RAG Approach | Powered by LangChain
Step-by-step document intelligence with Retrieval-Augmented Generation
Users upload documents (PDF, DOCX, text). The GenAI system processes and divides them into structured chunks for efficient understanding.
Each document segment is converted into high-dimensional vector embeddings using LangChain and OpenAI APIs for contextual search.
When a query is made, GenAI fetches the most relevant document chunks using Pinecone/FAISS vector database retrieval.
The system combines retrieved context with generative AI to craft accurate, explainable, and human-like responses in real time.
GenAI generates concise summaries, keyword extraction, and insights for better decision-making and analytics visualization.