id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
28,330 | import re
from abc import ABC, abstractmethod
from typing import (
Any,
Callable,
Dict,
List,
Literal,
Optional,
Sequence,
Union,
)
from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
from langchain_core.pydantic_v1 import (
... | Combine a ResultItem title and excerpt into a single string. Args: item: the ResultItem of a Kendra search. Returns: A combined text of the title and excerpt of the given item. |
28,331 | import warnings
from typing import Any, Dict, List, Optional
from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import root_validator
from langchain_core.retrievers import Base... | Deprecated ZillizRetreiver. Please use ZillizRetriever ('i' before 'e') instead. Args: *args: **kwargs: Returns: ZillizRetriever |
28,332 | from __future__ import annotations
from typing import TYPE_CHECKING, Any, Dict, List
from langchain_core.callbacks import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain_core.documents import Document
from langchain_core.language_models.chat_models import BaseChatModel
fr... | null |
28,333 | from __future__ import annotations
import concurrent.futures
from typing import Any, Iterable, List, Optional
import numpy as np
from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.retrieve... | Create an index of embeddings for a list of contexts. Args: contexts: List of contexts to embed. embeddings: Embeddings model to use. Returns: Index of embeddings. |
28,334 | import warnings
from typing import Any, Dict, List, Optional
from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import root_validator
from langchain_core.retrievers import Base... | Deprecated MilvusRetreiver. Please use MilvusRetriever ('i' before 'e') instead. Args: *args: **kwargs: Returns: MilvusRetriever |
28,335 | from __future__ import annotations
import concurrent.futures
from typing import Any, List, Optional
import numpy as np
from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.retrievers import ... | Create an index of embeddings for a list of contexts. Args: contexts: List of contexts to embed. embeddings: Embeddings model to use. Returns: Index of embeddings. |
28,336 | import hashlib
from typing import Any, Dict, List, Optional
from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import Extra, root_validator
from langchain_core.retrievers impor... | Create an index from a list of contexts. It modifies the index argument in-place! Args: contexts: List of contexts to embed. index: Index to use. embeddings: Embeddings model to use. sparse_encoder: Sparse encoder to use. ids: List of ids to use for the documents. metadatas: List of metadata to use for the documents. |
28,337 | from __future__ import annotations
from typing import Any, Callable, Dict, Iterable, List, Optional
from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
from langchain_core.pydantic_v1 import Field
from langchain_core.retrievers import BaseRetriever
def defa... | null |
28,338 | import asyncio
import logging
import warnings
from concurrent.futures import ThreadPoolExecutor
from typing import Any, Dict, Iterator, List, Optional, Union, cast
import aiohttp
import requests
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
The provided ... | Build metadata from BeautifulSoup output. |
28,339 | import concurrent
import logging
import random
from pathlib import Path
from typing import Any, List, Optional, Sequence, Type, Union
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
from langchain_community.document_loaders.html_bs import BSHTMLLoader
from ... | null |
28,340 | from __future__ import annotations
import os
from typing import (
TYPE_CHECKING,
Any,
Dict,
Iterable,
Iterator,
List,
Optional,
Sequence,
)
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
def _dependable_mastodon_import() ->... | null |
28,341 | import json
from pathlib import Path
from typing import Any, List
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
The provided code snippet includes necessary dependencies for implementing the `concatenate_cells` function. Write a Python function `def conc... | Combine cells information in a readable format ready to be used. Args: cell: A dictionary include_outputs: Whether to include the outputs of the cell. max_output_length: Maximum length of the output to be displayed. traceback: Whether to return a traceback of the error. Returns: A string with the cell information. |
28,342 | import json
from pathlib import Path
from typing import Any, List
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
The provided code snippet includes necessary dependencies for implementing the `remove_newlines` function. Write a Python function `def remove... | Recursively remove newlines, no matter the data structure they are stored in. |
28,343 | import re
from pathlib import Path
from typing import Iterator
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
The provided code snippet includes necessary dependencies for implementing the `concatenate_rows` function. Write a Python function `def concaten... | Combine message information in a readable format ready to be used. |
28,344 | from __future__ import annotations
import asyncio
import json
from pathlib import Path
from typing import TYPE_CHECKING, Dict, List, Optional, Union
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
The provided code snippet includes necessary dependencies f... | Combine message information in a readable format ready to be used. |
28,345 | from __future__ import annotations
import asyncio
import json
from pathlib import Path
from typing import TYPE_CHECKING, Dict, List, Optional, Union
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
The provided code snippet includes necessary dependencies f... | Convert a string or list of strings to a list of Documents with metadata. |
28,346 | from __future__ import annotations
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Sequence, Union
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
def _dependable_tweepy_import() -> tweepy:
try:
import tweepy
except I... | null |
28,347 | from __future__ import annotations
import logging
import os
import tempfile
from abc import abstractmethod
from enum import Enum
from pathlib import Path
from typing import TYPE_CHECKING, Dict, Iterable, List, Sequence, Union
from langchain_core.pydantic_v1 import (
BaseModel,
BaseSettings,
Field,
FileP... | Fetch the mime types for the specified file types. |
28,348 | from __future__ import annotations
import asyncio
import logging
import re
from typing import (
TYPE_CHECKING,
Callable,
Iterator,
List,
Optional,
Sequence,
Set,
Union,
)
import requests
from langchain_core.documents import Document
from langchain_core.utils.html import extract_sub_links... | Extract metadata from raw html using BeautifulSoup. |
28,349 | from __future__ import annotations
import warnings
from typing import (
TYPE_CHECKING,
Any,
Iterable,
Iterator,
Mapping,
Optional,
Sequence,
Union,
)
from urllib.parse import urlparse
import numpy as np
from langchain_core.documents import Document
from langchain_community.document_loade... | 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. |
28,350 | 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... | Get default mime-type based parser. |
28,351 | 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... | Get a parser by parser name. |
28,352 | from typing import Any, Callable, Iterator, List, Optional, Tuple
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
The provided code snippet includes necessary dependencies for implementing the `default_joiner` function. Write a Python function `def default... | Default joiner for content columns. |
28,353 | from __future__ import annotations
from pathlib import Path
from typing import TYPE_CHECKING, Any, Iterator, List, Optional, Sequence, Tuple, Union
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
def _process_element(
element: Union[Tag, NavigableString... | Returns cleaned text with newlines preserved and irrelevant elements removed. |
28,354 | from __future__ import annotations
from pathlib import Path
from typing import TYPE_CHECKING, Any, Iterator, List, Optional, Sequence, Tuple, Union
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
def _get_link_ratio(section: Tag) -> float:
links = sect... | null |
28,355 | from __future__ import annotations
import logging
from enum import Enum
from pathlib import Path
from typing import Any, Dict, List, Optional, Sequence, Union
from urllib.parse import parse_qs, urlparse
from langchain_core.documents import Document
from langchain_core.pydantic_v1 import root_validator
from langchain_co... | Parse a youtube url and return the video id if valid, otherwise None. |
28,356 | 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")
The provided code snippet includes necessary dependencies for implementing the `_make_iterator` function. W... | Create a function that optionally wraps an iterable in tqdm. |
28,357 | import concurrent.futures
from typing import List, NamedTuple, Optional, cast
class FileEncoding(NamedTuple):
"""File encoding as the NamedTuple."""
encoding: Optional[str]
"""The encoding of the file."""
confidence: float
"""The confidence of the encoding."""
language: Optional[str]
"""The ... | Try to detect the file encoding. Returns a list of `FileEncoding` tuples with the detected encodings ordered by confidence. Args: file_path: The path to the file to detect the encoding for. timeout: The timeout in seconds for the encoding detection. |
28,358 | import datetime
import json
from typing import List
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
The provided code snippet includes necessary dependencies for implementing the `concatenate_rows` function. Write a Python function `def concatenate_rows(me... | Combine message information in a readable format ready to be used. Args: message: Message to be concatenated title: Title of the conversation Returns: Concatenated message |
28,359 | import collections
from abc import ABC, abstractmethod
from typing import IO, Any, Callable, Dict, Iterator, List, Optional, Sequence, Union
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
def satisfies_min_unstructured_version(min_version: str) -> bool:
... | Raise an error if the `Unstructured` version does not exceed the specified minimum. |
28,360 | import collections
from abc import ABC, abstractmethod
from typing import IO, Any, Callable, Dict, Iterator, List, Optional, Sequence, Union
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
The provided code snippet includes necessary dependencies for imple... | Retrieve a list of elements from the `Unstructured API`. |
28,361 | import asyncio
import logging
import warnings
from typing import Any, Dict, Iterator, List, Optional, Sequence, Union
import aiohttp
import requests
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
The provided code snippet includes necessary dependencies f... | Build metadata from BeautifulSoup output. |
28,362 | import itertools
import re
from typing import Any, Callable, Generator, Iterable, Iterator, List, Optional, Tuple
from urllib.parse import urlparse
from langchain_core.documents import Document
from langchain_community.document_loaders.web_base import WebBaseLoader
def _default_parsing_function(content: Any) -> str:
... | null |
28,363 | import itertools
import re
from typing import Any, Callable, Generator, Iterable, Iterator, List, Optional, Tuple
from urllib.parse import urlparse
from langchain_core.documents import Document
from langchain_community.document_loaders.web_base import WebBaseLoader
def _default_meta_function(meta: dict, _content: Any)... | null |
28,364 | import itertools
import re
from typing import Any, Callable, Generator, Iterable, Iterator, List, Optional, Tuple
from urllib.parse import urlparse
from langchain_core.documents import Document
from langchain_community.document_loaders.web_base import WebBaseLoader
def _batch_block(iterable: Iterable, size: int) -> Ge... | null |
28,365 | import itertools
import re
from typing import Any, Callable, Generator, Iterable, Iterator, List, Optional, Tuple
from urllib.parse import urlparse
from langchain_core.documents import Document
from langchain_community.document_loaders.web_base import WebBaseLoader
The provided code snippet includes necessary dependen... | Extract the scheme + domain from a given URL. Args: url (str): The input URL. Returns: return a 2-tuple of scheme and domain |
28,366 | import datetime
import json
from pathlib import Path
from typing import Iterator
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
The provided code snippet includes necessary dependencies for implementing the `concatenate_rows` function. Write a Python func... | Combine message information in a readable format ready to be used. Args: row: dictionary containing message information. |
28,367 | from __future__ import annotations
from typing import TYPE_CHECKING, Iterable, List, Optional, Sequence
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
def _dependable_praw_import() -> praw:
try:
import praw
except ImportError:
rais... | null |
28,368 | import os
from typing import Any, Dict, List, Optional
from uuid import UUID
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.messages import (
AIMessage,
BaseMessage,
ChatMessage,
FunctionMessage,
HumanMessage,
SystemMessage,
)
from langchain_core.outputs import LLMR... | null |
28,369 | from __future__ import annotations
import logging
from copy import deepcopy
from typing import TYPE_CHECKING, Any, Dict, List, Tuple
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.outputs import LLMResult
from langchain_community.c... | Import flytekit and flytekitplugins-deck-standard. |
28,370 | from __future__ import annotations
import logging
from copy import deepcopy
from typing import TYPE_CHECKING, Any, Dict, List, Tuple
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.outputs import LLMResult
from langchain_community.c... | Analyze text using textstat and spacy. Parameters: text (str): The text to analyze. nlp (spacy.lang): The spacy language model to use for visualization. Returns: (dict): A dictionary containing the complexity metrics and visualization files serialized to HTML string. |
28,371 | import json
import tempfile
from copy import deepcopy
from pathlib import Path
from typing import Any, Dict, List, Optional, Sequence, Union
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.outputs import LLMResult
from langchain_com... | Load json file to a dictionary. Parameters: json_path (str): The path to the json file. Returns: (dict): The dictionary representation of the json file. |
28,372 | import json
import tempfile
from copy import deepcopy
from pathlib import Path
from typing import Any, Dict, List, Optional, Sequence, Union
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.outputs import LLMResult
from langchain_com... | Analyze text using textstat and spacy. Parameters: text (str): The text to analyze. complexity_metrics (bool): Whether to compute complexity metrics. visualize (bool): Whether to visualize the text. nlp (spacy.lang): The spacy language model to use for visualization. output_dir (str): The directory to save the visualiz... |
28,373 | import json
import tempfile
from copy import deepcopy
from pathlib import Path
from typing import Any, Dict, List, Optional, Sequence, Union
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.outputs import LLMResult
from langchain_com... | Construct an html element from a prompt and a generation. Parameters: prompt (str): The prompt. generation (str): The generation. Returns: (wandb.Html): The html element. |
28,374 | import hashlib
from pathlib import Path
from typing import Any, Dict, Iterable, Tuple, Union
def _flatten_dict(
nested_dict: Dict[str, Any], parent_key: str = "", sep: str = "_"
) -> Iterable[Tuple[str, Any]]:
"""
Generator that yields flattened items from a nested dictionary for a flat dict.
Parameters... | Flattens a nested dictionary into a flat dictionary. Parameters: nested_dict (dict): The nested dictionary to flatten. parent_key (str): The prefix to prepend to the keys of the flattened dict. sep (str): The separator to use between the parent key and the key of the flattened dictionary. Returns: (dict): A flat dictio... |
28,375 | import hashlib
from pathlib import Path
from typing import Any, Dict, Iterable, Tuple, Union
The provided code snippet includes necessary dependencies for implementing the `load_json` function. Write a Python function `def load_json(json_path: Union[str, Path]) -> str` to solve the following problem:
Load json file to... | Load json file to a string. Parameters: json_path (str): The path to the json file. Returns: (str): The string representation of the json file. |
28,376 | from __future__ import annotations
import tempfile
from copy import deepcopy
from pathlib import Path
from typing import TYPE_CHECKING, Any, Dict, List, Mapping, Optional, Sequence
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.out... | Import the clearml python package and raise an error if it is not installed. |
28,377 | import importlib.metadata
import logging
import os
import traceback
import warnings
from contextvars import ContextVar
from typing import Any, Dict, List, Union, cast
from uuid import UUID
import requests
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.callbacks import BaseCallbackHandler... | Builds an LLMonitor UserContextManager Parameters: - `user_id`: The user id. - `user_props`: The user properties. Returns: A context manager that sets the user context. |
28,378 | import importlib.metadata
import logging
import os
import traceback
import warnings
from contextvars import ContextVar
from typing import Any, Dict, List, Union, cast
from uuid import UUID
import requests
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.callbacks import BaseCallbackHandler... | null |
28,379 | import importlib.metadata
import logging
import os
import traceback
import warnings
from contextvars import ContextVar
from typing import Any, Dict, List, Union, cast
from uuid import UUID
import requests
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.callbacks import BaseCallbackHandler... | null |
28,380 | import importlib.metadata
import logging
import os
import traceback
import warnings
from contextvars import ContextVar
from typing import Any, Dict, List, Union, cast
from uuid import UUID
import requests
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.callbacks import BaseCallbackHandler... | null |
28,381 | import importlib.metadata
import logging
import os
import traceback
import warnings
from contextvars import ContextVar
from typing import Any, Dict, List, Union, cast
from uuid import UUID
import requests
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.callbacks import BaseCallbackHandler... | null |
28,382 | import importlib.metadata
import logging
import os
import traceback
import warnings
from contextvars import ContextVar
from typing import Any, Dict, List, Union, cast
from uuid import UUID
import requests
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.callbacks import BaseCallbackHandler... | null |
28,383 | import logging
import os
import random
import string
import tempfile
import traceback
from copy import deepcopy
from pathlib import Path
from typing import Any, Dict, List, Optional, Sequence, Union
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.callbacks import BaseCallbackHandler
from ... | Import the mlflow python package and raise an error if it is not installed. |
28,384 | import logging
import os
import random
import string
import tempfile
import traceback
from copy import deepcopy
from pathlib import Path
from typing import Any, Dict, List, Optional, Sequence, Union
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.callbacks import BaseCallbackHandler
from ... | Get the metrics to log to MLFlow. |
28,385 | import logging
import os
import random
import string
import tempfile
import traceback
from copy import deepcopy
from pathlib import Path
from typing import Any, Dict, List, Optional, Sequence, Union
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.callbacks import BaseCallbackHandler
from ... | Analyze text using textstat and spacy. Parameters: text (str): The text to analyze. nlp (spacy.lang): The spacy language model to use for visualization. textstat: The textstat library to use for complexity metrics calculation. Returns: (dict): A dictionary containing the complexity metrics and visualization files seria... |
28,386 | import logging
import os
import random
import string
import tempfile
import traceback
from copy import deepcopy
from pathlib import Path
from typing import Any, Dict, List, Optional, Sequence, Union
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.callbacks import BaseCallbackHandler
from ... | Construct an html element from a prompt and a generation. Parameters: prompt (str): The prompt. generation (str): The generation. Returns: (str): The html string. |
28,387 | import tempfile
from copy import deepcopy
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Sequence
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.outputs import Generation, LLMResult
import langchai... | null |
28,388 | import tempfile
from copy import deepcopy
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Sequence
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.outputs import Generation, LLMResult
import langchai... | null |
28,389 | import tempfile
from copy import deepcopy
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Sequence
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.outputs import Generation, LLMResult
import langchai... | null |
28,390 | from __future__ import annotations
import os
import uuid
from collections import defaultdict
from datetime import datetime
from time import time
from typing import TYPE_CHECKING, Any, DefaultDict, Dict, List, Optional
import numpy as np
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.call... | Lazy load Arthur. |
28,391 | from __future__ import annotations
import json
from typing import (
TYPE_CHECKING,
Any,
Dict,
List,
Optional,
Sequence,
Tuple,
TypedDict,
Union,
)
from langchain_core.tracers.base import BaseTracer
from langchain_core.tracers.schemas import Run
def _serialize_io(run_inputs: Optional... | null |
28,392 | from types import ModuleType, SimpleNamespace
from typing import TYPE_CHECKING, Any, Callable, Dict
from langchain_core.tracers import BaseTracer
def _get_run_type(run: "Run") -> str:
if isinstance(run.run_type, str):
return run.run_type
elif hasattr(run.run_type, "value"):
return run.run_type.... | null |
28,393 | from types import ModuleType, SimpleNamespace
from typing import TYPE_CHECKING, Any, Callable, Dict
from langchain_core.tracers import BaseTracer
The provided code snippet includes necessary dependencies for implementing the `import_comet_llm_api` function. Write a Python function `def import_comet_llm_api() -> Simple... | Import comet_llm api and raise an error if it is not installed. |
28,394 | import os
import warnings
from datetime import datetime
from enum import Enum
from typing import Any, Dict, List, Optional, Tuple, Union
from uuid import UUID
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.messages import BaseMessa... | Get default Label Studio configs for the given mode. Parameters: mode: Label Studio mode ("prompt" or "chat") Returns: Tuple of Label Studio config and mode |
28,395 | from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any, Optional
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.utils import get_from_env
The provided code snippet includes necessary dependencies for implementing the `import_langkit` function. Write a Pyth... | Import the langkit python package and raise an error if it is not installed. Args: sentiment: Whether to import the langkit.sentiment module. Defaults to False. toxicity: Whether to import the langkit.toxicity module. Defaults to False. themes: Whether to import the langkit.themes module. Defaults to False. Returns: Th... |
28,396 | from __future__ import annotations
from enum import Enum
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.outputs import LLMResult
from langchain_community.callb... | Convert newline characters to markdown newline sequences (space, space, newline). |
28,397 | from __future__ import annotations
import logging
from contextlib import contextmanager
from contextvars import ContextVar
from typing import (
Generator,
Optional,
)
from langchain_core.tracers.context import register_configure_hook
from langchain_community.callbacks.openai_info import OpenAICallbackHandler
fr... | Get the OpenAI callback handler in a context manager. which conveniently exposes token and cost information. Returns: OpenAICallbackHandler: The OpenAI callback handler. Example: >>> with get_openai_callback() as cb: ... # Use the OpenAI callback handler |
28,398 | from __future__ import annotations
import logging
from contextlib import contextmanager
from contextvars import ContextVar
from typing import (
Generator,
Optional,
)
from langchain_core.tracers.context import register_configure_hook
from langchain_community.callbacks.openai_info import OpenAICallbackHandler
fr... | Get the WandbTracer in a context manager. Args: session_name (str, optional): The name of the session. Defaults to "default". Returns: None Example: >>> with wandb_tracing_enabled() as session: ... # Use the WandbTracer session |
28,399 | from __future__ import annotations
import datetime
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple
from uuid import UUID
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.messages import (
AIMessage,
BaseMessage,
ChatMessage,
HumanMessage,
Syst... | Lazy import promptlayer to avoid circular imports. |
28,400 | import json
import os
import shutil
import tempfile
from copy import deepcopy
from typing import Any, Dict, List, Optional
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.outputs import LLMResult
from langchain_community.callbacks.u... | Save dict to local file path. Parameters: data (dict): The dictionary to be saved. file_path (str): Local file path. |
28,401 | from typing import Any, Awaitable, Callable, Dict, Optional
from uuid import UUID
from langchain_core.callbacks import AsyncCallbackHandler, BaseCallbackHandler
def _default_approve(_input: str) -> bool:
msg = (
"Do you approve of the following input? "
"Anything except 'Y'/'Yes' (case-insensitive)... | null |
28,402 | from typing import Any, Awaitable, Callable, Dict, Optional
from uuid import UUID
from langchain_core.callbacks import AsyncCallbackHandler, BaseCallbackHandler
async def _adefault_approve(_input: str) -> bool:
msg = (
"Do you approve of the following input? "
"Anything except 'Y'/'Yes' (case-insen... | null |
28,403 | from typing import Any, Awaitable, Callable, Dict, Optional
from uuid import UUID
from langchain_core.callbacks import AsyncCallbackHandler, BaseCallbackHandler
def _default_true(_: Dict[str, Any]) -> bool:
return True | null |
28,404 | import os
from typing import Any, Dict, List
from uuid import UUID
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.messages import BaseMessage
from langchain_core.outputs import LLMResult
The provided code snippet includes necessary dependencies for implementing the `import_context` functi... | Import the `getcontext` package. |
28,405 | import threading
from typing import Any, Dict, List
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.outputs import LLMResult
MODEL_COST_PER_1K_TOKENS = {
# GPT-4 input
"gpt-4": 0.03,
"gpt-4-0314": 0.03,
"gpt-4-0613": 0.03,
"gpt-4-32k": 0.06,
"gpt-4-32k-0314": 0.06,
... | Get the cost in USD for a given model and number of tokens. Args: model_name: Name of the model num_tokens: Number of tokens. is_completion: Whether the model is used for completion or not. Defaults to False. Returns: Cost in USD. |
28,406 | from copy import deepcopy
from typing import Any, Dict, List, Optional
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.outputs import LLMResult
The provided code snippet includes necessary dependencies for implementing the `import_... | Import the aim python package and raise an error if it is not installed. |
28,407 | import time
from typing import Any, Dict, List, Optional
from uuid import UUID
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.outputs import LLMResult
from langchain_community.callbacks.utils import import_pandas
The provided code snippet includes necessary dependencies for implementing t... | Import the fiddler python package and raise an error if it is not installed. |
28,408 | import time
from typing import Any, Dict, List, Optional, cast
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.messages import BaseMessage
from langchain_core.outputs import ChatGeneration, LLMResult
The provided code snippet inclu... | Import the infino client. |
28,409 | import time
from typing import Any, Dict, List, Optional, cast
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.messages import BaseMessage
from langchain_core.outputs import ChatGeneration, LLMResult
def import_tiktoken() -> Any:
... | Calculate num tokens for OpenAI with tiktoken package. Official documentation: https://github.com/openai/openai-cookbook/blob/main /examples/How_to_count_tokens_with_tiktoken.ipynb |
28,410 | from typing import Any, Dict, Optional, Sequence, Type, Union
from langchain_core.documents import BaseDocumentTransformer, Document
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel
class OpenAIMetadataTag... | Create a DocumentTransformer that uses an OpenAI function chain to automatically tag documents with metadata based on their content and an input schema. Args: metadata_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, ... |
28,411 | from typing import Any, Iterator, List, Sequence, cast
from langchain_core.documents import BaseDocumentTransformer, Document
The provided code snippet includes necessary dependencies for implementing the `get_navigable_strings` function. Write a Python function `def get_navigable_strings(element: Any) -> Iterator[str... | Get all navigable strings from a BeautifulSoup element. Args: element: A BeautifulSoup element. Returns: A generator of strings. |
28,412 | from typing import Any, List, Sequence
from langchain_core.documents import BaseDocumentTransformer, Document
from langchain_core.pydantic_v1 import BaseModel
The provided code snippet includes necessary dependencies for implementing the `_litm_reordering` function. Write a Python function `def _litm_reordering(docume... | Lost in the middle reorder: the less relevant documents will be at the middle of the list and more relevant elements at beginning / end. See: https://arxiv.org/abs//2307.03172 |
28,413 | from typing import Any, Callable, List, Sequence
import numpy as np
from langchain_core.documents import BaseDocumentTransformer, Document
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_community.utils.math import cosine_similarity
class _Document... | Convert a list of documents to a list of documents with state. Args: documents: The documents to convert. Returns: A list of documents with state. |
28,414 | from typing import Any, Callable, List, Sequence
import numpy as np
from langchain_core.documents import BaseDocumentTransformer, Document
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_community.utils.math import cosine_similarity
The provided c... | Filter redundant documents based on the similarity of their embeddings. |
28,415 | from typing import Any, Callable, List, Sequence
import numpy as np
from langchain_core.documents import BaseDocumentTransformer, Document
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_community.utils.math import cosine_similarity
class _Document... | null |
28,416 | from typing import Any, Callable, List, Sequence
import numpy as np
from langchain_core.documents import BaseDocumentTransformer, Document
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_community.utils.math import cosine_similarity
The provided c... | Filter documents based on proximity of their embeddings to clusters. |
28,417 | import importlib
import logging
from typing import Any, Callable, List, Optional
from langchain_community.embeddings.self_hosted import SelfHostedEmbeddings
The provided code snippet includes necessary dependencies for implementing the `_embed_documents` function. Write a Python function `def _embed_documents(client: ... | Inference function to send to the remote hardware. Accepts a sentence_transformer model_id and returns a list of embeddings for each document in the batch. |
28,418 | import importlib
import logging
from typing import Any, Callable, List, Optional
from langchain_community.embeddings.self_hosted import SelfHostedEmbeddings
logger = logging.getLogger(__name__)
The provided code snippet includes necessary dependencies for implementing the `load_embedding_model` function. Write a Pytho... | Load the embedding model. |
28,419 | from __future__ import annotations
import asyncio
import json
from typing import Any, Dict, List, Optional
import aiohttp
import requests
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, root_validator
The provided code snippet includes necessary dependencies for imple... | Check if an endpoint is live by sending a GET request to the specified URL. Args: url (str): The URL of the endpoint to check. Returns: bool: True if the endpoint is live (status code 200), False otherwise. Raises: Exception: If the endpoint returns a non-successful status code or if there is an error querying the endp... |
28,420 | from __future__ import annotations
import logging
from typing import Any, Callable, Dict, List, Optional
import requests
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, Extra, SecretStr, root_validator
from langchain_core.utils import convert_to_secret_str, get_from_di... | Use tenacity to retry the completion call. |
28,421 | from __future__ import annotations
import logging
import warnings
from typing import (
Any,
Callable,
Dict,
List,
Literal,
Optional,
Sequence,
Set,
Tuple,
Union,
)
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, Extra, Field, roo... | Use tenacity to retry the embedding call. |
28,422 | from __future__ import annotations
import logging
import warnings
from typing import (
Any,
Callable,
Dict,
List,
Literal,
Optional,
Sequence,
Set,
Tuple,
Union,
)
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, Extra, Field, roo... | Use tenacity to retry the embedding call. |
28,423 | from __future__ import annotations
import json
import logging
from typing import (
Any,
Callable,
Dict,
List,
Optional,
Tuple,
Union,
cast,
)
import requests
from langchain_core._api.deprecation import deprecated
from langchain_core.embeddings import Embeddings
from langchain_core.pydant... | Use tenacity to retry the embedding call. |
28,424 | from __future__ import annotations
import logging
import time
from typing import Any, Callable, Dict, List
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, SecretStr, root_validator
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
from tenaci... | Use tenacity to retry the embedding call. |
28,425 | from __future__ import annotations
from typing import Any, Iterator, List, Optional
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel
def _chunk(texts: List[str], size: int) -> Iterator[List[str]]:
for i in range(0, len(texts), size):
yield texts[i : i + size... | null |
28,426 | from __future__ import annotations
from typing import Any, Dict, Iterator, List
from urllib.parse import urlparse
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, PrivateAttr
def _chunk(texts: List[str], size: int) -> Iterator[List[str]]:
for i in range(0, len(text... | null |
28,427 | from __future__ import annotations
from typing import Iterator, List
from urllib.parse import urlparse
from langchain_community.embeddings.mlflow import MlflowEmbeddings
def _chunk(texts: List[str], size: int) -> Iterator[List[str]]:
for i in range(0, len(texts), size):
yield texts[i : i + size] | null |
28,428 | from __future__ import annotations
import logging
from typing import Any, Callable, Dict, List, Optional
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, root_validator
from langchain_core.utils import get_from_dict_or_env
from tenacity import (
before_sleep_log,
... | Use tenacity to retry the completion call. |
28,429 | from __future__ import annotations
import warnings
from typing import Any, Iterator, List, Optional
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel
def _chunk(texts: List[str], size: int) -> Iterator[List[str]]:
for i in range(0, len(texts), size):
yield te... | null |
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