`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)[source]¶

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)	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)	Run more documents through the embeddings and add to 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])	Delete by vector ID or other criteria.
`afrom_documents`(documents, embedding, **kwargs)	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.
`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)	Return docs most similar to query using 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)	Return docs and relevance scores in the range [0, 1], asynchronously.
`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.
`delete`([ids])	Delete by ID.
`deserialize_from_bytes`(serialized, ...)	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.
`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 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.

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)[source]¶

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]¶

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

Parameters

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

Returns

List of IDs of the added texts.

Return type

List[str]

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

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]¶

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

Parameters

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

Returns

List of IDs of the added texts.

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][source]¶

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][source]¶

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]¶

Delete by vector ID or other criteria.

Parameters

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

Return VectorStore initialized from documents and embeddings.

Parameters

documents (List[Document]) –
embedding (Embeddings) –
kwargs (Any) –

Return type

VST

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[source]¶

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[source]¶

Construct FAISS wrapper from raw documents asynchronously.

This is a user friendly interface that:

Embeds documents.
Creates an in memory docstore
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 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][source]¶

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][source]¶

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]][source]¶

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

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
kwargs (Any) –

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]¶

Return docs most similar to query using specified search type.

Parameters

query (str) –
search_type (str) –
kwargs (Any) –

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][source]¶

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][source]¶

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]]¶

Return docs and relevance scores in the range [0, 1], asynchronously.

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]][source]¶

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]][source]¶

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]]

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

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, **kwargs: Any) → FAISS[source]¶

Deserialize FAISS index, docstore, and index_to_docstore_id from bytes.

Parameters

serialized (bytes) –
embeddings (Embeddings) –
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]) –
embedding (Embeddings) –
kwargs (Any) –

Return type

VST

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[source]¶

Construct FAISS wrapper from raw documents.

This is a user friendly interface that:

Embeds documents.
Creates an in memory docstore
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[source]¶

Construct FAISS wrapper from raw documents.

This is a user friendly interface that:

Embeds documents.
Creates an in memory docstore
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

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

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.
asynchronous – whether to use async version or not
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][source]¶

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][source]¶

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]][source]¶

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[source]¶

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[source]¶

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 specified search type.

Parameters

query (str) –
search_type (str) –
kwargs (Any) –

Return type

List[Document]

serialize_to_bytes() → bytes[source]¶

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][source]¶

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][source]¶

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]][source]¶

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]][source]¶

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]]

langchain_community.vectorstores.faiss.FAISS¶

Examples using FAISS¶

`langchain_community.vectorstores.faiss`.FAISS¶