Our project is a startup that leverages the power of the GPT4 and LLM/ML models. The main objective is to create a unified application that combines the comprehensive functionality offered ML models with our UI mechanics. By harnessing the capabilities of the GPT like models, we aim to provide users with a seamless and intelligent conversational experience within a single application.
For the frontend, we have selected React.js, while for the backend, we have decided to use Django Rest Framework. The communication between the frontend and backend is facilitated through APIs. We have made significant progress in the implementation phase, completing the following tasks:
- Application UI Design: We have designed and developed the user interface of the application, ensuring an intuitive and visually appealing experience for our users.
- User Authentication with JWT: The application features secure user authentication using JSON Web Tokens (JWT), providing a seamless and reliable authentication mechanism.
- Email Confirmation Functionality: We have implemented email confirmation functionality, ensuring that user accounts are verified through a confirmation link sent to their registered email addresses.
- Integration with the Language Model: We have successfully integrated the Chat-GPT language model into the application, enabling advanced natural language processing capabilities and intelligent conversations.
- Swagger Documentation: To ensure clarity and facilitate API usage, we have provided documentation using Swagger, outlining the available endpoints, request/response structures, and parameters.
There are the upcoming tasks and features on our todo list that we plan to implement:
- Single sign-on: Enable users to authenticate and log in to the application using their social media accounts such as Facebook, Twitter, or Google. This will provide a convenient and streamlined login experience for our users.
- Access to different types of LLM/ML models: Allow users to smoothly switch to the best model for solving an exact task using model name @model_name like we do with real users into chats.
- Make a copy of nginx.conf.template -> nginx.conf;
- Uncomment into frontend/nginx.conf lines for SSL certificate;
- Insatall cerbot:
apt install certbot python3-certbot-nginx
certbot --nginx -d chat.fosterflow.com
The project is launched via Docker-Compose. Docker works with volumes, so any changes will be automatically applied. To install Docker Desktop, follow the instructions on the official website, depending on your platform, link.
The following are the steps to launch:
- Download the project;
- Go to the project directory
- Rename .env.template to .env (make changes if you need it);
- Run Docker Desktop application;
- In the command line, write:
docker-compose build
docker-compose up
After following this instruction, the interaction is available at localhost/chat
We welcome and encourage contributions from the community to enhance our project. Whether you're a developer, designer, tester, or a user with valuable feedback, your contributions are highly appreciated. Here's how you can contribute to our project:
- Reporting Issues: If you come across any bugs, errors, or have suggestions for improvements, please open an issue on our GitHub repository. Provide a detailed description of the problem or suggestion, along with any relevant information that can help us understand and reproduce the issue.
- Submitting Pull Requests: If you'd like to contribute code or features, you can submit a pull request on our GitHub repository. Our team will review your pull request and provide feedback or merge it if it aligns with the project's goals.
- Feedback and Ideas: We value your feedback and ideas for improving the project. If you have suggestions, feature request, or any other input, please don't hesitate to reach out to us via email at hello@fosterflow.com