Django Ollama Chat App

This project demonstrates how to build a simple chat application using Django and Ollama, allowing you to interact with large language models (LLMs) locally. This application uses the Llama 3 model by default, but can be configured to use other Ollama-compatible models.

Features

• Local LLM Interaction: Runs the LLM (Llama 3 by default) locally using Ollama, ensuring data privacy and low latency.
• Streaming Responses: Displays the LLM's response in real-time as it's generated, providing a more interactive chat experience.
• Simple Chat Interface: A basic web interface for sending messages to the LLM and viewing its responses.
• Easy Setup: Uses Docker for Ollama, simplifying model management and ensuring consistency.

Prerequisites

• Docker: Required for running the Ollama container. Install Docker Desktop (for Windows/macOS) or Docker Engine (for Linux). See https://docs.docker.com/get-docker/ for installation instructions.
• Python 3: Required for running the Django application.
• Git: Optional, but recommended for cloning the repository.

Installation

1. Clone the repository:

   git clone https://github.com/randyungaro/django-ollama.git  
   cd django-ollama
   

2. Set up the Ollama container:

   docker pull ollama/ollama
   docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
   docker exec -it ollama ollama run llama3  # Or any other Ollama-compatible model
   

Note: You can replace llama3 with the name of any other model you have downloaded using ollama pull <model_name>.

3. Create a virtual environment:

   python3 -m venv env
   source env/bin/activate  # On Linux/macOS
   env\Scripts\activate  # On Windows
   

4. Install dependencies:

   pip install django ollama
   

5. Run the Django development server:

   python manage.py runserver
   

6. Access the application:

Open your web browser and go to http://127.0.0.1:8000/chat/ to interact with the chat application.

7. Usage:

Type your message in the input box. Click the "Send" button. The LLM's response will appear in the chat history.

License MIT License

Acknowledgements This project uses Ollama for local LLM interaction.

The Llama 3 model is used by default.