Class OpenSearchVectorStore

constructor

• new OpenSearchVectorStore(embeddings, args): OpenSearchVectorStore

• Parameters

  • embeddings: EmbeddingsInterface

  • args: OpenSearchClientArgs

  Returns OpenSearchVectorStore

FilterType

addDocuments

• addDocuments(documents): Promise<void>

• Method to add documents to the OpenSearch index. It first converts the documents to vectors using the embeddings, then adds the vectors to the index.

  Parameters

  • documents: Document[]

    The documents to be added to the OpenSearch index.

  Returns Promise<void>

  Promise resolving to void.

addVectors

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

• Method to add vectors to the OpenSearch index. It ensures the index exists, then adds the vectors and associated documents to the index.

  Parameters

  • vectors: number[][]

    The vectors to be added to the OpenSearch index.

  • documents: Document[]

    The documents associated with the vectors.

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

    Optional parameter that can contain the IDs for the documents.

    • Optional ids?: string[]

  Returns Promise<void>

  Promise resolving to void.

deleteIfExists

• deleteIfExists(): Promise<void>

• Method to delete the OpenSearch index if it exists.

  Returns Promise<void>

  Promise resolving to void.

doesIndexExist

• doesIndexExist(): Promise<boolean>

• Method to check if the OpenSearch index exists.

  Returns Promise<boolean>

  Promise resolving to a boolean indicating whether the index exists.

similaritySearchVectorWithScore

• similaritySearchVectorWithScore(query, k, filter?): Promise<[Document, number][]>

• Method to perform a similarity search on the OpenSearch index using a query vector. It returns the k most similar documents and their scores.

  Parameters

  • query: number[]

    The query vector.

  • k: number

    The number of similar documents to return.

  • Optional filter: object

    Optional filter for the OpenSearch query.

  Returns Promise<[Document, number][]>

  Promise resolving to an array of tuples, each containing a Document and its score.

Static fromDocuments

• fromDocuments(docs, embeddings, dbConfig): Promise<OpenSearchVectorStore>

• Static method to create a new OpenSearchVectorStore from an array of Documents, embeddings, and OpenSearch client arguments.

  Parameters

  • docs: Document[]

    The documents to be added to the OpenSearch index.

  • embeddings: EmbeddingsInterface

    The embeddings used to convert the documents into vectors.

  • dbConfig: OpenSearchClientArgs

    The OpenSearch client arguments.

  Returns Promise<OpenSearchVectorStore>

  Promise resolving to a new instance of OpenSearchVectorStore.

Static fromExistingIndex

• fromExistingIndex(embeddings, dbConfig): Promise<OpenSearchVectorStore>

• Static method to create a new OpenSearchVectorStore from an existing OpenSearch index, embeddings, and OpenSearch client arguments.

  Parameters

  • embeddings: EmbeddingsInterface

    The embeddings used to convert the documents into vectors.

  • dbConfig: OpenSearchClientArgs

    The OpenSearch client arguments.

  Returns Promise<OpenSearchVectorStore>

  Promise resolving to a new instance of OpenSearchVectorStore.

Static fromTexts

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

• Static method to create a new OpenSearchVectorStore from an array of texts, their metadata, embeddings, and OpenSearch client arguments.

  Parameters

  • texts: string[]

    The texts to be converted into documents and added to the OpenSearch index.

  • metadatas: object | object[]

    The metadata associated with the texts. Can be an array of objects or a single object.

  • embeddings: EmbeddingsInterface

    The embeddings used to convert the texts into vectors.

  • args: OpenSearchClientArgs

    The OpenSearch client arguments.

  Returns Promise<OpenSearchVectorStore>

  Promise resolving to a new instance of OpenSearchVectorStore.