Class VectorStoreAbstract

constructor

• new VectorStore(embeddings, dbConfig): VectorStore

• Initializes a new vector store with embeddings and database configuration.

  Parameters

  • embeddings: EmbeddingsInterface<number[]>

    Instance of EmbeddingsInterface used to embed queries.
  • dbConfig: Record<string, any>

    Configuration settings for the database or storage system.

  Returns VectorStore

FilterType

embeddings

AbstractaddDocuments

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

• Adds documents to the vector store, embedding them first through the embeddings instance.

  Parameters

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

    Array of documents to embed and add.
  • Optionaloptions: AddDocumentOptions

    Optional configuration for embedding and storing documents.

  Returns Promise<void | string[]>

  A promise resolving to an array of document IDs or void, based on implementation.

  Abstract

AbstractaddVectors

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

• Adds precomputed vectors and corresponding documents to the vector store.

  Parameters

  • vectors: number[][]

    An array of vectors representing each document.
  • documents: DocumentInterface<Record<string, any>>[]

    Array of documents associated with each vector.
  • Optionaloptions: AddDocumentOptions

    Optional configuration for adding vectors, such as indexing.

  Returns Promise<void | string[]>

  A promise resolving to an array of document IDs or void, based on implementation.

  Abstract

asRetriever

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

• Creates a VectorStoreRetriever instance with flexible configuration options.

  Parameters

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

    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<VectorStore>

  • A configured VectorStoreRetriever instance based on the provided parameters.

  Example

  Basic usage with a k value:

  const retriever = myVectorStore.asRetriever(5);
  Copy

  Usage with a configuration object:

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

delete

• delete(_params?): Promise<void>

• Deletes documents from the vector store based on the specified parameters.

  Parameters

  • Optional_params: Record<string, any>

    Flexible key-value pairs defining conditions for document deletion.

  Returns Promise<void>

  A promise that resolves once the deletion is complete.

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.

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.
  • k: number = 4

    Number of similar results to return. Defaults to 4.
  • filter: undefined | string | object = undefined

    Optional filter based on FilterType.
  • _callbacks: undefined | Callbacks = undefined

    Optional callbacks for monitoring search progress

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

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

AbstractsimilaritySearchVectorWithScore

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

• Performs a similarity search using a vector query and returns results along with their similarity scores.

  Parameters

  • query: number[]

    Vector representing the search query.
  • k: number

    Number of similar results to return.
  • Optionalfilter: string | object

    Optional filter based on FilterType to restrict results.

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

  A promise resolving to an array of tuples containing documents and their similarity scores.

  Abstract

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.
  • k: number = 4

    Number of similar results to return. Defaults to 4.
  • filter: undefined | string | object = undefined

    Optional filter based on FilterType.
  • _callbacks: undefined | Callbacks = undefined

    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.

StaticfromDocuments

• fromDocuments(_docs, _embeddings, _dbConfig): Promise<VectorStore>

• Creates a VectorStore instance from an array of documents, using the specified embeddings and database configuration.

  Subclasses must implement this method to define how documents are embedded and stored. Throws an error if not overridden.

  Parameters

  • _docs: DocumentInterface<Record<string, any>>[]

    Array of DocumentInterface instances representing the documents to be stored.
  • _embeddings: EmbeddingsInterface<number[]>

    Instance of EmbeddingsInterface to embed the documents.
  • _dbConfig: Record<string, any>

    Database configuration settings.

  Returns Promise<VectorStore>

  A promise that resolves to a new VectorStore instance.

  Throws

  Throws an error if this method is not overridden by a subclass.

StaticfromTexts

• fromTexts(_texts, _metadatas, _embeddings, _dbConfig): Promise<VectorStore>

• Creates a VectorStore instance from an array of text strings and optional metadata, using the specified embeddings and database configuration.

  Subclasses must implement this method to define how text and metadata are embedded and stored in the vector store. Throws an error if not overridden.

  Parameters

  • _texts: string[]

    Array of strings representing the text documents to be stored.
  • _metadatas: object | object[]

    Metadata for the texts, either as an array (one for each text) or a single object (applied to all texts).
  • _embeddings: EmbeddingsInterface<number[]>

    Instance of EmbeddingsInterface to embed the texts.
  • _dbConfig: Record<string, any>

    Database configuration settings.

  Returns Promise<VectorStore>

  A promise that resolves to a new VectorStore instance.

  Throws

  Throws an error if this method is not overridden by a subclass.