How to Use LlamaIndex for Data-Connected AI Apps
Build AI applications that connect to and reason about your data using LlamaIndex's advanced RAG and data indexing framework.
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What You'll Learn
This intermediate-level guide walks you through how to use llamaindex for data-connected ai apps step by step. Estimated time: 12 min.
Step 1: Install LlamaIndex and connectors
Set up LlamaIndex in your Python project with the appropriate data connectors for your sources — PDFs, databases, APIs, and more.
Step 2: Ingest and index your data
Use LlamaIndex's data loaders to ingest documents, create semantic indexes, and configure chunking strategies.
Step 3: Build query engines
Create query engines that combine vector search, keyword matching, and LLM reasoning to answer questions from your data.
Step 4: Implement advanced RAG patterns
Apply sentence window retrieval, auto-merging, and reranking for improved retrieval quality and answer accuracy.
Step 5: Deploy and evaluate
Deploy your data-connected AI app and use LlamaIndex's evaluation tools to measure retrieval and answer quality.
Frequently Asked Questions
LlamaIndex vs LangChain for RAG applications?▾
LlamaIndex is purpose-built for RAG with more advanced retrieval strategies. LangChain is more general-purpose for chains, agents, and broader LLM applications.
What data sources does LlamaIndex support?▾
LlamaIndex has 100+ data connectors for PDFs, databases, APIs, Notion, Slack, GitHub, Google Drive, and many more sources.
Can LlamaIndex handle enterprise data volumes?▾
Yes. LlamaIndex supports production-scale deployments with managed indexes, caching, and streaming for responsive applications.