Class FakeVectorStore

Class that extends VectorStore to store vectors in memory. Provides methods for adding documents, performing similarity searches, and creating instances from texts, documents, or an existing index.

Hierarchy

  • VectorStore
    • FakeVectorStore

Constructors

constructor

Properties

FilterType embeddings memoryVectors similarity

Methods

addDocuments addVectors asRetriever delete maxMarginalRelevanceSearch? similaritySearch similaritySearchVectorWithScore similaritySearchWithScore toJSON toJSONNotImplemented fromDocuments fromExistingIndex fromTexts

Constructors

constructor

  • new FakeVectorStore(embeddings, __namedParameters?): FakeVectorStore

  • Parameters

    Returns FakeVectorStore

Properties

FilterType

FilterType: ((doc: Document<Record<string, any>>) => boolean)

embeddings

embeddings: EmbeddingsInterface

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

memoryVectors

memoryVectors: MemoryVector[]

similarity

similarity: ((a: number[], b: number[]) => number)

Type declaration

    • (a, b): number

    • Returns the average of cosine distances between vectors a and b

      Parameters

      • a: number[]

        first vector

      • b: number[]

        second vector

      Returns number

Methods

addDocuments

  • addDocuments(documents): Promise<void>

  • Method to add documents to the memory vector store. It extracts the text from each document, generates embeddings for them, and adds the resulting vectors to the store.

    Parameters

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

      Array of Document instances to be added to the store.

    Returns Promise<void>

    Promise that resolves when all documents have been added.

addVectors

  • addVectors(vectors, documents): Promise<void>

  • Method to add vectors to the memory vector store. It creates MemoryVector instances for each vector and document pair and adds them to the store.

    Parameters

    • vectors: number[][]

      Array of vectors to be added to the store.

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

      Array of Document instances corresponding to the vectors.

    Returns Promise<void>

    Promise that resolves when all vectors have been added.

asRetriever

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

  • Creates a VectorStoreRetriever instance with flexible configuration options.

    Parameters

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

      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: ((doc: Document<Record<string, any>>) => boolean)

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

        • (doc): boolean

        • Parameters

          • doc: Document<Record<string, any>>

          Returns boolean

    • 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<FakeVectorStore>

    • 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>

  • 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<((doc: Document<Record<string, any>>) => boolean)>
    • _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.

    • Optionalk: number

      Number of similar results to return. Defaults to 4.

    • Optionalfilter: ((doc: Document<Record<string, any>>) => boolean)

      Optional filter based on FilterType.

        • (doc): boolean

        • Parameters

          • doc: Document<Record<string, any>>

          Returns boolean

    • 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, filter?): Promise<[Document<Record<string, any>>, number][]>

  • Method to perform a similarity search in the memory vector store. It calculates the similarity between the query vector and each vector in the store, sorts the results by similarity, and returns the top k results along with their scores.

    Parameters

    • query: number[]

      Query vector to compare against the vectors in the store.

    • k: number

      Number of top results to return.

    • Optionalfilter: ((doc: Document<Record<string, any>>) => boolean)

      Optional filter function to apply to the vectors before performing the search.

        • (doc): boolean

        • Parameters

          • doc: Document<Record<string, any>>

          Returns boolean

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

    Promise that resolves with an array of tuples, each containing a Document and its similarity 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: ((doc: Document<Record<string, any>>) => boolean)

      Optional filter based on FilterType.

        • (doc): boolean

        • Parameters

          • doc: Document<Record<string, any>>

          Returns boolean

    • 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<FakeVectorStore>

  • Static method to create a FakeVectorStore instance from an array of Document instances. It adds the documents to the store.

    Parameters

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

      Array of Document instances to be added to the store.

    • embeddings: EmbeddingsInterface

      Embeddings instance used to generate embeddings for the documents.

    • OptionaldbConfig: FakeVectorStoreArgs

      Optional FakeVectorStoreArgs to configure the FakeVectorStore instance.

    Returns Promise<FakeVectorStore>

    Promise that resolves with a new FakeVectorStore instance.

StaticfromExistingIndex

  • fromExistingIndex(embeddings, dbConfig?): Promise<FakeVectorStore>

  • Static method to create a FakeVectorStore instance from an existing index. It creates a new FakeVectorStore instance without adding any documents or vectors.

    Parameters

    • embeddings: EmbeddingsInterface

      Embeddings instance used to generate embeddings for the documents.

    • OptionaldbConfig: FakeVectorStoreArgs

      Optional FakeVectorStoreArgs to configure the FakeVectorStore instance.

    Returns Promise<FakeVectorStore>

    Promise that resolves with a new FakeVectorStore instance.

StaticfromTexts

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

  • Static method to create a FakeVectorStore instance from an array of texts. It creates a Document for each text and metadata pair, and adds them to the store.

    Parameters

    • texts: string[]

      Array of texts to be added to the store.

    • metadatas: object | object[]

      Array or single object of metadata corresponding to the texts.

    • embeddings: EmbeddingsInterface

      Embeddings instance used to generate embeddings for the texts.

    • OptionaldbConfig: FakeVectorStoreArgs

      Optional FakeVectorStoreArgs to configure the FakeVectorStore instance.

    Returns Promise<FakeVectorStore>

    Promise that resolves with a new FakeVectorStore instance.

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