LlamaIndex

What Is LlamaIndex Used For?

LlamaIndex is used to build RAG (retrieval-augmented generation) pipelines that let LLMs query your own documents, databases, and data sources instead of relying solely on training data. Common use cases: document Q&A systems, chatbots over internal knowledge bases, structured data extraction, and multi-step agentic pipelines that route across multiple data sources. It handles the full pipeline: document ingestion, chunking, embedding, indexing, and retrieval.

Also see: LangChain · Haystack

LlamaIndex vs LangChain: Which Should You Use in 2026?

LlamaIndex wins for document-heavy RAG applications — its abstractions for chunking strategies, hybrid search, and retrieval evaluation are more mature than LangChain's. LangChain has a broader ecosystem for agent workflows and tool use. If your core use case is 'query documents with an LLM,' start with LlamaIndex. If you're building complex multi-step agents that use tools beyond document retrieval, LangChain or LangGraph are more flexible. Many production systems use both: LlamaIndex for retrieval, LangChain for agent orchestration.

Also see: LangChain · Haystack · Pinecone

How Do I Build a Basic RAG Pipeline with LlamaIndex?

1. Install: pip install llama-index
2. Load documents: SimpleDirectoryReader('./docs').load_data()
3. Create an index: VectorStoreIndex.from_documents(documents)
4. Build a query engine: index.as_query_engine()
5. Query: response = query_engine.query('What does the document say about X?')
6. For production: replace the default in-memory store with Pinecone, Qdrant, or pgvector

Also see: Pinecone · pgvector