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
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.
Performs 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.
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.
A wrapper for an open-source database for vector-search with persistent storage. It simplifies retrieval, filtering, and management of embeddings.