Search index

Storage backends persist processed chunks and enable efficient search across your document collection. Vector stores enable semantic search by storing chunk embeddings and finding similar vectors.

Available backends

Officially supported

Backend	Purpose
Vespa	Vector database with schema management, ranking, clustering, and replication
Postgres	PostgreSQL with `pgvector` and `pg_textsearch` for dense and hybrid search

Both built-in backends implement the VectorStoreIndex protocol, so they use the same ingestion and retrieval pipeline interfaces. Their provisioning, schema management, and query-scoping behavior differ. See each backend's page for details.

Community

Backend	Purpose
Qdrant	Qdrant vector search engine, maintained as a third-party plugin

Community backends are not part of the mistralai-search-toolkit package. Refer to each plugin's repository for installation and support.

Custom backends

Backend	Purpose
Custom vector stores	Implement your own storage backend