🤖 An Intelligent Chatbot: Powered by the locally hosted Ollama 3.2 LLM 🧠 and ChromaDB 🗂️, this chatbot offers semantic search 🔍, session-aware responses 🗨️, and an interactive Streamlit interface 🎨 for seamless user interaction. 🚀
The AI-Based Pre-Screening Assistant Bot is an AI-powered technical interview assistant that evaluates candidates using adaptive questioning, real-time feedback, and automated report generation. Built with Streamlit and RAGPipeline, it dynamically adjusts question complexity, tracks candidate performance, and emails interview summaries
iKOMA – Autonomous local AI assistant with secure internet search, continuous plan-execute-reflect loops, real-time dashboards, and safety-first guardrails.
A knowledge based LLM that runs on your own PC.
A production-ready AI agent pipeline using LangChain for LLM workflows, LangGraph for agent orchestration, and LangSmith for observability supporting document processing, weather queries, and general Q&A with built-in monitoring and debugging.
KardiaFlow is a specialized medical RAG system for analyzing clinical documents. Leveraging Chroma, Sentence-Transformers, and Ollama (gemma2:2b), it transforms PDFs into insights. With FastAPI, Docker, and Azure CI/CD, it offers a robust, secure architecture for professional healthcare AI deployment.
Neuro-Semantic Document Research Assistance
An AI-powered medical documentation system that generates comprehensive discharge summaries using Retrieval-Augmented Generation (RAG) architecture. This application leverages advanced language models, medical domain embeddings, and vector databases to assist healthcare professionals in creating accurate, structured clinical documentation.
A Legal Assistant Chatbot for lawyers in which lawyers upload their cases in pdf and chatbot reads the all of the necessary points in the pdf It helps them to solve their cases faster.
GeneralPurposeChatBot with RAG. Adpated for Telegram / Чатбот общего назначения для решения задачи поиска информации в документах. Адаптирован под Telegram.
This repository contains experiments and code for working with Retrieval-Augmented Generation (RAG), vector embeddings, and document ingestion/parsing using Python and popular libraries such as LangChain, HuggingFace, FAISS, and ChromaDB.
RAG-PDF Assistant — A simple Retrieval-Augmented Generation (RAG) chatbot that answers questions using custom PDF documents. It uses HuggingFace embeddings for text representation, stores them in a Chroma vector database, and generates natural language answers with Google Gemini. In this example, the assistant is powered by a few school policy doc
AI Study Assistant
AI-assisted news aggregator
A high-performance, privacy-first local voice assistant powered by Llama 3.2, Faster-Whisper, and RAG. Features persistent conversation memory and multimodal (voice/text) input, running entirely on-device for maximum speed and security
A modern Retrieval-Augmented Generation (RAG) chatbot for apartment rentals. Features a beautiful Streamlit web interface and a terminal chat mode. Supports real-time property search, pricing info, and conversation history using ChromaDB, MongoDB, and Gemini AI. Easy to set up, extensible, and perfect for demos or production use.
Production-grade RAG system for Singapore government documents with OpenAI integration
Simple LLM chatbot using ollama
Verify academic claims without compromising data privacy. ScholarQA is a local RAG tool that processes PDFs into vector embeddings and audits hypotheses against the text using open-source models.
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