Typesense

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

Transform documents to Typesense records.

Transform the Typesense records to documents.

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

Add documents to the vector store. Will be updated if in the metadata there is a document with the same id if is using the default import function. Metadata will be added in the columns of the schema based on metadataColumnNames.

Adds vectors to the vector store.

Creates a VectorStoreRetriever instance with flexible configuration options.

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

Delete documents from the vector store.

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.

Search for similar documents with their similarity score.

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

Create a vector store from documents.

Create a vector store from texts.

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.