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# `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