TurbopufferVectorStore

Embeddings interface for generating vector embeddings from text queries, enabling vector-based similarity searches.

Returns a string representing the type of vector store, which subclasses must implement to identify their specific vector storage type.

Adds documents to the vector store, embedding them first through the embeddings instance.

Adds precomputed vectors and corresponding documents to the vector store.

Creates a VectorStoreRetriever instance with flexible configuration options.

Deletes documents from the vector store based on the specified parameters.

Return documents selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to the query AND diversity among selected documents.

Searches for documents similar to a text query by embedding the query and performing a similarity search on the resulting vector.

Performs a similarity search using a vector query and returns results along with their similarity scores.

Searches for documents similar to a text query by embedding the query, and returns results with similarity scores.

Creates a VectorStore instance from an array of documents, using the specified embeddings and database configuration.

Subclasses must implement this method to define how documents are embedded and stored. Throws an error if not overridden.

Creates a VectorStore instance from an array of text strings and optional metadata, using the specified embeddings and database configuration.

Subclasses must implement this method to define how text and metadata are embedded and stored in the vector store. Throws an error if not overridden.

The name of the serializable. Override to provide an alias or to preserve the serialized module name in minified environments.

Implemented as a static method to support loading logic.