Initial implementation of The Watchman - Ready for review

[Coldaine]

The Watchman - Initial Implementation

Overview

This PR contains the initial implementation of The Watchman, a computer-centric knowledge graph system that tracks:

• 📸 Visual Timeline: Screenshot capture + OCR + semantic search
• 📁 System Graph: Project & Docker infrastructure discovery
• 📝 Event Tracking: File system change monitoring
• 🔧 Infrastructure: Neo4j graph database + FastAPI + workers

What's Implemented

✅ Complete

• Foundation (100%): Docker Compose, Neo4j schema, FastAPI skeleton, shared utilities
• Visual Timeline (100% structure): Screenshot capture, Tesseract OCR, embedding generation
• System Graph (100% structure): Project scanner, Docker scanner with port/volume mapping
• Event Tracking (100% structure): File system watcher with change events

⚠️ Untested

IMPORTANT: This code is completely untested. It has:

• Never been run via `docker-compose up`
• Never captured a screenshot
• Never processed OCR
• Never scanned a project
• Never created a graph node

⏳ Stub/Incomplete

• MCP Registry: API stubs only, needs YAML parser + Docker control
• Query Routing: API stubs only, needs intent classification + LLM integration
• Integration Tests: Missing entirely

Statistics

• Lines of Code: ~6,062 total (3,252 Python)
• Files: 48 files across 4 domains
• Completion: ~50% (structure complete, integration missing)

What Needs Review

1. Architecture & Design

• Domain separation (visual_timeline, system_graph, memory_change)
• Neo4j schema design (12 node types, 15 relationship types)
• Graph model (appropriate relationships and constraints?)

2. Code Quality

• Python code style and organization
• Error handling patterns
• Logging approach (using loguru)
• Configuration management (pydantic-settings)

3. Implementation Concerns

• Docker setup: Will containers build? Missing dependencies?
• X11 screenshot capture: Will it work in Docker?
• OCR performance: Is queue-based approach correct?
• Neo4j vector indexes: Correct configuration?
• Embedding generation: Ollama integration correct?

4. Security & Privacy

• Docker socket access (security implications?)
• Privacy redaction (sufficient patterns?)
• File system watching (permission issues?)

5. Missing Pieces

• Docker event watcher (claimed complete, actually missing)
• Health checks for workers
• Retention cleanup implementation
• Error recovery mechanisms

Honest Assessment

What I Think Works:

• Structure and architecture seem sound
• Neo4j schema is well-designed
• Utilities (Neo4j client, embeddings) look correct
• FastAPI setup follows best practices

What I'm Unsure About:

• Will Docker Compose actually work?
• Are all Python dependencies correct?
• Will screenshot capture work from container?
• Will OCR produce useful results?
• Is error handling sufficient?

What I Know Doesn't Work:

• Integration between components (untested)
• Edge cases everywhere
• Performance tuning needed
• Production readiness (far from it)

Testing Needed

Before merge:

1. Run `docker-compose up` successfully
2. Initialize Neo4j schema
3. Capture at least one screenshot
4. Process OCR on that screenshot
5. Scan at least one project
6. Scan Docker containers
7. Verify graph queries work
8. Run file system watcher
9. Verify all API endpoints respond

Questions for Reviewers

1. Architecture: Is the domain separation appropriate? Should anything be reorganized?
2. Graph Model: Are the Neo4j relationships sensible? Missing any important connections?
3. Docker Security: Is mounting the Docker socket acceptable, or should we use API calls only?
4. OCR Approach: Queue-based worker vs real-time processing?
5. Embeddings: Using Ollama for local embeddings - good choice or use cloud service?
6. Privacy: Are the redaction patterns sufficient? What am I missing?
7. Testing: What should be the testing priority?

Deployment Risk

🔴 HIGH RISK - This is untested code. Expect:

• Build failures
• Runtime errors
• Missing dependencies
• Configuration issues
• Permission problems
• Performance issues

Next Steps (If Approved)

1. Fix any review feedback
2. Test basic functionality
3. Fix inevitable bugs
4. Implement MCP registry
5. Implement query routing
6. Add integration tests
7. Create deployment guide

Honesty Disclaimer

This code was generated in a single session and represents ~50% of a working system. The structure exists, the logic looks reasonable, but nothing has been validated. I'm requesting review to catch issues early before investing more time.

See CONTRIBUTING.md for how to help!

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Ready for honest feedback 🙏

🤖 Generated with Claude Code

[Copilot]

Pull Request Overview

This PR introduces the initial implementation of The Watchman, a comprehensive computer-centric knowledge graph system that tracks visual timelines, system infrastructure, and file system events. The implementation provides a foundation for capturing screenshots with OCR, discovering project and Docker infrastructure, and monitoring file system changes through a Neo4j-backed graph database with FastAPI services.

• Core infrastructure setup with Docker Compose, Neo4j database, and FastAPI services
• Complete structural implementation of visual timeline capture and OCR processing
• System graph discovery for projects and Docker containers with relationship mapping

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[Copilot]

Pull Request Overview

Copilot reviewed 1 out of 1 changed files in this pull request and generated no new comments.

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_{Tip: Customize your code reviews with copilot-instructions.md. Create the file or learn how to get started.}

[Copilot]

Pull Request Overview

Copilot reviewed 1 out of 1 changed files in this pull request and generated no new comments.

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_{Tip: Customize your code reviews with copilot-instructions.md. Create the file or learn how to get started.}