Build Production-Ready RAG Applications
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.
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Why teams switch
Hybrid search that works
Exact phrases matched precisely. Semantic similarity for related content. Filters for tenant, access level, and time range on every query.
GraphRAG-ready
Model your data as nodes and edges. Query in Cypher alongside vector search. One system, not a separate graph DB plus vector DB.
Multimodal RAG
Store pages, captions, layout metadata, and embeddings for PDFs. Index images and diagrams alongside text.
One retrieval layer
Hybrid search blends keyword, vectors, and filters in one request. Fewer systems, fewer joins, fewer failure modes.
Comparison
The Power of the Lance Format
Vector Search
- Fast scans and random access from the same table — no tradeoff
- Zero-copy access for high throughput without serialization overhead
Multi-Modal
- Raw data, embeddings, and metadata in one table — not pointers to blob storage
- No separate metadata store to keep in sync
Enterprise-Grade Requirements
Security
Granular RBAC, SSO integration, and VPC deployment options.
Governance
Data versioning and time-travel capabilities for auditability.
Support
Dedicated technical account management and guaranteed SLAs.
Talk to Engineering
Or try LanceDB OSS — same code, scales to Cloud.
