PDF Chatbot: RAG using Langchain, Groq (Llama3 LLM) & OpenAI Embeddings

One-line: A Retrieval-Augmented Generation (RAG) PDF chatbot that ingests PDFs, indexes them with OpenAI embeddings, and answers user queries by retrieving relevant context and generating answers with a Llama-3 model served via Groq — orchestrated using LangChain.

What it does

• Ingests PDF documents (single or batch), extracts text, cleans and splits into chunks.

• Generates vector embeddings for each chunk using OpenAI embeddings.

• Stores vectors in a vector store (e.g., FAISS, Chroma, Pinecone).

• Uses LangChain to build a retrieval pipeline: given a user query, it retrieves top-k relevant chunks.

• Feeds retrieved context + user query into a generative LLM (Llama 3 served on Groq) to produce grounded, citation-aware answers.

• Returns responses in a conversational chat UI, optionally showing source citations and confidence/metadata.

Core components / tech stack

• LangChain — orchestration, chains, prompt templates, retrieval QA.

• PDF parsing — pdfplumber / PyPDF2 / pdfminer.six for text extraction.

• Text splitting / preprocessing — chunking, stopword removal, optional language detection.

• Embeddings — OpenAI Embeddings API (or an alternative) to vectorize chunks.

• Vector store — FAISS / Chroma / Pinecone / Milvus for fast similarity search.

• LLM — Llama-3 hosted/accelerated on Groq hardware (inference endpoint).

• Frontend — simple chat UI (Streamlit / Flask / React) to interact with the bot.

• Optional — job queue (Redis/RQ), Docker, Kubernetes for scalability.

High-level flow

1. Upload PDF → extract text → split into chunks.

2. Create embeddings for chunks (OpenAI) → store vectors in chosen vector DB.

3. User asks question → retrieve top relevant chunks via similarity search.

4. Construct prompt (retrieved chunks + instructions) → call Llama-3 (Groq) to generate answer.

5. Return answer + sources; log conversation for future improvements.

Features to highlight

• Grounded answers using retrieved document context (minimizes hallucinations).

• Source citations (link to page/chunk + excerpt).

• Hybrid retrieval options: semantic + keyword filtering.

• Configurable prompts and chain-of-thought toggles.

• Batch ingestion and incremental indexing for updates.

• Access controls and usage logging for privacy/compliance.

Deployment & Ops notes

• Use OpenAI only for embeddings if you require best-in-class semantic vectors; consider local/open replacements for offline setups.

• Host Llama-3 inference on Groq for high-throughput low-latency serving; ensure prompt format and tokenization match model specifics.

• Secure API keys, enforce rate limits, and implement caching for repeated queries.

• Monitor vector-store size and re-chunking strategy for large corpora.

Limitations & improvements

• RAG quality depends on chunk size, embedding quality, and retrieval strategy.

• Llama-3 may still hallucinate—mitigate with stronger prompt engineering, answer verification, and grounding heuristics.

• Consider answer ranking ensemble (multiple prompts or models) and user-feedback loop to improve accuracy.