llamaindexlisted

# LlamaIndex - Data Framework for LLM Applications The leading framework for connecting LLMs with your data. ## When to use LlamaIndex **Use LlamaIndex when:** - Building RAG (retrieval-augmented generation) applications - Need document question-answering over private data - Ingesting data from multiple sources (300+ connectors) - Creating knowledge bases for LLMs - Building chatbots with enterprise data - Need structured data extraction from documents **Metrics**: - **45,100+ GitHub stars** - **23,000+ repositories** use LlamaIndex - **300+ data connectors** (LlamaHub) - **1,715+ contributors** - **v0.14.7** (stable) **Use alternatives instead**: - **LangChain**: More general-purpose, better for agents - **Haystack**: Production search pipelines - **txtai**: Lightweight semantic search - **Chroma**: Just need vector storage ## Quick start ### Installation ```bash # Starter package (recommended) pip install llama-index # Or minimal core + specific integrations pip install llama-index-core pip install llama-index-llms-openai pip install llama-index-embeddings-openai ``` ### 5-line RAG example ```python from llama_index.core import VectorStoreIndex, SimpleDirectoryReader # Load documents documents = SimpleDirectoryReader("data").load_data() # Create index index = VectorStoreIndex.from_documents(documents) # Query query_engine = index.as_query_engine() response = query_engine.query("What did the author do growing up?") print(response) ``` ## Core concepts ### 1.