Class that provides an interface to a Postgres vector database. It extends the VectorStore base class and implements methods for adding documents and vectors, performing similarity searches, and ensuring the existence of a table in the database.

Hierarchy

Properties

FilterType: Metadata
appDataSource: DataSource
documentEntity: EntitySchema<any>
tableName: string
filter?: Metadata

Methods

  • Method to add documents to the vector store. It ensures the existence of the table in the database, converts the documents into vectors, and adds them to the store.

    Parameters

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

      Array of Document instances.

    Returns Promise<void>

    Promise that resolves when the documents have been added.

  • Method to add vectors to the vector store. It converts the vectors into rows and inserts them into the database.

    Parameters

    • vectors: number[][]

      Array of vectors.

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

      Array of Document instances.

    Returns Promise<void>

    Promise that resolves when the vectors have been added.

  • Parameters

    • Optional _params: Record<string, any>

    Returns Promise<void>

  • Method to ensure the existence of the table in the database. It creates the table if it does not already exist.

    Returns Promise<void>

    Promise that resolves when the table has been ensured.

  • Parameters

    • query: string
    • Optional k: number
    • Optional filter: Metadata
    • Optional _callbacks: Callbacks

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

  • Method to perform a similarity search in the vector store. It returns the k most similar documents to the query vector, along with their similarity scores.

    Parameters

    • query: number[]

      Query vector.

    • k: number

      Number of most similar documents to return.

    • Optional filter: Metadata

      Optional filter to apply to the search.

    Returns Promise<[TypeORMVectorStoreDocument, number][]>

    Promise that resolves with an array of tuples, each containing a TypeORMVectorStoreDocument and its similarity score.

  • Parameters

    • query: string
    • Optional k: number
    • Optional filter: Metadata
    • Optional _callbacks: Callbacks

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

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

    Parameters

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

    • List of documents selected by maximal marginal relevance.
  • Static method to create a new TypeORMVectorStore instance from a DataSource. It initializes the DataSource if it is not already initialized.

    Parameters

    Returns Promise<TypeORMVectorStore>

    A new instance of TypeORMVectorStore.

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

    Parameters

    Returns Promise<TypeORMVectorStore>

    Promise that resolves with a new instance of TypeORMVectorStore.

  • Static method to create a new TypeORMVectorStore instance from an existing index.

    Parameters

    Returns Promise<TypeORMVectorStore>

    Promise that resolves with a new instance of TypeORMVectorStore.

  • Static method to create a new TypeORMVectorStore instance from an array of texts and their metadata. It converts the texts into Document instances and adds them to the store.

    Parameters

    • texts: string[]

      Array of texts.

    • metadatas: object | object[]

      Array of metadata objects or a single metadata object.

    • embeddings: EmbeddingsInterface

      Embeddings instance.

    • dbConfig: TypeORMVectorStoreArgs

      TypeORMVectorStoreArgs instance.

    Returns Promise<TypeORMVectorStore>

    Promise that resolves with a new instance of TypeORMVectorStore.

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