Adds documents to the database.
The documents to be added.
A Promise that resolves when the documents have been added.
Adds vectors and their corresponding documents to the database.
The vectors to be added.
The corresponding documents to be added.
A Promise that resolves when the vectors and documents have been added.
Optional kOrFields: number | Partial<VectorStoreRetrieverInput<LanceDB>>Optional filter: string | objectOptional callbacks: CallbacksOptional tags: string[]Optional metadata: Record<string, unknown>Optional verbose: booleanOptional k: numberOptional filter: string | objectOptional _callbacks: CallbacksPerforms a similarity search on the vectors in the database and returns the documents and their scores.
The query vector.
The number of results to return.
A Promise that resolves with an array of tuples, each containing a Document and its score.
Optional k: numberOptional filter: string | objectOptional _callbacks: CallbacksOptional maxReturn documents selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to the query AND diversity among selected documents.
Text to look up documents similar to.
Static fromCreates a new instance of LanceDB from documents.
The documents to be added to the database.
The embeddings to be managed.
The configuration for the LanceDB instance.
A Promise that resolves with a new instance of LanceDB.
Static fromCreates a new instance of LanceDB from texts.
The texts to be converted into documents.
The metadata for the texts.
The embeddings to be managed.
The configuration for the LanceDB instance.
A Promise that resolves with a new instance of LanceDB.
Generated using TypeDoc
A wrapper for an open-source database for vector-search with persistent storage. It simplifies retrieval, filtering, and management of embeddings.