A single-file tkinter-based Ollama GUI project with no external dependencies.

TalkNexus: Ollama Chatbot Multi-Model & RAG Interface

Check Your Password is Ever Cracked & Know About Strength of Your Password & Generate Passwords Using a Specialized AI Model (StrengthX-Dildo:V1) Dynamic Intelligent Lock & Defense Operator

Machine-learning agriculture fastapi deep-learning llm crop-recommendation plant-disease-detection weather-api ollama multilingual-ai

A basic yet swift ollama/llama.cpp/openai TUI Client

Ollama‑Chat is a Streamlit-based web UI for interacting with Ollama-hosted language models. It provides a clean, modern chat interface where users can: Choose from multiple local Ollama models Adjust parameters like temperature and system prompts View and preserve chat history Customize settings via a sidebar

A feature-rich Ollama client with enhanced terminal UI using the Rich library

This project is a Python-based voice assistant that enables spoken conversations with a locally hosted LLM using Ollama.

Two-way-RAG is a voice-interactive Retrieval-Augmented Generation (RAG) system that transforms your local documents into a conversational knowledge base. Built with a high-performance FastAPI backend and LangChain, it leverages Ollama (Llama 3.2) for private, local LLM inference and FAISS for efficient semantic search.

AI-powered multi-agent system built using the Google Agent Developer Toolkit, designed to streamline complex tasks across finance, web intelligence, and database interaction. This suite enables seamless orchestration between specialized agents, each with domain expertise, to collaboratively process and fulfill user intents in real-time.

A lightweight local AI chatbot powered by Ollama and LLMs. Built using Python sockets and multi-threading to handle multiple users at once. Designed for simple, friendly English conversations with emoji-rich replies. 🌟

This desktop application, built with customtkinter, provides an interactive chat interface for local Large Language Models (LLMs) served via Ollama.

This RAG system helps locate specific sections of video content and answer questions quickly, leveraging embeddings and LLaMA 3.2 1B. The upgraded pipeline is optimized for speed, accuracy, and multi-day datasets.

Basic RAG-based chatbot, that will generate response based on the pdf provided.

DictAi helps you understand any word by giving both its dictionary definition and a simple, beginner-friendly explanation. It runs entirely on your device using Ollama’s fast phi model — no internet required.

AI Model for Competitive Exams A Flask-based web app that uses RAG (Retrieval-Augmented Generation) with ChromaDB and OCR to answer MPSC/UPSC questions. Users can input queries via text or images, and the system retrieves relevant content from study materials, applies NLP for context, and generates accurate, syllabus-aligned responses.