RAG applications succeed or fail on retrieval. When models hallucinate, the problem is almost always missing or low-quality context. LanceDB is the retrieval layer for fast, accurate RAG with native hybrid search and multimodal support.
Exact phrases matched precisely. Semantic similarity for related content. Filters for tenant, access level, and time range on every query.
Model your data as nodes and edges. Query in Cypher alongside vector search. One system, not a separate graph DB plus vector DB.
Store pages, captions, layout metadata, and embeddings for PDFs. Index images and diagrams alongside text.
Hybrid search blends keyword, vectors, and filters in one request. Fewer systems, fewer joins, fewer failure modes.
| Traditional RAG Stack | LanceDB | |
|---|---|---|
| Cost | Multiple systems - vector DB + keyword engine + feature store. | One retrieval layer. Fewer services, lower cost. |
| Scale | Scaling means coordinating multiple systems. | 20 PB largest table. 20K+ QPS. Single system scales. |
| Search | Dense vectors miss exact matches. Needs separate keyword service. | Native vector, full-text, and SQL hybrid in one query. |
| Data model | Vectors here, documents there, metadata somewhere else. | Raw data (blobs), embeddings, and features in one table. |
| Purpose | Glued-together retrieval pipeline. | Purpose-built retrieval layer for RAG and agents. |
| Best for | Simple text-only RAG. | Production multimodal RAG at scale. |
Granular RBAC, SSO integration, and VPC deployment options.
Data versioning and time-travel capabilities for auditability.
Dedicated technical account management and guaranteed SLAs.
Or try LanceDB OSS — same code, scales to Cloud.