# ChatXBT Web3 AI Assitant Service

AI-powered unification and execution protocol for web3 applications


## Requirements

The list below contains all the requirements needed to have a complete developer environment to sart working on this project.
Follow the links to install or update these tools on your machine.

1. [Python 3.12+](https://www.python.org/downloads/release/python-3120/)
2. [Pyenv](https://github.com/pyenv/pyenv)
3. [Pip Installer](https://pip.pypa.io/en/stable/installation/)
4. [Python Venv](https://docs.python.org/3/library/venv.html)


## Installation

1. Clone the repository: `git clone https://github.com/ChatXBT/chatxbt-web3-ai-assistant.git`
2. Get a copy of the `\`.env`\` file from the project manager`
3. Create a virtual environment: `python -m venv env`
4. Activate the virtual environment: `source env/bin/activate`
4. Install the dependencies: `pip install -r requirements.txt`
5. Run the application: `chainlit run chatxbt-assistant.py -w`
6. Open the application in your browser: `http://localhost:8000`


## Project Overview

The project is structured as follows:

1. src/data_sources: `a list of data sources that can be used to retrieve data for the AI assistant.`
2. src/databases: `a list of databases that can be used to store data for the AI assistant.`
3. src/libs: `a list of libraries that can be used to extend the classes and functions servicing the AI assistant.`
4. src/llms: `a list of language models powered libs that can be used to power the AI assistant.`
5. src/search_services: `a list of search services that can be used to retrieve data for the AI assistant.`
6. src/tools: `a list of tools that can be used to extend the functionality of the AI assistant.`


## Guides

1. Follow the development, structure and documentation patterns of the `src/tools/coin_data_toolkit.py` to build custom toolkits
2. Follow the development, structure and documentation patterns of the `src/tools/coin_data_toolkit.py` to build classes 
3. Pay attention to the `constants` variables and `caching` decorators to optimize perfomance of class functions/methods across the project
4. Create new classes in the appropriate folders to maintain an organized codebase and project
5. After installing any new package run `` to update the dependency requirements list
6. Expose RPC functions for SDKs that do not support python runtime. use [DeepKit](https://deepkit.io/documentation/rpc) and [BunJS](https://bun.sh/) if possible.


## Git Workflow

For this project, we will be using a very simple version of the Gitflow workflow. This workflow is designed to manage the development, release, and maintenance of software projects. It provides a clear and structured approach to managing branches, releases, and feature development.

### Feature

1. Create a branch off of main branch: `git checkout -b feature/my-feature`
2. Make your changes and commit them: `git commit -m "Add my feature"`
3. Push your changes to the remote repository: `git push origin feature/my-feature`
4. Open a pull request on GitHub and request a review from a team member.
5. Once the pull request is approved, merge it into the main branch.
6. Delete the feature branch: `git branch -d feature/my-feature`
7. Update your local main branch: `git checkout main && git pull origin main`

### Bugfix

1. For bug fixes, create a branch off of main branch: `git checkout -b bugfix/my-bugfix`
2. Make your changes and commit them: `git commit -m "Fix my bug"`
3. Push your changes to the remote repository: `git push origin bugfix/my-bugfix`
4. Open a pull request on GitHub and request a review from a team member.
5. Deploy the bugfix branch to a staging environment for testing.
6. Once testing is finished, merge the pull  request into the main branch.
7. Once the pull request is approved, merge it into the main branch.
8. Delete the bugfix branch: `git branch -d bugfix/my-bugfix`
9. Update your local main branch: `git checkout main && git pull origin main`


## Deplopyment to producton

To be finalised and added


## TODO

1. Add deployment instructions
2. Setup CI/CD pipelines for automatic deployment
3. Setup and integrate `LiteLLM` instance for LLM service high availability
4. Add RPC class for remmote method calls to other chatxbt services written in other launguages such as `JS`, `TS` & `GO`
5. Write enough unit test to ensure class and function input and output correctness