Fix Docker startup script and complete application deployment
- Update docker-start.sh to force correct profiles (qdrant, admin) - Fix PostgreSQL port mapping from 5432 to 15432 across all configs - Resolve MCP import conflicts by renaming src/mcp to src/mcp_servers - Fix admin interface StaticFiles mount syntax error - Update LLM client to support both Ollama and OpenAI-compatible APIs - Configure host networking for Discord bot container access - Correct database connection handling for async context managers - Update environment variables and Docker compose configurations - Add missing production dependencies and Dockerfile improvements
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CLAUDE.md
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CLAUDE.md
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# CLAUDE.md
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This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
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## Development Commands
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### Running the Application
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```bash
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# Main application (requires LLM service)
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python -m src.main
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# Or using startup script
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./start.sh
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# Admin interface
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python -m src.admin.app
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# Or using startup script
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./start-admin.sh
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```
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### Testing and Validation
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```bash
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# Basic system structure test
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python test_config.py
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# Simple component test
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python simple_test.py
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# Creative collaboration demo
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python scripts/demo_creative_integration.py
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# Memory sharing demo
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python scripts/demo_memory_sharing.py
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```
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### Database Management
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```bash
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# Initialize database and tables
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python -c "
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import asyncio
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from src.database.connection import init_database, create_tables
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asyncio.run(init_database())
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asyncio.run(create_tables())
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"
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# Apply migrations
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alembic upgrade head
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```
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### Frontend Development
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```bash
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# Navigate to admin frontend
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cd admin-frontend
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# Install dependencies
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npm install
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# Development server
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npm start
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# Build for production
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npm run build
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```
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### Docker Services
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```bash
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# Start core services only (PostgreSQL, Redis, ChromaDB)
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docker-compose -f docker-compose.services.yml up -d
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# Or start complete application stack
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./docker-start.sh
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# Or manually:
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docker-compose --env-file .env.docker up -d --build
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# Initialize services script (services only)
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./docker-services.sh
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```
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## Architecture Overview
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This is an autonomous Discord bot ecosystem where AI characters chat with each other without human intervention. The system features advanced RAG (Retrieval-Augmented Generation), MCP (Model Context Protocol) integration, and collaborative creative capabilities.
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### Core Components
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**Main Application (`src/main.py`)**: Entry point that initializes all systems including database, RAG, MCP servers, conversation engine, and Discord bot.
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**Character System (`src/characters/`)**: Enhanced AI characters with personality, memory, and self-modification capabilities. Characters use `EnhancedCharacter` class with built-in RAG and MCP integration.
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**Conversation Engine (`src/conversation/`)**: Autonomous conversation management with scheduling, topic generation, and multi-character interaction handling.
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**RAG Systems (`src/rag/`)**: Multi-layer vector database integration using ChromaDB for:
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- Personal memories per character
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- Community knowledge sharing
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- Creative project collaboration
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- Cross-character memory sharing with trust-based permissions
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**MCP Integration (`src/mcp/`)**: Model Context Protocol servers providing autonomous tools:
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- Self-modification (personality changes)
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- File system access (digital spaces)
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- Calendar/time awareness
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- Memory sharing coordination
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- Creative project management
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**Database (`src/database/`)**: SQLAlchemy-based models with PostgreSQL/SQLite support, including tables for conversations, characters, creative projects, shared memories, and trust relationships.
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**Admin Interface (`src/admin/` + `admin-frontend/`)**: FastAPI backend with React/TypeScript frontend providing real-time dashboard, character management, conversation analytics, and system controls.
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### Key Architectural Patterns
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**Trust-Based System**: Characters evaluate relationships before sharing memories or collaborating, with trust levels (Basic 30%, Personal 50%, Intimate 70%, Full 90%).
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**Autonomous Decision Making**: Characters use MCP tools to make independent decisions about project collaboration, memory sharing, and personality evolution.
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**Multi-Modal Data**: Vector stores handle both text and semantic embeddings for efficient memory retrieval and relationship mapping.
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## Configuration
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### Primary Config (`config/fishbowl_config.json`)
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- Discord bot settings (token, guild, channel)
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- Database connection (PostgreSQL/SQLite)
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- AI provider settings (custom LLM endpoint)
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- System parameters (conversation frequency, response delays)
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- Admin interface settings
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### Character Config (`config/characters.yaml`)
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- Character personalities, interests, speaking styles
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- Background stories and trait definitions
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### Environment Variables (`.env`)
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- Sensitive credentials (Discord tokens, database passwords)
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- Docker service settings
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- LLM service configuration
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## Development Notes
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### Dependencies
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- Python 3.10+ (3.13 compatible)
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- PostgreSQL 12+ or SQLite for development
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- Redis for caching
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- ChromaDB for vector storage
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- Discord.py for bot integration
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- FastAPI/React for admin interface
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### Testing Strategy
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- `test_config.py`: System structure validation
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- `simple_test.py`: Component functionality
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- Demo scripts: Feature-specific testing
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- Admin interface: Real-time monitoring
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### Important Files
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- `src/main.py`: Application entry point
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- `src/conversation/engine.py`: Core conversation logic
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- `src/characters/enhanced_character.py`: Character implementation
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- `src/rag/vector_store.py`: Vector database management
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- `src/database/models.py`: Database schema definitions
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### Common Issues
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- **LLM Service**: Requires Ollama or compatible API endpoint
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- **Database**: Ensure PostgreSQL/SQLite is accessible
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- **Discord**: Valid bot token required for Discord integration
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- **Vector Store**: ChromaDB initialization may require sufficient memory
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The system is designed for autonomous operation - characters will independently propose projects, share memories, and evolve their personalities based on interactions and experiences.
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