Class that extends the VectorStore base class. It provides methods to interact with a Weaviate index, including adding vectors and documents, deleting data, and performing similarity searches.

Hierarchy

Constructors

Properties

FilterType: WeaviateFilter

Methods

  • Method to add documents to the Weaviate index. It first generates vectors for the documents using the embeddings, then adds the vectors and documents to the index.

    Parameters

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

      Array of documents to be added.

    • Optional options: {
          ids?: string[];
      }

      Optional parameter that can include specific IDs for the documents.

      • Optional ids?: string[]

    Returns Promise<string[]>

    An array of document IDs.

  • Method to add vectors and corresponding documents to the Weaviate index.

    Parameters

    • vectors: number[][]

      Array of vectors to be added.

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

      Array of documents corresponding to the vectors.

    • Optional options: {
          ids?: string[];
      }

      Optional parameter that can include specific IDs for the documents.

      • Optional ids?: string[]

    Returns Promise<string[]>

    An array of document IDs.

  • Method to delete data from the Weaviate index. It can delete data based on specific IDs or a filter.

    Parameters

    • params: {
          filter?: WeaviateFilter;
          ids?: string[];
      }

      Object that includes either an array of IDs or a filter for the data to be deleted.

    Returns Promise<void>

    Promise that resolves when the deletion is complete.

  • Parameters

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

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

  • Method to perform a similarity search on the stored vectors in the Weaviate index. It returns the top k most similar documents and their similarity scores.

    Parameters

    • query: number[]

      The query vector.

    • k: number

      The number of most similar documents to return.

    • Optional filter: WeaviateFilter

      Optional filter to apply to the search.

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

    An array of tuples, where each tuple contains a document and its similarity score.

  • Method to perform a similarity search on the stored vectors in the Weaviate index. It returns the top k most similar documents, their similarity scores and embedding vectors.

    Parameters

    • query: number[]

      The query vector.

    • k: number

      The number of most similar documents to return.

    • Optional filter: WeaviateFilter

      Optional filter to apply to the search.

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

    An array of tuples, where each tuple contains a document, its similarity score and its embedding vector.

  • Parameters

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

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

  • Static method to create a new WeaviateStore instance from a list of texts. It first creates documents from the texts and metadata, then adds the documents to the Weaviate index.

    Parameters

    • texts: string[]

      Array of texts.

    • metadatas: object | object[]

      Metadata for the texts. Can be a single object or an array of objects.

    • embeddings: EmbeddingsInterface

      Embeddings to be used for the texts.

    • args: WeaviateLibArgs

      Arguments required to create a new WeaviateStore instance.

    Returns Promise<WeaviateStore>

    A new WeaviateStore instance.

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