Class QdrantVectorStore

constructor

• new QdrantVectorStore(embeddings, args): QdrantVectorStore

• Parameters

  • embeddings: EmbeddingsInterface

  • args: QdrantLibArgs

  Returns QdrantVectorStore

FilterType

client

collectionName

contentPayloadKey

metadataPayloadKey

Optional collectionConfig

addDocuments

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

• Method to add documents to the Qdrant database. It generates vectors from the documents using the Embeddings instance and then adds the vectors to the database.

  Parameters

  • documents: Document[]

    Array of Document instances to be added to the Qdrant database.

  • Optional documentOptions: QdrantAddDocumentOptions

    Optional QdrantAddDocumentOptions which has a list of JSON objects for extra querying

  Returns Promise<void>

  Promise that resolves when the documents have been added to the database.

addVectors

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

• Method to add vectors to the Qdrant database. Each vector is associated with a document, which is stored as the payload for a point in the database.

  Parameters

  • vectors: number[][]

    Array of vectors to be added to the Qdrant database.

  • documents: Document[]

    Array of Document instances associated with the vectors.

  • Optional documentOptions: QdrantAddDocumentOptions

    Optional QdrantAddDocumentOptions which has a list of JSON objects for extra querying

  Returns Promise<void>

  Promise that resolves when the vectors have been added to the database.

ensureCollection

• ensureCollection(): Promise<void>

• Method to ensure the existence of a collection in the Qdrant database. If the collection does not exist, it is created.

  Returns Promise<void>

  Promise that resolves when the existence of the collection has been ensured.

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 to search for vectors in the Qdrant database that are similar to a given query vector. The search results include the score and payload (metadata and content) for each similar vector.

  Parameters

  • query: number[]

    Query vector to search for similar vectors in the Qdrant database.

  • Optional k: number

    Optional number of similar vectors to return. If not specified, all similar vectors are returned.

  • Optional filter: {}

    Optional filter to apply to the search results.

  Returns Promise<[Document, number][]>

  Promise that resolves with an array of tuples, where each tuple includes a Document instance and a score for a similar vector.

Static fromDocuments

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

• Static method to create a QdrantVectorStore instance from Document instances. The documents are added to the Qdrant database.

  Parameters

  • docs: Document[]

    Array of Document instances to be added to the Qdrant database.

  • embeddings: EmbeddingsInterface

    Embeddings instance used to generate vectors from the documents.

  • dbConfig: QdrantLibArgs

    QdrantLibArgs instance specifying the configuration for the Qdrant database.

  Returns Promise<QdrantVectorStore>

  Promise that resolves with a new QdrantVectorStore instance.

Static fromExistingCollection

• fromExistingCollection(embeddings, dbConfig): Promise<QdrantVectorStore>

• Static method to create a QdrantVectorStore instance from an existing collection in the Qdrant database.

  Parameters

  • embeddings: EmbeddingsInterface

    Embeddings instance used to generate vectors from the documents in the collection.

  • dbConfig: QdrantLibArgs

    QdrantLibArgs instance specifying the configuration for the Qdrant database.

  Returns Promise<QdrantVectorStore>

  Promise that resolves with a new QdrantVectorStore instance.

Static fromTexts

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

• Static method to create a QdrantVectorStore instance from texts. Each text is associated with metadata and converted to a Document instance, which is then added to the Qdrant database.

  Parameters

  • texts: string[]

    Array of texts to be converted to Document instances and added to the Qdrant database.

  • metadatas: object | object[]

    Array or single object of metadata to be associated with the texts.

  • embeddings: EmbeddingsInterface

    Embeddings instance used to generate vectors from the texts.

  • dbConfig: QdrantLibArgs

    QdrantLibArgs instance specifying the configuration for the Qdrant database.

  Returns Promise<QdrantVectorStore>

  Promise that resolves with a new QdrantVectorStore instance.