/ 2024Retrieval architecture · Semantic search
LangChain RAG — Grounded QA at Scale
A retrieval-augmented QA system that grounds LLM responses in custom document corpora using Hugging Face embeddings, FAISS vector search, and OpenAI's GPT-3.5-turbo.
/ The Problem
GPT models hallucinate when asked about proprietary documents. Teams needed a way to get grounded answers over their own corpora without retraining.
/ The Approach
Chunk and embed any document set with Hugging Face embeddings, index with FAISS for fast semantic retrieval, then feed the top-k chunks to GPT-3.5-turbo with citations. A drop-in pipeline that works over any corpus without fine-tuning.
/ The Impact
Built a semantic QA pipeline with Hugging Face embeddings and FAISS retrieval feeding GPT-3.5-turbo. Answers cite source chunks. Drop-in architecture for any custom document set.
/ Highlights
- Semantic retrieval over arbitrary document corpora with FAISS
- Hugging Face embeddings — no proprietary embedding API required
- Source-cited answers so every claim is traceable to a chunk
- Drop-in architecture: point it at a new corpus and go
/ Stack
- LangChain
- FAISS
- HF Embeddings
- OpenAI
- Python
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