblo_semantic_topics'" + ) + + +class ChainInput(BaseModel): + """Input for PebbloRetrievalQA chain.""" + + query: str + auth_context: Optional[AuthContext] = None + semantic_context: Optional[SemanticContext] = None + + def dict(self, **kwargs: Any) -> dict: + base_dict = super().dict(**kwargs) + # Keep auth_context and semantic_context as it is(Pydantic models) + base_dict["auth_context"] = self.auth_context + base_dict["semantic_context"] = self.semantic_context + return base_dict + + +class Runtime(BaseModel): + """ + OS, language details + """ + + type: Optional[str] = "" + host: str + path: str + ip: Optional[str] = "" + platform: str + os: str + os_version: str + language: str + language_version: str + runtime: Optional[str] = "" + + +class Framework(BaseModel): + """ + Langchain framework details + """ + + name: str + version: str + + +class Model(BaseModel): + vendor: Optional[str] + name: Optional[str] + + +class PkgInfo(BaseModel): + project_home_page: Optional[str] + documentation_url: Optional[str] + pypi_url: Optional[str] + liscence_type: Optional[str] + installed_via: Optional[str] + location: Optional[str] + + +class VectorDB(BaseModel): + name: Optional[str] = None + version: Optional[str] = None + location: Optional[str] = None + embedding_model: Optional[str] = None + + +class ChainInfo(BaseModel): + name: str + model: Optional[Model] + vector_dbs: Optional[List[VectorDB]] + + +class App(BaseModel): + name: str + owner: str + description: Optional[str] + runtime: Runtime + framework: Framework + chains: List[ChainInfo] + plugin_version: str + client_version: Framework + + +class Context(BaseModel): + retrieved_from: Optional[str] + doc: Optional[str] + vector_db: str + pb_checksum: Optional[str] + + +class Prompt(BaseModel): + data: Optional[Union[list, str]] + entityCount: Optional[int] = None + entities: Optional[dict] = None + prompt_gov_enabled: Optional[bool] = None + + +class Qa(BaseModel): + name: str + context: Union[List[Optional[Context]], Optional[Context]] + prompt: Optional[Prompt] + response: Optional[Prompt] + prompt_time: str + user: str + user_identities: Optional[List[str]] + classifier_location: str diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/pebblo_retrieval/utilities.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/pebblo_retrieval/utilities.py new file mode 100644 index 0000000000000000000000000000000000000000..25f6efe1542c40802b62d85941a8443f4d98e9b1 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chains/pebblo_retrieval/utilities.py @@ -0,0 +1,542 @@ +import json +import logging +import os +import platform +from enum import Enum +from http import HTTPStatus +from typing import Any, Dict, List, Optional, Tuple + +import aiohttp +from aiohttp import ClientTimeout +from langchain_core.documents import Document +from langchain_core.env import get_runtime_environment +from langchain_core.utils import get_from_dict_or_env +from langchain_core.vectorstores import VectorStoreRetriever +from pydantic import BaseModel +from requests import Response, request +from requests.exceptions import RequestException + +from langchain_community.chains.pebblo_retrieval.models import ( + App, + AuthContext, + Context, + Framework, + Prompt, + Qa, + Runtime, +) + +logger = logging.getLogger(__name__) + +PLUGIN_VERSION = "0.1.1" + +_DEFAULT_CLASSIFIER_URL = "http://localhost:8000" +_DEFAULT_PEBBLO_CLOUD_URL = "https://api.daxa.ai" + + +class Routes(str, Enum): + """Routes available for the Pebblo API as enumerator.""" + + retrieval_app_discover = "/v1/app/discover" + prompt = "/v1/prompt" + prompt_governance = "/v1/prompt/governance" + + +def get_runtime() -> Tuple[Framework, Runtime]: + """Fetch the current Framework and Runtime details. + + Returns: + Tuple[Framework, Runtime]: Framework and Runtime for the current app instance. + """ + runtime_env = get_runtime_environment() + framework = Framework( + name="langchain", version=runtime_env.get("library_version", "unknown") + ) + uname = platform.uname() + runtime = Runtime( + host=uname.node, + path=os.environ["PWD"], + platform=runtime_env.get("platform", "unknown"), + os=uname.system, + os_version=uname.version, + ip=get_ip(), + language=runtime_env.get("runtime", "unknown"), + language_version=runtime_env.get("runtime_version", "unknown"), + ) + + if "Darwin" in runtime.os: + runtime.type = "desktop" + runtime.runtime = "Mac OSX" + + logger.debug(f"framework {framework}") + logger.debug(f"runtime {runtime}") + return framework, runtime + + +def get_ip() -> str: + """Fetch local runtime ip address. + + Returns: + str: IP address + """ + import socket # lazy imports + + host = socket.gethostname() + try: + public_ip = socket.gethostbyname(host) + except Exception: + public_ip = socket.gethostbyname("localhost") + return public_ip + + +class PebbloRetrievalAPIWrapper(BaseModel): + """Wrapper for Pebblo Retrieval API.""" + + api_key: Optional[str] # Use SecretStr + """API key for Pebblo Cloud""" + classifier_location: str = "local" + """Location of the classifier, local or cloud. Defaults to 'local'""" + classifier_url: Optional[str] + """URL of the Pebblo Classifier""" + cloud_url: Optional[str] + """URL of the Pebblo Cloud""" + + def __init__(self, **kwargs: Any): + """Validate that api key in environment.""" + kwargs["api_key"] = get_from_dict_or_env( + kwargs, "api_key", "PEBBLO_API_KEY", "" + ) + kwargs["classifier_url"] = get_from_dict_or_env( + kwargs, "classifier_url", "PEBBLO_CLASSIFIER_URL", _DEFAULT_CLASSIFIER_URL + ) + kwargs["cloud_url"] = get_from_dict_or_env( + kwargs, "cloud_url", "PEBBLO_CLOUD_URL", _DEFAULT_PEBBLO_CLOUD_URL + ) + super().__init__(**kwargs) + + def send_app_discover(self, app: App) -> None: + """ + Send app discovery request to Pebblo server & cloud. + + Args: + app (App): App instance to be discovered. + """ + pebblo_resp = None + payload = app.dict(exclude_unset=True) + + if self.classifier_location == "local": + # Send app details to local classifier + headers = self._make_headers() + app_discover_url = ( + f"{self.classifier_url}{Routes.retrieval_app_discover.value}" + ) + pebblo_resp = self.make_request("POST", app_discover_url, headers, payload) + + if self.api_key: + # Send app details to Pebblo cloud if api_key is present + headers = self._make_headers(cloud_request=True) + if pebblo_resp: + pebblo_server_version = json.loads(pebblo_resp.text).get( + "pebblo_server_version" + ) + payload.update({"pebblo_server_version": pebblo_server_version}) + + payload.update({"pebblo_client_version": PLUGIN_VERSION}) + pebblo_cloud_url = f"{self.cloud_url}{Routes.retrieval_app_discover.value}" + _ = self.make_request("POST", pebblo_cloud_url, headers, payload) + + def send_prompt( + self, + app_name: str, + retriever: VectorStoreRetriever, + question: str, + answer: str, + auth_context: Optional[AuthContext], + docs: List[Document], + prompt_entities: Dict[str, Any], + prompt_time: str, + prompt_gov_enabled: bool = False, + ) -> None: + """ + Send prompt to Pebblo server for classification. + Then send prompt to Daxa cloud(If api_key is present). + + Args: + app_name (str): Name of the app. + retriever (VectorStoreRetriever): Retriever instance. + question (str): Question asked in the prompt. + answer (str): Answer generated by the model. + auth_context (Optional[AuthContext]): Authentication context. + docs (List[Document]): List of documents retrieved. + prompt_entities (Dict[str, Any]): Entities present in the prompt. + prompt_time (str): Time when the prompt was generated. + prompt_gov_enabled (bool): Whether prompt governance is enabled. + """ + pebblo_resp = None + payload = self.build_prompt_qa_payload( + app_name, + retriever, + question, + answer, + auth_context, + docs, + prompt_entities, + prompt_time, + prompt_gov_enabled, + ) + + if self.classifier_location == "local": + # Send prompt to local classifier + headers = self._make_headers() + prompt_url = f"{self.classifier_url}{Routes.prompt.value}" + pebblo_resp = self.make_request("POST", prompt_url, headers, payload) + + if self.api_key: + # Send prompt to Pebblo cloud if api_key is present + if self.classifier_location == "local": + # If classifier location is local, then response, context and prompt + # should be fetched from pebblo_resp and replaced in payload. + pebblo_resp = pebblo_resp.json() if pebblo_resp else None + self.update_cloud_payload(payload, pebblo_resp) + + headers = self._make_headers(cloud_request=True) + pebblo_cloud_prompt_url = f"{self.cloud_url}{Routes.prompt.value}" + _ = self.make_request("POST", pebblo_cloud_prompt_url, headers, payload) + elif self.classifier_location == "pebblo-cloud": + logger.warning("API key is missing for sending prompt to Pebblo cloud.") + raise NameError("API key is missing for sending prompt to Pebblo cloud.") + + async def asend_prompt( + self, + app_name: str, + retriever: VectorStoreRetriever, + question: str, + answer: str, + auth_context: Optional[AuthContext], + docs: List[Document], + prompt_entities: Dict[str, Any], + prompt_time: str, + prompt_gov_enabled: bool = False, + ) -> None: + """ + Send prompt to Pebblo server for classification. + Then send prompt to Daxa cloud(If api_key is present). + + Args: + app_name (str): Name of the app. + retriever (VectorStoreRetriever): Retriever instance. + question (str): Question asked in the prompt. + answer (str): Answer generated by the model. + auth_context (Optional[AuthContext]): Authentication context. + docs (List[Document]): List of documents retrieved. + prompt_entities (Dict[str, Any]): Entities present in the prompt. + prompt_time (str): Time when the prompt was generated. + prompt_gov_enabled (bool): Whether prompt governance is enabled. + """ + pebblo_resp = None + payload = self.build_prompt_qa_payload( + app_name, + retriever, + question, + answer, + auth_context, + docs, + prompt_entities, + prompt_time, + prompt_gov_enabled, + ) + + if self.classifier_location == "local": + # Send prompt to local classifier + headers = self._make_headers() + prompt_url = f"{self.classifier_url}{Routes.prompt.value}" + pebblo_resp = await self.amake_request("POST", prompt_url, headers, payload) + + if self.api_key: + # Send prompt to Pebblo cloud if api_key is present + if self.classifier_location == "local": + # If classifier location is local, then response, context and prompt + # should be fetched from pebblo_resp and replaced in payload. + self.update_cloud_payload(payload, pebblo_resp) + + headers = self._make_headers(cloud_request=True) + pebblo_cloud_prompt_url = f"{self.cloud_url}{Routes.prompt.value}" + _ = await self.amake_request( + "POST", pebblo_cloud_prompt_url, headers, payload + ) + elif self.classifier_location == "pebblo-cloud": + logger.warning("API key is missing for sending prompt to Pebblo cloud.") + raise NameError("API key is missing for sending prompt to Pebblo cloud.") + + def check_prompt_validity(self, question: str) -> Tuple[bool, Dict[str, Any]]: + """ + Check the validity of the given prompt using a remote classification service. + + This method sends a prompt to a remote classifier service and return entities + present in prompt or not. + + Args: + question (str): The prompt question to be validated. + + Returns: + bool: True if the prompt is valid (does not contain deny list entities), + False otherwise. + dict: The entities present in the prompt + """ + prompt_payload = {"prompt": question} + prompt_entities: dict = {"entities": {}, "entityCount": 0} + is_valid_prompt: bool = True + if self.classifier_location == "local": + headers = self._make_headers() + prompt_gov_api_url = ( + f"{self.classifier_url}{Routes.prompt_governance.value}" + ) + pebblo_resp = self.make_request( + "POST", prompt_gov_api_url, headers, prompt_payload + ) + if pebblo_resp: + prompt_entities["entities"] = pebblo_resp.json().get("entities", {}) + prompt_entities["entityCount"] = pebblo_resp.json().get( + "entityCount", 0 + ) + return is_valid_prompt, prompt_entities + + async def acheck_prompt_validity( + self, question: str + ) -> Tuple[bool, Dict[str, Any]]: + """ + Check the validity of the given prompt using a remote classification service. + + This method sends a prompt to a remote classifier service and return entities + present in prompt or not. + + Args: + question (str): The prompt question to be validated. + + Returns: + bool: True if the prompt is valid (does not contain deny list entities), + False otherwise. + dict: The entities present in the prompt + """ + prompt_payload = {"prompt": question} + prompt_entities: dict = {"entities": {}, "entityCount": 0} + is_valid_prompt: bool = True + if self.classifier_location == "local": + headers = self._make_headers() + prompt_gov_api_url = ( + f"{self.classifier_url}{Routes.prompt_governance.value}" + ) + pebblo_resp = await self.amake_request( + "POST", prompt_gov_api_url, headers, prompt_payload + ) + if pebblo_resp: + prompt_entities["entities"] = pebblo_resp.get("entities", {}) + prompt_entities["entityCount"] = pebblo_resp.get("entityCount", 0) + return is_valid_prompt, prompt_entities + + def _make_headers(self, cloud_request: bool = False) -> dict: + """ + Generate headers for the request. + + args: + cloud_request (bool): flag indicating whether the request is for Pebblo + cloud. + returns: + dict: Headers for the request. + + """ + headers = { + "Accept": "application/json", + "Content-Type": "application/json", + } + if cloud_request: + # Add API key for Pebblo cloud request + if self.api_key: + headers.update({"x-api-key": self.api_key}) + else: + logger.warning("API key is missing for Pebblo cloud request.") + return headers + + @staticmethod + def make_request( + method: str, + url: str, + headers: dict, + payload: Optional[dict] = None, + timeout: int = 20, + ) -> Optional[Response]: + """ + Make a request to the Pebblo server/cloud API. + + Args: + method (str): HTTP method (GET, POST, PUT, DELETE, etc.). + url (str): URL for the request. + headers (dict): Headers for the request. + payload (Optional[dict]): Payload for the request (for POST, PUT, etc.). + timeout (int): Timeout for the request in seconds. + + Returns: + Optional[Response]: Response object if the request is successful. + """ + try: + response = request( + method=method, url=url, headers=headers, json=payload, timeout=timeout + ) + logger.debug( + "Request: method %s, url %s, len %s response status %s", + method, + response.request.url, + str(len(response.request.body if response.request.body else [])), + str(response.status_code), + ) + + if response.status_code >= HTTPStatus.INTERNAL_SERVER_ERROR: + logger.warning(f"Pebblo Server: Error {response.status_code}") + elif response.status_code >= HTTPStatus.BAD_REQUEST: + logger.warning(f"Pebblo received an invalid payload: {response.text}") + elif response.status_code != HTTPStatus.OK: + logger.warning( + f"Pebblo returned an unexpected response code: " + f"{response.status_code}" + ) + + return response + except RequestException: + logger.warning("Unable to reach server %s", url) + except Exception as e: + logger.warning("An Exception caught in make_request: %s", e) + return None + + @staticmethod + def update_cloud_payload(payload: dict, pebblo_resp: Optional[dict]) -> None: + """ + Update the payload with response, prompt and context from Pebblo response. + + Args: + payload (dict): Payload to be updated. + pebblo_resp (Optional[dict]): Response from Pebblo server. + """ + if pebblo_resp: + # Update response, prompt and context from pebblo response + response = payload.get("response", {}) + response.update(pebblo_resp.get("retrieval_data", {}).get("response", {})) + response.pop("data", None) + prompt = payload.get("prompt", {}) + prompt.update(pebblo_resp.get("retrieval_data", {}).get("prompt", {})) + prompt.pop("data", None) + context = payload.get("context", []) + for context_data in context: + context_data.pop("doc", None) + else: + payload["response"] = {} + payload["prompt"] = {} + payload["context"] = [] + + @staticmethod + async def amake_request( + method: str, + url: str, + headers: dict, + payload: Optional[dict] = None, + timeout: int = 20, + ) -> Any: + """ + Make a async request to the Pebblo server/cloud API. + + Args: + method (str): HTTP method (GET, POST, PUT, DELETE, etc.). + url (str): URL for the request. + headers (dict): Headers for the request. + payload (Optional[dict]): Payload for the request (for POST, PUT, etc.). + timeout (int): Timeout for the request in seconds. + + Returns: + Any: Response json if the request is successful. + """ + try: + client_timeout = ClientTimeout(total=timeout) + async with aiohttp.ClientSession() as asession: + async with asession.request( + method=method, + url=url, + json=payload, + headers=headers, + timeout=client_timeout, + ) as response: + if response.status >= HTTPStatus.INTERNAL_SERVER_ERROR: + logger.warning(f"Pebblo Server: Error {response.status}") + elif response.status >= HTTPStatus.BAD_REQUEST: + logger.warning( + f"Pebblo received an invalid payload: {response.text}" + ) + elif response.status != HTTPStatus.OK: + logger.warning( + f"Pebblo returned an unexpected response code: " + f"{response.status}" + ) + response_json = await response.json() + return response_json + except RequestException: + logger.warning("Unable to reach server %s", url) + except Exception as e: + logger.warning("An Exception caught in amake_request: %s", e) + return None + + def build_prompt_qa_payload( + self, + app_name: str, + retriever: VectorStoreRetriever, + question: str, + answer: str, + auth_context: Optional[AuthContext], + docs: List[Document], + prompt_entities: Dict[str, Any], + prompt_time: str, + prompt_gov_enabled: bool = False, + ) -> dict: + """ + Build the QA payload for the prompt. + + Args: + app_name (str): Name of the app. + retriever (VectorStoreRetriever): Retriever instance. + question (str): Question asked in the prompt. + answer (str): Answer generated by the model. + auth_context (Optional[AuthContext]): Authentication context. + docs (List[Document]): List of documents retrieved. + prompt_entities (Dict[str, Any]): Entities present in the prompt. + prompt_time (str): Time when the prompt was generated. + prompt_gov_enabled (bool): Whether prompt governance is enabled. + + Returns: + dict: The QA payload for the prompt. + """ + qa = Qa( + name=app_name, + context=[ + Context( + retrieved_from=doc.metadata.get( + "full_path", doc.metadata.get("source") + ), + doc=doc.page_content, + vector_db=retriever.vectorstore.__class__.__name__, + pb_checksum=doc.metadata.get("pb_checksum"), + ) + for doc in docs + if isinstance(doc, Document) + ], + prompt=Prompt( + data=question, + entities=prompt_entities.get("entities", {}), + entityCount=prompt_entities.get("entityCount", 0), + prompt_gov_enabled=prompt_gov_enabled, + ), + response=Prompt(data=answer), + prompt_time=prompt_time, + user=auth_context.user_id if auth_context else "unknown", + user_identities=auth_context.user_auth + if auth_context and hasattr(auth_context, "user_auth") + else [], + classifier_location=self.classifier_location, + ) + return qa.dict(exclude_unset=True) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chat_loaders/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chat_loaders/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..737e9bcc21fd8201e263fb865207acf03334fa14 Binary files /dev/null and b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chat_loaders/__pycache__/__init__.cpython-311.pyc differ diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chat_loaders/__pycache__/base.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/chat_loaders/__pycache__/base.cpython-311.pyc new file mode 100644 index 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0000000000000000000000000000000000000000..95907e77ffc6af73c3e0f66f299fb33041169f89 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/blob_loaders/__init__.py @@ -0,0 +1,44 @@ +import importlib +from typing import TYPE_CHECKING, Any + +from langchain_core.document_loaders import Blob, BlobLoader + +if TYPE_CHECKING: + from langchain_community.document_loaders.blob_loaders.cloud_blob_loader import ( + CloudBlobLoader, + ) + from langchain_community.document_loaders.blob_loaders.file_system import ( + FileSystemBlobLoader, + ) + from langchain_community.document_loaders.blob_loaders.youtube_audio import ( + YoutubeAudioLoader, + ) + + +_module_lookup = { + "CloudBlobLoader": ( + "langchain_community.document_loaders.blob_loaders.cloud_blob_loader" + ), + "FileSystemBlobLoader": ( + "langchain_community.document_loaders.blob_loaders.file_system" + ), + "YoutubeAudioLoader": ( + "langchain_community.document_loaders.blob_loaders.youtube_audio" + ), +} + + +def __getattr__(name: str) -> Any: + if name in _module_lookup: + module = importlib.import_module(_module_lookup[name]) + return getattr(module, name) + raise AttributeError(f"module {__name__} has no attribute {name}") + + +__all__ = [ + "BlobLoader", + "Blob", + "CloudBlobLoader", + "FileSystemBlobLoader", + "YoutubeAudioLoader", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/blob_loaders/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/blob_loaders/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..9da5532d42c231f7ff57ee806591d1e3937b221b Binary files /dev/null and b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/blob_loaders/__pycache__/__init__.cpython-311.pyc differ diff 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file system.""" + +import contextlib +import mimetypes +import tempfile +from io import BufferedReader, BytesIO +from pathlib import Path +from typing import ( + TYPE_CHECKING, + Callable, + Generator, + Iterable, + Iterator, + Optional, + Sequence, + TypeVar, + Union, +) +from urllib.parse import urlparse + +if TYPE_CHECKING: + from cloudpathlib import AnyPath + +from langchain_community.document_loaders.blob_loaders.schema import ( + Blob, + BlobLoader, +) + +T = TypeVar("T") + + +class _CloudBlob(Blob): + def as_string(self) -> str: + """Read data as a string.""" + from cloudpathlib import AnyPath + + if self.data is None and self.path: + return AnyPath(self.path).read_text(encoding=self.encoding) + elif isinstance(self.data, bytes): + return self.data.decode(self.encoding) + elif isinstance(self.data, str): + return self.data + else: + raise ValueError(f"Unable to get string for blob {self}") + + def as_bytes(self) -> bytes: + """Read data as bytes.""" + from cloudpathlib import AnyPath + + if isinstance(self.data, bytes): + return self.data + elif isinstance(self.data, str): + return self.data.encode(self.encoding) + elif self.data is None and self.path: + return AnyPath(self.path).read_bytes() + else: + raise ValueError(f"Unable to get bytes for blob {self}") + + @contextlib.contextmanager + def as_bytes_io(self) -> Generator[Union[BytesIO, BufferedReader], None, None]: + """Read data as a byte stream.""" + from cloudpathlib import AnyPath + + if isinstance(self.data, bytes): + yield BytesIO(self.data) + elif self.data is None and self.path: + return AnyPath(self.path).read_bytes() + else: + raise NotImplementedError(f"Unable to convert blob {self}") + + +def _url_to_filename(url: str) -> str: + """ + Convert file:, s3:, az: or gs: url to localfile. + If the file is not here, download it in a temporary file. + """ + from cloudpathlib import AnyPath + + url_parsed = urlparse(url) + suffix = Path(url_parsed.path).suffix + if url_parsed.scheme in ["s3", "az", "gs"]: + with AnyPath(url).open("rb") as f: + temp_file = tempfile.NamedTemporaryFile(suffix=suffix, delete=False) + while True: + buf = f.read() + if not buf: + break + temp_file.write(buf) + temp_file.close() + file_path = temp_file.name + elif url_parsed.scheme in ["file", ""]: + file_path = url_parsed.path + else: + raise ValueError(f"Scheme {url_parsed.scheme} not supported") + return file_path + + +def _make_iterator( + length_func: Callable[[], int], show_progress: bool = False +) -> Callable[[Iterable[T]], Iterator[T]]: + """Create a function that optionally wraps an iterable in tqdm.""" + if show_progress: + try: + from tqdm.auto import tqdm + except ImportError: + raise ImportError( + "You must install tqdm to use show_progress=True." + "You can install tqdm with `pip install tqdm`." + ) + + # Make sure to provide `total` here so that tqdm can show + # a progress bar that takes into account the total number of files. + def _with_tqdm(iterable: Iterable[T]) -> Iterator[T]: + """Wrap an iterable in a tqdm progress bar.""" + return tqdm(iterable, total=length_func()) + + iterator = _with_tqdm + else: + iterator = iter # type: ignore[assignment] + + return iterator + + +# PUBLIC API + + +class CloudBlobLoader(BlobLoader): + """Load blobs from cloud URL or file:. + + Example: + + .. code-block:: python + + loader = CloudBlobLoader("s3://mybucket/id") + + for blob in loader.yield_blobs(): + print(blob) + """ # noqa: E501 + + def __init__( + self, + url: Union[str, "AnyPath"], + *, + glob: str = "**/[!.]*", + exclude: Sequence[str] = (), + suffixes: Optional[Sequence[str]] = None, + show_progress: bool = False, + ) -> None: + """Initialize with a url and how to glob over it. + + Use [CloudPathLib](https://cloudpathlib.drivendata.org/). + + Args: + url: Cloud URL to load from. + Supports s3://, az://, gs://, file:// schemes. + If no scheme is provided, it is assumed to be a local file. + If a path to a file is provided, glob/exclude/suffixes are ignored. + glob: Glob pattern relative to the specified path + by default set to pick up all non-hidden files + exclude: patterns to exclude from results, use glob syntax + suffixes: Provide to keep only files with these suffixes + Useful when wanting to keep files with different suffixes + Suffixes must include the dot, e.g. ".txt" + show_progress: If true, will show a progress bar as the files are loaded. + This forces an iteration through all matching files + to count them prior to loading them. + + Examples: + + .. code-block:: python + from langchain_community.document_loaders.blob_loaders import CloudBlobLoader + + # Load a single file. + loader = CloudBlobLoader("s3://mybucket/id") # az:// + + # Recursively load all text files in a directory. + loader = CloudBlobLoader("az://mybucket/id", glob="**/*.txt") + + # Recursively load all non-hidden files in a directory. + loader = CloudBlobLoader("gs://mybucket/id", glob="**/[!.]*") + + # Load all files in a directory without recursion. + loader = CloudBlobLoader("s3://mybucket/id", glob="*") + + # Recursively load all files in a directory, except for py or pyc files. + loader = CloudBlobLoader( + "s3://mybucket/id", + glob="**/*.txt", + exclude=["**/*.py", "**/*.pyc"] + ) + """ # noqa: E501 + from cloudpathlib import AnyPath + + url_parsed = urlparse(str(url)) + + if url_parsed.scheme == "file": + url = url_parsed.path + + if isinstance(url, str): + self.path = AnyPath(url) + else: + self.path = url + + self.glob = glob + self.suffixes = set(suffixes or []) + self.show_progress = show_progress + self.exclude = exclude + + def yield_blobs( + self, + ) -> Iterable[Blob]: + """Yield blobs that match the requested pattern.""" + iterator = _make_iterator( + length_func=self.count_matching_files, show_progress=self.show_progress + ) + + for path in iterator(self._yield_paths()): + # yield Blob.from_path(path) + yield self.from_path(path) + + def _yield_paths(self) -> Iterable["AnyPath"]: + """Yield paths that match the requested pattern.""" + if self.path.is_file(): + yield self.path + return + + paths = self.path.glob(self.glob) + for path in paths: + if self.exclude: + if any(path.match(glob) for glob in self.exclude): + continue + if path.is_file(): + if self.suffixes and path.suffix not in self.suffixes: + continue # FIXME + yield path + + def count_matching_files(self) -> int: + """Count files that match the pattern without loading them.""" + # Carry out a full iteration to count the files without + # materializing anything expensive in memory. + num = 0 + for _ in self._yield_paths(): + num += 1 + return num + + @classmethod + def from_path( + cls, + path: "AnyPath", + *, + encoding: str = "utf-8", + mime_type: Optional[str] = None, + guess_type: bool = True, + metadata: Optional[dict] = None, + ) -> Blob: + """Load the blob from a path like object. + + Args: + path: path like object to file to be read + Supports s3://, az://, gs://, file:// schemes. + If no scheme is provided, it is assumed to be a local file. + encoding: Encoding to use if decoding the bytes into a string + mime_type: if provided, will be set as the mime-type of the data + guess_type: If True, the mimetype will be guessed from the file extension, + if a mime-type was not provided + metadata: Metadata to associate with the blob + + Returns: + Blob instance + """ + if mime_type is None and guess_type: + _mimetype = mimetypes.guess_type(path)[0] if guess_type else None + else: + _mimetype = mime_type + + url_parsed = urlparse(str(path)) + if url_parsed.scheme in ["file", ""]: + if url_parsed.scheme == "file": + local_path = url_parsed.path + else: + local_path = str(path) + return Blob( + data=None, + mimetype=_mimetype, + encoding=encoding, + path=local_path, + metadata=metadata if metadata is not None else {}, + ) + + return _CloudBlob( + data=None, + mimetype=_mimetype, + encoding=encoding, + path=str(path), + metadata=metadata if metadata is not None else {}, + ) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/blob_loaders/file_system.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/blob_loaders/file_system.py new file mode 100644 index 0000000000000000000000000000000000000000..ee756f32ed73e90c8f2700f8d451d53fdddabdb5 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/blob_loaders/file_system.py @@ -0,0 +1,149 @@ +"""Use to load blobs from the local file system.""" + +from pathlib import Path +from typing import Callable, Iterable, Iterator, Optional, Sequence, TypeVar, Union + +from langchain_community.document_loaders.blob_loaders.schema import Blob, BlobLoader + +T = TypeVar("T") + + +def _make_iterator( + length_func: Callable[[], int], show_progress: bool = False +) -> Callable[[Iterable[T]], Iterator[T]]: + """Create a function that optionally wraps an iterable in tqdm.""" + iterator: Callable[[Iterable[T]], Iterator[T]] + if show_progress: + try: + from tqdm.auto import tqdm + except ImportError: + raise ImportError( + "You must install tqdm to use show_progress=True." + "You can install tqdm with `pip install tqdm`." + ) + + # Make sure to provide `total` here so that tqdm can show + # a progress bar that takes into account the total number of files. + def _with_tqdm(iterable: Iterable[T]) -> Iterator[T]: + """Wrap an iterable in a tqdm progress bar.""" + return tqdm(iterable, total=length_func()) + + iterator = _with_tqdm + else: + iterator = iter + + return iterator + + +# PUBLIC API + + +class FileSystemBlobLoader(BlobLoader): + """Load blobs in the local file system. + + Example: + + .. code-block:: python + + from langchain_community.document_loaders.blob_loaders import FileSystemBlobLoader + loader = FileSystemBlobLoader("/path/to/directory") + for blob in loader.yield_blobs(): + print(blob) # noqa: T201 + """ # noqa: E501 + + def __init__( + self, + path: Union[str, Path], + *, + glob: str = "**/[!.]*", + exclude: Sequence[str] = (), + suffixes: Optional[Sequence[str]] = None, + show_progress: bool = False, + ) -> None: + """Initialize with a path to directory and how to glob over it. + + Args: + path: Path to directory to load from or path to file to load. + If a path to a file is provided, glob/exclude/suffixes are ignored. + glob: Glob pattern relative to the specified path + by default set to pick up all non-hidden files + exclude: patterns to exclude from results, use glob syntax + suffixes: Provide to keep only files with these suffixes + Useful when wanting to keep files with different suffixes + Suffixes must include the dot, e.g. ".txt" + show_progress: If true, will show a progress bar as the files are loaded. + This forces an iteration through all matching files + to count them prior to loading them. + + Examples: + + .. code-block:: python + from langchain_community.document_loaders.blob_loaders import FileSystemBlobLoader + + # Load a single file. + loader = FileSystemBlobLoader("/path/to/file.txt") + + # Recursively load all text files in a directory. + loader = FileSystemBlobLoader("/path/to/directory", glob="**/*.txt") + + # Recursively load all non-hidden files in a directory. + loader = FileSystemBlobLoader("/path/to/directory", glob="**/[!.]*") + + # Load all files in a directory without recursion. + loader = FileSystemBlobLoader("/path/to/directory", glob="*") + + # Recursively load all files in a directory, except for py or pyc files. + loader = FileSystemBlobLoader( + "/path/to/directory", + glob="**/*.txt", + exclude=["**/*.py", "**/*.pyc"] + ) + """ # noqa: E501 + if isinstance(path, Path): + _path = path + elif isinstance(path, str): + _path = Path(path) + else: + raise TypeError(f"Expected str or Path, got {type(path)}") + + self.path = _path.expanduser() # Expand user to handle ~ + self.glob = glob + self.suffixes = set(suffixes or []) + self.show_progress = show_progress + self.exclude = exclude + + def yield_blobs( + self, + ) -> Iterable[Blob]: + """Yield blobs that match the requested pattern.""" + iterator = _make_iterator( + length_func=self.count_matching_files, show_progress=self.show_progress + ) + + for path in iterator(self._yield_paths()): + yield Blob.from_path(path) + + def _yield_paths(self) -> Iterable[Path]: + """Yield paths that match the requested pattern.""" + if self.path.is_file(): + yield self.path + return + + paths = self.path.glob(self.glob) + for path in paths: + if self.exclude: + if any(path.match(glob) for glob in self.exclude): + continue + if path.is_file(): + if self.suffixes and path.suffix not in self.suffixes: + continue + yield path + + def count_matching_files(self) -> int: + """Count files that match the pattern without loading them.""" + # Carry out a full iteration to count the files without + # materializing anything expensive in memory. + num = 0 + for _ in self._yield_paths(): + num += 1 + return num diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/blob_loaders/schema.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/blob_loaders/schema.py new file mode 100644 index 0000000000000000000000000000000000000000..208510eaeac507c0007b09aeee92f1d1d2ca4eaa --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/blob_loaders/schema.py @@ -0,0 +1,7 @@ +from langchain_core.document_loaders.blob_loaders import Blob, BlobLoader, PathLike + +__all__ = [ + "Blob", + "BlobLoader", + "PathLike", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/blob_loaders/youtube_audio.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/blob_loaders/youtube_audio.py new file mode 100644 index 0000000000000000000000000000000000000000..b0b2dc6daa8f4e23a42d5ddab12b4c21d5b44896 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/blob_loaders/youtube_audio.py @@ -0,0 +1,48 @@ +from typing import Iterable, List + +from langchain_community.document_loaders.blob_loaders import FileSystemBlobLoader +from langchain_community.document_loaders.blob_loaders.schema import Blob, BlobLoader + + +class YoutubeAudioLoader(BlobLoader): + """Load YouTube urls as audio file(s).""" + + def __init__(self, urls: List[str], save_dir: str): + if not isinstance(urls, list): + raise TypeError("urls must be a list") + + self.urls = urls + self.save_dir = save_dir + + def yield_blobs(self) -> Iterable[Blob]: + """Yield audio blobs for each url.""" + + try: + import yt_dlp + except ImportError: + raise ImportError( + "yt_dlp package not found, please install it with `pip install yt_dlp`" + ) + + # Use yt_dlp to download audio given a YouTube url + ydl_opts = { + "format": "m4a/bestaudio/best", + "noplaylist": True, + "outtmpl": self.save_dir + "/%(title)s.%(ext)s", + "postprocessors": [ + { + "key": "FFmpegExtractAudio", + "preferredcodec": "m4a", + } + ], + } + + for url in self.urls: + # Download file + with yt_dlp.YoutubeDL(ydl_opts) as ydl: + ydl.download(url) + + # Yield the written blobs + loader = FileSystemBlobLoader(self.save_dir, glob="*.m4a") + for blob in loader.yield_blobs(): + yield blob diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9712718e19714985e211135afdea820a01110245 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/__init__.py @@ -0,0 +1,85 @@ +import importlib +from typing import TYPE_CHECKING, Any + +if TYPE_CHECKING: + from langchain_community.document_loaders.parsers.audio import ( + OpenAIWhisperParser, + ) + from langchain_community.document_loaders.parsers.doc_intelligence import ( + AzureAIDocumentIntelligenceParser, + ) + from langchain_community.document_loaders.parsers.docai import ( + DocAIParser, + ) + from langchain_community.document_loaders.parsers.grobid import ( + GrobidParser, + ) + from langchain_community.document_loaders.parsers.html import ( + BS4HTMLParser, + ) + from langchain_community.document_loaders.parsers.images import ( + BaseImageBlobParser, + LLMImageBlobParser, + RapidOCRBlobParser, + TesseractBlobParser, + ) + from langchain_community.document_loaders.parsers.language import ( + LanguageParser, + ) + from langchain_community.document_loaders.parsers.pdf import ( + PDFMinerParser, + PDFPlumberParser, + PyMuPDFParser, + PyPDFium2Parser, + PyPDFParser, + ) + from langchain_community.document_loaders.parsers.vsdx import ( + VsdxParser, + ) + + +_module_lookup = { + "AzureAIDocumentIntelligenceParser": "langchain_community.document_loaders.parsers.doc_intelligence", # noqa: E501 + "BS4HTMLParser": "langchain_community.document_loaders.parsers.html", + "BaseImageBlobParser": "langchain_community.document_loaders.parsers.images", + "DocAIParser": "langchain_community.document_loaders.parsers.docai", + "GrobidParser": "langchain_community.document_loaders.parsers.grobid", + "LanguageParser": "langchain_community.document_loaders.parsers.language", + "LLMImageBlobParser": "langchain_community.document_loaders.parsers.images", + "OpenAIWhisperParser": "langchain_community.document_loaders.parsers.audio", + "PDFMinerParser": "langchain_community.document_loaders.parsers.pdf", + "PDFPlumberParser": "langchain_community.document_loaders.parsers.pdf", + "PyMuPDFParser": "langchain_community.document_loaders.parsers.pdf", + "PyPDFParser": "langchain_community.document_loaders.parsers.pdf", + "PyPDFium2Parser": "langchain_community.document_loaders.parsers.pdf", + "RapidOCRBlobParser": 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Optional, Tuple, Union + +from langchain_core.documents import Document + +from langchain_community.document_loaders.base import BaseBlobParser +from langchain_community.document_loaders.blob_loaders import Blob +from langchain_community.utils.openai import is_openai_v1 + +logger = logging.getLogger(__name__) + + +class AzureOpenAIWhisperParser(BaseBlobParser): + """ + Transcribe and parse audio files using Azure OpenAI Whisper. + + This parser integrates with the Azure OpenAI Whisper model to transcribe + audio files. It differs from the standard OpenAI Whisper parser, requiring + an Azure endpoint and credentials. The parser is limited to files under 25 MB. + + **Note**: + This parser uses the Azure OpenAI API, providing integration with the Azure + ecosystem, and making it suitable for workflows involving other Azure services. + + For files larger than 25 MB, consider using Azure AI Speech batch transcription: + https://learn.microsoft.com/azure/ai-services/speech-service/batch-transcription-create?pivots=rest-api#use-a-whisper-model + + Setup: + 1. Follow the instructions here to deploy Azure Whisper: + https://learn.microsoft.com/azure/ai-services/openai/whisper-quickstart?tabs=command-line%2Cpython-new&pivots=programming-language-python + 2. Install ``langchain`` and set the following environment variables: + + .. code-block:: bash + + pip install -U langchain langchain-community + + export AZURE_OPENAI_API_KEY="your-api-key" + export AZURE_OPENAI_ENDPOINT="https://your-endpoint.openai.azure.com/" + export OPENAI_API_VERSION="your-api-version" + + Example Usage: + .. code-block:: python + + from langchain_classic.community import AzureOpenAIWhisperParser + + whisper_parser = AzureOpenAIWhisperParser( + deployment_name="your-whisper-deployment", + api_version="2024-06-01", + api_key="your-api-key", + # other params... + ) + + audio_blob = Blob(path="your-audio-file-path") + response = whisper_parser.lazy_parse(audio_blob) + + for document in response: + print(document.page_content) + + Integration with Other Loaders: + The AzureOpenAIWhisperParser can be used with video/audio loaders and + `GenericLoader` to automate retrieval and parsing. + + YoutubeAudioLoader Example: + .. code-block:: python + + from langchain_community.document_loaders.blob_loaders import ( + YoutubeAudioLoader + ) + from langchain_community.document_loaders.generic import GenericLoader + + # Must be a list + youtube_url = ["https://your-youtube-url"] + save_dir = "directory-to-download-videos" + + loader = GenericLoader( + YoutubeAudioLoader(youtube_url, save_dir), + AzureOpenAIWhisperParser(deployment_name="your-deployment-name") + ) + + docs = loader.load() + """ + + def __init__( + self, + *, + api_key: Optional[str] = None, + azure_endpoint: Optional[str] = None, + api_version: Optional[str] = None, + azure_ad_token_provider: Union[Callable[[], str], None] = None, + language: Optional[str] = None, + prompt: Optional[str] = None, + response_format: Union[ + Literal["json", "text", "srt", "verbose_json", "vtt"], None + ] = None, + temperature: Optional[float] = None, + deployment_name: str, + max_retries: int = 3, + ): + """ + Initialize the AzureOpenAIWhisperParser. + + Args: + api_key (Optional[str]): + Azure OpenAI API key. If not provided, defaults to the + `AZURE_OPENAI_API_KEY` environment variable. + azure_endpoint (Optional[str]): + Azure OpenAI service endpoint. Defaults to `AZURE_OPENAI_ENDPOINT` + environment variable if not set. + api_version (Optional[str]): + API version to use, + defaults to the `OPENAI_API_VERSION` environment variable. + azure_ad_token_provider (Union[Callable[[], str], None]): + Azure Active Directory token for authentication (if applicable). + language (Optional[str]): + Language in which the request should be processed. + prompt (Optional[str]): + Custom instructions or prompt for the Whisper model. + response_format (Union[str, None]): + The desired output format. Options: "json", "text", "srt", + "verbose_json", "vtt". + temperature (Optional[float]): + Controls the randomness of the model's output. + deployment_name (str): + The deployment name of the Whisper model. + max_retries (int): + Maximum number of retries for failed API requests. + Raises: + ImportError: + If the required package `openai` is not installed. + """ + self.api_key = api_key or os.environ.get("AZURE_OPENAI_API_KEY") + self.azure_endpoint = azure_endpoint or os.environ.get("AZURE_OPENAI_ENDPOINT") + self.api_version = api_version or os.environ.get("OPENAI_API_VERSION") + self.azure_ad_token_provider = azure_ad_token_provider + + self.language = language + self.prompt = prompt + self.response_format = response_format + self.temperature = temperature + + self.deployment_name = deployment_name + self.max_retries = max_retries + + try: + import openai + except ImportError: + raise ImportError( + "openai package not found, please install it with `pip install openai`" + ) + + if is_openai_v1(): + self._client = openai.AzureOpenAI( + api_key=self.api_key, + azure_endpoint=self.azure_endpoint, + api_version=self.api_version, + max_retries=self.max_retries, + azure_ad_token_provider=self.azure_ad_token_provider, + ) + else: + if self.api_key: + openai.api_key = self.api_key + if self.azure_endpoint: + openai.api_base = self.azure_endpoint + if self.api_version: + openai.api_version = self.api_version + openai.api_type = "azure" + self._client = openai + + @property + def _create_params(self) -> Dict[str, Any]: + params = { + "language": self.language, + "prompt": self.prompt, + "response_format": self.response_format, + "temperature": self.temperature, + } + return {k: v for k, v in params.items() if v is not None} + + def lazy_parse(self, blob: Blob) -> Iterator[Document]: + """ + Lazily parse the provided audio blob for transcription. + + Args: + blob (Blob): + The audio file in Blob format to be transcribed. + + Yields: + Document: + Parsed transcription from the audio file. + + Raises: + Exception: + If an error occurs during transcription. + """ + + file_obj = open(str(blob.path), "rb") + + # Transcribe + try: + if is_openai_v1(): + transcript = self._client.audio.transcriptions.create( + model=self.deployment_name, + file=file_obj, + **self._create_params, + ) + else: + transcript = self._client.Audio.transcribe( + model=self.deployment_name, + deployment_id=self.deployment_name, + file=file_obj, + **self._create_params, + ) + except Exception: + raise + + yield Document( + page_content=transcript.text + if not isinstance(transcript, str) + else transcript, + metadata={"source": blob.source}, + ) + + +class OpenAIWhisperParser(BaseBlobParser): + """Transcribe and parse audio files. + + Audio transcription is with OpenAI Whisper model. + + Args: + api_key: OpenAI API key + chunk_duration_threshold: Minimum duration of a chunk in seconds + NOTE: According to the OpenAI API, the chunk duration should be at least 0.1 + seconds. If the chunk duration is less or equal than the threshold, + it will be skipped. + """ + + def __init__( + self, + api_key: Optional[str] = None, + *, + chunk_duration_threshold: float = 0.1, + base_url: Optional[str] = None, + language: Union[str, None] = None, + prompt: Union[str, None] = None, + response_format: Union[ + Literal["json", "text", "srt", "verbose_json", "vtt"], None + ] = None, + temperature: Union[float, None] = None, + model: str = "whisper-1", + ): + self.api_key = api_key + self.chunk_duration_threshold = chunk_duration_threshold + self.base_url = ( + base_url if base_url is not None else os.environ.get("OPENAI_API_BASE") + ) + self.language = language + self.prompt = prompt + self.response_format = response_format + self.temperature = temperature + self.model = model + + @property + def _create_params(self) -> Dict[str, Any]: + params = { + "language": self.language, + "prompt": self.prompt, + "response_format": self.response_format, + "temperature": self.temperature, + } + return {k: v for k, v in params.items() if v is not None} + + def lazy_parse(self, blob: Blob) -> Iterator[Document]: + """Lazily parse the blob.""" + + try: + import openai + except ImportError: + raise ImportError( + "openai package not found, please install it with `pip install openai`" + ) + + audio = _get_audio_from_blob(blob) + + if is_openai_v1(): + # api_key optional, defaults to `os.environ['OPENAI_API_KEY']` + client = openai.OpenAI(api_key=self.api_key, base_url=self.base_url) + else: + # Set the API key if provided + if self.api_key: + openai.api_key = self.api_key + if self.base_url: + openai.api_base = self.base_url + + # Define the duration of each chunk in minutes + # Need to meet 25MB size limit for Whisper API + chunk_duration = 20 + chunk_duration_ms = chunk_duration * 60 * 1000 + + # Split the audio into chunk_duration_ms chunks + for split_number, i in enumerate(range(0, len(audio), chunk_duration_ms)): + # Audio chunk + chunk = audio[i : i + chunk_duration_ms] + # Skip chunks that are too short to transcribe + if chunk.duration_seconds <= self.chunk_duration_threshold: + continue + file_obj = io.BytesIO(chunk.export(format="mp3").read()) + if blob.source is not None: + file_obj.name = blob.source + f"_part_{split_number}.mp3" + else: + file_obj.name = f"part_{split_number}.mp3" + + # Transcribe + print(f"Transcribing part {split_number + 1}!") # noqa: T201 + attempts = 0 + while attempts < 3: + try: + if is_openai_v1(): + transcript = client.audio.transcriptions.create( + model=self.model, file=file_obj, **self._create_params + ) + else: + transcript = openai.Audio.transcribe(self.model, file_obj) + break + except Exception as e: + attempts += 1 + print(f"Attempt {attempts} failed. Exception: {str(e)}") # noqa: T201 + time.sleep(5) + else: + print("Failed to transcribe after 3 attempts.") # noqa: T201 + continue + + yield Document( + page_content=transcript.text + if not isinstance(transcript, str) + else transcript, + metadata={"source": blob.source, "chunk": split_number}, + ) + + +class OpenAIWhisperParserLocal(BaseBlobParser): + """Transcribe and parse audio files with OpenAI Whisper model. + + Audio transcription with OpenAI Whisper model locally from transformers. + + Parameters: + device - device to use + NOTE: By default uses the gpu if available, + if you want to use cpu, please set device = "cpu" + lang_model - whisper model to use, for example "openai/whisper-medium" + forced_decoder_ids - id states for decoder in multilanguage model, + usage example: + from transformers import WhisperProcessor + processor = WhisperProcessor.from_pretrained("openai/whisper-medium") + forced_decoder_ids = WhisperProcessor.get_decoder_prompt_ids(language="french", + task="transcribe") + forced_decoder_ids = WhisperProcessor.get_decoder_prompt_ids(language="french", + task="translate") + + + + """ + + def __init__( + self, + device: str = "0", + lang_model: Optional[str] = None, + batch_size: int = 8, + chunk_length: int = 30, + forced_decoder_ids: Optional[Tuple[Dict]] = None, + ): + """Initialize the parser. + + Args: + device: device to use. + lang_model: whisper model to use, for example "openai/whisper-medium". + Defaults to None. + forced_decoder_ids: id states for decoder in a multilanguage model. + Defaults to None. + batch_size: batch size used for decoding + Defaults to 8. + chunk_length: chunk length used during inference. + Defaults to 30s. + """ + try: + from transformers import pipeline + except ImportError: + raise ImportError( + "transformers package not found, please install it with " + "`pip install transformers`" + ) + try: + import torch + except ImportError: + raise ImportError( + "torch package not found, please install it with `pip install torch`" + ) + + # Determine the device to use + if device == "cpu": + self.device = "cpu" + else: + self.device = "cuda:0" if torch.cuda.is_available() else "cpu" + + if self.device == "cpu": + default_model = "openai/whisper-base" + self.lang_model = lang_model if lang_model else default_model + else: + # Set the language model based on the device and available memory + mem = torch.cuda.get_device_properties(self.device).total_memory / (1024**2) + if mem < 5000: + rec_model = "openai/whisper-base" + elif mem < 7000: + rec_model = "openai/whisper-small" + elif mem < 12000: + rec_model = "openai/whisper-medium" + else: + rec_model = "openai/whisper-large" + self.lang_model = lang_model if lang_model else rec_model + + print("Using the following model: ", self.lang_model) # noqa: T201 + + self.batch_size = batch_size + + # load model for inference + self.pipe = pipeline( + "automatic-speech-recognition", + model=self.lang_model, + chunk_length_s=chunk_length, + device=self.device, + ) + if forced_decoder_ids is not None: + try: + self.pipe.model.config.forced_decoder_ids = forced_decoder_ids + except Exception as exception_text: + logger.info( + "Unable to set forced_decoder_ids parameter for whisper model" + f"Text of exception: {exception_text}" + "Therefore whisper model will use default mode for decoder" + ) + + def lazy_parse(self, blob: Blob) -> Iterator[Document]: + """Lazily parse the blob.""" + + try: + import librosa + except ImportError: + raise ImportError( + "librosa package not found, please install it with " + "`pip install librosa`" + ) + + audio = _get_audio_from_blob(blob) + + file_obj = io.BytesIO(audio.export(format="mp3").read()) + + # Transcribe + print(f"Transcribing part {blob.path}!") # noqa: T201 + + y, sr = librosa.load(file_obj, sr=16000) + + prediction = self.pipe(y.copy(), batch_size=self.batch_size)["text"] + + yield Document( + page_content=prediction, + metadata={"source": blob.source}, + ) + + +class YandexSTTParser(BaseBlobParser): + """Transcribe and parse audio files. + Audio transcription is with OpenAI Whisper model.""" + + def __init__( + self, + *, + api_key: Optional[str] = None, + iam_token: Optional[str] = None, + model: str = "general", + language: str = "auto", + ): + """Initialize the parser. + + Args: + api_key: API key for a service account + with the `ai.speechkit-stt.user` role. + iam_token: IAM token for a service account + with the `ai.speechkit-stt.user` role. + model: Recognition model name. + Defaults to general. + language: The language in ISO 639-1 format. + Defaults to automatic language recognition. + Either `api_key` or `iam_token` must be provided, but not both. + """ + if (api_key is None) == (iam_token is None): + raise ValueError( + "Either 'api_key' or 'iam_token' must be provided, but not both." + ) + self.api_key = api_key + self.iam_token = iam_token + self.model = model + self.language = language + + def lazy_parse(self, blob: Blob) -> Iterator[Document]: + """Lazily parse the blob.""" + + try: + from speechkit import configure_credentials, creds, model_repository + from speechkit.stt import AudioProcessingType + except ImportError: + raise ImportError( + "yandex-speechkit package not found, please install it with " + "`pip install yandex-speechkit`" + ) + + audio = _get_audio_from_blob(blob) + + if self.api_key: + configure_credentials( + yandex_credentials=creds.YandexCredentials(api_key=self.api_key) + ) + else: + configure_credentials( + yandex_credentials=creds.YandexCredentials(iam_token=self.iam_token) + ) + + model = model_repository.recognition_model() + + model.model = self.model + model.language = self.language + model.audio_processing_type = AudioProcessingType.Full + + result = model.transcribe(audio) + + for res in result: + yield Document( + page_content=res.normalized_text, + metadata={"source": blob.source}, + ) + + +class FasterWhisperParser(BaseBlobParser): + """Transcribe and parse audio files with faster-whisper. + + faster-whisper is a reimplementation of OpenAI's Whisper model using CTranslate2, + which is up to 4 times faster than openai/whisper for the same accuracy while using + less memory. The efficiency can be further improved with 8-bit quantization on both + CPU and GPU. + + It can automatically detect the following 14 languages and transcribe the text + into their respective languages: en, zh, fr, de, ja, ko, ru, es, th, it, pt, vi, + ar, tr. + + The gitbub repository for faster-whisper is : + https://github.com/SYSTRAN/faster-whisper + + Example: Load a YouTube video and transcribe the video speech into a document. + .. code-block:: python + + from langchain_classic.document_loaders.generic import GenericLoader + from langchain_community.document_loaders.parsers.audio + import FasterWhisperParser + from langchain_classic.document_loaders.blob_loaders.youtube_audio + import YoutubeAudioLoader + + + url="https://www.youtube.com/watch?v=your_video" + save_dir="your_dir/" + loader = GenericLoader( + YoutubeAudioLoader([url],save_dir), + FasterWhisperParser() + ) + docs = loader.load() + + """ + + def __init__( + self, + *, + device: Optional[str] = "cuda", + model_size: Optional[str] = None, + ): + """Initialize the parser. + + Args: + device: It can be "cuda" or "cpu" based on the available device. + model_size: There are four model sizes to choose from: "base", "small", + "medium", and "large-v3", based on the available GPU memory. + """ + try: + import torch + except ImportError: + raise ImportError( + "torch package not found, please install it with `pip install torch`" + ) + + # Determine the device to use + if device == "cpu": + self.device = "cpu" + else: + self.device = "cuda" if torch.cuda.is_available() else "cpu" + + # Determine the model_size + if self.device == "cpu": + self.model_size = "base" + else: + # Set the model_size based on the available memory + mem = torch.cuda.get_device_properties(self.device).total_memory / (1024**2) + if mem < 1000: + self.model_size = "base" + elif mem < 3000: + self.model_size = "small" + elif mem < 5000: + self.model_size = "medium" + else: + self.model_size = "large-v3" + # If the user has assigned a model size, then use the assigned size + if model_size is not None: + if model_size in ["base", "small", "medium", "large-v3"]: + self.model_size = model_size + + def lazy_parse(self, blob: Blob) -> Iterator[Document]: + """Lazily parse the blob.""" + + try: + from faster_whisper import WhisperModel + except ImportError: + raise ImportError( + "faster_whisper package not found, please install it with " + "`pip install faster-whisper`" + ) + + audio = _get_audio_from_blob(blob) + + file_obj = io.BytesIO(audio.export(format="mp3").read()) + + # Transcribe + model = WhisperModel(self.model_size, device=self.device) + + segments, info = model.transcribe(file_obj, beam_size=5) + + for segment in segments: + yield Document( + page_content=segment.text, + metadata={ + "source": blob.source, + "timestamps": "[%.2fs -> %.2fs]" % (segment.start, segment.end), + "language": info.language, + "probability": "%d%%" % round(info.language_probability * 100), + **blob.metadata, + }, + ) + + +def _get_audio_from_blob(blob: Blob) -> Any: + """Get audio data from blob. + + Args: + blob: Blob object containing the audio data. + + Returns: + AudioSegment: Audio data from the blob. + + Raises: + ImportError: If the required package `pydub` is not installed. + ValueError: If the audio data is not found in the blob + """ + try: + from pydub import AudioSegment + except ImportError: + raise ImportError( + "pydub package not found, please install it with `pip install pydub`" + ) + + if isinstance(blob.data, bytes): + audio = AudioSegment.from_file(io.BytesIO(blob.data)) + elif blob.data is None and blob.path: + audio = AudioSegment.from_file(blob.path) + else: + raise ValueError("Unable to get audio from blob") + + return audio diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/doc_intelligence.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/doc_intelligence.py new file mode 100644 index 0000000000000000000000000000000000000000..f122f4dbabb5e3c99bf2e16ef2fdc31256d469c5 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/doc_intelligence.py @@ -0,0 +1,136 @@ +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any, Iterator, List, Optional, Union + +from langchain_core.documents import Document + +from langchain_community.document_loaders.base import BaseBlobParser +from langchain_community.document_loaders.blob_loaders import Blob + +if TYPE_CHECKING: + from azure.core.credentials import TokenCredential + +logger = logging.getLogger(__name__) + + +class AzureAIDocumentIntelligenceParser(BaseBlobParser): + """Loads a PDF with Azure Document Intelligence + (formerly Forms Recognizer).""" + + def __init__( + self, + api_endpoint: str, + api_key: Optional[str] = None, + api_version: Optional[str] = None, + api_model: str = "prebuilt-layout", + mode: str = "markdown", + analysis_features: Optional[List[str]] = None, + azure_credential: Optional["TokenCredential"] = None, + ): + from azure.ai.documentintelligence import DocumentIntelligenceClient + from azure.ai.documentintelligence.models import DocumentAnalysisFeature + from azure.core.credentials import AzureKeyCredential + + kwargs = {} + + credential: Union[AzureKeyCredential, TokenCredential] + if azure_credential: + if api_key is not None: + raise ValueError( + "Only one of api_key or azure_credential should be provided." + ) + credential = azure_credential + elif api_key is not None: + credential = AzureKeyCredential(api_key) + else: + raise ValueError("Either api_key or azure_credential must be provided.") + + if api_version is not None: + kwargs["api_version"] = api_version + + self.client = DocumentIntelligenceClient( + endpoint=api_endpoint, + credential=credential, + headers={"x-ms-useragent": "langchain-parser/1.0.0"}, + **kwargs, + ) + self.api_model = api_model + self.mode = mode + self.features: Optional[List[DocumentAnalysisFeature]] = None + if analysis_features is not None: + self.features = [ + DocumentAnalysisFeature(feature) for feature in analysis_features + ] + assert self.mode in ["single", "page", "markdown"] + + def _generate_docs_page(self, result: Any) -> Iterator[Document]: + for p in result.pages: + content = " ".join([line.content for line in p.lines]) + + d = Document( + page_content=content, + metadata={ + "page": p.page_number, + }, + ) + yield d + + def _generate_docs_single(self, result: Any) -> Iterator[Document]: + yield Document(page_content=result.content, metadata=result.as_dict()) + + def lazy_parse(self, blob: Blob) -> Iterator[Document]: + """Lazily parse the blob.""" + + with blob.as_bytes_io() as file_obj: + poller = self.client.begin_analyze_document( + self.api_model, + body=file_obj, + content_type="application/octet-stream", + output_content_format="markdown" if self.mode == "markdown" else "text", + features=self.features, + ) + result = poller.result() + + if self.mode in ["single", "markdown"]: + yield from self._generate_docs_single(result) + elif self.mode in ["page"]: + yield from self._generate_docs_page(result) + else: + raise ValueError(f"Invalid mode: {self.mode}") + + def parse_url(self, url: str) -> Iterator[Document]: + from azure.ai.documentintelligence.models import AnalyzeDocumentRequest + + poller = self.client.begin_analyze_document( + self.api_model, + body=AnalyzeDocumentRequest(url_source=url), + output_content_format="markdown" if self.mode == "markdown" else "text", + features=self.features, + ) + result = poller.result() + + if self.mode in ["single", "markdown"]: + yield from self._generate_docs_single(result) + elif self.mode in ["page"]: + yield from self._generate_docs_page(result) + else: + raise ValueError(f"Invalid mode: {self.mode}") + + def parse_bytes(self, bytes_source: bytes) -> Iterator[Document]: + from azure.ai.documentintelligence.models import AnalyzeDocumentRequest + + poller = self.client.begin_analyze_document( + self.api_model, + body=AnalyzeDocumentRequest(bytes_source=bytes_source), + output_content_format="markdown" if self.mode == "markdown" else "text", + features=self.features, + ) + result = poller.result() + + if self.mode in ["single", "markdown"]: + yield from self._generate_docs_single(result) + elif self.mode in ["page"]: + yield from self._generate_docs_page(result) + else: + raise ValueError(f"Invalid mode: {self.mode}") diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/docai.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/docai.py new file mode 100644 index 0000000000000000000000000000000000000000..b17b52ebcb069929c838a6e15e637d2688897552 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/docai.py @@ -0,0 +1,395 @@ +"""Module contains a PDF parser based on Document AI from Google Cloud. + +You need to install two libraries to use this parser: +pip install google-cloud-documentai +pip install google-cloud-documentai-toolbox +""" + +import logging +import re +import time +from dataclasses import dataclass +from typing import TYPE_CHECKING, Iterator, List, Optional, Sequence + +from langchain_core._api.deprecation import deprecated +from langchain_core.documents import Document +from langchain_core.utils.iter import batch_iterate + +from langchain_community.document_loaders.base import BaseBlobParser +from langchain_community.document_loaders.blob_loaders import Blob +from langchain_community.utilities.vertexai import get_client_info + +if TYPE_CHECKING: + from google.api_core.operation import Operation + from google.cloud.documentai import DocumentProcessorServiceClient + + +logger = logging.getLogger(__name__) + + +@dataclass +class DocAIParsingResults: + """Dataclass to store Document AI parsing results.""" + + source_path: str + parsed_path: str + + +@deprecated( + since="0.0.32", + removal="1.0", + alternative_import="langchain_google_community.DocAIParser", +) +class DocAIParser(BaseBlobParser): + """`Google Cloud Document AI` parser. + + For a detailed explanation of Document AI, refer to the product documentation. + https://cloud.google.com/document-ai/docs/overview + """ + + def __init__( + self, + *, + client: Optional["DocumentProcessorServiceClient"] = None, + location: Optional[str] = None, + gcs_output_path: Optional[str] = None, + processor_name: Optional[str] = None, + ): + """Initializes the parser. + + Args: + client: a DocumentProcessorServiceClient to use + location: a Google Cloud location where a Document AI processor is located + gcs_output_path: a path on Google Cloud Storage to store parsing results + processor_name: full resource name of a Document AI processor or processor + version + + You should provide either a client or location (and then a client + would be instantiated). + """ + + if bool(client) == bool(location): + raise ValueError( + "You must specify either a client or a location to instantiate " + "a client." + ) + + pattern = r"projects\/[0-9]+\/locations\/[a-z\-0-9]+\/processors\/[a-z0-9]+" + if processor_name and not re.fullmatch(pattern, processor_name): + raise ValueError( + f"Processor name {processor_name} has the wrong format. If your " + "prediction endpoint looks like https://us-documentai.googleapis.com" + "/v1/projects/PROJECT_ID/locations/us/processors/PROCESSOR_ID:process," + " use only projects/PROJECT_ID/locations/us/processors/PROCESSOR_ID " + "part." + ) + + self._gcs_output_path = gcs_output_path + self._processor_name = processor_name + if client: + self._client = client + else: + try: + from google.api_core.client_options import ClientOptions + from google.cloud.documentai import DocumentProcessorServiceClient + except ImportError as exc: + raise ImportError( + "documentai package not found, please install it with" + " `pip install google-cloud-documentai`" + ) from exc + options = ClientOptions( + api_endpoint=f"{location}-documentai.googleapis.com" + ) + self._client = DocumentProcessorServiceClient( + client_options=options, + client_info=get_client_info(module="document-ai"), + ) + + def lazy_parse(self, blob: Blob) -> Iterator[Document]: + """Parses a blob lazily. + + Args: + blobs: a Blob to parse + + This is a long-running operation. A recommended way is to batch + documents together and use the `batch_parse()` method. + """ + yield from self.batch_parse([blob], gcs_output_path=self._gcs_output_path) + + def online_process( + self, + blob: Blob, + enable_native_pdf_parsing: bool = True, + field_mask: Optional[str] = None, + page_range: Optional[List[int]] = None, + ) -> Iterator[Document]: + """Parses a blob lazily using online processing. + + Args: + blob: a blob to parse. + enable_native_pdf_parsing: enable pdf embedded text extraction + field_mask: a comma-separated list of which fields to include in the + Document AI response. + suggested: "text,pages.pageNumber,pages.layout" + page_range: list of page numbers to parse. If `None`, + entire document will be parsed. + """ + try: + from google.cloud import documentai + from google.cloud.documentai_v1.types import ( + IndividualPageSelector, + OcrConfig, + ProcessOptions, + ) + except ImportError as exc: + raise ImportError( + "documentai package not found, please install it with" + " `pip install google-cloud-documentai`" + ) from exc + try: + from google.cloud.documentai_toolbox.wrappers.page import _text_from_layout + except ImportError as exc: + raise ImportError( + "documentai_toolbox package not found, please install it with" + " `pip install google-cloud-documentai-toolbox`" + ) from exc + ocr_config = ( + OcrConfig(enable_native_pdf_parsing=enable_native_pdf_parsing) + if enable_native_pdf_parsing + else None + ) + individual_page_selector = ( + IndividualPageSelector(pages=page_range) if page_range else None + ) + + response = self._client.process_document( + documentai.ProcessRequest( + name=self._processor_name, + gcs_document=documentai.GcsDocument( + gcs_uri=blob.path, + mime_type=blob.mimetype or "application/pdf", + ), + process_options=ProcessOptions( + ocr_config=ocr_config, + individual_page_selector=individual_page_selector, + ), + skip_human_review=True, + field_mask=field_mask, + ) + ) + yield from ( + Document( + page_content=_text_from_layout(page.layout, response.document.text), + metadata={ + "page": page.page_number, + "source": blob.path, + }, + ) + for page in response.document.pages + ) + + def batch_parse( + self, + blobs: Sequence[Blob], + gcs_output_path: Optional[str] = None, + timeout_sec: int = 3600, + check_in_interval_sec: int = 60, + ) -> Iterator[Document]: + """Parses a list of blobs lazily. + + Args: + blobs: a list of blobs to parse. + gcs_output_path: a path on Google Cloud Storage to store parsing results. + timeout_sec: a timeout to wait for Document AI to complete, in seconds. + check_in_interval_sec: an interval to wait until next check + whether parsing operations have been completed, in seconds + This is a long-running operation. A recommended way is to decouple + parsing from creating LangChain Documents: + >>> operations = parser.docai_parse(blobs, gcs_path) + >>> parser.is_running(operations) + You can get operations names and save them: + >>> names = [op.operation.name for op in operations] + And when all operations are finished, you can use their results: + >>> operations = parser.operations_from_names(operation_names) + >>> results = parser.get_results(operations) + >>> docs = parser.parse_from_results(results) + """ + output_path = gcs_output_path or self._gcs_output_path + if not output_path: + raise ValueError( + "An output path on Google Cloud Storage should be provided." + ) + operations = self.docai_parse(blobs, gcs_output_path=output_path) + operation_names = [op.operation.name for op in operations] + logger.debug( + "Started parsing with Document AI, submitted operations %s", operation_names + ) + time_elapsed = 0 + while self.is_running(operations): + time.sleep(check_in_interval_sec) + time_elapsed += check_in_interval_sec + if time_elapsed > timeout_sec: + raise TimeoutError( + f"Timeout exceeded! Check operations {operation_names} later!" + ) + logger.debug(".") + + results = self.get_results(operations=operations) + yield from self.parse_from_results(results) + + def parse_from_results( + self, results: List[DocAIParsingResults] + ) -> Iterator[Document]: + try: + from google.cloud.documentai_toolbox.utilities.gcs_utilities import ( + split_gcs_uri, + ) + from google.cloud.documentai_toolbox.wrappers.document import _get_shards + from google.cloud.documentai_toolbox.wrappers.page import _text_from_layout + except ImportError as exc: + raise ImportError( + "documentai_toolbox package not found, please install it with" + " `pip install google-cloud-documentai-toolbox`" + ) from exc + for result in results: + gcs_bucket_name, gcs_prefix = split_gcs_uri(result.parsed_path) + shards = _get_shards(gcs_bucket_name, gcs_prefix) + yield from ( + Document( + page_content=_text_from_layout(page.layout, shard.text), + metadata={"page": page.page_number, "source": result.source_path}, + ) + for shard in shards + for page in shard.pages + ) + + def operations_from_names(self, operation_names: List[str]) -> List["Operation"]: + """Initializes Long-Running Operations from their names.""" + try: + from google.longrunning.operations_pb2 import ( + GetOperationRequest, + ) + except ImportError as exc: + raise ImportError( + "long running operations package not found, please install it with" + " `pip install gapic-google-longrunning`" + ) from exc + + return [ + self._client.get_operation(request=GetOperationRequest(name=name)) + for name in operation_names + ] + + def is_running(self, operations: List["Operation"]) -> bool: + return any(not op.done() for op in operations) + + def docai_parse( + self, + blobs: Sequence[Blob], + *, + gcs_output_path: Optional[str] = None, + processor_name: Optional[str] = None, + batch_size: int = 1000, + enable_native_pdf_parsing: bool = True, + field_mask: Optional[str] = None, + ) -> List["Operation"]: + """Runs Google Document AI PDF Batch Processing on a list of blobs. + + Args: + blobs: a list of blobs to be parsed + gcs_output_path: a path (folder) on GCS to store results + processor_name: name of a Document AI processor. + batch_size: amount of documents per batch + enable_native_pdf_parsing: a config option for the parser + field_mask: a comma-separated list of which fields to include in the + Document AI response. + suggested: "text,pages.pageNumber,pages.layout" + + Document AI has a 1000 file limit per batch, so batches larger than that need + to be split into multiple requests. + Batch processing is an async long-running operation + and results are stored in a output GCS bucket. + """ + try: + from google.cloud import documentai + from google.cloud.documentai_v1.types import OcrConfig, ProcessOptions + except ImportError as exc: + raise ImportError( + "documentai package not found, please install it with" + " `pip install google-cloud-documentai`" + ) from exc + + output_path = gcs_output_path or self._gcs_output_path + if output_path is None: + raise ValueError( + "An output path on Google Cloud Storage should be provided." + ) + processor_name = processor_name or self._processor_name + if processor_name is None: + raise ValueError("A Document AI processor name should be provided.") + + operations = [] + for batch in batch_iterate(size=batch_size, iterable=blobs): + input_config = documentai.BatchDocumentsInputConfig( + gcs_documents=documentai.GcsDocuments( + documents=[ + documentai.GcsDocument( + gcs_uri=blob.path, + mime_type=blob.mimetype or "application/pdf", + ) + for blob in batch + ] + ) + ) + + output_config = documentai.DocumentOutputConfig( + gcs_output_config=documentai.DocumentOutputConfig.GcsOutputConfig( + gcs_uri=output_path, field_mask=field_mask + ) + ) + + process_options = ( + ProcessOptions( + ocr_config=OcrConfig( + enable_native_pdf_parsing=enable_native_pdf_parsing + ) + ) + if enable_native_pdf_parsing + else None + ) + operations.append( + self._client.batch_process_documents( + documentai.BatchProcessRequest( + name=processor_name, + input_documents=input_config, + document_output_config=output_config, + process_options=process_options, + skip_human_review=True, + ) + ) + ) + return operations + + def get_results(self, operations: List["Operation"]) -> List[DocAIParsingResults]: + try: + from google.cloud.documentai_v1 import BatchProcessMetadata + except ImportError as exc: + raise ImportError( + "documentai package not found, please install it with" + " `pip install google-cloud-documentai`" + ) from exc + + return [ + DocAIParsingResults( + source_path=status.input_gcs_source, + parsed_path=status.output_gcs_destination, + ) + for op in operations + for status in ( + op.metadata.individual_process_statuses + if isinstance(op.metadata, BatchProcessMetadata) + else BatchProcessMetadata.deserialize( + op.metadata.value + ).individual_process_statuses + ) + ] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/documentloader_adapter.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/documentloader_adapter.py new file mode 100644 index 0000000000000000000000000000000000000000..93f6bb9ea12d6f8ff198c7e0e8e58eb113c15a65 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/documentloader_adapter.py @@ -0,0 +1,67 @@ +import inspect +from typing import Any, Dict, Iterator, Type + +from langchain_classic.document_loaders.base import BaseBlobParser, BaseLoader +from langchain_core._api import beta +from langchain_core.documents import Document +from langchain_core.documents.base import Blob + + +@beta() +class DocumentLoaderAsParser(BaseBlobParser): + """A wrapper class that adapts a document loader to function as a parser. + + This class is a work-around that adapts a document loader to function as a parser. + It is recommended to use a proper parser, if available. + + Requires the document loader to accept a `file_path` parameter. + """ + + DocumentLoaderType: Type[BaseLoader] + doc_loader_kwargs: Dict[str, Any] + + def __init__(self, document_loader_class: Type[BaseLoader], **kwargs: Any) -> None: + """ + Initializes the DocumentLoaderAsParser with a specific document loader class + and additional arguments. + + Args: + document_loader_class (Type[BaseLoader]): The document loader class to adapt + as a parser. + **kwargs: Additional arguments passed to the document loader's constructor. + + Raises: + TypeError: If the specified document loader does not accept a `file_path` parameter, + an exception is raised, as only loaders with this parameter can be adapted. + + Example: + ``` + from langchain_community.document_loaders.excel import UnstructuredExcelLoader + + # Initialize parser adapter with a document loader + excel_parser = DocumentLoaderAsParser(UnstructuredExcelLoader, mode="elements") + ``` + """ # noqa: E501 + super().__init__() + self.DocumentLoaderClass = document_loader_class + self.document_loader_kwargs = kwargs + + # Ensure the document loader class has a `file_path` parameter + init_signature = inspect.signature(document_loader_class.__init__) + if "file_path" not in init_signature.parameters: + raise TypeError( + f"{document_loader_class.__name__} does not accept `file_path`." + "Only document loaders with `file_path` parameter" + "can be morphed into a parser." + ) + + def lazy_parse(self, blob: Blob) -> Iterator[Document]: + """ + Use underlying DocumentLoader to lazily parse the blob. + """ + doc_loader = self.DocumentLoaderClass( # type: ignore[call-arg] + file_path=blob.path, **self.document_loader_kwargs + ) + for document in doc_loader.lazy_load(): + document.metadata.update(blob.metadata) + yield document diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/generic.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/generic.py new file mode 100644 index 0000000000000000000000000000000000000000..75861ab346d9653431c0cdcac5b02d029ca93703 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/generic.py @@ -0,0 +1,71 @@ +"""Code for generic / auxiliary parsers. + +This module contains some logic to help assemble more sophisticated parsers. +""" + +from typing import Iterator, Mapping, Optional + +from langchain_core.documents import Document + +from langchain_community.document_loaders.base import BaseBlobParser +from langchain_community.document_loaders.blob_loaders.schema import Blob + + +class MimeTypeBasedParser(BaseBlobParser): + """Parser that uses `mime`-types to parse a blob. + + This parser is useful for simple pipelines where the mime-type is sufficient + to determine how to parse a blob. + + To use, configure handlers based on mime-types and pass them to the initializer. + + Example: + + .. code-block:: python + + from langchain_community.document_loaders.parsers.generic import MimeTypeBasedParser + + parser = MimeTypeBasedParser( + handlers={ + "application/pdf": ..., + }, + fallback_parser=..., + ) + """ # noqa: E501 + + def __init__( + self, + handlers: Mapping[str, BaseBlobParser], + *, + fallback_parser: Optional[BaseBlobParser] = None, + ) -> None: + """Define a parser that uses mime-types to determine how to parse a blob. + + Args: + handlers: A mapping from mime-types to functions that take a blob, parse it + and return a document. + fallback_parser: A fallback_parser parser to use if the mime-type is not + found in the handlers. If provided, this parser will be + used to parse blobs with all mime-types not found in + the handlers. + If not provided, a ValueError will be raised if the + mime-type is not found in the handlers. + """ + self.handlers = handlers + self.fallback_parser = fallback_parser + + def lazy_parse(self, blob: Blob) -> Iterator[Document]: + """Load documents from a blob.""" + mimetype = blob.mimetype + + if mimetype is None: + raise ValueError(f"{blob} does not have a mimetype.") + + if mimetype in self.handlers: + handler = self.handlers[mimetype] + yield from handler.lazy_parse(blob) + else: + if self.fallback_parser is not None: + yield from self.fallback_parser.lazy_parse(blob) + else: + raise ValueError(f"Unsupported mime type: {mimetype}") diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/grobid.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/grobid.py new file mode 100644 index 0000000000000000000000000000000000000000..4df05fe7b30f149416860e88af2c74f2cc70870a --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/grobid.py @@ -0,0 +1,153 @@ +import logging +from typing import Dict, Iterator, List, Union + +import requests +from langchain_core.documents import Document + +from langchain_community.document_loaders.base import BaseBlobParser +from langchain_community.document_loaders.blob_loaders import Blob + +logger = logging.getLogger(__name__) + + +class ServerUnavailableException(Exception): + """Exception raised when the Grobid server is unavailable.""" + + pass + + +class GrobidParser(BaseBlobParser): + """Load article `PDF` files using `Grobid`.""" + + def __init__( + self, + segment_sentences: bool, + grobid_server: str = "http://localhost:8070/api/processFulltextDocument", + ) -> None: + self.segment_sentences = segment_sentences + self.grobid_server = grobid_server + try: + requests.get(grobid_server) + except requests.exceptions.RequestException: + logger.error( + "GROBID server does not appear up and running, \ + please ensure Grobid is installed and the server is running" + ) + raise ServerUnavailableException + + def process_xml( + self, file_path: str, xml_data: str, segment_sentences: bool + ) -> Iterator[Document]: + """Process the XML file from Grobin.""" + + try: + from bs4 import BeautifulSoup + except ImportError: + raise ImportError( + "`bs4` package not found, please install it with `pip install bs4`" + ) + soup = BeautifulSoup(xml_data, "xml") + sections = soup.find_all("div") + titles = soup.find_all("title") + if titles: + title = titles[0].text + else: + title = "No title found" + chunks = [] + for section in sections: + sect = section.find("head") + if sect is not None: + for i, paragraph in enumerate(section.find_all("p")): + chunk_bboxes = [] + paragraph_text = [] + for i, sentence in enumerate(paragraph.find_all("s")): + paragraph_text.append(sentence.text) + sbboxes = [] + if sentence.get("coords") is not None: + for bbox in sentence.get("coords").split(";"): # type: ignore[union-attr] + box = bbox.split(",") + sbboxes.append( + { + "page": box[0], + "x": box[1], + "y": box[2], + "h": box[3], + "w": box[4], + } + ) + chunk_bboxes.append(sbboxes) + if (segment_sentences is True) and (len(sbboxes) > 0): + fpage, lpage = sbboxes[0]["page"], sbboxes[-1]["page"] + sentence_dict = { + "text": sentence.text, + "para": str(i), + "bboxes": [sbboxes], + "section_title": sect.text, + "section_number": sect.get("n"), + "pages": (fpage, lpage), + } + chunks.append(sentence_dict) + if segment_sentences is not True: + fpage, lpage = ( + chunk_bboxes[0][0]["page"], + chunk_bboxes[-1][-1]["page"], + ) + paragraph_dict = { + "text": "".join(paragraph_text), + "para": str(i), + "bboxes": chunk_bboxes, + "section_title": sect.text, + "section_number": sect.get("n"), + "pages": (fpage, lpage), + } + chunks.append(paragraph_dict) + + yield from [ + Document( + page_content=chunk["text"], + metadata=dict( + { + "text": str(chunk["text"]), + "para": str(chunk["para"]), + "bboxes": str(chunk["bboxes"]), + "pages": str(chunk["pages"]), + "section_title": str(chunk["section_title"]), + "section_number": str(chunk["section_number"]), + "paper_title": str(title), + "file_path": str(file_path), + } + ), + ) + for chunk in chunks + ] + + def lazy_parse(self, blob: Blob) -> Iterator[Document]: + file_path = blob.source + if file_path is None: + raise ValueError("blob.source cannot be None.") + pdf = open(file_path, "rb") + files = {"input": (file_path, pdf, "application/pdf", {"Expires": "0"})} + try: + data: Dict[str, Union[str, List[str]]] = {} + for param in ["generateIDs", "consolidateHeader", "segmentSentences"]: + data[param] = "1" + data["teiCoordinates"] = ["head", "s"] + files = files or {} + r = requests.request( + "POST", + self.grobid_server, + headers=None, + params=None, + files=files, + data=data, + timeout=60, + ) + xml_data = r.text + except requests.exceptions.ReadTimeout: + logger.error("GROBID server timed out. Return None.") + xml_data = None + + if xml_data is None: + return iter([]) + else: + return self.process_xml(file_path, xml_data, self.segment_sentences) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/html/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/html/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f59e804b30f7d66e8475dbd7576d0e3307786791 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/html/__init__.py @@ -0,0 +1,3 @@ +from langchain_community.document_loaders.parsers.html.bs4 import BS4HTMLParser + +__all__ = ["BS4HTMLParser"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/html/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/html/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6ea603576aa0aed5320991479ab1073abf4126eb Binary files /dev/null and b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/html/__pycache__/__init__.cpython-311.pyc differ diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/html/__pycache__/bs4.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/html/__pycache__/bs4.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..35e9289d0471c1ce3c52fa97fb411a6f8835e23b Binary files /dev/null and b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/html/__pycache__/bs4.cpython-311.pyc differ diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/html/bs4.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/html/bs4.py new file mode 100644 index 0000000000000000000000000000000000000000..d00af499dcf05ebb68b7bc4e68e6c4387c8c9df4 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/html/bs4.py @@ -0,0 +1,54 @@ +"""Loader that uses bs4 to load HTML files, enriching metadata with page title.""" + +import logging +from typing import Any, Dict, Iterator, Union + +from langchain_core.documents import Document + +from langchain_community.document_loaders.base import BaseBlobParser +from langchain_community.document_loaders.blob_loaders import Blob + +logger = logging.getLogger(__name__) + + +class BS4HTMLParser(BaseBlobParser): + """Parse HTML files using `Beautiful Soup`.""" + + def __init__( + self, + *, + features: str = "lxml", + get_text_separator: str = "", + **kwargs: Any, + ) -> None: + """Initialize a bs4 based HTML parser.""" + try: + import bs4 # noqa:F401 + except ImportError: + raise ImportError( + "beautifulsoup4 package not found, please install it with " + "`pip install beautifulsoup4`" + ) + + self.bs_kwargs = {"features": features, **kwargs} + self.get_text_separator = get_text_separator + + def lazy_parse(self, blob: Blob) -> Iterator[Document]: + """Load HTML document into document objects.""" + from bs4 import BeautifulSoup + + with blob.as_bytes_io() as f: + soup = BeautifulSoup(f, **self.bs_kwargs) + + text = soup.get_text(self.get_text_separator) + + if soup.title: + title = str(soup.title.string) + else: + title = "" + + metadata: Dict[str, Union[str, None]] = { + "source": blob.source, + "title": title, + } + yield Document(page_content=text, metadata=metadata) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/images.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/images.py new file mode 100644 index 0000000000000000000000000000000000000000..1b4e1474af53c73aa306b10b899436aec60d91be --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/images.py @@ -0,0 +1,224 @@ +import base64 +import io +import logging +from abc import abstractmethod +from typing import TYPE_CHECKING, Iterable, Iterator + +import numpy +import numpy as np +from langchain_core.documents import Document +from langchain_core.language_models import BaseChatModel +from langchain_core.messages import HumanMessage + +from langchain_community.document_loaders.base import BaseBlobParser +from langchain_community.document_loaders.blob_loaders import Blob + +if TYPE_CHECKING: + from PIL.Image import Image + +logger = logging.getLogger(__name__) + + +class BaseImageBlobParser(BaseBlobParser): + """Abstract base class for parsing image blobs into text.""" + + @abstractmethod + def _analyze_image(self, img: "Image") -> str: + """Abstract method to analyze an image and extract textual content. + + Args: + img: The image to be analyzed. + + Returns: + The extracted text content. + """ + + def lazy_parse(self, blob: Blob) -> Iterator[Document]: + """Lazily parse a blob and yields Documents containing the parsed content. + + Args: + blob (Blob): The blob to be parsed. + + Yields: + Document: + A document containing the parsed content and metadata. + """ + try: + from PIL import Image as Img + except ImportError: + raise ImportError( + "`Pillow` package not found, please install it with " + "`pip install Pillow`" + ) + + with blob.as_bytes_io() as buf: + if blob.mimetype == "application/x-npy": + array = numpy.load(buf) + if array.ndim == 3 and array.shape[2] == 1: # Grayscale image + img = Img.fromarray(numpy.squeeze(array, axis=2), mode="L") + else: + img = Img.fromarray(array) + else: + img = Img.open(buf) + content = self._analyze_image(img) + logger.debug("Image text: %s", content.replace("\n", "\\n")) + yield Document( + page_content=content, + metadata={**blob.metadata, **{"source": blob.source}}, + ) + + +class RapidOCRBlobParser(BaseImageBlobParser): + """Parser for extracting text from images using the RapidOCR library. + + Attributes: + ocr: + The RapidOCR instance for performing OCR. + """ + + def __init__( + self, + ) -> None: + """ + Initializes the RapidOCRBlobParser. + """ + super().__init__() + self.ocr = None + + def _analyze_image(self, img: "Image") -> str: + """ + Analyzes an image and extracts text using RapidOCR. + + Args: + img (Image): + The image to be analyzed. + + Returns: + str: + The extracted text content. + """ + if not self.ocr: + try: + from rapidocr_onnxruntime import RapidOCR + + self.ocr = RapidOCR() + except ImportError: + raise ImportError( + "`rapidocr-onnxruntime` package not found, please install it with " + "`pip install rapidocr-onnxruntime`" + ) + ocr_result, _ = self.ocr(np.array(img)) # type: ignore[misc] + content = "" + if ocr_result: + content = ("\n".join([text[1] for text in ocr_result])).strip() + return content + + +class TesseractBlobParser(BaseImageBlobParser): + """Parse for extracting text from images using the Tesseract OCR library.""" + + def __init__( + self, + *, + langs: Iterable[str] = ("eng",), + ): + """Initialize the TesseractBlobParser. + + Args: + langs (list[str]): + The languages to use for OCR. + """ + super().__init__() + self.langs = list(langs) + + def _analyze_image(self, img: "Image") -> str: + """Analyze an image and extracts text using Tesseract OCR. + + Args: + img: The image to be analyzed. + + Returns: + str: The extracted text content. + """ + try: + import pytesseract + except ImportError: + raise ImportError( + "`pytesseract` package not found, please install it with " + "`pip install pytesseract`" + ) + return pytesseract.image_to_string(img, lang="+".join(self.langs)).strip() + + +_PROMPT_IMAGES_TO_DESCRIPTION: str = ( + "You are an assistant tasked with summarizing images for retrieval. " + "1. These summaries will be embedded and used to retrieve the raw image. " + "Give a concise summary of the image that is well optimized for retrieval\n" + "2. extract all the text from the image. " + "Do not exclude any content from the page.\n" + "Format answer in markdown without explanatory text " + "and without markdown delimiter ``` at the beginning. " +) + + +class LLMImageBlobParser(BaseImageBlobParser): + """Parser for analyzing images using a language model (LLM). + + Attributes: + model (BaseChatModel): + The language model to use for analysis. + prompt (str): + The prompt to provide to the language model. + """ + + def __init__( + self, + *, + model: BaseChatModel, + prompt: str = _PROMPT_IMAGES_TO_DESCRIPTION, + ): + """Initializes the LLMImageBlobParser. + + Args: + model (BaseChatModel): + The language model to use for analysis. + prompt (str): + The prompt to provide to the language model. + """ + super().__init__() + self.model = model + self.prompt = prompt + + def _analyze_image(self, img: "Image") -> str: + """Analyze an image using the provided language model. + + Args: + img: The image to be analyzed. + + Returns: + The extracted textual content. + """ + image_bytes = io.BytesIO() + img.save(image_bytes, format="PNG") + img_base64 = base64.b64encode(image_bytes.getvalue()).decode("utf-8") + msg = self.model.invoke( + [ + HumanMessage( + content=[ + { + "type": "text", + "text": self.prompt.format(format=format), + }, + { + "type": "image_url", + "image_url": { + "url": f"data:image/jpeg;base64,{img_base64}" + }, + }, + ] + ) + ] + ) + result = msg.content + assert isinstance(result, str) + return result diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e56cc143cfda9cbe3f92b72608267a75e2668c3a --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/__init__.py @@ -0,0 +1,5 @@ +from langchain_community.document_loaders.parsers.language.language_parser import ( + LanguageParser, +) + +__all__ = ["LanguageParser"] diff --git 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mode 100644 index 0000000000000000000000000000000000000000..2db1ec99fca4a39ef07a3dcf7853a8fa0351c889 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/c.py @@ -0,0 +1,36 @@ +from typing import TYPE_CHECKING + +from langchain_community.document_loaders.parsers.language.tree_sitter_segmenter import ( # noqa: E501 + TreeSitterSegmenter, +) + +if TYPE_CHECKING: + from tree_sitter import Language + + +CHUNK_QUERY = """ + [ + (struct_specifier + body: (field_declaration_list)) @struct + (enum_specifier + body: (enumerator_list)) @enum + (union_specifier + body: (field_declaration_list)) @union + (function_definition) @function + ] +""".strip() + + +class CSegmenter(TreeSitterSegmenter): + """Code segmenter for C.""" + + def get_language(self) -> "Language": + from tree_sitter_languages import get_language + + return get_language("c") + + def get_chunk_query(self) -> str: + return CHUNK_QUERY + + def make_line_comment(self, text: str) -> str: + return f"// {text}" diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/cobol.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/cobol.py new file mode 100644 index 0000000000000000000000000000000000000000..2b598ba27029737740438a584d5dfbb0a963b851 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/cobol.py @@ -0,0 +1,98 @@ +import re +from typing import Callable, List, Pattern + +from langchain_community.document_loaders.parsers.language.code_segmenter import ( + CodeSegmenter, +) + + +class CobolSegmenter(CodeSegmenter): + """Code segmenter for `COBOL`.""" + + PARAGRAPH_PATTERN: Pattern = re.compile(r"^[A-Z0-9\-]+(\s+.*)?\.$", re.IGNORECASE) + DIVISION_PATTERN: Pattern = re.compile( + r"^\s*(IDENTIFICATION|DATA|PROCEDURE|ENVIRONMENT)\s+DIVISION.*$", re.IGNORECASE + ) + SECTION_PATTERN: Pattern = re.compile(r"^\s*[A-Z0-9\-]+\s+SECTION.$", re.IGNORECASE) + + def __init__(self, code: str): + super().__init__(code) + self.source_lines: List[str] = self.code.splitlines() + + def is_valid(self) -> bool: + # Identify presence of any division to validate COBOL code + return any(self.DIVISION_PATTERN.match(line) for line in self.source_lines) + + def _extract_code(self, start_idx: int, end_idx: int) -> str: + return "\n".join(self.source_lines[start_idx:end_idx]).rstrip("\n") + + def _is_relevant_code(self, line: str) -> bool: + """Check if a line is part of the procedure division or a relevant section.""" + if "PROCEDURE DIVISION" in line.upper(): + return True + # Add additional conditions for relevant sections if needed + return False + + def _process_lines(self, func: Callable) -> List[str]: + """A generic function to process COBOL lines based on provided func.""" + elements: List[str] = [] + start_idx = None + inside_relevant_section = False + + for i, line in enumerate(self.source_lines): + if self._is_relevant_code(line): + inside_relevant_section = True + + if inside_relevant_section and ( + self.PARAGRAPH_PATTERN.match(line.strip().split(" ")[0]) + or self.SECTION_PATTERN.match(line.strip()) + ): + if start_idx is not None: + func(elements, start_idx, i) + start_idx = i + + # Handle the last element if exists + if start_idx is not None: + func(elements, start_idx, len(self.source_lines)) + + return elements + + def extract_functions_classes(self) -> List[str]: + def extract_func(elements: List[str], start_idx: int, end_idx: int) -> None: + elements.append(self._extract_code(start_idx, end_idx)) + + return self._process_lines(extract_func) + + def simplify_code(self) -> str: + simplified_lines: List[str] = [] + inside_relevant_section = False + omitted_code_added = ( + False # To track if "* OMITTED CODE *" has been added after the last header + ) + + for line in self.source_lines: + is_header = ( + "PROCEDURE DIVISION" in line + or "DATA DIVISION" in line + or "IDENTIFICATION DIVISION" in line + or self.PARAGRAPH_PATTERN.match(line.strip().split(" ")[0]) + or self.SECTION_PATTERN.match(line.strip()) + ) + + if is_header: + inside_relevant_section = True + # Reset the flag since we're entering a new section/division or + # paragraph + omitted_code_added = False + + if inside_relevant_section: + if is_header: + # Add header and reset the omitted code added flag + simplified_lines.append(line) + elif not omitted_code_added: + # Add omitted code comment only if it hasn't been added directly + # after the last header + simplified_lines.append("* OMITTED CODE *") + omitted_code_added = True + + return "\n".join(simplified_lines) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/code_segmenter.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/code_segmenter.py new file mode 100644 index 0000000000000000000000000000000000000000..2efb2add448e3d9d315a0cbc9c5926e431273450 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/code_segmenter.py @@ -0,0 +1,20 @@ +from abc import ABC, abstractmethod +from typing import List + + +class CodeSegmenter(ABC): + """Abstract class for the code segmenter.""" + + def __init__(self, code: str): + self.code = code + + def is_valid(self) -> bool: + return True + + @abstractmethod + def simplify_code(self) -> str: + raise NotImplementedError() # pragma: no cover + + @abstractmethod + def extract_functions_classes(self) -> List[str]: + raise NotImplementedError() # pragma: no cover diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/cpp.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/cpp.py new file mode 100644 index 0000000000000000000000000000000000000000..9d09164a846e773631cd0d67ea8d53e507593cd6 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/cpp.py @@ -0,0 +1,36 @@ +from typing import TYPE_CHECKING + +from langchain_community.document_loaders.parsers.language.tree_sitter_segmenter import ( # noqa: E501 + TreeSitterSegmenter, +) + +if TYPE_CHECKING: + from tree_sitter import Language + + +CHUNK_QUERY = """ + [ + (class_specifier + body: (field_declaration_list)) @class + (struct_specifier + body: (field_declaration_list)) @struct + (union_specifier + body: (field_declaration_list)) @union + (function_definition) @function + ] +""".strip() + + +class CPPSegmenter(TreeSitterSegmenter): + """Code segmenter for C++.""" + + def get_language(self) -> "Language": + from tree_sitter_languages import get_language + + return get_language("cpp") + + def get_chunk_query(self) -> str: + return CHUNK_QUERY + + def make_line_comment(self, text: str) -> str: + return f"// {text}" diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/csharp.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/csharp.py new file mode 100644 index 0000000000000000000000000000000000000000..a9f809fa00a84992d8b6fb627b71d229dfbc2bc1 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/csharp.py @@ -0,0 +1,36 @@ +from typing import TYPE_CHECKING + +from langchain_community.document_loaders.parsers.language.tree_sitter_segmenter import ( # noqa: E501 + TreeSitterSegmenter, +) + +if TYPE_CHECKING: + from tree_sitter import Language + + +CHUNK_QUERY = """ + [ + (namespace_declaration) @namespace + (class_declaration) @class + (method_declaration) @method + (interface_declaration) @interface + (enum_declaration) @enum + (struct_declaration) @struct + (record_declaration) @record + ] +""".strip() + + +class CSharpSegmenter(TreeSitterSegmenter): + """Code segmenter for C#.""" + + def get_language(self) -> "Language": + from tree_sitter_languages import get_language + + return get_language("c_sharp") + + def get_chunk_query(self) -> str: + return CHUNK_QUERY + + def make_line_comment(self, text: str) -> str: + return f"// {text}" diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/elixir.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/elixir.py new file mode 100644 index 0000000000000000000000000000000000000000..780209767d89fadda03a80ff3e39c836baf9b0cc --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/elixir.py @@ -0,0 +1,35 @@ +from typing import TYPE_CHECKING + +from langchain_community.document_loaders.parsers.language.tree_sitter_segmenter import ( # noqa: E501 + TreeSitterSegmenter, +) + +if TYPE_CHECKING: + from tree_sitter import Language + + +CHUNK_QUERY = """ + [ + (call target: ((identifier) @_identifier + (#any-of? @_identifier "defmodule" "defprotocol" "defimpl"))) @module + (call target: ((identifier) @_identifier + (#any-of? @_identifier "def" "defmacro" "defmacrop" "defp"))) @function + (unary_operator operator: "@" operand: (call target: ((identifier) @_identifier + (#any-of? @_identifier "moduledoc" "typedoc""doc")))) @comment + ] +""".strip() + + +class ElixirSegmenter(TreeSitterSegmenter): + """Code segmenter for Elixir.""" + + def get_language(self) -> "Language": + from tree_sitter_languages import get_language + + return get_language("elixir") + + def get_chunk_query(self) -> str: + return CHUNK_QUERY + + def make_line_comment(self, text: str) -> str: + return f"# {text}" diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/go.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/go.py new file mode 100644 index 0000000000000000000000000000000000000000..f836ab3ad710c73e2629828a6cf0f9fdab4b3c7a --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/go.py @@ -0,0 +1,31 @@ +from typing import TYPE_CHECKING + +from langchain_community.document_loaders.parsers.language.tree_sitter_segmenter import ( # noqa: E501 + TreeSitterSegmenter, +) + +if TYPE_CHECKING: + from tree_sitter import Language + + +CHUNK_QUERY = """ + [ + (function_declaration) @function + (type_declaration) @type + ] +""".strip() + + +class GoSegmenter(TreeSitterSegmenter): + """Code segmenter for Go.""" + + def get_language(self) -> "Language": + from tree_sitter_languages import get_language + + return get_language("go") + + def get_chunk_query(self) -> str: + return CHUNK_QUERY + + def make_line_comment(self, text: str) -> str: + return f"// {text}" diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/java.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/java.py new file mode 100644 index 0000000000000000000000000000000000000000..c7293e1ed7f7845420a7a1907fffa3010da1f14d --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/java.py @@ -0,0 +1,32 @@ +from typing import TYPE_CHECKING + +from langchain_community.document_loaders.parsers.language.tree_sitter_segmenter import ( # noqa: E501 + TreeSitterSegmenter, +) + +if TYPE_CHECKING: + from tree_sitter import Language + + +CHUNK_QUERY = """ + [ + (class_declaration) @class + (interface_declaration) @interface + (enum_declaration) @enum + ] +""".strip() + + +class JavaSegmenter(TreeSitterSegmenter): + """Code segmenter for Java.""" + + def get_language(self) -> "Language": + from tree_sitter_languages import get_language + + return get_language("java") + + def get_chunk_query(self) -> str: + return CHUNK_QUERY + + def make_line_comment(self, text: str) -> str: + return f"// {text}" diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/javascript.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/javascript.py new file mode 100644 index 0000000000000000000000000000000000000000..27a360a2e6c6475890d2a7992c27fd35beac2e6a --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/javascript.py @@ -0,0 +1,72 @@ +from typing import Any, List, Tuple + +from langchain_community.document_loaders.parsers.language.code_segmenter import ( + CodeSegmenter, +) + + +class JavaScriptSegmenter(CodeSegmenter): + """Code segmenter for JavaScript.""" + + def __init__(self, code: str): + super().__init__(code) + self.source_lines = self.code.splitlines() + + try: + import esprima # noqa: F401 + except ImportError: + raise ImportError( + "Could not import esprima Python package. " + "Please install it with `pip install esprima`." + ) + + def is_valid(self) -> bool: + import esprima + + try: + esprima.parseScript(self.code) + return True + except esprima.Error: + return False + + def _extract_code(self, node: Any) -> str: + start = node.loc.start.line - 1 + end = node.loc.end.line + return "\n".join(self.source_lines[start:end]) + + def extract_functions_classes(self) -> List[str]: + import esprima + + tree = esprima.parseScript(self.code, loc=True) + functions_classes = [] + + for node in tree.body: + if isinstance( + node, + (esprima.nodes.FunctionDeclaration, esprima.nodes.ClassDeclaration), + ): + functions_classes.append(self._extract_code(node)) + + return functions_classes + + def simplify_code(self) -> str: + import esprima + + tree = esprima.parseScript(self.code, loc=True) + simplified_lines = self.source_lines[:] + + indices_to_del: List[Tuple[int, int]] = [] + for node in tree.body: + if isinstance( + node, + (esprima.nodes.FunctionDeclaration, esprima.nodes.ClassDeclaration), + ): + start, end = node.loc.start.line - 1, node.loc.end.line + simplified_lines[start] = f"// Code for: {simplified_lines[start]}" + + indices_to_del.append((start + 1, end)) + + for start, end in reversed(indices_to_del): + del simplified_lines[start + 0 : end] + + return "\n".join(line for line in simplified_lines) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/kotlin.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/kotlin.py new file mode 100644 index 0000000000000000000000000000000000000000..6f946f7b4a622004526249709b1f98885752aadb --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/kotlin.py @@ -0,0 +1,31 @@ +from typing import TYPE_CHECKING + +from langchain_community.document_loaders.parsers.language.tree_sitter_segmenter import ( # noqa: E501 + TreeSitterSegmenter, +) + +if TYPE_CHECKING: + from tree_sitter import Language + + +CHUNK_QUERY = """ + [ + (function_declaration) @function + (class_declaration) @class + ] +""".strip() + + +class KotlinSegmenter(TreeSitterSegmenter): + """Code segmenter for Kotlin.""" + + def get_language(self) -> "Language": + from tree_sitter_languages import get_language + + return get_language("kotlin") + + def get_chunk_query(self) -> str: + return CHUNK_QUERY + + def make_line_comment(self, text: str) -> str: + return f"// {text}" diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/language_parser.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/language_parser.py new file mode 100644 index 0000000000000000000000000000000000000000..e1d4e5ec664b983efed7041c380e3a168927569b --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/language_parser.py @@ -0,0 +1,249 @@ +from __future__ import annotations + +from typing import Any, Dict, Iterator, Literal, Optional + +from langchain_core.documents import Document + +from langchain_community.document_loaders.base import BaseBlobParser +from langchain_community.document_loaders.blob_loaders import Blob +from langchain_community.document_loaders.parsers.language.c import CSegmenter +from langchain_community.document_loaders.parsers.language.cobol import CobolSegmenter +from langchain_community.document_loaders.parsers.language.cpp import CPPSegmenter +from langchain_community.document_loaders.parsers.language.csharp import CSharpSegmenter +from langchain_community.document_loaders.parsers.language.elixir import ElixirSegmenter +from langchain_community.document_loaders.parsers.language.go import GoSegmenter +from langchain_community.document_loaders.parsers.language.java import JavaSegmenter +from langchain_community.document_loaders.parsers.language.javascript import ( + JavaScriptSegmenter, +) +from langchain_community.document_loaders.parsers.language.kotlin import KotlinSegmenter +from langchain_community.document_loaders.parsers.language.lua import LuaSegmenter +from langchain_community.document_loaders.parsers.language.perl import PerlSegmenter +from langchain_community.document_loaders.parsers.language.php import PHPSegmenter +from langchain_community.document_loaders.parsers.language.python import PythonSegmenter +from langchain_community.document_loaders.parsers.language.ruby import RubySegmenter +from langchain_community.document_loaders.parsers.language.rust import RustSegmenter +from langchain_community.document_loaders.parsers.language.scala import ScalaSegmenter +from langchain_community.document_loaders.parsers.language.sql import SQLSegmenter +from langchain_community.document_loaders.parsers.language.typescript import ( + TypeScriptSegmenter, +) + +LANGUAGE_EXTENSIONS: Dict[str, str] = { + "py": "python", + "js": "js", + "cobol": "cobol", + "c": "c", + "cpp": "cpp", + "cs": "csharp", + "rb": "ruby", + "scala": "scala", + "rs": "rust", + "go": "go", + "kt": "kotlin", + "lua": "lua", + "pl": "perl", + "ts": "ts", + "java": "java", + "php": "php", + "ex": "elixir", + "exs": "elixir", + "sql": "sql", +} + +LANGUAGE_SEGMENTERS: Dict[str, Any] = { + "python": PythonSegmenter, + "js": JavaScriptSegmenter, + "cobol": CobolSegmenter, + "c": CSegmenter, + "cpp": CPPSegmenter, + "csharp": CSharpSegmenter, + "ruby": RubySegmenter, + "rust": RustSegmenter, + "scala": ScalaSegmenter, + "go": GoSegmenter, + "kotlin": KotlinSegmenter, + "lua": LuaSegmenter, + "perl": PerlSegmenter, + "ts": TypeScriptSegmenter, + "java": JavaSegmenter, + "php": PHPSegmenter, + "elixir": ElixirSegmenter, + "sql": SQLSegmenter, +} + +Language = Literal[ + "cpp", + "go", + "java", + "kotlin", + "js", + "ts", + "php", + "proto", + "python", + "rst", + "ruby", + "rust", + "scala", + "markdown", + "latex", + "html", + "sol", + "csharp", + "cobol", + "c", + "lua", + "perl", + "elixir", + "sql", +] + + +class LanguageParser(BaseBlobParser): + """Parse using the respective programming language syntax. + + Each top-level function and class in the code is loaded into separate documents. + Furthermore, an extra document is generated, containing the remaining top-level code + that excludes the already segmented functions and classes. + + This approach can potentially improve the accuracy of QA models over source code. + + The supported languages for code parsing are: + + - C: "c" (*) + - C++: "cpp" (*) + - C#: "csharp" (*) + - COBOL: "cobol" + - Elixir: "elixir" + - Go: "go" (*) + - Java: "java" (*) + - JavaScript: "js" (requires package `esprima`) + - Kotlin: "kotlin" (*) + - Lua: "lua" (*) + - Perl: "perl" (*) + - Python: "python" + - Ruby: "ruby" (*) + - Rust: "rust" (*) + - Scala: "scala" (*) + - SQL: "sql" (*) + - TypeScript: "ts" (*) + + Items marked with (*) require the packages `tree_sitter` and + `tree_sitter_languages`. It is straightforward to add support for additional + languages using `tree_sitter`, although this currently requires modifying LangChain. + + The language used for parsing can be configured, along with the minimum number of + lines required to activate the splitting based on syntax. + + If a language is not explicitly specified, `LanguageParser` will infer one from + filename extensions, if present. + + Examples: + + .. code-block:: python + + from langchain_community.document_loaders.generic import GenericLoader + from langchain_community.document_loaders.parsers import LanguageParser + + loader = GenericLoader.from_filesystem( + "./code", + glob="**/*", + suffixes=[".py", ".js"], + parser=LanguageParser() + ) + docs = loader.load() + + Example instantiations to manually select the language: + + .. code-block:: python + + + loader = GenericLoader.from_filesystem( + "./code", + glob="**/*", + suffixes=[".py"], + parser=LanguageParser(language="python") + ) + + Example instantiations to set number of lines threshold: + + .. code-block:: python + + loader = GenericLoader.from_filesystem( + "./code", + glob="**/*", + suffixes=[".py"], + parser=LanguageParser(parser_threshold=200) + ) + """ + + def __init__(self, language: Optional[Language] = None, parser_threshold: int = 0): + """ + Language parser that split code using the respective language syntax. + + Args: + language: If None (default), it will try to infer language from source. + parser_threshold: Minimum lines needed to activate parsing (0 by default). + """ + if language and language not in LANGUAGE_SEGMENTERS: + raise Exception(f"No parser available for {language}") + self.language = language + self.parser_threshold = parser_threshold + + def lazy_parse(self, blob: Blob) -> Iterator[Document]: + code = blob.as_string() + + language = self.language or ( + LANGUAGE_EXTENSIONS.get(blob.source.rsplit(".", 1)[-1]) + if isinstance(blob.source, str) + else None + ) + + if language is None: + yield Document( + page_content=code, + metadata={ + "source": blob.source, + }, + ) + return + + if self.parser_threshold >= len(code.splitlines()): + yield Document( + page_content=code, + metadata={ + "source": blob.source, + "language": language, + }, + ) + return + + self.Segmenter = LANGUAGE_SEGMENTERS[language] + segmenter = self.Segmenter(blob.as_string()) + if not segmenter.is_valid(): + yield Document( + page_content=code, + metadata={ + "source": blob.source, + }, + ) + return + + for functions_classes in segmenter.extract_functions_classes(): + yield Document( + page_content=functions_classes, + metadata={ + "source": blob.source, + "content_type": "functions_classes", + "language": language, + }, + ) + yield Document( + page_content=segmenter.simplify_code(), + metadata={ + "source": blob.source, + "content_type": "simplified_code", + "language": language, + }, + ) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/lua.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/lua.py new file mode 100644 index 0000000000000000000000000000000000000000..3e0a762ba4b5ffd7911e28c1c83978b476e491e9 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/lua.py @@ -0,0 +1,33 @@ +from typing import TYPE_CHECKING + +from langchain_community.document_loaders.parsers.language.tree_sitter_segmenter import ( # noqa: E501 + TreeSitterSegmenter, +) + +if TYPE_CHECKING: + from tree_sitter import Language + + +CHUNK_QUERY = """ + [ + (function_definition_statement + name: (identifier)) @function + (local_function_definition_statement + name: (identifier)) @function + ] +""".strip() + + +class LuaSegmenter(TreeSitterSegmenter): + """Code segmenter for Lua.""" + + def get_language(self) -> "Language": + from tree_sitter_languages import get_language + + return get_language("lua") + + def get_chunk_query(self) -> str: + return CHUNK_QUERY + + def make_line_comment(self, text: str) -> str: + return f"-- {text}" diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/perl.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/perl.py new file mode 100644 index 0000000000000000000000000000000000000000..b68d52cef2b04d61d7666d878185c3f84ef86c72 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/perl.py @@ -0,0 +1,30 @@ +from typing import TYPE_CHECKING + +from langchain_community.document_loaders.parsers.language.tree_sitter_segmenter import ( # noqa: E501 + TreeSitterSegmenter, +) + +if TYPE_CHECKING: + from tree_sitter import Language + + +CHUNK_QUERY = """ + [ + (function_definition) @subroutine + ] +""".strip() + + +class PerlSegmenter(TreeSitterSegmenter): + """Code segmenter for Perl.""" + + def get_language(self) -> "Language": + from tree_sitter_languages import get_language + + return get_language("perl") + + def get_chunk_query(self) -> str: + return CHUNK_QUERY + + def make_line_comment(self, text: str) -> str: + return f"# {text}" diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/php.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/php.py new file mode 100644 index 0000000000000000000000000000000000000000..e7ec12a5ee8153ea18b78170f1963e3b4f3575ec --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/php.py @@ -0,0 +1,35 @@ +from typing import TYPE_CHECKING + +from langchain_community.document_loaders.parsers.language.tree_sitter_segmenter import ( # noqa: E501 + TreeSitterSegmenter, +) + +if TYPE_CHECKING: + from tree_sitter import Language + + +CHUNK_QUERY = """ + [ + (function_definition) @function + (class_declaration) @class + (interface_declaration) @interface + (trait_declaration) @trait + (enum_declaration) @enum + (namespace_definition) @namespace + ] +""".strip() + + +class PHPSegmenter(TreeSitterSegmenter): + """Code segmenter for PHP.""" + + def get_language(self) -> "Language": + from tree_sitter_languages import get_language + + return get_language("php") + + def get_chunk_query(self) -> str: + return CHUNK_QUERY + + def make_line_comment(self, text: str) -> str: + return f"// {text}" diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/python.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/python.py new file mode 100644 index 0000000000000000000000000000000000000000..52dbc68352a91962db6a34c71ee01c34f977b93a --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/python.py @@ -0,0 +1,53 @@ +import ast +from typing import Any, List, Tuple + +from langchain_community.document_loaders.parsers.language.code_segmenter import ( + CodeSegmenter, +) + + +class PythonSegmenter(CodeSegmenter): + """Code segmenter for `Python`.""" + + def __init__(self, code: str): + super().__init__(code) + self.source_lines = self.code.splitlines() + + def is_valid(self) -> bool: + try: + ast.parse(self.code) + return True + except SyntaxError: + return False + + def _extract_code(self, node: Any) -> str: + start = node.lineno - 1 + end = node.end_lineno + return "\n".join(self.source_lines[start:end]) + + def extract_functions_classes(self) -> List[str]: + tree = ast.parse(self.code) + functions_classes = [] + + for node in ast.iter_child_nodes(tree): + if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef, ast.ClassDef)): + functions_classes.append(self._extract_code(node)) + + return functions_classes + + def simplify_code(self) -> str: + tree = ast.parse(self.code) + simplified_lines = self.source_lines[:] + + indices_to_del: List[Tuple[int, int]] = [] + for node in ast.iter_child_nodes(tree): + if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef, ast.ClassDef)): + start, end = node.lineno - 1, node.end_lineno + simplified_lines[start] = f"# Code for: {simplified_lines[start]}" + assert isinstance(end, int) + indices_to_del.append((start + 1, end)) + + for start, end in reversed(indices_to_del): + del simplified_lines[start + 0 : end] + + return "\n".join(simplified_lines) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/ruby.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/ruby.py new file mode 100644 index 0000000000000000000000000000000000000000..767a1f94a4d378d8917ea8afb8d695b4cb5576b9 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/ruby.py @@ -0,0 +1,32 @@ +from typing import TYPE_CHECKING + +from langchain_community.document_loaders.parsers.language.tree_sitter_segmenter import ( # noqa: E501 + TreeSitterSegmenter, +) + +if TYPE_CHECKING: + from tree_sitter import Language + + +CHUNK_QUERY = """ + [ + (method) @method + (module) @module + (class) @class + ] +""".strip() + + +class RubySegmenter(TreeSitterSegmenter): + """Code segmenter for Ruby.""" + + def get_language(self) -> "Language": + from tree_sitter_languages import get_language + + return get_language("ruby") + + def get_chunk_query(self) -> str: + return CHUNK_QUERY + + def make_line_comment(self, text: str) -> str: + return f"# {text}" diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/rust.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/rust.py new file mode 100644 index 0000000000000000000000000000000000000000..bb73f96bf6d7cb4f7ea12d83f55576f656db017b --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/rust.py @@ -0,0 +1,34 @@ +from typing import TYPE_CHECKING + +from langchain_community.document_loaders.parsers.language.tree_sitter_segmenter import ( # noqa: E501 + TreeSitterSegmenter, +) + +if TYPE_CHECKING: + from tree_sitter import Language + + +CHUNK_QUERY = """ + [ + (function_item + name: (identifier) + body: (block)) @function + (struct_item) @struct + (trait_item) @trait + ] +""".strip() + + +class RustSegmenter(TreeSitterSegmenter): + """Code segmenter for Rust.""" + + def get_language(self) -> "Language": + from tree_sitter_languages import get_language + + return get_language("rust") + + def get_chunk_query(self) -> str: + return CHUNK_QUERY + + def make_line_comment(self, text: str) -> str: + return f"// {text}" diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/scala.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/scala.py new file mode 100644 index 0000000000000000000000000000000000000000..af62a4e748fedbe6b9ea86b77958d5e3a3ba81ad --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/scala.py @@ -0,0 +1,33 @@ +from typing import TYPE_CHECKING + +from langchain_community.document_loaders.parsers.language.tree_sitter_segmenter import ( # noqa: E501 + TreeSitterSegmenter, +) + +if TYPE_CHECKING: + from tree_sitter import Language + + +CHUNK_QUERY = """ + [ + (class_definition) @class + (function_definition) @function + (object_definition) @object + (trait_definition) @trait + ] +""".strip() + + +class ScalaSegmenter(TreeSitterSegmenter): + """Code segmenter for Scala.""" + + def get_language(self) -> "Language": + from tree_sitter_languages import get_language + + return get_language("scala") + + def get_chunk_query(self) -> str: + return CHUNK_QUERY + + def make_line_comment(self, text: str) -> str: + return f"// {text}" diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/sql.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/sql.py new file mode 100644 index 0000000000000000000000000000000000000000..1c11b7b36375869d3cf3d0a0a57295652d2d1079 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/sql.py @@ -0,0 +1,65 @@ +from typing import TYPE_CHECKING + +from langchain_community.document_loaders.parsers.language.tree_sitter_segmenter import ( # noqa: E501 + TreeSitterSegmenter, +) + +if TYPE_CHECKING: + from tree_sitter import Language + +CHUNK_QUERY = """ + [ + (create_table_statement) @create + (select_statement) @select + (insert_statement) @insert + (update_statement) @update + (delete_statement) @delete + ] +""" + + +class SQLSegmenter(TreeSitterSegmenter): + """Code segmenter for SQL. + This class uses Tree-sitter to segment SQL code into its + constituent statements (e.g., SELECT, CREATE TABLE). + It also provides functionality to extract these + statements and simplify the code into commented descriptions. + """ + + def get_language(self) -> "Language": + """Return the SQL language grammar for Tree-sitter.""" + from tree_sitter_languages import get_language + + return get_language("sql") + + def get_chunk_query(self) -> str: + """Return the Tree-sitter query for SQL segmentation.""" + return CHUNK_QUERY + + def extract_functions_classes(self) -> list[str]: + """Extract SQL statements from the code. + Ensures that all SQL statements end with a semicolon + for consistency. + """ + extracted = super().extract_functions_classes() + # Ensure all statements end with a semicolon + return [ + stmt.strip() + ";" if not stmt.strip().endswith(";") else stmt.strip() + for stmt in extracted + ] + + def simplify_code(self) -> str: + """Simplify the extracted SQL code into comments. + Converts SQL statements into commented descriptions + for easy readability. + """ + return "\n".join( + [ + f"-- Code for: {stmt.strip()}" + for stmt in self.extract_functions_classes() + ] + ) + + def make_line_comment(self, text: str) -> str: + """Create a line comment in SQL style.""" + return f"-- {text}" diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/tree_sitter_segmenter.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/tree_sitter_segmenter.py new file mode 100644 index 0000000000000000000000000000000000000000..a467c269f6ed613cb1485cbe135a4bf7f5c65c71 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/tree_sitter_segmenter.py @@ -0,0 +1,108 @@ +from abc import abstractmethod +from typing import TYPE_CHECKING, List + +from langchain_community.document_loaders.parsers.language.code_segmenter import ( + CodeSegmenter, +) + +if TYPE_CHECKING: + from tree_sitter import Language, Parser + + +class TreeSitterSegmenter(CodeSegmenter): + """Abstract class for `CodeSegmenter`s that use the tree-sitter library.""" + + def __init__(self, code: str): + super().__init__(code) + self.source_lines = self.code.splitlines() + + try: + import tree_sitter # noqa: F401 + import tree_sitter_languages # noqa: F401 + except ImportError: + raise ImportError( + "Could not import tree_sitter/tree_sitter_languages Python packages. " + "Please install them with " + "`pip install tree-sitter tree-sitter-languages`." + ) + + def is_valid(self) -> bool: + language = self.get_language() + error_query = language.query("(ERROR) @error") + + parser = self.get_parser() + tree = parser.parse(bytes(self.code, encoding="UTF-8")) + + return len(error_query.captures(tree.root_node)) == 0 + + def extract_functions_classes(self) -> List[str]: + language = self.get_language() + query = language.query(self.get_chunk_query()) + + parser = self.get_parser() + tree = parser.parse(bytes(self.code, encoding="UTF-8")) + captures = query.captures(tree.root_node) + + processed_lines = set() + chunks = [] + + for node, name in captures: + start_line = node.start_point[0] + end_line = node.end_point[0] + lines = list(range(start_line, end_line + 1)) + + if any(line in processed_lines for line in lines): + continue + + processed_lines.update(lines) + chunk_text = node.text.decode("UTF-8") + chunks.append(chunk_text) + + return chunks + + def simplify_code(self) -> str: + language = self.get_language() + query = language.query(self.get_chunk_query()) + + parser = self.get_parser() + tree = parser.parse(bytes(self.code, encoding="UTF-8")) + processed_lines = set() + + simplified_lines = self.source_lines[:] + for node, name in query.captures(tree.root_node): + start_line = node.start_point[0] + end_line = node.end_point[0] + + lines = list(range(start_line, end_line + 1)) + if any(line in processed_lines for line in lines): + continue + + simplified_lines[start_line] = self.make_line_comment( + f"Code for: {self.source_lines[start_line]}" + ) + + for line_num in range(start_line + 1, end_line + 1): + simplified_lines[line_num] = None # type: ignore[call-overload] + + processed_lines.update(lines) + + return "\n".join(line for line in simplified_lines if line is not None) + + def get_parser(self) -> "Parser": + from tree_sitter import Parser + + parser = Parser() + parser.set_language(self.get_language()) + return parser + + @abstractmethod + def get_language(self) -> "Language": + raise NotImplementedError() # pragma: no cover + + @abstractmethod + def get_chunk_query(self) -> str: + raise NotImplementedError() # pragma: no cover + + @abstractmethod + def make_line_comment(self, text: str) -> str: + raise NotImplementedError() # pragma: no cover diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/typescript.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/typescript.py new file mode 100644 index 0000000000000000000000000000000000000000..ab7158e2e821077c6a3b8ff243c6a95155250a4a --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/language/typescript.py @@ -0,0 +1,33 @@ +from typing import TYPE_CHECKING + +from langchain_community.document_loaders.parsers.language.tree_sitter_segmenter import ( # noqa: E501 + TreeSitterSegmenter, +) + +if TYPE_CHECKING: + from tree_sitter import Language + + +CHUNK_QUERY = """ + [ + (function_declaration) @function + (class_declaration) @class + (interface_declaration) @interface + (enum_declaration) @enum + ] +""".strip() + + +class TypeScriptSegmenter(TreeSitterSegmenter): + """Code segmenter for TypeScript.""" + + def get_language(self) -> "Language": + from tree_sitter_languages import get_language + + return get_language("typescript") + + def get_chunk_query(self) -> str: + return CHUNK_QUERY + + def make_line_comment(self, text: str) -> str: + return f"// {text}" diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/msword.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/msword.py new file mode 100644 index 0000000000000000000000000000000000000000..f2a03cc37da3cbcc4c0449409b6cc1cacbf7ad0c --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/msword.py @@ -0,0 +1,45 @@ +from typing import Iterator + +from langchain_core.documents import Document + +from langchain_community.document_loaders.base import BaseBlobParser +from langchain_community.document_loaders.blob_loaders import Blob + + +class MsWordParser(BaseBlobParser): + """Parse the Microsoft Word documents from a blob.""" + + def lazy_parse(self, blob: Blob) -> Iterator[Document]: + """Parse a Microsoft Word document into the Document iterator. + + Args: + blob: The blob to parse. + + Returns: An iterator of Documents. + + """ + try: + from unstructured.partition.doc import partition_doc + from unstructured.partition.docx import partition_docx + except ImportError as e: + raise ImportError( + "Could not import unstructured, please install with `pip install " + "unstructured`." + ) from e + + mime_type_parser = { + "application/msword": partition_doc, + "application/vnd.openxmlformats-officedocument.wordprocessingml.document": ( + partition_docx + ), + } + if blob.mimetype not in ( + "application/msword", + "application/vnd.openxmlformats-officedocument.wordprocessingml.document", + ): + raise ValueError("This blob type is not supported for this parser.") + with blob.as_bytes_io() as word_document: + elements = mime_type_parser[blob.mimetype](file=word_document) + text = "\n\n".join([str(el) for el in elements]) + metadata = {"source": blob.source} + yield Document(page_content=text, metadata=metadata) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/pdf.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/pdf.py new file mode 100644 index 0000000000000000000000000000000000000000..4cdfa1b9fa8ba8d879f56039592f67976e95340f --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/pdf.py @@ -0,0 +1,1677 @@ +"""Module contains common parsers for PDFs.""" + +from __future__ import annotations + +import html +import io +import logging +import threading +import warnings +from datetime import datetime +from pathlib import Path +from tempfile import TemporaryDirectory +from typing import ( + TYPE_CHECKING, + Any, + BinaryIO, + Iterable, + Iterator, + Literal, + Mapping, + Optional, + Sequence, + Union, + cast, +) +from urllib.parse import urlparse + +import numpy +import numpy as np +from langchain_core.documents import Document + +from langchain_community.document_loaders.base import BaseBlobParser +from langchain_community.document_loaders.blob_loaders import Blob +from langchain_community.document_loaders.parsers.images import ( + BaseImageBlobParser, + RapidOCRBlobParser, +) + +if TYPE_CHECKING: + import pdfplumber + import pymupdf + import pypdf + import pypdfium2 + from textractor.data.text_linearization_config import TextLinearizationConfig + +_PDF_FILTER_WITH_LOSS = ["DCTDecode", "DCT", "JPXDecode"] +_PDF_FILTER_WITHOUT_LOSS = [ + "LZWDecode", + "LZW", + "FlateDecode", + "Fl", + "ASCII85Decode", + "A85", + "ASCIIHexDecode", + "AHx", + "RunLengthDecode", + "RL", + "CCITTFaxDecode", + "CCF", + "JBIG2Decode", +] + + +def extract_from_images_with_rapidocr( + images: Sequence[Union[Iterable[np.ndarray], bytes]], +) -> str: + """Extract text from images with RapidOCR. + + Args: + images: Images to extract text from. + + Returns: + Text extracted from images. + + Raises: + ImportError: If `rapidocr-onnxruntime` package is not installed. + """ + try: + from rapidocr_onnxruntime import RapidOCR + except ImportError: + raise ImportError( + "`rapidocr-onnxruntime` package not found, please install it with " + "`pip install rapidocr-onnxruntime`" + ) + ocr = RapidOCR() + text = "" + for img in images: + result, _ = ocr(img) + if result: + result = [text[1] for text in result] + text += "\n".join(result) + return text + + +logger = logging.getLogger(__name__) + +_FORMAT_IMAGE_STR = "\n\n{image_text}\n\n" +_JOIN_IMAGES = "\n" +_JOIN_TABLES = "\n" +_DEFAULT_PAGES_DELIMITER = "\n\f" + +_STD_METADATA_KEYS = {"source", "total_pages", "creationdate", "creator", "producer"} + + +def _format_inner_image(blob: Blob, content: str, format: str) -> str: + """Format the content of the image with the source of the blob. + + blob: The blob containing the image. + format:: + The format for the parsed output. + - "text" = return the content as is + - "markdown-img" = wrap the content into an image markdown link, w/ link + pointing to (`![body)(#)`] + - "html-img" = wrap the content as the `alt` text of an tag and link to + (`{body}`) + """ + if content: + source = blob.source or "#" + if format == "markdown-img": + content = content.replace("]", r"\\]") + content = f"![{content}]({source})" + elif format == "html-img": + content = f'{html.escape(content, quote=True)} src=' + return content + + +def _validate_metadata(metadata: dict[str, Any]) -> dict[str, Any]: + """Validate that the metadata has all the standard keys and the page is an integer. + + The standard keys are: + - source + - total_page + - creationdate + - creator + - producer + + Validate that page is an integer if it is present. + """ + if not _STD_METADATA_KEYS.issubset(metadata.keys()): + raise ValueError("The PDF parser must valorize the standard metadata.") + if not isinstance(metadata.get("page", 0), int): + raise ValueError("The PDF metadata page must be a integer.") + return metadata + + +def _purge_metadata(metadata: dict[str, Any]) -> dict[str, Any]: + """Purge metadata from unwanted keys and normalize key names. + + Args: + metadata: The original metadata dictionary. + + Returns: + The cleaned and normalized the key format of metadata dictionary. + """ + new_metadata: dict[str, Any] = {} + map_key = { + "page_count": "total_pages", + "file_path": "source", + } + for k, v in metadata.items(): + if type(v) not in [str, int]: + v = str(v) + if k.startswith("/"): + k = k[1:] + k = k.lower() + if k in ["creationdate", "moddate"]: + try: + new_metadata[k] = datetime.strptime( + v.replace("'", ""), "D:%Y%m%d%H%M%S%z" + ).isoformat("T") + except ValueError: + new_metadata[k] = v + elif k in map_key: + # Normalize key with others PDF parser + new_metadata[map_key[k]] = v + new_metadata[k] = v + elif isinstance(v, str): + new_metadata[k] = v.strip() + elif isinstance(v, int): + new_metadata[k] = v + return new_metadata + + +_PARAGRAPH_DELIMITER = [ + "\n\n\n", + "\n\n", +] # To insert images or table in the middle of the page. + + +def _merge_text_and_extras(extras: list[str], text_from_page: str) -> str: + """Insert extras such as image/table in a text between two paragraphs if possible, + else at the end of the text. + + Args: + extras: List of extra content (images/tables) to insert. + text_from_page: The text content from the page. + + Returns: + The merged text with extras inserted. + """ + + def _recurs_merge_text_and_extras( + extras: list[str], text_from_page: str, recurs: bool + ) -> Optional[str]: + if extras: + for delim in _PARAGRAPH_DELIMITER: + pos = text_from_page.rfind(delim) + if pos != -1: + # search penultimate, to bypass an error in footer + previous_text = None + if recurs: + previous_text = _recurs_merge_text_and_extras( + extras, text_from_page[:pos], False + ) + if previous_text: + all_text = previous_text + text_from_page[pos:] + else: + all_extras = "" + str_extras = "\n\n".join(filter(lambda x: x, extras)) + if str_extras: + all_extras = delim + str_extras + all_text = ( + text_from_page[:pos] + all_extras + text_from_page[pos:] + ) + break + else: + all_text = None + else: + all_text = text_from_page + return all_text + + all_text = _recurs_merge_text_and_extras(extras, text_from_page, True) + if not all_text: + all_extras = "" + str_extras = "\n\n".join(filter(lambda x: x, extras)) + if str_extras: + all_extras = _PARAGRAPH_DELIMITER[-1] + str_extras + all_text = text_from_page + all_extras + + return all_text + + +class PyPDFParser(BaseBlobParser): + """Parse a blob from a PDF using `pypdf` library. + + This class provides methods to parse a blob from a PDF document, supporting various + configurations such as handling password-protected PDFs, extracting images. + It integrates the 'pypdf' library for PDF processing and offers synchronous blob + parsing. + + Examples: + Setup: + + .. code-block:: bash + + pip install -U langchain-community pypdf + + Load a blob from a PDF file: + + .. code-block:: python + + from langchain_core.documents.base import Blob + + blob = Blob.from_path("./example_data/layout-parser-paper.pdf") + + Instantiate the parser: + + .. code-block:: python + + from langchain_community.document_loaders.parsers import PyPDFParser + + parser = PyPDFParser( + # password = None, + mode = "single", + pages_delimiter = "\n\f", + # images_parser = TesseractBlobParser(), + ) + + Lazily parse the blob: + + .. code-block:: python + + docs = [] + docs_lazy = parser.lazy_parse(blob) + + for doc in docs_lazy: + docs.append(doc) + print(docs[0].page_content[:100]) + print(docs[0].metadata) + """ + + def __init__( + self, + password: Optional[Union[str, bytes]] = None, + extract_images: bool = False, + *, + mode: Literal["single", "page"] = "page", + pages_delimiter: str = _DEFAULT_PAGES_DELIMITER, + images_parser: Optional[BaseImageBlobParser] = None, + images_inner_format: Literal["text", "markdown-img", "html-img"] = "text", + extraction_mode: Literal["plain", "layout"] = "plain", + extraction_kwargs: Optional[dict[str, Any]] = None, + ): + """Initialize a parser based on PyPDF. + + Args: + password: Optional password for opening encrypted PDFs. + extract_images: Whether to extract images from the PDF. + mode: The extraction mode, either "single" for the entire document or "page" + for page-wise extraction. + pages_delimiter: A string delimiter to separate pages in single-mode + extraction. + images_parser: Optional image blob parser. + images_inner_format: The format for the parsed output. + - "text" = return the content as is + - "markdown-img" = wrap the content into an image markdown link, w/ link + pointing to (`![body)(#)`] + - "html-img" = wrap the content as the `alt` text of an tag and link to + (`{body}`) + extraction_mode: “plain” for legacy functionality, “layout” extract text + in a fixed width format that closely adheres to the rendered layout in + the source pdf. + extraction_kwargs: Optional additional parameters for the extraction + process. + + Raises: + ValueError: If the `mode` is not "single" or "page". + """ + super().__init__() + if mode not in ["single", "page"]: + raise ValueError("mode must be single or page") + self.extract_images = extract_images + if extract_images and not images_parser: + images_parser = RapidOCRBlobParser() + self.images_parser = images_parser + self.images_inner_format = images_inner_format + self.password = password + self.mode = mode + self.pages_delimiter = pages_delimiter + self.extraction_mode = extraction_mode + self.extraction_kwargs = extraction_kwargs or {} + + def lazy_parse(self, blob: Blob) -> Iterator[Document]: + """ + Lazily parse the blob. + Insert image, if possible, between two paragraphs. + In this way, a paragraph can be continued on the next page. + + Args: + blob: The blob to parse. + + Raises: + ImportError: If the `pypdf` package is not found. + + Yield: + An iterator over the parsed documents. + """ + try: + import pypdf + except ImportError: + raise ImportError( + "`pypdf` package not found, please install it with `pip install pypdf`" + ) + + def _extract_text_from_page(page: pypdf.PageObject) -> str: + """ + Extract text from image given the version of pypdf. + + Args: + page: The page object to extract text from. + + Returns: + str: The extracted text. + """ + if pypdf.__version__.startswith("3"): + return page.extract_text() + else: + return page.extract_text( + extraction_mode=self.extraction_mode, + **self.extraction_kwargs, + ) + + with blob.as_bytes_io() as pdf_file_obj: + pdf_reader = pypdf.PdfReader(pdf_file_obj, password=self.password) + + doc_metadata = _purge_metadata( + {"producer": "PyPDF", "creator": "PyPDF", "creationdate": ""} + | cast(dict, pdf_reader.metadata or {}) + | { + "source": blob.source, + "total_pages": len(pdf_reader.pages), + } + ) + single_texts = [] + for page_number, page in enumerate(pdf_reader.pages): + text_from_page = _extract_text_from_page(page=page) + images_from_page = self.extract_images_from_page(page) + all_text = _merge_text_and_extras( + [images_from_page], text_from_page + ).strip() + if self.mode == "page": + yield Document( + page_content=all_text, + metadata=_validate_metadata( + doc_metadata + | { + "page": page_number, + "page_label": pdf_reader.page_labels[page_number], + } + ), + ) + else: + single_texts.append(all_text) + if self.mode == "single": + yield Document( + page_content=self.pages_delimiter.join(single_texts), + metadata=_validate_metadata(doc_metadata), + ) + + def extract_images_from_page(self, page: pypdf._page.PageObject) -> str: + """Extract images from a PDF page and get the text using images_to_text. + + Args: + page: The page object from which to extract images. + + Returns: + str: The extracted text from the images on the page. + """ + if not self.images_parser: + return "" + import pypdf + from PIL import Image + + if "/XObject" not in cast(dict, page["/Resources"]).keys(): + return "" + + xObject = page["/Resources"]["/XObject"].get_object() + images = [] + for obj in xObject: + np_image: Any = None + if xObject[obj]["/Subtype"] == "/Image": + img_filter = ( + xObject[obj]["/Filter"][1:] + if type(xObject[obj]["/Filter"]) is pypdf.generic._base.NameObject + else xObject[obj]["/Filter"][0][1:] + ) + if img_filter in _PDF_FILTER_WITHOUT_LOSS: + height, width = xObject[obj]["/Height"], xObject[obj]["/Width"] + + np_image = np.frombuffer( + xObject[obj].get_data(), dtype=np.uint8 + ).reshape(height, width, -1) + elif img_filter in _PDF_FILTER_WITH_LOSS: + np_image = np.array(Image.open(io.BytesIO(xObject[obj].get_data()))) + + else: + logger.warning("Unknown PDF Filter!") + if np_image is not None: + image_bytes = io.BytesIO() + + if image_bytes.getbuffer().nbytes == 0: + continue + + Image.fromarray(np_image).save(image_bytes, format="PNG") + blob = Blob.from_data(image_bytes.getvalue(), mime_type="image/png") + image_text = next(self.images_parser.lazy_parse(blob)).page_content + images.append( + _format_inner_image(blob, image_text, self.images_inner_format) + ) + return _FORMAT_IMAGE_STR.format( + image_text=_JOIN_IMAGES.join(filter(None, images)) + ) + + +class PDFMinerParser(BaseBlobParser): + """Parse a blob from a PDF using `pdfminer.six` library. + + This class provides methods to parse a blob from a PDF document, supporting various + configurations such as handling password-protected PDFs, extracting images, and + defining extraction mode. + It integrates the 'pdfminer.six' library for PDF processing and offers synchronous + blob parsing. + + Examples: + Setup: + + .. code-block:: bash + + pip install -U langchain-community pdfminer.six pillow + + Load a blob from a PDF file: + + .. code-block:: python + + from langchain_core.documents.base import Blob + + blob = Blob.from_path("./example_data/layout-parser-paper.pdf") + + Instantiate the parser: + + .. code-block:: python + + from langchain_community.document_loaders.parsers import PDFMinerParser + + parser = PDFMinerParser( + # password = None, + mode = "single", + pages_delimiter = "\n\f", + # extract_images = True, + # images_to_text = convert_images_to_text_with_tesseract(), + ) + + Lazily parse the blob: + + .. code-block:: python + + docs = [] + docs_lazy = parser.lazy_parse(blob) + + for doc in docs_lazy: + docs.append(doc) + print(docs[0].page_content[:100]) + print(docs[0].metadata) + """ + + _warn_concatenate_pages = False + + def __init__( + self, + extract_images: bool = False, + *, + password: Optional[str] = None, + mode: Literal["single", "page"] = "single", + pages_delimiter: str = _DEFAULT_PAGES_DELIMITER, + images_parser: Optional[BaseImageBlobParser] = None, + images_inner_format: Literal["text", "markdown-img", "html-img"] = "text", + concatenate_pages: Optional[bool] = None, + ): + """Initialize a parser based on PDFMiner. + + Args: + password: Optional password for opening encrypted PDFs. + mode: Extraction mode to use. Either "single" or "page" for page-wise + extraction. + pages_delimiter: A string delimiter to separate pages in single-mode + extraction. + extract_images: Whether to extract images from PDF. + images_inner_format: The format for the parsed output. + - "text" = return the content as is + - "markdown-img" = wrap the content into an image markdown link, w/ link + pointing to (`![body)(#)`] + - "html-img" = wrap the content as the `alt` text of an tag and link to + (`{body}`) + concatenate_pages: Deprecated. If True, concatenate all PDF pages + into one a single document. Otherwise, return one document per page. + + Returns: + This method does not directly return data. Use the `parse` or `lazy_parse` + methods to retrieve parsed documents with content and metadata. + + Raises: + ValueError: If the `mode` is not "single" or "page". + + Warnings: + `concatenate_pages` parameter is deprecated. Use `mode='single' or 'page' + instead. + """ + super().__init__() + if mode not in ["single", "page"]: + raise ValueError("mode must be single or page") + if extract_images and not images_parser: + images_parser = RapidOCRBlobParser() + self.extract_images = extract_images + self.images_parser = images_parser + self.images_inner_format = images_inner_format + self.password = password + self.mode = mode + self.pages_delimiter = pages_delimiter + if concatenate_pages is not None: + if not PDFMinerParser._warn_concatenate_pages: + PDFMinerParser._warn_concatenate_pages = True + logger.warning( + "`concatenate_pages` parameter is deprecated. " + "Use `mode='single' or 'page'` instead." + ) + self.mode = "single" if concatenate_pages else "page" + + @staticmethod + def decode_text(s: Union[bytes, str]) -> str: + """ + Decodes a PDFDocEncoding string to Unicode. + Adds py3 compatibility to pdfminer's version. + + Args: + s: The string to decode. + + Returns: + str: The decoded Unicode string. + """ + from pdfminer.utils import PDFDocEncoding + + if isinstance(s, bytes) and s.startswith(b"\xfe\xff"): + return str(s[2:], "utf-16be", "ignore") + try: + ords = (ord(c) if isinstance(c, str) else c for c in s) + return "".join(PDFDocEncoding[o] for o in ords) + except IndexError: + return str(s) + + @staticmethod + def resolve_and_decode(obj: Any) -> Any: + """ + Recursively resolve the metadata values. + + Args: + obj: The object to resolve and decode. It can be of any type. + + Returns: + The resolved and decoded object. + """ + from pdfminer.psparser import PSLiteral + + if hasattr(obj, "resolve"): + obj = obj.resolve() + if isinstance(obj, list): + return list(map(PDFMinerParser.resolve_and_decode, obj)) + elif isinstance(obj, PSLiteral): + return PDFMinerParser.decode_text(obj.name) + elif isinstance(obj, (str, bytes)): + return PDFMinerParser.decode_text(obj) + elif isinstance(obj, dict): + for k, v in obj.items(): + obj[k] = PDFMinerParser.resolve_and_decode(v) + return obj + + return obj + + def _get_metadata( + self, + fp: BinaryIO, + password: str = "", + caching: bool = True, + ) -> dict[str, Any]: + """ + Extract metadata from a PDF file. + + Args: + fp: The file pointer to the PDF file. + password: The password for the PDF file, if encrypted. Defaults to an empty + string. + caching: Whether to cache the PDF structure. Defaults to True. + + Returns: + Metadata of the PDF file. + """ + from pdfminer.pdfpage import PDFDocument, PDFPage, PDFParser + + # Create a PDF parser object associated with the file object. + parser = PDFParser(fp) + # Create a PDF document object that stores the document structure. + doc = PDFDocument(parser, password=password, caching=caching) + metadata = {} + + for info in doc.info: + metadata.update(info) + for k, v in metadata.items(): + try: + metadata[k] = PDFMinerParser.resolve_and_decode(v) + except Exception as e: # pragma: nocover + # This metadata value could not be parsed. Instead of failing the PDF + # read, treat it as a warning only if `strict_metadata=False`. + logger.warning( + '[WARNING] Metadata key "%s" could not be parsed due to ' + "exception: %s", + k, + str(e), + ) + + # Count number of pages. + metadata["total_pages"] = len(list(PDFPage.create_pages(doc))) + + return metadata + + def lazy_parse(self, blob: Blob) -> Iterator[Document]: + """ + Lazily parse the blob. + Insert image, if possible, between two paragraphs. + In this way, a paragraph can be continued on the next page. + + Args: + blob: The blob to parse. + + Raises: + ImportError: If the `pdfminer.six` or `pillow` package is not found. + + Yield: + An iterator over the parsed documents. + """ + try: + import pdfminer + from pdfminer.converter import PDFLayoutAnalyzer + from pdfminer.layout import ( + LAParams, + LTContainer, + LTImage, + LTItem, + LTPage, + LTText, + LTTextBox, + ) + from pdfminer.pdfinterp import PDFPageInterpreter, PDFResourceManager + from pdfminer.pdfpage import PDFPage + + if int(pdfminer.__version__) < 20201018: + raise ImportError( + "This parser is tested with pdfminer.six version 20201018 or " + "later. Remove pdfminer, and install pdfminer.six with " + "`pip uninstall pdfminer && pip install pdfminer.six`." + ) + except ImportError: + raise ImportError( + "pdfminer package not found, please install it " + "with `pip install pdfminer.six`" + ) + + with blob.as_bytes_io() as pdf_file_obj, TemporaryDirectory() as tempdir: + pages = PDFPage.get_pages(pdf_file_obj, password=self.password or "") + rsrcmgr = PDFResourceManager() + doc_metadata = _purge_metadata( + {"producer": "PDFMiner", "creator": "PDFMiner", "creationdate": ""} + | self._get_metadata(pdf_file_obj, password=self.password or "") + ) + doc_metadata["source"] = blob.source + + class Visitor(PDFLayoutAnalyzer): + def __init__( + self, + rsrcmgr: PDFResourceManager, + pageno: int = 1, + laparams: Optional[LAParams] = None, + ) -> None: + super().__init__(rsrcmgr, pageno=pageno, laparams=laparams) + + def receive_layout(me, ltpage: LTPage) -> None: + def render(item: LTItem) -> None: + if isinstance(item, LTContainer): + for child in item: + render(child) + elif isinstance(item, LTText): + text_io.write(item.get_text()) + if isinstance(item, LTTextBox): + text_io.write("\n") + elif isinstance(item, LTImage): + if self.images_parser: + from pdfminer.image import ImageWriter + + image_writer = ImageWriter(tempdir) + filename = image_writer.export_image(item) + blob = Blob.from_path(Path(tempdir) / filename) + blob.metadata["source"] = "#" + image_text = next( + self.images_parser.lazy_parse(blob) + ).page_content + + text_io.write( + _format_inner_image( + blob, image_text, self.images_inner_format + ) + ) + else: + pass + + render(ltpage) + + text_io = io.StringIO() + visitor_for_all = PDFPageInterpreter( + rsrcmgr, Visitor(rsrcmgr, laparams=LAParams()) + ) + all_content = [] + for i, page in enumerate(pages): + text_io.truncate(0) + text_io.seek(0) + visitor_for_all.process_page(page) + + all_text = text_io.getvalue() + # For legacy compatibility, net strip() + all_text = all_text.strip() + if self.mode == "page": + text_io.truncate(0) + text_io.seek(0) + yield Document( + page_content=all_text, + metadata=_validate_metadata(doc_metadata | {"page": i}), + ) + else: + if all_text.endswith("\f"): + all_text = all_text[:-1] + all_content.append(all_text) + if self.mode == "single": + # Add pages_delimiter between pages + document_content = self.pages_delimiter.join(all_content) + yield Document( + page_content=document_content, + metadata=_validate_metadata(doc_metadata), + ) + + +class PyMuPDFParser(BaseBlobParser): + """Parse a blob from a PDF using `PyMuPDF` library. + + This class provides methods to parse a blob from a PDF document, supporting various + configurations such as handling password-protected PDFs, extracting images, and + defining extraction mode. + It integrates the 'PyMuPDF' library for PDF processing and offers synchronous blob + parsing. + + Examples: + Setup: + + .. code-block:: bash + + pip install -U langchain-community pymupdf + + Load a blob from a PDF file: + + .. code-block:: python + + from langchain_core.documents.base import Blob + + blob = Blob.from_path("./example_data/layout-parser-paper.pdf") + + Instantiate the parser: + + .. code-block:: python + + from langchain_community.document_loaders.parsers import PyMuPDFParser + + parser = PyMuPDFParser( + # password = None, + mode = "single", + pages_delimiter = "\n\f", + # images_parser = TesseractBlobParser(), + # extract_tables="markdown", + # extract_tables_settings=None, + # text_kwargs=None, + ) + + Lazily parse the blob: + + .. code-block:: python + + docs = [] + docs_lazy = parser.lazy_parse(blob) + + for doc in docs_lazy: + docs.append(doc) + print(docs[0].page_content[:100]) + print(docs[0].metadata) + """ + + # PyMuPDF is not thread safe. + # See https://pymupdf.readthedocs.io/en/latest/recipes-multiprocessing.html + _lock = threading.Lock() + + def __init__( + self, + text_kwargs: Optional[dict[str, Any]] = None, + extract_images: bool = False, + *, + password: Optional[str] = None, + mode: Literal["single", "page"] = "page", + pages_delimiter: str = _DEFAULT_PAGES_DELIMITER, + images_parser: Optional[BaseImageBlobParser] = None, + images_inner_format: Literal["text", "markdown-img", "html-img"] = "text", + extract_tables: Union[Literal["csv", "markdown", "html"], None] = None, + extract_tables_settings: Optional[dict[str, Any]] = None, + ) -> None: + """Initialize a parser based on PyMuPDF. + + Args: + password: Optional password for opening encrypted PDFs. + mode: The extraction mode, either "single" for the entire document or "page" + for page-wise extraction. + pages_delimiter: A string delimiter to separate pages in single-mode + extraction. + extract_images: Whether to extract images from the PDF. + images_parser: Optional image blob parser. + images_inner_format: The format for the parsed output. + - "text" = return the content as is + - "markdown-img" = wrap the content into an image markdown link, w/ link + pointing to (`![body)(#)`] + - "html-img" = wrap the content as the `alt` text of an tag and link to + (`{body}`) + extract_tables: Whether to extract tables in a specific format, such as + "csv", "markdown", or "html". + extract_tables_settings: Optional dictionary of settings for customizing + table extraction. + + Returns: + This method does not directly return data. Use the `parse` or `lazy_parse` + methods to retrieve parsed documents with content and metadata. + + Raises: + ValueError: If the mode is not "single" or "page". + ValueError: If the extract_tables format is not "markdown", "html", + or "csv". + """ + super().__init__() + if mode not in ["single", "page"]: + raise ValueError("mode must be single or page") + if extract_tables and extract_tables not in ["markdown", "html", "csv"]: + raise ValueError("mode must be markdown") + + self.mode = mode + self.pages_delimiter = pages_delimiter + self.password = password + self.text_kwargs = text_kwargs or {} + if extract_images and not images_parser: + images_parser = RapidOCRBlobParser() + self.extract_images = extract_images + self.images_inner_format = images_inner_format + self.images_parser = images_parser + self.extract_tables = extract_tables + self.extract_tables_settings = extract_tables_settings + + def lazy_parse(self, blob: Blob) -> Iterator[Document]: + return self._lazy_parse( + blob, + ) + + def _lazy_parse( + self, + blob: Blob, + # text-kwargs is present for backwards compatibility. + # Users should not use it directly. + text_kwargs: Optional[dict[str, Any]] = None, + ) -> Iterator[Document]: + """Lazily parse the blob. + Insert image, if possible, between two paragraphs. + In this way, a paragraph can be continued on the next page. + + Args: + blob: The blob to parse. + text_kwargs: Optional keyword arguments to pass to the `get_text` method. + If provided at run time, it will override the default text_kwargs. + + Raises: + ImportError: If the `pypdf` package is not found. + + Yield: + An iterator over the parsed documents. + """ + try: + import pymupdf + + text_kwargs = text_kwargs or self.text_kwargs + if not self.extract_tables_settings: + from pymupdf.table import ( + DEFAULT_JOIN_TOLERANCE, + DEFAULT_MIN_WORDS_HORIZONTAL, + DEFAULT_MIN_WORDS_VERTICAL, + DEFAULT_SNAP_TOLERANCE, + ) + + self.extract_tables_settings = { + # See https://pymupdf.readthedocs.io/en/latest/page.html#Page.find_tables + "clip": None, + "vertical_strategy": "lines", + "horizontal_strategy": "lines", + "vertical_lines": None, + "horizontal_lines": None, + "snap_tolerance": DEFAULT_SNAP_TOLERANCE, + "snap_x_tolerance": None, + "snap_y_tolerance": None, + "join_tolerance": DEFAULT_JOIN_TOLERANCE, + "join_x_tolerance": None, + "join_y_tolerance": None, + "edge_min_length": 3, + "min_words_vertical": DEFAULT_MIN_WORDS_VERTICAL, + "min_words_horizontal": DEFAULT_MIN_WORDS_HORIZONTAL, + "intersection_tolerance": 3, + "intersection_x_tolerance": None, + "intersection_y_tolerance": None, + "text_tolerance": 3, + "text_x_tolerance": 3, + "text_y_tolerance": 3, + "strategy": None, # offer abbreviation + "add_lines": None, # optional user-specified lines + } + except ImportError: + raise ImportError( + "pymupdf package not found, please install it " + "with `pip install pymupdf`" + ) + + with PyMuPDFParser._lock: + with blob.as_bytes_io() as file_path: + if blob.data is None: + doc = pymupdf.open(file_path) + else: + doc = pymupdf.open(stream=file_path, filetype="pdf") + if doc.is_encrypted: + doc.authenticate(self.password) + doc_metadata = { + "producer": "PyMuPDF", + "creator": "PyMuPDF", + "creationdate": "", + } | self._extract_metadata(doc, blob) + full_content = [] + for page in doc: + all_text = self._get_page_content(doc, page, text_kwargs).strip() + if self.mode == "page": + yield Document( + page_content=all_text, + metadata=_validate_metadata( + doc_metadata | {"page": page.number} + ), + ) + else: + full_content.append(all_text) + + if self.mode == "single": + yield Document( + page_content=self.pages_delimiter.join(full_content), + metadata=_validate_metadata(doc_metadata), + ) + + def _get_page_content( + self, + doc: pymupdf.Document, + page: pymupdf.Page, + text_kwargs: dict[str, Any], + ) -> str: + """Get the text of the page using PyMuPDF and RapidOCR and issue a warning + if it is empty. + + Args: + doc: The PyMuPDF document object. + page: The PyMuPDF page object. + blob: The blob being parsed. + + Returns: + str: The text content of the page. + """ + text_from_page = page.get_text(**{**self.text_kwargs, **text_kwargs}) + images_from_page = self._extract_images_from_page(doc, page) + tables_from_page = self._extract_tables_from_page(page) + extras = [] + if images_from_page: + extras.append(images_from_page) + if tables_from_page: + extras.append(tables_from_page) + all_text = _merge_text_and_extras(extras, text_from_page) + + return all_text + + def _extract_metadata(self, doc: pymupdf.Document, blob: Blob) -> dict: + """Extract metadata from the document and page. + + Args: + doc: The PyMuPDF document object. + blob: The blob being parsed. + + Returns: + dict: The extracted metadata. + """ + metadata = _purge_metadata( + { + **{ + "producer": "PyMuPDF", + "creator": "PyMuPDF", + "creationdate": "", + "source": blob.source, + "file_path": blob.source, + "total_pages": len(doc), + }, + **{ + k: doc.metadata[k] + for k in doc.metadata + if isinstance(doc.metadata[k], (str, int)) + }, + } + ) + for k in ("modDate", "creationDate"): + if k in doc.metadata: + metadata[k] = doc.metadata[k] + return metadata + + def _extract_images_from_page( + self, doc: pymupdf.Document, page: pymupdf.Page + ) -> str: + """Extract images from a PDF page and get the text using images_to_text. + + Args: + doc: The PyMuPDF document object. + page: The PyMuPDF page object. + + Returns: + str: The extracted text from the images on the page. + """ + if not self.images_parser: + return "" + import pymupdf + + img_list = page.get_images() + images = [] + for img in img_list: + if self.images_parser: + xref = img[0] + pix = pymupdf.Pixmap(doc, xref) + image = np.frombuffer(pix.samples, dtype=np.uint8).reshape( + pix.height, pix.width, -1 + ) + image_bytes = io.BytesIO() + if image_bytes.getbuffer().nbytes == 0: + continue + + numpy.save(image_bytes, image) + blob = Blob.from_data( + image_bytes.getvalue(), mime_type="application/x-npy" + ) + image_text = next(self.images_parser.lazy_parse(blob)).page_content + + images.append( + _format_inner_image(blob, image_text, self.images_inner_format) + ) + return _FORMAT_IMAGE_STR.format( + image_text=_JOIN_IMAGES.join(filter(None, images)) + ) + + def _extract_tables_from_page(self, page: pymupdf.Page) -> str: + """Extract tables from a PDF page. + + Args: + page: The PyMuPDF page object. + + Returns: + str: The extracted tables in the specified format. + """ + if self.extract_tables is None: + return "" + import pymupdf + + tables_list = list( + pymupdf.table.find_tables(page, **self.extract_tables_settings) + ) + if tables_list: + if self.extract_tables == "markdown": + return _JOIN_TABLES.join([table.to_markdown() for table in tables_list]) + elif self.extract_tables == "html": + return _JOIN_TABLES.join( + [ + table.to_pandas().to_html( + header=False, + index=False, + bold_rows=False, + ) + for table in tables_list + ] + ) + elif self.extract_tables == "csv": + return _JOIN_TABLES.join( + [ + table.to_pandas().to_csv( + header=False, + index=False, + ) + for table in tables_list + ] + ) + else: + raise ValueError( + f"extract_tables {self.extract_tables} not implemented" + ) + return "" + + +class PyPDFium2Parser(BaseBlobParser): + """Parse a blob from a PDF using `PyPDFium2` library. + + This class provides methods to parse a blob from a PDF document, supporting various + configurations such as handling password-protected PDFs, extracting images, and + defining extraction mode. + It integrates the 'PyPDFium2' library for PDF processing and offers synchronous + blob parsing. + + Examples: + Setup: + + .. code-block:: bash + + pip install -U langchain-community pypdfium2 + + Load a blob from a PDF file: + + .. code-block:: python + + from langchain_core.documents.base import Blob + + blob = Blob.from_path("./example_data/layout-parser-paper.pdf") + + Instantiate the parser: + + .. code-block:: python + + from langchain_community.document_loaders.parsers import PyPDFium2Parser + + parser = PyPDFium2Parser( + # password=None, + mode="page", + pages_delimiter="\n\f", + # extract_images = True, + # images_to_text = convert_images_to_text_with_tesseract(), + ) + + Lazily parse the blob: + + .. code-block:: python + + docs = [] + docs_lazy = parser.lazy_parse(blob) + + for doc in docs_lazy: + docs.append(doc) + print(docs[0].page_content[:100]) + print(docs[0].metadata) + """ + + # PyPDFium2 is not thread safe. + # See https://pypdfium2.readthedocs.io/en/stable/python_api.html#thread-incompatibility + _lock = threading.Lock() + + def __init__( + self, + extract_images: bool = False, + *, + password: Optional[str] = None, + mode: Literal["single", "page"] = "page", + pages_delimiter: str = _DEFAULT_PAGES_DELIMITER, + images_parser: Optional[BaseImageBlobParser] = None, + images_inner_format: Literal["text", "markdown-img", "html-img"] = "text", + ) -> None: + """Initialize a parser based on PyPDFium2. + + Args: + password: Optional password for opening encrypted PDFs. + mode: The extraction mode, either "single" for the entire document or "page" + for page-wise extraction. + pages_delimiter: A string delimiter to separate pages in single-mode + extraction. + extract_images: Whether to extract images from the PDF. + images_parser: Optional image blob parser. + images_inner_format: The format for the parsed output. + - "text" = return the content as is + - "markdown-img" = wrap the content into an image markdown link, w/ link + pointing to (`![body)(#)`] + - "html-img" = wrap the content as the `alt` text of an tag and link to + (`{body}`) + extraction_mode: “plain” for legacy functionality, “layout” for experimental + layout mode functionality + extraction_kwargs: Optional additional parameters for the extraction + process. + + Returns: + This method does not directly return data. Use the `parse` or `lazy_parse` + methods to retrieve parsed documents with content and metadata. + + Raises: + ValueError: If the mode is not "single" or "page". + """ + super().__init__() + if mode not in ["single", "page"]: + raise ValueError("mode must be single or page") + self.extract_images = extract_images + if extract_images and not images_parser: + images_parser = RapidOCRBlobParser() + self.images_parser = images_parser + self.images_inner_format = images_inner_format + self.password = password + self.mode = mode + self.pages_delimiter = pages_delimiter + + def lazy_parse(self, blob: Blob) -> Iterator[Document]: + """ + Lazily parse the blob. + Insert image, if possible, between two paragraphs. + In this way, a paragraph can be continued on the next page. + + Args: + blob: The blob to parse. + + Raises: + ImportError: If the `pypdf` package is not found. + + Yield: + An iterator over the parsed documents. + """ + try: + import pypdfium2 + except ImportError: + raise ImportError( + "pypdfium2 package not found, please install it with" + " `pip install pypdfium2`" + ) + + # pypdfium2 is really finicky with respect to closing things, + # if done incorrectly creates seg faults. + with PyPDFium2Parser._lock: + with blob.as_bytes_io() as file_path: + pdf_reader = None + try: + pdf_reader = pypdfium2.PdfDocument( + file_path, password=self.password, autoclose=True + ) + full_content = [] + + doc_metadata = { + "producer": "PyPDFium2", + "creator": "PyPDFium2", + "creationdate": "", + } | _purge_metadata(pdf_reader.get_metadata_dict()) + doc_metadata["source"] = blob.source + doc_metadata["total_pages"] = len(pdf_reader) + + for page_number, page in enumerate(pdf_reader): + text_page = page.get_textpage() + text_from_page = "\n".join( + text_page.get_text_range().splitlines() + ) # Replace \r\n + text_page.close() + image_from_page = self._extract_images_from_page(page) + all_text = _merge_text_and_extras( + [image_from_page], text_from_page + ).strip() + page.close() + + if self.mode == "page": + # For legacy compatibility, add the last '\n' + if not all_text.endswith("\n"): + all_text += "\n" + yield Document( + page_content=all_text, + metadata=_validate_metadata( + { + **doc_metadata, + "page": page_number, + } + ), + ) + else: + full_content.append(all_text) + + if self.mode == "single": + yield Document( + page_content=self.pages_delimiter.join(full_content), + metadata=_validate_metadata(doc_metadata), + ) + finally: + if pdf_reader: + pdf_reader.close() + + def _extract_images_from_page(self, page: pypdfium2._helpers.page.PdfPage) -> str: + """Extract images from a PDF page and get the text using images_to_text. + + Args: + page: The page object from which to extract images. + + Returns: + str: The extracted text from the images on the page. + """ + if not self.images_parser: + return "" + + import pypdfium2.raw as pdfium_c + + images = list(page.get_objects(filter=(pdfium_c.FPDF_PAGEOBJ_IMAGE,))) + if not images: + return "" + str_images = [] + for image in images: + image_bytes = io.BytesIO() + np_image = image.get_bitmap().to_numpy() + if np_image.size < 3: + continue + numpy.save(image_bytes, image.get_bitmap().to_numpy()) + blob = Blob.from_data(image_bytes.getvalue(), mime_type="application/x-npy") + text_from_image = next(self.images_parser.lazy_parse(blob)).page_content + str_images.append( + _format_inner_image(blob, text_from_image, self.images_inner_format) + ) + image.close() + return _FORMAT_IMAGE_STR.format(image_text=_JOIN_IMAGES.join(str_images)) + + +class PDFPlumberParser(BaseBlobParser): + """Parse `PDF` with `PDFPlumber`.""" + + def __init__( + self, + text_kwargs: Optional[Mapping[str, Any]] = None, + dedupe: bool = False, + extract_images: bool = False, + ) -> None: + """Initialize the parser. + + Args: + text_kwargs: Keyword arguments to pass to ``pdfplumber.Page.extract_text()`` + dedupe: Avoiding the error of duplicate characters if `dedupe=True`. + """ + try: + import PIL # noqa:F401 + except ImportError: + raise ImportError( + "pillow package not found, please install it with `pip install pillow`" + ) + self.text_kwargs = text_kwargs or {} + self.dedupe = dedupe + self.extract_images = extract_images + + def lazy_parse(self, blob: Blob) -> Iterator[Document]: + """Lazily parse the blob.""" + import pdfplumber + + with blob.as_bytes_io() as file_path: + doc = pdfplumber.open(file_path) # open document + + yield from [ + Document( + page_content=self._process_page_content(page) + + "\n" + + self._extract_images_from_page(page), + metadata=dict( + { + "source": blob.source, + "file_path": blob.source, + "page": page.page_number - 1, + "total_pages": len(doc.pages), + }, + **{ + k: doc.metadata[k] + for k in doc.metadata + if type(doc.metadata[k]) in [str, int] + }, + ), + ) + for page in doc.pages + ] + + def _process_page_content(self, page: pdfplumber.page.Page) -> str: + """Process the page content based on dedupe.""" + if self.dedupe: + return page.dedupe_chars().extract_text(**self.text_kwargs) + return page.extract_text(**self.text_kwargs) + + def _extract_images_from_page(self, page: pdfplumber.page.Page) -> str: + """Extract images from page and get the text with RapidOCR.""" + from PIL import Image + + if not self.extract_images: + return "" + + images = [] + for img in page.images: + if img["stream"]["Filter"].name in _PDF_FILTER_WITHOUT_LOSS: + if img["stream"]["BitsPerComponent"] == 1: + images.append( + np.array( + Image.frombytes( + "1", + (img["stream"]["Width"], img["stream"]["Height"]), + img["stream"].get_data(), + ).convert("L") + ) + ) + else: + images.append( + np.frombuffer(img["stream"].get_data(), dtype=np.uint8).reshape( + img["stream"]["Height"], img["stream"]["Width"], -1 + ) + ) + elif img["stream"]["Filter"].name in _PDF_FILTER_WITH_LOSS: + images.append(img["stream"].get_data()) + else: + warnings.warn("Unknown PDF Filter!") + + return extract_from_images_with_rapidocr(images) + + +class AmazonTextractPDFParser(BaseBlobParser): + """Send `PDF` files to `Amazon Textract` and parse them. + + For parsing multi-page PDFs, they have to reside on S3. + + The AmazonTextractPDFLoader calls the + [Amazon Textract Service](https://aws.amazon.com/textract/) + to convert PDFs into a Document structure. + Single and multi-page documents are supported with up to 3000 pages + and 512 MB of size. + + For the call to be successful an AWS account is required, + similar to the + [AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-configure.html) + requirements. + + Besides the AWS configuration, it is very similar to the other PDF + loaders, while also supporting JPEG, PNG and TIFF and non-native + PDF formats. + + ```python + from langchain_community.document_loaders import AmazonTextractPDFLoader + loader=AmazonTextractPDFLoader("example_data/alejandro_rosalez_sample-small.jpeg") + documents = loader.load() + ``` + + One feature is the linearization of the output. + When using the features LAYOUT, FORMS or TABLES together with Textract + + ```python + from langchain_community.document_loaders import AmazonTextractPDFLoader + # you can mix and match each of the features + loader=AmazonTextractPDFLoader( + "example_data/alejandro_rosalez_sample-small.jpeg", + textract_features=["TABLES", "LAYOUT"]) + documents = loader.load() + ``` + + it will generate output that formats the text in reading order and + try to output the information in a tabular structure or + output the key/value pairs with a colon (key: value). + This helps most LLMs to achieve better accuracy when + processing these texts. + + ``Document`` objects are returned with metadata that includes the ``source`` and + a 1-based index of the page number in ``page``. Note that ``page`` represents + the index of the result returned from Textract, not necessarily the as-written + page number in the document. + + """ + + def __init__( + self, + textract_features: Optional[Sequence[int]] = None, + client: Optional[Any] = None, + *, + linearization_config: Optional[TextLinearizationConfig] = None, + ) -> None: + """Initializes the parser. + + Args: + textract_features: Features to be used for extraction, each feature + should be passed as an int that conforms to the enum + `Textract_Features`, see `amazon-textract-caller` pkg + client: boto3 textract client + linearization_config: Config to be used for linearization of the output + should be an instance of TextLinearizationConfig from + the `textractor` pkg + """ + + try: + import textractcaller as tc + import textractor.entities.document as textractor + + self.tc = tc + self.textractor = textractor + + if textract_features is not None: + self.textract_features = [ + tc.Textract_Features(f) for f in textract_features + ] + else: + self.textract_features = [] + + if linearization_config is not None: + self.linearization_config = linearization_config + else: + self.linearization_config = self.textractor.TextLinearizationConfig( + hide_figure_layout=True, + title_prefix="# ", + section_header_prefix="## ", + list_element_prefix="*", + ) + except ImportError: + raise ImportError( + "Could not import amazon-textract-caller or " + "amazon-textract-textractor python package. Please install it " + "with `pip install amazon-textract-caller` & " + "`pip install amazon-textract-textractor`." + ) + + if not client: + try: + import boto3 + + self.boto3_textract_client = boto3.client("textract") + except ImportError: + raise ImportError( + "Could not import boto3 python package. " + "Please install it with `pip install boto3`." + ) + else: + self.boto3_textract_client = client + + def lazy_parse(self, blob: Blob) -> Iterator[Document]: + """Iterates over the Blob pages and returns an Iterator with a Document + for each page, like the other parsers If multi-page document, blob.path + has to be set to the S3 URI and for single page docs + the blob.data is taken + """ + + url_parse_result = urlparse(str(blob.path)) if blob.path else None + # Either call with S3 path (multi-page) or with bytes (single-page) + if ( + url_parse_result + and url_parse_result.scheme == "s3" + and url_parse_result.netloc + ): + textract_response_json = self.tc.call_textract( + input_document=str(blob.path), + features=self.textract_features, + boto3_textract_client=self.boto3_textract_client, + ) + else: + textract_response_json = self.tc.call_textract( + input_document=blob.as_bytes(), + features=self.textract_features, + call_mode=self.tc.Textract_Call_Mode.FORCE_SYNC, + boto3_textract_client=self.boto3_textract_client, + ) + + document = self.textractor.Document.open(textract_response_json) + + for idx, page in enumerate(document.pages): + yield Document( + page_content=page.get_text(config=self.linearization_config), + metadata={"source": blob.source, "page": idx + 1}, + ) + + +class DocumentIntelligenceParser(BaseBlobParser): + """Loads a PDF with Azure Document Intelligence + (formerly Form Recognizer) and chunks at character level.""" + + def __init__(self, client: Any, model: str): + warnings.warn( + "langchain_community.document_loaders.parsers.pdf.DocumentIntelligenceParser" + "and langchain_community.document_loaders.pdf.DocumentIntelligenceLoader" + " are deprecated. Please upgrade to " + "langchain_community.document_loaders.DocumentIntelligenceLoader " + "for any file parsing purpose using Azure Document Intelligence " + "service." + ) + self.client = client + self.model = model + + def _generate_docs(self, blob: Blob, result: Any) -> Iterator[Document]: + for p in result.pages: + content = " ".join([line.content for line in p.lines]) + + d = Document( + page_content=content, + metadata={ + "source": blob.source, + "page": p.page_number, + }, + ) + yield d + + def lazy_parse(self, blob: Blob) -> Iterator[Document]: + """Lazily parse the blob.""" + + with blob.as_bytes_io() as file_obj: + poller = self.client.begin_analyze_document(self.model, file_obj) + result = poller.result() + + docs = self._generate_docs(blob, result) + + yield from docs diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/registry.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/registry.py new file mode 100644 index 0000000000000000000000000000000000000000..46074e5e698fdb0c4dfd944432a6c126de4edd73 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/registry.py @@ -0,0 +1,36 @@ +"""Module includes a registry of default parser configurations.""" + +from langchain_community.document_loaders.base import BaseBlobParser +from langchain_community.document_loaders.parsers.generic import MimeTypeBasedParser +from langchain_community.document_loaders.parsers.msword import MsWordParser +from langchain_community.document_loaders.parsers.pdf import PyMuPDFParser +from langchain_community.document_loaders.parsers.txt import TextParser + + +def _get_default_parser() -> BaseBlobParser: + """Get default mime-type based parser.""" + return MimeTypeBasedParser( + handlers={ + "application/pdf": PyMuPDFParser(), + "text/plain": TextParser(), + "application/msword": MsWordParser(), + "application/vnd.openxmlformats-officedocument.wordprocessingml.document": ( + MsWordParser() + ), + }, + fallback_parser=None, + ) + + +_REGISTRY = { + "default": _get_default_parser, +} + +# PUBLIC API + + +def get_parser(parser_name: str) -> BaseBlobParser: + """Get a parser by parser name.""" + if parser_name not in _REGISTRY: + raise ValueError(f"Unknown parser combination: {parser_name}") + return _REGISTRY[parser_name]() diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/txt.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/txt.py new file mode 100644 index 0000000000000000000000000000000000000000..5b2da3074317d2865dd6393749d6988d88234c6f --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/txt.py @@ -0,0 +1,16 @@ +"""Module for parsing text files..""" + +from typing import Iterator + +from langchain_core.documents import Document + +from langchain_community.document_loaders.base import BaseBlobParser +from langchain_community.document_loaders.blob_loaders import Blob + + +class TextParser(BaseBlobParser): + """Parser for text blobs.""" + + def lazy_parse(self, blob: Blob) -> Iterator[Document]: + """Lazily parse the blob.""" + yield Document(page_content=blob.as_string(), metadata={"source": blob.source}) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/vsdx.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/vsdx.py new file mode 100644 index 0000000000000000000000000000000000000000..aeb414453ad60c8a3ec79fe97739170f2f3ae4df --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/document_loaders/parsers/vsdx.py @@ -0,0 +1,207 @@ +import json +import re +import zipfile +from abc import ABC +from pathlib import Path +from typing import Iterator, List, Set, Tuple + +from langchain_community.docstore.document import Document +from langchain_community.document_loaders.base import BaseBlobParser +from langchain_community.document_loaders.blob_loaders import Blob + + +class VsdxParser(BaseBlobParser, ABC): + """Parser for vsdx files.""" + + def parse(self, blob: Blob) -> Iterator[Document]: # type: ignore[override] + """Parse a vsdx file.""" + return self.lazy_parse(blob) + + def lazy_parse(self, blob: Blob) -> Iterator[Document]: + """Retrieve the contents of pages from a .vsdx file + and insert them into documents, one document per page.""" + + with blob.as_bytes_io() as pdf_file_obj: + with zipfile.ZipFile(pdf_file_obj, "r") as zfile: + pages = self.get_pages_content(zfile, blob.source) # type: ignore[arg-type] + + yield from [ + Document( + page_content=page_content, + metadata={ + "source": blob.source, + "page": page_number, + "page_name": page_name, + }, + ) + for page_number, page_name, page_content in pages + ] + + def get_pages_content( + self, zfile: zipfile.ZipFile, source: str + ) -> List[Tuple[int, str, str]]: + """Get the content of the pages of a vsdx file. + + Attributes: + zfile (zipfile.ZipFile): The vsdx file under zip format. + source (str): The path of the vsdx file. + + Returns: + list[tuple[int, str, str]]: A list of tuples containing the page number, + the name of the page and the content of the page + for each page of the vsdx file. + """ + + try: + import xmltodict + except ImportError: + raise ImportError( + "The xmltodict library is required to parse vsdx files. " + "Please install it with `pip install xmltodict`." + ) + + if "visio/pages/pages.xml" not in zfile.namelist(): + print("WARNING - No pages.xml file found in {}".format(source)) # noqa: T201 + return # type: ignore[return-value] + if "visio/pages/_rels/pages.xml.rels" not in zfile.namelist(): + print("WARNING - No pages.xml.rels file found in {}".format(source)) # noqa: T201 + return # type: ignore[return-value] + if "docProps/app.xml" not in zfile.namelist(): + print("WARNING - No app.xml file found in {}".format(source)) # noqa: T201 + return # type: ignore[return-value] + + pagesxml_content: dict = xmltodict.parse(zfile.read("visio/pages/pages.xml")) + appxml_content: dict = xmltodict.parse(zfile.read("docProps/app.xml")) + pagesxmlrels_content: dict = xmltodict.parse( + zfile.read("visio/pages/_rels/pages.xml.rels") + ) + + if isinstance(pagesxml_content["Pages"]["Page"], list): + disordered_names: List[str] = [ + rel["@Name"].strip() for rel in pagesxml_content["Pages"]["Page"] + ] + else: + disordered_names: List[str] = [ # type: ignore[no-redef] + pagesxml_content["Pages"]["Page"]["@Name"].strip() + ] + if isinstance(pagesxmlrels_content["Relationships"]["Relationship"], list): + disordered_paths: List[str] = [ + "visio/pages/" + rel["@Target"] + for rel in pagesxmlrels_content["Relationships"]["Relationship"] + ] + else: + disordered_paths: List[str] = [ # type: ignore[no-redef] + "visio/pages/" + + pagesxmlrels_content["Relationships"]["Relationship"]["@Target"] + ] + ordered_names: List[str] = appxml_content["Properties"]["TitlesOfParts"][ + "vt:vector" + ]["vt:lpstr"][: len(disordered_names)] + ordered_names = [name.strip() for name in ordered_names] + ordered_paths = [ + disordered_paths[disordered_names.index(name.strip())] + for name in ordered_names + ] + + # Pages out of order and without content of their relationships + disordered_pages = [] + for path in ordered_paths: + content = zfile.read(path) + string_content = json.dumps(xmltodict.parse(content)) + + samples = re.findall( + r'"#text"\s*:\s*"([^\\"]*(?:\\.[^\\"]*)*)"', string_content + ) + if len(samples) > 0: + page_content = "\n".join(samples) + map_symboles = { + "\\n": "\n", + "\\t": "\t", + "\\u2013": "-", + "\\u2019": "'", + "\\u00e9r": "é", + "\\u00f4me": "ô", + } + for key, value in map_symboles.items(): + page_content = page_content.replace(key, value) + + disordered_pages.append({"page": path, "page_content": page_content}) + + # Direct relationships of each page in a dict format + pagexml_rels = [ + { + "path": page_path, + "content": xmltodict.parse( + zfile.read(f"visio/pages/_rels/{Path(page_path).stem}.xml.rels") + ), + } + for page_path in ordered_paths + if f"visio/pages/_rels/{Path(page_path).stem}.xml.rels" in zfile.namelist() + ] + + # Pages in order and with content of their relationships (direct and indirect) + ordered_pages: List[Tuple[int, str, str]] = [] + for page_number, (path, page_name) in enumerate( + zip(ordered_paths, ordered_names) + ): + relationships = self.get_relationships( + path, zfile, ordered_paths, pagexml_rels + ) + page_content = "\n".join( + [ + page_["page_content"] + for page_ in disordered_pages + if page_["page"] in relationships + ] + + [ + page_["page_content"] + for page_ in disordered_pages + if page_["page"] == path + ] + ) + ordered_pages.append((page_number, page_name, page_content)) + + return ordered_pages + + def get_relationships( + self, + page: str, + zfile: zipfile.ZipFile, + filelist: List[str], + pagexml_rels: List[dict], + ) -> Set[str]: + """Get the relationships of a page and the relationships of its relationships, + etc... recursively. + Pages are based on other pages (ex: background page), + so we need to get all the relationships to get all the content of a single page. + """ + + name_path = Path(page).name + parent_path = Path(page).parent + rels_path = parent_path / f"_rels/{name_path}.rels" + + if str(rels_path) not in zfile.namelist(): + 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0000000000000000000000000000000000000000..8d6a829ef61e8cb4d2b276d86e600eb0a2e3abf1 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/graph_vectorstores/extractors/__init__.py @@ -0,0 +1,41 @@ +from langchain_community.graph_vectorstores.extractors.gliner_link_extractor import ( + GLiNERInput, + GLiNERLinkExtractor, +) +from langchain_community.graph_vectorstores.extractors.hierarchy_link_extractor import ( + HierarchyInput, + HierarchyLinkExtractor, +) +from langchain_community.graph_vectorstores.extractors.html_link_extractor import ( + HtmlInput, + HtmlLinkExtractor, +) +from langchain_community.graph_vectorstores.extractors.keybert_link_extractor import ( + KeybertInput, + KeybertLinkExtractor, +) +from langchain_community.graph_vectorstores.extractors.link_extractor import ( + LinkExtractor, +) +from langchain_community.graph_vectorstores.extractors.link_extractor_adapter import ( + LinkExtractorAdapter, +) +from langchain_community.graph_vectorstores.extractors.link_extractor_transformer import ( # noqa: E501 + LinkExtractorTransformer, +) + +__all__ = [ + "GLiNERInput", + "GLiNERLinkExtractor", + "HierarchyInput", + "HierarchyLinkExtractor", + "HtmlInput", + "HtmlLinkExtractor", + "KeybertInput", + "KeybertLinkExtractor", + "LinkExtractor", + "LinkExtractor", + "LinkExtractorAdapter", + "LinkExtractorAdapter", + "LinkExtractorTransformer", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/graph_vectorstores/extractors/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/graph_vectorstores/extractors/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2dad8f0f92f6799a50b1dab161fca00652eecb57 Binary files /dev/null and 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b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/graph_vectorstores/extractors/gliner_link_extractor.py new file mode 100644 index 0000000000000000000000000000000000000000..f353ba4a1dc82bf07e2267472ce82934fe9d7154 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/graph_vectorstores/extractors/gliner_link_extractor.py @@ -0,0 +1,166 @@ +from typing import Any, Dict, Iterable, List, Optional, Set, Union + +from langchain_core._api import beta +from langchain_core.documents import Document + +from langchain_community.graph_vectorstores.extractors.link_extractor import ( + LinkExtractor, +) +from langchain_community.graph_vectorstores.links import Link + +# TypeAlias is not available in Python 3.9, we can't use that or the newer `type`. +GLiNERInput = Union[str, Document] + + +@beta() +class GLiNERLinkExtractor(LinkExtractor[GLiNERInput]): + """Link documents with common named entities using `GLiNER`_. + + `GLiNER`_ is a Named Entity Recognition (NER) model capable of identifying any + entity type using a bidirectional transformer encoder (BERT-like). + + The ``GLiNERLinkExtractor`` uses GLiNER to create links between documents that + have named entities in common. + + Example:: + + extractor = GLiNERLinkExtractor( + labels=["Person", "Award", "Date", "Competitions", "Teams"] + ) + results = extractor.extract_one("some long text...") + + .. _GLiNER: https://github.com/urchade/GLiNER + + .. seealso:: + + - :mod:`How to use a graph vector store ` + - :class:`How to create links between documents ` + + How to link Documents on common named entities + ============================================== + + Preliminaries + ------------- + + Install the ``gliner`` package: + + .. code-block:: bash + + pip install -q langchain_community gliner + + Usage + ----- + + We load the ``state_of_the_union.txt`` file, chunk it, then for each chunk we + extract named entity links and add them to the chunk. + + Using extract_one() + ^^^^^^^^^^^^^^^^^^^ + + We can use :meth:`extract_one` on a document to get the links and add the links + to the document metadata with + :meth:`~langchain_community.graph_vectorstores.links.add_links`:: + + from langchain_community.document_loaders import TextLoader + from langchain_community.graph_vectorstores import CassandraGraphVectorStore + from langchain_community.graph_vectorstores.extractors import GLiNERLinkExtractor + from langchain_community.graph_vectorstores.links import add_links + from langchain_text_splitters import CharacterTextSplitter + + loader = TextLoader("state_of_the_union.txt") + raw_documents = loader.load() + + text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) + documents = text_splitter.split_documents(raw_documents) + + ner_extractor = GLiNERLinkExtractor(["Person", "Topic"]) + for document in documents: + links = ner_extractor.extract_one(document) + add_links(document, links) + + print(documents[0].metadata) + + .. code-block:: output + + {'source': 'state_of_the_union.txt', 'links': [Link(kind='entity:Person', direction='bidir', tag='President Zelenskyy'), Link(kind='entity:Person', direction='bidir', tag='Vladimir Putin')]} + + Using LinkExtractorTransformer + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + + Using the :class:`~langchain_community.graph_vectorstores.extractors.link_extractor_transformer.LinkExtractorTransformer`, + we can simplify the link extraction:: + + from langchain_community.document_loaders import TextLoader + from langchain_community.graph_vectorstores.extractors import ( + GLiNERLinkExtractor, + LinkExtractorTransformer, + ) + from langchain_text_splitters import CharacterTextSplitter + + loader = TextLoader("state_of_the_union.txt") + raw_documents = loader.load() + + text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) + documents = text_splitter.split_documents(raw_documents) + + ner_extractor = GLiNERLinkExtractor(["Person", "Topic"]) + transformer = LinkExtractorTransformer([ner_extractor]) + documents = transformer.transform_documents(documents) + + print(documents[0].metadata) + + .. code-block:: output + + {'source': 'state_of_the_union.txt', 'links': [Link(kind='entity:Person', direction='bidir', tag='President Zelenskyy'), Link(kind='entity:Person', direction='bidir', tag='Vladimir Putin')]} + + The documents with named entity links can then be added to a :class:`~langchain_community.graph_vectorstores.base.GraphVectorStore`:: + + from langchain_community.graph_vectorstores import CassandraGraphVectorStore + + store = CassandraGraphVectorStore.from_documents(documents=documents, embedding=...) + + Args: + labels: List of kinds of entities to extract. + kind: Kind of links to produce with this extractor. + model: GLiNER model to use. + extract_kwargs: Keyword arguments to pass to GLiNER. + """ # noqa: E501 + + def __init__( + self, + labels: List[str], + *, + kind: str = "entity", + model: str = "urchade/gliner_mediumv2.1", + extract_kwargs: Optional[Dict[str, Any]] = None, + ): + try: + from gliner import GLiNER + + self._model = GLiNER.from_pretrained(model) + + except ImportError: + raise ImportError( + "gliner is required for GLiNERLinkExtractor. " + "Please install it with `pip install gliner`." + ) from None + + self._labels = labels + self._kind = kind + self._extract_kwargs = extract_kwargs or {} + + def extract_one(self, input: GLiNERInput) -> Set[Link]: # noqa: A002 + return next(iter(self.extract_many([input]))) + + def extract_many( + self, + inputs: Iterable[GLiNERInput], + ) -> Iterable[Set[Link]]: + strs = [i if isinstance(i, str) else i.page_content for i in inputs] + for entities in self._model.batch_predict_entities( + strs, self._labels, **self._extract_kwargs + ): + yield { + Link.bidir(kind=f"{self._kind}:{e['label']}", tag=e["text"]) + for e in entities + } diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/graph_vectorstores/extractors/hierarchy_link_extractor.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/graph_vectorstores/extractors/hierarchy_link_extractor.py new file mode 100644 index 0000000000000000000000000000000000000000..d838210aded319e90b3cd7031feb6c50fee50c21 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/graph_vectorstores/extractors/hierarchy_link_extractor.py @@ -0,0 +1,110 @@ +from typing import Callable, List, Set + +from langchain_core._api import beta +from langchain_core.documents import Document + +from langchain_community.graph_vectorstores.extractors.link_extractor import ( + LinkExtractor, +) +from langchain_community.graph_vectorstores.extractors.link_extractor_adapter import ( + LinkExtractorAdapter, +) +from langchain_community.graph_vectorstores.links import Link + +# TypeAlias is not available in Python 3.9, we can't use that or the newer `type`. +HierarchyInput = List[str] + +_PARENT: str = "p:" +_CHILD: str = "c:" +_SIBLING: str = "s:" + + +@beta() +class HierarchyLinkExtractor(LinkExtractor[HierarchyInput]): + def __init__( + self, + *, + kind: str = "hierarchy", + parent_links: bool = True, + child_links: bool = False, + sibling_links: bool = False, + ): + """Extract links from a document hierarchy. + + Example: + + .. code-block:: python + + # Given three paths (in this case, within the "Root" document): + h1 = ["Root", "H1"] + h1a = ["Root", "H1", "a"] + h1b = ["Root", "H1", "b"] + + # Parent links `h1a` and `h1b` to `h1`. + # Child links `h1` to `h1a` and `h1b`. + # Sibling links `h1a` and `h1b` together (both directions). + + Example use with documents: + .. code_block: python + transformer = LinkExtractorTransformer([ + HierarchyLinkExtractor().as_document_extractor( + # Assumes the "path" to each document is in the metadata. + # Could split strings, etc. + lambda doc: doc.metadata.get("path", []) + ) + ]) + linked = transformer.transform_documents(docs) + + Args: + kind: Kind of links to produce with this extractor. + parent_links: Link from a section to its parent. + child_links: Link from a section to its children. + sibling_links: Link from a section to other sections with the same parent. + """ + self._kind = kind + self._parent_links = parent_links + self._child_links = child_links + self._sibling_links = sibling_links + + def as_document_extractor( + self, hierarchy: Callable[[Document], HierarchyInput] + ) -> LinkExtractor[Document]: + """Create a LinkExtractor from `Document`. + + Args: + hierarchy: Function that returns the path for the given document. + + Returns: + A `LinkExtractor[Document]` suitable for application to `Documents` directly + or with `LinkExtractorTransformer`. + """ + return LinkExtractorAdapter(underlying=self, transform=hierarchy) + + def extract_one( + self, + input: HierarchyInput, + ) -> Set[Link]: + this_path = "/".join(input) + parent_path = None + + links = set() + if self._parent_links: + # This is linked from everything with this parent path. + links.add(Link.incoming(kind=self._kind, tag=_PARENT + this_path)) + if self._child_links: + # This is linked to every child with this as it's "parent" path. + links.add(Link.outgoing(kind=self._kind, tag=_CHILD + this_path)) + + if len(input) >= 1: + parent_path = "/".join(input[0:-1]) + if self._parent_links and len(input) > 1: + # This is linked to the nodes with the given parent path. + links.add(Link.outgoing(kind=self._kind, tag=_PARENT + parent_path)) + if self._child_links and len(input) > 1: + # This is linked from every node with the given parent path. + links.add(Link.incoming(kind=self._kind, tag=_CHILD + parent_path)) + if self._sibling_links: + # This is a sibling of everything with the same parent. + links.add(Link.bidir(kind=self._kind, tag=_SIBLING + parent_path)) + + return links diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/graph_vectorstores/extractors/html_link_extractor.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/graph_vectorstores/extractors/html_link_extractor.py new file mode 100644 index 0000000000000000000000000000000000000000..fddb852d6ee057084a73d81c4f72a3637713c8ad --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/graph_vectorstores/extractors/html_link_extractor.py @@ -0,0 +1,291 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import TYPE_CHECKING, List, Optional, Set, Union +from urllib.parse import urldefrag, urljoin, urlparse + +from langchain_core._api import beta +from langchain_core.documents import Document + +from langchain_community.graph_vectorstores import Link +from langchain_community.graph_vectorstores.extractors.link_extractor import ( + LinkExtractor, +) +from langchain_community.graph_vectorstores.extractors.link_extractor_adapter import ( + LinkExtractorAdapter, +) + +if TYPE_CHECKING: + from bs4 import BeautifulSoup + from bs4.element import Tag + + +def _parse_url(link: Tag, page_url: str, drop_fragments: bool = True) -> Optional[str]: + href = link.get("href") + if href is None: + return None + url = urlparse(href) # type: ignore[arg-type] + if url.scheme not in ["http", "https", ""]: + return None + + # Join the HREF with the page_url to convert relative paths to absolute. + url = str(urljoin(page_url, href)) # type: ignore[type-var, assignment] + + # Fragments would be useful if we chunked a page based on section. + # Then, each chunk would have a different URL based on the fragment. + # Since we aren't doing that yet, they just "break" links. So, drop + # the fragment. + if drop_fragments: + return urldefrag(url).url # type: ignore[call-overload] + return url # type: ignore[return-value] + + +def _parse_hrefs( + soup: BeautifulSoup, url: str, drop_fragments: bool = True +) -> Set[str]: + soup_links: List[Tag] = soup.find_all("a") + links: Set[str] = set() + + for link in soup_links: + parse_url = _parse_url(link, page_url=url, drop_fragments=drop_fragments) + # Remove self links and entries for any 'a' tag that failed to parse + # (didn't have href, or invalid domain, etc.) + if parse_url and parse_url != url: + links.add(parse_url) + + return links + + +@dataclass +class HtmlInput: + content: Union[str, BeautifulSoup] + base_url: str + + +@beta() +class HtmlLinkExtractor(LinkExtractor[HtmlInput]): + def __init__(self, *, kind: str = "hyperlink", drop_fragments: bool = True): + """Extract hyperlinks from HTML content. + + Expects the input to be an HTML string or a `BeautifulSoup` object. + + Example:: + + extractor = HtmlLinkExtractor() + results = extractor.extract_one(HtmlInput(html, url)) + + .. seealso:: + + - :mod:`How to use a graph vector store ` + - :class:`How to create links between documents ` + + How to link Documents on hyperlinks in HTML + =========================================== + + Preliminaries + ------------- + + Install the ``beautifulsoup4`` package: + + .. code-block:: bash + + pip install -q langchain_community beautifulsoup4 + + Usage + ----- + + For this example, we'll scrape 2 HTML pages that have an hyperlink from one + page to the other using an ``AsyncHtmlLoader``. + Then we use the ``HtmlLinkExtractor`` to create the links in the documents. + + Using extract_one() + ^^^^^^^^^^^^^^^^^^^ + + We can use :meth:`extract_one` on a document to get the links and add the links + to the document metadata with + :meth:`~langchain_community.graph_vectorstores.links.add_links`:: + + from langchain_community.document_loaders import AsyncHtmlLoader + from langchain_community.graph_vectorstores.extractors import ( + HtmlInput, + HtmlLinkExtractor, + ) + from langchain_community.graph_vectorstores.links import add_links + from langchain_core.documents import Document + + loader = AsyncHtmlLoader( + [ + "https://python.langchain.com/docs/integrations/providers/astradb/", + "https://docs.datastax.com/en/astra/home/astra.html", + ] + ) + + documents = loader.load() + + html_extractor = HtmlLinkExtractor() + + for doc in documents: + links = html_extractor.extract_one(HtmlInput(doc.page_content, url)) + add_links(doc, links) + + documents[0].metadata["links"][:5] + + .. code-block:: output + + [Link(kind='hyperlink', direction='out', tag='https://python.langchain.com/docs/integrations/providers/spreedly/'), + Link(kind='hyperlink', direction='out', tag='https://python.langchain.com/docs/integrations/providers/nvidia/'), + Link(kind='hyperlink', direction='out', tag='https://python.langchain.com/docs/integrations/providers/ray_serve/'), + Link(kind='hyperlink', direction='out', tag='https://python.langchain.com/docs/integrations/providers/bageldb/'), + Link(kind='hyperlink', direction='out', tag='https://python.langchain.com/docs/introduction/')] + + Using as_document_extractor() + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + + If you use a document loader that returns the raw HTML and that sets the source + key in the document metadata such as ``AsyncHtmlLoader``, + you can simplify by using :meth:`as_document_extractor` that takes directly a + ``Document`` as input:: + + from langchain_community.document_loaders import AsyncHtmlLoader + from langchain_community.graph_vectorstores.extractors import HtmlLinkExtractor + from langchain_community.graph_vectorstores.links import add_links + + loader = AsyncHtmlLoader( + [ + "https://python.langchain.com/docs/integrations/providers/astradb/", + "https://docs.datastax.com/en/astra/home/astra.html", + ] + ) + documents = loader.load() + html_extractor = HtmlLinkExtractor().as_document_extractor() + + for document in documents: + links = html_extractor.extract_one(document) + add_links(document, links) + + documents[0].metadata["links"][:5] + + .. code-block:: output + + [Link(kind='hyperlink', direction='out', tag='https://python.langchain.com/docs/integrations/providers/spreedly/'), + Link(kind='hyperlink', direction='out', tag='https://python.langchain.com/docs/integrations/providers/nvidia/'), + Link(kind='hyperlink', direction='out', tag='https://python.langchain.com/docs/integrations/providers/ray_serve/'), + Link(kind='hyperlink', direction='out', tag='https://python.langchain.com/docs/integrations/providers/bageldb/'), + Link(kind='hyperlink', direction='out', tag='https://python.langchain.com/docs/introduction/')] + + Using LinkExtractorTransformer + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + + Using the :class:`~langchain_community.graph_vectorstores.extractors.link_extractor_transformer.LinkExtractorTransformer`, + we can simplify the link extraction:: + + from langchain_community.document_loaders import AsyncHtmlLoader + from langchain_community.graph_vectorstores.extractors import ( + HtmlLinkExtractor, + LinkExtractorTransformer, + ) + from langchain_community.graph_vectorstores.links import add_links + + loader = AsyncHtmlLoader( + [ + "https://python.langchain.com/docs/integrations/providers/astradb/", + "https://docs.datastax.com/en/astra/home/astra.html", + ] + ) + + documents = loader.load() + transformer = LinkExtractorTransformer([HtmlLinkExtractor().as_document_extractor()]) + documents = transformer.transform_documents(documents) + + documents[0].metadata["links"][:5] + + .. code-block:: output + + [Link(kind='hyperlink', direction='out', tag='https://python.langchain.com/docs/integrations/providers/spreedly/'), + Link(kind='hyperlink', direction='out', tag='https://python.langchain.com/docs/integrations/providers/nvidia/'), + Link(kind='hyperlink', direction='out', tag='https://python.langchain.com/docs/integrations/providers/ray_serve/'), + Link(kind='hyperlink', direction='out', tag='https://python.langchain.com/docs/integrations/providers/bageldb/'), + Link(kind='hyperlink', direction='out', tag='https://python.langchain.com/docs/introduction/')] + + We can check that there is a link from the first document to the second:: + + for doc_to in documents: + for link_to in doc_to.metadata["links"]: + if link_to.direction == "in": + for doc_from in documents: + for link_from in doc_from.metadata["links"]: + if ( + link_to.direction == "in" + and link_from.direction == "out" + and link_to.tag == link_from.tag + ): + print( + f"Found link from {doc_from.metadata['source']} to {doc_to.metadata['source']}." + ) + + .. code-block:: output + + Found link from https://python.langchain.com/docs/integrations/providers/astradb/ to https://docs.datastax.com/en/astra/home/astra.html. + + The documents with URL links can then be added to a :class:`~langchain_community.graph_vectorstores.base.GraphVectorStore`:: + + from langchain_community.graph_vectorstores import CassandraGraphVectorStore + + store = CassandraGraphVectorStore.from_documents(documents=documents, embedding=...) + + Args: + kind: The kind of edge to extract. Defaults to ``hyperlink``. + drop_fragments: Whether fragments in URLs and links should be + dropped. Defaults to ``True``. + """ # noqa: E501 + try: + import bs4 # noqa:F401 + except ImportError as e: + raise ImportError( + "BeautifulSoup4 is required for HtmlLinkExtractor. " + "Please install it with `pip install beautifulsoup4`." + ) from e + + self._kind = kind + self.drop_fragments = drop_fragments + + def as_document_extractor( + self, url_metadata_key: str = "source" + ) -> LinkExtractor[Document]: + """Return a LinkExtractor that applies to documents. + + Note: + Since the HtmlLinkExtractor parses HTML, if you use with other similar + link extractors it may be more efficient to call the link extractors + directly on the parsed BeautifulSoup object. + + Args: + url_metadata_key: The name of the filed in document metadata with the URL of + the document. + """ + return LinkExtractorAdapter( + underlying=self, + transform=lambda doc: HtmlInput( + doc.page_content, doc.metadata[url_metadata_key] + ), + ) + + def extract_one( + self, + input: HtmlInput, # noqa: A002 + ) -> Set[Link]: + content = input.content + if isinstance(content, str): + from bs4 import BeautifulSoup + + content = BeautifulSoup(content, "html.parser") + + base_url = input.base_url + if self.drop_fragments: + base_url = urldefrag(base_url).url + + hrefs = _parse_hrefs(content, base_url, self.drop_fragments) + + links = {Link.outgoing(kind=self._kind, tag=url) for url in hrefs} + links.add(Link.incoming(kind=self._kind, tag=base_url)) + return links diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/graph_vectorstores/extractors/keybert_link_extractor.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/graph_vectorstores/extractors/keybert_link_extractor.py new file mode 100644 index 0000000000000000000000000000000000000000..3844df84f76ef4f696f7eec68fa77e3e6cf09abe --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/graph_vectorstores/extractors/keybert_link_extractor.py @@ -0,0 +1,167 @@ +from typing import Any, Dict, Iterable, Optional, Set, Union + +from langchain_core._api import beta +from langchain_core.documents import Document + +from langchain_community.graph_vectorstores.extractors.link_extractor import ( + LinkExtractor, +) +from langchain_community.graph_vectorstores.links import Link + +KeybertInput = Union[str, Document] + + +@beta() +class KeybertLinkExtractor(LinkExtractor[KeybertInput]): + def __init__( + self, + *, + kind: str = "kw", + embedding_model: str = "all-MiniLM-L6-v2", + extract_keywords_kwargs: Optional[Dict[str, Any]] = None, + ): + """Extract keywords using `KeyBERT `_. + + KeyBERT is a minimal and easy-to-use keyword extraction technique that + leverages BERT embeddings to create keywords and keyphrases that are most + similar to a document. + + The KeybertLinkExtractor uses KeyBERT to create links between documents that + have keywords in common. + + Example:: + + extractor = KeybertLinkExtractor() + results = extractor.extract_one("lorem ipsum...") + + .. seealso:: + + - :mod:`How to use a graph vector store ` + - :class:`How to create links between documents ` + + How to link Documents on common keywords using Keybert + ====================================================== + + Preliminaries + ------------- + + Install the keybert package: + + .. code-block:: bash + + pip install -q langchain_community keybert + + Usage + ----- + + We load the ``state_of_the_union.txt`` file, chunk it, then for each chunk we + extract keyword links and add them to the chunk. + + Using extract_one() + ^^^^^^^^^^^^^^^^^^^ + + We can use :meth:`extract_one` on a document to get the links and add the links + to the document metadata with + :meth:`~langchain_community.graph_vectorstores.links.add_links`:: + + from langchain_community.document_loaders import TextLoader + from langchain_community.graph_vectorstores import CassandraGraphVectorStore + from langchain_community.graph_vectorstores.extractors import KeybertLinkExtractor + from langchain_community.graph_vectorstores.links import add_links + from langchain_text_splitters import CharacterTextSplitter + + loader = TextLoader("state_of_the_union.txt") + + raw_documents = loader.load() + text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) + + documents = text_splitter.split_documents(raw_documents) + keyword_extractor = KeybertLinkExtractor() + + for document in documents: + links = keyword_extractor.extract_one(document) + add_links(document, links) + + print(documents[0].metadata) + + .. code-block:: output + + {'source': 'state_of_the_union.txt', 'links': [Link(kind='kw', direction='bidir', tag='ukraine'), Link(kind='kw', direction='bidir', tag='ukrainian'), Link(kind='kw', direction='bidir', tag='putin'), Link(kind='kw', direction='bidir', tag='vladimir'), Link(kind='kw', direction='bidir', tag='russia')]} + + Using LinkExtractorTransformer + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + + Using the :class:`~langchain_community.graph_vectorstores.extractors.link_extractor_transformer.LinkExtractorTransformer`, + we can simplify the link extraction:: + + from langchain_community.document_loaders import TextLoader + from langchain_community.graph_vectorstores.extractors import ( + KeybertLinkExtractor, + LinkExtractorTransformer, + ) + from langchain_text_splitters import CharacterTextSplitter + + loader = TextLoader("state_of_the_union.txt") + raw_documents = loader.load() + + text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) + documents = text_splitter.split_documents(raw_documents) + + transformer = LinkExtractorTransformer([KeybertLinkExtractor()]) + documents = transformer.transform_documents(documents) + + print(documents[0].metadata) + + .. code-block:: output + + {'source': 'state_of_the_union.txt', 'links': [Link(kind='kw', direction='bidir', tag='ukraine'), Link(kind='kw', direction='bidir', tag='ukrainian'), Link(kind='kw', direction='bidir', tag='putin'), Link(kind='kw', direction='bidir', tag='vladimir'), Link(kind='kw', direction='bidir', tag='russia')]} + + The documents with keyword links can then be added to a :class:`~langchain_community.graph_vectorstores.base.GraphVectorStore`:: + + from langchain_community.graph_vectorstores import CassandraGraphVectorStore + + store = CassandraGraphVectorStore.from_documents(documents=documents, embedding=...) + + Args: + kind: Kind of links to produce with this extractor. + embedding_model: Name of the embedding model to use with KeyBERT. + extract_keywords_kwargs: Keyword arguments to pass to KeyBERT's + ``extract_keywords`` method. + """ # noqa: E501 + try: + import keybert + + self._kw_model = keybert.KeyBERT(model=embedding_model) + except ImportError: + raise ImportError( + "keybert is required for KeybertLinkExtractor. " + "Please install it with `pip install keybert`." + ) from None + + self._kind = kind + self._extract_keywords_kwargs = extract_keywords_kwargs or {} + + def extract_one(self, input: KeybertInput) -> Set[Link]: # noqa: A002 + keywords = self._kw_model.extract_keywords( + input if isinstance(input, str) else input.page_content, + **self._extract_keywords_kwargs, + ) + return {Link.bidir(kind=self._kind, tag=kw[0]) for kw in keywords} + + def extract_many( + self, + inputs: Iterable[KeybertInput], + ) -> Iterable[Set[Link]]: + inputs = list(inputs) + if len(inputs) == 1: + # Even though we pass a list, if it contains one item, keybert will + # flatten it. This means it's easier to just call the special case + # for one item. + yield self.extract_one(inputs[0]) + elif len(inputs) > 1: + strs = [i if isinstance(i, str) else i.page_content for i in inputs] + extracted = self._kw_model.extract_keywords( + strs, **self._extract_keywords_kwargs + ) + for keywords in extracted: + yield {Link.bidir(kind=self._kind, tag=kw[0]) for kw in keywords} diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/graph_vectorstores/extractors/link_extractor.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/graph_vectorstores/extractors/link_extractor.py new file mode 100644 index 0000000000000000000000000000000000000000..bb141dccc53cddb754fd0082a1d9c9ca1f51cd4a --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/graph_vectorstores/extractors/link_extractor.py @@ -0,0 +1,39 @@ +from __future__ import annotations + +from abc import ABC, abstractmethod +from typing import Generic, Iterable, Set, TypeVar + +from langchain_core._api import beta + +from langchain_community.graph_vectorstores import Link + +InputT = TypeVar("InputT") + +METADATA_LINKS_KEY = "links" + + +@beta() +class LinkExtractor(ABC, Generic[InputT]): + """Interface for extracting links (incoming, outgoing, bidirectional).""" + + @abstractmethod + def extract_one(self, input: InputT) -> Set[Link]: + """Add edges from each `input` to the corresponding documents. + + Args: + input: The input content to extract edges from. + + Returns: + Set of links extracted from the input. + """ + + def extract_many(self, inputs: Iterable[InputT]) -> Iterable[Set[Link]]: + """Add edges from each `input` to the corresponding documents. + + Args: + inputs: The input content to extract edges from. + + Returns: + Iterable over the set of links extracted from the input. + """ + return map(self.extract_one, inputs) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/graph_vectorstores/extractors/link_extractor_adapter.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/graph_vectorstores/extractors/link_extractor_adapter.py new file mode 100644 index 0000000000000000000000000000000000000000..73b6761eff92a117e55259341f9c80e291bc4fc5 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/graph_vectorstores/extractors/link_extractor_adapter.py @@ -0,0 +1,29 @@ +from typing import Callable, Iterable, Set, TypeVar + +from langchain_core._api import beta + +from langchain_community.graph_vectorstores import Link +from langchain_community.graph_vectorstores.extractors.link_extractor import ( + LinkExtractor, +) + +InputT = TypeVar("InputT") +UnderlyingInputT = TypeVar("UnderlyingInputT") + + +@beta() +class LinkExtractorAdapter(LinkExtractor[InputT]): + def __init__( + self, + underlying: LinkExtractor[UnderlyingInputT], + transform: Callable[[InputT], UnderlyingInputT], + ) -> None: + self._underlying = underlying + self._transform = transform + + def extract_one(self, input: InputT) -> Set[Link]: # noqa: A002 + return self._underlying.extract_one(self._transform(input)) + + def extract_many(self, inputs: Iterable[InputT]) -> Iterable[Set[Link]]: + underlying_inputs = map(self._transform, inputs) + return self._underlying.extract_many(underlying_inputs) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/graph_vectorstores/extractors/link_extractor_transformer.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/graph_vectorstores/extractors/link_extractor_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..ba78fa2100fb4bb4bb4ccaa1892b9f5f8bab42c2 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/graph_vectorstores/extractors/link_extractor_transformer.py @@ -0,0 +1,45 @@ +from typing import Any, Sequence + +from langchain_core._api import beta +from langchain_core.documents import Document +from langchain_core.documents.transformers import BaseDocumentTransformer + +from langchain_community.graph_vectorstores.extractors.link_extractor import ( + LinkExtractor, +) +from langchain_community.graph_vectorstores.links import copy_with_links + + +@beta() +class LinkExtractorTransformer(BaseDocumentTransformer): + """DocumentTransformer for applying one or more LinkExtractors. + + Example: + .. code-block:: python + + extract_links = LinkExtractorTransformer([ + HtmlLinkExtractor().as_document_extractor(), + ]) + extract_links.transform_documents(docs) + """ + + def __init__(self, link_extractors: Sequence[LinkExtractor[Document]]): + """Create a DocumentTransformer which adds extracted links to each document.""" + self.link_extractors = link_extractors + + def transform_documents( + self, documents: Sequence[Document], **kwargs: Any + ) -> Sequence[Document]: + # Implement `transform_docments` directly, so that LinkExtractors which operate + # better in batch (`extract_many`) get a chance to do so. + + # Run each extractor over all documents. + links_per_extractor = [e.extract_many(documents) for e in self.link_extractors] + + # Transpose the list of lists to pair each document with the tuple of links. + links_per_document = zip(*links_per_extractor) + + return [ + copy_with_links(document, *links) + for document, links in zip(documents, links_per_document) + ] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/graphs/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/graphs/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1b3bfef72b6e6018148853c398d11d73fc5dd7ad Binary files /dev/null and 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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 ::= "-"? 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https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.1.0.md#parameterIn +# for more info. +class APIPropertyLocation(Enum): + """The location of the property.""" + + QUERY = "query" + PATH = "path" + HEADER = "header" + COOKIE = "cookie" # Not yet supported + + @classmethod + def from_str(cls, location: str) -> "APIPropertyLocation": + """Parse an APIPropertyLocation.""" + try: + return cls(location) + except ValueError: + raise ValueError( + f"Invalid APIPropertyLocation. Valid values are {cls.__members__}" + ) + + +_SUPPORTED_MEDIA_TYPES = ("application/json",) + +SUPPORTED_LOCATIONS = { + APIPropertyLocation.HEADER, + APIPropertyLocation.QUERY, + APIPropertyLocation.PATH, +} +INVALID_LOCATION_TEMPL = ( + 'Unsupported APIPropertyLocation "{location}"' + " for parameter {name}. " + + f"Valid values are {[loc.value for loc in SUPPORTED_LOCATIONS]}" +) + +SCHEMA_TYPE = Union[str, Type, tuple, None, Enum] + + +class APIPropertyBase(BaseModel): + """Base model for an API property.""" + + # The name of the parameter is required and is case-sensitive. + # If "in" is "path", the "name" field must correspond to a template expression + # within the path field in the Paths Object. + # If "in" is "header" and the "name" field is "Accept", "Content-Type", + # or "Authorization", the parameter definition is ignored. + # For all other cases, the "name" corresponds to the parameter + # name used by the "in" property. + name: str = Field(alias="name") + """The name of the property.""" + + required: bool = Field(alias="required") + """Whether the property is required.""" + + type: SCHEMA_TYPE = Field(alias="type") + """The type of the property. + + Either a primitive type, a component/parameter type, + or an array or 'object' (dict) of the above.""" + + default: Optional[Any] = Field(alias="default", default=None) + """The default value of the property.""" + + description: Optional[str] = Field(alias="description", default=None) + """The description of the property.""" + + +if TYPE_CHECKING: + from openapi_pydantic import ( + MediaType, + Parameter, + RequestBody, + Schema, + ) + + +class APIProperty(APIPropertyBase): + """A model for a property in the query, path, header, or cookie params.""" + + location: APIPropertyLocation = Field(alias="location") + """The path/how it's being passed to the endpoint.""" + + @staticmethod + def _cast_schema_list_type( + schema: Schema, + ) -> Optional[Union[str, Tuple[str, ...]]]: + type_ = schema.type + if not isinstance(type_, list): + return type_ + else: + return tuple(type_) + + @staticmethod + def _get_schema_type_for_enum(parameter: Parameter, schema: Schema) -> Enum: + """Get the schema type when the parameter is an enum.""" + param_name = f"{parameter.name}Enum" + return Enum(param_name, {str(v): v for v in schema.enum}) + + @staticmethod + def _get_schema_type_for_array( + schema: Schema, + ) -> Optional[Union[str, Tuple[str, ...]]]: + from openapi_pydantic import ( + Reference, + Schema, + ) + + items = schema.items + if isinstance(items, Schema): + schema_type = APIProperty._cast_schema_list_type(items) + elif isinstance(items, Reference): + ref_name = items.ref.split("/")[-1] + schema_type = ref_name # TODO: Add ref definitions to make his valid + else: + raise ValueError(f"Unsupported array items: {items}") + + if isinstance(schema_type, str): + # TODO: recurse + schema_type = (schema_type,) + + return schema_type + + @staticmethod + def _get_schema_type(parameter: Parameter, schema: Optional[Schema]) -> SCHEMA_TYPE: + if schema is None: + return None + schema_type: SCHEMA_TYPE = APIProperty._cast_schema_list_type(schema) + if schema_type == "array": + schema_type = APIProperty._get_schema_type_for_array(schema) + elif schema_type == "object": + # TODO: Resolve array and object types to components. + raise NotImplementedError("Objects not yet supported") + elif schema_type in PRIMITIVE_TYPES: + if schema.enum: + schema_type = APIProperty._get_schema_type_for_enum(parameter, schema) + else: + # Directly use the primitive type + pass + else: + raise NotImplementedError(f"Unsupported type: {schema_type}") + + return schema_type + + @staticmethod + def _validate_location(location: APIPropertyLocation, name: str) -> None: + if location not in SUPPORTED_LOCATIONS: + raise NotImplementedError( + INVALID_LOCATION_TEMPL.format(location=location, name=name) + ) + + @staticmethod + def _validate_content(content: Optional[Dict[str, MediaType]]) -> None: + if content: + raise ValueError( + "API Properties with media content not supported. " + "Media content only supported within APIRequestBodyProperty's" + ) + + @staticmethod + def _get_schema(parameter: Parameter, spec: OpenAPISpec) -> Optional[Schema]: + from openapi_pydantic import ( + Reference, + Schema, + ) + + schema = parameter.param_schema + if isinstance(schema, Reference): + schema = spec.get_referenced_schema(schema) + elif schema is None: + return None + elif not isinstance(schema, Schema): + raise ValueError(f"Error dereferencing schema: {schema}") + + return schema + + @staticmethod + def is_supported_location(location: str) -> bool: + """Return whether the provided location is supported.""" + try: + return APIPropertyLocation.from_str(location) in SUPPORTED_LOCATIONS + except ValueError: + return False + + @classmethod + def from_parameter(cls, parameter: Parameter, spec: OpenAPISpec) -> "APIProperty": + """Instantiate from an OpenAPI Parameter.""" + location = APIPropertyLocation.from_str(parameter.param_in) + cls._validate_location( + location, + parameter.name, + ) + cls._validate_content(parameter.content) + schema = cls._get_schema(parameter, spec) + schema_type = cls._get_schema_type(parameter, schema) + default_val = schema.default if schema is not None else None + return cls( + name=parameter.name, + location=location, + default=default_val, + description=parameter.description, + required=parameter.required, + type=schema_type, + ) + + +class APIRequestBodyProperty(APIPropertyBase): + """A model for a request body property.""" + + properties: List["APIRequestBodyProperty"] = Field(alias="properties") + """The sub-properties of the property.""" + + # This is useful for handling nested property cycles. + # We can define separate types in that case. + references_used: List[str] = Field(alias="references_used") + """The references used by the property.""" + + @classmethod + def _process_object_schema( + cls, schema: Schema, spec: OpenAPISpec, references_used: List[str] + ) -> Tuple[Union[str, List[str], None], List["APIRequestBodyProperty"]]: + from openapi_pydantic import ( + Reference, + ) + + properties = [] + required_props = schema.required or [] + if schema.properties is None: + raise ValueError( + f"No properties found when processing object schema: {schema}" + ) + for prop_name, prop_schema in schema.properties.items(): + if isinstance(prop_schema, Reference): + ref_name = prop_schema.ref.split("/")[-1] + if ref_name not in references_used: + references_used.append(ref_name) + prop_schema = spec.get_referenced_schema(prop_schema) + else: + continue + + properties.append( + cls.from_schema( + schema=prop_schema, + name=prop_name, + required=prop_name in required_props, + spec=spec, + references_used=references_used, + ) + ) + return schema.type, properties + + @classmethod + def _process_array_schema( + cls, + schema: Schema, + name: str, + spec: OpenAPISpec, + references_used: List[str], + ) -> str: + from openapi_pydantic import Reference, Schema + + items = schema.items + if items is not None: + if isinstance(items, Reference): + ref_name = items.ref.split("/")[-1] + if ref_name not in references_used: + references_used.append(ref_name) + items = spec.get_referenced_schema(items) + else: + pass + return f"Array<{ref_name}>" + else: + pass + + if isinstance(items, Schema): + array_type = cls.from_schema( + schema=items, + name=f"{name}Item", + required=True, # TODO: Add required + spec=spec, + references_used=references_used, + ) + return f"Array<{array_type.type}>" + + return "array" + + @classmethod + def from_schema( + cls, + schema: Schema, + name: str, + required: bool, + spec: OpenAPISpec, + references_used: Optional[List[str]] = None, + ) -> "APIRequestBodyProperty": + """Recursively populate from an OpenAPI Schema.""" + if references_used is None: + references_used = [] + + schema_type = schema.type + properties: List[APIRequestBodyProperty] = [] + if schema_type == "object" and schema.properties: + schema_type, properties = cls._process_object_schema( + schema, spec, references_used + ) + elif schema_type == "array": + schema_type = cls._process_array_schema(schema, name, spec, references_used) + elif schema_type in PRIMITIVE_TYPES: + # Use the primitive type directly + pass + elif schema_type is None: + # No typing specified/parsed. WIll map to 'any' + pass + else: + raise ValueError(f"Unsupported type: {schema_type}") + + return cls( + name=name, + required=required, + type=schema_type, + default=schema.default, + description=schema.description, + properties=properties, + references_used=references_used, + ) + + +# class APIRequestBodyProperty(APIPropertyBase): +class APIRequestBody(BaseModel): + """A model for a request body.""" + + description: Optional[str] = Field(alias="description") + """The description of the request body.""" + + properties: List[APIRequestBodyProperty] = Field(alias="properties") + + # E.g., application/json - we only support JSON at the moment. + media_type: str = Field(alias="media_type") + """The media type of the request body.""" + + @classmethod + def _process_supported_media_type( + cls, + media_type_obj: MediaType, + spec: OpenAPISpec, + ) -> List[APIRequestBodyProperty]: + """Process the media type of the request body.""" + from openapi_pydantic import Reference + + references_used = [] + schema = media_type_obj.media_type_schema + if isinstance(schema, Reference): + references_used.append(schema.ref.split("/")[-1]) + schema = spec.get_referenced_schema(schema) + if schema is None: + raise ValueError( + f"Could not resolve schema for media type: {media_type_obj}" + ) + api_request_body_properties = [] + required_properties = schema.required or [] + if schema.type == "object" and schema.properties: + for prop_name, prop_schema in schema.properties.items(): + if isinstance(prop_schema, Reference): + prop_schema = spec.get_referenced_schema(prop_schema) + + api_request_body_properties.append( + APIRequestBodyProperty.from_schema( + schema=prop_schema, + name=prop_name, + required=prop_name in required_properties, + spec=spec, + ) + ) + else: + api_request_body_properties.append( + APIRequestBodyProperty( + name="body", + required=True, + type=schema.type, + default=schema.default, + description=schema.description, + properties=[], + references_used=references_used, + ) + ) + + return api_request_body_properties + + @classmethod + def from_request_body( + cls, request_body: RequestBody, spec: OpenAPISpec + ) -> "APIRequestBody": + """Instantiate from an OpenAPI RequestBody.""" + properties = [] + for media_type, media_type_obj in request_body.content.items(): + if media_type not in _SUPPORTED_MEDIA_TYPES: + continue + api_request_body_properties = cls._process_supported_media_type( + media_type_obj, + spec, + ) + properties.extend(api_request_body_properties) + + return cls( + description=request_body.description, + properties=properties, + media_type=media_type, + ) + + +# class APIRequestBodyProperty(APIPropertyBase): +# class APIRequestBody(BaseModel): +class APIOperation(BaseModel): + """A model for a single API operation.""" + + operation_id: str = Field(alias="operation_id") + """The unique identifier of the operation.""" + + description: Optional[str] = Field(alias="description") + """The description of the operation.""" + + base_url: str = Field(alias="base_url") + """The base URL of the operation.""" + + path: str = Field(alias="path") + """The path of the operation.""" + + method: HTTPVerb = Field(alias="method") + """The HTTP method of the operation.""" + + properties: Sequence[APIProperty] = Field(alias="properties") + + # TODO: Add parse in used components to be able to specify what type of + # referenced object it is. + # """The properties of the operation.""" + # components: Dict[str, BaseModel] = Field(alias="components") + + request_body: Optional[APIRequestBody] = Field(alias="request_body") + """The request body of the operation.""" + + @staticmethod + def _get_properties_from_parameters( + parameters: List[Parameter], spec: OpenAPISpec + ) -> List[APIProperty]: + """Get the properties of the operation.""" + properties = [] + for param in parameters: + if APIProperty.is_supported_location(param.param_in): + properties.append(APIProperty.from_parameter(param, spec)) + elif param.required: + raise ValueError( + INVALID_LOCATION_TEMPL.format( + location=param.param_in, name=param.name + ) + ) + else: + logger.warning( + INVALID_LOCATION_TEMPL.format( + location=param.param_in, name=param.name + ) + + " Ignoring optional parameter" + ) + pass + return properties + + @classmethod + def from_openapi_url( + cls, + spec_url: str, + path: str, + method: str, + ) -> "APIOperation": + """Create an APIOperation from an OpenAPI URL.""" + spec = OpenAPISpec.from_url(spec_url) + return cls.from_openapi_spec(spec, path, method) + + @classmethod + def from_openapi_spec( + cls, + spec: OpenAPISpec, + path: str, + method: str, + ) -> "APIOperation": + """Create an APIOperation from an OpenAPI spec.""" + operation = spec.get_operation(path, method) + parameters = spec.get_parameters_for_operation(operation) + properties = cls._get_properties_from_parameters(parameters, spec) + operation_id = OpenAPISpec.get_cleaned_operation_id(operation, path, method) + request_body = spec.get_request_body_for_operation(operation) + api_request_body = ( + APIRequestBody.from_request_body(request_body, spec) + if request_body is not None + else None + ) + description = operation.description or operation.summary + if not description and spec.paths is not None: + description = spec.paths[path].description or spec.paths[path].summary + return cls( + operation_id=operation_id, + description=description or "", + base_url=spec.base_url, + path=path, + method=method, # type: ignore[arg-type] + properties=properties, + request_body=api_request_body, + ) + + @staticmethod + def ts_type_from_python(type_: SCHEMA_TYPE) -> str: + if type_ is None: + # TODO: Handle Nones better. These often result when + # parsing specs that are < v3 + return "any" + elif isinstance(type_, str): + return { + "str": "string", + "integer": "number", + "float": "number", + "date-time": "string", + }.get(type_, type_) + elif isinstance(type_, tuple): + return f"Array<{APIOperation.ts_type_from_python(type_[0])}>" + elif isinstance(type_, type) and issubclass(type_, Enum): + return " | ".join([f"'{e.value}'" for e in type_]) + else: + return str(type_) + + def _format_nested_properties( + self, properties: List[APIRequestBodyProperty], indent: int = 2 + ) -> str: + """Format nested properties.""" + formatted_props = [] + + for prop in properties: + prop_name = prop.name + prop_type = self.ts_type_from_python(prop.type) + prop_required = "" if prop.required else "?" + prop_desc = f"/* {prop.description} */" if prop.description else "" + + if prop.properties: + nested_props = self._format_nested_properties( + prop.properties, indent + 2 + ) + prop_type = f"{{\n{nested_props}\n{' ' * indent}}}" + + formatted_props.append( + f"{prop_desc}\n{' ' * indent}{prop_name}{prop_required}: {prop_type}," + ) + + return "\n".join(formatted_props) + + def to_typescript(self) -> str: + """Get typescript string representation of the operation.""" + operation_name = self.operation_id + params = [] + + if self.request_body: + formatted_request_body_props = self._format_nested_properties( + self.request_body.properties + ) + params.append(formatted_request_body_props) + + for prop in self.properties: + prop_name = prop.name + prop_type = self.ts_type_from_python(prop.type) + prop_required = "" if prop.required else "?" + prop_desc = f"/* {prop.description} */" if prop.description else "" + params.append(f"{prop_desc}\n\t\t{prop_name}{prop_required}: {prop_type},") + + formatted_params = "\n".join(params).strip() + description_str = f"/* {self.description} */" if self.description else "" + typescript_definition = f""" +{description_str} +type {operation_name} = (_: {{ +{formatted_params} +}}) => any; +""" + return typescript_definition.strip() + + @property + def query_params(self) -> List[str]: + return [ + property.name + for property in self.properties + if property.location == APIPropertyLocation.QUERY + ] + + @property + def path_params(self) -> List[str]: + return [ + property.name + for property in self.properties + if property.location == APIPropertyLocation.PATH + ] + + @property + def body_params(self) -> List[str]: + if self.request_body is None: + return [] + return [prop.name for prop in self.request_body.properties] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/tools/openapi/utils/openapi_utils.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/tools/openapi/utils/openapi_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..7ed0ade18d09bb3bc55d2e7379fb4ba58445c319 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/tools/openapi/utils/openapi_utils.py @@ -0,0 +1,5 @@ +"""Utility functions for parsing an OpenAPI spec. Kept for backwards compat.""" + +from langchain_community.utilities.openapi import HTTPVerb, OpenAPISpec + +__all__ = ["HTTPVerb", "OpenAPISpec"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/tools/openweathermap/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/tools/openweathermap/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f88b8d5f8a257a1a03a7cdc152709ff2b8ead092 Binary files /dev/null and b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/tools/openweathermap/__pycache__/__init__.cpython-311.pyc differ diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/tools/openweathermap/__pycache__/tool.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/langchain_community/tools/openweathermap/__pycache__/tool.cpython-311.pyc new file mode 100644 index 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