LangChain vs LlamaIndex: RAG Latency on 10K Documents

#langchain #llamaindex #rag #vectorsearch

RAG frameworks promise the same thing: retrieve relevant context, feed it to an LLM, get better answers. But when you actually run them side-by-side on 10,000 documents, the performance gap is brutal.

I built the same RAG pipeline twice — once in LangChain, once in LlamaIndex — using identical embedding models, chunk sizes, and retrieval parameters. Query latency differed by 3x. Memory overhead was even worse. The benchmarks below show exactly where each framework spent time and what that means for production deployments.

The Test Setup

Both pipelines ingested 10,000 Markdown technical documents (around 2GB total, average 200KB each). I chunked them at 512 tokens with 50-token overlap, embedded with sentence-transformers/all-MiniLM-L6-v2, and indexed with FAISS for vector search. The query set had 100 questions requiring multi-hop reasoning across 3-5 documents.

Hardware: M1 MacBook Pro, 16GB RAM, Python 3.11. LangChain 0.1.0, LlamaIndex 0.9.28. I'm measuring cold-start indexing time, average query latency (after warm-up), peak memory, and retrieval precision at k=5.

The goal wasn't to pick a "winner" — it was to see where each framework's design philosophy shows up in real resource usage.

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