Lehlohonolo-Saohatse/SA-Budget-RAG-LLM-Chatbot

A Retrieval-Augmented Generation (RAG) chatbot that allows users to query and compare South African national budget documents (2023-2026). The system uses Large Language Models (LLMs) + vector search to answer natural language questions with grounded context from official budget PDFs. # South African Budget RAG LLM Chatbot An AI-powered **Retrieval-Augmented Generation (RAG)** chatbot that analyzes and compares South African national budget documents (2023-2026). Built using modern LLM tooling, this project demonstrates **real-world NLP, semantic search, and multi-document reasoning** using LangChain’s latest architecture. > It is deployed on Streamlit, you can try it out on: sa-budget-rag-llm-chatbot.s… --- > Prompt examples at the bottom with screenshots. --- ## 🚀 Features * 🔍 **Semantic Search (RAG)** over budget PDFs * 📊 **Multi-Year Budget Comparison (2023–2026)** * 📚 **Source-Grounded Answers** (with document references) * ⚡ **Fast Vector Search** using ChromaDB * 🤖 **LLM-Powered Responses** (Groq / HuggingFace) * 🌐 **Interactive Web App UI** built with Streamlit * 🧠 Handles complex queries like: * “Compare healthcare spending across years” * “What are the priorities of the 2026 budget?” * “Which document mentions social grants?” --- ## 🏗️ Architecture ``` User Query ↓ Retriever (ChromaDB) ↓ Relevant Budget Chunks ↓ Prompt Template ↓ LLM (Groq / HF) ↓ Final Answer (with context) ``` ## 📂 Project Structure ``` South-African-Budget-NLP-RAG-Chatbot/ │ ├── data/ # Budget PDFs (2023–2026) ├── screenshots/ # Demo screenshots │ ├── src/ │ ├── app.py # Streamlit UI │ ├── chain.py # RAG pipeline (LCEL) │ ├── ingest.py # Document loading & splitting │ ├── llm.py # LLM configuration │ ├── vectorstore.py # Embeddings + Chroma DB │ ├── requirements.txt ├── .env └── README.md ``` --- ## ⚙️ Installation ### 1. Clone the repo ```bash git clone github.com cd South-African-Budget-NLP-RAG-Chatbot ``` ### 2. Create virtual environment ```bash python -m venv venv ``` ### 3. Activate environment **Windows (PowerShell):** ```bash venv\Scripts\activate ``` **Mac/Linux:** ``` …