Class WeaviateStore

Deprecated

Prefer the @langchain/weaviate package.

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

  • VectorStore
    • WeaviateStore

Constructors

constructor

Properties

FilterType embeddings

Methods

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

Constructors

constructor

Properties

FilterType

FilterType: WeaviateFilter

embeddings

embeddings: EmbeddingsInterface

Methods

addDocuments

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

  • 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.

    • Optionaloptions: {
          ids?: string[];
      }

      Optional parameter that can include specific IDs for the documents.

      • Optionalids?: string[]

    Returns Promise<string[]>

    An array of document IDs.

addVectors

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

  • 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.

    • Optionaloptions: {
          ids?: string[];
      }

      Optional parameter that can include specific IDs for the documents.

      • Optionalids?: string[]

    Returns Promise<string[]>

    An array of document IDs.

asRetriever

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

  • Creates a VectorStoreRetriever instance with flexible configuration options.

    Parameters

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

      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: WeaviateFilter

      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<WeaviateStore>

    • 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>

  • 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.

maxMarginalRelevanceSearch

  • maxMarginalRelevanceSearch(query, options, _callbacks?): Promise<Document<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<WeaviateFilter>
    • Optional_callbacks: undefined

    Returns Promise<Document<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: WeaviateFilter

      Optional filter based on FilterType.

    • 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 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.

    • Optionalfilter: 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.

similaritySearchVectorWithScoreAndEmbedding

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

  • 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.

    • Optionalfilter: 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.

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: WeaviateFilter

      Optional filter based on FilterType.

    • 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, args): Promise<WeaviateStore>

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

    Parameters

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

      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.

StaticfromExistingIndex

StaticfromTexts

  • fromTexts(texts, metadatas, embeddings, args): Promise<WeaviateStore>

  • 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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