from __future__ import annotations
import logging
from abc import abstractmethod
from collections.abc import AsyncIterable, Collection, Iterable, Iterator
from typing import (
Any,
ClassVar,
Optional,
Sequence,
cast,
)
from langchain_core._api import beta
from langchain_core.callbacks import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain_core.documents import Document
from langchain_core.load import Serializable
from langchain_core.runnables import run_in_executor
from langchain_core.vectorstores import VectorStore, VectorStoreRetriever
from pydantic import Field
from langchain_community.graph_vectorstores.links import METADATA_LINKS_KEY, Link
logger = logging.getLogger(__name__)
def _has_next(iterator: Iterator) -> bool:
"""Checks if the iterator has more elements.
Warning: consumes an element from the iterator"""
sentinel = object()
return next(iterator, sentinel) is not sentinel
[docs]
@beta()
class Node(Serializable):
"""Node in the GraphVectorStore.
Edges exist from nodes with an outgoing link to nodes with a matching incoming link.
For instance two nodes `a` and `b` connected over a hyperlink ``https://some-url``
would look like:
.. code-block:: python
[
Node(
id="a",
text="some text a",
links= [
Link(kind="hyperlink", tag="https://some-url", direction="incoming")
],
),
Node(
id="b",
text="some text b",
links= [
Link(kind="hyperlink", tag="https://some-url", direction="outgoing")
],
)
]
"""
id: Optional[str] = None
"""Unique ID for the node. Will be generated by the GraphVectorStore if not set."""
text: str
"""Text contained by the node."""
metadata: dict = Field(default_factory=dict)
"""Metadata for the node."""
links: list[Link] = Field(default_factory=list)
"""Links associated with the node."""
def _texts_to_nodes(
texts: Iterable[str],
metadatas: Optional[Iterable[dict]],
ids: Optional[Iterable[str]],
) -> Iterator[Node]:
metadatas_it = iter(metadatas) if metadatas else None
ids_it = iter(ids) if ids else None
for text in texts:
try:
_metadata = next(metadatas_it).copy() if metadatas_it else {}
except StopIteration as e:
raise ValueError("texts iterable longer than metadatas") from e
try:
_id = next(ids_it) if ids_it else None
except StopIteration as e:
raise ValueError("texts iterable longer than ids") from e
links = _metadata.pop(METADATA_LINKS_KEY, [])
if not isinstance(links, list):
links = list(links)
yield Node(
id=_id,
metadata=_metadata,
text=text,
links=links,
)
if ids_it and _has_next(ids_it):
raise ValueError("ids iterable longer than texts")
if metadatas_it and _has_next(metadatas_it):
raise ValueError("metadatas iterable longer than texts")
def _documents_to_nodes(documents: Iterable[Document]) -> Iterator[Node]:
for doc in documents:
metadata = doc.metadata.copy()
links = metadata.pop(METADATA_LINKS_KEY, [])
if not isinstance(links, list):
links = list(links)
yield Node(
id=doc.id,
metadata=metadata,
text=doc.page_content,
links=links,
)
[docs]
@beta()
def nodes_to_documents(nodes: Iterable[Node]) -> Iterator[Document]:
"""Convert nodes to documents.
Args:
nodes: The nodes to convert to documents.
Returns:
The documents generated from the nodes.
"""
for node in nodes:
metadata = node.metadata.copy()
metadata[METADATA_LINKS_KEY] = [
# Convert the core `Link` (from the node) back to the local `Link`.
Link(kind=link.kind, direction=link.direction, tag=link.tag)
for link in node.links
]
yield Document(
id=node.id,
page_content=node.text,
metadata=metadata,
)
[docs]
@beta(message="Added in version 0.3.1 of langchain_community. API subject to change.")
class GraphVectorStore(VectorStore):
"""A hybrid vector-and-graph graph store.
Document chunks support vector-similarity search as well as edges linking
chunks based on structural and semantic properties.
.. versionadded:: 0.3.1
"""
[docs]
@abstractmethod
def add_nodes(
self,
nodes: Iterable[Node],
**kwargs: Any,
) -> Iterable[str]:
"""Add nodes to the graph store.
Args:
nodes: the nodes to add.
**kwargs: Additional keyword arguments.
"""
[docs]
async def aadd_nodes(
self,
nodes: Iterable[Node],
**kwargs: Any,
) -> AsyncIterable[str]:
"""Add nodes to the graph store.
Args:
nodes: the nodes to add.
**kwargs: Additional keyword arguments.
"""
iterator = iter(await run_in_executor(None, self.add_nodes, nodes, **kwargs))
done = object()
while True:
doc = await run_in_executor(None, next, iterator, done)
if doc is done:
break
yield doc # type: ignore[misc]
[docs]
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[Iterable[dict]] = None,
*,
ids: Optional[Iterable[str]] = None,
**kwargs: Any,
) -> list[str]:
"""Run more texts through the embeddings and add to the vector store.
The Links present in the metadata field `links` will be extracted to create
the `Node` links.
Eg if nodes `a` and `b` are connected over a hyperlink `https://some-url`, the
function call would look like:
.. code-block:: python
store.add_texts(
ids=["a", "b"],
texts=["some text a", "some text b"],
metadatas=[
{
"links": [
Link.incoming(kind="hyperlink", tag="https://some-url")
]
},
{
"links": [
Link.outgoing(kind="hyperlink", tag="https://some-url")
]
},
],
)
Args:
texts: Iterable of strings to add to the vector store.
metadatas: Optional list of metadatas associated with the texts.
The metadata key `links` shall be an iterable of
:py:class:`~langchain_community.graph_vectorstores.links.Link`.
ids: Optional list of IDs associated with the texts.
**kwargs: vector store specific parameters.
Returns:
List of ids from adding the texts into the vector store.
"""
nodes = _texts_to_nodes(texts, metadatas, ids)
return list(self.add_nodes(nodes, **kwargs))
[docs]
async def aadd_texts(
self,
texts: Iterable[str],
metadatas: Optional[Iterable[dict]] = None,
*,
ids: Optional[Iterable[str]] = None,
**kwargs: Any,
) -> list[str]:
"""Run more texts through the embeddings and add to the vector store.
The Links present in the metadata field `links` will be extracted to create
the `Node` links.
Eg if nodes `a` and `b` are connected over a hyperlink `https://some-url`, the
function call would look like:
.. code-block:: python
await store.aadd_texts(
ids=["a", "b"],
texts=["some text a", "some text b"],
metadatas=[
{
"links": [
Link.incoming(kind="hyperlink", tag="https://some-url")
]
},
{
"links": [
Link.outgoing(kind="hyperlink", tag="https://some-url")
]
},
],
)
Args:
texts: Iterable of strings to add to the vector store.
metadatas: Optional list of metadatas associated with the texts.
The metadata key `links` shall be an iterable of
:py:class:`~langchain_community.graph_vectorstores.links.Link`.
ids: Optional list of IDs associated with the texts.
**kwargs: vector store specific parameters.
Returns:
List of ids from adding the texts into the vector store.
"""
nodes = _texts_to_nodes(texts, metadatas, ids)
return [_id async for _id in self.aadd_nodes(nodes, **kwargs)]
[docs]
def add_documents(
self,
documents: Iterable[Document],
**kwargs: Any,
) -> list[str]:
"""Run more documents through the embeddings and add to the vector store.
The Links present in the document metadata field `links` will be extracted to
create the `Node` links.
Eg if nodes `a` and `b` are connected over a hyperlink `https://some-url`, the
function call would look like:
.. code-block:: python
store.add_documents(
[
Document(
id="a",
page_content="some text a",
metadata={
"links": [
Link.incoming(kind="hyperlink", tag="http://some-url")
]
}
),
Document(
id="b",
page_content="some text b",
metadata={
"links": [
Link.outgoing(kind="hyperlink", tag="http://some-url")
]
}
),
]
)
Args:
documents: Documents to add to the vector store.
The document's metadata key `links` shall be an iterable of
:py:class:`~langchain_community.graph_vectorstores.links.Link`.
Returns:
List of IDs of the added texts.
"""
nodes = _documents_to_nodes(documents)
return list(self.add_nodes(nodes, **kwargs))
[docs]
async def aadd_documents(
self,
documents: Iterable[Document],
**kwargs: Any,
) -> list[str]:
"""Run more documents through the embeddings and add to the vector store.
The Links present in the document metadata field `links` will be extracted to
create the `Node` links.
Eg if nodes `a` and `b` are connected over a hyperlink `https://some-url`, the
function call would look like:
.. code-block:: python
store.add_documents(
[
Document(
id="a",
page_content="some text a",
metadata={
"links": [
Link.incoming(kind="hyperlink", tag="http://some-url")
]
}
),
Document(
id="b",
page_content="some text b",
metadata={
"links": [
Link.outgoing(kind="hyperlink", tag="http://some-url")
]
}
),
]
)
Args:
documents: Documents to add to the vector store.
The document's metadata key `links` shall be an iterable of
:py:class:`~langchain_community.graph_vectorstores.links.Link`.
Returns:
List of IDs of the added texts.
"""
nodes = _documents_to_nodes(documents)
return [_id async for _id in self.aadd_nodes(nodes, **kwargs)]
[docs]
@abstractmethod
def traversal_search(
self,
query: str,
*,
k: int = 4,
depth: int = 1,
filter: dict[str, Any] | None = None, # noqa: A002
**kwargs: Any,
) -> Iterable[Document]:
"""Retrieve documents from traversing this graph store.
First, `k` nodes are retrieved using a search for each `query` string.
Then, additional nodes are discovered up to the given `depth` from those
starting nodes.
Args:
query: The query string.
k: The number of Documents to return from the initial search.
Defaults to 4. Applies to each of the query strings.
depth: The maximum depth of edges to traverse. Defaults to 1.
filter: Optional metadata to filter the results.
**kwargs: Additional keyword arguments.
Returns:
Collection of retrieved documents.
"""
[docs]
async def atraversal_search(
self,
query: str,
*,
k: int = 4,
depth: int = 1,
filter: dict[str, Any] | None = None, # noqa: A002
**kwargs: Any,
) -> AsyncIterable[Document]:
"""Retrieve documents from traversing this graph store.
First, `k` nodes are retrieved using a search for each `query` string.
Then, additional nodes are discovered up to the given `depth` from those
starting nodes.
Args:
query: The query string.
k: The number of Documents to return from the initial search.
Defaults to 4. Applies to each of the query strings.
depth: The maximum depth of edges to traverse. Defaults to 1.
filter: Optional metadata to filter the results.
**kwargs: Additional keyword arguments.
Returns:
Collection of retrieved documents.
"""
iterator = iter(
await run_in_executor(
None,
self.traversal_search,
query,
k=k,
depth=depth,
filter=filter,
**kwargs,
)
)
done = object()
while True:
doc = await run_in_executor(None, next, iterator, done)
if doc is done:
break
yield doc # type: ignore[misc]
[docs]
@abstractmethod
def mmr_traversal_search(
self,
query: str,
*,
initial_roots: Sequence[str] = (),
k: int = 4,
depth: int = 2,
fetch_k: int = 100,
adjacent_k: int = 10,
lambda_mult: float = 0.5,
score_threshold: float = float("-inf"),
filter: dict[str, Any] | None = None, # noqa: A002
**kwargs: Any,
) -> Iterable[Document]:
"""Retrieve documents from this graph store using MMR-traversal.
This strategy first retrieves the top `fetch_k` results by similarity to
the question. It then selects the top `k` results based on
maximum-marginal relevance using the given `lambda_mult`.
At each step, it considers the (remaining) documents from `fetch_k` as
well as any documents connected by edges to a selected document
retrieved based on similarity (a "root").
Args:
query: The query string to search for.
initial_roots: Optional list of document IDs to use for initializing search.
The top `adjacent_k` nodes adjacent to each initial root will be
included in the set of initial candidates. To fetch only in the
neighborhood of these nodes, set `fetch_k = 0`.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch via similarity.
Defaults to 100.
adjacent_k: Number of adjacent Documents to fetch.
Defaults to 10.
depth: Maximum depth of a node (number of edges) from a node
retrieved via similarity. Defaults to 2.
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.
score_threshold: Only documents with a score greater than or equal
this threshold will be chosen. Defaults to negative infinity.
filter: Optional metadata to filter the results.
**kwargs: Additional keyword arguments.
"""
[docs]
async def ammr_traversal_search(
self,
query: str,
*,
initial_roots: Sequence[str] = (),
k: int = 4,
depth: int = 2,
fetch_k: int = 100,
adjacent_k: int = 10,
lambda_mult: float = 0.5,
score_threshold: float = float("-inf"),
filter: dict[str, Any] | None = None, # noqa: A002
**kwargs: Any,
) -> AsyncIterable[Document]:
"""Retrieve documents from this graph store using MMR-traversal.
This strategy first retrieves the top `fetch_k` results by similarity to
the question. It then selects the top `k` results based on
maximum-marginal relevance using the given `lambda_mult`.
At each step, it considers the (remaining) documents from `fetch_k` as
well as any documents connected by edges to a selected document
retrieved based on similarity (a "root").
Args:
query: The query string to search for.
initial_roots: Optional list of document IDs to use for initializing search.
The top `adjacent_k` nodes adjacent to each initial root will be
included in the set of initial candidates. To fetch only in the
neighborhood of these nodes, set `fetch_k = 0`.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch via similarity.
Defaults to 100.
adjacent_k: Number of adjacent Documents to fetch.
Defaults to 10.
depth: Maximum depth of a node (number of edges) from a node
retrieved via similarity. Defaults to 2.
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.
score_threshold: Only documents with a score greater than or equal
this threshold will be chosen. Defaults to negative infinity.
filter: Optional metadata to filter the results.
**kwargs: Additional keyword arguments.
"""
iterator = iter(
await run_in_executor(
None,
self.mmr_traversal_search,
query,
initial_roots=initial_roots,
k=k,
fetch_k=fetch_k,
adjacent_k=adjacent_k,
depth=depth,
lambda_mult=lambda_mult,
score_threshold=score_threshold,
filter=filter,
**kwargs,
)
)
done = object()
while True:
doc = await run_in_executor(None, next, iterator, done)
if doc is done:
break
yield doc # type: ignore[misc]
[docs]
def similarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> list[Document]:
return list(self.traversal_search(query, k=k, depth=0))
[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]:
if kwargs.get("depth", 0) > 0:
logger.warning(
"'mmr' search started with depth > 0. "
"Maybe you meant to do a 'mmr_traversal' search?"
)
return list(
self.mmr_traversal_search(
query, k=k, fetch_k=fetch_k, lambda_mult=lambda_mult, depth=0
)
)
[docs]
async def asimilarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> list[Document]:
return [doc async for doc in self.atraversal_search(query, k=k, depth=0)]
[docs]
def search(self, query: str, search_type: str, **kwargs: Any) -> list[Document]:
if search_type == "similarity":
return self.similarity_search(query, **kwargs)
elif search_type == "similarity_score_threshold":
docs_and_similarities = self.similarity_search_with_relevance_scores(
query, **kwargs
)
return [doc for doc, _ in docs_and_similarities]
elif search_type == "mmr":
return self.max_marginal_relevance_search(query, **kwargs)
elif search_type == "traversal":
return list(self.traversal_search(query, **kwargs))
elif search_type == "mmr_traversal":
return list(self.mmr_traversal_search(query, **kwargs))
else:
raise ValueError(
f"search_type of {search_type} not allowed. Expected "
"search_type to be 'similarity', 'similarity_score_threshold', "
"'mmr', 'traversal', or 'mmr_traversal'."
)
[docs]
async def asearch(
self, query: str, search_type: str, **kwargs: Any
) -> list[Document]:
if search_type == "similarity":
return await self.asimilarity_search(query, **kwargs)
elif search_type == "similarity_score_threshold":
docs_and_similarities = await self.asimilarity_search_with_relevance_scores(
query, **kwargs
)
return [doc for doc, _ in docs_and_similarities]
elif search_type == "mmr":
return await self.amax_marginal_relevance_search(query, **kwargs)
elif search_type == "traversal":
return [doc async for doc in self.atraversal_search(query, **kwargs)]
elif search_type == "mmr_traversal":
return [doc async for doc in self.ammr_traversal_search(query, **kwargs)]
else:
raise ValueError(
f"search_type of {search_type} not allowed. Expected "
"search_type to be 'similarity', 'similarity_score_threshold', "
"'mmr', 'traversal', or 'mmr_traversal'."
)
[docs]
def as_retriever(self, **kwargs: Any) -> GraphVectorStoreRetriever:
"""Return GraphVectorStoreRetriever initialized from this GraphVectorStore.
Args:
**kwargs: Keyword arguments to pass to the search function.
Can include:
- search_type (Optional[str]): Defines the type of search that
the Retriever should perform.
Can be ``traversal`` (default), ``similarity``, ``mmr``,
``mmr_traversal``, or ``similarity_score_threshold``.
- search_kwargs (Optional[Dict]): Keyword arguments to pass to the
search function. Can include things like:
- k(int): Amount of documents to return (Default: 4).
- depth(int): The maximum depth of edges to traverse (Default: 1).
Only applies to search_type: ``traversal`` and ``mmr_traversal``.
- score_threshold(float): Minimum relevance threshold
for similarity_score_threshold.
- fetch_k(int): Amount of documents to pass to MMR algorithm
(Default: 20).
- lambda_mult(float): Diversity of results returned by MMR;
1 for minimum diversity and 0 for maximum. (Default: 0.5).
Returns:
Retriever for this GraphVectorStore.
Examples:
.. code-block:: python
# Retrieve documents traversing edges
docsearch.as_retriever(
search_type="traversal",
search_kwargs={'k': 6, 'depth': 2}
)
# Retrieve documents with higher diversity
# Useful if your dataset has many similar documents
docsearch.as_retriever(
search_type="mmr_traversal",
search_kwargs={'k': 6, 'lambda_mult': 0.25, 'depth': 2}
)
# Fetch more documents for the MMR algorithm to consider
# But only return the top 5
docsearch.as_retriever(
search_type="mmr_traversal",
search_kwargs={'k': 5, 'fetch_k': 50, 'depth': 2}
)
# 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})
"""
return GraphVectorStoreRetriever(vectorstore=self, **kwargs)
[docs]
@beta(message="Added in version 0.3.1 of langchain_community. API subject to change.")
class GraphVectorStoreRetriever(VectorStoreRetriever):
"""Retriever for GraphVectorStore.
A graph vector store retriever is a retriever that uses a graph vector store to
retrieve documents.
It is similar to a vector store retriever, except that it uses both vector
similarity and graph connections to retrieve documents.
It uses the search methods implemented by a graph vector store, like traversal
search and MMR traversal search, to query the texts in the graph vector store.
Example::
store = CassandraGraphVectorStore(...)
retriever = store.as_retriever()
retriever.invoke("What is ...")
.. seealso::
:mod:`How to use a graph vector store <langchain_community.graph_vectorstores>`
How to use a graph vector store as a retriever
==============================================
Creating a retriever from a graph vector store
----------------------------------------------
You can build a retriever from a graph vector store using its
:meth:`~langchain_community.graph_vectorstores.base.GraphVectorStore.as_retriever`
method.
First we instantiate a graph vector store.
We will use a store backed by Cassandra
:class:`~langchain_community.graph_vectorstores.cassandra.CassandraGraphVectorStore`
graph vector store::
from langchain_community.document_loaders import TextLoader
from langchain_community.graph_vectorstores import CassandraGraphVectorStore
from langchain_community.graph_vectorstores.extractors import (
KeybertLinkExtractor,
LinkExtractorTransformer,
)
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
loader = TextLoader("state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
pipeline = LinkExtractorTransformer([KeybertLinkExtractor()])
pipeline.transform_documents(texts)
embeddings = OpenAIEmbeddings()
graph_vectorstore = CassandraGraphVectorStore.from_documents(texts, embeddings)
We can then instantiate a retriever::
retriever = graph_vectorstore.as_retriever()
This creates a retriever (specifically a ``GraphVectorStoreRetriever``), which we
can use in the usual way::
docs = retriever.invoke("what did the president say about ketanji brown jackson?")
Maximum marginal relevance traversal retrieval
----------------------------------------------
By default, the graph vector store retriever uses similarity search, then expands
the retrieved set by following a fixed number of graph edges.
If the underlying graph vector store supports maximum marginal relevance traversal,
you can specify that as the search type.
MMR-traversal is a retrieval method combining MMR and graph traversal.
The strategy first retrieves the top fetch_k results by similarity to the question.
It then iteratively expands the set of fetched documents by following adjacent_k
graph edges and selects the top k results based on maximum-marginal relevance using
the given ``lambda_mult``::
retriever = graph_vectorstore.as_retriever(search_type="mmr_traversal")
Passing search parameters
-------------------------
We can pass parameters to the underlying graph vector store's search methods using
``search_kwargs``.
Specifying graph traversal depth
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
For example, we can set the graph traversal depth to only return documents
reachable through a given number of graph edges::
retriever = graph_vectorstore.as_retriever(search_kwargs={"depth": 3})
Specifying MMR parameters
^^^^^^^^^^^^^^^^^^^^^^^^^
When using search type ``mmr_traversal``, several parameters of the MMR algorithm
can be configured.
The ``fetch_k`` parameter determines how many documents are fetched using vector
similarity and ``adjacent_k`` parameter determines how many documents are fetched
using graph edges.
The ``lambda_mult`` parameter controls how the MMR re-ranking weights similarity to
the query string vs diversity among the retrieved documents as fetched documents
are selected for the set of ``k`` final results::
retriever = graph_vectorstore.as_retriever(
search_type="mmr",
search_kwargs={"fetch_k": 20, "adjacent_k": 20, "lambda_mult": 0.25},
)
Specifying top k
^^^^^^^^^^^^^^^^
We can also limit the number of documents ``k`` returned by the retriever.
Note that if ``depth`` is greater than zero, the retriever may return more documents
than is specified by ``k``, since both the original ``k`` documents retrieved using
vector similarity and any documents connected via graph edges will be returned::
retriever = graph_vectorstore.as_retriever(search_kwargs={"k": 1})
Similarity score threshold retrieval
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
For example, we can set a similarity score threshold and only return documents with
a score above that threshold::
retriever = graph_vectorstore.as_retriever(search_kwargs={"score_threshold": 0.5})
""" # noqa: E501
vectorstore: VectorStore
"""VectorStore to use for retrieval."""
search_type: str = "traversal"
"""Type of search to perform. Defaults to "traversal"."""
allowed_search_types: ClassVar[Collection[str]] = (
"similarity",
"similarity_score_threshold",
"mmr",
"traversal",
"mmr_traversal",
)
@property
def graph_vectorstore(self) -> GraphVectorStore:
return cast(GraphVectorStore, self.vectorstore)
def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
) -> list[Document]:
if self.search_type == "traversal":
return list(
self.graph_vectorstore.traversal_search(query, **self.search_kwargs)
)
elif self.search_type == "mmr_traversal":
return list(
self.graph_vectorstore.mmr_traversal_search(query, **self.search_kwargs)
)
else:
return super()._get_relevant_documents(query, run_manager=run_manager)
async def _aget_relevant_documents(
self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
) -> list[Document]:
if self.search_type == "traversal":
return [
doc
async for doc in self.graph_vectorstore.atraversal_search(
query, **self.search_kwargs
)
]
elif self.search_type == "mmr_traversal":
return [
doc
async for doc in self.graph_vectorstore.ammr_traversal_search(
query, **self.search_kwargs
)
]
else:
return await super()._aget_relevant_documents(
query, run_manager=run_manager
)
