An insurance PDF RAG system leveraging MongoDB Atlas Vector Search capabilities
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Updated
Nov 28, 2024 - Python
An insurance PDF RAG system leveraging MongoDB Atlas Vector Search capabilities
LLM-based application leveraging LangChain for Retrieval-Augmented Generation (RAG) on imported PDF documents. Enables users to interactively query and converse with PDF content using vector-based retrieval.
✌️ A dynamic Retrieval-Augmented Generation (RAG) system with support for PDF indexing, website crawling, and semantic Q&A powered by OpenAI, Qdrant, and Streamlit.
Backend service for Retrieval-Augmented Generation (RAG) using AWS Bedrock, Superduper, and MongoDB Atlas Vector Search.
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