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LangChain to LlamaIndex Migration: RAG Refactor in 5 Steps

The 40% Latency Drop That Made Me Reconsider Everything

Switching our RAG pipeline from LangChain to LlamaIndex cut query latency from 1.2s to 720ms on the same 10K document corpus. That wasn't the goalβ€”I was just trying to fix a memory leak in our RetrievalQA chain. But halfway through debugging, I realized the architectural assumptions baked into each framework were so different that a full migration made more sense than patching.

This isn't a "LangChain bad, LlamaIndex good" post. I've written about the latency differences between these frameworks before, and both have legitimate use cases. But if you're running into LangChain's abstraction overhead or fighting with its callback system, here's the step-by-step refactor I wish I'd had.

A woman in a gray shirt and red overalls cleans a kitchen counter using a blue bucket and white rag. Modern setting.

Photo by Tima Miroshnichenko on Pexels

Why Migrate? The Architecture Mismatch


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