LlamaIndex (formerly GPT Index)
LlamaIndex is a powerful framework that helps developers connect LLMs to external data sources like documents, databases, PDFs, websites, and more — turning static content into queryable, searchable, AI-ready knowledge.
It acts as a data framework layer between your data and your LLM — making RAG (Retrieval-Augmented Generation) easy to build.
🔑 Why Use LlamaIndex?
LLMs (like ChatGPT) are smart, but they:
Can’t remember personal or private data
Don’t know your PDFs, Notion notes, or spreadsheets
Hallucinate when they lack access to the right context
LlamaIndex solves this by:
Ingesting your custom data
Structuring it into indexes
Letting the LLM access it during response generation
🧩 Key Features
Data Connectors
Load data from PDFs, Notion, Google Docs, APIs, SQL, websites
Chunking Strategies
Splits large documents into LLM-friendly parts
Indexing & Embedding
Builds vector or keyword-based indexes (with FAISS, Weaviate, Qdrant, etc.)
Query Engines
Routes user questions through indexes for context-aware answers
Memory & Agents
Supports long-term memory and multi-step agent workflows
🧪 Example Use Case
Want to ask ChatGPT questions about your company’s internal documentation? With LlamaIndex, you can:
Load your docs
Create an index
Ask: “What’s our refund policy for enterprise clients?” 🎯 And the LLM answers using your actual data — not internet guesses!
🔧 Works Well With
LangChain
FastAPI, Streamlit, Flask
Vector DBs: FAISS, Qdrant, Pinecone, Weaviate
Embeddings: OpenAI, Cohere, HuggingFace
🚀 Ideal For
RAG systems (custom chatbots with private data)
Internal search engines
PDF/document QA apps
Enterprise knowledge bots
🧠 Summary
LlamaIndex = Bridge between your data and LLMs
Makes it easy to build powerful RAG pipelines
Flexible, extensible, and works with most GenAI tools
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