"""**Retriever** class returns Documents given a text **query**.
It is more general than a vector store. A retriever does not need to be able to
store documents, only to return (or retrieve) it. Vector stores can be used as
the backbone of a retriever, but there are other types of retrievers as well.
**Class hierarchy:**
.. code-block::
BaseRetriever --> <name>Retriever # Examples: ArxivRetriever, MergerRetriever
**Main helpers:**
.. code-block::
RetrieverInput, RetrieverOutput, RetrieverLike, RetrieverOutputLike,
Document, Serializable, Callbacks,
CallbackManagerForRetrieverRun, AsyncCallbackManagerForRetrieverRun
"""
from __future__ import annotations
import warnings
from abc import ABC, abstractmethod
from inspect import signature
from typing import TYPE_CHECKING, Any, Optional
from pydantic import ConfigDict
from typing_extensions import TypedDict
from langchain_core._api import deprecated
from langchain_core.documents import Document
from langchain_core.runnables import (
Runnable,
RunnableConfig,
RunnableSerializable,
ensure_config,
)
from langchain_core.runnables.config import run_in_executor
if TYPE_CHECKING:
from langchain_core.callbacks.manager import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
Callbacks,
)
RetrieverInput = str
RetrieverOutput = list[Document]
RetrieverLike = Runnable[RetrieverInput, RetrieverOutput]
RetrieverOutputLike = Runnable[Any, RetrieverOutput]
[docs]
class LangSmithRetrieverParams(TypedDict, total=False):
"""LangSmith parameters for tracing."""
ls_retriever_name: str
"""Retriever name."""
ls_vector_store_provider: Optional[str]
"""Vector store provider."""
ls_embedding_provider: Optional[str]
"""Embedding provider."""
ls_embedding_model: Optional[str]
"""Embedding model."""
[docs]
class BaseRetriever(RunnableSerializable[RetrieverInput, RetrieverOutput], 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.
Usage:
A retriever follows the standard Runnable interface, and should be used
via the standard Runnable methods of `invoke`, `ainvoke`, `batch`, `abatch`.
Implementation:
When implementing a custom retriever, the class should implement
the `_get_relevant_documents` method to define the logic for retrieving documents.
Optionally, an async native implementations can be provided by overriding the
`_aget_relevant_documents` method.
Example: A retriever that returns the first 5 documents from a list of documents
.. code-block:: python
from langchain_core import Document, BaseRetriever
from typing import List
class SimpleRetriever(BaseRetriever):
docs: List[Document]
k: int = 5
def _get_relevant_documents(self, query: str) -> List[Document]:
\"\"\"Return the first k documents from the list of documents\"\"\"
return self.docs[:self.k]
async def _aget_relevant_documents(self, query: str) -> List[Document]:
\"\"\"(Optional) async native implementation.\"\"\"
return self.docs[:self.k]
Example: A simple retriever based on a scikit-learn vectorizer
.. code-block:: python
from sklearn.metrics.pairwise import cosine_similarity
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]:
# 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
model_config = ConfigDict(
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,
stacklevel=4,
)
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")
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,
stacklevel=4,
)
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
)
def _get_ls_params(self, **kwargs: Any) -> LangSmithRetrieverParams:
"""Get standard params for tracing."""
default_retriever_name = self.get_name()
if default_retriever_name.startswith("Retriever"):
default_retriever_name = default_retriever_name[9:]
elif default_retriever_name.endswith("Retriever"):
default_retriever_name = default_retriever_name[:-9]
default_retriever_name = default_retriever_name.lower()
ls_params = LangSmithRetrieverParams(ls_retriever_name=default_retriever_name)
return ls_params
[docs]
def invoke(
self, input: str, config: Optional[RunnableConfig] = None, **kwargs: Any
) -> list[Document]:
"""Invoke the retriever to get relevant documents.
Main entry point for synchronous retriever invocations.
Args:
input: The query string.
config: Configuration for the retriever. Defaults to None.
kwargs: Additional arguments to pass to the retriever.
Returns:
List of relevant documents.
Examples:
.. code-block:: python
retriever.invoke("query")
"""
from langchain_core.callbacks.manager import CallbackManager
config = ensure_config(config)
inheritable_metadata = {
**(config.get("metadata") or {}),
**self._get_ls_params(**kwargs),
}
callback_manager = CallbackManager.configure(
config.get("callbacks"),
None,
verbose=kwargs.get("verbose", False),
inheritable_tags=config.get("tags"),
local_tags=self.tags,
inheritable_metadata=inheritable_metadata,
local_metadata=self.metadata,
)
run_manager = callback_manager.on_retriever_start(
None,
input,
name=config.get("run_name") or self.get_name(),
run_id=kwargs.pop("run_id", None),
)
try:
_kwargs = kwargs if self._expects_other_args else {}
if self._new_arg_supported:
result = self._get_relevant_documents(
input, run_manager=run_manager, **_kwargs
)
else:
result = self._get_relevant_documents(input, **_kwargs)
except Exception as e:
run_manager.on_retriever_error(e)
raise e
else:
run_manager.on_retriever_end(
result,
)
return result
[docs]
async def ainvoke(
self,
input: str,
config: Optional[RunnableConfig] = None,
**kwargs: Any,
) -> list[Document]:
"""Asynchronously invoke the retriever to get relevant documents.
Main entry point for asynchronous retriever invocations.
Args:
input: The query string.
config: Configuration for the retriever. Defaults to None.
kwargs: Additional arguments to pass to the retriever.
Returns:
List of relevant documents.
Examples:
.. code-block:: python
await retriever.ainvoke("query")
"""
from langchain_core.callbacks.manager import AsyncCallbackManager
config = ensure_config(config)
inheritable_metadata = {
**(config.get("metadata") or {}),
**self._get_ls_params(**kwargs),
}
callback_manager = AsyncCallbackManager.configure(
config.get("callbacks"),
None,
verbose=kwargs.get("verbose", False),
inheritable_tags=config.get("tags"),
local_tags=self.tags,
inheritable_metadata=inheritable_metadata,
local_metadata=self.metadata,
)
run_manager = await callback_manager.on_retriever_start(
None,
input,
name=config.get("run_name") or self.get_name(),
run_id=kwargs.pop("run_id", None),
)
try:
_kwargs = kwargs if self._expects_other_args else {}
if self._new_arg_supported:
result = await self._aget_relevant_documents(
input, run_manager=run_manager, **_kwargs
)
else:
result = await self._aget_relevant_documents(input, **_kwargs)
except Exception as e:
await run_manager.on_retriever_error(e)
raise e
else:
await run_manager.on_retriever_end(
result,
)
return result
@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 callback 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 callback handler to use
Returns:
List of relevant documents
"""
return await run_in_executor(
None,
self._get_relevant_documents,
query,
run_manager=run_manager.get_sync(),
)
[docs]
@deprecated(since="0.1.46", alternative="invoke", removal="1.0")
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.
Users should favor using `.invoke` or `.batch` rather than
`get_relevant_documents directly`.
Args:
query: string to find relevant documents for.
callbacks: Callback manager or list of callbacks. Defaults to None.
tags: Optional list of tags associated with the retriever.
These tags will be associated with each call to this retriever,
and passed as arguments to the handlers defined in `callbacks`.
Defaults to None.
metadata: Optional metadata associated with the retriever.
This metadata will be associated with each call to this retriever,
and passed as arguments to the handlers defined in `callbacks`.
Defaults to None.
run_name: Optional name for the run. Defaults to None.
kwargs: Additional arguments to pass to the retriever.
Returns:
List of relevant documents.
"""
config: RunnableConfig = {}
if callbacks:
config["callbacks"] = callbacks
if tags:
config["tags"] = tags
if metadata:
config["metadata"] = metadata
if run_name:
config["run_name"] = run_name
return self.invoke(query, config, **kwargs)
[docs]
@deprecated(since="0.1.46", alternative="ainvoke", removal="1.0")
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,
) -> list[Document]:
"""Asynchronously get documents relevant to a query.
Users should favor using `.ainvoke` or `.abatch` rather than
`aget_relevant_documents directly`.
Args:
query: string to find relevant documents for.
callbacks: Callback manager or list of callbacks.
tags: Optional list of tags associated with the retriever.
These tags will be associated with each call to this retriever,
and passed as arguments to the handlers defined in `callbacks`.
Defaults to None.
metadata: Optional metadata associated with the retriever.
This metadata will be associated with each call to this retriever,
and passed as arguments to the handlers defined in `callbacks`.
Defaults to None.
run_name: Optional name for the run. Defaults to None.
kwargs: Additional arguments to pass to the retriever.
Returns:
List of relevant documents.
"""
config: RunnableConfig = {}
if callbacks:
config["callbacks"] = callbacks
if tags:
config["tags"] = tags
if metadata:
config["metadata"] = metadata
if run_name:
config["run_name"] = run_name
return await self.ainvoke(query, config, **kwargs)
