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Add Ollama LLM Support for Privacy-First AI Messages

🎯 Overview

This PR adds Ollama support to Stream Daemon, enabling users to run AI-generated messages completely locally with full privacy and zero API costs. Users can now choose between Google Gemini (cloud) or Ollama (local) for AI message generation.

✨ What's New

🤖 Ollama Provider Support

New Features:

  • Local LLM Execution - Run AI models on your own hardware
  • 100% Privacy - Stream data never leaves your network
  • Zero API Costs - Unlimited message generation
  • Offline Capable - Works without internet connection
  • Multiple Models - Support for gemma2, llama3.2, qwen2.5, mistral, phi3, and more
  • Provider Selection - Easy switching between Ollama and Gemini via LLM_PROVIDER config

📝 Documentation Updates

  • Consolidated .env.example - Merged Ollama configuration into main example file
  • Updated README - Added Ollama as recommended AI provider option
  • AI Messages Guide - Enhanced with detailed Ollama setup instructions
  • Migration Guide - Help existing Gemini users understand Ollama option

🧪 Enhanced Testing

  • Moved all tests to tests/ folder - Better organization
  • test_connection.py - Comprehensive integration test using real .env data
    • Tests streaming platform authentication (Twitch, YouTube, Kick)
    • Tests social platform authentication (Mastodon, Bluesky, Discord, Matrix)
    • Tests LLM generation with actual live stream data
    • Tests AI message posting to all social platforms
    • Production readiness validation
  • test_ollama.py - Dedicated Ollama integration testing
  • test_local_install.py - Dependency validation

All tests can now run from the tests/ folder with proper import paths.

🔧 Technical Implementation

New Configuration Options

# Choose your AI provider
LLM_PROVIDER=ollama  # or 'gemini'

# Ollama-specific settings
LLM_OLLAMA_HOST=http://192.168.1.100
LLM_OLLAMA_PORT=11434
LLM_MODEL=gemma2:2b  # or gemma3:4b, llama3.2:3b, etc.

Code Changes

New Files:

  • tests/test_connection.py - Production-ready integration testing
  • tests/test_ollama.py - Ollama-specific testing (moved from root)
  • tests/test_local_install.py - Dependency validation (moved from root)

Modified Files:

  • stream_daemon/ai/generator.py - Added Ollama provider support
  • .env.example - Consolidated Ollama and Gemini configuration
  • README.md - Updated to highlight Ollama as recommended option
  • docs/features/ai-messages.md - Enhanced with Ollama setup guide

Removed Files:

  • .env.ollama.example - Consolidated into main .env.example

Architecture

class AIMessageGenerator:
    def __init__(self):
        self.provider = os.getenv('LLM_PROVIDER', 'gemini').lower()
        
        if self.provider == 'ollama':
            self.client = ollama.Client(
                host=f"{ollama_host}:{ollama_port}"
            )
        elif self.provider == 'gemini':
            self.client = genai.Client(api_key=api_key)
    
    def authenticate(self) -> bool:
        """Verify connection to selected provider"""
        if self.provider == 'ollama':
            return self._authenticate_ollama()
        elif self.provider == 'gemini':
            return self._authenticate_gemini()

🧪 Testing

Manual Testing Performed

  1. Ollama Provider Tests:

    # Test Ollama connection and message generation
python3 tests/test_ollama.py

# Output:
# ✅ SUCCESS: Ollama connection initialized!
# ✅ SUCCESS: Generated stream start message!
# ✅ SUCCESS: Generated Mastodon message!
# ✅ SUCCESS: Generated stream end message!

  2. Production Integration Test:

    # Test with real .env data and live streams
python3 tests/test_connection.py

# Results:
# ✅ Twitch - LIVE - 110 viewers
# ✅ YouTube - LIVE - 245 viewers  
# ✅ Kick - LIVE - 11,002 viewers
# ✅ AI generated message posted to all 4 social platforms
# ✅ Stream Daemon is PRODUCTION READY!

  3. Dependency Validation:

    python3 tests/test_local_install.py

# ✅ SUCCESS - All dependencies installed correctly!

Test Coverage

  • ✅ Ollama server connectivity
  • ✅ Model availability verification
  • ✅ Message generation for all social platforms (Bluesky, Mastodon, Discord, Matrix)
  • ✅ Character limit enforcement (300 for Bluesky, 500 for Mastodon)
  • ✅ Stream start and end message generation
  • ✅ Error handling and retry logic
  • ✅ Fallback to static messages when AI unavailable
  • ✅ Real production environment testing with live streams

🔄 Migration Path

For Existing Users (Using Gemini)

No changes required! Your current setup continues to work:

# Current setup - still works perfectly
LLM_ENABLE=True
GEMINI_API_KEY=AIzaSyA_your_key_here
LLM_MODEL=gemini-2.0-flash-lite

Default provider is gemini, so existing deployments are unaffected.

For New Users or Migration to Ollama

# 1. Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# 2. Pull a model
ollama pull gemma2:2b

# 3. Start server
ollama serve

# 4. Update .env
LLM_ENABLE=True
LLM_PROVIDER=ollama  # ← New setting
LLM_OLLAMA_HOST=http://localhost
LLM_OLLAMA_PORT=11434
LLM_MODEL=gemma2:2b

See docs/features/ollama-migration.md for complete guide.

📊 Performance & Resource Usage

Ollama (Local)

  • Latency: 0.5-2 seconds (hardware dependent)
  • Cost: $0 per message
  • Rate Limit: None (unlimited)
  • Privacy: 100% local
  • Hardware:
    • Minimum: 4GB RAM, CPU-only (slower)
    • Recommended: 8GB+ RAM, NVIDIA/AMD/Apple GPU
    • Optimal: 16GB+ RAM, dedicated GPU

Gemini (Cloud)

  • Latency: 1-2 seconds
  • Cost: ~$0.0001 per message (Gemini 2.0 Flash Lite)
  • Rate Limit: 10-30 requests/minute (model dependent)
  • Privacy: Data sent to Google Cloud
  • Hardware: None required

🔗 Related Documentation

✅ Checklist

  • Code changes tested locally
  • Documentation updated
  • Tests pass (test_ollama.py, test_connection.py)
  • Backward compatibility maintained
  • Example configuration provided
  • Migration guide created
  • Tests organized in tests/ folder
  • .env.example consolidated

🎉 Impact

This PR enables privacy-conscious users to run AI-generated messages completely locally while maintaining full compatibility with existing Gemini users. No breaking changes, zero configuration changes required for existing deployments.

  • ✅ Minimum 2-second delay between requests (stays under 30 RPM limit)
  • ✅ Thread-safe coordination across all platforms
  • ✅ Maintains existing retry logic with exponential backoff
  • ✅ Prevents quota exhaustion from simultaneous streams

Request Pattern Example:

Twitch goes live → 4 social platforms
  ├─ Request 1: Bluesky  (0s)
  ├─ Request 2: Mastodon (2s delay)
  ├─ Request 3: Discord  (4s delay)
  └─ Request 4: Matrix   (6s delay)

YouTube goes live → waits for semaphore slots
  ├─ Request 5: Bluesky  (8s)
  └─ ...continues with 2s spacing

📊 Changes Summary

Files Modified

stream_daemon/platforms/streaming/youtube.py (22 insertions, 4 deletions)

  • Added error_cooldown_time tracking
  • Implemented 10-minute cooldown check in is_live()
  • Enhanced error logging with X/5 counter
  • Automatic reset after cooldown expires

stream_daemon/ai/generator.py (35 insertions, 6 deletions)

  • Added threading import for Semaphore
  • Implemented global semaphore (max 4 concurrent)
  • Added minimum delay enforcement (2 seconds)
  • Thread-safe coordination with locks
  • Updated docstrings to reflect actual implementation

Backward Compatibility

✅ Fully backward compatible - no configuration changes required:

  • YouTube monitoring works exactly as before, just with automatic recovery
  • AI message generation works exactly as before, just with rate limiting
  • No breaking changes to APIs or configuration
  • Existing behavior preserved, only adds resilience

🧪 Testing

YouTube Error Recovery

Test Scenario: Simulate consecutive YouTube API errors

# After 5 errors, enters 10-minute cooldown
# Logs: "YouTube: Maximum consecutive errors (5) reached. Entering 10-minute cooldown."
# During cooldown: "YouTube: In error cooldown. X minutes remaining."
# After cooldown: "YouTube: Error cooldown period ended. Resetting consecutive errors."

Manual Testing:

  • Tested with invalid API key (triggers errors)
  • Verified cooldown countdown in logs
  • Confirmed automatic recovery after 10 minutes
  • Verified error counter resets correctly

Gemini Rate Limiting

Test Scenario: Multiple streams go live simultaneously

# 3 streaming platforms × 4 social networks = 12 potential requests
# Semaphore limits to 4 concurrent
# 2-second delay between each request
# Total time: ~24 seconds for 12 requests (stays well under 30 RPM)

Expected Behavior:

  • Requests queue automatically when 4 concurrent limit reached
  • Debug logs show: "Rate limiting: waiting Xs before API call"
  • No 429 errors from Gemini API
  • All announcements eventually post successfully

🚀 Deployment

Recommended Deployment Steps

  1. Merge this PR to main
  2. Deploy to production server (192.168.213.210): 
    ssh user@192.168.213.210
cd /path/to/stream-daemon
git pull origin main
docker build -f Docker/Dockerfile -t stream-daemon:local .
docker stop stream-daemon && docker rm stream-daemon
docker-compose up -d  # or your container restart method
  3. Monitor logs for recovery messages: 
    docker logs -f stream-daemon | grep -E "cooldown|rate limit"
  4. Verify YouTube resumes after errors
  5. Watch for Gemini rate limiting working correctly

Rollback Plan

If issues occur, rollback is simple:

git checkout <previous-commit>
docker build -f Docker/Dockerfile -t stream-daemon:local .
docker-compose restart

📝 Documentation Updates

Documentation has been updated to reflect new error recovery behavior:

  • README.md: Updated YouTube feature description to mention automatic error recovery
  • docs/platforms/streaming/youtube.md: Added "Error Recovery" section documenting cooldown behavior
  • docs/features/ai-messages.md: Added "Rate Limiting" section documenting semaphore and delay

🔍 Review Checklist

  • Code follows project style guidelines
  • Backward compatible (no breaking changes)
  • Error handling is comprehensive
  • Logging is clear and helpful
  • Thread-safe implementation (rate limiting)
  • Documentation updated
  • Manual testing completed
  • Production-ready

🎉 Expected Outcomes

After merging:

  1. YouTube monitoring is resilient: Automatic recovery from temporary API issues without manual intervention
  2. Gemini API stays under limits: Proactive rate limiting prevents 429 errors even with simultaneous streams
  3. Better user experience: No lost announcements due to platform errors or rate limits
  4. Reduced manual intervention: Daemon self-heals from common error conditions
  5. Production stability: Both issues observed in production logs are resolved

📚 Related Issues

  • Fixes production issue: YouTube "5 consecutive errors" permanent disable
  • Addresses missing implementation: Gemini rate limiting mentioned in comments but not coded
  • Improves resilience: Both YouTube and AI generation become self-healing

Branch: hotfix/youtube-error-recovery
Commits:

  • e483316 - fix: Add automatic recovery for YouTube consecutive errors
  • 0e17562 - feat: Add Gemini API rate limiting

Ready for: Immediate merge to main and production deployment