All Tools
C
Vector DBPaid
COUCHBASE
Enterprise NoSQL with built-in vector search
Apache-2.0
ABOUT
Enterprise applications need to store structured data, run full-text search, and power semantic AI queries — but most databases specialize in only one. Couchbase combines a distributed JSON document store, full-text search via Bleve, and Approximate Nearest Neighbor vector search in a single cluster. This means your product catalog, search index, and embedding vectors all live in the same database with the same replication, security, and backup policies. No ETL jobs, no separate vector infrastructure, no data synchronization lag.
INSTALL
pip install couchbaseINTEGRATION GUIDE
1. Build a hybrid e-commerce search that combines product metadata filters, full-text search, and visual similarity search
2. Create a document RAG pipeline that queries JSON documents by field, full-text match, and semantic similarity in one query
3. Deploy an enterprise knowledge base with role-based access control where vectors, documents, and metadata share the same security model
4. Power a recommendation engine that stores user profiles, behavioral vectors, and product data in a single Couchbase cluster
5. Implement a fraud detection system that joins transaction vectors with structured account data in real time
TAGS
nosqlvector-searchembeddingsdocument-databasepythonsemantic-searchfull-text-searchenterprise