VectaraStore

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 Vectara store.

Vectara provides a way to add documents directly via their API. This API handles pre-processing and chunking internally in an optimal manner. This method is a wrapper to utilize that API within LangChain.

Throws an error, as this method is not implemented. Use addDocuments instead.

Creates a VectorStoreRetriever instance with flexible configuration options.

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

Method to delete data from the Vectara corpus.

Returns a header for Vectara API calls.

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

Performs a similarity search and returns documents.

Throws an error, as this method is not implemented. Use similaritySearch or similaritySearchWithScore instead.

Performs a similarity search and returns documents along with their scores.

Performs a Vectara API call based on the arguments provided.

Creates a VectaraStore instance from documents.

Creates a VectaraStore instance 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.