Multimodal Document Processing RAG with LangChain
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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.
In this project I have built an end to end advanced RAG project using open source llm model, Mistral using groq inferencing engine.
A knowledge base constructed based on Langchain+RAG+LLM
Chat With Documents is a Streamlit application designed to facilitate interactive, context-aware conversations with large language models (LLMs) by leveraging Retrieval-Augmented Generation (RAG). Users can upload documents or provide URLs, and the app indexes the content using a vector store called Chroma to supply relevant context during chats.
Implement RAG using LangChain and HuggingFace embedding models
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.
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.
SDLC AI Agent is an AI-powered tool that streamlines the entire Software Development Lifecycle from requirements gathering to code generation and testing.
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.
This project demonstrates a routing agent setup using LlamaIndex, Groq's LLaMA3-70B model, and HuggingFace Embeddings for answering queries from multiple domain-specific documents.
his is my own custom-built offline AI bot that lets you chat with PDFs and web pages using **local embeddings** and **local LLMs** like LLaMA 3. I built it step by step using LangChain, FAISS, HuggingFace, and Ollama — without relying on OpenAI or DeepSeek APIs anymore (they just kept failing or costing too much)
Analysis Agent on Llamaindex Typescript with a simple caching mechanism
Retrieval-Augmented Generation on YouTube transcripts and PDFs to deliver accurate and contextual answers.
Conversational RAG with PDF and chat history
Ask questions, get answers from your PDFs
A RAG Model ChatBot for jamia Millia Islamia
In this project I have built an advanced RAG Q&A chatbot with chain and retrievers using Langchain
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