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d0c3c407787e-0 | Source code for langchain.schema.retriever
from __future__ import annotations
import asyncio
import warnings
from abc import ABC, abstractmethod
from functools import partial
from inspect import signature
from typing import TYPE_CHECKING, Any, Dict, List, Optional
from langchain.load.dump import dumpd
from langchain.schema.document import Document
from langchain.schema.runnable import RunnableConfig, RunnableSerializable
if TYPE_CHECKING:
from langchain.callbacks.manager import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
Callbacks,
)
[docs]class BaseRetriever(RunnableSerializable[str, List[Document]], ABC):
"""Abstract base class for a Document retrieval system.
A retrieval system is defined as something that can take string queries and return
the most 'relevant' Documents from some source.
Example:
.. code-block:: python
class TFIDFRetriever(BaseRetriever, BaseModel):
vectorizer: Any
docs: List[Document]
tfidf_array: Any
k: int = 4
class Config:
arbitrary_types_allowed = True
def get_relevant_documents(self, query: str) -> List[Document]:
from sklearn.metrics.pairwise import cosine_similarity
# Ip -- (n_docs,x), Op -- (n_docs,n_Feats)
query_vec = self.vectorizer.transform([query])
# Op -- (n_docs,1) -- Cosine Sim with each doc
results = cosine_similarity(self.tfidf_array, query_vec).reshape((-1,))
return [self.docs[i] for i in results.argsort()[-self.k :][::-1]]
""" # noqa: E501
class Config:
"""Configuration for this pydantic object.""" | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/retriever.html |
d0c3c407787e-1 | class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
_new_arg_supported: bool = False
_expects_other_args: bool = False
tags: Optional[List[str]] = None
"""Optional list of tags associated with the retriever. Defaults to None
These tags will be associated with each call to this retriever,
and passed as arguments to the handlers defined in `callbacks`.
You can use these to eg identify a specific instance of a retriever with its
use case.
"""
metadata: Optional[Dict[str, Any]] = None
"""Optional metadata associated with the retriever. Defaults to None
This metadata will be associated with each call to this retriever,
and passed as arguments to the handlers defined in `callbacks`.
You can use these to eg identify a specific instance of a retriever with its
use case.
"""
def __init_subclass__(cls, **kwargs: Any) -> None:
super().__init_subclass__(**kwargs)
# Version upgrade for old retrievers that implemented the public
# methods directly.
if cls.get_relevant_documents != BaseRetriever.get_relevant_documents:
warnings.warn(
"Retrievers must implement abstract `_get_relevant_documents` method"
" instead of `get_relevant_documents`",
DeprecationWarning,
)
swap = cls.get_relevant_documents
cls.get_relevant_documents = ( # type: ignore[assignment]
BaseRetriever.get_relevant_documents
)
cls._get_relevant_documents = swap # type: ignore[assignment]
if (
hasattr(cls, "aget_relevant_documents") | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/retriever.html |
d0c3c407787e-2 | if (
hasattr(cls, "aget_relevant_documents")
and cls.aget_relevant_documents != BaseRetriever.aget_relevant_documents
):
warnings.warn(
"Retrievers must implement abstract `_aget_relevant_documents` method"
" instead of `aget_relevant_documents`",
DeprecationWarning,
)
aswap = cls.aget_relevant_documents
cls.aget_relevant_documents = ( # type: ignore[assignment]
BaseRetriever.aget_relevant_documents
)
cls._aget_relevant_documents = aswap # type: ignore[assignment]
parameters = signature(cls._get_relevant_documents).parameters
cls._new_arg_supported = parameters.get("run_manager") is not None
# If a V1 retriever broke the interface and expects additional arguments
cls._expects_other_args = (
len(set(parameters.keys()) - {"self", "query", "run_manager"}) > 0
)
[docs] def invoke(
self, input: str, config: Optional[RunnableConfig] = None
) -> List[Document]:
config = config or {}
return self.get_relevant_documents(
input,
callbacks=config.get("callbacks"),
tags=config.get("tags"),
metadata=config.get("metadata"),
run_name=config.get("run_name"),
)
[docs] async def ainvoke(
self,
input: str,
config: Optional[RunnableConfig] = None,
**kwargs: Optional[Any],
) -> List[Document]:
config = config or {}
return await self.aget_relevant_documents(
input,
callbacks=config.get("callbacks"),
tags=config.get("tags"), | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/retriever.html |
d0c3c407787e-3 | input,
callbacks=config.get("callbacks"),
tags=config.get("tags"),
metadata=config.get("metadata"),
run_name=config.get("run_name"),
)
@abstractmethod
def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
) -> List[Document]:
"""Get documents relevant to a query.
Args:
query: String to find relevant documents for
run_manager: The callbacks handler to use
Returns:
List of relevant documents
"""
async def _aget_relevant_documents(
self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
) -> List[Document]:
"""Asynchronously get documents relevant to a query.
Args:
query: String to find relevant documents for
run_manager: The callbacks handler to use
Returns:
List of relevant documents
"""
return await asyncio.get_running_loop().run_in_executor(
None, partial(self._get_relevant_documents, run_manager=run_manager), query
)
[docs] def get_relevant_documents(
self,
query: str,
*,
callbacks: Callbacks = None,
tags: Optional[List[str]] = None,
metadata: Optional[Dict[str, Any]] = None,
run_name: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
"""Retrieve documents relevant to a query.
Args:
query: string to find relevant documents for
callbacks: Callback manager or list of callbacks
tags: Optional list of tags associated with the retriever. Defaults to None
These tags will be associated with each call to this retriever, | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/retriever.html |
d0c3c407787e-4 | These tags will be associated with each call to this retriever,
and passed as arguments to the handlers defined in `callbacks`.
metadata: Optional metadata associated with the retriever. Defaults to None
This metadata will be associated with each call to this retriever,
and passed as arguments to the handlers defined in `callbacks`.
Returns:
List of relevant documents
"""
from langchain.callbacks.manager import CallbackManager
callback_manager = CallbackManager.configure(
callbacks,
None,
verbose=kwargs.get("verbose", False),
inheritable_tags=tags,
local_tags=self.tags,
inheritable_metadata=metadata,
local_metadata=self.metadata,
)
run_manager = callback_manager.on_retriever_start(
dumpd(self),
query,
name=run_name,
**kwargs,
)
try:
_kwargs = kwargs if self._expects_other_args else {}
if self._new_arg_supported:
result = self._get_relevant_documents(
query, run_manager=run_manager, **_kwargs
)
else:
result = self._get_relevant_documents(query, **_kwargs)
except Exception as e:
run_manager.on_retriever_error(e)
raise e
else:
run_manager.on_retriever_end(
result,
**kwargs,
)
return result
[docs] async def aget_relevant_documents(
self,
query: str,
*,
callbacks: Callbacks = None,
tags: Optional[List[str]] = None,
metadata: Optional[Dict[str, Any]] = None,
run_name: Optional[str] = None,
**kwargs: Any, | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/retriever.html |
d0c3c407787e-5 | run_name: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
"""Asynchronously get documents relevant to a query.
Args:
query: string to find relevant documents for
callbacks: Callback manager or list of callbacks
tags: Optional list of tags associated with the retriever. Defaults to None
These tags will be associated with each call to this retriever,
and passed as arguments to the handlers defined in `callbacks`.
metadata: Optional metadata associated with the retriever. Defaults to None
This metadata will be associated with each call to this retriever,
and passed as arguments to the handlers defined in `callbacks`.
Returns:
List of relevant documents
"""
from langchain.callbacks.manager import AsyncCallbackManager
callback_manager = AsyncCallbackManager.configure(
callbacks,
None,
verbose=kwargs.get("verbose", False),
inheritable_tags=tags,
local_tags=self.tags,
inheritable_metadata=metadata,
local_metadata=self.metadata,
)
run_manager = await callback_manager.on_retriever_start(
dumpd(self),
query,
name=run_name,
**kwargs,
)
try:
_kwargs = kwargs if self._expects_other_args else {}
if self._new_arg_supported:
result = await self._aget_relevant_documents(
query, run_manager=run_manager, **_kwargs
)
else:
result = await self._aget_relevant_documents(query, **_kwargs)
except Exception as e:
await run_manager.on_retriever_error(e)
raise e
else:
await run_manager.on_retriever_end(
result,
**kwargs, | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/retriever.html |
d0c3c407787e-6 | await run_manager.on_retriever_end(
result,
**kwargs,
)
return result | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/retriever.html |
2d5047ee1d6d-0 | Source code for langchain.schema.prompt
from __future__ import annotations
from abc import ABC, abstractmethod
from typing import List
from langchain.load.serializable import Serializable
from langchain.schema.messages import BaseMessage
[docs]class PromptValue(Serializable, ABC):
"""Base abstract class for inputs to any language model.
PromptValues can be converted to both LLM (pure text-generation) inputs and
ChatModel inputs.
"""
[docs] @classmethod
def is_lc_serializable(cls) -> bool:
"""Return whether this class is serializable."""
return True
[docs] @abstractmethod
def to_string(self) -> str:
"""Return prompt value as string."""
[docs] @abstractmethod
def to_messages(self) -> List[BaseMessage]:
"""Return prompt as a list of Messages.""" | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/prompt.html |
b618006e1d91-0 | Source code for langchain.schema.chat_history
from __future__ import annotations
from abc import ABC, abstractmethod
from typing import List
from langchain.schema.messages import AIMessage, BaseMessage, HumanMessage
[docs]class BaseChatMessageHistory(ABC):
"""Abstract base class for storing chat message history.
See `ChatMessageHistory` for default implementation.
Example:
.. code-block:: python
class FileChatMessageHistory(BaseChatMessageHistory):
storage_path: str
session_id: str
@property
def messages(self):
with open(os.path.join(storage_path, session_id), 'r:utf-8') as f:
messages = json.loads(f.read())
return messages_from_dict(messages)
def add_message(self, message: BaseMessage) -> None:
messages = self.messages.append(_message_to_dict(message))
with open(os.path.join(storage_path, session_id), 'w') as f:
json.dump(f, messages)
def clear(self):
with open(os.path.join(storage_path, session_id), 'w') as f:
f.write("[]")
"""
messages: List[BaseMessage]
"""A list of Messages stored in-memory."""
[docs] def add_user_message(self, message: str) -> None:
"""Convenience method for adding a human message string to the store.
Args:
message: The string contents of a human message.
"""
self.add_message(HumanMessage(content=message))
[docs] def add_ai_message(self, message: str) -> None:
"""Convenience method for adding an AI message string to the store.
Args:
message: The string contents of an AI message.
""" | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/chat_history.html |
b618006e1d91-1 | Args:
message: The string contents of an AI message.
"""
self.add_message(AIMessage(content=message))
[docs] @abstractmethod
def add_message(self, message: BaseMessage) -> None:
"""Add a Message object to the store.
Args:
message: A BaseMessage object to store.
"""
raise NotImplementedError()
[docs] @abstractmethod
def clear(self) -> None:
"""Remove all messages from the store""" | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/chat_history.html |
9cc02dfe64d1-0 | Source code for langchain.schema.vectorstore
from __future__ import annotations
import asyncio
import logging
import math
import warnings
from abc import ABC, abstractmethod
from functools import partial
from typing import (
TYPE_CHECKING,
Any,
Callable,
ClassVar,
Collection,
Dict,
Iterable,
List,
Optional,
Tuple,
Type,
TypeVar,
)
from langchain.pydantic_v1 import Field, root_validator
from langchain.schema import BaseRetriever
from langchain.schema.document import Document
from langchain.schema.embeddings import Embeddings
if TYPE_CHECKING:
from langchain.callbacks.manager import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
logger = logging.getLogger(__name__)
VST = TypeVar("VST", bound="VectorStore")
[docs]class VectorStore(ABC):
"""Interface for vector store."""
[docs] @abstractmethod
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
kwargs: vectorstore specific parameters
Returns:
List of ids from adding the texts into the vectorstore.
"""
@property
def embeddings(self) -> Optional[Embeddings]:
"""Access the query embedding object if available."""
logger.debug( | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/vectorstore.html |
9cc02dfe64d1-1 | """Access the query embedding object if available."""
logger.debug(
f"{Embeddings.__name__} is not implemented for {self.__class__.__name__}"
)
return None
[docs] def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[bool]:
"""Delete by vector ID or other criteria.
Args:
ids: List of ids to delete.
**kwargs: Other keyword arguments that subclasses might use.
Returns:
Optional[bool]: True if deletion is successful,
False otherwise, None if not implemented.
"""
raise NotImplementedError("delete method must be implemented by subclass.")
[docs] async def adelete(
self, ids: Optional[List[str]] = None, **kwargs: Any
) -> Optional[bool]:
"""Delete by vector ID or other criteria.
Args:
ids: List of ids to delete.
**kwargs: Other keyword arguments that subclasses might use.
Returns:
Optional[bool]: True if deletion is successful,
False otherwise, None if not implemented.
"""
raise NotImplementedError("delete method must be implemented by subclass.")
[docs] async def aadd_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore."""
return await asyncio.get_running_loop().run_in_executor(
None, partial(self.add_texts, **kwargs), texts, metadatas
)
[docs] def add_documents(self, documents: List[Document], **kwargs: Any) -> List[str]: | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/vectorstore.html |
9cc02dfe64d1-2 | """Run more documents through the embeddings and add to the vectorstore.
Args:
documents (List[Document]: Documents to add to the vectorstore.
Returns:
List[str]: List of IDs of the added texts.
"""
# TODO: Handle the case where the user doesn't provide ids on the Collection
texts = [doc.page_content for doc in documents]
metadatas = [doc.metadata for doc in documents]
return self.add_texts(texts, metadatas, **kwargs)
[docs] async def aadd_documents(
self, documents: List[Document], **kwargs: Any
) -> List[str]:
"""Run more documents through the embeddings and add to the vectorstore.
Args:
documents (List[Document]: Documents to add to the vectorstore.
Returns:
List[str]: List of IDs of the added texts.
"""
texts = [doc.page_content for doc in documents]
metadatas = [doc.metadata for doc in documents]
return await self.aadd_texts(texts, metadatas, **kwargs)
[docs] def search(self, query: str, search_type: str, **kwargs: Any) -> List[Document]:
"""Return docs most similar to query using specified search type."""
if search_type == "similarity":
return self.similarity_search(query, **kwargs)
elif search_type == "mmr":
return self.max_marginal_relevance_search(query, **kwargs)
else:
raise ValueError(
f"search_type of {search_type} not allowed. Expected "
"search_type to be 'similarity' or 'mmr'."
)
[docs] async def asearch( | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/vectorstore.html |
9cc02dfe64d1-3 | )
[docs] async def asearch(
self, query: str, search_type: str, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query using specified search type."""
if search_type == "similarity":
return await self.asimilarity_search(query, **kwargs)
elif search_type == "mmr":
return await self.amax_marginal_relevance_search(query, **kwargs)
else:
raise ValueError(
f"search_type of {search_type} not allowed. Expected "
"search_type to be 'similarity' or 'mmr'."
)
[docs] @abstractmethod
def similarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query."""
@staticmethod
def _euclidean_relevance_score_fn(distance: float) -> float:
"""Return a similarity score on a scale [0, 1]."""
# The 'correct' relevance function
# may differ depending on a few things, including:
# - the distance / similarity metric used by the VectorStore
# - the scale of your embeddings (OpenAI's are unit normed. Many
# others are not!)
# - embedding dimensionality
# - etc.
# This function converts the euclidean norm of normalized embeddings
# (0 is most similar, sqrt(2) most dissimilar)
# to a similarity function (0 to 1)
return 1.0 - distance / math.sqrt(2)
@staticmethod
def _cosine_relevance_score_fn(distance: float) -> float: | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/vectorstore.html |
9cc02dfe64d1-4 | def _cosine_relevance_score_fn(distance: float) -> float:
"""Normalize the distance to a score on a scale [0, 1]."""
return 1.0 - distance
@staticmethod
def _max_inner_product_relevance_score_fn(distance: float) -> float:
"""Normalize the distance to a score on a scale [0, 1]."""
if distance > 0:
return 1.0 - distance
return -1.0 * distance
def _select_relevance_score_fn(self) -> Callable[[float], float]:
"""
The 'correct' relevance function
may differ depending on a few things, including:
- the distance / similarity metric used by the VectorStore
- the scale of your embeddings (OpenAI's are unit normed. Many others are not!)
- embedding dimensionality
- etc.
Vectorstores should define their own selection based method of relevance.
"""
raise NotImplementedError
[docs] def similarity_search_with_score(
self, *args: Any, **kwargs: Any
) -> List[Tuple[Document, float]]:
"""Run similarity search with distance."""
raise NotImplementedError
[docs] async def asimilarity_search_with_score(
self, *args: Any, **kwargs: Any
) -> List[Tuple[Document, float]]:
"""Run similarity search with distance asynchronously."""
# This is a temporary workaround to make the similarity search
# asynchronous. The proper solution is to make the similarity search
# asynchronous in the vector store implementations.
func = partial(self.similarity_search_with_score, *args, **kwargs)
return await asyncio.get_event_loop().run_in_executor(None, func) | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/vectorstore.html |
9cc02dfe64d1-5 | return await asyncio.get_event_loop().run_in_executor(None, func)
def _similarity_search_with_relevance_scores(
self,
query: str,
k: int = 4,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""
Default similarity search with relevance scores. Modify if necessary
in subclass.
Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
Args:
query: input text
k: Number of Documents to return. Defaults to 4.
**kwargs: kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns:
List of Tuples of (doc, similarity_score)
"""
relevance_score_fn = self._select_relevance_score_fn()
docs_and_scores = self.similarity_search_with_score(query, k, **kwargs)
return [(doc, relevance_score_fn(score)) for doc, score in docs_and_scores]
async def _asimilarity_search_with_relevance_scores(
self,
query: str,
k: int = 4,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""
Default async similarity search with relevance scores. Modify if necessary
in subclass.
Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
Args:
query: input text
k: Number of Documents to return. Defaults to 4. | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/vectorstore.html |
9cc02dfe64d1-6 | k: Number of Documents to return. Defaults to 4.
**kwargs: kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns:
List of Tuples of (doc, similarity_score)
"""
relevance_score_fn = self._select_relevance_score_fn()
docs_and_scores = await self.asimilarity_search_with_score(query, k, **kwargs)
return [(doc, relevance_score_fn(score)) for doc, score in docs_and_scores]
[docs] def similarity_search_with_relevance_scores(
self,
query: str,
k: int = 4,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
Args:
query: input text
k: Number of Documents to return. Defaults to 4.
**kwargs: kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns:
List of Tuples of (doc, similarity_score)
"""
score_threshold = kwargs.pop("score_threshold", None)
docs_and_similarities = self._similarity_search_with_relevance_scores(
query, k=k, **kwargs
)
if any(
similarity < 0.0 or similarity > 1.0
for _, similarity in docs_and_similarities
):
warnings.warn(
"Relevance scores must be between" | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/vectorstore.html |
9cc02dfe64d1-7 | ):
warnings.warn(
"Relevance scores must be between"
f" 0 and 1, got {docs_and_similarities}"
)
if score_threshold is not None:
docs_and_similarities = [
(doc, similarity)
for doc, similarity in docs_and_similarities
if similarity >= score_threshold
]
if len(docs_and_similarities) == 0:
warnings.warn(
"No relevant docs were retrieved using the relevance score"
f" threshold {score_threshold}"
)
return docs_and_similarities
[docs] async def asimilarity_search_with_relevance_scores(
self,
query: str,
k: int = 4,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs and relevance scores in the range [0, 1], asynchronously.
0 is dissimilar, 1 is most similar.
Args:
query: input text
k: Number of Documents to return. Defaults to 4.
**kwargs: kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns:
List of Tuples of (doc, similarity_score)
"""
score_threshold = kwargs.pop("score_threshold", None)
docs_and_similarities = await self._asimilarity_search_with_relevance_scores(
query, k=k, **kwargs
)
if any(
similarity < 0.0 or similarity > 1.0
for _, similarity in docs_and_similarities
):
warnings.warn( | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/vectorstore.html |
9cc02dfe64d1-8 | for _, similarity in docs_and_similarities
):
warnings.warn(
"Relevance scores must be between"
f" 0 and 1, got {docs_and_similarities}"
)
if score_threshold is not None:
docs_and_similarities = [
(doc, similarity)
for doc, similarity in docs_and_similarities
if similarity >= score_threshold
]
if len(docs_and_similarities) == 0:
warnings.warn(
"No relevant docs were retrieved using the relevance score"
f" threshold {score_threshold}"
)
return docs_and_similarities
[docs] async def asimilarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query."""
# This is a temporary workaround to make the similarity search
# asynchronous. The proper solution is to make the similarity search
# asynchronous in the vector store implementations.
func = partial(self.similarity_search, query, k=k, **kwargs)
return await asyncio.get_event_loop().run_in_executor(None, func)
[docs] def similarity_search_by_vector(
self, embedding: List[float], k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query vector.
"""
raise NotImplementedError
[docs] async def asimilarity_search_by_vector( | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/vectorstore.html |
9cc02dfe64d1-9 | raise NotImplementedError
[docs] async def asimilarity_search_by_vector(
self, embedding: List[float], k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to embedding vector."""
# This is a temporary workaround to make the similarity search
# asynchronous. The proper solution is to make the similarity search
# asynchronous in the vector store implementations.
func = partial(self.similarity_search_by_vector, embedding, k=k, **kwargs)
return await asyncio.get_event_loop().run_in_executor(None, func)
[docs] def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns:
List of Documents selected by maximal marginal relevance.
"""
raise NotImplementedError
[docs] async def amax_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20, | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/vectorstore.html |
9cc02dfe64d1-10 | k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance."""
# This is a temporary workaround to make the similarity search
# asynchronous. The proper solution is to make the similarity search
# asynchronous in the vector store implementations.
func = partial(
self.max_marginal_relevance_search,
query,
k=k,
fetch_k=fetch_k,
lambda_mult=lambda_mult,
**kwargs,
)
return await asyncio.get_event_loop().run_in_executor(None, func)
[docs] def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns:
List of Documents selected by maximal marginal relevance.
"""
raise NotImplementedError | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/vectorstore.html |
9cc02dfe64d1-11 | List of Documents selected by maximal marginal relevance.
"""
raise NotImplementedError
[docs] async def amax_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance."""
raise NotImplementedError
[docs] @classmethod
def from_documents(
cls: Type[VST],
documents: List[Document],
embedding: Embeddings,
**kwargs: Any,
) -> VST:
"""Return VectorStore initialized from documents and embeddings."""
texts = [d.page_content for d in documents]
metadatas = [d.metadata for d in documents]
return cls.from_texts(texts, embedding, metadatas=metadatas, **kwargs)
[docs] @classmethod
async def afrom_documents(
cls: Type[VST],
documents: List[Document],
embedding: Embeddings,
**kwargs: Any,
) -> VST:
"""Return VectorStore initialized from documents and embeddings."""
texts = [d.page_content for d in documents]
metadatas = [d.metadata for d in documents]
return await cls.afrom_texts(texts, embedding, metadatas=metadatas, **kwargs)
[docs] @classmethod
@abstractmethod
def from_texts(
cls: Type[VST],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
**kwargs: Any, | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/vectorstore.html |
9cc02dfe64d1-12 | **kwargs: Any,
) -> VST:
"""Return VectorStore initialized from texts and embeddings."""
[docs] @classmethod
async def afrom_texts(
cls: Type[VST],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> VST:
"""Return VectorStore initialized from texts and embeddings."""
return await asyncio.get_running_loop().run_in_executor(
None, partial(cls.from_texts, **kwargs), texts, embedding, metadatas
)
def _get_retriever_tags(self) -> List[str]:
"""Get tags for retriever."""
tags = [self.__class__.__name__]
if self.embeddings:
tags.append(self.embeddings.__class__.__name__)
return tags
[docs] def as_retriever(self, **kwargs: Any) -> VectorStoreRetriever:
"""Return VectorStoreRetriever initialized from this VectorStore.
Args:
search_type (Optional[str]): Defines the type of search that
the Retriever should perform.
Can be "similarity" (default), "mmr", or
"similarity_score_threshold".
search_kwargs (Optional[Dict]): Keyword arguments to pass to the
search function. Can include things like:
k: Amount of documents to return (Default: 4)
score_threshold: Minimum relevance threshold
for similarity_score_threshold
fetch_k: Amount of documents to pass to MMR algorithm (Default: 20)
lambda_mult: Diversity of results returned by MMR;
1 for minimum diversity and 0 for maximum. (Default: 0.5) | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/vectorstore.html |
9cc02dfe64d1-13 | filter: Filter by document metadata
Returns:
VectorStoreRetriever: Retriever class for VectorStore.
Examples:
.. code-block:: python
# Retrieve more documents with higher diversity
# Useful if your dataset has many similar documents
docsearch.as_retriever(
search_type="mmr",
search_kwargs={'k': 6, 'lambda_mult': 0.25}
)
# Fetch more documents for the MMR algorithm to consider
# But only return the top 5
docsearch.as_retriever(
search_type="mmr",
search_kwargs={'k': 5, 'fetch_k': 50}
)
# Only retrieve documents that have a relevance score
# Above a certain threshold
docsearch.as_retriever(
search_type="similarity_score_threshold",
search_kwargs={'score_threshold': 0.8}
)
# Only get the single most similar document from the dataset
docsearch.as_retriever(search_kwargs={'k': 1})
# Use a filter to only retrieve documents from a specific paper
docsearch.as_retriever(
search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}}
)
"""
tags = kwargs.pop("tags", None) or []
tags.extend(self._get_retriever_tags())
return VectorStoreRetriever(vectorstore=self, **kwargs, tags=tags)
[docs]class VectorStoreRetriever(BaseRetriever):
"""Base Retriever class for VectorStore."""
vectorstore: VectorStore
"""VectorStore to use for retrieval."""
search_type: str = "similarity" | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/vectorstore.html |
9cc02dfe64d1-14 | search_type: str = "similarity"
"""Type of search to perform. Defaults to "similarity"."""
search_kwargs: dict = Field(default_factory=dict)
"""Keyword arguments to pass to the search function."""
allowed_search_types: ClassVar[Collection[str]] = (
"similarity",
"similarity_score_threshold",
"mmr",
)
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
@root_validator()
def validate_search_type(cls, values: Dict) -> Dict:
"""Validate search type."""
search_type = values["search_type"]
if search_type not in cls.allowed_search_types:
raise ValueError(
f"search_type of {search_type} not allowed. Valid values are: "
f"{cls.allowed_search_types}"
)
if search_type == "similarity_score_threshold":
score_threshold = values["search_kwargs"].get("score_threshold")
if (score_threshold is None) or (not isinstance(score_threshold, float)):
raise ValueError(
"`score_threshold` is not specified with a float value(0~1) "
"in `search_kwargs`."
)
return values
def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
) -> List[Document]:
if self.search_type == "similarity":
docs = self.vectorstore.similarity_search(query, **self.search_kwargs)
elif self.search_type == "similarity_score_threshold":
docs_and_similarities = (
self.vectorstore.similarity_search_with_relevance_scores(
query, **self.search_kwargs
)
) | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/vectorstore.html |
9cc02dfe64d1-15 | query, **self.search_kwargs
)
)
docs = [doc for doc, _ in docs_and_similarities]
elif self.search_type == "mmr":
docs = self.vectorstore.max_marginal_relevance_search(
query, **self.search_kwargs
)
else:
raise ValueError(f"search_type of {self.search_type} not allowed.")
return docs
async def _aget_relevant_documents(
self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
) -> List[Document]:
if self.search_type == "similarity":
docs = await self.vectorstore.asimilarity_search(
query, **self.search_kwargs
)
elif self.search_type == "similarity_score_threshold":
docs_and_similarities = (
await self.vectorstore.asimilarity_search_with_relevance_scores(
query, **self.search_kwargs
)
)
docs = [doc for doc, _ in docs_and_similarities]
elif self.search_type == "mmr":
docs = await self.vectorstore.amax_marginal_relevance_search(
query, **self.search_kwargs
)
else:
raise ValueError(f"search_type of {self.search_type} not allowed.")
return docs
[docs] def add_documents(self, documents: List[Document], **kwargs: Any) -> List[str]:
"""Add documents to vectorstore."""
return self.vectorstore.add_documents(documents, **kwargs)
[docs] async def aadd_documents(
self, documents: List[Document], **kwargs: Any
) -> List[str]:
"""Add documents to vectorstore.""" | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/vectorstore.html |
9cc02dfe64d1-16 | ) -> List[str]:
"""Add documents to vectorstore."""
return await self.vectorstore.aadd_documents(documents, **kwargs) | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/vectorstore.html |
52bb77ea5ac5-0 | Source code for langchain.schema.output_parser
from __future__ import annotations
import asyncio
import functools
from abc import ABC, abstractmethod
from typing import (
Any,
AsyncIterator,
Dict,
Generic,
Iterator,
List,
Optional,
Type,
TypeVar,
Union,
)
from typing_extensions import get_args
from langchain.schema.messages import AnyMessage, BaseMessage, BaseMessageChunk
from langchain.schema.output import (
ChatGeneration,
ChatGenerationChunk,
Generation,
GenerationChunk,
)
from langchain.schema.prompt import PromptValue
from langchain.schema.runnable import RunnableConfig, RunnableSerializable
T = TypeVar("T")
[docs]class BaseLLMOutputParser(Generic[T], ABC):
"""Abstract base class for parsing the outputs of a model."""
[docs] @abstractmethod
def parse_result(self, result: List[Generation], *, partial: bool = False) -> T:
"""Parse a list of candidate model Generations into a specific format.
Args:
result: A list of Generations to be parsed. The Generations are assumed
to be different candidate outputs for a single model input.
Returns:
Structured output.
"""
[docs] async def aparse_result(
self, result: List[Generation], *, partial: bool = False
) -> T:
"""Parse a list of candidate model Generations into a specific format.
Args:
result: A list of Generations to be parsed. The Generations are assumed
to be different candidate outputs for a single model input.
Returns:
Structured output.
"""
return await asyncio.get_running_loop().run_in_executor( | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/output_parser.html |
52bb77ea5ac5-1 | """
return await asyncio.get_running_loop().run_in_executor(
None, self.parse_result, result
)
[docs]class BaseGenerationOutputParser(
BaseLLMOutputParser, RunnableSerializable[Union[str, BaseMessage], T]
):
"""Base class to parse the output of an LLM call."""
@property
def InputType(self) -> Any:
return Union[str, AnyMessage]
@property
def OutputType(self) -> Type[T]:
# even though mypy complains this isn't valid,
# it is good enough for pydantic to build the schema from
return T # type: ignore[misc]
[docs] def invoke(
self, input: Union[str, BaseMessage], config: Optional[RunnableConfig] = None
) -> T:
if isinstance(input, BaseMessage):
return self._call_with_config(
lambda inner_input: self.parse_result(
[ChatGeneration(message=inner_input)]
),
input,
config,
run_type="parser",
)
else:
return self._call_with_config(
lambda inner_input: self.parse_result([Generation(text=inner_input)]),
input,
config,
run_type="parser",
)
[docs] async def ainvoke(
self,
input: str | BaseMessage,
config: Optional[RunnableConfig] = None,
**kwargs: Optional[Any],
) -> T:
if isinstance(input, BaseMessage):
return await self._acall_with_config(
lambda inner_input: self.aparse_result(
[ChatGeneration(message=inner_input)]
),
input,
config, | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/output_parser.html |
52bb77ea5ac5-2 | ),
input,
config,
run_type="parser",
)
else:
return await self._acall_with_config(
lambda inner_input: self.aparse_result([Generation(text=inner_input)]),
input,
config,
run_type="parser",
)
[docs]class BaseOutputParser(
BaseLLMOutputParser, RunnableSerializable[Union[str, BaseMessage], T]
):
"""Base class to parse the output of an LLM call.
Output parsers help structure language model responses.
Example:
.. code-block:: python
class BooleanOutputParser(BaseOutputParser[bool]):
true_val: str = "YES"
false_val: str = "NO"
def parse(self, text: str) -> bool:
cleaned_text = text.strip().upper()
if cleaned_text not in (self.true_val.upper(), self.false_val.upper()):
raise OutputParserException(
f"BooleanOutputParser expected output value to either be "
f"{self.true_val} or {self.false_val} (case-insensitive). "
f"Received {cleaned_text}."
)
return cleaned_text == self.true_val.upper()
@property
def _type(self) -> str:
return "boolean_output_parser"
""" # noqa: E501
@property
def InputType(self) -> Any:
return Union[str, AnyMessage]
@property
def OutputType(self) -> Type[T]:
for cls in self.__class__.__orig_bases__: # type: ignore[attr-defined]
type_args = get_args(cls)
if type_args and len(type_args) == 1:
return type_args[0] | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/output_parser.html |
52bb77ea5ac5-3 | return type_args[0]
raise TypeError(
f"Runnable {self.__class__.__name__} doesn't have an inferable OutputType. "
"Override the OutputType property to specify the output type."
)
[docs] def invoke(
self, input: Union[str, BaseMessage], config: Optional[RunnableConfig] = None
) -> T:
if isinstance(input, BaseMessage):
return self._call_with_config(
lambda inner_input: self.parse_result(
[ChatGeneration(message=inner_input)]
),
input,
config,
run_type="parser",
)
else:
return self._call_with_config(
lambda inner_input: self.parse_result([Generation(text=inner_input)]),
input,
config,
run_type="parser",
)
[docs] async def ainvoke(
self,
input: str | BaseMessage,
config: Optional[RunnableConfig] = None,
**kwargs: Optional[Any],
) -> T:
if isinstance(input, BaseMessage):
return await self._acall_with_config(
lambda inner_input: self.aparse_result(
[ChatGeneration(message=inner_input)]
),
input,
config,
run_type="parser",
)
else:
return await self._acall_with_config(
lambda inner_input: self.aparse_result([Generation(text=inner_input)]),
input,
config,
run_type="parser",
)
[docs] def parse_result(self, result: List[Generation], *, partial: bool = False) -> T:
"""Parse a list of candidate model Generations into a specific format. | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/output_parser.html |
52bb77ea5ac5-4 | """Parse a list of candidate model Generations into a specific format.
The return value is parsed from only the first Generation in the result, which
is assumed to be the highest-likelihood Generation.
Args:
result: A list of Generations to be parsed. The Generations are assumed
to be different candidate outputs for a single model input.
Returns:
Structured output.
"""
return self.parse(result[0].text)
[docs] @abstractmethod
def parse(self, text: str) -> T:
"""Parse a single string model output into some structure.
Args:
text: String output of a language model.
Returns:
Structured output.
"""
[docs] async def aparse_result(
self, result: List[Generation], *, partial: bool = False
) -> T:
"""Parse a list of candidate model Generations into a specific format.
The return value is parsed from only the first Generation in the result, which
is assumed to be the highest-likelihood Generation.
Args:
result: A list of Generations to be parsed. The Generations are assumed
to be different candidate outputs for a single model input.
Returns:
Structured output.
"""
return await asyncio.get_running_loop().run_in_executor(
None, functools.partial(self.parse_result, partial=partial), result
)
[docs] async def aparse(self, text: str) -> T:
"""Parse a single string model output into some structure.
Args:
text: String output of a language model.
Returns:
Structured output.
"""
return await asyncio.get_running_loop().run_in_executor(None, self.parse, text) | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/output_parser.html |
52bb77ea5ac5-5 | return await asyncio.get_running_loop().run_in_executor(None, self.parse, text)
# TODO: rename 'completion' -> 'text'.
[docs] def parse_with_prompt(self, completion: str, prompt: PromptValue) -> Any:
"""Parse the output of an LLM call with the input prompt for context.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Args:
completion: String output of a language model.
prompt: Input PromptValue.
Returns:
Structured output
"""
return self.parse(completion)
[docs] def get_format_instructions(self) -> str:
"""Instructions on how the LLM output should be formatted."""
raise NotImplementedError
@property
def _type(self) -> str:
"""Return the output parser type for serialization."""
raise NotImplementedError(
f"_type property is not implemented in class {self.__class__.__name__}."
" This is required for serialization."
)
[docs] def dict(self, **kwargs: Any) -> Dict:
"""Return dictionary representation of output parser."""
output_parser_dict = super().dict(**kwargs)
try:
output_parser_dict["_type"] = self._type
except NotImplementedError:
pass
return output_parser_dict
[docs]class BaseTransformOutputParser(BaseOutputParser[T]):
"""Base class for an output parser that can handle streaming input."""
def _transform(self, input: Iterator[Union[str, BaseMessage]]) -> Iterator[T]:
for chunk in input:
if isinstance(chunk, BaseMessage):
yield self.parse_result([ChatGeneration(message=chunk)])
else: | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/output_parser.html |
52bb77ea5ac5-6 | yield self.parse_result([ChatGeneration(message=chunk)])
else:
yield self.parse_result([Generation(text=chunk)])
async def _atransform(
self, input: AsyncIterator[Union[str, BaseMessage]]
) -> AsyncIterator[T]:
async for chunk in input:
if isinstance(chunk, BaseMessage):
yield self.parse_result([ChatGeneration(message=chunk)])
else:
yield self.parse_result([Generation(text=chunk)])
[docs] def transform(
self,
input: Iterator[Union[str, BaseMessage]],
config: Optional[RunnableConfig] = None,
**kwargs: Any,
) -> Iterator[T]:
yield from self._transform_stream_with_config(
input, self._transform, config, run_type="parser"
)
[docs] async def atransform(
self,
input: AsyncIterator[Union[str, BaseMessage]],
config: Optional[RunnableConfig] = None,
**kwargs: Any,
) -> AsyncIterator[T]:
async for chunk in self._atransform_stream_with_config(
input, self._atransform, config, run_type="parser"
):
yield chunk
[docs]class BaseCumulativeTransformOutputParser(BaseTransformOutputParser[T]):
"""Base class for an output parser that can handle streaming input."""
diff: bool = False
"""In streaming mode, whether to yield diffs between the previous and current
parsed output, or just the current parsed output.
"""
def _diff(self, prev: Optional[T], next: T) -> T:
"""Convert parsed outputs into a diff format. The semantics of this are
up to the output parser."""
raise NotImplementedError() | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/output_parser.html |
52bb77ea5ac5-7 | up to the output parser."""
raise NotImplementedError()
def _transform(self, input: Iterator[Union[str, BaseMessage]]) -> Iterator[Any]:
prev_parsed = None
acc_gen = None
for chunk in input:
if isinstance(chunk, BaseMessageChunk):
chunk_gen: Generation = ChatGenerationChunk(message=chunk)
elif isinstance(chunk, BaseMessage):
chunk_gen = ChatGenerationChunk(
message=BaseMessageChunk(**chunk.dict())
)
else:
chunk_gen = GenerationChunk(text=chunk)
if acc_gen is None:
acc_gen = chunk_gen
else:
acc_gen += chunk_gen
parsed = self.parse_result([acc_gen], partial=True)
if parsed is not None and parsed != prev_parsed:
if self.diff:
yield self._diff(prev_parsed, parsed)
else:
yield parsed
prev_parsed = parsed
async def _atransform(
self, input: AsyncIterator[Union[str, BaseMessage]]
) -> AsyncIterator[T]:
prev_parsed = None
acc_gen = None
async for chunk in input:
if isinstance(chunk, BaseMessageChunk):
chunk_gen: Generation = ChatGenerationChunk(message=chunk)
elif isinstance(chunk, BaseMessage):
chunk_gen = ChatGenerationChunk(
message=BaseMessageChunk(**chunk.dict())
)
else:
chunk_gen = GenerationChunk(text=chunk)
if acc_gen is None:
acc_gen = chunk_gen
else:
acc_gen += chunk_gen
parsed = self.parse_result([acc_gen], partial=True)
if parsed is not None and parsed != prev_parsed:
if self.diff: | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/output_parser.html |
52bb77ea5ac5-8 | if parsed is not None and parsed != prev_parsed:
if self.diff:
yield self._diff(prev_parsed, parsed)
else:
yield parsed
prev_parsed = parsed
[docs]class StrOutputParser(BaseTransformOutputParser[str]):
"""OutputParser that parses LLMResult into the top likely string."""
[docs] @classmethod
def is_lc_serializable(cls) -> bool:
"""Return whether this class is serializable."""
return True
@property
def _type(self) -> str:
"""Return the output parser type for serialization."""
return "default"
[docs] def parse(self, text: str) -> str:
"""Returns the input text with no changes."""
return text
# TODO: Deprecate
NoOpOutputParser = StrOutputParser
[docs]class OutputParserException(ValueError):
"""Exception that output parsers should raise to signify a parsing error.
This exists to differentiate parsing errors from other code or execution errors
that also may arise inside the output parser. OutputParserExceptions will be
available to catch and handle in ways to fix the parsing error, while other
errors will be raised.
Args:
error: The error that's being re-raised or an error message.
observation: String explanation of error which can be passed to a
model to try and remediate the issue.
llm_output: String model output which is error-ing.
send_to_llm: Whether to send the observation and llm_output back to an Agent
after an OutputParserException has been raised. This gives the underlying
model driving the agent the context that the previous output was improperly
structured, in the hopes that it will update the output to the correct
format.
""" | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/output_parser.html |
52bb77ea5ac5-9 | format.
"""
def __init__(
self,
error: Any,
observation: Optional[str] = None,
llm_output: Optional[str] = None,
send_to_llm: bool = False,
):
super(OutputParserException, self).__init__(error)
if send_to_llm:
if observation is None or llm_output is None:
raise ValueError(
"Arguments 'observation' & 'llm_output'"
" are required if 'send_to_llm' is True"
)
self.observation = observation
self.llm_output = llm_output
self.send_to_llm = send_to_llm | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/output_parser.html |
cf75cb40b062-0 | Source code for langchain.schema.language_model
from __future__ import annotations
from abc import ABC, abstractmethod
from functools import lru_cache
from typing import (
TYPE_CHECKING,
Any,
List,
Optional,
Sequence,
Set,
TypeVar,
Union,
)
from typing_extensions import TypeAlias
from langchain.schema.messages import AnyMessage, BaseMessage, get_buffer_string
from langchain.schema.output import LLMResult
from langchain.schema.prompt import PromptValue
from langchain.schema.runnable import RunnableSerializable
from langchain.utils import get_pydantic_field_names
if TYPE_CHECKING:
from langchain.callbacks.manager import Callbacks
@lru_cache(maxsize=None) # Cache the tokenizer
def get_tokenizer() -> Any:
try:
from transformers import GPT2TokenizerFast
except ImportError:
raise ImportError(
"Could not import transformers python package. "
"This is needed in order to calculate get_token_ids. "
"Please install it with `pip install transformers`."
)
# create a GPT-2 tokenizer instance
return GPT2TokenizerFast.from_pretrained("gpt2")
def _get_token_ids_default_method(text: str) -> List[int]:
"""Encode the text into token IDs."""
# get the cached tokenizer
tokenizer = get_tokenizer()
# tokenize the text using the GPT-2 tokenizer
return tokenizer.encode(text)
LanguageModelInput = Union[PromptValue, str, List[BaseMessage]]
LanguageModelOutput = TypeVar("LanguageModelOutput")
[docs]class BaseLanguageModel(
RunnableSerializable[LanguageModelInput, LanguageModelOutput], ABC
):
"""Abstract base class for interfacing with language models. | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/language_model.html |
cf75cb40b062-1 | ):
"""Abstract base class for interfacing with language models.
All language model wrappers inherit from BaseLanguageModel.
Exposes three main methods:
- generate_prompt: generate language model outputs for a sequence of prompt
values. A prompt value is a model input that can be converted to any language
model input format (string or messages).
- predict: pass in a single string to a language model and return a string
prediction.
- predict_messages: pass in a sequence of BaseMessages (corresponding to a single
model call) to a language model and return a BaseMessage prediction.
Each of these has an equivalent asynchronous method.
"""
@property
def InputType(self) -> TypeAlias:
"""Get the input type for this runnable."""
from langchain.prompts.base import StringPromptValue
from langchain.prompts.chat import ChatPromptValueConcrete
# This is a version of LanguageModelInput which replaces the abstract
# base class BaseMessage with a union of its subclasses, which makes
# for a much better schema.
return Union[
str,
Union[StringPromptValue, ChatPromptValueConcrete],
List[AnyMessage],
]
[docs] @abstractmethod
def generate_prompt(
self,
prompts: List[PromptValue],
stop: Optional[List[str]] = None,
callbacks: Callbacks = None,
**kwargs: Any,
) -> LLMResult:
"""Pass a sequence of prompts to the model and return model generations.
This method should make use of batched calls for models that expose a batched
API.
Use this method when you want to:
1. take advantage of batched calls, | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/language_model.html |
cf75cb40b062-2 | 1. take advantage of batched calls,
2. need more output from the model than just the top generated value,
3. are building chains that are agnostic to the underlying language model
type (e.g., pure text completion models vs chat models).
Args:
prompts: List of PromptValues. A PromptValue is an object that can be
converted to match the format of any language model (string for pure
text generation models and BaseMessages for chat models).
stop: Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
callbacks: Callbacks to pass through. Used for executing additional
functionality, such as logging or streaming, throughout generation.
**kwargs: Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns:
An LLMResult, which contains a list of candidate Generations for each input
prompt and additional model provider-specific output.
"""
[docs] @abstractmethod
async def agenerate_prompt(
self,
prompts: List[PromptValue],
stop: Optional[List[str]] = None,
callbacks: Callbacks = None,
**kwargs: Any,
) -> LLMResult:
"""Asynchronously pass a sequence of prompts and return model generations.
This method should make use of batched calls for models that expose a batched
API.
Use this method when you want to:
1. take advantage of batched calls,
2. need more output from the model than just the top generated value,
3. are building chains that are agnostic to the underlying language model
type (e.g., pure text completion models vs chat models). | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/language_model.html |
cf75cb40b062-3 | type (e.g., pure text completion models vs chat models).
Args:
prompts: List of PromptValues. A PromptValue is an object that can be
converted to match the format of any language model (string for pure
text generation models and BaseMessages for chat models).
stop: Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
callbacks: Callbacks to pass through. Used for executing additional
functionality, such as logging or streaming, throughout generation.
**kwargs: Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns:
An LLMResult, which contains a list of candidate Generations for each input
prompt and additional model provider-specific output.
"""
[docs] @abstractmethod
def predict(
self, text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any
) -> str:
"""Pass a single string input to the model and return a string prediction.
Use this method when passing in raw text. If you want to pass in specific
types of chat messages, use predict_messages.
Args:
text: String input to pass to the model.
stop: Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
**kwargs: Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns:
Top model prediction as a string.
"""
[docs] @abstractmethod
def predict_messages(
self,
messages: List[BaseMessage],
*,
stop: Optional[Sequence[str]] = None,
**kwargs: Any, | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/language_model.html |
cf75cb40b062-4 | stop: Optional[Sequence[str]] = None,
**kwargs: Any,
) -> BaseMessage:
"""Pass a message sequence to the model and return a message prediction.
Use this method when passing in chat messages. If you want to pass in raw text,
use predict.
Args:
messages: A sequence of chat messages corresponding to a single model input.
stop: Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
**kwargs: Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns:
Top model prediction as a message.
"""
[docs] @abstractmethod
async def apredict(
self, text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any
) -> str:
"""Asynchronously pass a string to the model and return a string prediction.
Use this method when calling pure text generation models and only the top
candidate generation is needed.
Args:
text: String input to pass to the model.
stop: Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
**kwargs: Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns:
Top model prediction as a string.
"""
[docs] @abstractmethod
async def apredict_messages(
self,
messages: List[BaseMessage],
*,
stop: Optional[Sequence[str]] = None,
**kwargs: Any,
) -> BaseMessage:
"""Asynchronously pass messages to the model and return a message prediction. | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/language_model.html |
cf75cb40b062-5 | """Asynchronously pass messages to the model and return a message prediction.
Use this method when calling chat models and only the top
candidate generation is needed.
Args:
messages: A sequence of chat messages corresponding to a single model input.
stop: Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
**kwargs: Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns:
Top model prediction as a message.
"""
[docs] def get_token_ids(self, text: str) -> List[int]:
"""Return the ordered ids of the tokens in a text.
Args:
text: The string input to tokenize.
Returns:
A list of ids corresponding to the tokens in the text, in order they occur
in the text.
"""
return _get_token_ids_default_method(text)
[docs] def get_num_tokens(self, text: str) -> int:
"""Get the number of tokens present in the text.
Useful for checking if an input will fit in a model's context window.
Args:
text: The string input to tokenize.
Returns:
The integer number of tokens in the text.
"""
return len(self.get_token_ids(text))
[docs] def get_num_tokens_from_messages(self, messages: List[BaseMessage]) -> int:
"""Get the number of tokens in the messages.
Useful for checking if an input will fit in a model's context window.
Args:
messages: The message inputs to tokenize.
Returns:
The sum of the number of tokens across the messages.
""" | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/language_model.html |
cf75cb40b062-6 | Returns:
The sum of the number of tokens across the messages.
"""
return sum([self.get_num_tokens(get_buffer_string([m])) for m in messages])
@classmethod
def _all_required_field_names(cls) -> Set:
"""DEPRECATED: Kept for backwards compatibility.
Use get_pydantic_field_names.
"""
return get_pydantic_field_names(cls) | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/language_model.html |
c0e052313c97-0 | Source code for langchain.schema.storage
from abc import ABC, abstractmethod
from typing import Generic, Iterator, List, Optional, Sequence, Tuple, TypeVar, Union
K = TypeVar("K")
V = TypeVar("V")
[docs]class BaseStore(Generic[K, V], ABC):
"""Abstract interface for a key-value store."""
[docs] @abstractmethod
def mget(self, keys: Sequence[K]) -> List[Optional[V]]:
"""Get the values associated with the given keys.
Args:
keys (Sequence[K]): A sequence of keys.
Returns:
A sequence of optional values associated with the keys.
If a key is not found, the corresponding value will be None.
"""
[docs] @abstractmethod
def mset(self, key_value_pairs: Sequence[Tuple[K, V]]) -> None:
"""Set the values for the given keys.
Args:
key_value_pairs (Sequence[Tuple[K, V]]): A sequence of key-value pairs.
"""
[docs] @abstractmethod
def mdelete(self, keys: Sequence[K]) -> None:
"""Delete the given keys and their associated values.
Args:
keys (Sequence[K]): A sequence of keys to delete.
"""
[docs] @abstractmethod
def yield_keys(
self, *, prefix: Optional[str] = None
) -> Union[Iterator[K], Iterator[str]]:
"""Get an iterator over keys that match the given prefix.
Args:
prefix (str): The prefix to match.
Returns:
Iterator[K | str]: An iterator over keys that match the given prefix.
This method is allowed to return an iterator over either K or str | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/storage.html |
c0e052313c97-1 | This method is allowed to return an iterator over either K or str
depending on what makes more sense for the given store.
""" | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/storage.html |
cf826ffac3d0-0 | Source code for langchain.schema.embeddings
import asyncio
from abc import ABC, abstractmethod
from typing import List
[docs]class Embeddings(ABC):
"""Interface for embedding models."""
[docs] @abstractmethod
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Embed search docs."""
[docs] @abstractmethod
def embed_query(self, text: str) -> List[float]:
"""Embed query text."""
[docs] async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
"""Asynchronous Embed search docs."""
return await asyncio.get_running_loop().run_in_executor(
None, self.embed_documents, texts
)
[docs] async def aembed_query(self, text: str) -> List[float]:
"""Asynchronous Embed query text."""
return await asyncio.get_running_loop().run_in_executor(
None, self.embed_query, text
) | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/embeddings.html |
c54a189d87e1-0 | Source code for langchain.schema.memory
from __future__ import annotations
from abc import ABC, abstractmethod
from typing import Any, Dict, List
from langchain.load.serializable import Serializable
[docs]class BaseMemory(Serializable, ABC):
"""Abstract base class for memory in Chains.
Memory refers to state in Chains. Memory can be used to store information about
past executions of a Chain and inject that information into the inputs of
future executions of the Chain. For example, for conversational Chains Memory
can be used to store conversations and automatically add them to future model
prompts so that the model has the necessary context to respond coherently to
the latest input.
Example:
.. code-block:: python
class SimpleMemory(BaseMemory):
memories: Dict[str, Any] = dict()
@property
def memory_variables(self) -> List[str]:
return list(self.memories.keys())
def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
return self.memories
def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
pass
def clear(self) -> None:
pass
""" # noqa: E501
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
@property
@abstractmethod
def memory_variables(self) -> List[str]:
"""The string keys this memory class will add to chain inputs."""
[docs] @abstractmethod
def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
"""Return key-value pairs given the text input to the chain."""
[docs] @abstractmethod | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/memory.html |
c54a189d87e1-1 | [docs] @abstractmethod
def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
"""Save the context of this chain run to memory."""
[docs] @abstractmethod
def clear(self) -> None:
"""Clear memory contents.""" | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/memory.html |
abf03308bc75-0 | Source code for langchain.schema.messages
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Dict, List, Sequence, Union
from typing_extensions import Literal
from langchain.load.serializable import Serializable
from langchain.pydantic_v1 import Extra, Field
if TYPE_CHECKING:
from langchain.prompts.chat import ChatPromptTemplate
[docs]def get_buffer_string(
messages: Sequence[BaseMessage], human_prefix: str = "Human", ai_prefix: str = "AI"
) -> str:
"""Convert sequence of Messages to strings and concatenate them into one string.
Args:
messages: Messages to be converted to strings.
human_prefix: The prefix to prepend to contents of HumanMessages.
ai_prefix: THe prefix to prepend to contents of AIMessages.
Returns:
A single string concatenation of all input messages.
Example:
.. code-block:: python
from langchain.schema import AIMessage, HumanMessage
messages = [
HumanMessage(content="Hi, how are you?"),
AIMessage(content="Good, how are you?"),
]
get_buffer_string(messages)
# -> "Human: Hi, how are you?\nAI: Good, how are you?"
"""
string_messages = []
for m in messages:
if isinstance(m, HumanMessage):
role = human_prefix
elif isinstance(m, AIMessage):
role = ai_prefix
elif isinstance(m, SystemMessage):
role = "System"
elif isinstance(m, FunctionMessage):
role = "Function"
elif isinstance(m, ChatMessage):
role = m.role
else:
raise ValueError(f"Got unsupported message type: {m}") | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/messages.html |
abf03308bc75-1 | else:
raise ValueError(f"Got unsupported message type: {m}")
message = f"{role}: {m.content}"
if isinstance(m, AIMessage) and "function_call" in m.additional_kwargs:
message += f"{m.additional_kwargs['function_call']}"
string_messages.append(message)
return "\n".join(string_messages)
[docs]class BaseMessage(Serializable):
"""The base abstract Message class.
Messages are the inputs and outputs of ChatModels.
"""
content: Union[str, List[Union[str, Dict]]]
"""The string contents of the message."""
additional_kwargs: dict = Field(default_factory=dict)
"""Any additional information."""
type: str
class Config:
extra = Extra.allow
[docs] @classmethod
def is_lc_serializable(cls) -> bool:
"""Return whether this class is serializable."""
return True
def __add__(self, other: Any) -> ChatPromptTemplate:
from langchain.prompts.chat import ChatPromptTemplate
prompt = ChatPromptTemplate(messages=[self])
return prompt + other
[docs]def merge_content(
first_content: Union[str, List[Union[str, Dict]]],
second_content: Union[str, List[Union[str, Dict]]],
) -> Union[str, List[Union[str, Dict]]]:
# If first chunk is a string
if isinstance(first_content, str):
# If the second chunk is also a string, then merge them naively
if isinstance(second_content, str):
return first_content + second_content
# If the second chunk is a list, add the first chunk to the start of the list
else: | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/messages.html |
abf03308bc75-2 | else:
return_list: List[Union[str, Dict]] = [first_content]
return return_list + second_content
# If both are lists, merge them naively
elif isinstance(second_content, List):
return first_content + second_content
# If the first content is a list, and the second content is a string
else:
# If the last element of the first content is a string
# Add the second content to the last element
if isinstance(first_content[-1], str):
return first_content[:-1] + [first_content[-1] + second_content]
else:
# Otherwise, add the second content as a new element of the list
return first_content + [second_content]
[docs]class BaseMessageChunk(BaseMessage):
"""A Message chunk, which can be concatenated with other Message chunks."""
def _merge_kwargs_dict(
self, left: Dict[str, Any], right: Dict[str, Any]
) -> Dict[str, Any]:
"""Merge additional_kwargs from another BaseMessageChunk into this one."""
merged = left.copy()
for k, v in right.items():
if k not in merged:
merged[k] = v
elif type(merged[k]) != type(v):
raise ValueError(
f'additional_kwargs["{k}"] already exists in this message,'
" but with a different type."
)
elif isinstance(merged[k], str):
merged[k] += v
elif isinstance(merged[k], dict):
merged[k] = self._merge_kwargs_dict(merged[k], v)
else:
raise ValueError(
f"Additional kwargs key {k} already exists in this message."
)
return merged | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/messages.html |
abf03308bc75-3 | )
return merged
def __add__(self, other: Any) -> BaseMessageChunk: # type: ignore
if isinstance(other, BaseMessageChunk):
# If both are (subclasses of) BaseMessageChunk,
# concat into a single BaseMessageChunk
if isinstance(self, ChatMessageChunk):
return self.__class__(
role=self.role,
content=merge_content(self.content, other.content),
additional_kwargs=self._merge_kwargs_dict(
self.additional_kwargs, other.additional_kwargs
),
)
return self.__class__(
content=merge_content(self.content, other.content),
additional_kwargs=self._merge_kwargs_dict(
self.additional_kwargs, other.additional_kwargs
),
)
else:
raise TypeError(
'unsupported operand type(s) for +: "'
f"{self.__class__.__name__}"
f'" and "{other.__class__.__name__}"'
)
[docs]class HumanMessage(BaseMessage):
"""A Message from a human."""
example: bool = False
"""Whether this Message is being passed in to the model as part of an example
conversation.
"""
type: Literal["human"] = "human"
HumanMessage.update_forward_refs()
[docs]class HumanMessageChunk(HumanMessage, BaseMessageChunk):
"""A Human Message chunk."""
# Ignoring mypy re-assignment here since we're overriding the value
# to make sure that the chunk variant can be discriminated from the
# non-chunk variant.
type: Literal["HumanMessageChunk"] = "HumanMessageChunk" # type: ignore[assignment] # noqa: E501
[docs]class AIMessage(BaseMessage): | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/messages.html |
abf03308bc75-4 | [docs]class AIMessage(BaseMessage):
"""A Message from an AI."""
example: bool = False
"""Whether this Message is being passed in to the model as part of an example
conversation.
"""
type: Literal["ai"] = "ai"
AIMessage.update_forward_refs()
[docs]class AIMessageChunk(AIMessage, BaseMessageChunk):
"""A Message chunk from an AI."""
# Ignoring mypy re-assignment here since we're overriding the value
# to make sure that the chunk variant can be discriminated from the
# non-chunk variant.
type: Literal["AIMessageChunk"] = "AIMessageChunk" # type: ignore[assignment] # noqa: E501
def __add__(self, other: Any) -> BaseMessageChunk: # type: ignore
if isinstance(other, AIMessageChunk):
if self.example != other.example:
raise ValueError(
"Cannot concatenate AIMessageChunks with different example values."
)
return self.__class__(
example=self.example,
content=merge_content(self.content, other.content),
additional_kwargs=self._merge_kwargs_dict(
self.additional_kwargs, other.additional_kwargs
),
)
return super().__add__(other)
[docs]class SystemMessage(BaseMessage):
"""A Message for priming AI behavior, usually passed in as the first of a sequence
of input messages.
"""
type: Literal["system"] = "system"
SystemMessage.update_forward_refs()
[docs]class SystemMessageChunk(SystemMessage, BaseMessageChunk):
"""A System Message chunk."""
# Ignoring mypy re-assignment here since we're overriding the value | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/messages.html |
abf03308bc75-5 | # Ignoring mypy re-assignment here since we're overriding the value
# to make sure that the chunk variant can be discriminated from the
# non-chunk variant.
type: Literal["SystemMessageChunk"] = "SystemMessageChunk" # type: ignore[assignment] # noqa: E501
[docs]class FunctionMessage(BaseMessage):
"""A Message for passing the result of executing a function back to a model."""
name: str
"""The name of the function that was executed."""
type: Literal["function"] = "function"
FunctionMessage.update_forward_refs()
[docs]class FunctionMessageChunk(FunctionMessage, BaseMessageChunk):
"""A Function Message chunk."""
# Ignoring mypy re-assignment here since we're overriding the value
# to make sure that the chunk variant can be discriminated from the
# non-chunk variant.
type: Literal["FunctionMessageChunk"] = "FunctionMessageChunk" # type: ignore[assignment]
def __add__(self, other: Any) -> BaseMessageChunk: # type: ignore
if isinstance(other, FunctionMessageChunk):
if self.name != other.name:
raise ValueError(
"Cannot concatenate FunctionMessageChunks with different names."
)
return self.__class__(
name=self.name,
content=merge_content(self.content, other.content),
additional_kwargs=self._merge_kwargs_dict(
self.additional_kwargs, other.additional_kwargs
),
)
return super().__add__(other)
[docs]class ToolMessage(BaseMessage):
"""A Message for passing the result of executing a tool back to a model."""
tool_call_id: str
"""Tool call that this message is responding to.""" | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/messages.html |
abf03308bc75-6 | tool_call_id: str
"""Tool call that this message is responding to."""
type: Literal["tool"] = "tool"
ToolMessage.update_forward_refs()
[docs]class ToolMessageChunk(ToolMessage, BaseMessageChunk):
"""A Tool Message chunk."""
# Ignoring mypy re-assignment here since we're overriding the value
# to make sure that the chunk variant can be discriminated from the
# non-chunk variant.
type: Literal["ToolMessageChunk"] = "ToolMessageChunk" # type: ignore[assignment]
def __add__(self, other: Any) -> BaseMessageChunk: # type: ignore
if isinstance(other, ToolMessageChunk):
if self.tool_call_id != other.tool_call_id:
raise ValueError(
"Cannot concatenate ToolMessageChunks with different names."
)
return self.__class__(
tool_call_id=self.tool_call_id,
content=merge_content(self.content, other.content),
additional_kwargs=self._merge_kwargs_dict(
self.additional_kwargs, other.additional_kwargs
),
)
return super().__add__(other)
[docs]class ChatMessage(BaseMessage):
"""A Message that can be assigned an arbitrary speaker (i.e. role)."""
role: str
"""The speaker / role of the Message."""
type: Literal["chat"] = "chat"
ChatMessage.update_forward_refs()
[docs]class ChatMessageChunk(ChatMessage, BaseMessageChunk):
"""A Chat Message chunk."""
# Ignoring mypy re-assignment here since we're overriding the value
# to make sure that the chunk variant can be discriminated from the
# non-chunk variant. | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/messages.html |
abf03308bc75-7 | # non-chunk variant.
type: Literal["ChatMessageChunk"] = "ChatMessageChunk" # type: ignore
def __add__(self, other: Any) -> BaseMessageChunk: # type: ignore
if isinstance(other, ChatMessageChunk):
if self.role != other.role:
raise ValueError(
"Cannot concatenate ChatMessageChunks with different roles."
)
return self.__class__(
role=self.role,
content=merge_content(self.content, other.content),
additional_kwargs=self._merge_kwargs_dict(
self.additional_kwargs, other.additional_kwargs
),
)
return super().__add__(other)
AnyMessage = Union[
AIMessage, HumanMessage, ChatMessage, SystemMessage, FunctionMessage, ToolMessage
]
def _message_to_dict(message: BaseMessage) -> dict:
return {"type": message.type, "data": message.dict()}
[docs]def messages_to_dict(messages: Sequence[BaseMessage]) -> List[dict]:
"""Convert a sequence of Messages to a list of dictionaries.
Args:
messages: Sequence of messages (as BaseMessages) to convert.
Returns:
List of messages as dicts.
"""
return [_message_to_dict(m) for m in messages]
def _message_from_dict(message: dict) -> BaseMessage:
_type = message["type"]
if _type == "human":
return HumanMessage(**message["data"])
elif _type == "ai":
return AIMessage(**message["data"])
elif _type == "system":
return SystemMessage(**message["data"])
elif _type == "chat":
return ChatMessage(**message["data"])
elif _type == "function": | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/messages.html |
abf03308bc75-8 | return ChatMessage(**message["data"])
elif _type == "function":
return FunctionMessage(**message["data"])
elif _type == "tool":
return ToolMessage(**message["data"])
else:
raise ValueError(f"Got unexpected message type: {_type}")
[docs]def messages_from_dict(messages: List[dict]) -> List[BaseMessage]:
"""Convert a sequence of messages from dicts to Message objects.
Args:
messages: Sequence of messages (as dicts) to convert.
Returns:
List of messages (BaseMessages).
"""
return [_message_from_dict(m) for m in messages] | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/messages.html |
f7c3fcd00905-0 | Source code for langchain.schema.output
from __future__ import annotations
from copy import deepcopy
from typing import Any, Dict, List, Literal, Optional
from uuid import UUID
from langchain.load.serializable import Serializable
from langchain.pydantic_v1 import BaseModel, root_validator
from langchain.schema.messages import BaseMessage, BaseMessageChunk
[docs]class Generation(Serializable):
"""A single text generation output."""
text: str
"""Generated text output."""
generation_info: Optional[Dict[str, Any]] = None
"""Raw response from the provider. May include things like the
reason for finishing or token log probabilities.
"""
type: Literal["Generation"] = "Generation"
"""Type is used exclusively for serialization purposes."""
# TODO: add log probs as separate attribute
[docs] @classmethod
def is_lc_serializable(cls) -> bool:
"""Return whether this class is serializable."""
return True
[docs]class GenerationChunk(Generation):
"""A Generation chunk, which can be concatenated with other Generation chunks."""
def __add__(self, other: GenerationChunk) -> GenerationChunk:
if isinstance(other, GenerationChunk):
generation_info = (
{**(self.generation_info or {}), **(other.generation_info or {})}
if self.generation_info is not None or other.generation_info is not None
else None
)
return GenerationChunk(
text=self.text + other.text,
generation_info=generation_info,
)
else:
raise TypeError(
f"unsupported operand type(s) for +: '{type(self)}' and '{type(other)}'"
)
[docs]class ChatGeneration(Generation):
"""A single chat generation output.""" | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/output.html |
f7c3fcd00905-1 | [docs]class ChatGeneration(Generation):
"""A single chat generation output."""
text: str = ""
"""*SHOULD NOT BE SET DIRECTLY* The text contents of the output message."""
message: BaseMessage
"""The message output by the chat model."""
# Override type to be ChatGeneration, ignore mypy error as this is intentional
type: Literal["ChatGeneration"] = "ChatGeneration" # type: ignore[assignment]
"""Type is used exclusively for serialization purposes."""
@root_validator
def set_text(cls, values: Dict[str, Any]) -> Dict[str, Any]:
"""Set the text attribute to be the contents of the message."""
try:
values["text"] = values["message"].content
except (KeyError, AttributeError) as e:
raise ValueError("Error while initializing ChatGeneration") from e
return values
[docs]class ChatGenerationChunk(ChatGeneration):
"""A ChatGeneration chunk, which can be concatenated with other
ChatGeneration chunks.
Attributes:
message: The message chunk output by the chat model.
"""
message: BaseMessageChunk
# Override type to be ChatGeneration, ignore mypy error as this is intentional
type: Literal["ChatGenerationChunk"] = "ChatGenerationChunk" # type: ignore[assignment] # noqa: E501
"""Type is used exclusively for serialization purposes."""
def __add__(self, other: ChatGenerationChunk) -> ChatGenerationChunk:
if isinstance(other, ChatGenerationChunk):
generation_info = (
{**(self.generation_info or {}), **(other.generation_info or {})}
if self.generation_info is not None or other.generation_info is not None
else None
) | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/output.html |
f7c3fcd00905-2 | else None
)
return ChatGenerationChunk(
message=self.message + other.message,
generation_info=generation_info,
)
else:
raise TypeError(
f"unsupported operand type(s) for +: '{type(self)}' and '{type(other)}'"
)
[docs]class RunInfo(BaseModel):
"""Class that contains metadata for a single execution of a Chain or model."""
run_id: UUID
"""A unique identifier for the model or chain run."""
[docs]class ChatResult(BaseModel):
"""Class that contains all results for a single chat model call."""
generations: List[ChatGeneration]
"""List of the chat generations. This is a List because an input can have multiple
candidate generations.
"""
llm_output: Optional[dict] = None
"""For arbitrary LLM provider specific output."""
[docs]class LLMResult(BaseModel):
"""Class that contains all results for a batched LLM call."""
generations: List[List[Generation]]
"""List of generated outputs. This is a List[List[]] because
each input could have multiple candidate generations."""
llm_output: Optional[dict] = None
"""Arbitrary LLM provider-specific output."""
run: Optional[List[RunInfo]] = None
"""List of metadata info for model call for each input."""
[docs] def flatten(self) -> List[LLMResult]:
"""Flatten generations into a single list.
Unpack List[List[Generation]] -> List[LLMResult] where each returned LLMResult
contains only a single Generation. If token usage information is available,
it is kept only for the LLMResult corresponding to the top-choice | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/output.html |
f7c3fcd00905-3 | it is kept only for the LLMResult corresponding to the top-choice
Generation, to avoid over-counting of token usage downstream.
Returns:
List of LLMResults where each returned LLMResult contains a single
Generation.
"""
llm_results = []
for i, gen_list in enumerate(self.generations):
# Avoid double counting tokens in OpenAICallback
if i == 0:
llm_results.append(
LLMResult(
generations=[gen_list],
llm_output=self.llm_output,
)
)
else:
if self.llm_output is not None:
llm_output = deepcopy(self.llm_output)
llm_output["token_usage"] = dict()
else:
llm_output = None
llm_results.append(
LLMResult(
generations=[gen_list],
llm_output=llm_output,
)
)
return llm_results
def __eq__(self, other: object) -> bool:
"""Check for LLMResult equality by ignoring any metadata related to runs."""
if not isinstance(other, LLMResult):
return NotImplemented
return (
self.generations == other.generations
and self.llm_output == other.llm_output
) | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/output.html |
8cf9574238da-0 | Source code for langchain.schema.callbacks.manager
from __future__ import annotations
import asyncio
import functools
import logging
import os
import uuid
from concurrent.futures import ThreadPoolExecutor
from contextlib import asynccontextmanager, contextmanager
from contextvars import ContextVar
from typing import (
TYPE_CHECKING,
Any,
AsyncGenerator,
Coroutine,
Dict,
Generator,
List,
Optional,
Sequence,
Tuple,
Type,
TypeVar,
Union,
cast,
)
from uuid import UUID
from langsmith import utils as ls_utils
from langsmith.run_helpers import get_run_tree_context
from tenacity import RetryCallState
from langchain.schema import (
AgentAction,
AgentFinish,
Document,
LLMResult,
)
from langchain.schema.callbacks.base import (
BaseCallbackHandler,
BaseCallbackManager,
Callbacks,
ChainManagerMixin,
LLMManagerMixin,
RetrieverManagerMixin,
RunManagerMixin,
ToolManagerMixin,
)
from langchain.schema.callbacks.stdout import StdOutCallbackHandler
from langchain.schema.callbacks.tracers import run_collector
from langchain.schema.callbacks.tracers.langchain import (
LangChainTracer,
)
from langchain.schema.callbacks.tracers.langchain_v1 import (
LangChainTracerV1,
TracerSessionV1,
)
from langchain.schema.callbacks.tracers.stdout import ConsoleCallbackHandler
from langchain.schema.messages import BaseMessage, get_buffer_string
from langchain.schema.output import ChatGenerationChunk, GenerationChunk
if TYPE_CHECKING:
from langsmith import Client as LangSmithClient
logger = logging.getLogger(__name__) | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/callbacks/manager.html |
8cf9574238da-1 | from langsmith import Client as LangSmithClient
logger = logging.getLogger(__name__)
tracing_callback_var: ContextVar[Optional[LangChainTracerV1]] = ContextVar( # noqa: E501
"tracing_callback", default=None
)
tracing_v2_callback_var: ContextVar[Optional[LangChainTracer]] = ContextVar( # noqa: E501
"tracing_callback_v2", default=None
)
run_collector_var: ContextVar[
Optional[run_collector.RunCollectorCallbackHandler]
] = ContextVar( # noqa: E501
"run_collector", default=None
)
def _get_debug() -> bool:
from langchain.globals import get_debug
return get_debug()
[docs]@contextmanager
def tracing_enabled(
session_name: str = "default",
) -> Generator[TracerSessionV1, None, None]:
"""Get the Deprecated LangChainTracer in a context manager.
Args:
session_name (str, optional): The name of the session.
Defaults to "default".
Returns:
TracerSessionV1: The LangChainTracer session.
Example:
>>> with tracing_enabled() as session:
... # Use the LangChainTracer session
"""
cb = LangChainTracerV1()
session = cast(TracerSessionV1, cb.load_session(session_name))
try:
tracing_callback_var.set(cb)
yield session
finally:
tracing_callback_var.set(None)
[docs]@contextmanager
def tracing_v2_enabled(
project_name: Optional[str] = None,
*,
example_id: Optional[Union[str, UUID]] = None, | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/callbacks/manager.html |
8cf9574238da-2 | *,
example_id: Optional[Union[str, UUID]] = None,
tags: Optional[List[str]] = None,
client: Optional[LangSmithClient] = None,
) -> Generator[LangChainTracer, None, None]:
"""Instruct LangChain to log all runs in context to LangSmith.
Args:
project_name (str, optional): The name of the project.
Defaults to "default".
example_id (str or UUID, optional): The ID of the example.
Defaults to None.
tags (List[str], optional): The tags to add to the run.
Defaults to None.
Returns:
None
Example:
>>> with tracing_v2_enabled():
... # LangChain code will automatically be traced
You can use this to fetch the LangSmith run URL:
>>> with tracing_v2_enabled() as cb:
... chain.invoke("foo")
... run_url = cb.get_run_url()
"""
if isinstance(example_id, str):
example_id = UUID(example_id)
cb = LangChainTracer(
example_id=example_id,
project_name=project_name,
tags=tags,
client=client,
)
try:
tracing_v2_callback_var.set(cb)
yield cb
finally:
tracing_v2_callback_var.set(None)
[docs]@contextmanager
def collect_runs() -> Generator[run_collector.RunCollectorCallbackHandler, None, None]:
"""Collect all run traces in context.
Returns:
run_collector.RunCollectorCallbackHandler: The run collector callback handler.
Example:
>>> with collect_runs() as runs_cb:
chain.invoke("foo") | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/callbacks/manager.html |
8cf9574238da-3 | >>> with collect_runs() as runs_cb:
chain.invoke("foo")
run_id = runs_cb.traced_runs[0].id
"""
cb = run_collector.RunCollectorCallbackHandler()
run_collector_var.set(cb)
yield cb
run_collector_var.set(None)
def _get_trace_callbacks(
project_name: Optional[str] = None,
example_id: Optional[Union[str, UUID]] = None,
callback_manager: Optional[Union[CallbackManager, AsyncCallbackManager]] = None,
) -> Callbacks:
if _tracing_v2_is_enabled():
project_name_ = project_name or _get_tracer_project()
tracer = tracing_v2_callback_var.get() or LangChainTracer(
project_name=project_name_,
example_id=example_id,
)
if callback_manager is None:
cb = cast(Callbacks, [tracer])
else:
if not any(
isinstance(handler, LangChainTracer)
for handler in callback_manager.handlers
):
callback_manager.add_handler(tracer, True)
# If it already has a LangChainTracer, we don't need to add another one.
# this would likely mess up the trace hierarchy.
cb = callback_manager
else:
cb = None
return cb
[docs]@contextmanager
def trace_as_chain_group(
group_name: str,
callback_manager: Optional[CallbackManager] = None,
*,
inputs: Optional[Dict[str, Any]] = None,
project_name: Optional[str] = None,
example_id: Optional[Union[str, UUID]] = None,
run_id: Optional[UUID] = None, | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/callbacks/manager.html |
8cf9574238da-4 | run_id: Optional[UUID] = None,
tags: Optional[List[str]] = None,
) -> Generator[CallbackManagerForChainGroup, None, None]:
"""Get a callback manager for a chain group in a context manager.
Useful for grouping different calls together as a single run even if
they aren't composed in a single chain.
Args:
group_name (str): The name of the chain group.
callback_manager (CallbackManager, optional): The callback manager to use.
inputs (Dict[str, Any], optional): The inputs to the chain group.
project_name (str, optional): The name of the project.
Defaults to None.
example_id (str or UUID, optional): The ID of the example.
Defaults to None.
run_id (UUID, optional): The ID of the run.
tags (List[str], optional): The inheritable tags to apply to all runs.
Defaults to None.
Note: must have LANGCHAIN_TRACING_V2 env var set to true to see the trace in LangSmith.
Returns:
CallbackManagerForChainGroup: The callback manager for the chain group.
Example:
.. code-block:: python
llm_input = "Foo"
with trace_as_chain_group("group_name", inputs={"input": llm_input}) as manager:
# Use the callback manager for the chain group
res = llm.predict(llm_input, callbacks=manager)
manager.on_chain_end({"output": res})
""" # noqa: E501
cb = _get_trace_callbacks(
project_name, example_id, callback_manager=callback_manager
)
cm = CallbackManager.configure(
inheritable_callbacks=cb,
inheritable_tags=tags,
) | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/callbacks/manager.html |
8cf9574238da-5 | inheritable_callbacks=cb,
inheritable_tags=tags,
)
run_manager = cm.on_chain_start({"name": group_name}, inputs or {}, run_id=run_id)
child_cm = run_manager.get_child()
group_cm = CallbackManagerForChainGroup(
child_cm.handlers,
child_cm.inheritable_handlers,
child_cm.parent_run_id,
parent_run_manager=run_manager,
tags=child_cm.tags,
inheritable_tags=child_cm.inheritable_tags,
metadata=child_cm.metadata,
inheritable_metadata=child_cm.inheritable_metadata,
)
try:
yield group_cm
except Exception as e:
if not group_cm.ended:
run_manager.on_chain_error(e)
raise e
else:
if not group_cm.ended:
run_manager.on_chain_end({})
[docs]@asynccontextmanager
async def atrace_as_chain_group(
group_name: str,
callback_manager: Optional[AsyncCallbackManager] = None,
*,
inputs: Optional[Dict[str, Any]] = None,
project_name: Optional[str] = None,
example_id: Optional[Union[str, UUID]] = None,
run_id: Optional[UUID] = None,
tags: Optional[List[str]] = None,
) -> AsyncGenerator[AsyncCallbackManagerForChainGroup, None]:
"""Get an async callback manager for a chain group in a context manager.
Useful for grouping different async calls together as a single run even if
they aren't composed in a single chain.
Args:
group_name (str): The name of the chain group.
callback_manager (AsyncCallbackManager, optional): The async callback manager to use, | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/callbacks/manager.html |
8cf9574238da-6 | callback_manager (AsyncCallbackManager, optional): The async callback manager to use,
which manages tracing and other callback behavior.
project_name (str, optional): The name of the project.
Defaults to None.
example_id (str or UUID, optional): The ID of the example.
Defaults to None.
run_id (UUID, optional): The ID of the run.
tags (List[str], optional): The inheritable tags to apply to all runs.
Defaults to None.
Returns:
AsyncCallbackManager: The async callback manager for the chain group.
Note: must have LANGCHAIN_TRACING_V2 env var set to true to see the trace in LangSmith.
Example:
.. code-block:: python
llm_input = "Foo"
async with atrace_as_chain_group("group_name", inputs={"input": llm_input}) as manager:
# Use the async callback manager for the chain group
res = await llm.apredict(llm_input, callbacks=manager)
await manager.on_chain_end({"output": res})
""" # noqa: E501
cb = _get_trace_callbacks(
project_name, example_id, callback_manager=callback_manager
)
cm = AsyncCallbackManager.configure(inheritable_callbacks=cb, inheritable_tags=tags)
run_manager = await cm.on_chain_start(
{"name": group_name}, inputs or {}, run_id=run_id
)
child_cm = run_manager.get_child()
group_cm = AsyncCallbackManagerForChainGroup(
child_cm.handlers,
child_cm.inheritable_handlers,
child_cm.parent_run_id,
parent_run_manager=run_manager,
tags=child_cm.tags,
inheritable_tags=child_cm.inheritable_tags, | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/callbacks/manager.html |
8cf9574238da-7 | inheritable_tags=child_cm.inheritable_tags,
metadata=child_cm.metadata,
inheritable_metadata=child_cm.inheritable_metadata,
)
try:
yield group_cm
except Exception as e:
if not group_cm.ended:
await run_manager.on_chain_error(e)
raise e
else:
if not group_cm.ended:
await run_manager.on_chain_end({})
[docs]def handle_event(
handlers: List[BaseCallbackHandler],
event_name: str,
ignore_condition_name: Optional[str],
*args: Any,
**kwargs: Any,
) -> None:
"""Generic event handler for CallbackManager.
Note: This function is used by langserve to handle events.
Args:
handlers: The list of handlers that will handle the event
event_name: The name of the event (e.g., "on_llm_start")
ignore_condition_name: Name of the attribute defined on handler
that if True will cause the handler to be skipped for the given event
*args: The arguments to pass to the event handler
**kwargs: The keyword arguments to pass to the event handler
"""
coros: List[Coroutine[Any, Any, Any]] = []
try:
message_strings: Optional[List[str]] = None
for handler in handlers:
try:
if ignore_condition_name is None or not getattr(
handler, ignore_condition_name
):
event = getattr(handler, event_name)(*args, **kwargs)
if asyncio.iscoroutine(event):
coros.append(event)
except NotImplementedError as e:
if event_name == "on_chat_model_start":
if message_strings is None: | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/callbacks/manager.html |
8cf9574238da-8 | if event_name == "on_chat_model_start":
if message_strings is None:
message_strings = [get_buffer_string(m) for m in args[1]]
handle_event(
[handler],
"on_llm_start",
"ignore_llm",
args[0],
message_strings,
*args[2:],
**kwargs,
)
else:
handler_name = handler.__class__.__name__
logger.warning(
f"NotImplementedError in {handler_name}.{event_name}"
f" callback: {repr(e)}"
)
except Exception as e:
logger.warning(
f"Error in {handler.__class__.__name__}.{event_name} callback:"
f" {repr(e)}"
)
if handler.raise_error:
raise e
finally:
if coros:
try:
# Raises RuntimeError if there is no current event loop.
asyncio.get_running_loop()
loop_running = True
except RuntimeError:
loop_running = False
if loop_running:
# If we try to submit this coroutine to the running loop
# we end up in a deadlock, as we'd have gotten here from a
# running coroutine, which we cannot interrupt to run this one.
# The solution is to create a new loop in a new thread.
with ThreadPoolExecutor(1) as executor:
executor.submit(_run_coros, coros).result()
else:
_run_coros(coros)
def _run_coros(coros: List[Coroutine[Any, Any, Any]]) -> None:
if hasattr(asyncio, "Runner"):
# Python 3.11+ | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/callbacks/manager.html |
8cf9574238da-9 | if hasattr(asyncio, "Runner"):
# Python 3.11+
# Run the coroutines in a new event loop, taking care to
# - install signal handlers
# - run pending tasks scheduled by `coros`
# - close asyncgens and executors
# - close the loop
with asyncio.Runner() as runner:
# Run the coroutine, get the result
for coro in coros:
runner.run(coro)
# Run pending tasks scheduled by coros until they are all done
while pending := asyncio.all_tasks(runner.get_loop()):
runner.run(asyncio.wait(pending))
else:
# Before Python 3.11 we need to run each coroutine in a new event loop
# as the Runner api is not available.
for coro in coros:
asyncio.run(coro)
async def _ahandle_event_for_handler(
handler: BaseCallbackHandler,
event_name: str,
ignore_condition_name: Optional[str],
*args: Any,
**kwargs: Any,
) -> None:
try:
if ignore_condition_name is None or not getattr(handler, ignore_condition_name):
event = getattr(handler, event_name)
if asyncio.iscoroutinefunction(event):
await event(*args, **kwargs)
else:
if handler.run_inline:
event(*args, **kwargs)
else:
await asyncio.get_event_loop().run_in_executor(
None, functools.partial(event, *args, **kwargs)
)
except NotImplementedError as e:
if event_name == "on_chat_model_start":
message_strings = [get_buffer_string(m) for m in args[1]] | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/callbacks/manager.html |
8cf9574238da-10 | message_strings = [get_buffer_string(m) for m in args[1]]
await _ahandle_event_for_handler(
handler,
"on_llm_start",
"ignore_llm",
args[0],
message_strings,
*args[2:],
**kwargs,
)
else:
logger.warning(
f"NotImplementedError in {handler.__class__.__name__}.{event_name}"
f" callback: {repr(e)}"
)
except Exception as e:
logger.warning(
f"Error in {handler.__class__.__name__}.{event_name} callback:"
f" {repr(e)}"
)
if handler.raise_error:
raise e
[docs]async def ahandle_event(
handlers: List[BaseCallbackHandler],
event_name: str,
ignore_condition_name: Optional[str],
*args: Any,
**kwargs: Any,
) -> None:
"""Generic event handler for AsyncCallbackManager.
Note: This function is used by langserve to handle events.
Args:
handlers: The list of handlers that will handle the event
event_name: The name of the event (e.g., "on_llm_start")
ignore_condition_name: Name of the attribute defined on handler
that if True will cause the handler to be skipped for the given event
*args: The arguments to pass to the event handler
**kwargs: The keyword arguments to pass to the event handler
"""
for handler in [h for h in handlers if h.run_inline]:
await _ahandle_event_for_handler(
handler, event_name, ignore_condition_name, *args, **kwargs
)
await asyncio.gather(
*( | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/callbacks/manager.html |
8cf9574238da-11 | )
await asyncio.gather(
*(
_ahandle_event_for_handler(
handler, event_name, ignore_condition_name, *args, **kwargs
)
for handler in handlers
if not handler.run_inline
)
)
BRM = TypeVar("BRM", bound="BaseRunManager")
[docs]class BaseRunManager(RunManagerMixin):
"""Base class for run manager (a bound callback manager)."""
[docs] def __init__(
self,
*,
run_id: UUID,
handlers: List[BaseCallbackHandler],
inheritable_handlers: List[BaseCallbackHandler],
parent_run_id: Optional[UUID] = None,
tags: Optional[List[str]] = None,
inheritable_tags: Optional[List[str]] = None,
metadata: Optional[Dict[str, Any]] = None,
inheritable_metadata: Optional[Dict[str, Any]] = None,
) -> None:
"""Initialize the run manager.
Args:
run_id (UUID): The ID of the run.
handlers (List[BaseCallbackHandler]): The list of handlers.
inheritable_handlers (List[BaseCallbackHandler]):
The list of inheritable handlers.
parent_run_id (UUID, optional): The ID of the parent run.
Defaults to None.
tags (Optional[List[str]]): The list of tags.
inheritable_tags (Optional[List[str]]): The list of inheritable tags.
metadata (Optional[Dict[str, Any]]): The metadata.
inheritable_metadata (Optional[Dict[str, Any]]): The inheritable metadata.
"""
self.run_id = run_id
self.handlers = handlers
self.inheritable_handlers = inheritable_handlers | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/callbacks/manager.html |
8cf9574238da-12 | self.handlers = handlers
self.inheritable_handlers = inheritable_handlers
self.parent_run_id = parent_run_id
self.tags = tags or []
self.inheritable_tags = inheritable_tags or []
self.metadata = metadata or {}
self.inheritable_metadata = inheritable_metadata or {}
[docs] @classmethod
def get_noop_manager(cls: Type[BRM]) -> BRM:
"""Return a manager that doesn't perform any operations.
Returns:
BaseRunManager: The noop manager.
"""
return cls(
run_id=uuid.uuid4(),
handlers=[],
inheritable_handlers=[],
tags=[],
inheritable_tags=[],
metadata={},
inheritable_metadata={},
)
[docs]class RunManager(BaseRunManager):
"""Sync Run Manager."""
[docs] def on_text(
self,
text: str,
**kwargs: Any,
) -> Any:
"""Run when text is received.
Args:
text (str): The received text.
Returns:
Any: The result of the callback.
"""
handle_event(
self.handlers,
"on_text",
None,
text,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
tags=self.tags,
**kwargs,
)
[docs] def on_retry(
self,
retry_state: RetryCallState,
**kwargs: Any,
) -> None:
handle_event(
self.handlers,
"on_retry",
"ignore_retry",
retry_state,
run_id=self.run_id,
parent_run_id=self.parent_run_id, | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/callbacks/manager.html |
8cf9574238da-13 | run_id=self.run_id,
parent_run_id=self.parent_run_id,
tags=self.tags,
**kwargs,
)
[docs]class ParentRunManager(RunManager):
"""Sync Parent Run Manager."""
[docs] def get_child(self, tag: Optional[str] = None) -> CallbackManager:
"""Get a child callback manager.
Args:
tag (str, optional): The tag for the child callback manager.
Defaults to None.
Returns:
CallbackManager: The child callback manager.
"""
manager = CallbackManager(handlers=[], parent_run_id=self.run_id)
manager.set_handlers(self.inheritable_handlers)
manager.add_tags(self.inheritable_tags)
manager.add_metadata(self.inheritable_metadata)
if tag is not None:
manager.add_tags([tag], False)
return manager
[docs]class AsyncRunManager(BaseRunManager):
"""Async Run Manager."""
[docs] async def on_text(
self,
text: str,
**kwargs: Any,
) -> Any:
"""Run when text is received.
Args:
text (str): The received text.
Returns:
Any: The result of the callback.
"""
await ahandle_event(
self.handlers,
"on_text",
None,
text,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
tags=self.tags,
**kwargs,
)
[docs] async def on_retry(
self,
retry_state: RetryCallState,
**kwargs: Any,
) -> None:
await ahandle_event(
self.handlers, | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/callbacks/manager.html |
8cf9574238da-14 | ) -> None:
await ahandle_event(
self.handlers,
"on_retry",
"ignore_retry",
retry_state,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
tags=self.tags,
**kwargs,
)
[docs]class AsyncParentRunManager(AsyncRunManager):
"""Async Parent Run Manager."""
[docs] def get_child(self, tag: Optional[str] = None) -> AsyncCallbackManager:
"""Get a child callback manager.
Args:
tag (str, optional): The tag for the child callback manager.
Defaults to None.
Returns:
AsyncCallbackManager: The child callback manager.
"""
manager = AsyncCallbackManager(handlers=[], parent_run_id=self.run_id)
manager.set_handlers(self.inheritable_handlers)
manager.add_tags(self.inheritable_tags)
manager.add_metadata(self.inheritable_metadata)
if tag is not None:
manager.add_tags([tag], False)
return manager
[docs]class CallbackManagerForLLMRun(RunManager, LLMManagerMixin):
"""Callback manager for LLM run."""
[docs] def on_llm_new_token(
self,
token: str,
*,
chunk: Optional[Union[GenerationChunk, ChatGenerationChunk]] = None,
**kwargs: Any,
) -> None:
"""Run when LLM generates a new token.
Args:
token (str): The new token.
"""
handle_event(
self.handlers,
"on_llm_new_token",
"ignore_llm",
token=token,
run_id=self.run_id,
parent_run_id=self.parent_run_id, | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/callbacks/manager.html |
8cf9574238da-15 | run_id=self.run_id,
parent_run_id=self.parent_run_id,
tags=self.tags,
chunk=chunk,
**kwargs,
)
[docs] def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
"""Run when LLM ends running.
Args:
response (LLMResult): The LLM result.
"""
handle_event(
self.handlers,
"on_llm_end",
"ignore_llm",
response,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
tags=self.tags,
**kwargs,
)
[docs] def on_llm_error(
self,
error: BaseException,
**kwargs: Any,
) -> None:
"""Run when LLM errors.
Args:
error (Exception or KeyboardInterrupt): The error.
"""
handle_event(
self.handlers,
"on_llm_error",
"ignore_llm",
error,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
tags=self.tags,
**kwargs,
)
[docs]class AsyncCallbackManagerForLLMRun(AsyncRunManager, LLMManagerMixin):
"""Async callback manager for LLM run."""
[docs] async def on_llm_new_token(
self,
token: str,
*,
chunk: Optional[Union[GenerationChunk, ChatGenerationChunk]] = None,
**kwargs: Any,
) -> None:
"""Run when LLM generates a new token.
Args:
token (str): The new token.
""" | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/callbacks/manager.html |
8cf9574238da-16 | Args:
token (str): The new token.
"""
await ahandle_event(
self.handlers,
"on_llm_new_token",
"ignore_llm",
token,
chunk=chunk,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
tags=self.tags,
**kwargs,
)
[docs] async def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
"""Run when LLM ends running.
Args:
response (LLMResult): The LLM result.
"""
await ahandle_event(
self.handlers,
"on_llm_end",
"ignore_llm",
response,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
tags=self.tags,
**kwargs,
)
[docs] async def on_llm_error(
self,
error: BaseException,
**kwargs: Any,
) -> None:
"""Run when LLM errors.
Args:
error (Exception or KeyboardInterrupt): The error.
"""
await ahandle_event(
self.handlers,
"on_llm_error",
"ignore_llm",
error,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
tags=self.tags,
**kwargs,
)
[docs]class CallbackManagerForChainRun(ParentRunManager, ChainManagerMixin):
"""Callback manager for chain run."""
[docs] def on_chain_end(self, outputs: Union[Dict[str, Any], Any], **kwargs: Any) -> None:
"""Run when chain ends running. | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/callbacks/manager.html |
8cf9574238da-17 | """Run when chain ends running.
Args:
outputs (Union[Dict[str, Any], Any]): The outputs of the chain.
"""
handle_event(
self.handlers,
"on_chain_end",
"ignore_chain",
outputs,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
tags=self.tags,
**kwargs,
)
[docs] def on_chain_error(
self,
error: BaseException,
**kwargs: Any,
) -> None:
"""Run when chain errors.
Args:
error (Exception or KeyboardInterrupt): The error.
"""
handle_event(
self.handlers,
"on_chain_error",
"ignore_chain",
error,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
tags=self.tags,
**kwargs,
)
[docs] def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:
"""Run when agent action is received.
Args:
action (AgentAction): The agent action.
Returns:
Any: The result of the callback.
"""
handle_event(
self.handlers,
"on_agent_action",
"ignore_agent",
action,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
tags=self.tags,
**kwargs,
)
[docs] def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> Any:
"""Run when agent finish is received.
Args:
finish (AgentFinish): The agent finish.
Returns: | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/callbacks/manager.html |
8cf9574238da-18 | Args:
finish (AgentFinish): The agent finish.
Returns:
Any: The result of the callback.
"""
handle_event(
self.handlers,
"on_agent_finish",
"ignore_agent",
finish,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
tags=self.tags,
**kwargs,
)
[docs]class AsyncCallbackManagerForChainRun(AsyncParentRunManager, ChainManagerMixin):
"""Async callback manager for chain run."""
[docs] async def on_chain_end(
self, outputs: Union[Dict[str, Any], Any], **kwargs: Any
) -> None:
"""Run when chain ends running.
Args:
outputs (Union[Dict[str, Any], Any]): The outputs of the chain.
"""
await ahandle_event(
self.handlers,
"on_chain_end",
"ignore_chain",
outputs,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
tags=self.tags,
**kwargs,
)
[docs] async def on_chain_error(
self,
error: BaseException,
**kwargs: Any,
) -> None:
"""Run when chain errors.
Args:
error (Exception or KeyboardInterrupt): The error.
"""
await ahandle_event(
self.handlers,
"on_chain_error",
"ignore_chain",
error,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
tags=self.tags,
**kwargs,
)
[docs] async def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any: | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/callbacks/manager.html |
8cf9574238da-19 | """Run when agent action is received.
Args:
action (AgentAction): The agent action.
Returns:
Any: The result of the callback.
"""
await ahandle_event(
self.handlers,
"on_agent_action",
"ignore_agent",
action,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
tags=self.tags,
**kwargs,
)
[docs] async def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> Any:
"""Run when agent finish is received.
Args:
finish (AgentFinish): The agent finish.
Returns:
Any: The result of the callback.
"""
await ahandle_event(
self.handlers,
"on_agent_finish",
"ignore_agent",
finish,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
tags=self.tags,
**kwargs,
)
[docs]class CallbackManagerForToolRun(ParentRunManager, ToolManagerMixin):
"""Callback manager for tool run."""
[docs] def on_tool_end(
self,
output: str,
**kwargs: Any,
) -> None:
"""Run when tool ends running.
Args:
output (str): The output of the tool.
"""
handle_event(
self.handlers,
"on_tool_end",
"ignore_agent",
output,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
tags=self.tags,
**kwargs,
)
[docs] def on_tool_error(
self,
error: BaseException, | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/callbacks/manager.html |
8cf9574238da-20 | self,
error: BaseException,
**kwargs: Any,
) -> None:
"""Run when tool errors.
Args:
error (Exception or KeyboardInterrupt): The error.
"""
handle_event(
self.handlers,
"on_tool_error",
"ignore_agent",
error,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
tags=self.tags,
**kwargs,
)
[docs]class AsyncCallbackManagerForToolRun(AsyncParentRunManager, ToolManagerMixin):
"""Async callback manager for tool run."""
[docs] async def on_tool_end(self, output: str, **kwargs: Any) -> None:
"""Run when tool ends running.
Args:
output (str): The output of the tool.
"""
await ahandle_event(
self.handlers,
"on_tool_end",
"ignore_agent",
output,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
tags=self.tags,
**kwargs,
)
[docs] async def on_tool_error(
self,
error: BaseException,
**kwargs: Any,
) -> None:
"""Run when tool errors.
Args:
error (Exception or KeyboardInterrupt): The error.
"""
await ahandle_event(
self.handlers,
"on_tool_error",
"ignore_agent",
error,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
tags=self.tags,
**kwargs,
)
[docs]class CallbackManagerForRetrieverRun(ParentRunManager, RetrieverManagerMixin): | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/callbacks/manager.html |
8cf9574238da-21 | """Callback manager for retriever run."""
[docs] def on_retriever_end(
self,
documents: Sequence[Document],
**kwargs: Any,
) -> None:
"""Run when retriever ends running."""
handle_event(
self.handlers,
"on_retriever_end",
"ignore_retriever",
documents,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
tags=self.tags,
**kwargs,
)
[docs] def on_retriever_error(
self,
error: BaseException,
**kwargs: Any,
) -> None:
"""Run when retriever errors."""
handle_event(
self.handlers,
"on_retriever_error",
"ignore_retriever",
error,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
tags=self.tags,
**kwargs,
)
[docs]class AsyncCallbackManagerForRetrieverRun(
AsyncParentRunManager,
RetrieverManagerMixin,
):
"""Async callback manager for retriever run."""
[docs] async def on_retriever_end(
self, documents: Sequence[Document], **kwargs: Any
) -> None:
"""Run when retriever ends running."""
await ahandle_event(
self.handlers,
"on_retriever_end",
"ignore_retriever",
documents,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
tags=self.tags,
**kwargs,
)
[docs] async def on_retriever_error(
self, | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/callbacks/manager.html |
8cf9574238da-22 | )
[docs] async def on_retriever_error(
self,
error: BaseException,
**kwargs: Any,
) -> None:
"""Run when retriever errors."""
await ahandle_event(
self.handlers,
"on_retriever_error",
"ignore_retriever",
error,
run_id=self.run_id,
parent_run_id=self.parent_run_id,
tags=self.tags,
**kwargs,
)
[docs]class CallbackManager(BaseCallbackManager):
"""Callback manager that handles callbacks from LangChain."""
[docs] def on_llm_start(
self,
serialized: Dict[str, Any],
prompts: List[str],
**kwargs: Any,
) -> List[CallbackManagerForLLMRun]:
"""Run when LLM starts running.
Args:
serialized (Dict[str, Any]): The serialized LLM.
prompts (List[str]): The list of prompts.
run_id (UUID, optional): The ID of the run. Defaults to None.
Returns:
List[CallbackManagerForLLMRun]: A callback manager for each
prompt as an LLM run.
"""
managers = []
for prompt in prompts:
run_id_ = uuid.uuid4()
handle_event(
self.handlers,
"on_llm_start",
"ignore_llm",
serialized,
[prompt],
run_id=run_id_,
parent_run_id=self.parent_run_id,
tags=self.tags,
metadata=self.metadata,
**kwargs,
)
managers.append(
CallbackManagerForLLMRun(
run_id=run_id_, | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/callbacks/manager.html |
8cf9574238da-23 | CallbackManagerForLLMRun(
run_id=run_id_,
handlers=self.handlers,
inheritable_handlers=self.inheritable_handlers,
parent_run_id=self.parent_run_id,
tags=self.tags,
inheritable_tags=self.inheritable_tags,
metadata=self.metadata,
inheritable_metadata=self.inheritable_metadata,
)
)
return managers
[docs] def on_chat_model_start(
self,
serialized: Dict[str, Any],
messages: List[List[BaseMessage]],
**kwargs: Any,
) -> List[CallbackManagerForLLMRun]:
"""Run when LLM starts running.
Args:
serialized (Dict[str, Any]): The serialized LLM.
messages (List[List[BaseMessage]]): The list of messages.
run_id (UUID, optional): The ID of the run. Defaults to None.
Returns:
List[CallbackManagerForLLMRun]: A callback manager for each
list of messages as an LLM run.
"""
managers = []
for message_list in messages:
run_id_ = uuid.uuid4()
handle_event(
self.handlers,
"on_chat_model_start",
"ignore_chat_model",
serialized,
[message_list],
run_id=run_id_,
parent_run_id=self.parent_run_id,
tags=self.tags,
metadata=self.metadata,
**kwargs,
)
managers.append(
CallbackManagerForLLMRun(
run_id=run_id_,
handlers=self.handlers,
inheritable_handlers=self.inheritable_handlers,
parent_run_id=self.parent_run_id,
tags=self.tags, | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/callbacks/manager.html |
8cf9574238da-24 | parent_run_id=self.parent_run_id,
tags=self.tags,
inheritable_tags=self.inheritable_tags,
metadata=self.metadata,
inheritable_metadata=self.inheritable_metadata,
)
)
return managers
[docs] def on_chain_start(
self,
serialized: Dict[str, Any],
inputs: Union[Dict[str, Any], Any],
run_id: Optional[UUID] = None,
**kwargs: Any,
) -> CallbackManagerForChainRun:
"""Run when chain starts running.
Args:
serialized (Dict[str, Any]): The serialized chain.
inputs (Union[Dict[str, Any], Any]): The inputs to the chain.
run_id (UUID, optional): The ID of the run. Defaults to None.
Returns:
CallbackManagerForChainRun: The callback manager for the chain run.
"""
if run_id is None:
run_id = uuid.uuid4()
handle_event(
self.handlers,
"on_chain_start",
"ignore_chain",
serialized,
inputs,
run_id=run_id,
parent_run_id=self.parent_run_id,
tags=self.tags,
metadata=self.metadata,
**kwargs,
)
return CallbackManagerForChainRun(
run_id=run_id,
handlers=self.handlers,
inheritable_handlers=self.inheritable_handlers,
parent_run_id=self.parent_run_id,
tags=self.tags,
inheritable_tags=self.inheritable_tags,
metadata=self.metadata,
inheritable_metadata=self.inheritable_metadata,
)
[docs] def on_tool_start(
self,
serialized: Dict[str, Any], | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/callbacks/manager.html |
8cf9574238da-25 | self,
serialized: Dict[str, Any],
input_str: str,
run_id: Optional[UUID] = None,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> CallbackManagerForToolRun:
"""Run when tool starts running.
Args:
serialized (Dict[str, Any]): The serialized tool.
input_str (str): The input to the tool.
run_id (UUID, optional): The ID of the run. Defaults to None.
parent_run_id (UUID, optional): The ID of the parent run. Defaults to None.
Returns:
CallbackManagerForToolRun: The callback manager for the tool run.
"""
if run_id is None:
run_id = uuid.uuid4()
handle_event(
self.handlers,
"on_tool_start",
"ignore_agent",
serialized,
input_str,
run_id=run_id,
parent_run_id=self.parent_run_id,
tags=self.tags,
metadata=self.metadata,
**kwargs,
)
return CallbackManagerForToolRun(
run_id=run_id,
handlers=self.handlers,
inheritable_handlers=self.inheritable_handlers,
parent_run_id=self.parent_run_id,
tags=self.tags,
inheritable_tags=self.inheritable_tags,
metadata=self.metadata,
inheritable_metadata=self.inheritable_metadata,
)
[docs] def on_retriever_start(
self,
serialized: Dict[str, Any],
query: str,
run_id: Optional[UUID] = None,
parent_run_id: Optional[UUID] = None,
**kwargs: Any, | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/callbacks/manager.html |
8cf9574238da-26 | parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> CallbackManagerForRetrieverRun:
"""Run when retriever starts running."""
if run_id is None:
run_id = uuid.uuid4()
handle_event(
self.handlers,
"on_retriever_start",
"ignore_retriever",
serialized,
query,
run_id=run_id,
parent_run_id=self.parent_run_id,
tags=self.tags,
metadata=self.metadata,
**kwargs,
)
return CallbackManagerForRetrieverRun(
run_id=run_id,
handlers=self.handlers,
inheritable_handlers=self.inheritable_handlers,
parent_run_id=self.parent_run_id,
tags=self.tags,
inheritable_tags=self.inheritable_tags,
metadata=self.metadata,
inheritable_metadata=self.inheritable_metadata,
)
[docs] @classmethod
def configure(
cls,
inheritable_callbacks: Callbacks = None,
local_callbacks: Callbacks = None,
verbose: bool = False,
inheritable_tags: Optional[List[str]] = None,
local_tags: Optional[List[str]] = None,
inheritable_metadata: Optional[Dict[str, Any]] = None,
local_metadata: Optional[Dict[str, Any]] = None,
) -> CallbackManager:
"""Configure the callback manager.
Args:
inheritable_callbacks (Optional[Callbacks], optional): The inheritable
callbacks. Defaults to None.
local_callbacks (Optional[Callbacks], optional): The local callbacks.
Defaults to None.
verbose (bool, optional): Whether to enable verbose mode. Defaults to False. | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/callbacks/manager.html |
8cf9574238da-27 | verbose (bool, optional): Whether to enable verbose mode. Defaults to False.
inheritable_tags (Optional[List[str]], optional): The inheritable tags.
Defaults to None.
local_tags (Optional[List[str]], optional): The local tags.
Defaults to None.
inheritable_metadata (Optional[Dict[str, Any]], optional): The inheritable
metadata. Defaults to None.
local_metadata (Optional[Dict[str, Any]], optional): The local metadata.
Defaults to None.
Returns:
CallbackManager: The configured callback manager.
"""
return _configure(
cls,
inheritable_callbacks,
local_callbacks,
verbose,
inheritable_tags,
local_tags,
inheritable_metadata,
local_metadata,
)
[docs]class CallbackManagerForChainGroup(CallbackManager):
"""Callback manager for the chain group."""
[docs] def __init__(
self,
handlers: List[BaseCallbackHandler],
inheritable_handlers: Optional[List[BaseCallbackHandler]] = None,
parent_run_id: Optional[UUID] = None,
*,
parent_run_manager: CallbackManagerForChainRun,
**kwargs: Any,
) -> None:
super().__init__(
handlers,
inheritable_handlers,
parent_run_id,
**kwargs,
)
self.parent_run_manager = parent_run_manager
self.ended = False
[docs] def copy(self) -> CallbackManagerForChainGroup:
return self.__class__(
handlers=self.handlers,
inheritable_handlers=self.inheritable_handlers,
parent_run_id=self.parent_run_id,
tags=self.tags,
inheritable_tags=self.inheritable_tags, | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/callbacks/manager.html |
8cf9574238da-28 | tags=self.tags,
inheritable_tags=self.inheritable_tags,
metadata=self.metadata,
inheritable_metadata=self.inheritable_metadata,
parent_run_manager=self.parent_run_manager,
)
[docs] def on_chain_end(self, outputs: Union[Dict[str, Any], Any], **kwargs: Any) -> None:
"""Run when traced chain group ends.
Args:
outputs (Union[Dict[str, Any], Any]): The outputs of the chain.
"""
self.ended = True
return self.parent_run_manager.on_chain_end(outputs, **kwargs)
[docs] def on_chain_error(
self,
error: BaseException,
**kwargs: Any,
) -> None:
"""Run when chain errors.
Args:
error (Exception or KeyboardInterrupt): The error.
"""
self.ended = True
return self.parent_run_manager.on_chain_error(error, **kwargs)
[docs]class AsyncCallbackManager(BaseCallbackManager):
"""Async callback manager that handles callbacks from LangChain."""
@property
def is_async(self) -> bool:
"""Return whether the handler is async."""
return True
[docs] async def on_llm_start(
self,
serialized: Dict[str, Any],
prompts: List[str],
**kwargs: Any,
) -> List[AsyncCallbackManagerForLLMRun]:
"""Run when LLM starts running.
Args:
serialized (Dict[str, Any]): The serialized LLM.
prompts (List[str]): The list of prompts.
run_id (UUID, optional): The ID of the run. Defaults to None.
Returns: | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/callbacks/manager.html |
8cf9574238da-29 | Returns:
List[AsyncCallbackManagerForLLMRun]: The list of async
callback managers, one for each LLM Run corresponding
to each prompt.
"""
tasks = []
managers = []
for prompt in prompts:
run_id_ = uuid.uuid4()
tasks.append(
ahandle_event(
self.handlers,
"on_llm_start",
"ignore_llm",
serialized,
[prompt],
run_id=run_id_,
parent_run_id=self.parent_run_id,
tags=self.tags,
metadata=self.metadata,
**kwargs,
)
)
managers.append(
AsyncCallbackManagerForLLMRun(
run_id=run_id_,
handlers=self.handlers,
inheritable_handlers=self.inheritable_handlers,
parent_run_id=self.parent_run_id,
tags=self.tags,
inheritable_tags=self.inheritable_tags,
metadata=self.metadata,
inheritable_metadata=self.inheritable_metadata,
)
)
await asyncio.gather(*tasks)
return managers
[docs] async def on_chat_model_start(
self,
serialized: Dict[str, Any],
messages: List[List[BaseMessage]],
**kwargs: Any,
) -> List[AsyncCallbackManagerForLLMRun]:
"""Run when LLM starts running.
Args:
serialized (Dict[str, Any]): The serialized LLM.
messages (List[List[BaseMessage]]): The list of messages.
run_id (UUID, optional): The ID of the run. Defaults to None.
Returns:
List[AsyncCallbackManagerForLLMRun]: The list of | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/callbacks/manager.html |
8cf9574238da-30 | Returns:
List[AsyncCallbackManagerForLLMRun]: The list of
async callback managers, one for each LLM Run
corresponding to each inner message list.
"""
tasks = []
managers = []
for message_list in messages:
run_id_ = uuid.uuid4()
tasks.append(
ahandle_event(
self.handlers,
"on_chat_model_start",
"ignore_chat_model",
serialized,
[message_list],
run_id=run_id_,
parent_run_id=self.parent_run_id,
tags=self.tags,
metadata=self.metadata,
**kwargs,
)
)
managers.append(
AsyncCallbackManagerForLLMRun(
run_id=run_id_,
handlers=self.handlers,
inheritable_handlers=self.inheritable_handlers,
parent_run_id=self.parent_run_id,
tags=self.tags,
inheritable_tags=self.inheritable_tags,
metadata=self.metadata,
inheritable_metadata=self.inheritable_metadata,
)
)
await asyncio.gather(*tasks)
return managers
[docs] async def on_chain_start(
self,
serialized: Dict[str, Any],
inputs: Union[Dict[str, Any], Any],
run_id: Optional[UUID] = None,
**kwargs: Any,
) -> AsyncCallbackManagerForChainRun:
"""Run when chain starts running.
Args:
serialized (Dict[str, Any]): The serialized chain.
inputs (Union[Dict[str, Any], Any]): The inputs to the chain.
run_id (UUID, optional): The ID of the run. Defaults to None.
Returns: | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/callbacks/manager.html |
8cf9574238da-31 | Returns:
AsyncCallbackManagerForChainRun: The async callback manager
for the chain run.
"""
if run_id is None:
run_id = uuid.uuid4()
await ahandle_event(
self.handlers,
"on_chain_start",
"ignore_chain",
serialized,
inputs,
run_id=run_id,
parent_run_id=self.parent_run_id,
tags=self.tags,
metadata=self.metadata,
**kwargs,
)
return AsyncCallbackManagerForChainRun(
run_id=run_id,
handlers=self.handlers,
inheritable_handlers=self.inheritable_handlers,
parent_run_id=self.parent_run_id,
tags=self.tags,
inheritable_tags=self.inheritable_tags,
metadata=self.metadata,
inheritable_metadata=self.inheritable_metadata,
)
[docs] async def on_tool_start(
self,
serialized: Dict[str, Any],
input_str: str,
run_id: Optional[UUID] = None,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> AsyncCallbackManagerForToolRun:
"""Run when tool starts running.
Args:
serialized (Dict[str, Any]): The serialized tool.
input_str (str): The input to the tool.
run_id (UUID, optional): The ID of the run. Defaults to None.
parent_run_id (UUID, optional): The ID of the parent run.
Defaults to None.
Returns:
AsyncCallbackManagerForToolRun: The async callback manager
for the tool run.
"""
if run_id is None:
run_id = uuid.uuid4()
await ahandle_event( | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/callbacks/manager.html |
8cf9574238da-32 | run_id = uuid.uuid4()
await ahandle_event(
self.handlers,
"on_tool_start",
"ignore_agent",
serialized,
input_str,
run_id=run_id,
parent_run_id=self.parent_run_id,
tags=self.tags,
metadata=self.metadata,
**kwargs,
)
return AsyncCallbackManagerForToolRun(
run_id=run_id,
handlers=self.handlers,
inheritable_handlers=self.inheritable_handlers,
parent_run_id=self.parent_run_id,
tags=self.tags,
inheritable_tags=self.inheritable_tags,
metadata=self.metadata,
inheritable_metadata=self.inheritable_metadata,
)
[docs] async def on_retriever_start(
self,
serialized: Dict[str, Any],
query: str,
run_id: Optional[UUID] = None,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> AsyncCallbackManagerForRetrieverRun:
"""Run when retriever starts running."""
if run_id is None:
run_id = uuid.uuid4()
await ahandle_event(
self.handlers,
"on_retriever_start",
"ignore_retriever",
serialized,
query,
run_id=run_id,
parent_run_id=self.parent_run_id,
tags=self.tags,
metadata=self.metadata,
**kwargs,
)
return AsyncCallbackManagerForRetrieverRun(
run_id=run_id,
handlers=self.handlers,
inheritable_handlers=self.inheritable_handlers,
parent_run_id=self.parent_run_id,
tags=self.tags, | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/callbacks/manager.html |
8cf9574238da-33 | parent_run_id=self.parent_run_id,
tags=self.tags,
inheritable_tags=self.inheritable_tags,
metadata=self.metadata,
inheritable_metadata=self.inheritable_metadata,
)
[docs] @classmethod
def configure(
cls,
inheritable_callbacks: Callbacks = None,
local_callbacks: Callbacks = None,
verbose: bool = False,
inheritable_tags: Optional[List[str]] = None,
local_tags: Optional[List[str]] = None,
inheritable_metadata: Optional[Dict[str, Any]] = None,
local_metadata: Optional[Dict[str, Any]] = None,
) -> AsyncCallbackManager:
"""Configure the async callback manager.
Args:
inheritable_callbacks (Optional[Callbacks], optional): The inheritable
callbacks. Defaults to None.
local_callbacks (Optional[Callbacks], optional): The local callbacks.
Defaults to None.
verbose (bool, optional): Whether to enable verbose mode. Defaults to False.
inheritable_tags (Optional[List[str]], optional): The inheritable tags.
Defaults to None.
local_tags (Optional[List[str]], optional): The local tags.
Defaults to None.
inheritable_metadata (Optional[Dict[str, Any]], optional): The inheritable
metadata. Defaults to None.
local_metadata (Optional[Dict[str, Any]], optional): The local metadata.
Defaults to None.
Returns:
AsyncCallbackManager: The configured async callback manager.
"""
return _configure(
cls,
inheritable_callbacks,
local_callbacks,
verbose,
inheritable_tags,
local_tags,
inheritable_metadata,
local_metadata,
) | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/callbacks/manager.html |
8cf9574238da-34 | local_tags,
inheritable_metadata,
local_metadata,
)
[docs]class AsyncCallbackManagerForChainGroup(AsyncCallbackManager):
"""Async callback manager for the chain group."""
[docs] def __init__(
self,
handlers: List[BaseCallbackHandler],
inheritable_handlers: Optional[List[BaseCallbackHandler]] = None,
parent_run_id: Optional[UUID] = None,
*,
parent_run_manager: AsyncCallbackManagerForChainRun,
**kwargs: Any,
) -> None:
super().__init__(
handlers,
inheritable_handlers,
parent_run_id,
**kwargs,
)
self.parent_run_manager = parent_run_manager
self.ended = False
[docs] def copy(self) -> AsyncCallbackManagerForChainGroup:
return self.__class__(
handlers=self.handlers,
inheritable_handlers=self.inheritable_handlers,
parent_run_id=self.parent_run_id,
tags=self.tags,
inheritable_tags=self.inheritable_tags,
metadata=self.metadata,
inheritable_metadata=self.inheritable_metadata,
parent_run_manager=self.parent_run_manager,
)
[docs] async def on_chain_end(
self, outputs: Union[Dict[str, Any], Any], **kwargs: Any
) -> None:
"""Run when traced chain group ends.
Args:
outputs (Union[Dict[str, Any], Any]): The outputs of the chain.
"""
self.ended = True
await self.parent_run_manager.on_chain_end(outputs, **kwargs)
[docs] async def on_chain_error(
self,
error: BaseException,
**kwargs: Any,
) -> None: | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/callbacks/manager.html |
8cf9574238da-35 | error: BaseException,
**kwargs: Any,
) -> None:
"""Run when chain errors.
Args:
error (Exception or KeyboardInterrupt): The error.
"""
self.ended = True
await self.parent_run_manager.on_chain_error(error, **kwargs)
T = TypeVar("T", CallbackManager, AsyncCallbackManager)
[docs]def env_var_is_set(env_var: str) -> bool:
"""Check if an environment variable is set.
Args:
env_var (str): The name of the environment variable.
Returns:
bool: True if the environment variable is set, False otherwise.
"""
return env_var in os.environ and os.environ[env_var] not in (
"",
"0",
"false",
"False",
)
def _tracing_v2_is_enabled() -> bool:
return (
env_var_is_set("LANGCHAIN_TRACING_V2")
or tracing_v2_callback_var.get() is not None
or get_run_tree_context() is not None
)
def _get_tracer_project() -> str:
run_tree = get_run_tree_context()
return getattr(
run_tree,
"session_name",
getattr(
# Note, if people are trying to nest @traceable functions and the
# tracing_v2_enabled context manager, this will likely mess up the
# tree structure.
tracing_v2_callback_var.get(),
"project",
# Have to set this to a string even though it always will return
# a string because `get_tracer_project` technically can return
# None, but only when a specific argument is supplied.
# Therefore, this just tricks the mypy type checker | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/callbacks/manager.html |
8cf9574238da-36 | # Therefore, this just tricks the mypy type checker
str(ls_utils.get_tracer_project()),
),
)
_configure_hooks: List[
Tuple[
ContextVar[Optional[BaseCallbackHandler]],
bool,
Optional[Type[BaseCallbackHandler]],
Optional[str],
]
] = []
H = TypeVar("H", bound=BaseCallbackHandler, covariant=True)
[docs]def register_configure_hook(
context_var: ContextVar[Optional[Any]],
inheritable: bool,
handle_class: Optional[Type[BaseCallbackHandler]] = None,
env_var: Optional[str] = None,
) -> None:
if env_var is not None and handle_class is None:
raise ValueError(
"If env_var is set, handle_class must also be set to a non-None value."
)
_configure_hooks.append(
(
# the typings of ContextVar do not have the generic arg set as covariant
# so we have to cast it
cast(ContextVar[Optional[BaseCallbackHandler]], context_var),
inheritable,
handle_class,
env_var,
)
)
register_configure_hook(run_collector_var, False)
def _configure(
callback_manager_cls: Type[T],
inheritable_callbacks: Callbacks = None,
local_callbacks: Callbacks = None,
verbose: bool = False,
inheritable_tags: Optional[List[str]] = None,
local_tags: Optional[List[str]] = None,
inheritable_metadata: Optional[Dict[str, Any]] = None,
local_metadata: Optional[Dict[str, Any]] = None,
) -> T:
"""Configure the callback manager.
Args: | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/callbacks/manager.html |
8cf9574238da-37 | ) -> T:
"""Configure the callback manager.
Args:
callback_manager_cls (Type[T]): The callback manager class.
inheritable_callbacks (Optional[Callbacks], optional): The inheritable
callbacks. Defaults to None.
local_callbacks (Optional[Callbacks], optional): The local callbacks.
Defaults to None.
verbose (bool, optional): Whether to enable verbose mode. Defaults to False.
inheritable_tags (Optional[List[str]], optional): The inheritable tags.
Defaults to None.
local_tags (Optional[List[str]], optional): The local tags. Defaults to None.
inheritable_metadata (Optional[Dict[str, Any]], optional): The inheritable
metadata. Defaults to None.
local_metadata (Optional[Dict[str, Any]], optional): The local metadata.
Defaults to None.
Returns:
T: The configured callback manager.
"""
run_tree = get_run_tree_context()
parent_run_id = None if run_tree is None else getattr(run_tree, "id")
callback_manager = callback_manager_cls(handlers=[], parent_run_id=parent_run_id)
if inheritable_callbacks or local_callbacks:
if isinstance(inheritable_callbacks, list) or inheritable_callbacks is None:
inheritable_callbacks_ = inheritable_callbacks or []
callback_manager = callback_manager_cls(
handlers=inheritable_callbacks_.copy(),
inheritable_handlers=inheritable_callbacks_.copy(),
parent_run_id=parent_run_id,
)
else:
callback_manager = callback_manager_cls(
handlers=inheritable_callbacks.handlers.copy(),
inheritable_handlers=inheritable_callbacks.inheritable_handlers.copy(),
parent_run_id=inheritable_callbacks.parent_run_id,
tags=inheritable_callbacks.tags.copy(), | lang/api.python.langchain.com/en/latest/_modules/langchain/schema/callbacks/manager.html |
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