⚠️ Deprecated ⚠️

Prefer the @langchain/weaviate package.

This feature is deprecated and will be removed in the future.

It is not recommended for use.

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
embeddings: EmbeddingsInterface

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[]

      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[]

      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.

  • 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<this["FilterType"]>
    • Optional _callbacks: undefined

    Returns Promise<Document[]>

    • List of documents selected by maximal marginal relevance.
  • 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, 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, number, number[]][]>

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

  • Static method to create a new WeaviateStore instance from a list of documents. It adds the documents to the Weaviate index.

    Parameters

    • docs: Document[]

      Array of documents.

    • embeddings: EmbeddingsInterface

      Embeddings to be used for the documents.

    • args: WeaviateLibArgs

      Arguments required to create a new WeaviateStore instance.

    Returns Promise<WeaviateStore>

    A new WeaviateStore instance.

  • 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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