About this resource
LlamaIndex focuses on the data side of AI products. It helps teams ingest content, build indexes, and return grounded answers from their own data.
Capabilities
- Retrieval-first design
- Strong knowledge pipelines
- Flexible data connectors
Use cases
- Document Q&A
- Internal knowledge search
- Context-aware support bots
Getting started
- Start with a single document collection
- Measure retrieval accuracy
- Keep source grounding visible
Strengths
- Great retrieval primitives
- Good ecosystem for RAG
- Useful beyond chat interfaces
Considerations
- Needs careful chunking and retrieval tuning
- Can feel technical for non-developers
Questions
Is LlamaIndex only for RAG?
RAG is the core use case, but it is also useful for general knowledge workflows.
Can I use my own vector store?
Yes. It integrates with popular vector databases and storage backends.