Initial implementation of autonomous Discord LLM fishbowl

Core Features:
- Full autonomous AI character ecosystem with multi-personality support
- Advanced RAG system with personal, community, and creative memory layers
- MCP integration for character self-modification and file system access
- PostgreSQL database with comprehensive character relationship tracking
- Redis caching and ChromaDB vector storage for semantic memory retrieval
- Dynamic personality evolution based on interactions and self-reflection
- Community knowledge management with tradition and norm identification
- Sophisticated conversation engine with natural scheduling and topic management
- Docker containerization and production-ready deployment configuration

Architecture:
- Multi-layer vector databases for personal, community, and creative knowledge
- Character file systems with personal and shared digital spaces
- Autonomous self-modification with safety validation and audit trails
- Memory importance scoring with time-based decay and consolidation
- Community health monitoring and cultural evolution tracking
- RAG-powered conversation context and relationship optimization

Characters can:
- Develop authentic personalities through experience-based learning
- Create and build upon original creative works and philosophical insights
- Form complex relationships with memory of past interactions
- Modify their own personality traits through self-reflection cycles
- Contribute to and learn from shared community knowledge
- Manage personal digital spaces with diaries, creative works, and reflections
- Engage in collaborative projects and community decision-making

System supports indefinite autonomous operation with continuous character
development, community culture evolution, and creative collaboration.

Dockerfile

@@ -0,0 +1,25 @@
+FROM python:3.11-slim
+
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    gcc \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy requirements first for better caching
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy application code
+COPY src/ ./src/
+COPY config/ ./config/
+
+# Create logs directory
+RUN mkdir -p logs
+
+# Set Python path
+ENV PYTHONPATH=/app/src
+
+# Run the application
+CMD ["python", "-m", "src.main"]