A local AI chatbot built using Ollama (LLaMA 2) and Streamlit. Runs fully offline with no API key required.

A multi-character AI chatbot built with Streamlit and powered by Llama 3.2 via Ollama.

A lightweight chatbot built with LLaMA 3.2, LangChain, and Streamlit using Ollama for local inference.

Medical RAG Chatbot: A Streamlit-powered AI assistant that processes medical books, theses, PDFs, and images using OCR. Features ChromaDB vector search, Ollama LLM (Llama 3.2), and citation-backed answers for accurate, explainable medical queries.

This Streamlit app is a Document AI Chatbot for Q&A with LLMs. It processes PDFs, PPTs, & Excels into a FAISS vector database for RAG. Integrates Ollama (Llama 3.2) & Gemini API. Features persistent chat history & intuitive UI with `+` button for file uploads. Built with Python, Streamlit, LangChain, RAG, custom CSS.

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A context-aware chatbot using Llama 3.2 via Ollama and LangChain, deployed with Streamlit.

Local RAG chatbot for asking questions to your PDFs using LangChain, Ollama (Llama 3.2), ChromaDB & Streamlit — 100% offline & private

A simple local AI chatbot built with Streamlit and Ollama, using the LLaMA 3.2 model for interactive conversations without external APIs.

A chatbot built with LangChain, powered by either OpenAI's GPT-4 or Llama 2 from Ollama, with a simple Streamlit interface.

Building a chatbot using Streamlit, LangChain, and Ollama. This chatbot will run locally and allow us to chat with powerful models like LLaMA 3.2 and DeepSeek.

📦 Langchain – LLM Chatbot with OpenAI & Ollama This repository demonstrates how to build an interactive chatbot using LangChain, Streamlit, and two different LLM backends: OpenAI (gpt-3.5-turbo) and Ollama (LLaMA 2).

Offline RAG chatbot answering Space-related questions using Wikipedia sources, Python, Llama 3.2 (Ollama), Chroma vector DB, and LangChain 1.0+ with a Streamlit interface.

A conversational AI chatbot built with Ollama Llama 3.2, Streamlit, and LangChain, offering interactive and dynamic responses. Designed for seamless integration and efficient natural language understanding.

A locally hosted chatbot built with Python and Streamlit. Supports multiple Ollama models (Gemma 3, Llama 2) and integrates additional Hugging Face models for flexible, offline AI experimentation.

A Streamlit-based chatbot app that leverages the Ollama Llama 3.2 model and stores chat history in PostgreSQL. The app is containerized using Docker and orchestrated with Docker Compose.

A simple Streamlit chatbot using LangChain and Ollama’s LLaMA 3.2:1b model. Users can enter text and receive AI-generated responses. Clean UI with quick deployment, ideal for experimenting with local LLMs via langchain_ollama.

LangChain + Streamlit Chatbot (Llama2 / OpenAI GPT Integration) A simple yet powerful chatbot built with LangChain, Streamlit, and Ollama’s Llama 2 model, designed to demonstrate LLM-powered conversations through a clean web interface. The project also supports switching to OpenAI GPT models for improved performance and flexibility.

Emotion-Aware Chatbot for Mental Health Support – A Streamlit-based AI chatbot using LLaMA 3.2 (via LangChain-Ollama) to provide empathetic, non-clinical emotional support. Deployed on Google Cloud. Includes full source code, deployment instructions, and supplementary documentation.

A Streamlit-based chatbot using LangChain, the Ollama LLM (Llama 3.2 by Meta), and LangSmith for tracking. It processes user questions with dynamic prompts and delivers AI-driven responses in real time.

This project is an interactive chatbot built using LangChain, Ollama LLM, and Streamlit. It leverages the power of local LLMs (like LLaMA 2) for generating natural language responses to user queries. The chatbot interface is built with Streamlit, enabling real-time user interaction via a simple web app.

his is a web-based chatbot built using Streamlit, powered by the Llama 2 model via the Ollama framework, and enhanced with voice output functionality using Google Text-to-Speech (gTTS). The chatbot provides a clean, interactive interface for chatting with the Llama 2 model, with optional voice output that can be enabled or disabled.

Enhanced Q&A Chatbot with OpenAI and Ollama This project is a Streamlit‑based web application that provides an interactive chatbot interface. Users can ask questions and receive AI‑powered responses in real time. The app supports both OpenAI models (like GPT‑4) and open‑source models via Ollama (such as LLaMA 2 and mxbai‑embed‑large).

A smart medical chatbot built using open-source tools. Utilizes HuggingFace for embeddings, FAISS (CPU) for vector storage, and LLaMA 2 via Ollama for language generation. Streamlit is used for the conversational interface, enabling efficient and interactive healthcare-focused dialogue.

A Retrieval-Augmented Generation (RAG) based chatbot that allows users to upload PDF documents and ask questions related to their content. Built using Streamlit for the user interface, LangChain for orchestration, and the LLaMA 2 model served locally via Ollama.

A streamlit-based chatbot for analyzing diagrams and flowcharts, using Ollama (LLaMA 3.2 Vision, LLaVA) and Gemini 1.5 Flash models, supporting image upload, persistent chat with chat history retention, JSON export, and automated diagram parsing into structured json..

A simple and interactive AI Chatbot built using Python, Streamlit, and Ollama. It runs locally on your computer using open-source models like Llama 3.2. This chatbot can answer questions on various topics including general knowledge, coding, education, and more without requiring any internet connection or API keys.

A lightweight AI chatbot built using Ollama's Llama 2 model. It runs locally with Python and Streamlit/Flask, offering smart, real-time responses for Q&A, resume analysis, or educational help. Fast, private, and customizable—ideal for offline use and personal AI exploration.

Bhim is an AI-powered chatbot built with Streamlit, LangChain, and Ollama using the LLaMA 3.2 model. It provides accurate, context-aware, and ethical answers through an interactive web interface. The system maintains chat history, ensuring meaningful, real-time, and responsible conversations for problem-solving.