Multimodal Document Processing RAG with LangChain
ChatPDF leverages Retrieval Augmented Generation (RAG) to let users chat with their PDF documents using natural language. Simply upload a PDF, and interactively query its content with ease. Perfect for extracting information, summarizing text, and enhancing document accessibility.
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In this project I have built an end to end advanced RAG project using open source llm model, Mistral using groq inferencing engine.
A ChatBot designed to assist WhatsAgenda customers in configuring their calendar. This tool streamlines the setup of scheduling, managing appointments, and customizing service hours, ensuring an efficient and user-friendly experience.
Memomind is a sleek note-taking app built with React 18, Next.js 14, and TypeScript. It features a chat-based RAG workflow, AI-powered insights with Langchain and Llama3, and secure authentication via Clerk. It uses Tailwind CSS for styling and Shadcn-UI for components.
In this end to end project I have built a RAG app using ObjectBox Vector Databse and LangChain. With Objectbox you can do OnDevice AI, without the data ever needing to leave the device.
Repo for DermAssist: Your AI Assitant for Skin Problems. Powered by a vision model and a reliable RAG system.
Implement RAG using LangChain and HuggingFace embedding models
"SmartRAG-Assistant/GenAI-Assistant leverages advanced LLM models and Nvidia APIs for efficient query handling and document summarization. It integrates LlamaParse for structured data extraction, HuggingFace embeddings for vectorization, and PineconeDB for efficient retrieval, ensuring precise answers to user queries."
In this project I have built an advanced RAG Q&A chatbot with chain and retrievers using Langchain
Retrieval-Augmented Generation on YouTube transcripts and PDFs to deliver accurate and contextual answers.
Analysis Agent on Llamaindex Typescript with a simple caching mechanism
Conversational RAG with PDF and chat history
A RAG Model ChatBot for jamia Millia Islamia
The project involves developing a chatbot to enhance learning by answering common FAQs and providing hints within the scope of each sprint. Below is the deployed link demonstrating frontend and node backend. Flask app is not deployed due to size issue, please run locally and use google api key to check the functionality of our RAG based chatbot
A Streamlit-based chatbot application utilizing Groq API and Langchain for conversational AI.
Document Based Question Answering System Using LangChain and LLMs
Upload documents 📄 and get instant, accurate answers to your questions with InstaDoc: Intelligent QnA Powered by RAG. Enjoy quick summaries 📜 and precise Q&A, all through an intuitive interface. InstaDoc leverages advanced technologies 🚀 to help you understand your documents better and faster, making document analysis efficient and user-friendly
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