This project is an AI-powered medical chatbot using Llama-2 (7B) LLM, Pinecone, and LangChain to answer medical queries from a vectorized knowledge base.
- Conversational AI: Context-aware responses to medical queries
- Semantic Search: Powered by Pinecone Vector Store
- Frontend: HTML, CSS, Javascript (Bootstrap)
- Backend: Python (Flask)
- LLM: Llama-2 (7B) via CTransformers
- Database: Pinecone (AWS, us-east-1) with LangChain
- Embeddings: Hugging Face Models
✅ Developed an AI-powered medical chatbot using Python (Flask), Llama-2, Pinecone, and LangChain. ✅ Implemented semantic search with Pinecone Vector Database to retrieve relevant medical information. ✅ Integrated Llama-2 (7B model) with CTransformers to generate context-aware medical responses. ✅ Pre-processed and embedded medical documents for improved query retrieval using LangChain. ✅ Optimized retrieval-based QA pipeline using cosine similarity metric for high-accuracy results. ✅ Designed a custom prompt template to ensure accurate, reliable, and structured medical responses. ✅ Deployed serverless infrastructure on Pinecone (AWS - us-east-1 region) for scalable and fast vector searches.
