danlumumba/Kenya-finance-bill-2025-RAG

This is a simple Retrieval-Augmented Generation (RAG) application that allows users to ask natural language questions about the Kenya Finance Bill 2025 and receive intelligent, context-aware answers. # Kenya-finance-bill-2025-RAG This is a simple Retrieval-Augmented Generation (RAG) application that allows users to ask natural language questions about the Kenya Finance Bill 2025 and receive intelligent, context-aware answers. # 🇰🇪 Kenya Finance Bill 2025 - RAG Q&A System This is a simple Retrieval-Augmented Generation (RAG) application that allows users to ask natural language questions about the **Kenya Finance Bill 2025** and receive intelligent, context-aware answers. --- ## 🚀 Features - ✅ Loads and processes the full Finance Bill text. - ✅ Splits the text into manageable chunks with context overlap. - ✅ Uses vector embeddings for efficient similarity search. - ✅ Employs a Large Language Model (LLM) to generate accurate responses. - ✅ Supports conversational memory (optional). - ✅ Fully implemented in Python using LangChain and FAISS. --- ## 🧠 How It Works 1. **Ingest Bill Text** The full text is split into overlapping chunks to maintain context. 2. **Embed the Text** Chunks are converted into numerical vectors using a transformer model. 3. **Store in Vector DB** Embeddings are indexed using FAISS for fast similarity search. 4. **Query and Retrieve** User queries are converted to vectors, and relevant chunks are retrieved. 5. **Answer with LLM** A language model (like GPT-3.5) generates a response using the retrieved chunks. --- ## 🛠️ Technologies Used - LangChain - FAISS - OpenAI GPT or Mistral - Python - Optional: Streamlit or Gradio for UI --- ## 📦 Setup Instructions 1. Clone the repository: ```bash git clone github.com cd kenya-finance-bill-rag