AutoAI-AgentRAG

AutoAI-AgentRAG is an open-source PyPI library designed to enhance automation workflows by integrating AI Agents, Retrieval-Augmented Generation (RAG), and Machine Learning (ML). It empowers developers to build intelligent automation systems that efficiently manage complex tasks with minimal manual intervention.

Features

• AI Agent Framework: A modular system for designing and managing AI Agents that autonomously execute tasks and make context-informed decisions.
• Retrieval-Augmented Generation (RAG) Integration: Connects to external knowledge bases (APIs, databases, web sources) for real-time data retrieval, enhancing contextual awareness.
• Machine Learning Models: Offers pre-trained and customizable ML models compatible with TensorFlow, PyTorch, and other frameworks for predictive analytics and pattern recognition.
• Command-Line Interface (CLI): A user-friendly CLI for initializing projects, training models, and deploying agents, facilitating ease of use for developers.
• Extensible Plugin System: Enables users to add custom data sources, ML models, or agent behaviors to adapt the library to specific use cases.
• Comprehensive Documentation: Detailed guides, API references, and tutorials hosted on ReadTheDocs to accelerate user onboarding and development.
• Unit Testing and CI/CD: Built-in unit tests and GitHub Actions for continuous integration and deployment, ensuring code reliability and maintainability.
• Cross-Platform Compatibility: Optimized for Windows, macOS, and Linux, with Docker support for flexible deployment across environments.
• Real-Time Monitoring Dashboard: An optional web interface to track agent performance, task progress, and ML model metrics for better visibility and control.
• Community Support: A Discord server and GitHub Issues page for collaboration, troubleshooting, and feature requests to foster an active user community.

Installation

pip install autoai-agentrag

Quick Start

from autoai_agentrag import Agent, RAGConnector, MLModel

# Initialize an AI agent
agent = Agent("my_agent")

# Connect to knowledge sources
rag = RAGConnector()
rag.add_source("web", url="https://example.com/api")
rag.add_source("database", connection_string="sqlite:///my_data.db")

# Attach RAG to the agent
agent.add_rag(rag)

# Add ML capabilities
model = MLModel.from_pretrained("text-classification")
agent.add_model(model)

# Define agent behavior
@agent.task
def analyze_data(input_text):
    # Retrieve relevant information
    context = agent.rag.query(input_text)
    
    # Process with ML model
    result = agent.model.predict(input_text, context=context)
    
    return result

# Run the agent
response = agent.run("Analyze the latest market trends")
print(response)

Command Line Interface

# Initialize a new project
autoai init my_project

# Create a new agent
autoai create agent my_agent

# Add RAG capabilities
autoai add rag --source web --url https://example.com/api

# Train or use a model
autoai add model --type text-classification

# Run your agent
autoai run my_agent --input "Analyze the latest market trends"

# Start monitoring dashboard
autoai dashboard

Documentation

For detailed documentation, visit https://autoai-agentrag.readthedocs.io

Contributing

We welcome contributions! Please see our Contributing Guide for more details.

License

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