Multimodal Document Processing RAG with LangChain
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.
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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基于LangGraph的智能保险合同 PDF 分析与问答助手，支持要点提取、检索、风险高亮、公式解析与可视化。AI-powered insurance contract PDF assistant: summarization, semantic/keyword search, risk highlighting, formula extraction, and visualization.
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.
SDLC AI Agent is an AI-powered tool that streamlines the entire Software Development Lifecycle from requirements gathering to code generation and testing.
In this project I have built an end to end advanced RAG project using open source llm model, Mistral using groq inferencing engine.
Implement RAG using LangChain and HuggingFace embedding models
Repo for DermAssist: Your AI Assitant for Skin Problems. Powered by a vision model and a reliable RAG system.
A knowledge base constructed based on Langchain+RAG+LLM
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.
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)
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 implements a classic Retrieval-Augmented Generation (RAG) system using HuggingFace models with quantization techniques. The system processes PDF documents, extracts their content, and enables interactive question-answering through a Streamlit web application.
Agentic Chatbot: for Navigating Red Hat Internal resources from THE SOURCE
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.
A Fast API server that provides local text and multi-modal embedding using LlamaIndex Hugging Face Embedding
Conversational RAG with PDF and chat history
In this project I have built an advanced RAG Q&A chatbot with chain and retrievers using Langchain
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