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
Available backends
| Backend | Purpose |
|---|---|
| Vespa | Vector database with schema management, ranking, clustering, and replication |
| Postgres | PostgreSQL with pgvector and pg_textsearch for dense and hybrid search |
| Custom vector stores | Implement your own storage backend |
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.