Enhanced-QA-Chatbot-With-Ollama 🤖

This repository contains an enhanced Question Answering (QA) chatbot built using LangChain, Ollama, Python, Streamlit, and LangSmith. It offers the capability to switch between two different language models: "llama3.2" and "gemma:2b". 🧠

Overview

This chatbot leverages the power of Ollama to run large language models locally. 🏠 It provides a user-friendly interface through Streamlit, allowing users to interact with the chosen model for question answering tasks. LangChain facilitates the integration of different components and provides powerful tools for chain-of-thought prompting and other advanced features. LangSmith is used for experiment tracking and evaluation of the chatbot performance. 📈

Technologies Used

• LangChain: Framework for building applications with LLMs. ⛓️
• Ollama: Tool for running and managing Large Language Models locally. 🦙
• Python: Programming language used for development. 🐍
• Streamlit: Framework for creating interactive web applications. streamlit
• LangSmith: Platform for debugging, testing, and evaluating LLM applications. 🔬
• llama3.2: One of the supported language models.
• gemma:2b: The other supported language model.

Features

• Model Switching: Easily switch between the "llama3.2" and "gemma:2b" models. 🔄
• User-Friendly Interface: Interactive web interface built with Streamlit. ✨
• Local LLM Execution: Leverages Ollama for running models locally, ensuring privacy and potentially faster inference. 🔒
• LangChain Integration: Utilizes LangChain for advanced prompting techniques and chain management. 🚀
• Experiment Tracking: Uses LangSmith for experiment tracking and evaluation. 📊

Installation

1. Clone the repository:

   git clone [https://github.com/laavanjan/Enhanced-QA-Chatbot-With-Ollama.git](https://github.com/laavanjan/Enhanced-QA-Chatbot-With-Ollama.git)
   cd Enhanced-QA-Chatbot-With-Ollama

2. Install dependencies:

   pip install -r requirements.txt

3. Install Ollama and download the models:

   Follow the instructions on the Ollama website to install Ollama. Then, download the "llama3.2" and "gemma:2b" models. Make sure Ollama is running. ✅

4. Set up LangSmith:

   Create an account on LangSmith and obtain your API key. Set the environment variables LANGSMITH_API_KEY and optionally LANGSMITH_HOST as described in the LangSmith documentation. 🔑

Usage

1. Run the Streamlit application:

   streamlit run app.py

2. Access the application:

   Open your web browser and navigate to the URL provided by Streamlit (usually http://localhost:8501). 🌐

3. Interact with the chatbot:

   Use the interface to ask questions and switch between the available models. 💬

Contributing

Contributions are welcome! 🎉 Please open an issue or submit a pull request. 🤝

License

This project is licensed under the GPL License - see the LICENSE file for details. 📜

Acknowledgements

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