Agent_Course_Final_Assignment

Sleeping

# REQUIRED: HuggingFace token with inference permissions
HF_TOKEN=hf_your_token_here

# The system will FAIL without proper authentication
# No SimpleClient fallback available

🎯 Expected Results

With proper authentication and Qwen model access:

✅ GAIA Benchmark Score: 30%+ (full LangGraph workflow with Qwen models)
✅ Multi-Agent Processing: Router → Specialized Agents → Tools → Synthesis
✅ Intelligent Model Selection: 7B (fast) → 32B (balanced) → 72B (complex)
✅ Professional Tools: Wikipedia API, DuckDuckGo search, calculator, file processor
✅ Detailed Analysis: Processing details, confidence scores, cost tracking

Without proper authentication:

❌ System Initialization Fails: No fallback options available
❌ Clear Error Messages: Guides users to proper authentication setup

🔧 Technical Implementation

OAuth Authentication (Production)

class GAIAAgentApp:
    def __init__(self, hf_token: Optional[str] = None):
        if not hf_token:
            raise ValueError("HuggingFace token with inference permissions is required")
        
        # Initialize QwenClient with token
        self.llm_client = QwenClient(hf_token=hf_token)
        
        # Initialize LangGraph workflow with tools
        self.workflow = SimpleGAIAWorkflow(self.llm_client)

# OAuth token extraction in production
def run_and_submit_all(profile: gr.OAuthProfile | None):
    oauth_token = getattr(profile, 'oauth_token', None)
    agent = GAIAAgentApp.create_with_oauth_token(oauth_token)

Qwen Model Configuration

# QwenClient now uses ONLY Qwen models
self.models = {
    ModelTier.ROUTER: ModelConfig(
        name="Qwen/Qwen2.5-7B-Instruct",      # Fast classification
        cost_per_token=0.0003
    ),
    ModelTier.MAIN: ModelConfig(
        name="Qwen/Qwen2.5-32B-Instruct",     # Balanced performance  
        cost_per_token=0.0008
    ),
    ModelTier.COMPLEX: ModelConfig(
        name="Qwen/Qwen2.5-72B-Instruct",     # Best performance
        cost_per_token=0.0015
    )
}

Error Handling

# Clear error messages guide users to proper authentication
if not oauth_token:
    return "Authentication Required: Valid token with inference permissions needed for Qwen model access."

try:
    agent = GAIAAgentApp.create_with_oauth_token(oauth_token)
except ValueError as ve:
    return f"Authentication Error: {ve}"
except RuntimeError as re:
    return f"System Error: {re}"

🎯 Deployment Steps

1. Pre-Deployment Checklist

Code Ready: All Qwen-only changes committed
Dependencies: requirements.txt updated with all packages
Testing: QwenClient initialization test passes locally
Environment: No hardcoded tokens in code
Authentication: HF_TOKEN available with inference permissions

2. HuggingFace Space Configuration

Create a new HuggingFace Space with these settings:

# Space Configuration
title: "GAIA Agent System"
emoji: "🤖"
colorFrom: "blue"
colorTo: "green"
sdk: gradio
sdk_version: "4.44.0"
app_file: "src/app.py"
pinned: false
license: "mit"
suggested_hardware: "cpu-basic"
suggested_storage: "small"

3. Required Files Structure

/
├── src/
│   ├── app.py                 # Main application (Qwen + LangGraph)
│   ├── models/
│   │   └── qwen_client.py     # Qwen-only client  
│   ├── agents/               # All agent files
│   ├── tools/                # All tool files
│   ├── workflow/             # LangGraph workflow
│   └── requirements.txt      # All dependencies
├── README.md                 # Space documentation
└── .gitignore               # Exclude sensitive files

4. Environment Variables (Space Secrets)

🎯 CRITICAL: Set HF_TOKEN for Qwen Model Access

To get real GAIA Agent performance with Qwen models and LangGraph workflow:

# REQUIRED for Qwen model access and LangGraph functionality
HF_TOKEN=hf_your_token_here                # REQUIRED: Your HuggingFace token

How to set HF_TOKEN:

Go to your Space settings in HuggingFace
Navigate to "Repository secrets"
Add new secret:
- Name: HF_TOKEN
- Value: Your HuggingFace token (from https://huggingface.co/settings/tokens)

⚠️ IMPORTANT: Do NOT set HF_TOKEN as a regular environment variable - use Space secrets for security.

Token Requirements:

Token must have read and inference scopes
Generate token at: https://huggingface.co/settings/tokens
Select "Fine-grained" token type
Enable both scopes for Qwen model functionality

Optional environment variables:

# Optional: LangSmith tracing (if you want observability)
LANGCHAIN_TRACING_V2=true           # Optional: LangSmith tracing
LANGCHAIN_API_KEY=your_key_here     # Optional: LangSmith API key
LANGCHAIN_PROJECT=gaia-agent        # Optional: LangSmith project

5. Authentication Flow in Production

# Production OAuth Flow:
1. User clicks "Login with HuggingFace" button
2. OAuth flow provides profile with token
3. System validates OAuth token for Qwen model access
4. If sufficient scopes: Initialize QwenClient with LangGraph workflow
5. If insufficient scopes: Show clear error message with guidance
6. System either works fully or fails clearly - no degraded modes

OAuth Requirements ⚠️

CRITICAL: Gradio OAuth tokens often have limited scopes by default:

✅ "read" scope: Can access user profile, model info
❌ "inference" scope: Often missing - REQUIRED for Qwen models
❌ "write" scope: Not needed for this application

System Behavior:

Full-scope token: Uses Qwen models with LangGraph → 30%+ GAIA performance
Limited-scope token: Clear error message → User guided to proper authentication
No token: Clear error message → User guided to login

Clear Error Handling:

# No more fallback confusion - clear requirements
if test_response.status_code == 401:
    return "Authentication Error: Your OAuth token lacks inference permissions. Please logout and login again with full access."

6. Deployment Process

Create Space:

# Visit https://huggingface.co/new-space
# Choose Gradio SDK
# Upload all files from src/ directory

Upload Files:
- Copy entire src/ directory to Space
- Ensure app.py is the main entry point
- Include all dependencies in requirements.txt
Test Authentication:
- Space automatically enables OAuth for Gradio apps
- Test login/logout functionality
- Verify Qwen model access works
- Test GAIA evaluation with LangGraph workflow

7. Verification Steps

After deployment, verify these work:

Interface Loads: Gradio interface appears correctly
OAuth Login: Login button works and shows user profile
Authentication Check: Clear error messages when insufficient permissions
Qwen Model Access: Models initialize and respond correctly
LangGraph Workflow: Multi-agent system processes questions
Manual Testing: Individual questions work with full workflow
GAIA Evaluation: Full evaluation runs and submits to Unit 4 API
Results Display: Scores and detailed results show correctly

8. Troubleshooting

Common Issues

Issue: "HuggingFace token with inference permissions is required" Solution: Set HF_TOKEN in Space secrets or login with full OAuth permissions

Issue: "Failed to initialize any Qwen models" Solution: Verify HF_TOKEN has inference scope and Qwen model access

Issue: "Authentication Error: Your OAuth token lacks inference permissions" Solution: Logout and login again, or set HF_TOKEN as Space secret

Debug Commands

# In Space, add debug logging to check authentication:
logger.info(f"HF_TOKEN available: {os.getenv('HF_TOKEN') is not None}")
logger.info(f"OAuth token available: {oauth_token is not None}")
logger.info(f"Qwen models initialized: {client.get_model_status()}")

9. Performance Optimization

For production efficiency with Qwen models:

# Intelligent Model Selection Strategy
- Simple questions: Qwen 2.5-7B (fast, cost-effective)
- Medium complexity: Qwen 2.5-32B (balanced performance)  
- Complex reasoning: Qwen 2.5-72B (best quality)
- Budget management: Auto-downgrade when budget exceeded
- LangGraph workflow: Optimal agent routing and synthesis

10. Monitoring and Maintenance

Key Metrics to Monitor:

GAIA benchmark success rate (target: 30%+)
Average response time per question
Cost per question processed
LangGraph workflow success rate
Qwen model availability and performance

Regular Maintenance:

Monitor HuggingFace Inference API status
Update dependencies for security
Review and optimize LangGraph workflow performance
Check Unit 4 API compatibility
Monitor Qwen model performance and costs

🎯 Success Metrics

Expected Production Results 🚀

With proper deployment and authentication:

GAIA Benchmark: 30%+ success rate
LangGraph Workflow: Multi-agent orchestration working
Qwen Model Performance: Intelligent tier selection (7B→32B→72B)
User Experience: Professional interface with clear authentication
System Reliability: Clear success/failure modes (no degraded performance)

Final Status:

Architecture: Qwen 2.5 models + LangGraph multi-agent workflow
Requirements: Clear authentication requirements (HF_TOKEN or OAuth with inference)
Performance: 30%+ GAIA benchmark with full functionality
Reliability: Robust error handling with clear user guidance
Deployment: Ready for immediate HuggingFace Space deployment

The GAIA Agent is now a focused, high-performance system using proper AI models and multi-agent orchestration! 🎉