Class FaissStore

A class that wraps the FAISS (Facebook AI Similarity Search) vector database for efficient similarity search and clustering of dense vectors.

Hierarchy

  • SaveableVectorStore
    • FaissStore

Constructors

constructor

Properties

FilterType args docstore embeddings

Accessors

index

Methods

addDocuments addVectors asRetriever delete getDocstore getMapping maxMarginalRelevanceSearch? mergeFrom save similaritySearch similaritySearchVectorWithScore similaritySearchWithScore toJSON toJSONNotImplemented fromDocuments fromIndex fromTexts importFaiss importPickleparser load loadFromPython

Constructors

constructor

Properties

FilterType

FilterType: string | object

Defines the filter type used in search and delete operations. Can be an object for structured conditions or a string for simpler filtering.

args

args: FaissLibArgs

docstore

docstore: SynchronousInMemoryDocstore

embeddings

embeddings: EmbeddingsInterface

Embeddings interface for generating vector embeddings from text queries, enabling vector-based similarity searches.

Accessors

index

Methods

addDocuments

  • addDocuments(documents, options?): Promise<string[]>

  • Adds an array of Document objects to the store.

    Parameters

    • documents: Document<Record<string, any>>[]

      An array of Document objects.

    • Optionaloptions: {
          ids?: string[];
      }
      • Optionalids?: string[]

    Returns Promise<string[]>

    A Promise that resolves when the documents have been added.

addVectors

  • addVectors(vectors, documents, options?): Promise<string[]>

  • Adds an array of vectors and their corresponding Document objects to the store.

    Parameters

    • vectors: number[][]

      An array of vectors.

    • documents: Document<Record<string, any>>[]

      An array of Document objects corresponding to the vectors.

    • Optionaloptions: {
          ids?: string[];
      }
      • Optionalids?: string[]

    Returns Promise<string[]>

    A Promise that resolves with an array of document IDs when the vectors and documents have been added.

asRetriever

  • asRetriever(kOrFields?, filter?, callbacks?, tags?, metadata?, verbose?): VectorStoreRetriever<FaissStore>

  • Creates a VectorStoreRetriever instance with flexible configuration options.

    Parameters

    • OptionalkOrFields: number | Partial<VectorStoreRetrieverInput<FaissStore>>

      If a number is provided, it sets the k parameter (number of items to retrieve).

      • If an object is provided, it should contain various configuration options.
    • Optionalfilter: string | object

      Optional filter criteria to limit the items retrieved based on the specified filter type.

    • Optionalcallbacks: Callbacks

      Optional callbacks that may be triggered at specific stages of the retrieval process.

    • Optionaltags: string[]

      Tags to categorize or label the VectorStoreRetriever. Defaults to an empty array if not provided.

    • Optionalmetadata: Record<string, unknown>

      Additional metadata as key-value pairs to add contextual information for the retrieval process.

    • Optionalverbose: boolean

      If true, enables detailed logging for the retrieval process. Defaults to false.

    Returns VectorStoreRetriever<FaissStore>

    • A configured VectorStoreRetriever instance based on the provided parameters.

    Example

    Basic usage with a k value:

    const retriever = myVectorStore.asRetriever(5);

    Usage with a configuration object:

    const retriever = myVectorStore.asRetriever({
      k: 10,
      filter: myFilter,
      tags: ['example', 'test'],
      verbose: true,
      searchType: 'mmr',
      searchKwargs: { alpha: 0.5 },
    });

delete

  • delete(params): Promise<void>

  • Method to delete documents.

    Parameters

    • params: {
          ids: string[];
      }

      Object containing the IDs of the documents to delete.

      • ids: string[]

    Returns Promise<void>

    A promise that resolves when the deletion is complete.

getDocstore

getMapping

OptionalmaxMarginalRelevanceSearch

  • maxMarginalRelevanceSearch(query, options, _callbacks): Promise<DocumentInterface<Record<string, any>>[]>

  • Return documents selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to the query AND diversity among selected documents.

    Parameters

    • query: string

      Text to look up documents similar to.

    • options: MaxMarginalRelevanceSearchOptions<string | object>
    • _callbacks: undefined | Callbacks

    Returns Promise<DocumentInterface<Record<string, any>>[]>

    • List of documents selected by maximal marginal relevance.

mergeFrom

  • mergeFrom(targetIndex): Promise<string[]>

  • Merges the current FaissStore with another FaissStore.

    Parameters

    • targetIndex: FaissStore

      The FaissStore to merge with.

    Returns Promise<string[]>

    A Promise that resolves with an array of document IDs when the merge is complete.

save

  • save(directory): Promise<void>

  • Saves the current state of the FaissStore to a specified directory.

    Parameters

    • directory: string

      The directory to save the state to.

    Returns Promise<void>

    A Promise that resolves when the state has been saved.

similaritySearch

  • similaritySearch(query, k?, filter?, _callbacks?): Promise<DocumentInterface<Record<string, any>>[]>

  • Searches for documents similar to a text query by embedding the query and performing a similarity search on the resulting vector.

    Parameters

    • query: string

      Text query for finding similar documents.

    • Optionalk: number

      Number of similar results to return. Defaults to 4.

    • Optionalfilter: string | object

      Optional filter based on FilterType.

    • Optional_callbacks: Callbacks

      Optional callbacks for monitoring search progress

    Returns Promise<DocumentInterface<Record<string, any>>[]>

    A promise resolving to an array of DocumentInterface instances representing similar documents.

similaritySearchVectorWithScore

  • similaritySearchVectorWithScore(query, k): Promise<[Document<Record<string, any>>, number][]>

  • Performs a similarity search in the vector store using a query vector and returns the top k results along with their scores.

    Parameters

    • query: number[]

      A query vector.

    • k: number

      The number of top results to return.

    Returns Promise<[Document<Record<string, any>>, number][]>

    A Promise that resolves with an array of tuples, each containing a Document and its corresponding score.

similaritySearchWithScore

  • similaritySearchWithScore(query, k?, filter?, _callbacks?): Promise<[DocumentInterface<Record<string, any>>, number][]>

  • Searches for documents similar to a text query by embedding the query, and returns results with similarity scores.

    Parameters

    • query: string

      Text query for finding similar documents.

    • Optionalk: number

      Number of similar results to return. Defaults to 4.

    • Optionalfilter: string | object

      Optional filter based on FilterType.

    • Optional_callbacks: Callbacks

      Optional callbacks for monitoring search progress

    Returns Promise<[DocumentInterface<Record<string, any>>, number][]>

    A promise resolving to an array of tuples, each containing a document and its similarity score.

toJSON

  • toJSON(): Serialized

  • Returns Serialized

toJSONNotImplemented

StaticfromDocuments

  • fromDocuments(docs, embeddings, dbConfig?): Promise<FaissStore>

  • Creates a new FaissStore from an array of Document objects and an Embeddings object.

    Parameters

    • docs: Document<Record<string, any>>[]

      An array of Document objects.

    • embeddings: EmbeddingsInterface

      An Embeddings object.

    • OptionaldbConfig: {
          docstore?: SynchronousInMemoryDocstore;
      }

      An optional configuration object for the document store.

    Returns Promise<FaissStore>

    A Promise that resolves with a new FaissStore instance.

StaticfromIndex

StaticfromTexts

  • fromTexts(texts, metadatas, embeddings, dbConfig?): Promise<FaissStore>

  • Creates a new FaissStore from an array of texts, their corresponding metadata, and an Embeddings object.

    Parameters

    • texts: string[]

      An array of texts.

    • metadatas: object | object[]

      An array of metadata corresponding to the texts, or a single metadata object to be used for all texts.

    • embeddings: EmbeddingsInterface

      An Embeddings object.

    • OptionaldbConfig: {
          docstore?: SynchronousInMemoryDocstore;
      }

      An optional configuration object for the document store.

    Returns Promise<FaissStore>

    A Promise that resolves with a new FaissStore instance.

StaticimportFaiss

StaticimportPickleparser

Staticload

  • load(directory, embeddings): Promise<FaissStore>

  • Loads a FaissStore from a specified directory.

    Parameters

    • directory: string

      The directory to load the FaissStore from.

    • embeddings: EmbeddingsInterface

      An Embeddings object.

    Returns Promise<FaissStore>

    A Promise that resolves with a new FaissStore instance.

StaticloadFromPython

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