langchain_community.vectorstores.faiss.FAISS

class langchain_community.vectorstores.faiss.FAISS(embedding_function: Union[Callable[[str], List[float]], Embeddings], index: Any, docstore: Docstore, index_to_docstore_id: Dict[int, str], relevance_score_fn: Optional[Callable[[float], float]] = None, normalize_L2: bool = False, distance_strategy: DistanceStrategy = DistanceStrategy.EUCLIDEAN_DISTANCE)

Meta Faiss vector store.

To use, you must have the faiss python package installed.

Example

from langchain_community.embeddings.openai import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS

embeddings = OpenAIEmbeddings()
texts = ["FAISS is an important library", "LangChain supports FAISS"]
faiss = FAISS.from_texts(texts, embeddings)

Initialize with necessary components.

Attributes

embeddings

Access the query embedding object if available.

Methods

__init__(embedding_function, index, ...[, ...])	Initialize with necessary components.
aadd_documents(documents, **kwargs)	Async run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas, ids])	Run more texts through the embeddings and add to the vectorstore
add_documents(documents, **kwargs)	Add or update documents in the vectorstore.
add_embeddings(text_embeddings[, metadatas, ids])	Add the given texts and embeddings to the vectorstore.
add_texts(texts[, metadatas, ids])	Run more texts through the embeddings and add to the vectorstore.
adelete([ids])	Async delete by vector ID or other criteria.
afrom_documents(documents, embedding, **kwargs)	Async return VectorStore initialized from documents and embeddings.
afrom_embeddings(text_embeddings, embedding)	Construct FAISS wrapper from raw documents asynchronously.
afrom_texts(texts, embedding[, metadatas, ids])	Construct FAISS wrapper from raw documents asynchronously.
aget_by_ids(ids, /)	Async get documents by their IDs.
amax_marginal_relevance_search(query[, k, ...])	Return docs selected using the maximal marginal relevance asynchronously.
amax_marginal_relevance_search_by_vector(...)	Return docs selected using the maximal marginal relevance asynchronously.
amax_marginal_relevance_search_with_score_by_vector(...)	Return docs and their similarity scores selected using the maximal marginal
as_retriever(**kwargs)	Return VectorStoreRetriever initialized from this VectorStore.
asearch(query, search_type, **kwargs)	Async return docs most similar to query using a specified search type.
asimilarity_search(query[, k, filter, fetch_k])	Return docs most similar to query asynchronously.
asimilarity_search_by_vector(embedding[, k, ...])	Return docs most similar to embedding vector asynchronously.
asimilarity_search_with_relevance_scores(query)	Async return docs and relevance scores in the range [0, 1].
asimilarity_search_with_score(query[, k, ...])	Return docs most similar to query asynchronously.
asimilarity_search_with_score_by_vector(...)	Return docs most similar to query asynchronously.
astreaming_upsert(items, /, batch_size, **kwargs)
aupsert(items, /, **kwargs)
delete([ids])	Delete by ID.
deserialize_from_bytes(serialized, embeddings, *)	Deserialize FAISS index, docstore, and index_to_docstore_id from bytes.
from_documents(documents, embedding, **kwargs)	Return VectorStore initialized from documents and embeddings.
from_embeddings(text_embeddings, embedding)	Construct FAISS wrapper from raw documents.
from_texts(texts, embedding[, metadatas, ids])	Construct FAISS wrapper from raw documents.
get_by_ids(ids, /)	Get documents by their IDs.
load_local(folder_path, embeddings[, ...])	Load FAISS index, docstore, and index_to_docstore_id from disk.
max_marginal_relevance_search(query[, k, ...])	Return docs selected using the maximal marginal relevance.
max_marginal_relevance_search_by_vector(...)	Return docs selected using the maximal marginal relevance.
max_marginal_relevance_search_with_score_by_vector(...)	Return docs and their similarity scores selected using the maximal marginal
merge_from(target)	Merge another FAISS object with the current one.
save_local(folder_path[, index_name])	Save FAISS index, docstore, and index_to_docstore_id to disk.
search(query, search_type, **kwargs)	Return docs most similar to query using a specified search type.
serialize_to_bytes()	Serialize FAISS index, docstore, and index_to_docstore_id to bytes.
similarity_search(query[, k, filter, fetch_k])	Return docs most similar to query.
similarity_search_by_vector(embedding[, k, ...])	Return docs most similar to embedding vector.
similarity_search_with_relevance_scores(query)	Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query[, k, ...])	Return docs most similar to query.
similarity_search_with_score_by_vector(embedding)	Return docs most similar to query.
streaming_upsert(items, /, batch_size, **kwargs)
upsert(items, /, **kwargs)

Parameters

• embedding_function (Union[Callable[[str], List[float]], Embeddings]) –

• index (Any) –

• docstore (Docstore) –

• index_to_docstore_id (Dict[int, str]) –

• relevance_score_fn (Optional[Callable[[float], float]]) –

• normalize_L2 (bool) –

• distance_strategy (DistanceStrategy) –

__init__(embedding_function: Union[Callable[[str], List[float]], Embeddings], index: Any, docstore: Docstore, index_to_docstore_id: Dict[int, str], relevance_score_fn: Optional[Callable[[float], float]] = None, normalize_L2: bool = False, distance_strategy: DistanceStrategy = DistanceStrategy.EUCLIDEAN_DISTANCE)

Initialize with necessary components.

Parameters

• embedding_function (Union[Callable[[str], List[float]], Embeddings]) –

• index (Any) –

• docstore (Docstore) –

• index_to_docstore_id (Dict[int, str]) –

• relevance_score_fn (Optional[Callable[[float], float]]) –

• normalize_L2 (bool) –

• distance_strategy (DistanceStrategy) –

async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]

Async run more documents through the embeddings and add to the vectorstore.

Parameters

• documents (List[Document]) – Documents to add to the vectorstore.

• kwargs (Any) – Additional keyword arguments.

Returns

List of IDs of the added texts.

Raises

ValueError – If the number of IDs does not match the number of documents.

Return type

List[str]

async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → List[str]

Run more texts through the embeddings and add to the vectorstore

asynchronously.

Parameters

• texts (Iterable[str]) – Iterable of strings to add to the vectorstore.

• metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.

• ids (Optional[List[str]]) – Optional list of unique IDs.

• kwargs (Any) –

Returns

List of ids from adding the texts into the vectorstore.

Return type

List[str]

add_documents(documents: List[Document], **kwargs: Any) → List[str]

Add or update documents in the vectorstore.

Parameters

• documents (List[Document]) – Documents to add to the vectorstore.

• kwargs (Any) – Additional keyword arguments. if kwargs contains ids and documents contain ids, the ids in the kwargs will receive precedence.

Returns

List of IDs of the added texts.

Raises

ValueError – If the number of ids does not match the number of documents.

Return type

List[str]

add_embeddings(text_embeddings: Iterable[Tuple[str, List[float]]], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → List[str]

Add the given texts and embeddings to the vectorstore.

Parameters

• text_embeddings (Iterable[Tuple[str, List[float]]]) – Iterable pairs of string and embedding to add to the vectorstore.

• metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.

• ids (Optional[List[str]]) – Optional list of unique IDs.

• kwargs (Any) –

Returns

List of ids from adding the texts into the vectorstore.

Return type

List[str]

add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → List[str]

Run more texts through the embeddings and add to the vectorstore.

Parameters

• texts (Iterable[str]) – Iterable of strings to add to the vectorstore.

• metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.

• ids (Optional[List[str]]) – Optional list of unique IDs.

• kwargs (Any) –

Returns

List of ids from adding the texts into the vectorstore.

Return type

List[str]

async adelete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]

Async delete by vector ID or other criteria.

Parameters

• ids (Optional[List[str]]) – List of ids to delete. If None, delete all. Default is None.

• **kwargs (Any) – Other keyword arguments that subclasses might use.

Returns

True if deletion is successful, False otherwise, None if not implemented.

Return type

Optional[bool]

async classmethod afrom_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST

Async return VectorStore initialized from documents and embeddings.

Parameters

• documents (List[Document]) – List of Documents to add to the vectorstore.

• embedding (Embeddings) – Embedding function to use.

• kwargs (Any) – Additional keyword arguments.

Returns

VectorStore initialized from documents and embeddings.

Return type

VectorStore

async classmethod afrom_embeddings(text_embeddings: Iterable[Tuple[str, List[float]]], embedding: Embeddings, metadatas: Optional[Iterable[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → FAISS

Construct FAISS wrapper from raw documents asynchronously.

Parameters

• text_embeddings (Iterable[Tuple[str, List[float]]]) –

• embedding (Embeddings) –

• metadatas (Optional[Iterable[dict]]) –

• ids (Optional[List[str]]) –

• kwargs (Any) –

Return type

FAISS

async classmethod afrom_texts(texts: list[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → FAISS

Construct FAISS wrapper from raw documents asynchronously.

This is a user friendly interface that:

1. Embeds documents.

2. Creates an in memory docstore

3. Initializes the FAISS database

This is intended to be a quick way to get started.

Example

from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()
faiss = await FAISS.afrom_texts(texts, embeddings)

Parameters

• texts (list[str]) –

• embedding (Embeddings) –

• metadatas (Optional[List[dict]]) –

• ids (Optional[List[str]]) –

• kwargs (Any) –

Return type

FAISS

async aget_by_ids(ids: Sequence[str], /) → List[Document]

Async get documents by their IDs.

The returned documents are expected to have the ID field set to the ID of the document in the vector store.

Fewer documents may be returned than requested if some IDs are not found or if there are duplicated IDs.

Users should not assume that the order of the returned documents matches the order of the input IDs. Instead, users should rely on the ID field of the returned documents.

This method should NOT raise exceptions if no documents are found for some IDs.

Parameters

ids (Sequence[str]) – List of ids to retrieve.

Returns

List of Documents.

Return type

List[Document]

New in version 0.2.11.

async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Union[Callable, Dict[str, Any]]] = None, **kwargs: Any) → List[Document]

Return docs selected using the maximal marginal relevance asynchronously.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Parameters

• query (str) – Text to look up documents similar to.

• k (int) – Number of Documents to return. Defaults to 4.

• fetch_k (int) – Number of Documents to fetch before filtering (if needed) to pass to MMR algorithm.

• lambda_mult (float) – 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.

• filter (Optional[Union[Callable, Dict[str, Any]]]) –

• kwargs (Any) –

Returns

List of Documents selected by maximal marginal relevance.

Return type

List[Document]

async amax_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Union[Callable, Dict[str, Any]]] = None, **kwargs: Any) → List[Document]

Return docs selected using the maximal marginal relevance asynchronously.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Parameters

• embedding (List[float]) – Embedding to look up documents similar to.

• k (int) – Number of Documents to return. Defaults to 4.

• fetch_k (int) – Number of Documents to fetch before filtering to pass to MMR algorithm.

• lambda_mult (float) – 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.

• filter (Optional[Union[Callable, Dict[str, Any]]]) –

• kwargs (Any) –

Returns

List of Documents selected by maximal marginal relevance.

Return type

List[Document]

async amax_marginal_relevance_search_with_score_by_vector(embedding: List[float], *, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Union[Callable, Dict[str, Any]]] = None) → List[Tuple[Document, float]]

Return docs and their similarity scores selected using the maximal marginal

relevance asynchronously.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Parameters

• embedding (List[float]) – Embedding to look up documents similar to.

• k (int) – Number of Documents to return. Defaults to 4.

• fetch_k (int) – Number of Documents to fetch before filtering to pass to MMR algorithm.

• lambda_mult (float) – 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.

• filter (Optional[Union[Callable, Dict[str, Any]]]) –

Returns

List of Documents and similarity scores selected by maximal marginal

relevance and score for each.

Return type

List[Tuple[Document, float]]

as_retriever(**kwargs: Any) → VectorStoreRetriever

Return VectorStoreRetriever initialized from this VectorStore.

Parameters

**kwargs (Any) –

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 “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)

filter: Filter by document metadata

Returns

Retriever class for VectorStore.

Return type

VectorStoreRetriever

Examples:

# 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'}}
)

async asearch(query: str, search_type: str, **kwargs: Any) → List[Document]

Async return docs most similar to query using a specified search type.

Parameters

• query (str) – Input text.

• search_type (str) – Type of search to perform. Can be “similarity”, “mmr”, or “similarity_score_threshold”.

• **kwargs (Any) – Arguments to pass to the search method.

Returns

List of Documents most similar to the query.

Raises

ValueError – If search_type is not one of “similarity”, “mmr”, or “similarity_score_threshold”.

Return type

List[Document]

async asimilarity_search(query: str, k: int = 4, filter: Optional[Union[Callable, Dict[str, Any]]] = None, fetch_k: int = 20, **kwargs: Any) → List[Document]

Return docs most similar to query asynchronously.

Parameters

• query (str) – Text to look up documents similar to.

• k (int) – Number of Documents to return. Defaults to 4.

• filter (Optional[Union[Callable, Dict[str, Any]]]) – (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.

• fetch_k (int) – (Optional[int]) Number of Documents to fetch before filtering. Defaults to 20.

• kwargs (Any) –

Returns

List of Documents most similar to the query.

Return type

List[Document]

async asimilarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[Union[Callable, Dict[str, Any]]] = None, fetch_k: int = 20, **kwargs: Any) → List[Document]

Return docs most similar to embedding vector asynchronously.

Parameters

• embedding (List[float]) – Embedding to look up documents similar to.

• k (int) – Number of Documents to return. Defaults to 4.

• filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None. If a callable, it must take as input the metadata dict of Document and return a bool.

• fetch_k (int) – (Optional[int]) Number of Documents to fetch before filtering. Defaults to 20.

• kwargs (Any) –

Returns

List of Documents most similar to the embedding.

Return type

List[Document]

async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]

Async return docs and relevance scores in the range [0, 1].

0 is dissimilar, 1 is most similar.

Parameters

• query (str) – Input text.

• k (int) – Number of Documents to return. Defaults to 4.

• **kwargs (Any) –

  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)

Return type

List[Tuple[Document, float]]

async asimilarity_search_with_score(query: str, k: int = 4, filter: Optional[Union[Callable, Dict[str, Any]]] = None, fetch_k: int = 20, **kwargs: Any) → List[Tuple[Document, float]]

Return docs most similar to query asynchronously.

Parameters

• query (str) – Text to look up documents similar to.

• k (int) – Number of Documents to return. Defaults to 4.

• filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None. If a callable, it must take as input the metadata dict of Document and return a bool.

• fetch_k (int) – (Optional[int]) Number of Documents to fetch before filtering. Defaults to 20.

• kwargs (Any) –

Returns

List of documents most similar to the query text with L2 distance in float. Lower score represents more similarity.

Return type

List[Tuple[Document, float]]

async asimilarity_search_with_score_by_vector(embedding: List[float], k: int = 4, filter: Optional[Union[Callable, Dict[str, Any]]] = None, fetch_k: int = 20, **kwargs: Any) → List[Tuple[Document, float]]

Return docs most similar to query asynchronously.

Parameters

• embedding (List[float]) – Embedding vector to look up documents similar to.

• k (int) – Number of Documents to return. Defaults to 4.

• filter (Optional[Dict[str, Any]]) – Filter by metadata. Defaults to None. If a callable, it must take as input the metadata dict of Document and return a bool.

• fetch_k (int) – (Optional[int]) Number of Documents to fetch before filtering. Defaults to 20.

• **kwargs (Any) –

  kwargs to be passed to similarity search. Can include: score_threshold: Optional, a floating point value between 0 to 1 to

  filter the resulting set of retrieved docs

Returns

List of documents most similar to the query text and L2 distance in float for each. Lower score represents more similarity.

Return type

List[Tuple[Document, float]]

astreaming_upsert(items: AsyncIterable[Document], /, batch_size: int, **kwargs: Any) → AsyncIterator[UpsertResponse]

Beta

Added in 0.2.11. The API is subject to change.

Upsert documents in a streaming fashion. Async version of streaming_upsert.

Parameters

• items (AsyncIterable[Document]) – Iterable of Documents to add to the vectorstore.

• batch_size (int) – The size of each batch to upsert.

• kwargs (Any) – Additional keyword arguments. kwargs should only include parameters that are common to all documents. (e.g., timeout for indexing, retry policy, etc.) kwargs should not include ids to avoid ambiguous semantics. Instead the ID should be provided as part of the Document object.

Yields

UpsertResponse – A response object that contains the list of IDs that were successfully added or updated in the vectorstore and the list of IDs that failed to be added or updated.

Return type

AsyncIterator[UpsertResponse]

New in version 0.2.11.

async aupsert(items: Sequence[Document], /, **kwargs: Any) → UpsertResponse

Beta

Added in 0.2.11. The API is subject to change.

Add or update documents in the vectorstore. Async version of upsert.

The upsert functionality should utilize the ID field of the Document object if it is provided. If the ID is not provided, the upsert method is free to generate an ID for the document.

When an ID is specified and the document already exists in the vectorstore, the upsert method should update the document with the new data. If the document does not exist, the upsert method should add the document to the vectorstore.

Parameters

• items (Sequence[Document]) – Sequence of Documents to add to the vectorstore.

• kwargs (Any) – Additional keyword arguments.

Returns

A response object that contains the list of IDs that were successfully added or updated in the vectorstore and the list of IDs that failed to be added or updated.

Return type

UpsertResponse

New in version 0.2.11.

delete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]

Delete by ID. These are the IDs in the vectorstore.

Parameters

• ids (Optional[List[str]]) – List of ids to delete.

• kwargs (Any) –

Returns

True if deletion is successful, False otherwise, None if not implemented.

Return type

Optional[bool]

classmethod deserialize_from_bytes(serialized: bytes, embeddings: Embeddings, *, allow_dangerous_deserialization: bool = False, **kwargs: Any) → FAISS

Deserialize FAISS index, docstore, and index_to_docstore_id from bytes.

Parameters

• serialized (bytes) –

• embeddings (Embeddings) –

• allow_dangerous_deserialization (bool) –

• kwargs (Any) –

Return type

FAISS

classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST

Return VectorStore initialized from documents and embeddings.

Parameters

• documents (List[Document]) – List of Documents to add to the vectorstore.

• embedding (Embeddings) – Embedding function to use.

• kwargs (Any) – Additional keyword arguments.

Returns

VectorStore initialized from documents and embeddings.

Return type

VectorStore

classmethod from_embeddings(text_embeddings: Iterable[Tuple[str, List[float]]], embedding: Embeddings, metadatas: Optional[Iterable[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → FAISS

Construct FAISS wrapper from raw documents.

This is a user friendly interface that:

1. Embeds documents.

2. Creates an in memory docstore

3. Initializes the FAISS database

This is intended to be a quick way to get started.

Example

from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()
text_embeddings = embeddings.embed_documents(texts)
text_embedding_pairs = zip(texts, text_embeddings)
faiss = FAISS.from_embeddings(text_embedding_pairs, embeddings)

Parameters

• text_embeddings (Iterable[Tuple[str, List[float]]]) –

• embedding (Embeddings) –

• metadatas (Optional[Iterable[dict]]) –

• ids (Optional[List[str]]) –

• kwargs (Any) –

Return type

FAISS

classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → FAISS

Construct FAISS wrapper from raw documents.

This is a user friendly interface that:

1. Embeds documents.

2. Creates an in memory docstore

3. Initializes the FAISS database

This is intended to be a quick way to get started.

Example

from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()
faiss = FAISS.from_texts(texts, embeddings)

Parameters

• texts (List[str]) –

• embedding (Embeddings) –

• metadatas (Optional[List[dict]]) –

• ids (Optional[List[str]]) –

• kwargs (Any) –

Return type

FAISS

get_by_ids(ids: Sequence[str], /) → List[Document]

Get documents by their IDs.

The returned documents are expected to have the ID field set to the ID of the document in the vector store.

Fewer documents may be returned than requested if some IDs are not found or if there are duplicated IDs.

Users should not assume that the order of the returned documents matches the order of the input IDs. Instead, users should rely on the ID field of the returned documents.

This method should NOT raise exceptions if no documents are found for some IDs.

Parameters

ids (Sequence[str]) – List of ids to retrieve.

Returns

List of Documents.

Return type

List[Document]

New in version 0.2.11.

classmethod load_local(folder_path: str, embeddings: Embeddings, index_name: str = 'index', *, allow_dangerous_deserialization: bool = False, **kwargs: Any) → FAISS

Load FAISS index, docstore, and index_to_docstore_id from disk.

Parameters

• folder_path (str) – folder path to load index, docstore, and index_to_docstore_id from.

• embeddings (Embeddings) – Embeddings to use when generating queries

• index_name (str) – for saving with a specific index file name

• allow_dangerous_deserialization (bool) – whether to allow deserialization of the data which involves loading a pickle file. Pickle files can be modified by malicious actors to deliver a malicious payload that results in execution of arbitrary code on your machine.

• kwargs (Any) –

Return type

FAISS

max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Union[Callable, Dict[str, Any]]] = None, **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.

Parameters

• query (str) – Text to look up documents similar to.

• k (int) – Number of Documents to return. Defaults to 4.

• fetch_k (int) – Number of Documents to fetch before filtering (if needed) to pass to MMR algorithm.

• lambda_mult (float) – 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.

• filter (Optional[Union[Callable, Dict[str, Any]]]) –

• kwargs (Any) –

Returns

List of Documents selected by maximal marginal relevance.

Return type

List[Document]

max_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Union[Callable, Dict[str, Any]]] = None, **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.

Parameters

• embedding (List[float]) – Embedding to look up documents similar to.

• k (int) – Number of Documents to return. Defaults to 4.

• fetch_k (int) – Number of Documents to fetch before filtering to pass to MMR algorithm.

• lambda_mult (float) – 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.

• filter (Optional[Union[Callable, Dict[str, Any]]]) –

• kwargs (Any) –

Returns

List of Documents selected by maximal marginal relevance.

Return type

List[Document]

max_marginal_relevance_search_with_score_by_vector(embedding: List[float], *, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Union[Callable, Dict[str, Any]]] = None) → List[Tuple[Document, float]]

Return docs and their similarity scores selected using the maximal marginal

relevance.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Parameters

• embedding (List[float]) – Embedding to look up documents similar to.

• k (int) – Number of Documents to return. Defaults to 4.

• fetch_k (int) – Number of Documents to fetch before filtering to pass to MMR algorithm.

• lambda_mult (float) – 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.

• filter (Optional[Union[Callable, Dict[str, Any]]]) –

Returns

List of Documents and similarity scores selected by maximal marginal

relevance and score for each.

Return type

List[Tuple[Document, float]]

merge_from(target: FAISS) → None

Merge another FAISS object with the current one.

Add the target FAISS to the current one.

Parameters

target (FAISS) – FAISS object you wish to merge into the current one

Returns

None.

Return type

None

save_local(folder_path: str, index_name: str = 'index') → None

Save FAISS index, docstore, and index_to_docstore_id to disk.

Parameters

• folder_path (str) – folder path to save index, docstore, and index_to_docstore_id to.

• index_name (str) – for saving with a specific index file name

Return type

None

search(query: str, search_type: str, **kwargs: Any) → List[Document]

Return docs most similar to query using a specified search type.

Parameters

• query (str) – Input text

• search_type (str) – Type of search to perform. Can be “similarity”, “mmr”, or “similarity_score_threshold”.

• **kwargs (Any) – Arguments to pass to the search method.

Returns

List of Documents most similar to the query.

Raises

ValueError – If search_type is not one of “similarity”, “mmr”, or “similarity_score_threshold”.

Return type

List[Document]

serialize_to_bytes() → bytes

Serialize FAISS index, docstore, and index_to_docstore_id to bytes.

Return type

bytes

similarity_search(query: str, k: int = 4, filter: Optional[Union[Callable, Dict[str, Any]]] = None, fetch_k: int = 20, **kwargs: Any) → List[Document]

Return docs most similar to query.

Parameters

• query (str) – Text to look up documents similar to.

• k (int) – Number of Documents to return. Defaults to 4.

• filter (Optional[Union[Callable, Dict[str, Any]]]) – (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.

• fetch_k (int) – (Optional[int]) Number of Documents to fetch before filtering. Defaults to 20.

• kwargs (Any) –

Returns

List of Documents most similar to the query.

Return type

List[Document]

similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[Dict[str, Any]] = None, fetch_k: int = 20, **kwargs: Any) → List[Document]

Return docs most similar to embedding vector.

Parameters

• embedding (List[float]) – Embedding to look up documents similar to.

• k (int) – Number of Documents to return. Defaults to 4.

• filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None. If a callable, it must take as input the metadata dict of Document and return a bool.

• fetch_k (int) – (Optional[int]) Number of Documents to fetch before filtering. Defaults to 20.

• kwargs (Any) –

Returns

List of Documents most similar to the embedding.

Return type

List[Document]

similarity_search_with_relevance_scores(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.

Parameters

• query (str) – Input text.

• k (int) – Number of Documents to return. Defaults to 4.

• **kwargs (Any) –

  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).

Return type

List[Tuple[Document, float]]

similarity_search_with_score(query: str, k: int = 4, filter: Optional[Union[Callable, Dict[str, Any]]] = None, fetch_k: int = 20, **kwargs: Any) → List[Tuple[Document, float]]

Return docs most similar to query.

Parameters

• query (str) – Text to look up documents similar to.

• k (int) – Number of Documents to return. Defaults to 4.

• filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None. If a callable, it must take as input the metadata dict of Document and return a bool.

• fetch_k (int) – (Optional[int]) Number of Documents to fetch before filtering. Defaults to 20.

• kwargs (Any) –

Returns

List of documents most similar to the query text with L2 distance in float. Lower score represents more similarity.

Return type

List[Tuple[Document, float]]

similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, filter: Optional[Union[Callable, Dict[str, Any]]] = None, fetch_k: int = 20, **kwargs: Any) → List[Tuple[Document, float]]

Return docs most similar to query.

Parameters

• embedding (List[float]) – Embedding vector to look up documents similar to.

• k (int) – Number of Documents to return. Defaults to 4.

• filter (Optional[Union[Callable, Dict[str, Any]]]) – Filter by metadata. Defaults to None. If a callable, it must take as input the metadata dict of Document and return a bool.

• fetch_k (int) – (Optional[int]) Number of Documents to fetch before filtering. Defaults to 20.

• **kwargs (Any) –

  kwargs to be passed to similarity search. Can include: score_threshold: Optional, a floating point value between 0 to 1 to

  filter the resulting set of retrieved docs

Returns

List of documents most similar to the query text and L2 distance in float for each. Lower score represents more similarity.

Return type

List[Tuple[Document, float]]

streaming_upsert(items: Iterable[Document], /, batch_size: int, **kwargs: Any) → Iterator[UpsertResponse]

Beta

Added in 0.2.11. The API is subject to change.

Upsert documents in a streaming fashion.

Parameters

• items (Iterable[Document]) – Iterable of Documents to add to the vectorstore.

• batch_size (int) – The size of each batch to upsert.

• kwargs (Any) – Additional keyword arguments. kwargs should only include parameters that are common to all documents. (e.g., timeout for indexing, retry policy, etc.) kwargs should not include ids to avoid ambiguous semantics. Instead, the ID should be provided as part of the Document object.

Yields

UpsertResponse – A response object that contains the list of IDs that were successfully added or updated in the vectorstore and the list of IDs that failed to be added or updated.

Return type

Iterator[UpsertResponse]

New in version 0.2.11.

upsert(items: Sequence[Document], /, **kwargs: Any) → UpsertResponse

Beta

Added in 0.2.11. The API is subject to change.

Add or update documents in the vectorstore.

The upsert functionality should utilize the ID field of the Document object if it is provided. If the ID is not provided, the upsert method is free to generate an ID for the document.

When an ID is specified and the document already exists in the vectorstore, the upsert method should update the document with the new data. If the document does not exist, the upsert method should add the document to the vectorstore.

Parameters

• items (Sequence[Document]) – Sequence of Documents to add to the vectorstore.

• kwargs (Any) – Additional keyword arguments.

Returns

A response object that contains the list of IDs that were successfully added or updated in the vectorstore and the list of IDs that failed to be added or updated.

Return type

UpsertResponse

New in version 0.2.11.

Examples using FAISS

• # Oracle Cloud Infrastructure Generative AI

• Build a Question/Answering system over SQL data

• Build an Agent

• Caching

• Cohere reranker

• Cross Encoder Reranker

• Document Comparison

• Facebook - Meta

• Faiss

• Faiss (Async)

• FlashRank reranker

• Fleet AI Context

• How to add values to a chain’s state

• How to better prompt when doing SQL question-answering

• How to combine results from multiple retrievers

• How to create and query vector stores

• How to deal with large databases when doing SQL question-answering

• How to do retrieval with contextual compression

• How to handle long text when doing extraction

• How to inspect runnables

• How to invoke runnables in parallel

• How to load PDFs

• How to pass through arguments from one step to the next

• How to select examples by maximal marginal relevance (MMR)

• How to stream runnables

• How to use a time-weighted vector store retriever

• How to use a vectorstore as a retriever

• Jina Reranker

• LLMLingua Document Compressor

• NVIDIA NIMs

• OpenVINO Reranker

• RAGatouille

• RankLLM Reranker

• UpTrain

• VoyageAI Reranker

• YouTube audio