Advanced AI Development Ecosystem

Automated Issue Resolution and Closure System

The automated issue resolution and closure system is designed to automatically manage and close issues based on predefined criteria. It aims to streamline the resolution process and ensure that issues are efficiently handled without manual intervention.

Welcome to our advanced AI development ecosystem, featuring the formidable Mixtral-8x7B Large Language Model (LLM). This pretrained, generative Sparse Mixture of Experts surpasses Llama 2 70B across most benchmarks, highlighting its exceptional capabilities.

Key Features

• AI Model Customization
• Tool Access
• Persistentt Conversations
• File Support
• Issue Management

Issue Management Features

• Searching for Bugs, Features, Refactors, and Tests
• Identifying and Reporting Issues
• Creating Sweep issues using the newly created template

Issue management features allow users to create, update, and delete issues. It also provides the functionality to list all existing issues and retrieve specific issues based on user-defined criteria. Users can effectively utilize the issue management feature to track and address software bugs, feature requests, and other project-related tasks.

• API Extensibility

Primary Components

• AI Assistant
• Thread
• Message
• Run
• Run Step

Getting Started

Configuring and Using the Recurring Issue Management System

To configure the recurring issue management system, modify the settings in the recurring_issues section of the sweep.yml file. This section allows users to define repeat schedules, specify recurrence patterns, and configure notification settings. For detailed instructions on setting up, customizing, and using the recurring issue management system, refer to the system documentation provided in the sweep.yml file.

To get started with the project, clone the repository and install the required dependencies.

git clone https://github.com/reconsumeralization/AssistantMix/advanced-ai-development-ecosystem.git
cd advanced-ai-development-ecosystem
pip install -r requirements.txt

Configuration

The project configuration can be found in the config.py file. Here you can set the AI model name, the number of tools, thread context window, run status check interval, authorization method, and other settings.

# config.py

# AI Model Configuration
AI_MODEL_NAME = 'Mixtral-8x7B'
AI_MODEL_TOOLS = 128

# Thread Configuration
THREAD_CONTEXT_WINDOW = 2048

# Run Configuration
RUN_STATUS_CHECK_INTERVAL = 60  # in seconds

# Authorization Configuration
AUTHORIZATION_METHOD = 'API_KEY'

# Open Interpreter Configuration
OPEN_INTERPRETER_SANDBOXED = True

# Knowledge Retrieval Configuration
KNOWLEDGE_RETRIEVAL_INDEXING = True

# Function Calling Configuration
FUNCTION_CALLING_ENABLED = True

# Code Refactoring Platform Configuration
CODE_REFACTORING_PLATFORM_INTEGRATED = True

# UI Configuration
UI_ENABLED = True

# Error Handling Configuration
ERROR_HANDLING_METHOD = 'LOGGING'

# Logging Configuration
LOGGING_ENABLED = True
LOGGING_LEVEL = 'INFO'  # Options: DEBUG, INFO, WARNING, ERROR, CRITICAL

Usage

Contributing to Issue Management Optimization

Contributions to the improvement and optimization of the issue management features are welcome! Please refer to our Contributing Guide for details on the process for submitting changes related to issue management.

The main components of the project are the AI model, the assistant, and the thread. You can customize the AI model's characteristics, equip the assistant with tools, and initiate threads with initial messages to commence a conversation.

# ai_model.py

class Mixtral8x7B:
    """
    Class representing the Mixtral-8x7B Large Language Model (LLM).
    """

    def __init__(self):
        self.name = config.AI_MODEL_NAME
        self.tools = [None] * config.AI_MODEL_TOOLS
        logger.info(f"AI Model {self.name} initialized with {len(self.tools)} tools.")

    def customize(self, characteristics):
        """
        Customize the AI model's characteristics.
        """
        self.characteristics = characteristics
        logger.info(f"AI Model {self.name} customized with characteristics: {characteristics}")

# assistant.py

class Assistant:
    """
    Class representing an AI Assistant powered by the Mixtral-8x7B Large Language Model (LLM).
    """

# thread.py

Providing Feedback

Guidelines for Identifying and Reporting Issues Users can provide feedback, report bugs, suggest improvements, and provide general feedback through the following channels:

• Bug Reports: Users can report bugs by sending an email to bugs@example.com or by creating an issue in the issue tracker.
• Improvement Suggestions: Users can suggest improvements by creating an issue in the issue tracker or by sending an email to improvement@example.com.
• General Feedback: Users can provide general feedback by emailing feedback@example.com.

The development team will review and address the feedback according to the following process:

1. Bug reports will be reviewed, and if valid, issues will be created in the issue tracker. These issues will then be prioritized and addressed in upcoming releases.
2. Improvement suggestions will be tracked, reviewed, and considered for future enhancements.
3. General feedback will be reviewed, and constructive feedback will be used to inform improvements to the system.

We value your feedback and strive to continuously improve the system based on user input. Thank you for contributing to the development of our AI ecosystem.

Logging

The project uses Python's built-in logging module to record events, track errors, and capture relevant information during execution. The logging configuration can be found in the config.py file.

# utilities.py

# Initialize logger
logger = logging.getLogger(__name__)
logger.setLevel(config.LOGGING_LEVEL)

# Create a file handler
handler = logging.FileHandler('ai_development.log')
handler.setLevel(config.LOGGING_LEVEL)

# Create a logging format
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)

# Add the handlers to the logger
logger.addHandler(handler)

Testing

Expected Behavior and Logs

Upon the execution of the automated issue resolution and closure system, the expected behavior includes the automatic resolution and closure of issues based on the predefined criteria. Additionally, the system generates notification and audit logs to provide clear records of the automated resolution actions. These logs can be accessed to track the resolution process and review the actions taken by the system.

Reporting System Testing

The project includes test cases for the reporting system.

To run the tests, use the following command:

python -m unittest discover -s tests

The project includes test cases for the AI model and the assistant. To run the tests, use the following command:

python -m unittest discover -s tests

Documentation

For more detailed information about the project, please refer to the system_documentation.md file.

Contributing

Contributions are welcome! Please read our Contributing Guide and our Code of Conduct for details on our code of conduct and the process for submitting pull requests to us.

License

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