holyasea/URAChatBot

The URAChatBot (also referred to as URA TaxBot) is a specialized AI-powered intelligence system designed to help users interact with and retrieve information from PDF documents, specifically tailored for the Uganda Revenue Authority (URA). # URAChatBot - PDF Intelligence System with Retrieval Augmented Generation (RAG) ## Overview The goal of this project is to create a user-centric and intelligent system that enhances information retrieval from PDF documents through natural language queries. The project focuses on streamlining the user experience by developing an intuitive interface, allowing users to interact with PDF content using language they are comfortable with. To achieve this, we leverage the Retrieval Augmented Generation (RAG) methodology introduced by Meta AI researchers. **URAChatBot** brings together cutting-edge AI technologies to provide seamless document interaction. ## Retrieval Augmented Generation (RAG) ### Introduction RAG is a method designed to address knowledge-intensive tasks, particularly in information retrieval. It combines an information retrieval component with a text generator model to achieve adaptive and efficient knowledge processing. Unlike traditional methods that require retraining the entire model for knowledge updates, RAG allows for fine-tuning and modification of internal knowledge without extensive retraining. ### Workflow 1. **Input**: RAG takes multiple pdf as input. 2. **VectoreStore**: The pdf's are then converted to vectorstore using FAISS and all-MiniLM-L6-v2 Embeddings model from Hugging Face. 3. **Memory**: Conversation buffer memory is used to maintain a track of previous conversation which are fed to the llm model along with the user query. 4. **Text Generation with GPT-4o-mini via OpenRouter**: The embedded input is fed to OpenAI's GPT-4o-mini model through OpenRouter API - fast, affordable, and reliable. 5. **User Interface**: Streamlit is used to create the interface for the application. ### Benefits - **Adaptability**: RAG adapts to situations where facts may evolve over time, making it suitable for dynamic knowledge domains. - **Efficiency**: By combining retrieval and generation, RAG provides access to the latest information without …