This project leverages Retrieval Augmented Generation (RAG) to create a conversational AI system designed to provide accurate and timely first-aid advice. By integrating the capabilities of large language models (LLMs) with efficient information retrieval techniques, this system helps users answer common first aid questions by retrieving relevant information from a large corpus of first aid documents.
- Technology: Combines LLMs with RAG to ensure contextually relevant and precise responses.
- Embeddings: Utilizes Google Gemini text embedding to create high-quality embeddings optimized for retrieval tasks.
- Database: Employs ChromaDB to store and manage document embeddings, enabling efficient and accurate context retrieval.
- Language Model: Uses Google Gemini ChatGoogleGenerativeAI to generate responses based on retrieved context.
This project is designed to assist users with first aid guidance by retrieving and synthesizing information from a comprehensive first aid guide. The system demonstrates the effective integration of LLMs and RAG to create a practical, user-focused application.
