Best Vector Database for RAG & Hybrid Search | LanceDB

Tomorrow's AI is being built on LanceDB today

Build Production-Ready RAG Applications

RAG applications succeed or fail on retrieval. When models hallucinate, the issue is almost always missing or low quality context rather than the model itself. That hurts trust and drives up cost: more model API calls, more verbose prompts, and extra GPU time just to recover from bad answers.

Dense vector search on its own is fuzzy. It misses exact matches and often bypasses critical filters, so you overfetch and overspend.

LanceDB is the retrieval layer for accurate, fast, production-scale RAG. It’s built on the open source Lance lakehouse format, optimized for high-throughput random access and column pruning, so retrieval stays fast and cost-efficient as datasets grow. It gives you native hybrid search, GraphRAG-ready storage, and multimodal retrieval so your models see the right data at the right time with fewer calls, smaller prompts, and better infra efficiency.

Why Hybrid Search Wins at RAG

Vectors alone are not enough for serious RAG workloads. Dense embeddings capture semantics but struggle with exact matching and strict constraints. The most reliable systems use hybrid search that blends keyword and vector signals in a single retrieval step.

With hybrid search in LanceDB you get:

  • Exact phrases matched precisely
  • Semantic similarity that still surfaces related content users expect
  • Filters for tenant, access level, and time range enforced on every query

Because this runs in one engine:

  • You avoid extra hops through separate keyword services
  • You return more relevant context so prompts can stay concise
  • You reduce redundant queries that waste tokens and GPU time

LanceDB makes this pattern native rather than another sidecar service. In one engine you can:

  • Combine BM25-style keyword ranking with dense vector similarity via reranking
  • Apply filters and access control rules in the same pass
  • Cut hallucinations and lower per-query cost by consistently retrieving the most relevant, policy-compliant context

Ready for GraphRAG

Good retrieval is more than just a flat list of relevant chunks. Documents reference other documents. Sections cite prior cases. Services depend on upstream systems. GraphRAG uses these relationships to give models deeper, structured context.

A typical GraphRAG stack means running a graph database plus a vector store and gluing them together. That’s more services to manage and more spend.

LanceDB is ready for GraphRAG-style workloads without that sprawl. With open source extensions like lance-graph , you can model your existing Lance tables as nodes and edges and query them in Cypher, alongside your vector search query workloads. All your data can remain in one system, with multiple secondary indexes, so traversal and retrieval logic run on a single, consistent data layer:

  • When you model your data as a graph, nodes represent documents, sections, and entities, each with embeddings and metadata
  • Edges capture links, citations, and dependencies that GraphRAG traverses
  • Queries can mix graph structure, keyword filters, and vector search without leaving the engine

You get the benefits of GraphRAG (richer, more grounded context) without paying for and operating a separate graph DB + vector DB stack.

RAG Vector Search for Images and Documents

Production RAG is not text only. Real workloads include PDFs, charts, screenshots, diagrams, and other media. If your retrieval stack only understands text, your coverage and answer quality are limited, and you start bolting on extra services to fill the gaps.

LanceDB treats all media as first class in the retrieval layer:

  • Store pages, captions, layout metadata, and embeddings for PDFs
  • Index images, diagrams, and UI screenshots alongside text chunks
  • Keep labels, tags, and file paths in the same rows as their vector representations

This lets you:

  • Retrieve charts, diagrams, and screenshots alongside relevant passages
  • Run vector search or full-text search on billions of embeddings across text, images, and documents in a single, scalable system
  • Build applications that surface the exact PDF page, chart, or screenshot that supports the answer

Because multimodal retrieval is built into the same engine:

  • No separate media search system to run and pay for
  • Supports datasets larger than memory, including multi-terabyte document and media collections.
  • Less custom glue to orchestrate multiple indexes
  • Lower operational and infrastructure cost for the same (or better) user experience

Trusted by Teams Building Advanced AI

“Law firms, professional service providers, and enterprises rely on Harvey to process a large number of complex documents in a scalable and secure manner. LanceDB’s search and retrieval infrastructure has been instrumental in helping us meet those demands.”
- Gabriel Pereyra, Co-Founder, Harvey

Teams choose LanceDB when accuracy, speed, multimodal retrieval, and price–performance all matter at once.

Fix Your Retrieval Stack

If your current RAG pipeline is a mix of vector indexes, keyword engines, feature stores, and custom glue code, you are carrying extra complexity and extra cost: more services, more data movement, more teams keeping it all running.

LanceDB gives you:

  • Hybrid search that blends keyword, vectors, and filters in one request
  • GraphRAG-ready storage for nodes, edges, and embeddings together
  • Multimodal retrieval for text, PDFs, images, and other assets
  • One retrieval layer that your RAG applications and models can depend on

Fewer systems mean:

  • Fewer joins and less network I/O
  • Fewer failure modes and maintenance cycles
  • A better cost profile at the same or higher retrieval quality

Use the Get a Demo form to see how LanceDB can simplify your retrieval stack, cut down infrastructure and token spend, and help your RAG applications stop hallucinating.