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LLM Frameworks in Action: Building RAG Systems with LangChain, LlamaIndex, and Haystack!
The article comprehensively explains RAG (Retrieval-Augmented Generation) as a solution to LLM hallucinations, covering its implementation through major frameworks like LangChain, LlamaIndex, and Hays
tack, while introducing Vectorize as a platform for building production-ready RAG pipelines. It provides detailed comparisons of different frameworks' approaches and their specific use cases in the LLM ecosystem.

Reasons to Read -- Learn:

  • how RAG technology can effectively reduce LLM hallucinations through a structured two-phase approach of preprocessing and inference runtime
  • distinct capabilities and best use cases of three major RAG frameworks (LangChain, LlamaIndex, and Haystack), helping you choose the right framework for your specific project needs
  • how to build production-ready RAG pipelines using Vectorize, which offers features like automated embedding model evaluation, chunking strategy optimization, and integration with various vector databases
    • 9 min readauthor: Pavan Belagatti
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