LlamaIndex
Data framework for ingestion, indexing and retrieval — strongest on document-heavy RAG.
LlamaIndex focuses on the unglamorous half of RAG: getting messy enterprise documents parsed, chunked and indexed well. It ships hundreds of connectors and a query-engine abstraction that makes swapping retrieval strategies practical.
Best for: Document-heavy enterprise RAG pipelines
Deploy: Self-hostable
What to do about it
- Check licensing terms
- Verify data handling
- Assess lock-in potential
What it is
LlamaIndex is a data framework that handles ingestion, indexing, and retrieval of documents. It focuses on parsing, chunking, and indexing enterprise documents. The framework includes hundreds of connectors and a query-engine abstraction.
This abstraction allows for practical swapping of retrieval strategies. LlamaIndex is particularly strong on document-heavy RAG.
Where it fits in a deployment stack
RAG tools like LlamaIndex solve the problem of retrieving relevant information from large collections of documents. They normally sit next to natural language processing and machine learning components in a production system.
These tools take over the work of indexing and retrieving documents, allowing other components to focus on higher-level tasks. They often interact with data storage and search systems.
In a production system, RAG tools can be used to power search interfaces, chatbots, and other applications that require retrieving information from documents.
How to evaluate it
To evaluate LlamaIndex, you need to consider its performance, scalability, and flexibility. This involves testing its retrieval capabilities, indexing speed, and query performance. You should also inspect its output and measure its latency.
- Measure p95 latency of the retrieval step under 50 concurrent sessions
- Inspect the indexing output for a sample set of documents
- Test the query performance with different retrieval strategies
- Feed it a large dataset of documents and measure its indexing speed
- Inject failures into the system and observe its recovery behavior
- Verify the correctness of its retrieval results for a set of test queries
What to verify before you commit
Before committing to LlamaIndex, you should verify its licensing terms, data handling practices, and operational costs. You should also check its failure behavior, lock-in potential, and compatibility with your existing systems.
- Check the licensing terms for any restrictions on usage or distribution
- Verify the data handling practices for compliance with your organization's policies
- Estimate the operational costs of running LlamaIndex in your production environment
- Test its failure behavior and recovery capabilities
- Assess the potential for lock-in and vendor dependence
- Check its compatibility with your existing data storage and search systems
Where this fits in the FDE path
- FDE career path — the levels, the titles that map to them, and what changes at each step
- Interview guide — what the loop tests beyond coding
- Pay data — 223 postings — midpoint median $231,000, employer-published ranges only
- 922 live FDE roles — pulled daily from employers’ own job boards
RAG in Production Playbook
Chunking strategies, reranking setups, evaluation harnesses and the seven failure modes that kill every RAG demo in week three.