Class AstraDBVectorStore

constructor

• new AstraDBVectorStore(embeddings, args): AstraDBVectorStore

• Parameters

  • embeddings: EmbeddingsInterface
  • args: AstraLibArgs

  Returns AstraDBVectorStore

FilterType

caller

addDocuments

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

• Method that adds documents to AstraDB.

  Parameters

  • documents: Document[]

    Array of documents to add to AstraDB.
  • Optionaloptions: string[]

    Optional ids for the documents.

  Returns Promise<void>

  Promise that resolves the documents have been added.

addVectors

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

• Method to save vectors to AstraDB.

  Parameters

  • vectors: number[][]

    Vectors to save.
  • documents: Document[]

    The documents associated with the vectors.
  • Optionaloptions: string[]

  Returns Promise<void>

  Promise that resolves when the vectors have been added.

delete

• delete(params): Promise<void>

• Method that deletes documents from AstraDB.

  Parameters

  • params: AstraDeleteParams

    AstraDeleteParameters for the delete.

  Returns Promise<void>

  Promise that resolves when the documents have been deleted.

initialize

• initialize(): Promise<void>

• Create a new collection in your Astra DB vector database and then connects to it. If the collection already exists, it will connect to it as well.

  Returns Promise<void>

  Promise that resolves if connected to the collection.

maxMarginalRelevanceSearch

• maxMarginalRelevanceSearch(query, options): Promise<Document[]>

• 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"]>

  Returns Promise<Document[]>

  • List of documents selected by maximal marginal relevance.

similaritySearchVectorWithScore

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

• Method that performs a similarity search in AstraDB and returns and similarity scores.

  Parameters

  • query: number[]

    Query vector for the similarity search.
  • k: number

    Number of top results to return.
  • Optionalfilter: CollectionFilter

    Optional filter to apply to the search.

  Returns Promise<[Document, number][]>

  Promise that resolves with an array of documents and their scores.

StaticfromDocuments

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

• Static method to create an instance of AstraDBVectorStore from documents.

  Parameters

  • docs: Document[]

    The Documents to use.
  • embeddings: EmbeddingsInterface

    The embeddings to use.
  • dbConfig: AstraLibArgs

    The arguments for the AstraDBVectorStore.

  Returns Promise<AstraDBVectorStore>

  Promise that resolves with a new instance of AstraDBVectorStore.

StaticfromExistingIndex

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

• Static method to create an instance of AstraDBVectorStore from an existing index.

  Parameters

  • embeddings: EmbeddingsInterface

    The embeddings to use.
  • dbConfig: AstraLibArgs

    The arguments for the AstraDBVectorStore.

  Returns Promise<AstraDBVectorStore>

  Promise that resolves with a new instance of AstraDBVectorStore.

StaticfromTexts

• fromTexts(texts, metadatas, embeddings, dbConfig): Promise<AstraDBVectorStore>

• Static method to create an instance of AstraDBVectorStore from texts.

  Parameters

  • texts: string[]

    The texts to use.
  • metadatas: object | object[]

    The metadata associated with the texts.
  • embeddings: EmbeddingsInterface

    The embeddings to use.
  • dbConfig: AstraLibArgs

    The arguments for the AstraDBVectorStore.

  Returns Promise<AstraDBVectorStore>

  Promise that resolves with a new instance of AstraDBVectorStore.