mauke-231/acity-rag-assistant

RAG System to answer questions based on the 2025 Ghana Budget and Election Results dataset # ACity RAG Assistant ### CS4241 — Introduction to Artificial Intelligence | End of Semester Examination 2026 **Author:** Maukewonge Yaw Nyarko-Tetteh **Index Number:** 10022200107 **Lecturer:** Godwin Danso **Academic City University — Faculty of Computational Sciences and Informatics** --- ## Project Description A fully custom Retrieval-Augmented Generation (RAG) chatbot for Academic City University. Allows users to chat with: - Ghana's **2025 Budget Statement and Economic Policy** (MOFEP) - **Ghana Presidential Election Results** (by constituency, region, year) > ⚠️ Built **without** LangChain, LlamaIndex, or any pre-built RAG pipeline. All components — chunking, embedding, vector storage, retrieval, and prompt construction — implemented manually. ## Live Demo ai10022200107-mauke.streaml… ## Features - 🔍 Hybrid semantic + keyword retrieval (FAISS + BM25) - 🧠 Memory-based RAG — remembers past exchanges semantically - 📄 Retrieved chunk display with similarity scores - 🛡️ Hallucination control via prompt templates - 🔬 Pipeline debug view (all 7 stages logged) - 🤖 Side-by-side RAG vs Pure-LLM comparison - 🌐 Provider-agnostic LLM (Anthropic / OpenAI / Google) ## Setup & Run ```bash # 1. Clone the repo git clone github.com cd ai_10022200107 # 2. Install dependencies pip install -r requirements.txt or python -m pip install -r requirements.txt # 3. Set your LLM API key export LLM_PROVIDER="groq" # or "openai", "google" or "anthropic" export LLM_API_KEY="your-api-key" # 4. Run the app streamlit run app.py or python -m streamlit run app.py ``` ## Project Structure ``` ├── app.py → Streamlit UI ├── requirements.txt ├── src/ │ ├── data_engineering.py → Part A: Cleaning + chunking │ ├── retrieval.py → Part B: FAISS + BM25 + hybrid │ ├── prompt_engineering.py → Part C: Prompt templates │ ├── pipeline.py → Part D: Full pipeline + logging │ └── memory.py …