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Retrieval Augmented Generation (RAG)
/Let's Build a Simple RAG System

Let's Build a Simple RAG System

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Let's Build a Simple RAG System

The implementation layer provides pre-built components that can be assembled into functional RAG systems with minimal custom code. These frameworks handle the core functionalities of document processing, retrieval, and generation, allowing developers to focus on application-specific needs rather than rebuilding foundational components.

Frameworks like LangChain and LlamaIndex represent the most efficient path to learning and implementing RAG systems. Rather than wrestling with low-level vector operations, embedding generation, and context management, these frameworks provide battle-tested abstractions that dramatically accelerate development:

Framework Comparison: LangChain vs. LlamaIndex

While there is significant overlap between these frameworks, they have different strengths and focus areas:

  • LangChain excels at building modular, composable pipelines and workflows. Its strength lies in orchestrating end-to-end LLM applications with extensive integration capabilities across various tools, APIs, and services. LangChain provides robust abstractions for creating agents, managing complex chains of operations, and connecting different components in a flexible architecture.
  • LlamaIndex (formerly GPT Index) specializes in sophisticated data indexing and retrieval mechanisms. Its core strength is in document processing, chunking strategies, and creating efficient data structures optimized for semantic search. LlamaIndex offers advanced query routing, transformation, and response synthesis specifically designed for knowledge-intensive applications.

Many production RAG systems leverage both frameworks together—using LlamaIndex for the indexing and retrieval components while employing LangChain for the broader application structure and integration with external systems.