A three-part system that captures Discord conversations, analyzes them for themes, and displays real-time insights.

Discord → Part 1 (Bot) → Redis → Part 2 (Intelligence) → Redis → Part 3 (Dashboard)
                ↓                         ↓                          ↓
          Audio + Text              Theme Analysis            Web Interface

• Docker and Docker Compose
• Discord Bot Token
• OpenAI API Key (optional for transcription/LLM)

# Clone and setup
git clone <repository>
cd collective-intelligence-platform

# Copy environment template
cp .env.example .env

# Edit .env with your credentials
nano .env

# Build and start all services
docker-compose up -d

# View logs
docker-compose logs -f

# Access dashboard
open http://localhost:8000

• File: transcription_bot.py
• Function: Captures voice and text from Discord
• Output: Sends to transcription_queue in Redis
• Providers: Local Whisper, OpenAI API, or Ollama

• File: intelligence_processor.py
• Function: Analyzes messages for themes using LangChain
• Input: Reads from transcription_queue
• Output: Sends to intelligence_queue
• Features: Theme identification, bridge concepts, synthesis

• File: dashboard.py
• Function: Web interface for viewing insights
• Input: Reads from intelligence_queue
• Features: Live updates, theme visualization, Discord notifications

# Test Part 1 (Discord Bot)
python test_suite.py discord

# Test Part 2 (Intelligence)
python test_suite.py intelligence

# Test Part 3 (Dashboard)
python test_suite.py dashboard

# Test Complete Pipeline
python test_suite.py pipeline

All configuration is done through .env file:

# Discord
DISCORD_BOT_TOKEN=your_token_here

# OpenAI (optional)
OPENAI_API_KEY=your_key_here

# Transcription
TRANSCRIPTION_PROVIDER=local  # local, openai, ollama
WHISPER_MODEL=base            # tiny, base, small, medium, large

# Intelligence Processing
LLM_PROVIDER=openai           # openai, local
EMBEDDING_PROVIDER=openai     # openai, local
SYNTHESIS_INTERVAL=30         # seconds
MAX_THEMES=5

# Dashboard (optional)
DISCORD_WEBHOOK_URL=your_webhook_url

• Dashboard: http://localhost:8000
• API Health: http://localhost:8000/api/health
• Logs: docker-compose logs -f [service-name]

1. Discord → Redis

   • Text messages and voice transcriptions
   • Format: {type, content, username, channel, timestamp}

2. Redis → Intelligence

   • Processes every 30 seconds
   • Identifies themes and connections
   • Format: {themes, bridge_concepts, summary}

3. Redis → Dashboard

   • Real-time WebSocket updates
   • REST API for historical data

From Discord Bot:

{
  "type": "voice_transcription",
  "content": "I think we should use AI for code reviews",
  "username": "Alice",
  "channel": "dev-talk",
  "timestamp": "2024-01-20T10:30:00Z"
}

From Intelligence Processor:

{
  "themes": [
    {
      "title": "AI in Development",
      "description": "Discussion about AI tools for coding",
      "keywords": ["AI", "code review", "automation"],
      "confidence": 0.85
    }
  ],
  "bridge_concepts": [...],
  "summary": "Active discussion about integrating AI into development workflows"
}

• Check bot has voice permissions in Discord
• Verify !join command in a voice channel

• Check !status shows recording
• Verify transcription provider is configured
• Check logs: docker-compose logs discord-bot

• Ensure enough messages (minimum 5)
• Check intelligence processor logs
• Verify OpenAI API key if using OpenAI

• Check WebSocket connection in browser console
• Verify Redis is running: docker-compose ps
• Check dashboard logs

• Reduce Whisper model size for faster transcription
• Increase SYNTHESIS_INTERVAL to reduce processing frequency
• Use local embeddings to avoid API costs
• Adjust MESSAGE_BUFFER_SIZE based on activity level

# Run services individually for development
docker-compose up redis
python transcription_bot.py
python intelligence_processor.py
python dashboard.py

# Or use development mode
docker-compose -f docker-compose.dev.yml up

MIT