agents-llamaindex

原文涵盖的主题

• LlamaIndex - Data Framework for LLM Applications
• When to use LlamaIndex
• Quick start
• Installation
• 5-line RAG example
• Core concepts
• 1. Data connectors - Load documents
• 2. Indices - Structure data

The leading framework for connecting LLMs with your data. 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 1. Use vector indices for most cases - Best performance 2. Save indices to disk - Avoid re-indexing 3. Chunk documents properly - 512-1024 tokens optimal 4. Add metadata - Enables filtering and tracking 5. Use streaming - Better UX for long responses 6. Enable verbose during dev - See retrieval process 7. Evaluate responses - Check relevance and faithfulness 8. Use chat engine for conversations - Built-in memory 9. Persist storage - Don't lose your index 10. Monitor costs - Track embedding and LLM usage | Operati

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