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Building Agentic Retrieval-Augmented Generation (RAG) with LlamaIndex
A detailed technical guide for implementing a Retrieval-Augmented Generation system using LlamaIndex framework, covering everything from environment setup to creating specialized query engines. The tu
torial focuses on enhancing LLM capabilities by integrating external data sources for more accurate and context-aware responses.

Reasons to Read -- Learn:

  • how to build a production-ready RAG system using LlamaIndex, with step-by-step instructions for implementing both Summary and Vector Store indices for efficient information retrieval
  • how to integrate OpenAI's GPT-3.5-turbo and embedding models with your custom datasets, enabling context-aware responses for domain-specific applications
  • practical implementation techniques for creating modular and scalable query engines that can handle both summarization and contextual retrieval tasks effectively
  • 3 min readauthor: Wamiq Raza
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