Observed Signal · Apr 27, 2026 · Technical Tutorial · Source: DEV Community · Impact: 2/5 · Sentiment: Neutral
PDF Q&A App Built with RAG, FAISS, Llama 3.1
A developer built an end-to-end Retrieval-Augmented Generation (RAG) PDF Q&A application called PDF Q&A Pro. The app extracts text from uploaded PDFs, splits content into overlapping 500-token chunks, embeds chunks with sentence-transformers (all-MiniLM-L6-v2), and stores vectors in FAISS for millisecond retrieval. Queries embed the question, retrieve top‑k (k=4) chunks, and call Llama 3.1 (8B) via Groq for generative answers. The project uses LangChain loaders/text splitters, Streamlit for the frontend, and runs on free Groq inference (author notes a 14,400 requests/day free tier). The article includes full code examples, a GitHub repo link, a list of bugs and fixes encountered, and suggested extensions (persistent index, streaming, hybrid search).
Practical, hands-on RAG implementation demonstrating a low-cost stack (FAISS + all-MiniLM embeddings + Groq-hosted Llama 3.1) and troubleshooting guidance; useful to practitioners but not industry‑shifting.
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Key Takeaways & Evidence Grounding
- Author built a PDF Q&A app (PDF Q&A Pro) using a RAG pipeline.
- Vector search uses FAISS; embeddings use sentence-transformers/all-MiniLM-L6-v2.
- LLM inference uses Llama 3.1 (llama-3.1-8b-instant) via Groq; Groq free tier cited as 14,400 requests/day.
- Indexing splits text into chunks of size 500 with 50-token overlap and retrieves k=4 chunks per query.
- Source code and examples published at github.com/naimulkarim/pdf-qa-app.
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