mali-109/Rag-project

KrishiMitr RAG is an AI-powered agricultural assistant built using Retrieval-Augmented Generation (RAG). It retrieves relevant information from agricultural documents to provide accurate, context-aware answers. Built with LangChain, ChromaDB, sentence-transformer embeddings, and Groq LLM for fast, reliable, and intelligent farming assistance. Short Description KrishiMitr RAG is an AI-powered Retrieval-Augmented Generation system that provides accurate agricultural information by retrieving knowledge from agricultural documents using ChromaDB, LangChain, and Groq LLM. GitHub Repository Description KrishiMitr RAG is an intelligent agricultural knowledge assistant built using Retrieval-Augmented Generation (RAG). Instead of relying only on an LLM's pretrained knowledge, the system retrieves relevant information from agricultural documents and generates accurate, context-aware responses for farmers and agricultural professionals. The project uses LangChain for orchestration, ChromaDB as the vector database, sentence-transformer embeddings for semantic search, and Groq LLM for fast response generation. Features 🌾 Agricultural Question Answering 📄 PDF Document Knowledge Base 🔍 Semantic Search using Vector Embeddings 🗂️ ChromaDB Vector Database ⚡ Fast inference with Groq LLM 🤖 Context-aware AI responses 📚 Easy addition of new agricultural documents 🔄 Retrieval-Augmented Generation (RAG) pipeline Tech Stack Python LangChain ChromaDB Groq API Sentence Transformers Hugging Face Embeddings PDF Loader Recursive Text Splitter Workflow Agricultural PDF documents are loaded. Documents are split into smaller chunks. Each chunk is converted into vector embeddings. Embeddings are stored in ChromaDB. User asks an agricultural question. Relevant document chunks are retrieved. Retrieved context is sent to the Groq LLM. The AI generates an accurate, context-based answer. Use Cases Farmer assistance Crop management guidance Disease information Pest management Fertilizer recommendations Government scheme information Agricultural education Offline agricultural knowledge base (with local documents) Future Enhancements Voice-enabled multilingual assistant Integration with the KrishiMitr mobile app Image-based crop disease diagnosis Weather and market price integration Personalized farming recommendations Multi-language suppor …