81 lines
No EOL
2.9 KiB
Markdown
81 lines
No EOL
2.9 KiB
Markdown
# AI Agent Project Specification
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## Executive Summary
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Develop a modular, multi-modal AI agent system capable of handling personal assistance, home automation, DevOps tasks, and intelligent information retrieval through an extensible plugin architecture.
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## Core Objectives
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1. Implement foundational modular architecture with clear role-based access control
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2. Establish MCP (Multi-Context Provider) integration framework
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3. Deliver multi-modal interaction capabilities (CLI/Web/REST)
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4. Create persistent memory system with SQLite backend
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5. Enable proactive task execution capabilities
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## Functional Requirements
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### Core System
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- Dynamic role management (roles.d)
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- Tool/module registry (tools.d)
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- MCP runtime integration (mcps.d)
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- Configuration management (conf.d)
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### Interfaces
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- CLI interface with Typer integration
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- FastAPI-based web interface
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- REST API with OpenAPI documentation
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- WebSocket support for real-time updates
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### Operational Requirements
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- Systemd service integration
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- Structured logging with rotation
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- Health monitoring endpoints
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- Automated testing framework
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## Non-Functional Requirements
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### Performance
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- <500ms response time for local commands
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- <2s response time for cloud-integrated tasks
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- Support 100 concurrent API connections
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### Security
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- Role-based access control
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- Secrets encryption at rest
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- Audit logging of privileged operations
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### Scalability
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- SQLite → PostgreSQL migration path
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- Horizontal scaling support for MCPs
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- Load-balanced API endpoints
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## Technology Stack
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| Component | Technology Choices |
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|--------------------|---------------------------------------------|
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| Core Language | Python 3.11+ |
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| Web Framework | FastAPI + Uvicorn |
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| CLI Framework | Typer |
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| Database | SQLite (initial), PostgreSQL (future) |
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| Task Queue | Celery + Redis |
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| NLP Integration | LangChain + Local LLMs |
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| Monitoring | Prometheus + Grafana |
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## Integration Points
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1. Home Automation (Home Assistant API)
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2. Calendar Services (Google Calendar API)
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3. Infrastructure Management (Docker API)
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4. External AI Services (OpenAI/Anthropic)
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5. MCP Service Discovery Protocol
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## Success Criteria
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- Demonstrate core assistant capabilities within local environment
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- Show MCP integration with 3 sample providers
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- Achieve 90% test coverage on core modules
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- Document full API surface with examples
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## Constraints
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- Initial deployment targets Linux systems
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- Must maintain compatibility with Python 3.11+
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- All external integrations must support offline operation
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- Core system memory footprint <512MB RAM
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## Assumptions
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- Primary users are technical operators
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- Initial deployment environment has Python 3.11+ installed
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- Networking connectivity available for cloud integrations |