id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
28,630 | from __future__ import annotations
from datetime import datetime
from pathlib import Path
from typing import TYPE_CHECKING, Iterator, List, Optional, Union
from langchain_core.chat_sessions import ChatSession
from langchain_core.messages import HumanMessage
from langchain_community.chat_loaders.base import BaseChatLoad... | null |
28,631 | from __future__ import annotations
import importlib
from typing import (
Any,
AsyncIterator,
Dict,
Iterable,
List,
Mapping,
Sequence,
Union,
overload,
)
from langchain_core.chat_sessions import ChatSession
from langchain_core.messages import (
AIMessage,
AIMessageChunk,
B... | Async version of enumerate function. |
28,632 | from __future__ import annotations
import importlib
from typing import (
Any,
AsyncIterator,
Dict,
Iterable,
List,
Mapping,
Sequence,
Union,
overload,
)
from langchain_core.chat_sessions import ChatSession
from langchain_core.messages import (
AIMessage,
AIMessageChunk,
B... | Convert dictionaries representing OpenAI messages to LangChain format. Args: messages: List of dictionaries representing OpenAI messages Returns: List of LangChain BaseMessage objects. |
28,633 | from __future__ import annotations
import importlib
from typing import (
Any,
AsyncIterator,
Dict,
Iterable,
List,
Mapping,
Sequence,
Union,
overload,
)
from langchain_core.chat_sessions import ChatSession
from langchain_core.messages import (
AIMessage,
AIMessageChunk,
B... | null |
28,634 | from __future__ import annotations
import importlib
from typing import (
Any,
AsyncIterator,
Dict,
Iterable,
List,
Mapping,
Sequence,
Union,
overload,
)
from langchain_core.chat_sessions import ChatSession
from langchain_core.messages import (
AIMessage,
AIMessageChunk,
B... | Convert messages to a list of lists of dictionaries for fine-tuning. Args: sessions: The chat sessions. Returns: The list of lists of dictionaries. |
28,635 | import os
from math import ceil
from typing import Any, Dict, List, Optional
The provided code snippet includes necessary dependencies for implementing the `get_arangodb_client` function. Write a Python function `def get_arangodb_client( url: Optional[str] = None, dbname: Optional[str] = None, username: Op... | Get the Arango DB client from credentials. Args: url: Arango DB url. Can be passed in as named arg or set as environment var ``ARANGODB_URL``. Defaults to "http://localhost:8529". dbname: Arango DB name. Can be passed in as named arg or set as environment var ``ARANGODB_DBNAME``. Defaults to "_system". username: Can be... |
28,636 | from __future__ import annotations
from typing import Any, List, NamedTuple, Optional, Tuple
KG_TRIPLE_DELIMITER = "<|>"
class KnowledgeTriple(NamedTuple):
"""A triple in the graph."""
subject: str
predicate: str
object_: str
def from_string(cls, triple_string: str) -> "KnowledgeTriple":
"""... | Parse knowledge triples from the knowledge string. |
28,637 | from __future__ import annotations
from typing import Any, List, NamedTuple, Optional, Tuple
The provided code snippet includes necessary dependencies for implementing the `get_entities` function. Write a Python function `def get_entities(entity_str: str) -> List[str]` to solve the following problem:
Extract entities ... | Extract entities from entity string. |
28,638 | from hashlib import md5
from typing import Any, Dict, List, Optional
from langchain_core.utils import get_from_dict_or_env
from langchain_community.graphs.graph_document import GraphDocument
from langchain_community.graphs.graph_store import GraphStore
The provided code snippet includes necessary dependencies for impl... | Sanitize the input dictionary or list. Sanitizes the input by removing embedding-like values, lists with more than 128 elements, that are mostly irrelevant for generating answers in a LLM context. These properties, if left in results, can occupy significant context space and detract from the LLM's performance by introd... |
28,639 | from hashlib import md5
from typing import Any, Dict, List, Optional
from langchain_core.utils import get_from_dict_or_env
from langchain_community.graphs.graph_document import GraphDocument
from langchain_community.graphs.graph_store import GraphStore
BASE_ENTITY_LABEL = "__Entity__"
include_docs_query = (
"MERGE ... | null |
28,640 | from hashlib import md5
from typing import Any, Dict, List, Optional
from langchain_core.utils import get_from_dict_or_env
from langchain_community.graphs.graph_document import GraphDocument
from langchain_community.graphs.graph_store import GraphStore
BASE_ENTITY_LABEL = "__Entity__"
def _get_rel_import_query(baseEnt... | null |
28,641 | import os
import tempfile
from urllib.parse import urlparse
import requests
The provided code snippet includes necessary dependencies for implementing the `detect_file_src_type` function. Write a Python function `def detect_file_src_type(file_path: str) -> str` to solve the following problem:
Detect if the file is loc... | Detect if the file is local or remote. |
28,642 | import os
import tempfile
from urllib.parse import urlparse
import requests
The provided code snippet includes necessary dependencies for implementing the `download_audio_from_url` function. Write a Python function `def download_audio_from_url(audio_url: str) -> str` to solve the following problem:
Download audio from... | Download audio from url to local. |
28,643 | from __future__ import annotations
import logging
import os
from typing import TYPE_CHECKING
logger = logging.getLogger(__name__)
The provided code snippet includes necessary dependencies for implementing the `authenticate` function. Write a Python function `def authenticate() -> Client` to solve the following problem... | Authenticate using the Amadeus API |
28,644 | import json
from typing import Any, Dict, Optional, Union
from langchain_core.pydantic_v1 import BaseModel
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain_community.utilities.requests import GenericRequestsWrapper
from langchain_core.tools impor... | Parse the json string into a dict. |
28,645 | import json
from typing import Any, Dict, Optional, Union
from langchain_core.pydantic_v1 import BaseModel
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain_community.utilities.requests import GenericRequestsWrapper
from langchain_core.tools impor... | Strips quotes from the url. |
28,646 | from __future__ import annotations
import json
from typing import Optional, Type
import requests
import yaml
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain_core.pydantic_v1 import BaseModel
from langchain_core.tools import BaseTool
The provide... | Convert the yaml or json serialized spec to a dict. Args: txt: The yaml or json serialized spec. Returns: dict: The spec as a dict. |
28,647 | import ast
import io
import sys
import tokenize
The provided code snippet includes necessary dependencies for implementing the `interleave` function. Write a Python function `def interleave(inter, f, seq)` to solve the following problem:
Call f on each item in seq, calling inter() in between.
Here is the function:
d... | Call f on each item in seq, calling inter() in between. |
28,648 | import ast
import io
import sys
import tokenize
class Unparser:
"""Methods in this class recursively traverse an AST and
output source code for the abstract syntax; original formatting
is disregarded."""
def __init__(self, tree, file=sys.stdout):
"""Unparser(tree, file=sys.stdout) -> None.
... | Parse a file and pretty-print it to output. The output is formatted as valid Python source code. Args: filename: The name of the file to parse. output: The output stream to write to. |
28,649 | from __future__ import annotations
import ast
import json
import os
from io import StringIO
from sys import version_info
from typing import IO, TYPE_CHECKING, Any, Callable, List, Optional, Type, Union
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManager,
CallbackManagerFor... | Add print statement to the last line if it's missing. Sometimes, the LLM-generated code doesn't have `print(variable_name)`, instead the LLM tries to print the variable only by writing `variable_name` (as you would in REPL, for example). This methods checks the AST of the generated Python code and adds the print statem... |
28,650 | from __future__ import annotations
import tempfile
from typing import TYPE_CHECKING, Any, Optional
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.tools import BaseTool
from langchain_community.utilities.vertexai import get_client_info
def _import_google_cloud_texttospeech() -> Any:
... | null |
28,651 | import json
from typing import Any, Dict, Optional
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain_core.language_models import BaseLanguageModel
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_core.tools import BaseTool
fr... | null |
28,652 | from __future__ import annotations
import logging
import os
from typing import TYPE_CHECKING, List, Optional, Tuple
def import_googleapiclient_resource_builder() -> build_resource:
"""Import googleapiclient.discovery.build function.
Returns:
build_resource: googleapiclient.discovery.build function.
... | Build a Gmail service. |
28,653 | from __future__ import annotations
import logging
import os
from typing import TYPE_CHECKING, List, Optional, Tuple
logger = logging.getLogger(__name__)
The provided code snippet includes necessary dependencies for implementing the `clean_email_body` function. Write a Python function `def clean_email_body(body: str) -... | Clean email body. |
28,654 | from __future__ import annotations
import uuid
from typing import TYPE_CHECKING
The provided code snippet includes necessary dependencies for implementing the `make_image_public` function. Write a Python function `def make_image_public(client: Steamship, block: Block) -> str` to solve the following problem:
Upload a b... | Upload a block to a signed URL and return the public URL. |
28,655 | from __future__ import annotations
import asyncio
from typing import TYPE_CHECKING, Any, Coroutine, List, Optional, TypeVar
The provided code snippet includes necessary dependencies for implementing the `aget_current_page` function. Write a Python function `async def aget_current_page(browser: AsyncBrowser) -> AsyncPa... | Asynchronously get the current page of the browser. Args: browser: The browser (AsyncBrowser) to get the current page from. Returns: AsyncPage: The current page. |
28,656 | from __future__ import annotations
import asyncio
from typing import TYPE_CHECKING, Any, Coroutine, List, Optional, TypeVar
The provided code snippet includes necessary dependencies for implementing the `get_current_page` function. Write a Python function `def get_current_page(browser: SyncBrowser) -> SyncPage` to sol... | Get the current page of the browser. Args: browser: The browser to get the current page from. Returns: SyncPage: The current page. |
28,657 | from __future__ import annotations
import asyncio
from typing import TYPE_CHECKING, Any, Coroutine, List, Optional, TypeVar
def run_async(coro: Coroutine[Any, Any, T]) -> T:
"""Run an async coroutine.
Args:
coro: The coroutine to run. Coroutine[Any, Any, T]
Returns:
T: The result of the coro... | Create an async playwright browser. Args: headless: Whether to run the browser in headless mode. Defaults to True. args: arguments to pass to browser.chromium.launch Returns: AsyncBrowser: The playwright browser. |
28,658 | from __future__ import annotations
import asyncio
from typing import TYPE_CHECKING, Any, Coroutine, List, Optional, TypeVar
The provided code snippet includes necessary dependencies for implementing the `create_sync_playwright_browser` function. Write a Python function `def create_sync_playwright_browser( headless... | Create a playwright browser. Args: headless: Whether to run the browser in headless mode. Defaults to True. args: arguments to pass to browser.chromium.launch Returns: SyncBrowser: The playwright browser. |
28,659 | from __future__ import annotations
import json
from typing import TYPE_CHECKING, List, Optional, Sequence, Type
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_community.tools.playwright.ba... | Get elements matching the given CSS selector. |
28,660 | from __future__ import annotations
import json
from typing import TYPE_CHECKING, List, Optional, Sequence, Type
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_community.tools.playwright.ba... | Get elements matching the given CSS selector. |
28,661 | from __future__ import annotations
from typing import TYPE_CHECKING, Optional, Tuple, Type
from langchain_core.pydantic_v1 import root_validator
from langchain_core.tools import BaseTool
The provided code snippet includes necessary dependencies for implementing the `lazy_import_playwright_browsers` function. Write a P... | Lazy import playwright browsers. Returns: Tuple[Type[AsyncBrowser], Type[SyncBrowser]]: AsyncBrowser and SyncBrowser classes. |
28,662 | from typing import Callable, Optional
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.pydantic_v1 import Field
from langchain_core.tools import BaseTool
def _print_func(text: str) -> None:
print("\n") # noqa: T201
print(text) # noqa: T201 | null |
28,663 | import logging
import platform
import warnings
from typing import Any, List, Optional, Type, Union
from langchain_core.callbacks import (
CallbackManagerForToolRun,
)
from langchain_core.pydantic_v1 import BaseModel, Field, root_validator
from langchain_core.tools import BaseTool
class BashProcess:
"""Wrapper ... | Get default bash process. |
28,664 | import logging
import platform
import warnings
from typing import Any, List, Optional, Type, Union
from langchain_core.callbacks import (
CallbackManagerForToolRun,
)
from langchain_core.pydantic_v1 import BaseModel, Field, root_validator
from langchain_core.tools import BaseTool
The provided code snippet includes... | Get platform. |
28,665 | import warnings
from typing import Any
from langchain_community.tools.human.tool import HumanInputRun
class HumanInputRun(BaseTool):
"""Tool that asks user for input."""
name: str = "human"
description: str = (
"You can ask a human for guidance when you think you "
"got stuck or you are no... | Tool for asking the user for input. |
28,666 | from __future__ import annotations
import json
import re
from pathlib import Path
from typing import Dict, List, Optional, Union
from langchain_core.pydantic_v1 import BaseModel
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain_core.tools import B... | Parse input of the form data["key1"][0]["key2"] into a list of keys. |
28,667 | import tempfile
from enum import Enum
from typing import Any, Dict, Optional, Union
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.pydantic_v1 import root_validator
from langchain_core.tools import BaseTool
from langchain_core.utils import get_from_dict_or_env
def _import_elevenlabs... | null |
28,668 | from __future__ import annotations
import logging
import os
from typing import TYPE_CHECKING
The provided code snippet includes necessary dependencies for implementing the `clean_body` function. Write a Python function `def clean_body(body: str) -> str` to solve the following problem:
Clean body of a message or event.... | Clean body of a message or event. |
28,669 | from __future__ import annotations
import logging
import os
from typing import TYPE_CHECKING
logger = logging.getLogger(__name__)
The provided code snippet includes necessary dependencies for implementing the `authenticate` function. Write a Python function `def authenticate() -> Account` to solve the following proble... | Authenticate using the Microsoft Grah API |
28,670 | from __future__ import annotations
import logging
import os
from typing import TYPE_CHECKING
logger = logging.getLogger(__name__)
The provided code snippet includes necessary dependencies for implementing the `login` function. Write a Python function `def login() -> WebClient` to solve the following problem:
Authentic... | Authenticate using the Slack API. |
28,671 | import warnings
from typing import Any, Optional, Type
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_core.tools import BaseTool
from langchain_community.utilities.duckduckgo_search import DuckDuckGoSearchAPIWrapper
class DuckDuckGoS... | Deprecated. Use DuckDuckGoSearchRun instead. Args: *args: **kwargs: Returns: DuckDuckGoSearchRun |
28,672 | import sys
from pathlib import Path
from typing import Optional
from langchain_core.pydantic_v1 import BaseModel
def is_relative_to(path: Path, root: Path) -> bool:
"""Check if path is relative to root."""
if sys.version_info >= (3, 9):
# No need for a try/except block in Python 3.8+.
return pat... | Resolve a relative path, raising an error if not within the root directory. |
28,673 | from __future__ import annotations
import os
from typing import TYPE_CHECKING, Literal, Optional
The provided code snippet includes necessary dependencies for implementing the `authenticate` function. Write a Python function `def authenticate(network: Optional[Literal["mainnet", "testnet"]] = "testnet") -> Ain` to sol... | Authenticate using the AIN Blockchain |
28,674 | import base64
import itertools
import json
import re
from pathlib import Path
from typing import Dict, List, Type
import requests
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_community.tools import Tool
The provided code snippet includes necessary dependencies for implementing the `strip_mark... | Strip markdown code from a string. |
28,675 | import base64
import itertools
import json
import re
from pathlib import Path
from typing import Dict, List, Type
import requests
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_community.tools import Tool
The provided code snippet includes necessary dependencies for implementing the `head_file`... | Get the first n lines of a file. |
28,676 | import base64
import itertools
import json
import re
from pathlib import Path
from typing import Dict, List, Type
import requests
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_community.tools import Tool
The provided code snippet includes necessary dependencies for implementing the `file_to_ba... | Convert a file to base64. |
28,677 | import logging
from typing import List, Optional, Tuple, Union
import numpy as np
Matrix = Union[List[List[float]], List[np.ndarray], np.ndarray]
def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray:
"""Row-wise cosine similarity between two equal-width matrices."""
if len(X) == 0 or len(Y) == 0:
r... | Row-wise cosine similarity with optional top-k and score threshold filtering. Args: X: Matrix. Y: Matrix, same width as X. top_k: Max number of results to return. score_threshold: Minimum cosine similarity of results. Returns: Tuple of two lists. First contains two-tuples of indices (X_idx, Y_idx), second contains corr... |
28,678 | from importlib import metadata
from typing import Any, Optional
The provided code snippet includes necessary dependencies for implementing the `get_client_info` function. Write a Python function `def get_client_info(module: Optional[str] = None) -> Any` to solve the following problem:
r"""Returns a custom user agent h... | r"""Returns a custom user agent header. Args: module (Optional[str]): Optional. The module for a custom user agent header. Returns: google.api_core.gapic_v1.client_info.ClientInfo |
28,679 | from typing import Literal, Optional, Type, TypedDict
from langchain_core.pydantic_v1 import BaseModel
from langchain_core.utils.json_schema import dereference_refs
class ToolDescription(TypedDict):
"""Representation of a callable function to the Ernie API."""
type: Literal["function"]
function: FunctionDes... | Converts a Pydantic model to a function description for the Ernie API. |
28,680 | import os
from pathlib import Path
import pypdfium2 as pdfium
from langchain_community.vectorstores import Chroma
from langchain_experimental.open_clip import OpenCLIPEmbeddings
The provided code snippet includes necessary dependencies for implementing the `get_images_from_pdf` function. Write a Python function `def g... | Extract images from each page of a PDF document and save as JPEG files. :param pdf_path: A string representing the path to the PDF file. :param img_dump_path: A string representing the path to dummp images. |
28,681 | import base64
import io
from pathlib import Path
from langchain_community.chat_models import ChatOpenAI
from langchain_community.vectorstores import Chroma
from langchain_core.documents import Document
from langchain_core.messages import HumanMessage
from langchain_core.output_parsers import StrOutputParser
from langch... | Multi-modal RAG chain, :param retriever: A function that retrieves the necessary context for the model. :return: A chain of functions representing the multi-modal RAG process. |
28,682 | import json
from langchain_core.pydantic_v1 import BaseModel, Field, conint
class LLMPlateResponse(BaseModel):
row_start: conint(ge=0) = Field(
..., description="The starting row of the plate (0-indexed)"
)
row_end: conint(ge=0) = Field(
..., description="The ending row of the plate (0-index... | Based on the prompt we expect the result to be a string that looks like: '[{"row_start": 12, "row_end": 19, "col_start": 1, \ "col_end": 12, "contents": "Entity ID"}]' We'll load that JSON and turn it into a Pydantic model |
28,683 | import base64
import json
from langchain_community.chat_models import ChatOpenAI
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate, SystemMessagePromptTemplate
from langchain_core.pydantic_v1 import Field
from langserve import CustomUserType
from .prompts im... | null |
28,684 | import base64
import json
from langchain_community.chat_models import ChatOpenAI
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate, SystemMessagePromptTemplate
from langchain_core.pydantic_v1 import Field
from langserve import CustomUserType
from .prompts im... | null |
28,685 | import base64
import json
from langchain_community.chat_models import ChatOpenAI
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate, SystemMessagePromptTemplate
from langchain_core.pydantic_v1 import Field
from langserve import CustomUserType
from .prompts im... | null |
28,686 | import base64
import json
from langchain_community.chat_models import ChatOpenAI
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate, SystemMessagePromptTemplate
from langchain_core.pydantic_v1 import Field
from langserve import CustomUserType
from .prompts im... | null |
28,687 | from langchain.utilities import DuckDuckGoSearchAPIWrapper
from langchain_community.chat_models import ChatOpenAI
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel
from langchain_core.runnables import Runnable... | null |
28,688 | from langchain.utilities import DuckDuckGoSearchAPIWrapper
from langchain_community.chat_models import ChatOpenAI
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel
from langchain_core.runnables import Runnable... | null |
28,689 | from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
class AgentAction(Serializable):
"""A full description of an action for an ActionAgent to execute."""
tool: str
"""The name of the Tool to execute."""
tool_input: Union... | null |
28,690 | from typing import List, Tuple
from langchain.agents import AgentExecutor
from langchain.agents.format_scratchpad import format_xml
from langchain.tools import DuckDuckGoSearchRun
from langchain.tools.render import render_text_description
from langchain_community.chat_models import ChatAnthropic
from langchain_core.mes... | null |
28,691 | import os
from langchain_community.chat_models import ChatOpenAI
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores import MongoDBAtlasVectorSearch
from langchain_core.documents import Document
from langchain_core.output_parsers import StrOutputParser
from langchain_core.p... | null |
28,692 | import os
import uuid
from langchain_community.document_loaders import PyPDFLoader
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores import MongoDBAtlasVectorSearch
from langchain_text_splitters import RecursiveCharacterTextSplitter
from pymongo import MongoClient
PARENT_... | null |
28,693 | import os
BASE_DIR = os.path.abspath(os.path.dirname(__file__))
def populate(vector_store):
# is the store empty? find out with a probe search
hits = vector_store.similarity_search_by_vector(
embedding=[0.001] * 1536,
k=1,
)
#
if len(hits) == 0:
# this seems a first run:
... | null |
28,694 | import os
from cassandra.auth import PlainTextAuthProvider
from cassandra.cluster import Cluster
def get_cassandra_connection():
contact_points = [
cp.strip()
for cp in os.environ.get("CASSANDRA_CONTACT_POINTS", "").split(",")
if cp.strip()
]
CASSANDRA_KEYSPACE = os.environ["CASSAND... | null |
28,695 | from langchain_community.chat_models import ChatOpenAI
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel
from langchain_core.runnables import RunnablePassthrough
def parse_numbered_list(input_str):
"""Pars... | null |
28,696 | from langchain_community.chat_models import ChatOpenAI
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel
from langchain_core.runnables import RunnablePassthrough
def get_final_answer(expanded_list):
final... | null |
28,698 | import os
def get_boolean_env_var(var_name, default_value=False):
"""Retrieve the boolean value of an environment variable.
Args:
var_name (str): The name of the environment variable to retrieve.
default_value (bool): The default value to return if the variable
is not found.
Returns:
bool: T... | null |
28,699 | import os
from langchain_community.document_loaders import UnstructuredFileLoader
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Redis
from langchain_text_splitters import RecursiveCharacterTextSplitter
from rag_redis.config import EMBED_MODEL, INDEX_NAME, ... | Ingest PDF to Redis from the data/ directory that contains Edgar 10k filings data for Nike. |
28,700 | from langchain import hub
from langchain_community.chat_models import ChatAnthropic
from langchain_community.utilities import WikipediaAPIWrapper
from langchain_core.output_parsers import StrOutputParser
from langchain_core.pydantic_v1 import BaseModel
from langchain_core.runnables import RunnableLambda, RunnablePassth... | null |
28,701 | from typing import Optional, Type
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.pydantic_v1 import BaseModel, Field
from langchain.tools import BaseTool
from neo4j_semantic_ollama.utils import get_candidates, get_user_id, graph
recommendati... | Recommends movies based on user's history and preference for a specific movie and/or genre. Returns: str: A string containing a list of recommended movies, or an error message. |
28,702 | from typing import Optional, Type
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.pydantic_v1 import BaseModel, Field
from langchain.tools import BaseTool
from neo4j_semantic_ollama.utils import get_candidates, graph
description_query = """
M... | null |
28,703 | import os
from typing import List, Tuple
from langchain.agents import AgentExecutor
from langchain.agents.format_scratchpad import format_log_to_messages
from langchain.agents.output_parsers import (
ReActJsonSingleInputOutputParser,
)
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
from langc... | null |
28,704 | from typing import Optional, Type
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.pydantic_v1 import BaseModel, Field
from langchain.tools import BaseTool
from neo4j_semantic_ollama.utils import get_candidates, get_user_id, graph
store_rating... | null |
28,705 | import getpass
import os
from langchain_community.document_loaders import PyPDFLoader
from langchain_community.vectorstores import Milvus
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel
from langchain_core.r... | Load and ingest the PDF file from the URL |
28,706 | import os
from langchain_community.chat_models import ChatOpenAI
from langchain_community.document_loaders import PyPDFLoader
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores import MongoDBAtlasVectorSearch
from langchain_core.output_parsers import StrOutputParser
from l... | null |
28,707 | import os
from operator import itemgetter
from typing import List, Tuple
from langchain_community.chat_models import ChatOpenAI
from langchain_community.vectorstores.zep import CollectionConfig, ZepVectorStore
from langchain_core.documents import Document
from langchain_core.messages import AIMessage, BaseMessage, Huma... | null |
28,708 | import os
from operator import itemgetter
from typing import List, Tuple
from langchain_community.chat_models import ChatOpenAI
from langchain_community.vectorstores.zep import CollectionConfig, ZepVectorStore
from langchain_core.documents import Document
from langchain_core.messages import AIMessage, BaseMessage, Huma... | null |
28,709 | from typing import List, Tuple
from langchain.agents import AgentExecutor
from langchain.agents.format_scratchpad import format_to_openai_function_messages
from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
from langchain.utilities.tavily_search import TavilySearchAPIWrapper
from langchain_com... | null |
28,710 | from langchain_core.agents import AgentAction, AgentFinish
class AgentAction(Serializable):
"""A full description of an action for an ActionAgent to execute."""
tool: str
"""The name of the Tool to execute."""
tool_input: Union[str, dict]
"""The input to pass in to the Tool."""
log: str
""... | null |
28,711 | import base64
import io
from pathlib import Path
from langchain_community.chat_models import ChatOllama
from langchain_community.vectorstores import Chroma
from langchain_core.documents import Document
from langchain_core.messages import HumanMessage
from langchain_core.output_parsers import StrOutputParser
from langch... | Resize an image encoded as a Base64 string. :param base64_string: A Base64 encoded string of the image to be resized. :param size: A tuple representing the new size (width, height) for the image. :return: A Base64 encoded string of the resized image. |
28,712 | import base64
import io
from pathlib import Path
from langchain_community.chat_models import ChatOllama
from langchain_community.vectorstores import Chroma
from langchain_core.documents import Document
from langchain_core.messages import HumanMessage
from langchain_core.output_parsers import StrOutputParser
from langch... | Multi-modal RAG chain, :param retriever: A function that retrieves the necessary context for the model. :return: A chain of functions representing the multi-modal RAG process. |
28,713 | from operator import itemgetter
import numpy as np
from langchain.retrievers import (
ArxivRetriever,
KayAiRetriever,
PubMedRetriever,
WikipediaRetriever,
)
from langchain.utils.math import cosine_similarity
from langchain_community.chat_models import ChatOpenAI
from langchain_community.embeddings impor... | null |
28,714 | from operator import itemgetter
import numpy as np
from langchain.retrievers import (
ArxivRetriever,
KayAiRetriever,
PubMedRetriever,
WikipediaRetriever,
)
from langchain.utils.math import cosine_similarity
from langchain_community.chat_models import ChatOpenAI
from langchain_community.embeddings impor... | null |
28,715 | from typing import List, Optional
from langchain_community.graphs import Neo4jGraph
from langchain_core.documents import Document
from langchain_experimental.graph_transformers import LLMGraphTransformer
from langchain_openai import ChatOpenAI
graph = Neo4jGraph()
llm = ChatOpenAI(model="gpt-3.5-turbo-16k", temperature... | Process the given text to extract graph data and constructs a graph document from the extracted information. The constructed graph document is then added to the graph. Parameters: - text (str): The input text from which the information will be extracted to construct the graph. - allowed_nodes (Optional[List[str]]): A l... |
28,716 | import os
import re
import subprocess
import tempfile
from langchain.agents import AgentType, initialize_agent
from langchain.agents.tools import Tool
from langchain.pydantic_v1 import BaseModel, Field, ValidationError, validator
from langchain_community.chat_models import ChatOpenAI
from langchain_core.language_model... | null |
28,717 | import os
import re
import subprocess
import tempfile
from langchain.agents import AgentType, initialize_agent
from langchain.agents.tools import Tool
from langchain.pydantic_v1 import BaseModel, Field, ValidationError, validator
from langchain_community.chat_models import ChatOpenAI
from langchain_core.language_model... | Format a file with black. |
28,718 | import os
import re
import subprocess
import tempfile
from langchain.agents import AgentType, initialize_agent
from langchain.agents.tools import Tool
from langchain.pydantic_v1 import BaseModel, Field, ValidationError, validator
from langchain_community.chat_models import ChatOpenAI
from langchain_core.language_model... | Run ruff format on a file. |
28,719 | import os
import re
import subprocess
import tempfile
from langchain.agents import AgentType, initialize_agent
from langchain.agents.tools import Tool
from langchain.pydantic_v1 import BaseModel, Field, ValidationError, validator
from langchain_community.chat_models import ChatOpenAI
from langchain_core.language_model... | Run ruff check on a file. |
28,720 | import os
import re
import subprocess
import tempfile
from langchain.agents import AgentType, initialize_agent
from langchain.agents.tools import Tool
from langchain.pydantic_v1 import BaseModel, Field, ValidationError, validator
from langchain_community.chat_models import ChatOpenAI
from langchain_core.language_model... | Run mypy on a file. |
28,721 | import os
import re
import subprocess
import tempfile
from langchain.agents import AgentType, initialize_agent
from langchain.agents.tools import Tool
from langchain.pydantic_v1 import BaseModel, Field, ValidationError, validator
from langchain_community.chat_models import ChatOpenAI
from langchain_core.language_model... | null |
28,722 | import os
import re
import subprocess
import tempfile
from langchain.agents import AgentType, initialize_agent
from langchain.agents.tools import Tool
from langchain.pydantic_v1 import BaseModel, Field, ValidationError, validator
from langchain_community.chat_models import ChatOpenAI
from langchain_core.language_model... | null |
28,723 | import logging
import uuid
from typing import Sequence
from bs4 import BeautifulSoup as Soup
from langchain_core.documents import Document
from langchain_core.runnables import Runnable
from propositional_retrieval.constants import DOCSTORE_ID_KEY
from propositional_retrieval.proposal_chain import proposition_chain
from... | Create retriever that indexes docs and their propositions :param docs: Documents to index :param indexer: Runnable creates additional propositions per doc :param docstore_id_key: Key to use to store the docstore id :return: Retriever |
28,724 | from langchain_community.chat_models import ChatOpenAI
from langchain_core.load import load
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel
from langchain_core.runnables import RunnablePassthrough
from propo... | The RAG chain :param retriever: A function that retrieves the necessary context for the model. :return: A chain of functions representing the multi-modal RAG process. |
28,725 | import logging
from langchain.output_parsers.openai_tools import JsonOutputToolsParser
from langchain_community.chat_models import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnableLambda
def get_propositions(tool_calls: list) -> list:
if not tool_calls:
... | null |
28,726 | import logging
from langchain.output_parsers.openai_tools import JsonOutputToolsParser
from langchain_community.chat_models import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnableLambda
def empty_proposals(x):
# Model couldn't generate proposals
ret... | null |
28,728 | import os
from operator import itemgetter
from typing import List, Tuple
from langchain_community.chat_models import ChatOpenAI
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_core.messages import AIMessage, HumanMessage
from langchain_core.output_parsers import StrOutputParser
from langchain... | null |
28,729 | import os
from operator import itemgetter
from typing import List, Tuple
from langchain_community.chat_models import ChatOpenAI
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_core.messages import AIMessage, HumanMessage
from langchain_core.output_parsers import StrOutputParser
from langchain... | null |
28,730 | import os
from typing import List, Tuple
from langchain.agents import AgentExecutor
from langchain.agents.format_scratchpad import format_to_openai_function_messages
from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
from langchain_community.chat_models import ChatOpenAI
from langchain_communi... | A search engine optimized for comprehensive, accurate, \ and trusted results. Useful for when you need to answer questions \ about current events or about recent information. \ Input should be a search query. \ If the user is asking about something that you don't know about, \ you should probably use this tool to see i... |
28,731 | import os
from typing import List, Tuple
from langchain.agents import AgentExecutor
from langchain.agents.format_scratchpad import format_to_openai_function_messages
from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
from langchain_community.chat_models import ChatOpenAI
from langchain_communi... | null |
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