M4A

LlamaIndex

Data framework for connecting LLMs with external data

freemiumproductionpythontypescriptragdata-connectorsopen-source
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Memory Types

semantic, contextual, document

Integrations

openai, anthropic, cohere, pinecone, weaviate, qdrant, chroma


Overview


LlamaIndex (formerly GPT Index) is a data framework specifically designed for connecting LLMs with external data sources. Having raised $10 million, LlamaIndex has established itself as the go-to framework for building RAG (Retrieval-Augmented Generation) applications. While LangChain is more general-purpose, LlamaIndex focuses deeply on data ingestion, indexing, and retrieval.


The framework excels at creating custom knowledge bases from diverse data sources, with sophisticated indexing strategies and retrieval methods. LlamaIndex provides simple interfaces for common use cases while offering advanced customization for complex scenarios, making it accessible to both beginners and experts.


Key Features


  • **Data Connectors**: 160+ connectors for diverse data sources
  • **Advanced Indexing**: Multiple index types for different use cases
  • **Query Engines**: Sophisticated retrieval and query strategies
  • **Chat Engines**: Conversation interfaces with context retention
  • **Agent Tools**: Convert indexes into agent tools
  • **Evaluation**: Built-in evaluation framework for RAG quality
  • **LlamaCloud**: Managed parsing and ingestion service
  • **LlamaParse**: Advanced document parsing for PDFs and complex formats

    • When to Use LlamaIndex


    LlamaIndex is ideal for:

  • RAG applications with complex data sources
  • Building searchable knowledge bases from documents
  • Applications requiring sophisticated retrieval strategies
  • Projects needing strong document parsing capabilities
  • Teams focused specifically on data-augmented LLM apps
  • Use cases requiring evaluation of RAG quality

    • Pros


  • Best-in-class for RAG applications
  • Extensive data connector library
  • Advanced indexing and retrieval strategies
  • Strong focus on document parsing
  • Excellent documentation and examples
  • Active community and development
  • Both simple and advanced APIs
  • LlamaParse for complex document handling

    • Cons


  • More focused scope than general frameworks
  • Smaller ecosystem than LangChain
  • Less suitable for non-RAG use cases
  • TypeScript version less mature than Python
  • Some advanced features only in paid tiers
  • Can be complex for simple use cases
  • Less emphasis on agents compared to LangChain

    • Pricing


  • **Open Source**: Free framework
  • **LlamaCloud**: Free tier, then $69/month
  • **LlamaParse**: $0/month for 1k pages, then paid tiers
  • **Enterprise**: Custom pricing for support and features

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