Quick Start Guide

This guide will help you get started with AIECS quickly.

Prerequisites

Before you begin, ensure you have:

Python 3.10 or higher installed
AIECS installed (see Installation)
Required environment variables configured

Basic Usage

Starting the Server

Start the AIECS FastAPI server:

# Using the CLI
aiecs

# Or using Python module
python -m aiecs

The server will start on http://localhost:8000 by default.

Using the Client

Here’s a simple example of using the AIECS client:

from aiecs.aiecs_client import AIECSClient
import asyncio

async def main():
    # Initialize the client
    client = AIECSClient(base_url="http://localhost:8000")

    # Execute a simple task
    result = await client.execute_task(
        task_type="text_generation",
        parameters={
            "prompt": "Explain what AIECS is in one sentence.",
            "max_tokens": 100
        }
    )

    print(result)

if __name__ == "__main__":
    asyncio.run(main())

Working with Tools

AIECS provides a rich set of tools for various tasks.

Document Processing

from aiecs.tools.docs.document_parser_tool import DocumentParserTool

# Initialize the tool
parser = DocumentParserTool()

# Parse a document
result = await parser.execute({
    "operation": "parse_document",
    "document_path": "/path/to/document.pdf"
})

print(result["content"])

Web Scraping

from aiecs.tools.web.web_scraper_tool import WebScraperTool

# Initialize the tool
scraper = WebScraperTool()

# Scrape a webpage
result = await scraper.execute({
    "operation": "scrape_url",
    "url": "https://example.com"
})

print(result["content"])

Data Analysis

from aiecs.tools.data.pandas_tool import PandasTool

# Initialize the tool
pandas_tool = PandasTool()

# Analyze data
result = await pandas_tool.execute({
    "operation": "read_csv",
    "file_path": "/path/to/data.csv"
})

print(result["dataframe_info"])

Using LLM Providers

OpenAI

from aiecs.llm.llm_integration import LLMIntegration

# Initialize with OpenAI
llm = LLMIntegration(provider="openai")

# Generate text
response = await llm.generate(
    prompt="What is artificial intelligence?",
    max_tokens=150
)

print(response)

Google Vertex AI

from aiecs.llm.llm_integration import LLMIntegration

# Initialize with Vertex AI
llm = LLMIntegration(provider="vertex")

# Generate text
response = await llm.generate(
    prompt="Explain machine learning.",
    max_tokens=150
)

print(response)

Task Execution with Celery

AIECS uses Celery for asynchronous task execution.

Starting Workers

# Start a Celery worker
celery -A aiecs.tasks.worker worker --loglevel=info

Submitting Tasks

from aiecs.tasks.worker import execute_task

# Submit a task
task = execute_task.delay(
    task_type="data_processing",
    parameters={"input": "data"}
)

# Get task result
result = task.get(timeout=30)
print(result)

WebSocket Communication

AIECS supports real-time communication via WebSockets.

import socketio

# Create a Socket.IO client
sio = socketio.AsyncClient()

@sio.on('task_update')
async def on_task_update(data):
    print(f"Task update: {data}")

async def main():
    await sio.connect('http://localhost:8000')
    await sio.wait()

if __name__ == '__main__':
    asyncio.run(main())

Next Steps

Read the Configuration guide for detailed configuration options
Explore the Tools API reference for available tools
Check out the Advanced Usage guide for advanced usage patterns