GAIA Agent Deployment Guide

This document provides instructions for deploying the GAIA agent in various environments, from local development to production settings.

Prerequisites

Before deploying the GAIA agent, ensure you have:

Python 3.8 or higher installed
All required dependencies installed
API keys for any external services (OpenAI, Serper, Supabase, etc.)
Sufficient resources for your expected workload

Local Deployment

Installation

Clone the repository:

git clone https://github.com/yourusername/gaia-agent.git
cd gaia-agent

Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Install dependencies:
```
pip install -r requirements.txt
```

Create a .env file with your configuration:

OPENAI_API_KEY=your-openai-api-key
SERPER_API_KEY=your-serper-api-key
SUPABASE_URL=https://your-project-url.supabase.co
SUPABASE_KEY=your-supabase-api-key

Run the application:
```
python app.py
```

Testing

Before deploying to production, run the test suite to ensure everything is working correctly:

# Run all tests
python -m unittest discover tests

# Run benchmark tests
python -m tests.benchmark --output benchmark_results.json

Deployment Options

Hugging Face Spaces

The GAIA agent is designed to be easily deployed to Hugging Face Spaces:

Create a new Space on Hugging Face:
- Go to https://huggingface.co/spaces
- Click "Create new Space"
- Select "Gradio" as the SDK
- Choose a name for your Space
Configure your Space:
- Add your API keys as secrets in the Space settings
- Set the Python version to 3.8 or higher

Push your code to the Space:

git remote add space https://huggingface.co/spaces/yourusername/your-space-name
git push space main

Your agent will be automatically deployed and available at https://huggingface.co/spaces/yourusername/your-space-name

Docker Deployment

For containerized deployment, you can use Docker:

Create a Dockerfile:

FROM python:3.9-slim

WORKDIR /app

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY . .

CMD ["python", "app.py"]

Build the Docker image:
```
docker build -t gaia-agent .
```

Run the container:

docker run -p 7860:7860 --env-file .env gaia-agent

Cloud Deployment

AWS Elastic Beanstalk

Install the EB CLI:
```
pip install awsebcli
```
Initialize your EB application:
```
eb init -p python-3.8 gaia-agent
```
Create an environment:
```
eb create gaia-agent-env
```

Configure environment variables:

eb setenv OPENAI_API_KEY=your-openai-api-key SERPER_API_KEY=your-serper-api-key

Deploy your application:
```
eb deploy
```

Google Cloud Run

Build and push your Docker image to Google Container Registry:

gcloud builds submit --tag gcr.io/your-project-id/gaia-agent

Deploy to Cloud Run:

gcloud run deploy gaia-agent \
  --image gcr.io/your-project-id/gaia-agent \
  --platform managed \
  --set-env-vars OPENAI_API_KEY=your-openai-api-key,SERPER_API_KEY=your-serper-api-key

Scaling Considerations

Memory Usage

The GAIA agent can be memory-intensive, especially when processing complex questions or using large language models. Consider the following:

Monitor memory usage during benchmark testing
Adjust max_tokens and other model parameters to control memory usage
Consider using a memory cache for frequently asked questions

API Rate Limits

Be aware of rate limits for external APIs:

OpenAI has rate limits based on your subscription tier
Serper and other search APIs typically have daily or monthly quotas
Implement retry logic with exponential backoff for rate limit errors

Cost Management

Using external APIs can incur costs:

Monitor your API usage and costs
Consider caching results to reduce API calls
Use smaller or less expensive models for simpler tasks

Production Best Practices

Logging

Implement comprehensive logging:

import logging

logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    filename='gaia_agent.log'
)

Monitoring

Set up monitoring for:

API response times
Error rates
Memory and CPU usage
Cost metrics

Security

Protect sensitive information:

Never hardcode API keys in your code
Use environment variables or a secure secrets manager
Implement proper authentication for your API endpoints
Validate and sanitize all user inputs

Backup and Recovery

Implement backup and recovery procedures:

Regularly backup your Supabase database
Create snapshots of your deployment environment
Document recovery procedures

Continuous Integration/Continuous Deployment (CI/CD)

Set up CI/CD pipelines to automate testing and deployment:

GitHub Actions Example

Create a .github/workflows/main.yml file:

name: CI/CD Pipeline

on:
  push:
    branches: [ main ]
  pull_request:
    branches: [ main ]

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v2
    - name: Set up Python
      uses: actions/setup-python@v2
      with:
        python-version: '3.9'
    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install -r requirements.txt
    - name: Run tests
      run: |
        python -m unittest discover tests

  deploy:
    needs: test
    if: github.event_name == 'push' && github.ref == 'refs/heads/main'
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v2
    - name: Deploy to Hugging Face Spaces
      uses: huggingface/huggingface-spaces-deploy-action@main
      with:
        space_id: ${{ secrets.SPACE_ID }}
        token: ${{ secrets.HF_TOKEN }}

Troubleshooting

Common Issues

API Key Errors:
- Ensure all API keys are correctly set in your environment variables
- Check for typos or trailing spaces in your API keys
Memory Issues:
- Reduce the max_tokens parameter in your model configuration
- Implement memory cleanup after processing each question
Timeout Errors:
- Increase the timeout values in your configuration
- Optimize your agent's workflow to reduce processing time
Rate Limit Errors:
- Implement retry logic with exponential backoff
- Consider upgrading your API subscription tier

Getting Help

If you encounter issues not covered in this guide:

Check the project's GitHub issues for similar problems
Consult the documentation for the specific components (LangChain, LangGraph, etc.)
Reach out to the community for assistance

Next Steps

For usage examples, see Usage Examples.

For configuration options, see Configuration Guide.