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)	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)

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]

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]

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]

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

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 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

• 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]

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]]

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]]

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]]

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, **kwargs: Any) → FAISS

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

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

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.

• 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]

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 specified search type.

Parameters

• query (str) –

• search_type (str) –

• kwargs (Any) –

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]]

Examples using FAISS

• 1. Vanilla RAG {#vanilla-rag-1}

• Adding values to chain state {#adding-values-to-chain-state}

• Backed by a Vector Store

• Define your embedding model

• Download the content

• Examples of a pretend task of creating antonyms.

• Facebook - Meta

• Formatting inputs & output {#formatting-inputs-output}

• Helper function for printing docs

• OR

• OR (depending on Python version)

• PDF

• Passing data through {#passing-data-through}

• QA with private data protection {#qa-with-private-data-protection}

• Quickstart

• Quickstart {#quickstart}

• Streaming With LangChain {#streaming-with-langchain}

• Swapping the `ByteStore` {#swapping-the-bytestore}

• The input schema of the chain is the input schema of its first part, the prompt.

• Uncomment the below to use LangSmith. Not required.

• Vector stores

• conversational_retrieval_agents.md

• del os.environ[‘NVIDIA_API_KEY’] ## delete key and reset

• document_comparison_toolkit.md

• fleet_context.md

• initialize the bm25 retriever and faiss retriever

• inspect.md

• os.environ[“LANGCHAIN_TRACING_V2”] = “true”

• os.environ[“OPENAI_API_KEY”] = getpass.getpass()

• ragatouille.md

• set a flag to switch between local and remote parsing

• use default authN method API-key

• vectorstore.md