CouchbaseSearchVectorStore

The embeddings generated for the input texts.

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

Adds an array of documents to the collection. The documents are first converted to vectors using the embedDocuments method of the embeddings instance.

Adds an array of vectors and corresponding documents to the collection. The vectors and documents are batch inserted into the database.

Creates a VectorStoreRetriever instance with flexible configuration options.

Deletes rows from the Cassandra table that match the specified WHERE clause conditions.

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.

Return documents that are most similar to the vector embedding.

Performs a similarity search on the vectors in the collection. The search is performed using the given query vector and returns the top k most similar vectors along with their corresponding documents and similarity scores.

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

Creates an instance of AnalyticDBVectorStore from an array of texts and corresponding metadata. The texts are first converted to Document instances before being added to the collection.

Initializes the llama_cpp model for usage in the chat models wrapper.

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.