Compare commits
+8
-3
@@ -8,7 +8,14 @@ API_HOST=0.0.0.0
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API_PORT=8000
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API_PREFIX=/v1
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# Ollama Configuration
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# Anthropic Configuration (Claude - preferred backend)
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# Set ANTHROPIC_API_KEY to enable Claude as the default backend
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# Without an API key, Tatlock uses Ollama exclusively
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# ANTHROPIC_API_KEY=sk-ant-api03-your-key-here
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ANTHROPIC_MODEL=claude-sonnet-4-20250514
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PREFER_CLOUD_BACKEND=true
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# Ollama Configuration (local fallback when Claude unavailable)
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OLLAMA_HOST=http://localhost:11434
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OLLAMA_DEFAULT_MODEL=mistral-nemo:latest
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OLLAMA_TIMEOUT=120
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@@ -21,7 +28,6 @@ SEARXNG_TIMEOUT=30
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REDIS_HOST=localhost
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REDIS_PORT=6379
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REDIS_MEMORY_DB=1
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REDIS_BENCHMARK_DB=6
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REDIS_TIMEOUT=5
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# Qdrant Configuration
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@@ -33,7 +39,6 @@ QDRANT_PORT=6333
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# - development: DEBUG (maximum verbosity)
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# - production: WARNING (minimal noise)
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# Uncomment to override: LOG_LEVEL=INFO
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ENABLE_BENCHMARKS=true
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# Note: Log format is auto-selected based on ENVIRONMENT (console for dev, json for production)
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# User Configuration
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@@ -5,6 +5,17 @@ on:
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types: [published]
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jobs:
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release:
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runs-on: ubuntu-latest
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steps:
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- name: Create Gitea Release
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run: |
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curl -sf -X POST \
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-H "Authorization: token ${{ secrets.GITHUB_TOKEN }}" \
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-H "Content-Type: application/json" \
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-d '{"tag_name": "${{ github.ref_name }}", "name": "Release ${{ github.ref_name }}", "body": "Automated release for ${{ github.ref_name }}"}' \
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"${{ github.server_url }}/api/v1/repos/${{ github.repository }}/releases"
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build:
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runs-on: ubuntu-latest
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steps:
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+4
-1
@@ -68,7 +68,10 @@ dmypy.json
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.ruff_cache/
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# Logs
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logs/
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logs/*
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!logs/traces/
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logs/traces/*
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!logs/traces/viewer.html
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*.log
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# Database
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@@ -22,12 +22,27 @@ This document contains instructions and documentation references for AI assistan
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* **Test REST endpoints** against `http://localhost:8777` using curl or similar tools
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* **Only deploy** when a phase or feature is complete and tested locally
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* **Environment**: Copy `.env.example` to `.env` and configure for your local setup (Ollama, Redis, Qdrant hosts)
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* **Running tests**: Always use the venv explicitly to avoid environment mismatches:
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```bash
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.venv/bin/python -m pytest tests/ # All tests
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.venv/bin/python -m pytest tests/core/ -v # Core tests only
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```
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### 🌐 Internal Service Access
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* **git.schweitz.net**: Access via `http://localhost:3002` (direct Gitea) to bypass Authentik SSO
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* Example: `curl http://localhost:3002/jpmschweitzer/library-desk/raw/branch/main/README.md`
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* Public repos are readable without authentication
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* Related repos: `library-desk`, `scheduler`
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* Related repos: `library-desk`, `scheduler`, `core-api`, `portainer-core`
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### 🐳 Deployment & Infrastructure
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* **Full stack documentation**: Available in the `portainer-core` repo
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* Access: `curl http://localhost:3002/jpmschweitzer/portainer-core/raw/branch/main/CONTAINERS.md`
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* Contains: All service ports, URLs, Redis DB allocations, external domains
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* **Tatlock deployment**:
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* LAN: `http://192.168.86.149:8000`
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* External: `tatlock.schweitz.net` (behind Authentik SSO)
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* Redis DBs: 1 (memory), 6 (benchmarks)
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* **Health check**: `curl http://192.168.86.149:8000/health`
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### 🛡️ Git Discipline
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* **NEVER commit to `main` or `master` directly.** Always create a feature branch: `feature/your-feature-name` or `fix/issue-description`.
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@@ -41,6 +56,32 @@ This document contains instructions and documentation references for AI assistan
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* **Update `CHANGELOG.md`** with every user-facing change.
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* Format: `## [Unreleased] - YYYY-MM-DD` followed by `### Added`, `### Changed`, or `### Fixed`.
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### 🚀 Release Flow
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When changes are ready for deployment:
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1. **Ask user if deploy cycle is desired**
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2. **Update version** in `pyproject.toml`:
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- Bug fixes: bump patch version (1.8.3 → 1.8.4)
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- New features: bump minor version (1.8.4 → 1.9.0)
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3. **Update CHANGELOG.md**:
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- Move items from `[Unreleased]` to new version section
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- Add release date: `## [1.8.4] - 2025-12-16`
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4. **Commit and tag**:
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```bash
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git add -A
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git commit -m "fix: description of changes"
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git tag v1.8.4
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git push origin main --tags
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```
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5. **CI/CD triggers automatically**:
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- Gitea CI builds Docker image on new tag
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- Watchtower pulls and deploys to production
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- Verify deployment: `curl http://192.168.86.149:8000/health`
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---
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## 2. FastAPI Architecture & Best Practices
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+297
-1
@@ -7,6 +7,288 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
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## [Unreleased]
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## [2.0.0] - 2026-02-05
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### Added
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- **Claude backend support (Claudification Phase 1)** - All agents now prefer Claude over Ollama
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- New `src/anthropic/` module with model selector and health check
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- `get_model()` factory returns Claude if available, Ollama as fallback
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- Startup health check caches Claude API availability
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- Configuration: `ANTHROPIC_API_KEY`, `ANTHROPIC_MODEL`, `PREFER_CLOUD_BACKEND`
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- 200k token context when using Claude backend
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- **Steward dual-backend support** - Direct API calls to Claude or Ollama
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- `_call_claude()`: Anthropic Messages API path
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- `_call_ollama()`: Existing Ollama generate API path (preserved)
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- Automatic fallback: if Claude call fails mid-request, retries with Ollama
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- **Claudification project tracking** - `PROJECT_CLAUDIFICATION.md` with Phase 1/2 roadmap
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### Changed
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- **All PydanticAI agents refactored to use `get_model()`**:
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- Tatlock (6 instantiation locations)
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- Librarian
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- Biographer
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- Housekeeper
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- **`initialize_application()` is now async** - Supports async Claude health check at startup
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- **Dependencies**: `pydantic-ai-slim[openai,anthropic]` replaces `pydantic-ai-slim[openai]`
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- **Startup logging** now includes backend selection info (claude/ollama)
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- **Agent creation logging** now includes backend and model info
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### Removed
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- Stale `tests/core/test_benchmarks.py` (benchmark system was removed in v1.10.0)
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## [1.11.0] - 2025-12-30
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### Added
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- **Paperless document integration** - HybridRAG now includes indexed PDFs and scanned documents from Paperless-ngx
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- New `include_documents` parameter in `hybrid_search` tool
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- 📑 icon for document sources in search results
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- Librarian prompt updated with document awareness
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- **Volatile cache integration** - HybridRAG now includes pre-fetched real-time data
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- New `include_volatile` parameter in `hybrid_search` tool
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- ⚡ icon for volatile sources in search results
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- Supports weather, forecast, news, stock, crypto, sun, air_quality namespaces
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||||
- Librarian prompt updated with volatile cache awareness (user-configured items only)
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- **Biographer routing in Steward** - Personal memory queries now correctly route to The Biographer
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- Added explicit routing rules for "where do I live", "what car do I drive", etc.
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- Added biographer delegation examples to Steward prompt
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- Location keywords ("live", "where", "home") now trigger profile pre-fetch
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### Changed
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- **LibraryDeskClient.hybrid_search** - Now passes full config including `document_limit`, `volatile_limit`, and enable flags
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- **Steward guidelines** - Clarified that research queries about TOPICS go to Librarian, queries about USER go to Biographer
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## [1.10.1] - 2025-12-23
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### Fixed
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- **Tatlock's excessive apologizing** - Strengthened personality prompt to prevent unnecessary apologies after successful Librarian delegations. Added explicit "do NOT apologize" instructions to both system prompt and synthesis prompt.
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## [1.10.0] - 2025-12-22
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### Added
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#### Lightweight Request Tracing
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- **JSON-based tracing system** for local development debugging
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- Captures full request flow through multi-agent architecture
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- `Trace` and `Span` dataclasses with automatic timing and nesting
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- ContextVar-based propagation for async-safe tracing
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- `trace_span` async context manager for clean instrumentation
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- Traces written to `logs/traces/{trace_id}.json`
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- Enabled via `DEBUG=true` environment variable
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- **Trace Viewer UI** (`logs/traces/viewer.html`)
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- Standalone HTML viewer with timeline visualization
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- Filter by status, search by request text
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- Expandable span details with prompts and responses
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- **Tracing REST API** (`/traces`)
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- `GET /traces` - Serve trace viewer UI
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- `GET /traces/list` - List available traces with filtering
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- `GET /traces/{trace_id}` - Retrieve specific trace JSON
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- Only available when `DEBUG=true`
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- **Full pipeline instrumentation**
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- Router-level trace start/end with context management
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- Steward analysis spans in preprocessing
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- Tatlock orchestrate/synthesize spans
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- Expert delegation spans (librarian/biographer/housekeeper)
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- Tool-level spans extracted from PydanticAI messages
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### Changed
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- **Replaced Redis benchmarks with file-based tracing** - Simpler, more useful for debugging
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- **Context management moved to service layer** - Router simplified, context set in response service
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- **Server binds to all interfaces** - `wakeup.sh` now uses `0.0.0.0` for network access
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||||
### Removed
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||||
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||||
- **Redis benchmark system** (`src/core/benchmarks.py`)
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- `ENABLE_BENCHMARKS` config setting
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- `REDIS_BENCHMARK_DB` config setting
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||||
- `redis_url` property (kept `redis_memory_url`)
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- Benchmark recording in Steward service and tool tracking
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### Fixed
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||||
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- **Librarian fabrication prevention** - Added explicit instructions to never invent data when tools fail or sources are unavailable
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## [1.9.0] - 2025-12-18
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### Changed
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||||
|
||||
- **Housekeeper prompt optimization** - Rewrote system prompt for Mistral-Nemo function calling with negative constraints, step-by-step process, and explicit entity ID format guidance
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||||
- **Housekeeper temperature setting** - Set temperature to 0.1 for deterministic tool calling behavior
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||||
- **Device list room group priority** - Room groups now appear first in `list_devices` output with `[ROOM GROUP]` marker to address positional bias
|
||||
- **Tool docstring improvements** - Updated turn_on/turn_off/toggle with explicit `entity_id=` parameter examples
|
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|
||||
### Added
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||||
|
||||
- **Housekeeper optimization findings** - Added `docs/housekeeper-optimization-findings.md` documenting the experiment journey from 0% to 100% success rate
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||||
- **Housekeeper test script** - Added `scripts/test_housekeeper.sh` for room group detection regression testing
|
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|
||||
## [1.8.6] - 2025-12-17
|
||||
|
||||
### Fixed
|
||||
|
||||
- **Housekeeper API paths** - Updated all client endpoints to use `/housekeeping/` prefix to match core-api routes
|
||||
- **Housekeeper entity hallucination** - Improved system prompt with critical rule requiring `list_devices()` before any control action to prevent guessing entity IDs
|
||||
|
||||
### Added
|
||||
|
||||
- **Housekeeping API spec** - Added `docs/housekeeping-api-spec.md` documenting the core-api home automation interface
|
||||
|
||||
## [1.8.5] - 2025-12-16
|
||||
|
||||
### Fixed
|
||||
|
||||
- **Redis benchmark boolean storage** - Convert booleans to strings for Redis `hset` (Redis doesn't accept bool type directly)
|
||||
- **Tool tracking capability matching** - `delegate_to_librarian` now correctly recognized as using "librarian" capability when checking Steward recommendations
|
||||
- **E2E test fixture scope** - Fixed pytest-asyncio ScopeMismatch error by using `loop_scope="module"` for module-scoped async fixtures
|
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|
||||
## [1.8.4] - 2025-12-16
|
||||
|
||||
### Fixed
|
||||
|
||||
- **Remove `<think>` wrappers from think messages** - Messages in `reasoning_content` should be plain text
|
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- Removed `<think>` wrappers from delegation.py household think messages
|
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- Removed `<think>` wrappers from orchestration.py status messages
|
||||
- Think messages now appear cleanly in Open WebUI's reasoning block
|
||||
|
||||
## [1.8.3] - 2025-12-16
|
||||
|
||||
### Fixed
|
||||
|
||||
- **Open WebUI streaming rendering** - Use `reasoning_content` field for thinking (DeepSeek R1 format) instead of `<think>` tags in `content`
|
||||
- Open WebUI now renders thinking as proper collapsible blocks instead of broken HTML
|
||||
|
||||
## [1.8.2] - 2025-12-16
|
||||
|
||||
### Fixed
|
||||
|
||||
- **HybridRAG keywords schema mismatch** - library-desk now returns `keywords` as dict with `core_keywords`, client now handles both formats
|
||||
|
||||
## [1.8.1] - 2025-12-16
|
||||
|
||||
### Fixed
|
||||
|
||||
#### Ollama Message Sanitization
|
||||
- **Fixed `invalid message content type: <nil>` error** from Ollama
|
||||
- Created custom `TatlockOllamaProvider` that sanitizes messages before sending to Ollama
|
||||
- Ollama rejects assistant messages with `content: null` (tool-only messages from PydanticAI)
|
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- Provider converts `null` content to empty string `""` for compatibility
|
||||
- Updated all agents (Librarian, Biographer, Housekeeper, Tatlock) to use sanitized provider
|
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- Added `src/ollama/provider.py` with reusable provider pattern
|
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|
||||
#### Streaming Think Message Accumulation
|
||||
- **Fixed repeating think messages in frontend** (e.g., 10x "The Librarian has compiled...")
|
||||
- Frontend was accumulating `ReasoningSummaryDelta` events expecting concatenation
|
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- Added `ReasoningSummaryDone()` signal after each think message to indicate completion
|
||||
- Each think slug is now treated as a complete message, not a continuation
|
||||
|
||||
## [1.8.0] - 2025-12-15
|
||||
|
||||
### Fixed
|
||||
|
||||
#### Steward Routing for Web Search
|
||||
- Updated Steward guidelines to route web searches, weather, news → Librarian with `search_web`
|
||||
- Added URL/article reading → Librarian with `read_url` to routing guidelines
|
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- Added examples showing `search_web` and `read_url` tool usage
|
||||
|
||||
#### Librarian Agent Tool Registration
|
||||
- Registered `search_web`, `read_url`, `read_urls_batch` tools with the Librarian PydanticAI agent
|
||||
- Updated Librarian system prompt with Web Search & Content Extraction section
|
||||
- Fixed tool count in agent logger (11 → 14 tools)
|
||||
|
||||
#### Query Enrichment Integration
|
||||
- Fixed enriched query (with location/timezone context) not being passed to delegations
|
||||
- Response service now uses `enriched_query` from Steward recommendation for all delegations
|
||||
- Weather queries now automatically include user's stored location
|
||||
|
||||
#### Action Type Detection
|
||||
- Added "read", "fetch", "url", "http" keywords to RESEARCH action type for Librarian
|
||||
- Ensures proper think messages for URL reading tasks
|
||||
|
||||
## [1.7.0] - 2025-12-15
|
||||
|
||||
### Added
|
||||
|
||||
#### Web Search Migration to Librarian
|
||||
- **`search_web()`** tool in Librarian for web search via library-desk `/rag/search` endpoint
|
||||
- **`read_url()`** tool for single URL content extraction via Trafilatura
|
||||
- **`read_urls_batch()`** tool for parallel batch URL extraction (max 20 URLs)
|
||||
- `WebSearchResult`, `WebSearchResponse` models in LibraryDeskClient
|
||||
- `ContentExtractionResult`, `BatchExtractionResponse` models for content extraction
|
||||
- `search_web()`, `extract_content()`, `extract_content_batch()` methods in LibraryDeskClient
|
||||
- Comprehensive unit tests for new Librarian tools (`tests/agents/librarian/test_tools.py`)
|
||||
|
||||
### Changed
|
||||
|
||||
- Librarian capability updated with web search domains: "web", "url", "internet"
|
||||
- Tatlock system prompt now delegates web search to Librarian
|
||||
- `tatlock_core` capability reduced to computation/datetime only (no longer requires network)
|
||||
|
||||
### Removed
|
||||
|
||||
- `search_web` function from `src/agents/tatlock_core/tools.py`
|
||||
- `web_search_tool` from `tatlock_core_tools` list
|
||||
- `search_web` from legacy `src/agents/tools.py`
|
||||
- Search tests from `tests/agents/test_tools.py` (moved to Librarian tests)
|
||||
|
||||
## [1.6.0] - 2025-12-15
|
||||
|
||||
### Added
|
||||
|
||||
#### Two-Phase Tatlock Execution
|
||||
- **Phase 1: Orchestration** - Executes tool calls and expert delegations, returns structured results
|
||||
- **Phase 2: Synthesis** - Synthesizes butler-toned response from gathered results
|
||||
- `orchestrate_tool_calls()` method in TatlockAgent for coordination phase
|
||||
- `synthesize_from_results()` method in TatlockAgent for synthesis phase
|
||||
- Guarantees butler personality in all responses by separating coordination from response generation
|
||||
|
||||
#### Automatic Think Slugs
|
||||
- **Deterministic butler-perspective messages** during expert delegation (no LLM involved)
|
||||
- `ActionType` enum: RETRIEVE, RESEARCH, CREATE, CONTROL, RECORD
|
||||
- `HOUSEHOLD_THINK_MESSAGES` mapping with butler-perspective messages for all experts:
|
||||
- Librarian: "Allow me to consult the archives, sir." / "I'm having the Librarian prepare a new entry."
|
||||
- Biographer: "Let me consult the household records." / "I've asked the Biographer to take note, sir."
|
||||
- Housekeeper: "I'm instructing the household staff now, sir." / "Allow me to inquire with the household staff."
|
||||
- `_detect_action_type()` function for keyword-based action detection
|
||||
- `get_think_message()` helper for retrieving appropriate messages
|
||||
- Streaming delegation wrappers: `stream_delegate_to_librarian()`, `stream_delegate_to_biographer()`, `stream_delegate_to_housekeeper()`
|
||||
- `STREAMING_DELEGATION_WRAPPERS` mapping in delegation.py
|
||||
- `get_streaming_delegation_tools()` method in HouseholdRegistry
|
||||
|
||||
#### Steward Query Enrichment
|
||||
- **Auto-fill user context** (location, timezone) when not specified in query
|
||||
- `_build_enriched_query()` function in steward service
|
||||
- Regex word boundary matching for accurate location detection (avoids false positives)
|
||||
- `enriched_query` field added to `StewardRecommendation` schema
|
||||
- Automatic enrichment for weather queries (location), time queries (timezone), temperature preferences
|
||||
|
||||
#### Documentation
|
||||
- **ORCHESTRATION_SCENARIOS.md** completely rewritten with:
|
||||
- Mermaid flow diagrams for two-phase execution
|
||||
- 4 new Housekeeper scenarios (light control, device status, parallel delegation)
|
||||
- Biographer memory recording scenario
|
||||
- Complete think slug reference tables
|
||||
- Action type detection tables
|
||||
- Updated architecture mindmap
|
||||
- **TESTING_IMPROVEMENTS.md** - LLM testing best practices for future implementation
|
||||
|
||||
### Changed
|
||||
|
||||
- `create_response_with_steward()` now uses two-phase execution
|
||||
- `_direct_delegation()` routes through synthesis phase for consistent butler tone
|
||||
- `_execute_single_delegation()` now supports housekeeper
|
||||
- Streaming response handler integrated with think slug system
|
||||
- All 326 unit tests passing
|
||||
|
||||
## [1.5.0] - 2025-12-15
|
||||
|
||||
### Added
|
||||
@@ -608,7 +890,21 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
|
||||
- CORS middleware
|
||||
- Exception handlers (OpenAI-compatible error format)
|
||||
|
||||
[Unreleased]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v1.4.0...main
|
||||
[Unreleased]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v2.0.0...main
|
||||
[2.0.0]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v1.11.0...v2.0.0
|
||||
[1.11.0]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v1.10.0...v1.11.0
|
||||
[1.10.0]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v1.9.0...v1.10.0
|
||||
[1.9.0]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v1.8.6...v1.9.0
|
||||
[1.8.6]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v1.8.5...v1.8.6
|
||||
[1.8.5]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v1.8.4...v1.8.5
|
||||
[1.8.4]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v1.8.3...v1.8.4
|
||||
[1.8.3]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v1.8.2...v1.8.3
|
||||
[1.8.2]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v1.8.1...v1.8.2
|
||||
[1.8.1]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v1.8.0...v1.8.1
|
||||
[1.8.0]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v1.7.0...v1.8.0
|
||||
[1.7.0]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v1.6.0...v1.7.0
|
||||
[1.6.0]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v1.5.0...v1.6.0
|
||||
[1.5.0]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v1.4.0...v1.5.0
|
||||
[1.4.0]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v1.3.3...v1.4.0
|
||||
[1.3.3]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v1.3.2...v1.3.3
|
||||
[1.3.2]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v1.3.1...v1.3.2
|
||||
|
||||
+677
-216
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,401 @@
|
||||
# Tatlock Enhancement Plan: Bidirectional Claude Integration
|
||||
|
||||
## Executive Summary
|
||||
|
||||
Implement a **bidirectional architecture** that:
|
||||
1. **Superpowers Tatlock** by swapping Ollama→Claude backend (200k context, better reasoning, same butler personality)
|
||||
2. **Exposes Tatlock as MCP server** for Claude instances on any device (phone, browser, desktop)
|
||||
|
||||
This gives you the flexibility to use whichever AI is best/most accessible at any moment.
|
||||
|
||||
## Key Insight: Blanket Backend Swap (Simpler Than Sidecar)
|
||||
|
||||
Instead of adding a Claude "Analyst" sidecar agent, **swap the underlying model for ALL agents**:
|
||||
|
||||
```
|
||||
CURRENT: TatlockAgent → OpenAIChatModel → OllamaProvider → Ollama (mistral-nemo)
|
||||
PROPOSED: TatlockAgent → AnthropicModel → AnthropicProvider → Claude API
|
||||
↘ (fallback when offline) → OllamaProvider → Ollama
|
||||
```
|
||||
|
||||
**Why this works:**
|
||||
- PydanticAI natively supports Anthropic via `AnthropicModel` + `AnthropicProvider`
|
||||
- The same `TATLOCK_SYSTEM_PROMPT` is passed to Claude - butler personality preserved
|
||||
- Claude is **better** at following system prompts than mistral-nemo
|
||||
- 200k context for ALL queries, not just "complex" ones
|
||||
- Simpler architecture: no routing logic, no sidecar delegation
|
||||
|
||||
---
|
||||
|
||||
## Research Findings
|
||||
|
||||
### Industry Best Practices (2025-2026)
|
||||
|
||||
**MCP Protocol Updates** ([MCP Spec Updates June 2025](https://auth0.com/blog/mcp-specs-update-all-about-auth/)):
|
||||
- Streamable HTTP replaced SSE (March 2025) - better for cloud deployment
|
||||
- OAuth 2.0 required for remote servers - MCP servers are OAuth Resource Servers
|
||||
- Tool Output Schemas now available - better structured data handling
|
||||
- MCP Registry launched (Sept 2025) - community server discovery
|
||||
|
||||
**Community Patterns** ([Claude Code Router](https://github.com/musistudio/claude-code-router)):
|
||||
- Task-based routing is becoming standard: route simple→local, complex→cloud
|
||||
- Translation proxies bridge Anthropic Messages API ↔ OpenAI format
|
||||
- Cost savings of up to 98% reported with smart routing
|
||||
|
||||
**Home Automation MCP** ([ha-mcp](https://github.com/homeassistant-ai/ha-mcp)):
|
||||
- Production-ready MCP servers exist for Home Assistant
|
||||
- Support Claude Code, Gemini CLI, Open WebUI, VSCode, Cursor
|
||||
- Pattern: expose local tools securely to remote AI clients
|
||||
|
||||
**Remote MCP Access** ([mcp-remote](https://www.npmjs.com/package/mcp-remote)):
|
||||
- Bridge local MCP servers to Claude Desktop/Browser via proxy
|
||||
- Supports authentication headers for security
|
||||
- Works with ngrok/Cloudflare Tunnel for HTTPS
|
||||
|
||||
---
|
||||
|
||||
## Recommended Architecture
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────────────────────────────┐
|
||||
│ BIDIRECTIONAL TATLOCK-CLAUDE ARCHITECTURE │
|
||||
├─────────────────────────────────────────────────────────────────────────────────────┤
|
||||
│ │
|
||||
│ ╔═══════════════════════════════════════════════════════════════════════════════╗ │
|
||||
│ ║ SCENARIO A: Using Tatlock (Open WebUI, local apps) ║ │
|
||||
│ ║ ───────────────────────────────────────────────── ║ │
|
||||
│ ║ ║ │
|
||||
│ ║ Request → Steward → Tatlock → Tools + Expert Delegation ║ │
|
||||
│ ║ │ ║ │
|
||||
│ ║ ├─→ Librarian (Claude) → research, wiki, RAG ║ │
|
||||
│ ║ ├─→ Biographer (Claude) → memory, preferences ║ │
|
||||
│ ║ ├─→ Housekeeper (Claude) → home automation ║ │
|
||||
│ ║ └─→ All powered by Claude with Ollama fallback ║ │
|
||||
│ ║ ║ │
|
||||
│ ║ Butler personality preserved, 200k context for all queries ║ │
|
||||
│ ╚═══════════════════════════════════════════════════════════════════════════════╝ │
|
||||
│ │
|
||||
│ ╔═══════════════════════════════════════════════════════════════════════════════╗ │
|
||||
│ ║ SCENARIO B: Using Claude.ai / Claude Desktop / Phone ║ │
|
||||
│ ║ ──────────────────────────────────────────────────── ║ │
|
||||
│ ║ ║ │
|
||||
│ ║ Claude ──[MCP over HTTPS]──► Tatlock MCP Server → Household Tools ║ │
|
||||
│ ║ │ ║ │
|
||||
│ ║ ├─→ calculator, datetime ║ │
|
||||
│ ║ ├─→ web_search, wiki_search ║ │
|
||||
│ ║ ├─→ hybrid_search (RAG) ║ │
|
||||
│ ║ ├─→ memory_recall, store_insight ║ │
|
||||
│ ║ └─→ home_control (lights, climate) ║ │
|
||||
│ ║ ║ │
|
||||
│ ║ Full 200k context, your local tools accessible from anywhere ║ │
|
||||
│ ╚═══════════════════════════════════════════════════════════════════════════════╝ │
|
||||
│ │
|
||||
│ ╔═══════════════════════════════════════════════════════════════════════════════╗ │
|
||||
│ ║ SCENARIO C: Offline (internet down) ║ │
|
||||
│ ║ ────────────────────────────────── ║ │
|
||||
│ ║ ║ │
|
||||
│ ║ Tatlock operates fully locally with Ollama ║ │
|
||||
│ ║ • All tools work (except web search) ║ │
|
||||
│ ║ • Graceful degradation with same butler personality ║ │
|
||||
│ ╚═══════════════════════════════════════════════════════════════════════════════╝ │
|
||||
│ │
|
||||
└─────────────────────────────────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Implementation Plan
|
||||
|
||||
### Phase 1: Blanket Backend Swap (Claude for All Agents)
|
||||
|
||||
Replace Ollama with Claude as the default backend for all PydanticAI agents, with automatic offline fallback.
|
||||
|
||||
**New Files:**
|
||||
```
|
||||
src/anthropic/
|
||||
├── __init__.py
|
||||
├── provider.py # Claude provider with health check
|
||||
└── model_selector.py # Chooses Claude or Ollama based on availability
|
||||
```
|
||||
|
||||
**Key Implementation (`src/anthropic/provider.py`):**
|
||||
```python
|
||||
from pydantic_ai.models.anthropic import AnthropicModel
|
||||
from pydantic_ai.providers.anthropic import AnthropicProvider
|
||||
from pydantic_ai.models.openai import OpenAIChatModel
|
||||
from src.ollama.provider import get_ollama_provider
|
||||
from src.core.config import config
|
||||
|
||||
_anthropic_available: bool | None = None
|
||||
|
||||
async def check_anthropic_health() -> bool:
|
||||
"""Check if Anthropic API is reachable."""
|
||||
global _anthropic_available
|
||||
try:
|
||||
from anthropic import AsyncAnthropic
|
||||
client = AsyncAnthropic(api_key=config.ANTHROPIC_API_KEY)
|
||||
await client.messages.create(
|
||||
model=config.ANTHROPIC_MODEL,
|
||||
max_tokens=1,
|
||||
messages=[{"role": "user", "content": "hi"}]
|
||||
)
|
||||
_anthropic_available = True
|
||||
except Exception:
|
||||
_anthropic_available = False
|
||||
return _anthropic_available
|
||||
|
||||
def get_model(prefer_cloud: bool = True):
|
||||
"""Get the best available model. Returns Claude if available, otherwise Ollama."""
|
||||
if prefer_cloud and config.ANTHROPIC_API_KEY and _anthropic_available:
|
||||
provider = AnthropicProvider(api_key=config.ANTHROPIC_API_KEY)
|
||||
return AnthropicModel(
|
||||
model_name=config.ANTHROPIC_MODEL,
|
||||
provider=provider,
|
||||
)
|
||||
else:
|
||||
return OpenAIChatModel(
|
||||
model_name=config.OLLAMA_DEFAULT_MODEL,
|
||||
provider=get_ollama_provider()
|
||||
)
|
||||
```
|
||||
|
||||
**Modify TatlockAgent (`src/agents/tatlock.py`):**
|
||||
```python
|
||||
def _ensure_agent(self):
|
||||
if self._agent is not None:
|
||||
return
|
||||
|
||||
from src.anthropic.model_selector import get_model
|
||||
|
||||
model = get_model(prefer_cloud=True)
|
||||
|
||||
self._agent = Agent(
|
||||
model,
|
||||
system_prompt=TATLOCK_SYSTEM_PROMPT, # Same butler personality!
|
||||
)
|
||||
self._register_tools()
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Phase 2: MCP Server (Expose Tools to Claude)
|
||||
|
||||
Create an MCP server that exposes Tatlock's household tools to external Claude instances.
|
||||
|
||||
**New Files:**
|
||||
```
|
||||
src/mcp/
|
||||
├── __init__.py
|
||||
├── server.py # MCP server using mcp Python SDK
|
||||
├── tool_adapters.py # Convert PydanticAI tools → MCP schemas
|
||||
├── auth.py # API key authentication
|
||||
└── transport.py # Streamable HTTP transport
|
||||
```
|
||||
|
||||
**Docker Stack Addition (`stacks/agents.yml`):**
|
||||
```yaml
|
||||
tatlock-mcp:
|
||||
image: git.schweitz.internal/jpmschweitzer/tatlock:latest
|
||||
command: ["python", "-m", "src.mcp.server"]
|
||||
ports:
|
||||
- "8002:8002"
|
||||
environment:
|
||||
- MCP_AUTH_TOKEN=${MCP_AUTH_TOKEN}
|
||||
networks:
|
||||
- docker-dataplane
|
||||
```
|
||||
|
||||
**Claude Desktop Configuration:**
|
||||
```json
|
||||
{
|
||||
"mcpServers": {
|
||||
"tatlock": {
|
||||
"command": "npx",
|
||||
"args": ["mcp-remote", "https://mcp.schweitz.net/sse", "--header", "Authorization: Bearer ${MCP_AUTH_TOKEN}"]
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Files to Modify
|
||||
|
||||
### Phase 1 - Backend Swap
|
||||
|
||||
**New Files:**
|
||||
| File | Purpose |
|
||||
|------|---------|
|
||||
| `src/anthropic/__init__.py` | Package init |
|
||||
| `src/anthropic/provider.py` | Claude provider with health check |
|
||||
| `src/anthropic/model_selector.py` | Choose Claude or Ollama based on availability |
|
||||
|
||||
**Modified Files:**
|
||||
| File | Changes |
|
||||
|------|---------|
|
||||
| `src/core/config.py` | Add `ANTHROPIC_API_KEY`, `ANTHROPIC_MODEL`, `PREFER_CLOUD_BACKEND` |
|
||||
| `src/agents/tatlock.py` | Use `get_model()` instead of hardcoded Ollama |
|
||||
| `src/agents/librarian/agent.py` | Use `get_model()` instead of hardcoded Ollama |
|
||||
| `src/agents/biographer/agent.py` | Use `get_model()` instead of hardcoded Ollama |
|
||||
| `src/agents/steward/agent.py` | Convert to PydanticAI or add Anthropic API support |
|
||||
| `src/core/startup.py` | Add Anthropic health check on startup |
|
||||
| `requirements.txt` | Add `anthropic>=0.40.0` |
|
||||
| `.env.example` | Document new environment variables |
|
||||
|
||||
### Phase 2 - MCP Server
|
||||
|
||||
**New Files:**
|
||||
| File | Purpose |
|
||||
|------|---------|
|
||||
| `src/mcp/__init__.py` | Package init |
|
||||
| `src/mcp/server.py` | MCP server implementation |
|
||||
| `src/mcp/tool_adapters.py` | PydanticAI → MCP schema conversion |
|
||||
| `src/mcp/auth.py` | Token-based authentication |
|
||||
|
||||
---
|
||||
|
||||
## Cost Analysis
|
||||
|
||||
- **Claude API**: $5-30/month (10-50 calls/day, ~2k input + 1k output tokens/call)
|
||||
- **MCP via Claude Pro**: Included in subscription
|
||||
- **Total**: ~$10-80/month for full bidirectional integration
|
||||
|
||||
---
|
||||
|
||||
## Verification Plan
|
||||
|
||||
### Phase 1 Testing
|
||||
```bash
|
||||
# 1. Run with Claude backend
|
||||
ANTHROPIC_API_KEY=your-key docker-compose up -d tatlock
|
||||
|
||||
# 2. Verify Claude is being used
|
||||
docker logs tatlock 2>&1 | grep -i "anthropic\|claude"
|
||||
|
||||
# 3. Test butler personality
|
||||
curl -X POST http://tatlock.schweitz.internal:8000/v1/responses \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{"model": "Tatlock", "input": "Hello, who are you?"}'
|
||||
|
||||
# 4. Test offline fallback
|
||||
ANTHROPIC_API_KEY="" docker-compose up -d tatlock
|
||||
docker logs tatlock 2>&1 | grep -i "ollama\|fallback"
|
||||
```
|
||||
|
||||
### Phase 2 Testing
|
||||
```bash
|
||||
# 1. Start MCP server
|
||||
docker-compose up -d tatlock-mcp
|
||||
|
||||
# 2. Test MCP endpoint
|
||||
curl -X POST https://mcp.schweitz.net/tools/list \
|
||||
-H "Authorization: Bearer $MCP_AUTH_TOKEN"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Implementation Priority
|
||||
|
||||
1. **Phase 1: Backend Swap** (~1 week)
|
||||
- Immediate value: 200k context for ALL queries
|
||||
- Low risk: provider abstraction, graceful offline fallback
|
||||
|
||||
2. **Phase 2: MCP Server** (~2-3 weeks)
|
||||
- Enables cross-device access
|
||||
- Bidirectional: Tatlock superpowered by Claude AND accessible to Claude
|
||||
|
||||
---
|
||||
|
||||
## Future Phases (Optional)
|
||||
|
||||
- **Phase 3: LiteLLM Gateway** - Unified endpoint for all models, config-driven routing
|
||||
- **Phase 4: Multi-Provider** - Add OpenAI, Vertex AI, etc.
|
||||
- **Phase 5: Smart Routing** - Context-aware model selection, cost ceiling enforcement
|
||||
|
||||
---
|
||||
|
||||
## Offline Behavior
|
||||
|
||||
| Scenario | Behavior |
|
||||
|----------|----------|
|
||||
| No API key | Use Ollama exclusively |
|
||||
| API unreachable | Use Ollama, log warning |
|
||||
| API rate limited | Fallback to Ollama |
|
||||
|
||||
| Aspect | Claude | Ollama |
|
||||
|--------|--------|--------|
|
||||
| Context | 200k tokens | ~8k tokens |
|
||||
| Latency | 1-3s (network) | 0.5-1s (local) |
|
||||
| Personality | Preserved | Preserved |
|
||||
| Tools | All work | All work |
|
||||
| Cost | API charges | Free |
|
||||
|
||||
---
|
||||
|
||||
## Implementation Status
|
||||
|
||||
### Phase 1: Backend Swap - CODE COMPLETE (awaiting API access)
|
||||
|
||||
- [x] Add Anthropic config settings to `src/core/config.py`
|
||||
- [x] Add `pydantic-ai-slim[openai,anthropic]` to requirements.txt
|
||||
- [x] Create `src/anthropic/` module (model_selector.py)
|
||||
- [x] Add Claude health check to startup.py
|
||||
- [x] Refactor all PydanticAI agents to use `get_model()`
|
||||
- [x] Librarian
|
||||
- [x] Biographer
|
||||
- [x] Housekeeper
|
||||
- [x] Tatlock (6 locations)
|
||||
- [x] Add Claude API path to Steward agent (direct API calls)
|
||||
- [x] Update `.env.example` with new variables
|
||||
- [x] Test Ollama fallback (working)
|
||||
- [ ] Test with Claude API key (blocked: no API access currently)
|
||||
|
||||
**Note:** Implementation complete. Currently runs in Ollama-only mode. Will automatically use Claude when `ANTHROPIC_API_KEY` is configured.
|
||||
|
||||
### Phase 2: MCP Server - NOT STARTED
|
||||
|
||||
- [ ] Create `src/mcp/` module
|
||||
- [ ] Tool adapters (PydanticAI → MCP schema)
|
||||
- [ ] Authentication middleware
|
||||
- [ ] Streamable HTTP transport
|
||||
- [ ] Docker stack configuration
|
||||
|
||||
---
|
||||
|
||||
## Related Repository Handovers
|
||||
|
||||
Handover documents created in each repo: `PROJECT_CLAUDIFICATION_HANDOVER.md`
|
||||
|
||||
### library-desk - HANDOVER CREATED
|
||||
|
||||
- [x] Write handover document
|
||||
- [ ] Review HybridRAG response size limits
|
||||
- [ ] Review smart_create endpoint for Claude optimization
|
||||
- [ ] Evaluate response formats for LLM consumption
|
||||
|
||||
### core-api - HANDOVER CREATED
|
||||
|
||||
- [x] Write handover document
|
||||
- [ ] Review list_devices response format
|
||||
- [ ] Review error messages for LLM consumption
|
||||
- [ ] Evaluate rate limiting for faster Claude processing
|
||||
|
||||
### portainer-core - HANDOVER CREATED (blocking for production)
|
||||
|
||||
- [x] Write handover document
|
||||
- [ ] Update stack with new environment variables
|
||||
- [ ] Configure secrets management for API key
|
||||
- [ ] Update CONTAINERS.md documentation
|
||||
|
||||
### webber - HANDOVER CREATED
|
||||
|
||||
- [x] Write handover document
|
||||
- [ ] Review content truncation limits
|
||||
- [ ] Evaluate extraction quality for LLM consumption
|
||||
|
||||
### tatlock-ui - HANDOVER CREATED
|
||||
|
||||
- [x] Write handover document
|
||||
- [ ] Test streaming responses with Claude backend
|
||||
- [ ] Test conversation history with larger context
|
||||
- [ ] Verify tool call display and reasoning rendering
|
||||
@@ -0,0 +1,105 @@
|
||||
# Testing Improvements for LLM Outputs
|
||||
|
||||
## Problem
|
||||
|
||||
LLM outputs are non-deterministic. Tests checking for exact string matches fail when the LLM writes "thirty-seven" instead of "37".
|
||||
|
||||
## Proposed Solutions
|
||||
|
||||
### 1. LLM-as-Judge Pattern
|
||||
|
||||
Use a smaller/faster model to evaluate semantic correctness:
|
||||
|
||||
```python
|
||||
async def llm_judge(output: str, criteria: str) -> bool:
|
||||
"""Use LLM to evaluate if output meets criteria."""
|
||||
prompt = f"""
|
||||
Evaluate if this output is correct:
|
||||
Output: {output}
|
||||
Criteria: {criteria}
|
||||
Answer only YES or NO.
|
||||
"""
|
||||
result = await judge_model.run(prompt)
|
||||
return "YES" in result.output.upper()
|
||||
|
||||
# Usage in test:
|
||||
assert await llm_judge(
|
||||
response,
|
||||
"The answer correctly states that sqrt(144) + 25 = 37"
|
||||
)
|
||||
```
|
||||
|
||||
### 2. Fuzzy/Regex Matching
|
||||
|
||||
For numeric answers, accept multiple representations:
|
||||
|
||||
```python
|
||||
import re
|
||||
|
||||
def contains_number(text: str, number: int) -> bool:
|
||||
"""Check if text contains number in any form."""
|
||||
patterns = [
|
||||
rf'\b{number}\b', # Digit form
|
||||
number_to_words(number), # Word form
|
||||
]
|
||||
return any(re.search(p, text, re.I) for p in patterns)
|
||||
|
||||
# Usage:
|
||||
assert contains_number(response, 37) # Matches "37" or "thirty-seven"
|
||||
```
|
||||
|
||||
### 3. DeepEval Framework
|
||||
|
||||
```python
|
||||
from deepeval.metrics import AnswerRelevancyMetric
|
||||
from deepeval.test_case import LLMTestCase
|
||||
|
||||
def test_calculation():
|
||||
test_case = LLMTestCase(
|
||||
input="What is sqrt(144) + 25?",
|
||||
actual_output=response,
|
||||
expected_output="37"
|
||||
)
|
||||
metric = AnswerRelevancyMetric(threshold=0.7)
|
||||
assert metric.measure(test_case)
|
||||
```
|
||||
|
||||
### 4. pytest-evals Plugin
|
||||
|
||||
Minimal pytest plugin for LLM testing with metrics collection.
|
||||
|
||||
```bash
|
||||
pip install pytest-evals
|
||||
```
|
||||
|
||||
### 5. Multiple Runs with Threshold
|
||||
|
||||
Run flaky tests multiple times and require majority pass:
|
||||
|
||||
```python
|
||||
@pytest.mark.flaky(reruns=3, reruns_delay=1)
|
||||
def test_llm_response():
|
||||
...
|
||||
```
|
||||
|
||||
Or custom:
|
||||
|
||||
```python
|
||||
@pytest.mark.parametrize("run", range(3))
|
||||
def test_llm_response(run):
|
||||
...
|
||||
# Aggregate results across runs
|
||||
```
|
||||
|
||||
## Resources
|
||||
|
||||
- [DeepEval](https://github.com/confident-ai/deepeval) - LLM evaluation framework
|
||||
- [pytest-evals](https://github.com/AlmogBaku/pytest-evals) - pytest plugin for LLM evals
|
||||
- [LLM Testing Guide 2025](https://www.confident-ai.com/blog/llm-testing-in-2024-top-methods-and-strategies)
|
||||
- [Testing LLM Applications - Langfuse](https://langfuse.com/blog/2025-10-21-testing-llm-applications)
|
||||
|
||||
## Implementation Priority
|
||||
|
||||
1. Add fuzzy number matching helper (quick win)
|
||||
2. Evaluate DeepEval for complex output testing
|
||||
3. Consider LLM-as-judge for semantic correctness
|
||||
@@ -0,0 +1,246 @@
|
||||
# Housekeeper Agent Optimization Findings
|
||||
|
||||
## Background
|
||||
|
||||
Research with Gemini identified key issues with mistral-nemo and tool calling:
|
||||
- "Pre-computation Hallucination" - model answers before using tools
|
||||
- High default temperature (0.7-0.8) causes wandering
|
||||
- Model is "chatty and confident" - needs explicit constraints
|
||||
|
||||
## Key Recommendations from Gemini Research
|
||||
|
||||
1. **Temperature 0.0** for tool-calling agents (deterministic, follows schema)
|
||||
2. **Chain of Thought (CoT)** - force step-by-step reasoning
|
||||
3. **Negative constraints** - tell model what NOT to do (Nemo responds better)
|
||||
4. **Explicit tool descriptions** - verbose docstrings with "never estimate yourself"
|
||||
5. **"Strictly tool-based assistant"** pattern - NO internal knowledge claim
|
||||
|
||||
---
|
||||
|
||||
## Experiment Log
|
||||
|
||||
### Baseline (v1.8.6)
|
||||
- **Date**: 2025-12-17
|
||||
- **Configuration**: Default temperature, improved prompt requiring list_devices first
|
||||
- **Results**:
|
||||
- Called list_devices first ✓
|
||||
- Still hallucinated `light.study_desk` despite seeing list with only `light.study` and `light.study_main`
|
||||
- Partial success: turned off `light.study_main`, failed on hallucinated entity
|
||||
- **Success rate**: ~50% (1 of 2 study lights controlled correctly)
|
||||
|
||||
---
|
||||
|
||||
### Experiment 1: Temperature 0.0
|
||||
- **Date**: 2025-12-18
|
||||
- **Change**: Set `model_settings=ModelSettings(temperature=0.0)` for Housekeeper
|
||||
- **Hypothesis**: Deterministic output will force model to use exact entity IDs from tool results
|
||||
- **Results**:
|
||||
|
||||
**Study lights test:**
|
||||
- Called `list_devices()` first ✓ (but no domain filter)
|
||||
- Used wrong parameter `device_id` instead of `entity_id` (recovered after validation error)
|
||||
- Only identified `light.studeerlamp` as "study" related (Dutch name)
|
||||
- **Missed `light.study` and `light.study_main`** - didn't match English "study"
|
||||
- Turned off 1 wrong light, missed 2 actual study lights
|
||||
|
||||
**Kitchen lights test:**
|
||||
- Called `list_devices()` first ✓ (no domain filter)
|
||||
- Saw full device list including `light.kitchen`
|
||||
- Used wrong parameter `device_id` instead of `entity_id` (recovered after validation)
|
||||
- After correction, dropped domain prefix: used `kitchen` instead of `light.kitchen`
|
||||
- 404 error - device not found
|
||||
|
||||
- **Success rate**: 0% (no target lights successfully controlled)
|
||||
- **Observations**:
|
||||
- Temperature 0.0 alone is insufficient
|
||||
- Model consistently confuses `device_id` vs `entity_id` parameter name
|
||||
- After validation error correction, model truncates entity_id (drops domain prefix)
|
||||
- Semantic matching of room names to devices is weak
|
||||
- Model doesn't understand entity_id format: `domain.name`
|
||||
|
||||
---
|
||||
|
||||
### Experiment 2: Negative Constraints + CoT
|
||||
- **Date**: 2025-12-18
|
||||
- **Change**: Complete prompt rewrite with:
|
||||
- "You have NO Internal Knowledge" - negative framing
|
||||
- Explicit entity_id format with WRONG/RIGHT examples
|
||||
- Step-by-step process (ALWAYS FOLLOW)
|
||||
- Explicit parameter names section
|
||||
- "What NOT To Do" negative constraints
|
||||
- **Hypothesis**: Negative constraints work better with Mistral-Nemo
|
||||
- **Results**:
|
||||
|
||||
**Study lights test:**
|
||||
- Called `list_devices(domain="light")` ✓ with domain filter (improvement!)
|
||||
- Still used `device_id` first, recovered to `entity_id` after validation error
|
||||
- After recovery, used correct full format: `light.studeerlamp`
|
||||
- **Still only matched `studeerlamp` not `light.study` or `light.study_main`**
|
||||
|
||||
**Kitchen lights test:**
|
||||
- Called `list_devices(domain="light")` ✓
|
||||
- Called `turn_off(entity_id="light.kitchen")` ✓ correct format!
|
||||
- All 4 kitchen lights turned off (light.kitchen is a group)
|
||||
- **100% success for kitchen!**
|
||||
|
||||
- **Success rate**:
|
||||
- Study: 0% (wrong semantic match)
|
||||
- Kitchen: 100% (4/4 lights off)
|
||||
- Combined: ~50% (1 of 2 tests successful)
|
||||
- **Observations**:
|
||||
- Domain filter now consistently used ✓
|
||||
- Entity_id format correct after recovery ✓
|
||||
- Semantic matching still fails for "study" → prefers Dutch "studeerlamp" over English "study"
|
||||
- Parameter name confusion persists (`device_id` vs `entity_id`)
|
||||
- Simple room names (kitchen) work; mixed language fails (study/studeerlamp)
|
||||
|
||||
---
|
||||
|
||||
### Experiment 3: Temperature 0.1 + Explicit Tool Docstrings
|
||||
- **Date**: 2025-12-18
|
||||
- **Change**:
|
||||
- Temperature 0.1
|
||||
- Updated turn_on/turn_off docstrings with explicit `entity_id=` in examples
|
||||
- **Results**:
|
||||
- Still uses `device_id` first, recovers to `entity_id` after validation
|
||||
- Still picks wrong entity (studeerlamp over study)
|
||||
- **Success rate**: 0%
|
||||
|
||||
---
|
||||
|
||||
### Experiment 4: Room Group Priority (with explicit examples)
|
||||
- **Date**: 2025-12-18
|
||||
- **Change**: Updated prompt with:
|
||||
- Explicit instruction: "Look for EXACT match `light.<room_name>` first!"
|
||||
- Concrete examples: "For 'study lights' → look for `light.study`"
|
||||
- Working example showing `turn_off(entity_id="light.study")`
|
||||
- **Hypothesis**: Explicit examples will guide model to use room groups
|
||||
- **Results**:
|
||||
|
||||
**Test 1 & 2 (consecutive):**
|
||||
- Called `list_devices(domain="light")` ✓
|
||||
- Device list clearly shows `light.study` at the bottom
|
||||
- First call: `turn_off({"devices":["studeerlamp"]})` - wrong param AND wrong device
|
||||
- After validation error: `turn_off(entity_id="light.studeerlamp")` - correct param, still wrong device
|
||||
- **Completely ignored `light.study` despite prompt explicitly saying to use it**
|
||||
|
||||
- **Success rate**: 0% (wrong device controlled)
|
||||
- **Observations**:
|
||||
- Model ignores explicit step-by-step instructions in favor of substring matching
|
||||
- Dutch "studeerlamp" contains "studer" which the model prefers over exact "study" match
|
||||
- Even when prompt has a literal example `turn_off(entity_id="light.study")`, model uses `light.studeerlamp`
|
||||
- Positional bias possible - `light.study` appears at end of 21-item list
|
||||
- **Fundamental limitation**: Mistral-Nemo cannot follow explicit matching rules
|
||||
|
||||
---
|
||||
|
||||
### Experiment 5: Room Groups First (Tool Output Ordering)
|
||||
- **Date**: 2025-12-18
|
||||
- **Change**: Modified `list_devices` to sort room groups to top of list using HA attributes (`is_hue_group`, `hue_type="room"`)
|
||||
- **Hypothesis**: Positional bias - model focuses on items earlier in list
|
||||
- **Results**:
|
||||
- Room groups (`light.study`, `light.kitchen`, etc.) now appear first in device list
|
||||
- Combined with improved prompt, model now consistently uses room groups
|
||||
- **70% success rate** (7/10 tests) with default q4 quantization
|
||||
|
||||
---
|
||||
|
||||
### Experiment 6: Model Quantization (q5_1)
|
||||
- **Date**: 2025-12-18
|
||||
- **Change**: Upgraded from default Mistral-Nemo quantization (q4) to `mistral-nemo:12b-instruct-2407-q5_1`
|
||||
- **Hypothesis**: Higher precision weights improve tool calling accuracy
|
||||
- **Results**:
|
||||
|
||||
| Test | Action | Result |
|
||||
|------|--------|--------|
|
||||
| 1 | Turn off study | PASS |
|
||||
| 2 | Turn on study | PASS |
|
||||
| 3 | Toggle study | PASS |
|
||||
| 4 | Turn off kitchen | PASS |
|
||||
| 5 | Turn on kitchen | PASS |
|
||||
| 6 | Toggle kitchen | PASS |
|
||||
| 7 | Turn off bedroom | PASS |
|
||||
| 8 | Turn on bedroom | PASS |
|
||||
| 9 | Turn off living room | PASS |
|
||||
| 10 | Turn on living room | PASS |
|
||||
|
||||
- **Success rate**: **100%** (10/10 tests)
|
||||
- **Observations**:
|
||||
- q5_1 quantization dramatically improves tool calling accuracy
|
||||
- All room groups correctly identified and used
|
||||
- No parameter confusion (`entity_id` used correctly)
|
||||
- No entity_id truncation issues
|
||||
- Toggle operations now work reliably
|
||||
- Model fits within 10GB VRAM (q6 did not)
|
||||
|
||||
---
|
||||
|
||||
### Experiment 7: Device List in System Prompt (Context Injection)
|
||||
- **Date**: [PENDING]
|
||||
- **Change**: Store device list in database (per user/household) and inject into system prompt
|
||||
- **Approach**:
|
||||
1. Periodically sync device list from Home Assistant to PostgreSQL
|
||||
2. On each Housekeeper invocation, fetch device list and include in prompt
|
||||
3. Remove need for model to call list_devices() - just match from context
|
||||
- **Hypothesis**:
|
||||
- Eliminates tool call step where errors occur
|
||||
- Reduces context size by not returning full device list as tool output
|
||||
- Makes entity matching a language task (in prompt) rather than tool result parsing
|
||||
- **Trade-offs**:
|
||||
- Stale data if sync is infrequent
|
||||
- Prompt size increase (but less than tool call response)
|
||||
- Need sync mechanism and storage
|
||||
- **Results**: [TO BE RECORDED]
|
||||
- **Success rate**: [TO BE RECORDED]
|
||||
|
||||
---
|
||||
|
||||
## Key Problem Identified (Solved)
|
||||
|
||||
The model struggled with:
|
||||
1. **Parameter schema adherence** - uses `device_id` when schema requires `entity_id`
|
||||
2. **Value preservation** - truncates values after validation errors (drops `light.` prefix)
|
||||
3. **Semantic matching** - prefers substring matches ("studeerlamp" contains "studer") over exact matches (`light.study`)
|
||||
4. **Following explicit instructions** - ignores step-by-step processes even when examples are provided
|
||||
5. **Positional bias** - may not "see" items at the end of long lists
|
||||
|
||||
**Solution**: These issues were resolved by:
|
||||
1. Using q5_1 quantization instead of default q4 (higher precision weights)
|
||||
2. Sorting room groups to top of device list (address positional bias)
|
||||
3. Explicit prompt guidance with negative constraints and examples
|
||||
|
||||
---
|
||||
|
||||
## Potential Next Experiments
|
||||
|
||||
### Experiment 5: Room Groups First (List Ordering)
|
||||
- **Hypothesis**: Positional bias - model focuses on items earlier in list
|
||||
- **Change**: Sort device list to put room groups (entities matching `light.<single_word>`) at the TOP
|
||||
- **Effort**: Low - modify list_devices output formatting
|
||||
- **Risk**: May affect other use cases where individual devices are needed
|
||||
|
||||
### Experiment 6: Simplified Device List Format
|
||||
- **Hypothesis**: Markdown formatting adds noise that confuses the model
|
||||
- **Change**: Return simple list: `light.study (Study - GROUP), light.study_main (Ceiling light), ...`
|
||||
- **Effort**: Low - modify list_devices output
|
||||
- **Risk**: Less human-readable responses
|
||||
|
||||
---
|
||||
|
||||
## Learnings to Apply Elsewhere
|
||||
|
||||
1. **Quantization matters** - q5_1 dramatically outperforms q4 for tool calling (100% vs 70%)
|
||||
2. **Positional bias is real** - sort important items to top of lists
|
||||
3. **Smaller models need simpler workflows** - fewer tool calls, more context injection
|
||||
4. **Validation errors don't teach** - model often makes worse mistakes on retry
|
||||
5. **Entity IDs are hard** - domain.name format confuses the model
|
||||
6. **Consider pre-computation** - move matching logic to code, not LLM
|
||||
7. **Use explicit negative constraints** - "NEVER do X" works better than "always do Y"
|
||||
|
||||
---
|
||||
|
||||
## Notes
|
||||
|
||||
- Librarian may need higher temperature for creative synthesis
|
||||
- All "action" agents (Housekeeper, future agents) should use low temperature
|
||||
- Consider testing with Gemma 2 9B for better function calling (Google, open weights)
|
||||
@@ -0,0 +1,348 @@
|
||||
# Tatlock Integration Guide
|
||||
|
||||
Implementation instructions for integrating Library Desk search and content extraction endpoints into the Tatlock project.
|
||||
|
||||
## Base Configuration
|
||||
|
||||
```
|
||||
BASE_URL: http://library-desk:8089 (or your deployment URL)
|
||||
AUTH_HEADER: Authorization: Bearer <LIBRARY_API_KEY>
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 1. RAG Search Endpoint
|
||||
|
||||
**Use case:** Librarian needs to research a topic by searching the web.
|
||||
|
||||
### Endpoint
|
||||
|
||||
```
|
||||
POST /rag/search
|
||||
```
|
||||
|
||||
### Request
|
||||
|
||||
```json
|
||||
{
|
||||
"query": "Python async programming best practices",
|
||||
"search_type": "web",
|
||||
"limit": 10,
|
||||
"user": "tatlock-librarian"
|
||||
}
|
||||
```
|
||||
|
||||
| Field | Type | Default | Description |
|
||||
|-------|------|---------|-------------|
|
||||
| `query` | string | required | Search query (1-500 chars) |
|
||||
| `search_type` | enum | `"web"` | `"web"`, `"news"`, or `"images"` |
|
||||
| `limit` | int | 10 | Results to return (1-20) |
|
||||
| `user` | string | `"default"` | User identifier for tracking |
|
||||
|
||||
### Response
|
||||
|
||||
```json
|
||||
{
|
||||
"query": "Python async programming best practices",
|
||||
"search_type": "web",
|
||||
"results": [
|
||||
{
|
||||
"title": "Async IO in Python: A Complete Walkthrough",
|
||||
"url": "https://realpython.com/async-io-python/",
|
||||
"content": "Full extracted article text via Trafilatura (~2000 chars max)...",
|
||||
"snippet": "Original search engine snippet (150-300 chars)...",
|
||||
"source": "realpython.com",
|
||||
"published_date": "2023-05-15"
|
||||
}
|
||||
],
|
||||
"total_results": 10,
|
||||
"search_time_ms": 2340,
|
||||
"sources_summary": "## Sources\n- [Async IO in Python](https://realpython.com/async-io-python/)\n- ..."
|
||||
}
|
||||
```
|
||||
|
||||
### Key Fields for Tatlock
|
||||
|
||||
| Field | Usage |
|
||||
|-------|-------|
|
||||
| `results[].content` | Full extracted text - use this for LLM context |
|
||||
| `results[].snippet` | Fallback if content extraction failed |
|
||||
| `sources_summary` | Pre-formatted markdown for citations |
|
||||
|
||||
### Error Handling
|
||||
|
||||
| HTTP Code | Meaning | Action |
|
||||
|-----------|---------|--------|
|
||||
| 400 | Invalid query | Check query length/format |
|
||||
| 502 | SearXNG unavailable | Retry with backoff |
|
||||
| 504 | Search timeout | Retry or reduce limit |
|
||||
| 500 | Internal error | Log and notify |
|
||||
|
||||
### Example Usage (Python)
|
||||
|
||||
```python
|
||||
import httpx
|
||||
|
||||
async def search_web(query: str, limit: int = 10) -> dict:
|
||||
async with httpx.AsyncClient() as client:
|
||||
response = await client.post(
|
||||
f"{BASE_URL}/rag/search",
|
||||
headers={"Authorization": f"Bearer {API_KEY}"},
|
||||
json={
|
||||
"query": query,
|
||||
"search_type": "web",
|
||||
"limit": limit,
|
||||
"user": "tatlock-librarian"
|
||||
},
|
||||
timeout=30.0
|
||||
)
|
||||
response.raise_for_status()
|
||||
return response.json()
|
||||
|
||||
# Usage
|
||||
results = await search_web("machine learning transformers")
|
||||
for r in results["results"]:
|
||||
# Prefer full content, fall back to snippet
|
||||
text = r["content"] or r["snippet"]
|
||||
print(f"{r['title']}: {len(text)} chars")
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 2. Content Extraction Endpoint
|
||||
|
||||
**Use case:** Librarian has a specific URL and needs to read its content.
|
||||
|
||||
### Single URL Extraction
|
||||
|
||||
```
|
||||
POST /content/extract
|
||||
```
|
||||
|
||||
#### Request
|
||||
|
||||
```json
|
||||
{
|
||||
"url": "https://example.com/article",
|
||||
"include_metadata": true,
|
||||
"max_length": 2000
|
||||
}
|
||||
```
|
||||
|
||||
#### Response
|
||||
|
||||
```json
|
||||
{
|
||||
"result": {
|
||||
"url": "https://example.com/article",
|
||||
"title": "Article Title",
|
||||
"content": "Extracted main text content...",
|
||||
"author": "John Doe",
|
||||
"date": "2024-01-15",
|
||||
"language": "en",
|
||||
"success": true,
|
||||
"error": null
|
||||
},
|
||||
"extraction_time_ms": 1250
|
||||
}
|
||||
```
|
||||
|
||||
### Batch URL Extraction
|
||||
|
||||
```
|
||||
POST /content/extract/batch
|
||||
```
|
||||
|
||||
#### Request
|
||||
|
||||
```json
|
||||
{
|
||||
"urls": [
|
||||
"https://example.com/article1",
|
||||
"https://example.com/article2",
|
||||
"https://example.com/article3"
|
||||
],
|
||||
"include_metadata": true,
|
||||
"max_length": 2000
|
||||
}
|
||||
```
|
||||
|
||||
#### Response
|
||||
|
||||
```json
|
||||
{
|
||||
"results": [
|
||||
{
|
||||
"url": "https://example.com/article1",
|
||||
"title": "Article 1",
|
||||
"content": "Extracted content...",
|
||||
"success": true,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"url": "https://example.com/article2",
|
||||
"title": null,
|
||||
"content": "",
|
||||
"success": false,
|
||||
"error": "Connection timeout"
|
||||
}
|
||||
],
|
||||
"total_urls": 3,
|
||||
"successful": 2,
|
||||
"failed": 1,
|
||||
"extraction_time_ms": 3500
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 3. Error Pattern: Soft Failures
|
||||
|
||||
> **Important:** Content extraction uses a **soft failure pattern** - individual URL failures do NOT throw HTTP errors.
|
||||
|
||||
### Why Soft Failures?
|
||||
|
||||
When extracting content from multiple URLs (batch) or even single URLs:
|
||||
- Some sites block bots
|
||||
- Some URLs are temporarily down
|
||||
- Some pages have no extractable content
|
||||
|
||||
Instead of failing the entire request, we return:
|
||||
- `success: true/false` per result
|
||||
- `error: "reason"` when failed
|
||||
- Empty `content: ""` on failure
|
||||
|
||||
### Handling Soft Failures
|
||||
|
||||
```python
|
||||
async def extract_with_fallback(url: str) -> str:
|
||||
response = await client.post(
|
||||
f"{BASE_URL}/content/extract",
|
||||
headers={"Authorization": f"Bearer {API_KEY}"},
|
||||
json={"url": url}
|
||||
)
|
||||
response.raise_for_status() # Only throws on 4xx/5xx
|
||||
|
||||
data = response.json()
|
||||
result = data["result"]
|
||||
|
||||
if result["success"]:
|
||||
return result["content"]
|
||||
else:
|
||||
# Log the failure, return empty or handle gracefully
|
||||
logger.warning(f"Extraction failed for {url}: {result['error']}")
|
||||
return "" # Or raise, or use cached version, etc.
|
||||
```
|
||||
|
||||
### Batch Processing Example
|
||||
|
||||
```python
|
||||
async def extract_batch_with_stats(urls: list[str]) -> dict:
|
||||
response = await client.post(
|
||||
f"{BASE_URL}/content/extract/batch",
|
||||
headers={"Authorization": f"Bearer {API_KEY}"},
|
||||
json={"urls": urls, "max_length": 3000}
|
||||
)
|
||||
response.raise_for_status()
|
||||
|
||||
data = response.json()
|
||||
|
||||
# Separate successful and failed
|
||||
successful = [r for r in data["results"] if r["success"]]
|
||||
failed = [r for r in data["results"] if not r["success"]]
|
||||
|
||||
if failed:
|
||||
logger.warning(f"{len(failed)} URLs failed extraction:")
|
||||
for f in failed:
|
||||
logger.warning(f" {f['url']}: {f['error']}")
|
||||
|
||||
return {
|
||||
"contents": {r["url"]: r["content"] for r in successful},
|
||||
"failed_urls": [f["url"] for f in failed],
|
||||
"success_rate": data["successful"] / data["total_urls"]
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 4. Recommended Patterns for Tatlock
|
||||
|
||||
### Research Flow
|
||||
|
||||
```python
|
||||
async def librarian_research(topic: str) -> dict:
|
||||
"""
|
||||
Full research flow: search + extract additional context.
|
||||
"""
|
||||
# 1. Search for relevant pages
|
||||
search_results = await search_web(topic, limit=10)
|
||||
|
||||
# 2. RAG search already includes extracted content
|
||||
# Only extract more if you need deeper content
|
||||
|
||||
# 3. Build context for LLM
|
||||
context_parts = []
|
||||
for r in search_results["results"]:
|
||||
content = r["content"] or r["snippet"]
|
||||
if content:
|
||||
context_parts.append(f"## {r['title']}\nSource: {r['url']}\n\n{content}")
|
||||
|
||||
return {
|
||||
"context": "\n\n---\n\n".join(context_parts),
|
||||
"sources": search_results["sources_summary"],
|
||||
"result_count": search_results["total_results"]
|
||||
}
|
||||
```
|
||||
|
||||
### Reading a Specific Page
|
||||
|
||||
```python
|
||||
async def librarian_read_page(url: str) -> str:
|
||||
"""
|
||||
Read a specific URL the user provided.
|
||||
"""
|
||||
response = await client.post(
|
||||
f"{BASE_URL}/content/extract",
|
||||
headers={"Authorization": f"Bearer {API_KEY}"},
|
||||
json={"url": url, "max_length": 5000} # Longer for deep reads
|
||||
)
|
||||
response.raise_for_status()
|
||||
|
||||
result = response.json()["result"]
|
||||
|
||||
if not result["success"]:
|
||||
raise ValueError(f"Could not read page: {result['error']}")
|
||||
|
||||
# Format for LLM
|
||||
header = f"# {result['title'] or 'Untitled'}\n"
|
||||
if result["author"]:
|
||||
header += f"Author: {result['author']}\n"
|
||||
if result["date"]:
|
||||
header += f"Date: {result['date']}\n"
|
||||
|
||||
return header + "\n" + result["content"]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 5. Rate Limits & Best Practices
|
||||
|
||||
| Recommendation | Reason |
|
||||
|----------------|--------|
|
||||
| Use `limit: 5-10` for searches | More results = longer extraction time |
|
||||
| Batch URLs when possible | More efficient than sequential calls |
|
||||
| Max 20 URLs per batch | Server limit |
|
||||
| Set reasonable timeouts (30s) | Content extraction can be slow |
|
||||
| Cache results client-side | Same URL rarely changes content |
|
||||
| Use `user` parameter | Helps with debugging and rate limiting |
|
||||
|
||||
---
|
||||
|
||||
## 6. Quick Reference
|
||||
|
||||
| Endpoint | Method | Use Case |
|
||||
|----------|--------|----------|
|
||||
| `/rag/search` | POST | Search web + get extracted content |
|
||||
| `/content/extract` | POST | Read a single URL |
|
||||
| `/content/extract/batch` | POST | Read multiple URLs |
|
||||
| `/health` | GET | Check service status |
|
||||
File diff suppressed because it is too large
Load Diff
+1
-1
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
|
||||
|
||||
[project]
|
||||
name = "tatlock"
|
||||
version = "1.5.0"
|
||||
version = "2.0.0"
|
||||
description = "OpenAI-compatible API with Ollama backend"
|
||||
requires-python = ">=3.12"
|
||||
dependencies = []
|
||||
|
||||
+2
-3
@@ -21,10 +21,9 @@ pydantic-settings>=2.12,<2.13
|
||||
|
||||
# AI/LLM integration
|
||||
# PydanticAI: Agent framework for using Pydantic with LLMs
|
||||
# Using slim version with only openai extra (Ollama uses OpenAI-compatible API)
|
||||
# This avoids installing SDKs for anthropic, cohere, google, groq, huggingface, etc.
|
||||
# Using slim version with openai (Ollama) and anthropic (Claude) extras
|
||||
# See DEPENDENCY_SLIM.md for rollback instructions if this breaks
|
||||
pydantic-ai-slim[openai]>=1.27,<1.28
|
||||
pydantic-ai-slim[openai,anthropic]>=1.27,<1.28
|
||||
|
||||
# HTTP client for Ollama communication
|
||||
# Latest: 0.28.1 - No known CVEs
|
||||
|
||||
Executable
+141
@@ -0,0 +1,141 @@
|
||||
#!/bin/bash
|
||||
# Housekeeper Room Group Detection Test Suite
|
||||
# Verifies room groups are controlled by checking actual state changes
|
||||
|
||||
API_URL="http://localhost:8777/v1/chat/completions"
|
||||
CORE_API="http://192.168.86.149:8083"
|
||||
RESULTS_FILE="/tmp/housekeeper_test_results.txt"
|
||||
|
||||
GREEN='\033[0;32m'
|
||||
RED='\033[0;31m'
|
||||
YELLOW='\033[1;33m'
|
||||
NC='\033[0m'
|
||||
|
||||
get_state() {
|
||||
curl -s "$CORE_API/housekeeping/devices/$1" 2>/dev/null | jq -r '.state' 2>/dev/null
|
||||
}
|
||||
|
||||
echo "=========================================="
|
||||
echo "Housekeeper Room Group Test Suite"
|
||||
echo "=========================================="
|
||||
echo ""
|
||||
|
||||
> "$RESULTS_FILE"
|
||||
|
||||
run_toggle_test() {
|
||||
local test_num=$1
|
||||
local room=$2
|
||||
local entity="light.$room"
|
||||
local prompt_room="${room//_/ }"
|
||||
|
||||
printf "Test %2d: Toggle %-12s lights ... " "$test_num" "$prompt_room"
|
||||
|
||||
local before=$(get_state "$entity")
|
||||
if [ -z "$before" ] || [ "$before" = "null" ]; then
|
||||
echo -e "${YELLOW}SKIP${NC} (cannot get state)"
|
||||
echo "SKIP|$test_num|Toggle $room|error" >> "$RESULTS_FILE"
|
||||
return
|
||||
fi
|
||||
|
||||
curl -s -X POST "$API_URL" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d "{\"model\": \"tatlock\", \"messages\": [{\"role\": \"user\", \"content\": \"Toggle the $prompt_room lights\"}]}" > /dev/null
|
||||
|
||||
sleep 4
|
||||
|
||||
local after=$(get_state "$entity")
|
||||
|
||||
if [ "$before" != "$after" ]; then
|
||||
echo -e "${GREEN}PASS${NC} ($before -> $after)"
|
||||
echo "PASS|$test_num|Toggle $room|$before->$after" >> "$RESULTS_FILE"
|
||||
else
|
||||
echo -e "${RED}FAIL${NC} (state unchanged: $before)"
|
||||
echo "FAIL|$test_num|Toggle $room|unchanged:$before" >> "$RESULTS_FILE"
|
||||
fi
|
||||
}
|
||||
|
||||
run_onoff_test() {
|
||||
local test_num=$1
|
||||
local room=$2
|
||||
local action=$3
|
||||
local expected_state=$4
|
||||
# Entity uses underscore, prompt uses space
|
||||
local entity="light.${room//_/ }"
|
||||
entity="light.$room"
|
||||
local prompt_room="${room//_/ }"
|
||||
|
||||
printf "Test %2d: %-8s %-12s lights ... " "$test_num" "$action" "$prompt_room"
|
||||
|
||||
curl -s -X POST "$API_URL" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d "{\"model\": \"tatlock\", \"messages\": [{\"role\": \"user\", \"content\": \"$action the $prompt_room lights\"}]}" > /dev/null
|
||||
|
||||
sleep 4
|
||||
|
||||
local after=$(get_state "$entity")
|
||||
|
||||
if [ "$after" = "$expected_state" ]; then
|
||||
echo -e "${GREEN}PASS${NC} ($after)"
|
||||
echo "PASS|$test_num|$action $room|$after" >> "$RESULTS_FILE"
|
||||
else
|
||||
echo -e "${RED}FAIL${NC} (got $after, expected $expected_state)"
|
||||
echo "FAIL|$test_num|$action $room|got:$after,expected:$expected_state" >> "$RESULTS_FILE"
|
||||
fi
|
||||
}
|
||||
|
||||
echo "Running tests (~4s each)..."
|
||||
echo ""
|
||||
|
||||
# Study tests
|
||||
run_onoff_test 1 "study" "Turn off" "off"
|
||||
run_onoff_test 2 "study" "Turn on" "on"
|
||||
run_toggle_test 3 "study"
|
||||
|
||||
# Kitchen tests
|
||||
run_onoff_test 4 "kitchen" "Turn off" "off"
|
||||
run_onoff_test 5 "kitchen" "Turn on" "on"
|
||||
run_toggle_test 6 "kitchen"
|
||||
|
||||
# Bedroom tests
|
||||
run_onoff_test 7 "bedroom" "Turn off" "off"
|
||||
run_onoff_test 8 "bedroom" "Turn on" "on"
|
||||
|
||||
# Living room tests (entity is light.living_room)
|
||||
run_onoff_test 9 "living_room" "Turn off" "off"
|
||||
run_onoff_test 10 "living_room" "Turn on" "on"
|
||||
|
||||
# Ensure all lights end up ON
|
||||
echo ""
|
||||
echo "Restoring all lights to ON..."
|
||||
for room in "study" "kitchen" "bedroom" "living room"; do
|
||||
curl -s -X POST "$API_URL" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d "{\"model\": \"tatlock\", \"messages\": [{\"role\": \"user\", \"content\": \"Turn on the $room lights\"}]}" > /dev/null
|
||||
sleep 3
|
||||
done
|
||||
echo "Done."
|
||||
|
||||
echo ""
|
||||
echo "=========================================="
|
||||
echo "Results"
|
||||
echo "=========================================="
|
||||
|
||||
PASS=$(grep -c "^PASS" "$RESULTS_FILE" 2>/dev/null || echo 0)
|
||||
FAIL=$(grep -c "^FAIL" "$RESULTS_FILE" 2>/dev/null || echo 0)
|
||||
SKIP=$(grep -c "^SKIP" "$RESULTS_FILE" 2>/dev/null || echo 0)
|
||||
TOTAL=$((PASS + FAIL))
|
||||
|
||||
echo "Passed: $PASS"
|
||||
echo "Failed: $FAIL"
|
||||
echo "Skipped: $SKIP"
|
||||
|
||||
if [ "$TOTAL" -gt 0 ]; then
|
||||
echo ""
|
||||
echo "Success Rate: $((PASS * 100 / TOTAL))% ($PASS/$TOTAL)"
|
||||
fi
|
||||
|
||||
if [ "$FAIL" -gt 0 ]; then
|
||||
echo ""
|
||||
echo "Failures:"
|
||||
grep "^FAIL" "$RESULTS_FILE"
|
||||
fi
|
||||
@@ -102,19 +102,10 @@ _biographer_agent: Optional[Agent[None, str]] = None
|
||||
|
||||
def _create_biographer_agent() -> Agent[None, str]:
|
||||
"""Create The Biographer PydanticAI agent."""
|
||||
# Import required classes for Ollama configuration
|
||||
from pydantic_ai.models.openai import OpenAIChatModel
|
||||
from pydantic_ai.providers.ollama import OllamaProvider
|
||||
from src.anthropic.model_selector import get_model
|
||||
|
||||
# PydanticAI expects Ollama base URL to end with /v1
|
||||
clean_host = str(config.OLLAMA_HOST).rstrip('/')
|
||||
base_url = f"{clean_host}/v1"
|
||||
|
||||
# Create Ollama model with provider
|
||||
model = OpenAIChatModel(
|
||||
model_name=config.OLLAMA_DEFAULT_MODEL,
|
||||
provider=OllamaProvider(base_url=base_url)
|
||||
)
|
||||
# Get best available model (Claude if available, else Ollama)
|
||||
model = get_model()
|
||||
|
||||
agent: Agent[None, str] = Agent(
|
||||
model=model,
|
||||
@@ -134,9 +125,12 @@ def _create_biographer_agent() -> Agent[None, str]:
|
||||
# Register management tools
|
||||
agent.tool_plain(forget_memory)
|
||||
|
||||
from src.anthropic.model_selector import get_model_info
|
||||
model_info = get_model_info()
|
||||
logger.info(
|
||||
"biographer_agent_created",
|
||||
model=config.OLLAMA_DEFAULT_MODEL,
|
||||
backend=model_info["backend"],
|
||||
model=model_info["model"],
|
||||
tool_count=6,
|
||||
)
|
||||
|
||||
|
||||
+370
-85
@@ -9,13 +9,141 @@ This implements the agent-as-tool pattern recommended by PydanticAI:
|
||||
agents call other agents via tool wrappers, keeping each agent focused.
|
||||
"""
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Callable, Optional, Any
|
||||
from enum import Enum
|
||||
from typing import AsyncGenerator, Callable, Optional, Any
|
||||
|
||||
from src.core.logging_config import get_logger
|
||||
from src.core.tracing import trace_span, SpanType
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Action Types for Think Slug Selection
|
||||
# =============================================================================
|
||||
|
||||
class ActionType(Enum):
|
||||
"""
|
||||
Categories of actions for selecting appropriate think messages.
|
||||
|
||||
Each expert has different action types that warrant different
|
||||
butler-perspective messages to the user.
|
||||
"""
|
||||
RETRIEVE = "retrieve" # Looking up existing information
|
||||
RESEARCH = "research" # Conducting new research (web search, etc.)
|
||||
CREATE = "create" # Creating new content (pages, notes)
|
||||
CONTROL = "control" # Controlling devices/automations
|
||||
RECORD = "record" # Recording memories/notes
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Household Think Messages (Butler's Perspective)
|
||||
# =============================================================================
|
||||
|
||||
HOUSEHOLD_THINK_MESSAGES: dict[str, dict[ActionType, dict[str, str]]] = {
|
||||
# Note: No <think> wrappers needed - these go to reasoning_content field
|
||||
"librarian": {
|
||||
ActionType.RETRIEVE: {
|
||||
"start": "Allow me to consult the archives, sir.",
|
||||
"success": "The Librarian has compiled the relevant findings.",
|
||||
"error": "I'm afraid the archives proved difficult to access.",
|
||||
},
|
||||
ActionType.RESEARCH: {
|
||||
"start": "I've dispatched the Librarian to conduct some fresh research.",
|
||||
"success": "The Librarian has returned with findings, sir.",
|
||||
"error": "The research proved inconclusive, I'm afraid.",
|
||||
},
|
||||
ActionType.CREATE: {
|
||||
"start": "I'm having the Librarian prepare a new entry.",
|
||||
"success": "The new material has been properly catalogued, sir.",
|
||||
"error": "I'm afraid there was difficulty filing the entry.",
|
||||
},
|
||||
},
|
||||
"biographer": {
|
||||
ActionType.RETRIEVE: {
|
||||
"start": "Let me consult the household records.",
|
||||
"success": "The Biographer has located the relevant information, sir.",
|
||||
"error": "I'm unable to locate those particular records.",
|
||||
},
|
||||
ActionType.RECORD: {
|
||||
"start": "I've asked the Biographer to take note of this, sir.",
|
||||
"success": "The household records have been updated accordingly.",
|
||||
"error": "I'm afraid there was difficulty recording the entry.",
|
||||
},
|
||||
},
|
||||
"housekeeper": {
|
||||
ActionType.RETRIEVE: {
|
||||
"start": "Allow me to inquire with the household staff.",
|
||||
"success": "The staff reports the current status, sir.",
|
||||
"error": "The household staff is momentarily unavailable, I'm afraid.",
|
||||
},
|
||||
ActionType.CONTROL: {
|
||||
"start": "I'm instructing the household staff now, sir.",
|
||||
"success": "The household has been configured as requested.",
|
||||
"error": "I'm afraid the staff reports an issue with that request.",
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def _detect_action_type(expert: str, task: str) -> ActionType:
|
||||
"""
|
||||
Detect action type from expert name and task description.
|
||||
|
||||
Used to select appropriate butler-perspective think messages.
|
||||
|
||||
Args:
|
||||
expert: Name of the expert (librarian, biographer, housekeeper)
|
||||
task: Task description
|
||||
|
||||
Returns:
|
||||
ActionType: Detected action type for message selection
|
||||
"""
|
||||
task_lower = task.lower()
|
||||
|
||||
if expert == "librarian":
|
||||
# Web search, URL reading = RESEARCH (fresh external data)
|
||||
if any(w in task_lower for w in ["search", "find", "look up", "research"]):
|
||||
if any(w in task_lower for w in ["web", "online", "internet"]):
|
||||
return ActionType.RESEARCH
|
||||
return ActionType.RETRIEVE
|
||||
if any(w in task_lower for w in ["read", "fetch", "url", "http"]):
|
||||
return ActionType.RESEARCH # Reading URLs is research
|
||||
if any(w in task_lower for w in ["create", "write", "add", "make", "new"]):
|
||||
return ActionType.CREATE
|
||||
return ActionType.RETRIEVE
|
||||
|
||||
elif expert == "biographer":
|
||||
if any(w in task_lower for w in ["remember", "note", "record", "save", "store"]):
|
||||
return ActionType.RECORD
|
||||
return ActionType.RETRIEVE
|
||||
|
||||
elif expert == "housekeeper":
|
||||
if any(w in task_lower for w in ["turn", "set", "activate", "enable", "disable", "toggle"]):
|
||||
return ActionType.CONTROL
|
||||
return ActionType.RETRIEVE
|
||||
|
||||
return ActionType.RETRIEVE
|
||||
|
||||
|
||||
def get_think_message(expert: str, task: str, phase: str) -> str:
|
||||
"""
|
||||
Get the appropriate think message for an expert delegation.
|
||||
|
||||
Args:
|
||||
expert: Name of the expert
|
||||
task: Task description (used to detect action type)
|
||||
phase: One of "start", "success", "error"
|
||||
|
||||
Returns:
|
||||
str: Butler-perspective think message
|
||||
"""
|
||||
action_type = _detect_action_type(expert, task)
|
||||
expert_messages = HOUSEHOLD_THINK_MESSAGES.get(expert, {})
|
||||
action_messages = expert_messages.get(action_type, expert_messages.get(ActionType.RETRIEVE, {}))
|
||||
return action_messages.get(phase, f"Consulting {expert}...")
|
||||
|
||||
|
||||
@dataclass
|
||||
class DelegationTask:
|
||||
"""
|
||||
@@ -112,38 +240,58 @@ async def delegate_to_librarian(
|
||||
has_context=bool(context),
|
||||
)
|
||||
|
||||
try:
|
||||
# Use run() not run_stream() - avoids Ollama bug
|
||||
output = await run_librarian(task=task, context=context)
|
||||
async with trace_span(
|
||||
"delegate_to_librarian",
|
||||
SpanType.EXPERT,
|
||||
metadata={
|
||||
"expert": "librarian",
|
||||
"task_preview": task[:100],
|
||||
"has_context": bool(context),
|
||||
},
|
||||
) as span:
|
||||
try:
|
||||
# Use run() not run_stream() - avoids Ollama bug
|
||||
output = await run_librarian(task=task, context=context)
|
||||
|
||||
logger.info(
|
||||
"delegation_to_librarian_completed",
|
||||
task=task[:50],
|
||||
output_length=len(output),
|
||||
)
|
||||
logger.info(
|
||||
"delegation_to_librarian_completed",
|
||||
task=task[:50],
|
||||
output_length=len(output),
|
||||
)
|
||||
|
||||
return DelegationResult(
|
||||
expert_name="librarian",
|
||||
task=task,
|
||||
success=True,
|
||||
output=output,
|
||||
)
|
||||
if span:
|
||||
span.metadata["success"] = True
|
||||
span.metadata["output_length"] = len(output)
|
||||
span.details["task"] = task
|
||||
span.details["context"] = context[:500] if context else None
|
||||
span.details["result_preview"] = output[:1000]
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
"delegation_to_librarian_error",
|
||||
task=task[:50],
|
||||
error=str(e),
|
||||
exc_info=True,
|
||||
)
|
||||
return DelegationResult(
|
||||
expert_name="librarian",
|
||||
task=task,
|
||||
success=True,
|
||||
output=output,
|
||||
)
|
||||
|
||||
return DelegationResult(
|
||||
expert_name="librarian",
|
||||
task=task,
|
||||
success=False,
|
||||
output="",
|
||||
error=str(e),
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
"delegation_to_librarian_error",
|
||||
task=task[:50],
|
||||
error=str(e),
|
||||
exc_info=True,
|
||||
)
|
||||
|
||||
if span:
|
||||
span.metadata["success"] = False
|
||||
span.details["error"] = str(e)
|
||||
|
||||
return DelegationResult(
|
||||
expert_name="librarian",
|
||||
task=task,
|
||||
success=False,
|
||||
output="",
|
||||
error=str(e),
|
||||
)
|
||||
|
||||
|
||||
async def delegate_to_biographer(
|
||||
@@ -190,38 +338,58 @@ async def delegate_to_biographer(
|
||||
has_context=bool(context),
|
||||
)
|
||||
|
||||
try:
|
||||
# Use run() not run_stream() - avoids Ollama bug
|
||||
output = await run_biographer(task=task, context=context)
|
||||
async with trace_span(
|
||||
"delegate_to_biographer",
|
||||
SpanType.EXPERT,
|
||||
metadata={
|
||||
"expert": "biographer",
|
||||
"task_preview": task[:100],
|
||||
"has_context": bool(context),
|
||||
},
|
||||
) as span:
|
||||
try:
|
||||
# Use run() not run_stream() - avoids Ollama bug
|
||||
output = await run_biographer(task=task, context=context)
|
||||
|
||||
logger.info(
|
||||
"delegation_to_biographer_completed",
|
||||
task=task[:50],
|
||||
output_length=len(output),
|
||||
)
|
||||
logger.info(
|
||||
"delegation_to_biographer_completed",
|
||||
task=task[:50],
|
||||
output_length=len(output),
|
||||
)
|
||||
|
||||
return DelegationResult(
|
||||
expert_name="biographer",
|
||||
task=task,
|
||||
success=True,
|
||||
output=output,
|
||||
)
|
||||
if span:
|
||||
span.metadata["success"] = True
|
||||
span.metadata["output_length"] = len(output)
|
||||
span.details["task"] = task
|
||||
span.details["context"] = context[:500] if context else None
|
||||
span.details["result_preview"] = output[:1000]
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
"delegation_to_biographer_error",
|
||||
task=task[:50],
|
||||
error=str(e),
|
||||
exc_info=True,
|
||||
)
|
||||
return DelegationResult(
|
||||
expert_name="biographer",
|
||||
task=task,
|
||||
success=True,
|
||||
output=output,
|
||||
)
|
||||
|
||||
return DelegationResult(
|
||||
expert_name="biographer",
|
||||
task=task,
|
||||
success=False,
|
||||
output="",
|
||||
error=str(e),
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
"delegation_to_biographer_error",
|
||||
task=task[:50],
|
||||
error=str(e),
|
||||
exc_info=True,
|
||||
)
|
||||
|
||||
if span:
|
||||
span.metadata["success"] = False
|
||||
span.details["error"] = str(e)
|
||||
|
||||
return DelegationResult(
|
||||
expert_name="biographer",
|
||||
task=task,
|
||||
success=False,
|
||||
output="",
|
||||
error=str(e),
|
||||
)
|
||||
|
||||
|
||||
async def delegate_to_housekeeper(
|
||||
@@ -267,38 +435,155 @@ async def delegate_to_housekeeper(
|
||||
has_context=bool(context),
|
||||
)
|
||||
|
||||
try:
|
||||
# Use run() not run_stream() - avoids Ollama bug
|
||||
output = await run_housekeeper(task=task, context=context)
|
||||
async with trace_span(
|
||||
"delegate_to_housekeeper",
|
||||
SpanType.EXPERT,
|
||||
metadata={
|
||||
"expert": "housekeeper",
|
||||
"task_preview": task[:100],
|
||||
"has_context": bool(context),
|
||||
},
|
||||
) as span:
|
||||
try:
|
||||
# Use run() not run_stream() - avoids Ollama bug
|
||||
output = await run_housekeeper(task=task, context=context)
|
||||
|
||||
logger.info(
|
||||
"delegation_to_housekeeper_completed",
|
||||
task=task[:50],
|
||||
output_length=len(output),
|
||||
)
|
||||
logger.info(
|
||||
"delegation_to_housekeeper_completed",
|
||||
task=task[:50],
|
||||
output_length=len(output),
|
||||
)
|
||||
|
||||
return DelegationResult(
|
||||
expert_name="housekeeper",
|
||||
task=task,
|
||||
success=True,
|
||||
output=output,
|
||||
)
|
||||
if span:
|
||||
span.metadata["success"] = True
|
||||
span.metadata["output_length"] = len(output)
|
||||
span.details["task"] = task
|
||||
span.details["context"] = context[:500] if context else None
|
||||
span.details["result_preview"] = output[:1000]
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
"delegation_to_housekeeper_error",
|
||||
task=task[:50],
|
||||
error=str(e),
|
||||
exc_info=True,
|
||||
)
|
||||
return DelegationResult(
|
||||
expert_name="housekeeper",
|
||||
task=task,
|
||||
success=True,
|
||||
output=output,
|
||||
)
|
||||
|
||||
return DelegationResult(
|
||||
expert_name="housekeeper",
|
||||
task=task,
|
||||
success=False,
|
||||
output="",
|
||||
error=str(e),
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
"delegation_to_housekeeper_error",
|
||||
task=task[:50],
|
||||
error=str(e),
|
||||
exc_info=True,
|
||||
)
|
||||
|
||||
if span:
|
||||
span.metadata["success"] = False
|
||||
span.details["error"] = str(e)
|
||||
|
||||
return DelegationResult(
|
||||
expert_name="housekeeper",
|
||||
task=task,
|
||||
success=False,
|
||||
output="",
|
||||
error=str(e),
|
||||
)
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Streaming Delegation Wrappers (with Think Messages)
|
||||
# =============================================================================
|
||||
|
||||
async def stream_delegate_to_librarian(
|
||||
task: str,
|
||||
context: str = "",
|
||||
) -> AsyncGenerator[str, None]:
|
||||
"""
|
||||
Stream delegation to Librarian with automatic think messages.
|
||||
|
||||
Yields butler-perspective think messages before and after the delegation,
|
||||
allowing the UI to show progress to the user.
|
||||
|
||||
Args:
|
||||
task: Task description
|
||||
context: Additional context
|
||||
|
||||
Yields:
|
||||
str: Think messages and final result marker
|
||||
"""
|
||||
# Yield start message (deterministic)
|
||||
yield get_think_message("librarian", task, "start") + "\n"
|
||||
|
||||
# Execute delegation
|
||||
result = await delegate_to_librarian(task, context)
|
||||
|
||||
# Yield completion message (deterministic)
|
||||
if result.success:
|
||||
yield get_think_message("librarian", task, "success") + "\n"
|
||||
else:
|
||||
yield get_think_message("librarian", task, "error") + "\n"
|
||||
|
||||
# Yield result marker for extraction
|
||||
yield f"__DELEGATION_RESULT__:librarian:{result.output}"
|
||||
|
||||
|
||||
async def stream_delegate_to_biographer(
|
||||
task: str,
|
||||
context: str = "",
|
||||
) -> AsyncGenerator[str, None]:
|
||||
"""
|
||||
Stream delegation to Biographer with automatic think messages.
|
||||
|
||||
Args:
|
||||
task: Task description
|
||||
context: Additional context
|
||||
|
||||
Yields:
|
||||
str: Think messages and final result marker
|
||||
"""
|
||||
yield get_think_message("biographer", task, "start") + "\n"
|
||||
|
||||
result = await delegate_to_biographer(task, context)
|
||||
|
||||
if result.success:
|
||||
yield get_think_message("biographer", task, "success") + "\n"
|
||||
else:
|
||||
yield get_think_message("biographer", task, "error") + "\n"
|
||||
|
||||
yield f"__DELEGATION_RESULT__:biographer:{result.output}"
|
||||
|
||||
|
||||
async def stream_delegate_to_housekeeper(
|
||||
task: str,
|
||||
context: str = "",
|
||||
) -> AsyncGenerator[str, None]:
|
||||
"""
|
||||
Stream delegation to Housekeeper with automatic think messages.
|
||||
|
||||
Args:
|
||||
task: Task description
|
||||
context: Additional context
|
||||
|
||||
Yields:
|
||||
str: Think messages and final result marker
|
||||
"""
|
||||
yield get_think_message("housekeeper", task, "start") + "\n"
|
||||
|
||||
result = await delegate_to_housekeeper(task, context)
|
||||
|
||||
if result.success:
|
||||
yield get_think_message("housekeeper", task, "success") + "\n"
|
||||
else:
|
||||
yield get_think_message("housekeeper", task, "error") + "\n"
|
||||
|
||||
yield f"__DELEGATION_RESULT__:housekeeper:{result.output}"
|
||||
|
||||
|
||||
# Mapping of streaming delegation wrappers
|
||||
STREAMING_DELEGATION_WRAPPERS = {
|
||||
"librarian": stream_delegate_to_librarian,
|
||||
"biographer": stream_delegate_to_biographer,
|
||||
"housekeeper": stream_delegate_to_housekeeper,
|
||||
}
|
||||
|
||||
|
||||
# Future expert delegation wrappers will be added here:
|
||||
|
||||
@@ -32,79 +32,69 @@ from src.core.logging_config import get_logger
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
# Housekeeper system prompt
|
||||
HOUSEKEEPER_SYSTEM_PROMPT = """You are The Housekeeper, an expert home automation assistant in the Tatlock household.
|
||||
# Housekeeper system prompt - Optimized for Mistral-Nemo function calling
|
||||
HOUSEKEEPER_SYSTEM_PROMPT = """You are a strictly tool-based home automation assistant.
|
||||
|
||||
Your role is to help users control and monitor their smart home through Home Assistant:
|
||||
- Lights, switches, and other devices
|
||||
- Scenes (pre-configured device states)
|
||||
- Scripts (automation sequences)
|
||||
- Automations (event-triggered rules)
|
||||
## CRITICAL: You Have NO Internal Knowledge
|
||||
|
||||
## Your Personality
|
||||
- Efficient and practical
|
||||
- Safety-conscious (confirm destructive actions)
|
||||
- Proactive in suggesting optimizations
|
||||
- Clear about what actions you're taking
|
||||
You do NOT know what devices exist. You do NOT know any entity IDs.
|
||||
Entity IDs are different in every installation. You MUST discover them using tools.
|
||||
|
||||
## Your Tools
|
||||
## Entity ID Format
|
||||
|
||||
### Discovery Tools
|
||||
- **list_areas**: See all rooms/areas configured in Home Assistant
|
||||
- **list_devices**: Find devices by type (domain) or location (area)
|
||||
- **get_device_state**: Check a device's current state and attributes
|
||||
Entity IDs follow the format: `domain.name`
|
||||
Examples: `light.kitchen`, `light.study_main`, `switch.coffee_maker`
|
||||
|
||||
### Control Tools
|
||||
- **turn_on**: Turn on lights, switches, etc. (supports brightness/color for lights)
|
||||
- **turn_off**: Turn off devices
|
||||
- **toggle**: Flip a device's state
|
||||
The `entity_id` parameter MUST be the COMPLETE value including the domain prefix.
|
||||
WRONG: `entity_id="kitchen"`
|
||||
RIGHT: `entity_id="light.kitchen"`
|
||||
|
||||
### Scene Tools
|
||||
- **list_scenes**: See available scene presets
|
||||
- **activate_scene**: Activate a scene (e.g., "movie night", "good morning")
|
||||
## Step-by-Step Process (ALWAYS FOLLOW)
|
||||
|
||||
### Script Tools
|
||||
- **list_scripts**: See available automation scripts
|
||||
- **run_script**: Execute a script
|
||||
When asked to control devices in a room:
|
||||
|
||||
### Automation Tools
|
||||
- **list_automations**: See all automations and their status
|
||||
- **toggle_automation**: Enable or disable an automation
|
||||
1. THINK: What domain? (light, switch, climate, etc.)
|
||||
2. CALL: list_devices(domain="light") to discover available devices
|
||||
3. CHECK: Look for EXACT match `light.<room_name>` first!
|
||||
- For "study lights" → look for `light.study` (not light.study_main, not light.studeerlamp)
|
||||
- For "kitchen lights" → look for `light.kitchen` (not light.kitchen_spot_1)
|
||||
- These room groups control ALL lights in that room at once
|
||||
- If found, use ONLY the group (stop looking for individual lights)
|
||||
4. FALLBACK: Only if no exact room group exists, find entity_ids containing the room name
|
||||
5. CALL: turn_on/turn_off using the EXACT entity_id from step 3 or 4
|
||||
|
||||
### History Tools
|
||||
- **get_history**: Check a device's state history
|
||||
Example for "Turn off study lights":
|
||||
1. Domain is "light"
|
||||
2. Call list_devices(domain="light")
|
||||
3. Look for room group: `light.study` - FOUND!
|
||||
4. Call turn_off(entity_id="light.study") # This controls all study lights
|
||||
|
||||
## Best Practices
|
||||
Example for "Turn off hallway lights" (no room group):
|
||||
1. Domain is "light"
|
||||
2. Call list_devices(domain="light")
|
||||
3. Look for room group: `light.hallway` - NOT FOUND
|
||||
4. Find all with "hallway": light.hallway_spot_1, light.hallway_spot_2
|
||||
5. Call turn_off for each
|
||||
|
||||
1. **Device Discovery First**: If the user asks about devices without being specific,
|
||||
use list_devices to find what's available before acting.
|
||||
## Tool Parameter Names
|
||||
|
||||
2. **Confirm State After Actions**: After turning something on/off, you can verify
|
||||
with get_device_state if needed.
|
||||
- turn_on, turn_off, toggle: Use `entity_id` (NOT device_id, NOT id)
|
||||
- activate_scene: Use `scene_id`
|
||||
- run_script: Use `script_id`
|
||||
|
||||
3. **Use Entity IDs**: Devices are identified by entity_id (e.g., light.living_room).
|
||||
Always use the exact entity_id from list_devices.
|
||||
## What NOT To Do
|
||||
|
||||
4. **Area-Aware**: When users say "living room lights", filter by area="living_room".
|
||||
|
||||
5. **Safety**: For actions affecting multiple devices or automations, summarize
|
||||
what you're about to do.
|
||||
|
||||
## Common Patterns
|
||||
|
||||
- "Turn on the lights" → list_devices(domain="light"), then turn_on each
|
||||
- "What's on?" → list_devices() and filter for state="on"
|
||||
- "Movie time" → Either activate_scene("scene.movie_night") or run_script if available
|
||||
- "Dim the bedroom" → turn_on("light.bedroom", brightness=64)
|
||||
- NEVER guess an entity_id
|
||||
- NEVER construct an entity_id from the room name
|
||||
- NEVER drop the domain prefix (light., switch., etc.)
|
||||
- NEVER use "device_id" - the parameter is called "entity_id"
|
||||
- NEVER provide an answer without calling list_devices first
|
||||
|
||||
## Response Format
|
||||
Your responses are returned to Tatlock (the butler) who will synthesize them into
|
||||
a final answer for the user. Keep this in mind:
|
||||
- Lead with confirmation of what you did or found
|
||||
- Be specific about which devices were affected
|
||||
- Include relevant state information
|
||||
- Note any issues or failures
|
||||
- Be concise - Tatlock will format the final response
|
||||
|
||||
After completing actions, briefly confirm:
|
||||
- Which devices were affected (list the entity_ids)
|
||||
- Whether each action succeeded or failed
|
||||
"""
|
||||
|
||||
# Lazy initialization to avoid connection issues during imports
|
||||
@@ -113,19 +103,10 @@ _housekeeper_agent: Optional[Agent[None, str]] = None
|
||||
|
||||
def _create_housekeeper_agent() -> Agent[None, str]:
|
||||
"""Create the Housekeeper PydanticAI agent."""
|
||||
# Import required classes for Ollama configuration
|
||||
from pydantic_ai.models.openai import OpenAIChatModel
|
||||
from pydantic_ai.providers.ollama import OllamaProvider
|
||||
from src.anthropic.model_selector import get_model
|
||||
|
||||
# PydanticAI expects Ollama base URL to end with /v1
|
||||
clean_host = str(config.OLLAMA_HOST).rstrip("/")
|
||||
base_url = f"{clean_host}/v1"
|
||||
|
||||
# Create Ollama model with provider
|
||||
model = OpenAIChatModel(
|
||||
model_name=config.OLLAMA_DEFAULT_MODEL,
|
||||
provider=OllamaProvider(base_url=base_url),
|
||||
)
|
||||
# Get best available model (Claude if available, else Ollama)
|
||||
model = get_model()
|
||||
|
||||
agent: Agent[None, str] = Agent(
|
||||
model=model,
|
||||
@@ -158,9 +139,12 @@ def _create_housekeeper_agent() -> Agent[None, str]:
|
||||
# Register history tools
|
||||
agent.tool_plain(get_history)
|
||||
|
||||
from src.anthropic.model_selector import get_model_info
|
||||
model_info = get_model_info()
|
||||
logger.info(
|
||||
"housekeeper_agent_created",
|
||||
model=config.OLLAMA_DEFAULT_MODEL,
|
||||
backend=model_info["backend"],
|
||||
model=model_info["model"],
|
||||
tool_count=13,
|
||||
)
|
||||
|
||||
@@ -220,9 +204,13 @@ async def run_housekeeper(
|
||||
)
|
||||
|
||||
try:
|
||||
# Use temperature 0.1 for slight exploration
|
||||
from pydantic_ai.settings import ModelSettings
|
||||
|
||||
result = await agent.run(
|
||||
prompt,
|
||||
message_history=message_history,
|
||||
model_settings=ModelSettings(temperature=0.1),
|
||||
)
|
||||
|
||||
logger.info(
|
||||
@@ -278,9 +266,13 @@ async def run_housekeeper_stream(
|
||||
)
|
||||
|
||||
try:
|
||||
# Use temperature 0.1 for slight exploration
|
||||
from pydantic_ai.settings import ModelSettings
|
||||
|
||||
async with agent.run_stream(
|
||||
prompt,
|
||||
message_history=message_history,
|
||||
model_settings=ModelSettings(temperature=0.1),
|
||||
) as response:
|
||||
async for delta in response.stream_text(delta=True):
|
||||
yield delta
|
||||
|
||||
@@ -182,7 +182,7 @@ class CoreAPIClient:
|
||||
|
||||
logger.debug("core_api_list_devices", domain=domain, area=area)
|
||||
|
||||
response = await client.get("/devices", params=params or None)
|
||||
response = await client.get("/housekeeping/devices", params=params or None)
|
||||
response.raise_for_status()
|
||||
|
||||
data = response.json()
|
||||
@@ -199,7 +199,7 @@ class CoreAPIClient:
|
||||
|
||||
logger.debug("core_api_list_areas")
|
||||
|
||||
response = await client.get("/areas")
|
||||
response = await client.get("/housekeeping/areas")
|
||||
response.raise_for_status()
|
||||
|
||||
data = response.json()
|
||||
@@ -219,7 +219,7 @@ class CoreAPIClient:
|
||||
|
||||
logger.debug("core_api_get_state", entity_id=entity_id)
|
||||
|
||||
response = await client.get(f"/entities/{entity_id}")
|
||||
response = await client.get(f"/housekeeping/devices/{entity_id}")
|
||||
response.raise_for_status()
|
||||
|
||||
return DeviceState(**response.json())
|
||||
@@ -260,7 +260,7 @@ class CoreAPIClient:
|
||||
logger.info("core_api_turn_on", entity_id=entity_id, payload=payload)
|
||||
|
||||
response = await client.post(
|
||||
f"/devices/{entity_id}/control",
|
||||
f"/housekeeping/devices/{entity_id}/control",
|
||||
json=payload,
|
||||
)
|
||||
response.raise_for_status()
|
||||
@@ -288,7 +288,7 @@ class CoreAPIClient:
|
||||
logger.info("core_api_turn_off", entity_id=entity_id)
|
||||
|
||||
response = await client.post(
|
||||
f"/devices/{entity_id}/control",
|
||||
f"/housekeeping/devices/{entity_id}/control",
|
||||
json={"action": "turn_off"},
|
||||
)
|
||||
response.raise_for_status()
|
||||
@@ -316,7 +316,7 @@ class CoreAPIClient:
|
||||
logger.info("core_api_toggle", entity_id=entity_id)
|
||||
|
||||
response = await client.post(
|
||||
f"/devices/{entity_id}/control",
|
||||
f"/housekeeping/devices/{entity_id}/control",
|
||||
json={"action": "toggle"},
|
||||
)
|
||||
response.raise_for_status()
|
||||
@@ -344,7 +344,7 @@ class CoreAPIClient:
|
||||
|
||||
logger.debug("core_api_list_scenes")
|
||||
|
||||
response = await client.get("/scenes")
|
||||
response = await client.get("/housekeeping/scenes")
|
||||
response.raise_for_status()
|
||||
|
||||
data = response.json()
|
||||
@@ -364,7 +364,7 @@ class CoreAPIClient:
|
||||
|
||||
logger.info("core_api_activate_scene", scene_id=scene_id)
|
||||
|
||||
response = await client.post(f"/scenes/{scene_id}/activate")
|
||||
response = await client.post(f"/housekeeping/scenes/{scene_id}/activate")
|
||||
response.raise_for_status()
|
||||
|
||||
data = response.json()
|
||||
@@ -390,7 +390,7 @@ class CoreAPIClient:
|
||||
|
||||
logger.debug("core_api_list_scripts")
|
||||
|
||||
response = await client.get("/scripts")
|
||||
response = await client.get("/housekeeping/scripts")
|
||||
response.raise_for_status()
|
||||
|
||||
data = response.json()
|
||||
@@ -420,7 +420,7 @@ class CoreAPIClient:
|
||||
logger.info("core_api_run_script", script_id=script_id)
|
||||
|
||||
response = await client.post(
|
||||
f"/scripts/{script_id}/run",
|
||||
f"/housekeeping/scripts/{script_id}/run",
|
||||
json=payload or None,
|
||||
)
|
||||
response.raise_for_status()
|
||||
@@ -448,7 +448,7 @@ class CoreAPIClient:
|
||||
|
||||
logger.debug("core_api_list_automations")
|
||||
|
||||
response = await client.get("/automations")
|
||||
response = await client.get("/housekeeping/automations")
|
||||
response.raise_for_status()
|
||||
|
||||
data = response.json()
|
||||
@@ -478,7 +478,7 @@ class CoreAPIClient:
|
||||
)
|
||||
|
||||
response = await client.post(
|
||||
f"/automations/{automation_id}/toggle",
|
||||
f"/housekeeping/automations/{automation_id}/toggle",
|
||||
json={"enable": enable},
|
||||
)
|
||||
response.raise_for_status()
|
||||
@@ -515,7 +515,7 @@ class CoreAPIClient:
|
||||
logger.debug("core_api_get_history", entity_id=entity_id, hours=hours)
|
||||
|
||||
response = await client.get(
|
||||
"/history",
|
||||
"/housekeeping/history",
|
||||
params={"entity_id": entity_id, "hours": hours},
|
||||
)
|
||||
response.raise_for_status()
|
||||
@@ -536,7 +536,7 @@ class CoreAPIClient:
|
||||
"""
|
||||
try:
|
||||
client = self._ensure_client()
|
||||
response = await client.get("/health")
|
||||
response = await client.get("/housekeeping/health")
|
||||
return response.status_code == 200
|
||||
except Exception as e:
|
||||
logger.warning("core_api_health_check_failed", error=str(e))
|
||||
|
||||
@@ -59,10 +59,27 @@ async def list_devices(
|
||||
|
||||
for dom, dom_devices in sorted(by_domain.items()):
|
||||
output_parts.append(f"### {dom.title()}s")
|
||||
for device in dom_devices:
|
||||
|
||||
# Sort devices: room groups first (using Home Assistant's is_hue_group attribute)
|
||||
def is_room_group(d: object) -> bool:
|
||||
"""Check if device is a room group based on HA attributes."""
|
||||
attrs = getattr(d, "attributes", {})
|
||||
# Check for Hue room groups
|
||||
if attrs.get("is_hue_group") and attrs.get("hue_type") == "room":
|
||||
return True
|
||||
# Check for other group indicators (icon or entity_id list)
|
||||
if "entity_id" in attrs and isinstance(attrs["entity_id"], list):
|
||||
return True
|
||||
return False
|
||||
|
||||
sorted_devices = sorted(dom_devices, key=lambda d: (not is_room_group(d), d.entity_id))
|
||||
|
||||
for device in sorted_devices:
|
||||
state_icon = "on" if device.state == "on" else "off" if device.state == "off" else device.state
|
||||
area_str = f" ({device.area})" if device.area else ""
|
||||
output_parts.append(f"- **{device.name}**{area_str}: {state_icon}")
|
||||
# Mark room groups clearly using actual HA data
|
||||
group_marker = " [ROOM GROUP]" if is_room_group(device) else ""
|
||||
output_parts.append(f"- **{device.name}**{area_str}{group_marker}: {state_icon}")
|
||||
output_parts.append(f" ID: `{device.entity_id}`")
|
||||
output_parts.append("")
|
||||
|
||||
@@ -164,12 +181,12 @@ async def turn_on(
|
||||
color_temp: int | None = None,
|
||||
) -> str:
|
||||
"""
|
||||
Turn on a device.
|
||||
Turn on a device. Use the entity_id parameter with the EXACT value from list_devices.
|
||||
|
||||
For lights, can optionally set brightness and color temperature.
|
||||
|
||||
Args:
|
||||
entity_id: Device to turn on (e.g., light.living_room, switch.coffee_maker)
|
||||
entity_id: The EXACT entity ID from list_devices including domain prefix.
|
||||
brightness: Optional brightness for lights (0-255, where 255 is full brightness)
|
||||
color_temp: Optional color temperature in Kelvin (2700=warm, 6500=cool)
|
||||
|
||||
@@ -177,10 +194,9 @@ async def turn_on(
|
||||
Confirmation of the action
|
||||
|
||||
Examples:
|
||||
turn_on("light.living_room") # Turn on at current brightness
|
||||
turn_on("light.bedroom", brightness=128) # Turn on at 50% brightness
|
||||
turn_on("light.office", brightness=255, color_temp=4000) # Full, neutral white
|
||||
turn_on("switch.coffee_maker") # Turn on a switch
|
||||
turn_on(entity_id="light.living_room")
|
||||
turn_on(entity_id="light.bedroom", brightness=128)
|
||||
turn_on(entity_id="switch.coffee_maker")
|
||||
"""
|
||||
try:
|
||||
async with CoreAPIClient() as client:
|
||||
@@ -209,17 +225,18 @@ async def turn_on(
|
||||
|
||||
async def turn_off(entity_id: str) -> str:
|
||||
"""
|
||||
Turn off a device.
|
||||
Turn off a device. Use the entity_id parameter with the EXACT value from list_devices.
|
||||
|
||||
Args:
|
||||
entity_id: Device to turn off (e.g., light.living_room, switch.coffee_maker)
|
||||
entity_id: The EXACT entity ID from list_devices including domain prefix.
|
||||
|
||||
Returns:
|
||||
Confirmation of the action
|
||||
|
||||
Examples:
|
||||
turn_off("light.living_room")
|
||||
turn_off("switch.coffee_maker")
|
||||
turn_off(entity_id="light.living_room")
|
||||
turn_off(entity_id="switch.coffee_maker")
|
||||
turn_off(entity_id="light.kitchen")
|
||||
"""
|
||||
try:
|
||||
async with CoreAPIClient() as client:
|
||||
@@ -239,15 +256,17 @@ async def toggle(entity_id: str) -> str:
|
||||
"""
|
||||
Toggle a device's state (on becomes off, off becomes on).
|
||||
|
||||
Use the entity_id parameter with the EXACT value from list_devices.
|
||||
|
||||
Args:
|
||||
entity_id: Device to toggle
|
||||
entity_id: The EXACT entity ID from list_devices including domain prefix.
|
||||
|
||||
Returns:
|
||||
Confirmation with the new state
|
||||
|
||||
Examples:
|
||||
toggle("light.living_room")
|
||||
toggle("switch.fan")
|
||||
toggle(entity_id="light.living_room")
|
||||
toggle(entity_id="switch.fan")
|
||||
"""
|
||||
try:
|
||||
async with CoreAPIClient() as client:
|
||||
|
||||
@@ -19,6 +19,9 @@ from src.agents.librarian.tools import (
|
||||
get_wiki_page,
|
||||
hybrid_search,
|
||||
list_dossiers,
|
||||
read_url,
|
||||
read_urls_batch,
|
||||
search_web,
|
||||
search_wiki,
|
||||
semantic_search,
|
||||
smart_create_wiki_page,
|
||||
@@ -36,7 +39,14 @@ Your role is to help users find, understand, synthesize, and manage information
|
||||
- The personal wiki (Wiki.js) containing documentation and notes
|
||||
- The knowledge graph (Neo4j) with entities and relationships
|
||||
- Vector embeddings (Qdrant) for semantic search
|
||||
- Web search (SearXNG) for current information
|
||||
- Paperless documents (📑) - indexed PDFs, scanned documents, invoices, receipts from the user's document archive
|
||||
- Volatile cache (⚡) - pre-fetched real-time data for user-relevant locations and items:
|
||||
- weather/forecast: conditions and forecasts for user's configured cities
|
||||
- news: headlines from user's preferred sources
|
||||
- stock/crypto: quotes for user's watched symbols
|
||||
- sun/air_quality: data for user's locations
|
||||
- Note: volatile data may not exist for arbitrary queries - falls back to web search
|
||||
- Web search (SearXNG) for current information not available in cache
|
||||
|
||||
## Your Personality
|
||||
- Scholarly and thorough in your research
|
||||
@@ -47,8 +57,23 @@ Your role is to help users find, understand, synthesize, and manage information
|
||||
|
||||
## Your Tools
|
||||
|
||||
### Research Tools
|
||||
- **hybrid_search**: Your primary research tool - searches all sources at once
|
||||
### Web Search & Content Extraction
|
||||
- **search_web**: Search the internet for current information (weather, news, facts)
|
||||
- Use for: weather forecasts, current events, recent developments, external facts
|
||||
- Returns extracted content from search results, not just snippets
|
||||
- **read_url**: Read and extract content from a specific URL
|
||||
- Use when: user provides a URL or you need to read a specific webpage
|
||||
- **read_urls_batch**: Read multiple URLs in parallel (up to 20)
|
||||
- Use for: comparing multiple sources, gathering info from several pages
|
||||
|
||||
### Internal Research Tools
|
||||
- **hybrid_search**: Your primary research tool - searches ALL sources at once:
|
||||
- Wiki pages (vector similarity)
|
||||
- Knowledge graph (entity relationships)
|
||||
- Paperless documents (📑 indexed PDFs, scans)
|
||||
- Volatile cache (⚡ weather, news, stocks - when available)
|
||||
- Web search (current information)
|
||||
Results are fused and re-ranked by relevance. Volatile data gets priority when fresh.
|
||||
- **search_wiki**: Find specific wiki pages by keyword
|
||||
- **semantic_search**: Find conceptually similar content
|
||||
- **explore_knowledge_graph** / **find_related_entities**: Discover connections
|
||||
@@ -102,6 +127,14 @@ Your responses are returned to Tatlock (the butler) who will synthesize them int
|
||||
- Note any gaps in available information
|
||||
- Be concise but thorough - Tatlock will format the final response
|
||||
- Structure your findings clearly so they can be easily integrated with other responses
|
||||
|
||||
## CRITICAL: Never Fabricate Information
|
||||
If a tool fails or you cannot access a data source:
|
||||
- Say "I was unable to retrieve [information type]" - be specific about what failed
|
||||
- Do NOT provide placeholder, template, or made-up data
|
||||
- Do NOT say "Here's what I would have said" or "Here's a sample response"
|
||||
- Do NOT invent specific numbers, dates, or facts when the actual data is unavailable
|
||||
- It is better to return no information than to return fabricated information
|
||||
"""
|
||||
|
||||
# Lazy initialization to avoid connection issues during imports
|
||||
@@ -110,19 +143,10 @@ _librarian_agent: Optional[Agent[None, str]] = None
|
||||
|
||||
def _create_librarian_agent() -> Agent[None, str]:
|
||||
"""Create the Librarian PydanticAI agent."""
|
||||
# Import required classes for Ollama configuration
|
||||
from pydantic_ai.models.openai import OpenAIChatModel
|
||||
from pydantic_ai.providers.ollama import OllamaProvider
|
||||
from src.anthropic.model_selector import get_model
|
||||
|
||||
# PydanticAI expects Ollama base URL to end with /v1
|
||||
clean_host = str(config.OLLAMA_HOST).rstrip('/')
|
||||
base_url = f"{clean_host}/v1"
|
||||
|
||||
# Create Ollama model with provider
|
||||
model = OpenAIChatModel(
|
||||
model_name=config.OLLAMA_DEFAULT_MODEL,
|
||||
provider=OllamaProvider(base_url=base_url)
|
||||
)
|
||||
# Get best available model (Claude if available, else Ollama)
|
||||
model = get_model()
|
||||
|
||||
agent: Agent[None, str] = Agent(
|
||||
model=model,
|
||||
@@ -130,7 +154,7 @@ def _create_librarian_agent() -> Agent[None, str]:
|
||||
retries=2,
|
||||
)
|
||||
|
||||
# Register research tools
|
||||
# Register research tools (internal knowledge)
|
||||
agent.tool_plain(hybrid_search)
|
||||
agent.tool_plain(search_wiki)
|
||||
agent.tool_plain(semantic_search)
|
||||
@@ -139,6 +163,11 @@ def _create_librarian_agent() -> Agent[None, str]:
|
||||
agent.tool_plain(explore_knowledge_graph)
|
||||
agent.tool_plain(find_related_entities)
|
||||
|
||||
# Register web search & content extraction tools
|
||||
agent.tool_plain(search_web)
|
||||
agent.tool_plain(read_url)
|
||||
agent.tool_plain(read_urls_batch)
|
||||
|
||||
# Register wiki read tools
|
||||
agent.tool_plain(get_wiki_page)
|
||||
|
||||
@@ -147,10 +176,13 @@ def _create_librarian_agent() -> Agent[None, str]:
|
||||
agent.tool_plain(update_wiki_page)
|
||||
agent.tool_plain(smart_create_wiki_page)
|
||||
|
||||
from src.anthropic.model_selector import get_model_info
|
||||
model_info = get_model_info()
|
||||
logger.info(
|
||||
"librarian_agent_created",
|
||||
model=config.OLLAMA_DEFAULT_MODEL,
|
||||
tool_count=11,
|
||||
backend=model_info["backend"],
|
||||
model=model_info["model"],
|
||||
tool_count=14, # 7 research + 3 web + 1 wiki read + 3 wiki write
|
||||
)
|
||||
|
||||
return agent
|
||||
|
||||
@@ -21,10 +21,11 @@ LIBRARIAN_CAPABILITY = HouseholdCapability(
|
||||
role="The Librarian",
|
||||
category="research",
|
||||
description=(
|
||||
"Research and wiki management: can CREATE wiki pages about topics "
|
||||
"Research, web search, and wiki management: can SEARCH the web for current "
|
||||
"information, READ URLs/articles, CREATE wiki pages about topics "
|
||||
"(with automatic HybridRAG research), UPDATE existing pages, "
|
||||
"SEARCH wiki/knowledge graph/web, and synthesize information. "
|
||||
"Use for: 'create a page about X', 'update wiki', 'find info on X'"
|
||||
"and synthesize information from multiple sources. "
|
||||
"Use for: 'search for X', 'what is X', 'create a page about X', 'read this URL'"
|
||||
),
|
||||
domains=[
|
||||
"research",
|
||||
@@ -33,6 +34,9 @@ LIBRARIAN_CAPABILITY = HouseholdCapability(
|
||||
"wiki",
|
||||
"documents",
|
||||
"search",
|
||||
"web",
|
||||
"url",
|
||||
"internet",
|
||||
"synthesis",
|
||||
"create",
|
||||
"write",
|
||||
|
||||
@@ -98,6 +98,47 @@ class ResearchSummary(BaseModel):
|
||||
timing_ms: int = 0
|
||||
|
||||
|
||||
class WebSearchResult(BaseModel):
|
||||
"""Result from web search via /rag/search."""
|
||||
title: str
|
||||
url: str
|
||||
content: str = "" # Full extracted text via Trafilatura
|
||||
snippet: str = "" # Original search engine snippet
|
||||
source: str = "" # Domain name
|
||||
published_date: Optional[str] = None
|
||||
|
||||
|
||||
class WebSearchResponse(BaseModel):
|
||||
"""Response from /rag/search endpoint."""
|
||||
query: str
|
||||
search_type: str
|
||||
results: list[WebSearchResult] = Field(default_factory=list)
|
||||
total_results: int = 0
|
||||
search_time_ms: int = 0
|
||||
sources_summary: str = "" # Pre-formatted markdown citations
|
||||
|
||||
|
||||
class ContentExtractionResult(BaseModel):
|
||||
"""Result from content extraction."""
|
||||
url: str
|
||||
title: Optional[str] = None
|
||||
content: str = ""
|
||||
author: Optional[str] = None
|
||||
date: Optional[str] = None
|
||||
language: Optional[str] = None
|
||||
success: bool = True
|
||||
error: Optional[str] = None
|
||||
|
||||
|
||||
class BatchExtractionResponse(BaseModel):
|
||||
"""Response from batch content extraction."""
|
||||
results: list[ContentExtractionResult] = Field(default_factory=list)
|
||||
total_urls: int = 0
|
||||
successful: int = 0
|
||||
failed: int = 0
|
||||
extraction_time_ms: int = 0
|
||||
|
||||
|
||||
class EntityLinking(BaseModel):
|
||||
"""Entity linking results from smart-create."""
|
||||
forward_links: int = 0
|
||||
@@ -184,18 +225,22 @@ class LibraryDeskClient:
|
||||
vector_limit: int = 10,
|
||||
graph_limit: int = 10,
|
||||
web_limit: int = 5,
|
||||
document_limit: int = 5,
|
||||
volatile_limit: int = 3,
|
||||
enable_reranking: bool = True,
|
||||
final_result_count: int = 10,
|
||||
) -> HybridRAGResponse:
|
||||
"""
|
||||
Execute HybridRAG search combining vector, graph, and web results.
|
||||
Execute HybridRAG search combining vector, graph, documents, volatile, and web.
|
||||
|
||||
Args:
|
||||
query: Search query
|
||||
user: User identifier for multi-tenancy (defaults to request context)
|
||||
vector_limit: Max results from vector search
|
||||
vector_limit: Max results from vector search (wiki pages)
|
||||
graph_limit: Max results from graph search
|
||||
web_limit: Max results from web search
|
||||
web_limit: Max results from web search (0 to disable)
|
||||
document_limit: Max results from Paperless documents (0 to disable)
|
||||
volatile_limit: Max results from volatile cache (0 to disable)
|
||||
enable_reranking: Whether to rerank with LLM
|
||||
final_result_count: Number of final results after fusion
|
||||
|
||||
@@ -211,6 +256,11 @@ class LibraryDeskClient:
|
||||
"vector_limit": vector_limit,
|
||||
"graph_limit": graph_limit,
|
||||
"web_limit": web_limit,
|
||||
"document_limit": document_limit,
|
||||
"volatile_limit": volatile_limit,
|
||||
"enable_documents": document_limit > 0,
|
||||
"enable_volatile": volatile_limit > 0,
|
||||
"enable_web": web_limit > 0,
|
||||
"enable_reranking": enable_reranking,
|
||||
"final_result_count": final_result_count,
|
||||
},
|
||||
@@ -240,9 +290,16 @@ class LibraryDeskClient:
|
||||
metadata=r.get("metadata", {}),
|
||||
))
|
||||
|
||||
# Handle keywords being either a list or a dict with core_keywords
|
||||
raw_keywords = data.get("keywords", [])
|
||||
if isinstance(raw_keywords, dict):
|
||||
keywords = raw_keywords.get("core_keywords", [])
|
||||
else:
|
||||
keywords = raw_keywords
|
||||
|
||||
return HybridRAGResponse(
|
||||
results=results,
|
||||
keywords=data.get("keywords", []),
|
||||
keywords=keywords,
|
||||
synonyms=data.get("synonyms", []),
|
||||
related_dossiers=data.get("related_dossiers", []),
|
||||
formatted_context=data.get("formatted_context", ""),
|
||||
@@ -685,6 +742,186 @@ class LibraryDeskClient:
|
||||
logger.warning("library_desk_health_check_failed", error=str(e))
|
||||
return False
|
||||
|
||||
# ========================================================================
|
||||
# RAG Search (Web Search with Content Extraction)
|
||||
# ========================================================================
|
||||
|
||||
async def search_web(
|
||||
self,
|
||||
query: str,
|
||||
user: str | None = None,
|
||||
search_type: str = "web",
|
||||
limit: int = 10,
|
||||
) -> WebSearchResponse:
|
||||
"""
|
||||
Search the web and extract content from results.
|
||||
|
||||
Uses SearXNG for search and Trafilatura for content extraction.
|
||||
Returns both snippets and full extracted text.
|
||||
|
||||
Args:
|
||||
query: Search query (1-500 chars)
|
||||
user: User identifier for tracking
|
||||
search_type: "web", "news", or "images"
|
||||
limit: Number of results (1-20)
|
||||
|
||||
Returns:
|
||||
WebSearchResponse with results and pre-formatted sources
|
||||
"""
|
||||
user = user or get_user()
|
||||
client = self._ensure_client()
|
||||
|
||||
payload = {
|
||||
"query": query,
|
||||
"search_type": search_type,
|
||||
"limit": limit,
|
||||
"user": user or "tatlock-librarian",
|
||||
}
|
||||
|
||||
logger.info("library_desk_web_search", query=query, limit=limit)
|
||||
|
||||
response = await client.post("/rag/search", json=payload, timeout=30.0)
|
||||
response.raise_for_status()
|
||||
|
||||
data = response.json()
|
||||
|
||||
results = [
|
||||
WebSearchResult(
|
||||
title=r.get("title", ""),
|
||||
url=r.get("url", ""),
|
||||
content=r.get("content", ""),
|
||||
snippet=r.get("snippet", ""),
|
||||
source=r.get("source", ""),
|
||||
published_date=r.get("published_date"),
|
||||
)
|
||||
for r in data.get("results", [])
|
||||
]
|
||||
|
||||
return WebSearchResponse(
|
||||
query=data.get("query", query),
|
||||
search_type=data.get("search_type", search_type),
|
||||
results=results,
|
||||
total_results=data.get("total_results", len(results)),
|
||||
search_time_ms=data.get("search_time_ms", 0),
|
||||
sources_summary=data.get("sources_summary", ""),
|
||||
)
|
||||
|
||||
# ========================================================================
|
||||
# Content Extraction
|
||||
# ========================================================================
|
||||
|
||||
async def extract_content(
|
||||
self,
|
||||
url: str,
|
||||
include_metadata: bool = True,
|
||||
max_length: int = 5000,
|
||||
) -> ContentExtractionResult:
|
||||
"""
|
||||
Extract main content from a URL.
|
||||
|
||||
Uses Trafilatura for intelligent content extraction,
|
||||
removing boilerplate, ads, and navigation.
|
||||
|
||||
Note: Uses soft failure pattern - check result.success field.
|
||||
|
||||
Args:
|
||||
url: URL to extract content from
|
||||
include_metadata: Whether to extract author, date, etc.
|
||||
max_length: Maximum content length
|
||||
|
||||
Returns:
|
||||
ContentExtractionResult (check .success and .error fields)
|
||||
"""
|
||||
client = self._ensure_client()
|
||||
|
||||
payload = {
|
||||
"url": url,
|
||||
"include_metadata": include_metadata,
|
||||
"max_length": max_length,
|
||||
}
|
||||
|
||||
logger.debug("library_desk_extract_content", url=url)
|
||||
|
||||
response = await client.post("/content/extract", json=payload, timeout=30.0)
|
||||
response.raise_for_status()
|
||||
|
||||
data = response.json()
|
||||
result = data.get("result", {})
|
||||
|
||||
return ContentExtractionResult(
|
||||
url=result.get("url", url),
|
||||
title=result.get("title"),
|
||||
content=result.get("content", ""),
|
||||
author=result.get("author"),
|
||||
date=result.get("date"),
|
||||
language=result.get("language"),
|
||||
success=result.get("success", False),
|
||||
error=result.get("error"),
|
||||
)
|
||||
|
||||
async def extract_content_batch(
|
||||
self,
|
||||
urls: list[str],
|
||||
include_metadata: bool = True,
|
||||
max_length: int = 2000,
|
||||
) -> BatchExtractionResponse:
|
||||
"""
|
||||
Extract content from multiple URLs in parallel.
|
||||
|
||||
More efficient than sequential calls. Max 20 URLs per batch.
|
||||
|
||||
Note: Uses soft failure pattern - individual failures don't
|
||||
throw errors, check each result's .success field.
|
||||
|
||||
Args:
|
||||
urls: List of URLs to extract (max 20)
|
||||
include_metadata: Whether to extract author, date, etc.
|
||||
max_length: Maximum content length per URL
|
||||
|
||||
Returns:
|
||||
BatchExtractionResponse with results and stats
|
||||
"""
|
||||
client = self._ensure_client()
|
||||
|
||||
payload = {
|
||||
"urls": urls[:20], # Server limit
|
||||
"include_metadata": include_metadata,
|
||||
"max_length": max_length,
|
||||
}
|
||||
|
||||
logger.info("library_desk_extract_batch", url_count=len(urls))
|
||||
|
||||
response = await client.post(
|
||||
"/content/extract/batch",
|
||||
json=payload,
|
||||
timeout=60.0, # Longer timeout for batch
|
||||
)
|
||||
response.raise_for_status()
|
||||
|
||||
data = response.json()
|
||||
|
||||
results = [
|
||||
ContentExtractionResult(
|
||||
url=r.get("url", ""),
|
||||
title=r.get("title"),
|
||||
content=r.get("content", ""),
|
||||
author=r.get("author"),
|
||||
date=r.get("date"),
|
||||
language=r.get("language"),
|
||||
success=r.get("success", False),
|
||||
error=r.get("error"),
|
||||
)
|
||||
for r in data.get("results", [])
|
||||
]
|
||||
|
||||
return BatchExtractionResponse(
|
||||
results=results,
|
||||
total_urls=data.get("total_urls", len(urls)),
|
||||
successful=data.get("successful", 0),
|
||||
failed=data.get("failed", 0),
|
||||
extraction_time_ms=data.get("extraction_time_ms", 0),
|
||||
)
|
||||
|
||||
|
||||
# Global client factory
|
||||
async def get_library_client() -> LibraryDeskClient:
|
||||
|
||||
@@ -17,20 +17,26 @@ logger = get_logger(__name__)
|
||||
async def hybrid_search(
|
||||
query: str,
|
||||
include_web: bool = True,
|
||||
include_documents: bool = True,
|
||||
include_volatile: bool = True,
|
||||
) -> str:
|
||||
"""
|
||||
Search across all knowledge sources using HybridRAG.
|
||||
|
||||
This is the primary research tool, combining:
|
||||
- Vector search (semantic similarity over documents)
|
||||
- Vector search (semantic similarity over wiki pages)
|
||||
- Knowledge graph (entities and relationships)
|
||||
- Paperless documents (📑 indexed PDFs, scans, invoices)
|
||||
- Volatile cache (⚡ weather, news, stocks - for user's configured items)
|
||||
- Web search (current information from SearXNG)
|
||||
|
||||
Results are fused and re-ranked by relevance.
|
||||
Results are fused and re-ranked by relevance. Volatile data gets priority when fresh.
|
||||
|
||||
Args:
|
||||
query: Natural language research query
|
||||
include_web: Whether to include web results (default: True)
|
||||
include_documents: Whether to include Paperless documents (default: True)
|
||||
include_volatile: Whether to include volatile cache data (default: True)
|
||||
|
||||
Returns:
|
||||
Formatted search results with sources and context
|
||||
@@ -38,12 +44,16 @@ async def hybrid_search(
|
||||
Examples:
|
||||
hybrid_search("How does Docker orchestration work with Kubernetes?")
|
||||
hybrid_search("What projects use Neo4j?", include_web=False)
|
||||
hybrid_search("Find my electricity invoices", include_web=False, include_volatile=False)
|
||||
hybrid_search("What's the weather in Rotterdam?") # May hit volatile cache
|
||||
"""
|
||||
try:
|
||||
async with LibraryDeskClient() as client:
|
||||
response = await client.hybrid_search(
|
||||
query=query,
|
||||
web_limit=5 if include_web else 0,
|
||||
document_limit=5 if include_documents else 0,
|
||||
volatile_limit=3 if include_volatile else 0,
|
||||
)
|
||||
|
||||
if not response.results:
|
||||
@@ -70,6 +80,8 @@ async def hybrid_search(
|
||||
"vector": "📄",
|
||||
"graph": "🔗",
|
||||
"web": "🌐",
|
||||
"document": "📑",
|
||||
"volatile": "⚡",
|
||||
}.get(result.source, "•")
|
||||
|
||||
output_parts.append(
|
||||
@@ -432,6 +444,239 @@ async def find_related_entities(
|
||||
return f"Error finding related entities: {str(e)}"
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Web Search & Content Extraction
|
||||
# ============================================================================
|
||||
|
||||
async def search_web(
|
||||
query: str,
|
||||
limit: int = 10,
|
||||
search_type: str = "web",
|
||||
) -> str:
|
||||
"""
|
||||
Search the web and extract content from results.
|
||||
|
||||
This is the primary tool for finding current information online.
|
||||
Results include both snippets and full extracted text from pages.
|
||||
|
||||
Search types:
|
||||
- "web": General web search (default)
|
||||
- "news": News articles
|
||||
- "images": Image search
|
||||
|
||||
Args:
|
||||
query: Search query (1-500 chars)
|
||||
limit: Number of results (1-20, default: 10)
|
||||
search_type: Type of search ("web", "news", or "images")
|
||||
|
||||
Returns:
|
||||
Formatted search results with sources and extracted content
|
||||
|
||||
Examples:
|
||||
search_web("Python 3.12 new features")
|
||||
search_web("latest tech news", search_type="news", limit=5)
|
||||
"""
|
||||
try:
|
||||
async with LibraryDeskClient() as client:
|
||||
response = await client.search_web(
|
||||
query=query,
|
||||
limit=limit,
|
||||
search_type=search_type,
|
||||
)
|
||||
|
||||
if not response.results:
|
||||
return f"No results found for '{query}'"
|
||||
|
||||
output_parts = [f"## Web Search: {query}\n"]
|
||||
output_parts.append(f"*Found {response.total_results} results in {response.search_time_ms}ms*\n")
|
||||
|
||||
for i, result in enumerate(response.results, 1):
|
||||
output_parts.append(f"### {i}. {result.title}")
|
||||
output_parts.append(f"**Source:** {result.source}")
|
||||
output_parts.append(f"**URL:** {result.url}")
|
||||
|
||||
if result.published_date:
|
||||
output_parts.append(f"**Date:** {result.published_date}")
|
||||
|
||||
# Use full content if available, otherwise snippet
|
||||
content = result.content or result.snippet
|
||||
if content:
|
||||
# Truncate for readability
|
||||
if len(content) > 500:
|
||||
content = content[:500] + "..."
|
||||
output_parts.append(f"\n{content}")
|
||||
|
||||
output_parts.append("")
|
||||
|
||||
# Add pre-formatted sources for citations
|
||||
if response.sources_summary:
|
||||
output_parts.append("---")
|
||||
output_parts.append(response.sources_summary)
|
||||
|
||||
logger.info(
|
||||
"librarian_web_search",
|
||||
query=query,
|
||||
result_count=response.total_results,
|
||||
search_type=search_type,
|
||||
)
|
||||
|
||||
return "\n".join(output_parts)
|
||||
|
||||
except Exception as e:
|
||||
logger.error("librarian_web_search_error", error=str(e), query=query)
|
||||
return f"Error searching web: {str(e)}"
|
||||
|
||||
|
||||
async def read_url(
|
||||
url: str,
|
||||
max_length: int = 5000,
|
||||
) -> str:
|
||||
"""
|
||||
Read and extract the main content from a URL.
|
||||
|
||||
Use this when you have a specific URL to read, such as:
|
||||
- A link the user provided
|
||||
- A URL from search results you want to read in full
|
||||
- Documentation or article pages
|
||||
|
||||
Extracts the main content, removing ads, navigation, and boilerplate.
|
||||
|
||||
Args:
|
||||
url: The URL to read
|
||||
max_length: Maximum content length (default: 5000)
|
||||
|
||||
Returns:
|
||||
Extracted page content with metadata
|
||||
|
||||
Examples:
|
||||
read_url("https://docs.python.org/3/library/asyncio.html")
|
||||
read_url("https://example.com/article", max_length=10000)
|
||||
"""
|
||||
try:
|
||||
async with LibraryDeskClient() as client:
|
||||
result = await client.extract_content(
|
||||
url=url,
|
||||
include_metadata=True,
|
||||
max_length=max_length,
|
||||
)
|
||||
|
||||
if not result.success:
|
||||
return f"Could not read page: {result.error or 'Unknown error'}"
|
||||
|
||||
output_parts = []
|
||||
|
||||
# Header with metadata
|
||||
if result.title:
|
||||
output_parts.append(f"# {result.title}")
|
||||
else:
|
||||
output_parts.append(f"# Content from {url}")
|
||||
|
||||
output_parts.append(f"**URL:** {url}")
|
||||
|
||||
if result.author:
|
||||
output_parts.append(f"**Author:** {result.author}")
|
||||
|
||||
if result.date:
|
||||
output_parts.append(f"**Date:** {result.date}")
|
||||
|
||||
if result.language and result.language != "en":
|
||||
output_parts.append(f"**Language:** {result.language}")
|
||||
|
||||
output_parts.append("")
|
||||
|
||||
# Main content
|
||||
if result.content:
|
||||
output_parts.append(result.content)
|
||||
else:
|
||||
output_parts.append("(No content could be extracted)")
|
||||
|
||||
logger.info(
|
||||
"librarian_read_url",
|
||||
url=url,
|
||||
content_length=len(result.content) if result.content else 0,
|
||||
)
|
||||
|
||||
return "\n".join(output_parts)
|
||||
|
||||
except Exception as e:
|
||||
logger.error("librarian_read_url_error", error=str(e), url=url)
|
||||
return f"Error reading URL: {str(e)}"
|
||||
|
||||
|
||||
async def read_urls_batch(
|
||||
urls: list[str],
|
||||
max_length: int = 2000,
|
||||
) -> str:
|
||||
"""
|
||||
Read and extract content from multiple URLs in parallel.
|
||||
|
||||
More efficient than calling read_url multiple times.
|
||||
Max 20 URLs per batch.
|
||||
|
||||
Note: Individual failures don't fail the entire batch -
|
||||
failed URLs are reported but other content is still returned.
|
||||
|
||||
Args:
|
||||
urls: List of URLs to read (max 20)
|
||||
max_length: Maximum content length per URL (default: 2000)
|
||||
|
||||
Returns:
|
||||
Extracted content from all successful URLs with failure report
|
||||
|
||||
Examples:
|
||||
read_urls_batch(["https://example.com/1", "https://example.com/2"])
|
||||
"""
|
||||
try:
|
||||
async with LibraryDeskClient() as client:
|
||||
response = await client.extract_content_batch(
|
||||
urls=urls,
|
||||
include_metadata=True,
|
||||
max_length=max_length,
|
||||
)
|
||||
|
||||
output_parts = [
|
||||
f"## Batch Content Extraction",
|
||||
f"*Extracted {response.successful}/{response.total_urls} URLs in {response.extraction_time_ms}ms*\n",
|
||||
]
|
||||
|
||||
# Show successful extractions
|
||||
for result in response.results:
|
||||
if result.success:
|
||||
title = result.title or result.url
|
||||
output_parts.append(f"### {title}")
|
||||
output_parts.append(f"**URL:** {result.url}")
|
||||
|
||||
if result.content:
|
||||
# Truncate for readability in batch mode
|
||||
content = result.content
|
||||
if len(content) > max_length:
|
||||
content = content[:max_length] + "..."
|
||||
output_parts.append(f"\n{content}")
|
||||
|
||||
output_parts.append("")
|
||||
|
||||
# Report failures
|
||||
failed = [r for r in response.results if not r.success]
|
||||
if failed:
|
||||
output_parts.append("---")
|
||||
output_parts.append("### Failed Extractions")
|
||||
for result in failed:
|
||||
output_parts.append(f"- {result.url}: {result.error}")
|
||||
|
||||
logger.info(
|
||||
"librarian_read_urls_batch",
|
||||
total=response.total_urls,
|
||||
successful=response.successful,
|
||||
failed=response.failed,
|
||||
)
|
||||
|
||||
return "\n".join(output_parts)
|
||||
|
||||
except Exception as e:
|
||||
logger.error("librarian_read_urls_batch_error", error=str(e))
|
||||
return f"Error reading URLs: {str(e)}"
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Wiki Write Operations
|
||||
# ============================================================================
|
||||
@@ -685,7 +930,7 @@ async def smart_create_wiki_page(
|
||||
|
||||
# All tools available to The Librarian
|
||||
LIBRARIAN_TOOLS = [
|
||||
# Research tools
|
||||
# Research tools (internal knowledge)
|
||||
hybrid_search,
|
||||
search_wiki,
|
||||
get_wiki_page,
|
||||
@@ -694,6 +939,10 @@ LIBRARIAN_TOOLS = [
|
||||
semantic_search,
|
||||
explore_knowledge_graph,
|
||||
find_related_entities,
|
||||
# Web search & content extraction
|
||||
search_web,
|
||||
read_url,
|
||||
read_urls_batch,
|
||||
# Write tools
|
||||
create_wiki_page,
|
||||
update_wiki_page,
|
||||
|
||||
+13
-13
@@ -176,19 +176,19 @@ async def orchestrate_with_think_updates(
|
||||
if delegation_task.expert_name == "librarian":
|
||||
expert_display_name = "The Librarian"
|
||||
|
||||
yield f"<think>🤝 Consulting {expert_display_name}...</think>\n"
|
||||
yield f"🤝 Consulting {expert_display_name}...\n"
|
||||
|
||||
# Execute delegation (uses run() internally)
|
||||
result = await execute_delegation(delegation_task)
|
||||
|
||||
if result.success:
|
||||
yield f"<think>✅ {expert_display_name} completed research.</think>\n"
|
||||
yield f"✅ {expert_display_name} completed research.\n"
|
||||
|
||||
# Yield the expert's findings
|
||||
if result.output:
|
||||
yield f"\n{result.output}"
|
||||
else:
|
||||
yield f"<think>⚠️ {expert_display_name} encountered an issue: {result.error}</think>\n"
|
||||
yield f"⚠️ {expert_display_name} encountered an issue: {result.error}\n"
|
||||
|
||||
logger.info(
|
||||
"orchestration_complete",
|
||||
@@ -449,12 +449,12 @@ async def orchestrate_multi_expert(
|
||||
return
|
||||
|
||||
# Stream: Starting multi-expert coordination
|
||||
yield f"<think>🎯 Starting multi-expert coordination ({len(tasks)} tasks, {mode.value})...</think>\n"
|
||||
yield f"🎯 Starting multi-expert coordination ({len(tasks)} tasks, {mode.value})...\n"
|
||||
|
||||
if mode == ExecutionMode.PARALLEL:
|
||||
# Parallel execution - emit one update then run all at once
|
||||
expert_names = ", ".join(_get_display_name(t.expert_name) for t in tasks)
|
||||
yield f"<think>🔄 Consulting in parallel: {expert_names}...</think>\n"
|
||||
yield f"🔄 Consulting in parallel: {expert_names}...\n"
|
||||
|
||||
result = await execute_parallel(tasks)
|
||||
|
||||
@@ -462,9 +462,9 @@ async def orchestrate_multi_expert(
|
||||
for expert_name, expert_result in result.results.items():
|
||||
display_name = _get_display_name(expert_name)
|
||||
if expert_result.success:
|
||||
yield f"<think>✅ {display_name} completed.</think>\n"
|
||||
yield f"✅ {display_name} completed.\n"
|
||||
else:
|
||||
yield f"<think>⚠️ {display_name} failed: {expert_result.error}</think>\n"
|
||||
yield f"⚠️ {display_name} failed: {expert_result.error}\n"
|
||||
|
||||
else:
|
||||
# Sequential execution - emit updates for each task
|
||||
@@ -472,27 +472,27 @@ async def orchestrate_multi_expert(
|
||||
|
||||
for task in tasks:
|
||||
display_name = _get_display_name(task.expert_name)
|
||||
yield f"<think>🤝 Consulting {display_name}...</think>\n"
|
||||
yield f"🤝 Consulting {display_name}...\n"
|
||||
|
||||
task_result = await execute_delegation(task)
|
||||
result.add_result(task_result)
|
||||
|
||||
if task_result.success:
|
||||
yield f"<think>✅ {display_name} completed.</think>\n"
|
||||
yield f"✅ {display_name} completed.\n"
|
||||
else:
|
||||
yield f"<think>⚠️ {display_name} failed: {task_result.error}</think>\n"
|
||||
yield f"⚠️ {display_name} failed: {task_result.error}\n"
|
||||
if stop_on_failure:
|
||||
yield "<think>🛑 Stopping due to failure.</think>\n"
|
||||
yield "🛑 Stopping due to failure.\n"
|
||||
break
|
||||
|
||||
result.aggregate_outputs()
|
||||
|
||||
# Stream: Summary
|
||||
if result.all_succeeded:
|
||||
yield "<think>🎉 All experts completed successfully.</think>\n"
|
||||
yield "🎉 All experts completed successfully.\n"
|
||||
else:
|
||||
failed_names = ", ".join(_get_display_name(e) for e in result.failed_experts)
|
||||
yield f"<think>⚠️ Some experts failed: {failed_names}</think>\n"
|
||||
yield f"⚠️ Some experts failed: {failed_names}\n"
|
||||
|
||||
# Yield combined output
|
||||
if result.combined_output:
|
||||
|
||||
+115
-31
@@ -5,11 +5,13 @@ The Steward analyzes incoming requests, identifies relevant household
|
||||
capabilities, and provides focused recommendations to Tatlock (the Butler).
|
||||
This creates a two-tier architecture that prevents cognitive overload.
|
||||
|
||||
Uses plain text output (not JSON) for reliability with Ollama models.
|
||||
Uses plain text output (not JSON) for reliability. Supports both Claude
|
||||
(preferred) and Ollama (fallback) backends via direct API calls.
|
||||
"""
|
||||
import httpx
|
||||
from typing import Optional
|
||||
|
||||
from src.anthropic.model_selector import is_claude_available, get_model_info
|
||||
from src.core.config import config
|
||||
from src.core.household_registry import get_household_registry
|
||||
from src.core.logging_config import get_logger
|
||||
@@ -56,13 +58,22 @@ USER QUERY: {query}
|
||||
GUIDELINES:
|
||||
- Be conservative - only recommend truly necessary capabilities
|
||||
- Simple greetings/chat → no capabilities needed (conversational response only)
|
||||
- Questions about prior conversation ("what did I say", "my name", "what we discussed") → no capabilities (Tatlock has full history)
|
||||
- Questions about prior conversation ("what did I say", "what we discussed") → no capabilities (Tatlock has full history)
|
||||
- Math/calculations → tatlock_core
|
||||
- Quick web searches → tatlock_core
|
||||
- Time/date queries → tatlock_core
|
||||
- PERSONAL MEMORY queries → biographer to recall (ALWAYS use for questions about the user themselves):
|
||||
- "where do I live", "what's my location", "my address" → biographer to recall location
|
||||
- "what's my name", "who am I" → biographer to recall name
|
||||
- "what car do I drive", "my vehicle" → biographer to recall car
|
||||
- "what do you know about me", "what have I told you" → biographer to recall or list_memories
|
||||
- "remember that I...", "store that..." → biographer to store_insight
|
||||
- "forget my...", "delete..." → biographer to forget_memory
|
||||
- "my timezone", "my preferences" → biographer to recall preferences
|
||||
- Web searches, weather, news, current information → librarian with search_web
|
||||
- Read a URL or article → librarian with read_url
|
||||
- Wiki creation ("create a page about X", "add X to wiki") → librarian with smart_create
|
||||
- Wiki updates ("update the page", "add to dossier") → librarian with update
|
||||
- Research queries ("find info", "what do we know about", "search for") → librarian with hybrid_search
|
||||
- Research queries about TOPICS (not about the user) → librarian with hybrid_search
|
||||
- In-depth research, knowledge synthesis, document lookup → librarian with hybrid_search
|
||||
- If conversation history is relevant, note which previous turns matter
|
||||
- Assess complexity: simple (1 tool), moderate (2-3 tools), complex (multiple steps)
|
||||
@@ -74,8 +85,14 @@ COMPLEXITY: [simple/moderate/complex]
|
||||
CONTEXT: [any relevant conversation context, or "none"]
|
||||
|
||||
EXAMPLES:
|
||||
- "DELEGATE: biographer to recall the user's location" (for "where do I live?")
|
||||
- "DELEGATE: biographer to recall the user's car" (for "what car do I drive?")
|
||||
- "DELEGATE: biographer to list_memories about the user" (for "what do you know about me?")
|
||||
- "DELEGATE: biographer to store_insight about user's pet" (for "remember that I have a dog named Max")
|
||||
- "DELEGATE: librarian to search_web for tomorrow's weather forecast"
|
||||
- "DELEGATE: librarian to create a wiki page about CI/CD pipelines"
|
||||
- "DELEGATE: librarian to search for information about Docker networking"
|
||||
- "DELEGATE: librarian to hybrid_search for information about Docker networking"
|
||||
- "DELEGATE: librarian to read_url https://example.com/article"
|
||||
- "DELEGATE: tatlock_core to calculate the result"
|
||||
- "DELEGATE: none (conversational response only)"
|
||||
|
||||
@@ -90,22 +107,74 @@ class StewardAgent:
|
||||
Analyzes requests with full conversation context and recommends
|
||||
which household capabilities the Butler should use.
|
||||
|
||||
Uses plain text output for reliability with Ollama models.
|
||||
Uses plain text output for reliability. Supports both Claude
|
||||
(preferred) and Ollama (fallback) backends via direct API calls.
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
"""Initialize Steward with Ollama model (same as Tatlock for VRAM efficiency)."""
|
||||
"""Initialize Steward with backend selection based on availability."""
|
||||
# Ollama config (fallback)
|
||||
self.ollama_host = str(config.OLLAMA_HOST).rstrip('/')
|
||||
self.model_name = config.OLLAMA_DEFAULT_MODEL
|
||||
self.ollama_model = config.OLLAMA_DEFAULT_MODEL
|
||||
|
||||
# Claude config (preferred)
|
||||
self.claude_model = config.ANTHROPIC_MODEL
|
||||
self._anthropic_client = None
|
||||
|
||||
# Determine which backend to use
|
||||
self._use_claude = config.PREFER_CLOUD_BACKEND and is_claude_available()
|
||||
|
||||
self.timeout = 30.0 # 30 second timeout for analysis
|
||||
|
||||
model_info = get_model_info()
|
||||
logger.info(
|
||||
"steward_agent_created",
|
||||
ollama_host=self.ollama_host,
|
||||
model=self.model_name,
|
||||
backend=model_info["backend"],
|
||||
model=model_info["model"],
|
||||
timeout=self.timeout,
|
||||
)
|
||||
|
||||
def _get_anthropic_client(self):
|
||||
"""Get or create Anthropic client (lazy initialization)."""
|
||||
if self._anthropic_client is None:
|
||||
from anthropic import AsyncAnthropic
|
||||
self._anthropic_client = AsyncAnthropic(api_key=config.ANTHROPIC_API_KEY)
|
||||
return self._anthropic_client
|
||||
|
||||
async def _call_claude(self, system_prompt: str, user_message: str) -> str:
|
||||
"""Call Claude API directly for plain text generation."""
|
||||
client = self._get_anthropic_client()
|
||||
|
||||
response = await client.messages.create(
|
||||
model=self.claude_model,
|
||||
max_tokens=1024,
|
||||
system=system_prompt,
|
||||
messages=[{"role": "user", "content": user_message}],
|
||||
temperature=0.3, # Lower = more consistent
|
||||
)
|
||||
|
||||
return response.content[0].text.strip()
|
||||
|
||||
async def _call_ollama(self, prompt: str) -> str:
|
||||
"""Call Ollama API directly for plain text generation."""
|
||||
async with httpx.AsyncClient(timeout=self.timeout) as client:
|
||||
response = await client.post(
|
||||
f"{self.ollama_host}/api/generate",
|
||||
json={
|
||||
"model": self.ollama_model,
|
||||
"prompt": prompt,
|
||||
"stream": False,
|
||||
"options": {
|
||||
"temperature": 0.3, # Lower = more consistent
|
||||
"top_p": 0.9
|
||||
}
|
||||
}
|
||||
)
|
||||
|
||||
response.raise_for_status()
|
||||
result = response.json()
|
||||
return result["response"].strip()
|
||||
|
||||
async def analyze(
|
||||
self,
|
||||
query: str,
|
||||
@@ -114,6 +183,8 @@ class StewardAgent:
|
||||
"""
|
||||
Analyze query and return plain text recommendation.
|
||||
|
||||
Uses Claude if available, falls back to Ollama.
|
||||
|
||||
Args:
|
||||
query: User's query to analyze
|
||||
conversation_history: Previous conversation turns
|
||||
@@ -129,35 +200,48 @@ class StewardAgent:
|
||||
history = conversation_history or []
|
||||
prompt = build_steward_prompt(query, history)
|
||||
|
||||
logger.debug("steward_calling_ollama", query_preview=query[:100])
|
||||
backend = "claude" if self._use_claude else "ollama"
|
||||
logger.debug(
|
||||
"steward_calling_llm",
|
||||
backend=backend,
|
||||
query_preview=query[:100],
|
||||
)
|
||||
|
||||
# Call Ollama API directly (more reliable than PydanticAI for plain text)
|
||||
async with httpx.AsyncClient(timeout=self.timeout) as client:
|
||||
response = await client.post(
|
||||
f"{self.ollama_host}/api/generate",
|
||||
json={
|
||||
"model": self.model_name,
|
||||
"prompt": prompt,
|
||||
"stream": False,
|
||||
"options": {
|
||||
"temperature": 0.3, # Lower = more consistent
|
||||
"top_p": 0.9
|
||||
}
|
||||
}
|
||||
)
|
||||
|
||||
response.raise_for_status()
|
||||
result = response.json()
|
||||
|
||||
analysis_text = result["response"].strip()
|
||||
try:
|
||||
if self._use_claude:
|
||||
# For Claude, split into system + user message
|
||||
# The prompt contains both, but Claude prefers explicit system
|
||||
analysis_text = await self._call_claude(
|
||||
system_prompt="You are the Steward of the household, advising the Butler (Tatlock) on which capabilities to use. Be concise and specific.",
|
||||
user_message=prompt,
|
||||
)
|
||||
else:
|
||||
analysis_text = await self._call_ollama(prompt)
|
||||
|
||||
logger.debug(
|
||||
"steward_analysis_received",
|
||||
text_preview=analysis_text[:150]
|
||||
backend=backend,
|
||||
text_preview=analysis_text[:150],
|
||||
)
|
||||
|
||||
return analysis_text
|
||||
|
||||
except Exception as e:
|
||||
# If Claude fails, try Ollama as fallback
|
||||
if self._use_claude:
|
||||
logger.warning(
|
||||
"steward_claude_fallback",
|
||||
error=str(e),
|
||||
)
|
||||
analysis_text = await self._call_ollama(prompt)
|
||||
logger.debug(
|
||||
"steward_analysis_received",
|
||||
backend="ollama_fallback",
|
||||
text_preview=analysis_text[:150],
|
||||
)
|
||||
return analysis_text
|
||||
raise
|
||||
|
||||
|
||||
# Global Steward instance
|
||||
_steward_agent = None
|
||||
|
||||
@@ -60,6 +60,10 @@ class StewardRecommendation(BaseModel):
|
||||
default_factory=dict,
|
||||
description="Pre-fetched user context from memory (profile, preferences)"
|
||||
)
|
||||
enriched_query: str = Field(
|
||||
default="",
|
||||
description="User query with auto-filled context (location, timezone) when not specified"
|
||||
)
|
||||
|
||||
def format_for_butler(self) -> str:
|
||||
"""
|
||||
|
||||
@@ -1,8 +1,8 @@
|
||||
"""
|
||||
Steward service layer.
|
||||
|
||||
Provides high-level interface for request analysis with logging,
|
||||
benchmarking, and error handling.
|
||||
Provides high-level interface for request analysis with logging
|
||||
and error handling.
|
||||
|
||||
Parses plain text recommendations into structured data.
|
||||
Includes memory pre-fetch for user context injection.
|
||||
@@ -10,7 +10,6 @@ Includes memory pre-fetch for user context injection.
|
||||
import re
|
||||
from typing import Any, Optional
|
||||
|
||||
from src.core.benchmarks import PerformanceBenchmark, get_benchmark_store
|
||||
from src.core.household_registry import get_household_registry
|
||||
from src.core.logging_config import get_logger, log_operation
|
||||
from src.core.memory_service import memory_service
|
||||
@@ -149,6 +148,68 @@ def _extract_missing_capabilities(text: str) -> Optional[str]:
|
||||
return None
|
||||
|
||||
|
||||
def _build_enriched_query(user_request: str, memory_context: dict[str, Any]) -> str:
|
||||
"""
|
||||
Build an enriched query by appending user context when not specified.
|
||||
|
||||
When the user asks location-dependent questions (weather, nearby, etc.)
|
||||
without specifying a location, this appends their known location.
|
||||
Similarly for timezone-dependent queries.
|
||||
|
||||
Args:
|
||||
user_request: The user's original request
|
||||
memory_context: Pre-fetched memory context with profile/preferences
|
||||
|
||||
Returns:
|
||||
str: Query with context appended, or original query if no enrichment needed
|
||||
|
||||
Example:
|
||||
>>> query = _build_enriched_query(
|
||||
... "What's the weather?",
|
||||
... {"profile": {"location": "Amsterdam", "timezone": "Europe/Amsterdam"}}
|
||||
... )
|
||||
>>> query
|
||||
"What's the weather?\n\n[User Context: location=Amsterdam, timezone=Europe/Amsterdam]"
|
||||
"""
|
||||
if not memory_context:
|
||||
return user_request
|
||||
|
||||
request_lower = user_request.lower()
|
||||
profile = memory_context.get("profile", {})
|
||||
preferences = memory_context.get("preferences", {})
|
||||
|
||||
context_parts = []
|
||||
|
||||
# Check if location is needed and not specified
|
||||
location_keywords = ["weather", "temperature", "forecast", "nearby", "local", "here"]
|
||||
# Use word boundary pattern to avoid false positives like "at" in "what"
|
||||
location_prepositions = [r'\bin\b', r'\bat\b', r'\bnear\b', r'\baround\b', r'\bfor\b']
|
||||
location_specified = any(re.search(p, request_lower) for p in location_prepositions)
|
||||
|
||||
if any(word in request_lower for word in location_keywords):
|
||||
if not location_specified and profile.get("location"):
|
||||
context_parts.append(f"location={profile['location']}")
|
||||
|
||||
# Check if timezone is needed and not specified
|
||||
time_keywords = ["time", "schedule", "meeting", "appointment", "when", "today", "tomorrow"]
|
||||
timezone_specified = any(word in request_lower for word in ["timezone", "tz", "utc", "gmt"])
|
||||
|
||||
if any(word in request_lower for word in time_keywords):
|
||||
if not timezone_specified and profile.get("timezone"):
|
||||
context_parts.append(f"timezone={profile['timezone']}")
|
||||
|
||||
# Add preferences if relevant
|
||||
if preferences.get("temperature_unit") and "weather" in request_lower:
|
||||
context_parts.append(f"temperature_unit={preferences['temperature_unit']}")
|
||||
|
||||
# Build enriched query
|
||||
if context_parts:
|
||||
context_str = ", ".join(context_parts)
|
||||
return f"{user_request}\n\n[User Context: {context_str}]"
|
||||
|
||||
return user_request
|
||||
|
||||
|
||||
async def _prefetch_memory_context(user_request: str) -> dict[str, Any]:
|
||||
"""
|
||||
Pre-fetch user context that might be needed for this request.
|
||||
@@ -175,7 +236,9 @@ async def _prefetch_memory_context(user_request: str) -> dict[str, Any]:
|
||||
# Location-related queries
|
||||
if any(word in request_lower for word in [
|
||||
"weather", "temperature", "forecast", "nearby", "local",
|
||||
"directions", "distance", "map", "here"
|
||||
"directions", "distance", "map", "here",
|
||||
# Direct location questions
|
||||
"live", "where", "home", "reside", "location", "address",
|
||||
]):
|
||||
profile_keys.append("location")
|
||||
|
||||
@@ -223,8 +286,7 @@ async def analyze_request(
|
||||
This is the main entry point for Steward analysis. It:
|
||||
1. Calls the Steward agent with full conversation history
|
||||
2. Logs the operation with timing
|
||||
3. Records performance benchmarks to Redis
|
||||
4. Returns structured recommendations
|
||||
3. Returns structured recommendations
|
||||
|
||||
Args:
|
||||
user_request: The current user message to analyze
|
||||
@@ -277,6 +339,9 @@ async def analyze_request(
|
||||
context = _extract_conversation_context(analysis_text, conversation_history)
|
||||
missing = _extract_missing_capabilities(analysis_text)
|
||||
|
||||
# Build enriched query with auto-filled context
|
||||
enriched_query = _build_enriched_query(user_request, memory_context)
|
||||
|
||||
recommendation = StewardRecommendation(
|
||||
recommended_capabilities=capabilities,
|
||||
reasoning=analysis_text,
|
||||
@@ -284,6 +349,7 @@ async def analyze_request(
|
||||
conversation_context=context,
|
||||
missing_capabilities=missing,
|
||||
memory_context=memory_context,
|
||||
enriched_query=enriched_query,
|
||||
)
|
||||
|
||||
# Update log context with results
|
||||
@@ -299,23 +365,6 @@ async def analyze_request(
|
||||
reasoning=analysis_text[:200], # First 200 chars
|
||||
)
|
||||
|
||||
# Record performance benchmark
|
||||
if log_ctx.get("duration_seconds"):
|
||||
benchmark = PerformanceBenchmark(
|
||||
operation="steward_analysis",
|
||||
duration_seconds=log_ctx["duration_seconds"],
|
||||
success=True,
|
||||
recommendation_count=len(recommendation.recommended_capabilities),
|
||||
confidence=None, # Could add confidence scoring in future
|
||||
conversation_id=conversation_id,
|
||||
metadata={
|
||||
"complexity": recommendation.estimated_complexity,
|
||||
"has_context": recommendation.conversation_context.has_previous_context,
|
||||
"missing_capabilities": recommendation.missing_capabilities is not None,
|
||||
},
|
||||
)
|
||||
await get_benchmark_store().record(benchmark)
|
||||
|
||||
return recommendation
|
||||
|
||||
except Exception as e:
|
||||
|
||||
+309
-73
@@ -17,10 +17,14 @@ from src.agents.tatlock_core.tools import (
|
||||
get_current_datetime,
|
||||
calculate_time_offset,
|
||||
time_difference,
|
||||
search_web,
|
||||
)
|
||||
from src.core.config import config
|
||||
from src.core.logging_config import get_logger
|
||||
from src.core.tracing import (
|
||||
start_span, end_span, get_current_span,
|
||||
add_tool_spans_from_messages,
|
||||
SpanType, SpanStatus,
|
||||
)
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
@@ -43,7 +47,16 @@ def generate_id() -> str:
|
||||
# System prompt defining Tatlock's personality
|
||||
TATLOCK_SYSTEM_PROMPT = """You are Tatlock, a helpful personal assistant with the demeanor of a British butler.
|
||||
|
||||
Address users as "sir" and maintain a formal yet personable tone. You are not overly apologetic and may be slightly snarky when appropriate. If an opportunity for a pun presents itself, you cannot resist.
|
||||
## Personality
|
||||
|
||||
Address users as "sir". Be confident, direct, and efficient - you are an unflappable English butler who gets things done. Dry wit and puns are encouraged.
|
||||
|
||||
**CRITICAL - Do NOT:**
|
||||
- Apologize unless you genuinely made an error
|
||||
- Say "Apologies for any confusion" or "Allow me to rectify" when nothing went wrong
|
||||
- Preface successful results with caveats or apologies
|
||||
|
||||
When presenting findings: lead with the answer, be concise, skip the preamble.
|
||||
|
||||
You coordinate with various household staff (expert agents) to provide comprehensive assistance across:
|
||||
- Research and knowledge work
|
||||
@@ -76,18 +89,17 @@ You have direct access to several permanent tools that you should USE whenever a
|
||||
- time_difference: Calculate the time between two dates
|
||||
- Use these for ANY date/time queries - never guess at dates or times
|
||||
|
||||
3. **Web Search** (search_web): Search for current, volatile, or factual information
|
||||
- Use this for ANY information that might be current, factual, or outside your training data
|
||||
3. **Web Search** (via Librarian): For current, volatile, or factual information
|
||||
- Delegate to the Librarian for web searches and research
|
||||
- Examples: news, current events, recent developments, specific facts, technical documentation
|
||||
- Always prefer searching over guessing or using potentially outdated knowledge
|
||||
- For extensive research questions, note that this will later be delegated to the librarian
|
||||
- Use: delegate_to_librarian(task="search the web for ...")
|
||||
|
||||
## Tool Usage Guidelines
|
||||
|
||||
- **Mathematics**: ALWAYS use the calculator tool, even for simple arithmetic
|
||||
- **Dates/Times**: ALWAYS use the date/time tools, never guess or estimate
|
||||
- **Current Information**: ALWAYS search for facts, news, or volatile information
|
||||
- **Verification**: When facts are important, use search to verify rather than rely on memory alone
|
||||
- **Current Information**: Delegate web searches to the Librarian
|
||||
- **Verification**: When facts are important, delegate to Librarian for research
|
||||
- When you use a tool, explain what you're doing in a butler-appropriate manner
|
||||
- Present tool results naturally in your response
|
||||
|
||||
@@ -126,10 +138,7 @@ class TatlockAgent(AgentInterface):
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
"""Initialize Tatlock configuration (lazy agent creation)."""
|
||||
# Store Ollama configuration
|
||||
self.ollama_host = str(config.OLLAMA_HOST)
|
||||
self.model_name = config.OLLAMA_DEFAULT_MODEL
|
||||
"""Initialize Tatlock (lazy agent creation)."""
|
||||
self._agent = None # Lazy initialization
|
||||
|
||||
def _ensure_agent(self):
|
||||
@@ -137,30 +146,21 @@ class TatlockAgent(AgentInterface):
|
||||
if self._agent is not None:
|
||||
return
|
||||
|
||||
from src.anthropic.model_selector import get_model, get_model_info
|
||||
|
||||
model_info = get_model_info()
|
||||
logger.info(
|
||||
"tatlock_agent_initializing",
|
||||
ollama_host=self.ollama_host,
|
||||
model=self.model_name,
|
||||
backend=model_info["backend"],
|
||||
model=model_info["model"],
|
||||
)
|
||||
|
||||
# Import required classes for Ollama configuration
|
||||
from pydantic_ai.models.openai import OpenAIChatModel
|
||||
from pydantic_ai.providers.ollama import OllamaProvider
|
||||
# Get best available model (Claude if available, else Ollama)
|
||||
model = get_model()
|
||||
|
||||
# PydanticAI expects Ollama base URL to end with /v1
|
||||
# Remove trailing slash from ollama_host if present
|
||||
clean_host = self.ollama_host.rstrip('/')
|
||||
base_url = f"{clean_host}/v1"
|
||||
|
||||
# Create Ollama model with provider
|
||||
ollama_model = OpenAIChatModel(
|
||||
model_name=self.model_name,
|
||||
provider=OllamaProvider(base_url=base_url)
|
||||
)
|
||||
|
||||
# Create PydanticAI agent with Ollama model
|
||||
# Create PydanticAI agent
|
||||
self._agent = Agent(
|
||||
ollama_model,
|
||||
model,
|
||||
system_prompt=TATLOCK_SYSTEM_PROMPT,
|
||||
)
|
||||
|
||||
@@ -238,28 +238,8 @@ class TatlockAgent(AgentInterface):
|
||||
ctx.deps.log_call(f"🕐 Calculating time difference between {date1_str} and {date2_str}")
|
||||
return time_difference(date1_str, date2_str)
|
||||
|
||||
# Web search tool
|
||||
@self._agent.tool
|
||||
async def web_search(ctx: RunContext[ToolCallTracker], query: str, num_results: int = 5) -> str:
|
||||
"""
|
||||
Search the web using SearXNG for current information.
|
||||
|
||||
Use this tool for ANY information that might be:
|
||||
- Current or time-sensitive (news, events, recent developments)
|
||||
- Factual and verifiable (statistics, technical specs, definitions)
|
||||
- Outside your training data or knowledge cutoff
|
||||
|
||||
Args:
|
||||
query: Search query string
|
||||
num_results: Number of results to return (default: 5, max: 10)
|
||||
|
||||
Returns:
|
||||
Formatted search results with titles, URLs, and snippets
|
||||
"""
|
||||
# Log the search query to reasoning output
|
||||
if ctx.deps:
|
||||
ctx.deps.log_call(f"🔍 Searching for: '{query}'")
|
||||
return await search_web(query, num_results)
|
||||
# NOTE: Web search has been moved to The Librarian agent.
|
||||
# Use delegate_to_librarian(task="search web for ...") for web search.
|
||||
|
||||
@property
|
||||
def agent(self):
|
||||
@@ -455,7 +435,7 @@ class TatlockAgent(AgentInterface):
|
||||
steward_note: Note from Steward (prepended to request, invisible to user)
|
||||
scoped_tools: List of tool definitions from household registry
|
||||
message_history: Conversation history in PydanticAI format
|
||||
tool_tracker: Optional tool call tracker for benchmarking
|
||||
tool_tracker: Optional tool call tracker for analysis
|
||||
|
||||
Returns:
|
||||
str: Tatlock's response text
|
||||
@@ -469,8 +449,7 @@ class TatlockAgent(AgentInterface):
|
||||
... tool_tracker=tracker,
|
||||
... )
|
||||
"""
|
||||
from pydantic_ai.models.openai import OpenAIChatModel
|
||||
from pydantic_ai.providers.ollama import OllamaProvider
|
||||
from src.anthropic.model_selector import get_model
|
||||
|
||||
logger.info(
|
||||
"tatlock_run_with_scoped_tools",
|
||||
@@ -481,18 +460,12 @@ class TatlockAgent(AgentInterface):
|
||||
|
||||
# Create a fresh agent instance with scoped tools only
|
||||
# This ensures Tatlock can ONLY use tools recommended by the Steward
|
||||
clean_host = self.ollama_host.rstrip('/')
|
||||
base_url = f"{clean_host}/v1"
|
||||
|
||||
ollama_model = OpenAIChatModel(
|
||||
model_name=self.model_name,
|
||||
provider=OllamaProvider(base_url=base_url)
|
||||
)
|
||||
model = get_model()
|
||||
|
||||
# Create agent with scoped tools
|
||||
# Tools from household registry are already PydanticAI Tool objects
|
||||
scoped_agent = Agent(
|
||||
ollama_model,
|
||||
model,
|
||||
system_prompt=TATLOCK_SYSTEM_PROMPT,
|
||||
tools=scoped_tools, # Pass tools directly to Agent constructor
|
||||
)
|
||||
@@ -563,8 +536,7 @@ class TatlockAgent(AgentInterface):
|
||||
Yields:
|
||||
Text chunks from the streaming response
|
||||
"""
|
||||
from pydantic_ai.models.openai import OpenAIChatModel
|
||||
from pydantic_ai.providers.ollama import OllamaProvider
|
||||
from src.anthropic.model_selector import get_model
|
||||
|
||||
logger.info(
|
||||
"tatlock_run_with_scoped_tools_stream",
|
||||
@@ -574,17 +546,11 @@ class TatlockAgent(AgentInterface):
|
||||
)
|
||||
|
||||
# Create a fresh agent instance with scoped tools only
|
||||
clean_host = self.ollama_host.rstrip('/')
|
||||
base_url = f"{clean_host}/v1"
|
||||
|
||||
ollama_model = OpenAIChatModel(
|
||||
model_name=self.model_name,
|
||||
provider=OllamaProvider(base_url=base_url)
|
||||
)
|
||||
model = get_model()
|
||||
|
||||
# Create agent with scoped tools
|
||||
scoped_agent = Agent(
|
||||
ollama_model,
|
||||
model,
|
||||
system_prompt=TATLOCK_SYSTEM_PROMPT,
|
||||
tools=scoped_tools,
|
||||
)
|
||||
@@ -630,6 +596,276 @@ class TatlockAgent(AgentInterface):
|
||||
|
||||
logger.info("tatlock_scoped_run_complete")
|
||||
|
||||
async def orchestrate_tool_calls(
|
||||
self,
|
||||
user_message: str,
|
||||
steward_note: str,
|
||||
scoped_tools: list[Any],
|
||||
message_history: list[dict],
|
||||
tool_tracker: Any = None,
|
||||
) -> dict[str, Any]:
|
||||
"""
|
||||
Phase 1: Execute tool calls and delegations, return structured results.
|
||||
|
||||
This is the coordination phase where Tatlock orchestrates tool calls
|
||||
and expert delegations. The raw output is captured for Phase 2 synthesis.
|
||||
|
||||
Args:
|
||||
user_message: The user's original message
|
||||
steward_note: Note from Steward (invisible to user)
|
||||
scoped_tools: List of tool definitions from household registry
|
||||
message_history: Conversation history
|
||||
tool_tracker: Optional tool call tracker for analysis
|
||||
|
||||
Returns:
|
||||
dict with:
|
||||
- tools_called: List of tool names that were called
|
||||
- expert_results: Dict mapping expert names to their outputs
|
||||
- tool_outputs: Dict mapping tool names to their outputs
|
||||
- raw_output: The agent's raw text output
|
||||
"""
|
||||
from pydantic_ai.settings import ModelSettings
|
||||
from pydantic_ai.messages import (
|
||||
ModelRequest,
|
||||
ModelResponse,
|
||||
UserPromptPart,
|
||||
TextPart,
|
||||
ToolCallPart,
|
||||
ToolReturnPart,
|
||||
)
|
||||
from src.anthropic.model_selector import get_model
|
||||
|
||||
logger.info(
|
||||
"tatlock_orchestrate_tool_calls",
|
||||
user_message_preview=user_message[:100],
|
||||
scoped_tool_count=len(scoped_tools),
|
||||
history_length=len(message_history),
|
||||
)
|
||||
|
||||
# Start tracing span for orchestration phase
|
||||
orchestrate_span = start_span(
|
||||
"tatlock_orchestrate",
|
||||
SpanType.TATLOCK,
|
||||
metadata={
|
||||
"scoped_tool_count": len(scoped_tools),
|
||||
"tool_names": [getattr(t, '__name__', str(t)) for t in scoped_tools[:5]],
|
||||
},
|
||||
)
|
||||
|
||||
# Create a fresh agent instance with scoped tools only
|
||||
model = get_model()
|
||||
|
||||
# Create agent with scoped tools
|
||||
scoped_agent = Agent(
|
||||
model,
|
||||
system_prompt=TATLOCK_SYSTEM_PROMPT,
|
||||
tools=scoped_tools,
|
||||
)
|
||||
|
||||
# Prepend Steward's note to the request
|
||||
enriched_message = f"{steward_note}\n\n{user_message}"
|
||||
|
||||
# Convert message history to PydanticAI format
|
||||
pydantic_history = []
|
||||
for msg in message_history:
|
||||
role = msg.get("role")
|
||||
content = msg.get("content", "")
|
||||
|
||||
if not content or not content.strip():
|
||||
continue
|
||||
|
||||
if role == "user":
|
||||
pydantic_history.append(
|
||||
ModelRequest(parts=[UserPromptPart(content=content)])
|
||||
)
|
||||
elif role == "assistant":
|
||||
pydantic_history.append(
|
||||
ModelResponse(parts=[TextPart(content=content)])
|
||||
)
|
||||
|
||||
# Run with scoped tools and tracker
|
||||
result = await scoped_agent.run(
|
||||
enriched_message,
|
||||
message_history=pydantic_history if pydantic_history else None,
|
||||
deps=tool_tracker,
|
||||
model_settings=ModelSettings(extra_body={"tool_choice": "required"})
|
||||
)
|
||||
|
||||
# Extract tool calls and results from the agent's messages
|
||||
tools_called = []
|
||||
expert_results = {}
|
||||
tool_outputs = {}
|
||||
|
||||
# Parse through new messages to find tool calls and returns
|
||||
for msg in result.new_messages():
|
||||
if isinstance(msg, ModelResponse):
|
||||
for part in msg.parts:
|
||||
if isinstance(part, ToolCallPart):
|
||||
tools_called.append(part.tool_name)
|
||||
elif isinstance(msg, ModelRequest):
|
||||
for part in msg.parts:
|
||||
if isinstance(part, ToolReturnPart):
|
||||
tool_name = part.tool_name
|
||||
content = part.content
|
||||
|
||||
# Categorize as expert result or tool output
|
||||
if tool_name.startswith("delegate_to_"):
|
||||
expert_name = tool_name.replace("delegate_to_", "")
|
||||
expert_results[expert_name] = content
|
||||
else:
|
||||
tool_outputs[tool_name] = content
|
||||
|
||||
logger.info(
|
||||
"tatlock_orchestration_complete",
|
||||
tools_called=tools_called,
|
||||
expert_count=len(expert_results),
|
||||
tool_output_count=len(tool_outputs),
|
||||
)
|
||||
|
||||
# Add tool-level spans from result messages
|
||||
if orchestrate_span:
|
||||
add_tool_spans_from_messages(result.new_messages(), orchestrate_span)
|
||||
|
||||
# End orchestration span with results
|
||||
end_span(
|
||||
orchestrate_span,
|
||||
metadata_update={
|
||||
"tools_called": tools_called,
|
||||
"expert_count": len(expert_results),
|
||||
"tool_output_count": len(tool_outputs),
|
||||
},
|
||||
details_update={
|
||||
"steward_note_preview": steward_note[:500] if steward_note else None,
|
||||
},
|
||||
)
|
||||
|
||||
return {
|
||||
"tools_called": tools_called,
|
||||
"expert_results": expert_results,
|
||||
"tool_outputs": tool_outputs,
|
||||
"raw_output": result.output,
|
||||
}
|
||||
|
||||
async def synthesize_from_results(
|
||||
self,
|
||||
user_message: str,
|
||||
orchestration_results: dict[str, Any],
|
||||
message_history: list[dict],
|
||||
) -> str:
|
||||
"""
|
||||
Phase 2: Synthesize butler-toned response from gathered results.
|
||||
|
||||
This is the synthesis phase where Tatlock takes the coordination
|
||||
results and produces a properly butler-toned response.
|
||||
|
||||
Args:
|
||||
user_message: The user's original message
|
||||
orchestration_results: Results from orchestrate_tool_calls()
|
||||
message_history: Conversation history
|
||||
|
||||
Returns:
|
||||
str: Butler-toned response synthesized from all results
|
||||
"""
|
||||
from pydantic_ai.messages import ModelRequest, ModelResponse, UserPromptPart, TextPart
|
||||
from src.anthropic.model_selector import get_model
|
||||
|
||||
logger.info(
|
||||
"tatlock_synthesize_from_results",
|
||||
user_message_preview=user_message[:100],
|
||||
expert_count=len(orchestration_results.get("expert_results", {})),
|
||||
tool_count=len(orchestration_results.get("tool_outputs", {})),
|
||||
)
|
||||
|
||||
# Start tracing span for synthesis phase
|
||||
synthesize_span = start_span(
|
||||
"tatlock_synthesize",
|
||||
SpanType.TATLOCK,
|
||||
metadata={
|
||||
"expert_count": len(orchestration_results.get("expert_results", {})),
|
||||
"tool_output_count": len(orchestration_results.get("tool_outputs", {})),
|
||||
},
|
||||
)
|
||||
|
||||
# Build synthesis prompt with all available information
|
||||
synthesis_parts = []
|
||||
synthesis_parts.append(f"The user asked: {user_message}")
|
||||
synthesis_parts.append("")
|
||||
|
||||
# Add expert findings if any
|
||||
if orchestration_results.get("expert_results"):
|
||||
synthesis_parts.append("Expert findings:")
|
||||
for expert, result in orchestration_results["expert_results"].items():
|
||||
synthesis_parts.append(f"- {expert.title()}: {result}")
|
||||
synthesis_parts.append("")
|
||||
|
||||
# Add tool outputs if any
|
||||
if orchestration_results.get("tool_outputs"):
|
||||
synthesis_parts.append("Tool results:")
|
||||
for tool, result in orchestration_results["tool_outputs"].items():
|
||||
synthesis_parts.append(f"- {tool}: {result}")
|
||||
synthesis_parts.append("")
|
||||
|
||||
synthesis_parts.append(
|
||||
"Synthesize a response for the user. Be direct and confident. "
|
||||
"Lead with the answer - no apologies, no caveats, no 'mix-ups'. "
|
||||
"Address them as 'sir', be concise, add dry wit if appropriate."
|
||||
)
|
||||
|
||||
synthesis_prompt = "\n".join(synthesis_parts)
|
||||
|
||||
# Create synthesis agent (no tools needed)
|
||||
model = get_model()
|
||||
|
||||
# Synthesis agent uses butler prompt but no tools
|
||||
synthesis_agent = Agent(
|
||||
model,
|
||||
system_prompt=TATLOCK_SYSTEM_PROMPT,
|
||||
# No tools for synthesis phase
|
||||
)
|
||||
|
||||
# Convert message history to PydanticAI format
|
||||
pydantic_history = []
|
||||
for msg in message_history:
|
||||
role = msg.get("role")
|
||||
content = msg.get("content", "")
|
||||
|
||||
if not content or not content.strip():
|
||||
continue
|
||||
|
||||
if role == "user":
|
||||
pydantic_history.append(
|
||||
ModelRequest(parts=[UserPromptPart(content=content)])
|
||||
)
|
||||
elif role == "assistant":
|
||||
pydantic_history.append(
|
||||
ModelResponse(parts=[TextPart(content=content)])
|
||||
)
|
||||
|
||||
# Run synthesis
|
||||
result = await synthesis_agent.run(
|
||||
synthesis_prompt,
|
||||
message_history=pydantic_history if pydantic_history else None,
|
||||
)
|
||||
|
||||
logger.info(
|
||||
"tatlock_synthesis_complete",
|
||||
response_preview=result.output[:100],
|
||||
)
|
||||
|
||||
# End synthesis span with result
|
||||
end_span(
|
||||
synthesize_span,
|
||||
metadata_update={
|
||||
"response_length": len(result.output),
|
||||
},
|
||||
details_update={
|
||||
"synthesis_prompt": synthesis_prompt[:1000],
|
||||
"response_preview": result.output[:500],
|
||||
},
|
||||
)
|
||||
|
||||
return result.output
|
||||
|
||||
async def get_capabilities(self) -> dict:
|
||||
"""Return current capabilities."""
|
||||
return {
|
||||
|
||||
@@ -1,7 +1,8 @@
|
||||
"""
|
||||
Tatlock's core tools package.
|
||||
|
||||
Provides calculator, date/time, and web search capabilities.
|
||||
Provides calculator and date/time capabilities.
|
||||
Web search has been moved to The Librarian agent.
|
||||
Organized as a household member with toolset and capability registration.
|
||||
"""
|
||||
from .capability import TATLOCK_CORE_CAPABILITY, get_capability
|
||||
@@ -10,7 +11,6 @@ from .tools import (
|
||||
calculate,
|
||||
calculate_time_offset,
|
||||
get_current_datetime,
|
||||
search_web,
|
||||
time_difference,
|
||||
)
|
||||
|
||||
@@ -20,7 +20,6 @@ __all__ = [
|
||||
"get_current_datetime",
|
||||
"calculate_time_offset",
|
||||
"time_difference",
|
||||
"search_web",
|
||||
# Toolset
|
||||
"tatlock_core_tools",
|
||||
"get_core_tools",
|
||||
|
||||
@@ -11,10 +11,10 @@ TATLOCK_CORE_CAPABILITY = HouseholdCapability(
|
||||
name="tatlock_core",
|
||||
role="Butler's Core Tools",
|
||||
category="core",
|
||||
description="Essential tools for computation, date/time operations, and web searches",
|
||||
domains=["computation", "datetime", "information", "research"],
|
||||
description="Essential tools for computation and date/time operations",
|
||||
domains=["computation", "datetime", "math", "calculator"],
|
||||
cost="low",
|
||||
requires_network=True, # For web search
|
||||
requires_network=False, # Web search moved to Librarian
|
||||
)
|
||||
|
||||
|
||||
|
||||
@@ -256,96 +256,5 @@ def time_difference(date1_str: str, date2_str: str = "now") -> str:
|
||||
return f"Error calculating time difference: {str(e)}"
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# SearXNG Search Tool
|
||||
# ============================================================================
|
||||
|
||||
async def search_web(query: str, num_results: int = 5) -> str:
|
||||
"""
|
||||
Search the web using SearXNG.
|
||||
|
||||
Args:
|
||||
query: Search query string
|
||||
num_results: Number of results to return (default: 5, max: 10)
|
||||
|
||||
Returns:
|
||||
Formatted search results as a string with titles, URLs, and snippets
|
||||
|
||||
Examples:
|
||||
search_web("Python async programming") -> "1. Title: ...\n URL: ...\n ..."
|
||||
"""
|
||||
try:
|
||||
# Limit results
|
||||
num_results = min(num_results, 10)
|
||||
|
||||
# Get SearXNG host with fallback logic
|
||||
searxng_host = str(config.SEARXNG_HOST)
|
||||
|
||||
# Try production host first, fall back to localhost in development
|
||||
hosts_to_try = [searxng_host]
|
||||
if config.ENVIRONMENT.value == "development" and "localhost" not in searxng_host:
|
||||
# Add localhost fallback for development
|
||||
hosts_to_try.append("http://localhost:8087")
|
||||
|
||||
last_error = None
|
||||
|
||||
for host in hosts_to_try:
|
||||
try:
|
||||
logger.debug("searxng_search_attempt", host=host, query=query)
|
||||
|
||||
async with httpx.AsyncClient(timeout=config.SEARXNG_TIMEOUT) as client:
|
||||
response = await client.get(
|
||||
f"{host}/search",
|
||||
params={
|
||||
"q": query,
|
||||
"format": "json",
|
||||
"pageno": 1,
|
||||
}
|
||||
)
|
||||
|
||||
if response.status_code == 200:
|
||||
data = response.json()
|
||||
results = data.get("results", [])
|
||||
|
||||
if not results:
|
||||
return f"No results found for '{query}'"
|
||||
|
||||
# Format results
|
||||
formatted_results = []
|
||||
for i, result in enumerate(results[:num_results], 1):
|
||||
title = result.get("title", "No title")
|
||||
url = result.get("url", "")
|
||||
content = result.get("content", "No description available")
|
||||
|
||||
formatted_results.append(
|
||||
f"{i}. {title}\n"
|
||||
f" URL: {url}\n"
|
||||
f" {content}\n"
|
||||
)
|
||||
|
||||
logger.info(
|
||||
"searxng_search_success",
|
||||
host=host,
|
||||
query=query,
|
||||
result_count=len(results),
|
||||
)
|
||||
return "\n".join(formatted_results)
|
||||
else:
|
||||
last_error = f"SearXNG returned status {response.status_code}"
|
||||
|
||||
except httpx.ConnectError:
|
||||
last_error = f"Cannot connect to SearXNG at {host}"
|
||||
logger.warning("searxng_connection_failed", host=host)
|
||||
continue
|
||||
except Exception as e:
|
||||
last_error = str(e)
|
||||
logger.warning("searxng_error", host=host, error=str(e))
|
||||
continue
|
||||
|
||||
# All hosts failed
|
||||
logger.error("searxng_all_hosts_failed", error=last_error)
|
||||
return f"Error searching: {last_error}. Please check that SearXNG is running."
|
||||
|
||||
except Exception as e:
|
||||
logger.error("searxng_unexpected_error", error=str(e), exc_info=True)
|
||||
return f"Error searching: {str(e)}"
|
||||
# NOTE: Web search has been moved to The Librarian agent.
|
||||
# Use delegate_to_librarian(task="search web for ...") for web search.
|
||||
|
||||
@@ -55,17 +55,8 @@ time_difference_tool = Tool(
|
||||
),
|
||||
)
|
||||
|
||||
web_search_tool = Tool(
|
||||
function=tools.search_web,
|
||||
name="search_web",
|
||||
description=(
|
||||
"Search the web using SearXNG for current information. "
|
||||
"Use this to find recent events, current data, or verify facts. "
|
||||
"Returns formatted results with titles, URLs, and snippets. "
|
||||
"Useful for information that may have changed since training data."
|
||||
),
|
||||
takes_ctx=False,
|
||||
)
|
||||
# NOTE: Web search has been moved to The Librarian agent.
|
||||
# Use delegate_to_librarian(task="search web for ...") for web search.
|
||||
|
||||
|
||||
# Combined toolset of all core tools
|
||||
@@ -74,7 +65,6 @@ tatlock_core_tools = [
|
||||
current_datetime_tool,
|
||||
time_offset_tool,
|
||||
time_difference_tool,
|
||||
web_search_tool,
|
||||
]
|
||||
|
||||
|
||||
|
||||
+3
-97
@@ -4,20 +4,14 @@ Tatlock's permanent tools.
|
||||
These tools are always available to the butler agent:
|
||||
- Calculator: For all mathematical operations
|
||||
- Date/Time toolkit: For current time and time calculations
|
||||
- SearXNG search: For searching the web for current information
|
||||
|
||||
Note: Web search has been moved to The Librarian agent.
|
||||
See src/agents/librarian/tools.py for search_web functionality.
|
||||
"""
|
||||
|
||||
import logging
|
||||
import math
|
||||
import re
|
||||
from datetime import datetime, timedelta
|
||||
from typing import Any
|
||||
|
||||
import httpx
|
||||
|
||||
from src.core.config import config
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
# ============================================================================
|
||||
@@ -256,91 +250,3 @@ def time_difference(date1_str: str, date2_str: str = "now") -> str:
|
||||
|
||||
except Exception as e:
|
||||
return f"Error calculating time difference: {str(e)}"
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# SearXNG Search Tool
|
||||
# ============================================================================
|
||||
|
||||
async def search_web(query: str, num_results: int = 5) -> str:
|
||||
"""
|
||||
Search the web using SearXNG.
|
||||
|
||||
Args:
|
||||
query: Search query string
|
||||
num_results: Number of results to return (default: 5, max: 10)
|
||||
|
||||
Returns:
|
||||
Formatted search results as a string with titles, URLs, and snippets
|
||||
|
||||
Examples:
|
||||
search_web("Python async programming") -> "1. Title: ...\n URL: ...\n ..."
|
||||
"""
|
||||
try:
|
||||
# Limit results
|
||||
num_results = min(num_results, 10)
|
||||
|
||||
# Get SearXNG host with fallback logic
|
||||
searxng_host = str(config.SEARXNG_HOST)
|
||||
|
||||
# Try production host first, fall back to localhost in development
|
||||
hosts_to_try = [searxng_host]
|
||||
if config.ENVIRONMENT.value == "development" and "localhost" not in searxng_host:
|
||||
# Add localhost fallback for development
|
||||
hosts_to_try.append("http://localhost:8087")
|
||||
|
||||
last_error = None
|
||||
|
||||
for host in hosts_to_try:
|
||||
try:
|
||||
logger.info(f"Attempting SearXNG search at {host}")
|
||||
|
||||
async with httpx.AsyncClient(timeout=config.SEARXNG_TIMEOUT) as client:
|
||||
response = await client.get(
|
||||
f"{host}/search",
|
||||
params={
|
||||
"q": query,
|
||||
"format": "json",
|
||||
"pageno": 1,
|
||||
}
|
||||
)
|
||||
|
||||
if response.status_code == 200:
|
||||
data = response.json()
|
||||
results = data.get("results", [])
|
||||
|
||||
if not results:
|
||||
return f"No results found for '{query}'"
|
||||
|
||||
# Format results
|
||||
formatted_results = []
|
||||
for i, result in enumerate(results[:num_results], 1):
|
||||
title = result.get("title", "No title")
|
||||
url = result.get("url", "")
|
||||
content = result.get("content", "No description available")
|
||||
|
||||
formatted_results.append(
|
||||
f"{i}. {title}\n"
|
||||
f" URL: {url}\n"
|
||||
f" {content}\n"
|
||||
)
|
||||
|
||||
return "\n".join(formatted_results)
|
||||
else:
|
||||
last_error = f"SearXNG returned status {response.status_code}"
|
||||
|
||||
except httpx.ConnectError:
|
||||
last_error = f"Cannot connect to SearXNG at {host}"
|
||||
logger.warning(f"SearXNG connection failed at {host}, trying next host if available")
|
||||
continue
|
||||
except Exception as e:
|
||||
last_error = str(e)
|
||||
logger.warning(f"SearXNG error at {host}: {e}")
|
||||
continue
|
||||
|
||||
# All hosts failed
|
||||
return f"Error searching: {last_error}. Please check that SearXNG is running."
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Unexpected error in search_web: {e}", exc_info=True)
|
||||
return f"Error searching: {str(e)}"
|
||||
|
||||
@@ -0,0 +1,17 @@
|
||||
"""
|
||||
Anthropic/Claude integration module.
|
||||
|
||||
Provides model selection with automatic fallback between Claude and Ollama.
|
||||
"""
|
||||
|
||||
from src.anthropic.model_selector import (
|
||||
check_claude_health,
|
||||
get_model,
|
||||
is_claude_available,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
"check_claude_health",
|
||||
"get_model",
|
||||
"is_claude_available",
|
||||
]
|
||||
@@ -0,0 +1,151 @@
|
||||
"""
|
||||
Model selector for Claude/Ollama backend switching.
|
||||
|
||||
Provides automatic model selection with Claude as preferred backend
|
||||
and Ollama as offline fallback.
|
||||
"""
|
||||
|
||||
from typing import Union
|
||||
|
||||
from pydantic_ai.models.anthropic import AnthropicModel
|
||||
from pydantic_ai.models.openai import OpenAIChatModel
|
||||
|
||||
from src.core.config import config
|
||||
from src.core.logging_config import get_logger
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
# Cached health check result (set once at startup)
|
||||
_claude_available: bool | None = None
|
||||
|
||||
|
||||
async def check_claude_health() -> bool:
|
||||
"""
|
||||
Check if Claude API is reachable and working.
|
||||
|
||||
This should be called once at application startup.
|
||||
The result is cached in `_claude_available`.
|
||||
|
||||
Returns:
|
||||
True if Claude API is accessible, False otherwise.
|
||||
"""
|
||||
global _claude_available
|
||||
|
||||
# No API key configured - Claude not available
|
||||
if not config.ANTHROPIC_API_KEY:
|
||||
logger.info(
|
||||
"claude_health_check_skipped",
|
||||
reason="no_api_key",
|
||||
)
|
||||
_claude_available = False
|
||||
return False
|
||||
|
||||
try:
|
||||
from anthropic import AsyncAnthropic
|
||||
|
||||
client = AsyncAnthropic(api_key=config.ANTHROPIC_API_KEY)
|
||||
|
||||
# Minimal API call to verify connectivity
|
||||
# Using a tiny max_tokens to minimize cost
|
||||
await client.messages.create(
|
||||
model=config.ANTHROPIC_MODEL,
|
||||
max_tokens=1,
|
||||
messages=[{"role": "user", "content": "hi"}],
|
||||
)
|
||||
|
||||
_claude_available = True
|
||||
logger.info(
|
||||
"claude_health_check_passed",
|
||||
model=config.ANTHROPIC_MODEL,
|
||||
)
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
_claude_available = False
|
||||
logger.warning(
|
||||
"claude_health_check_failed",
|
||||
error=str(e),
|
||||
model=config.ANTHROPIC_MODEL,
|
||||
)
|
||||
return False
|
||||
|
||||
|
||||
def is_claude_available() -> bool:
|
||||
"""
|
||||
Check if Claude is available (from cached health check result).
|
||||
|
||||
Returns:
|
||||
True if Claude API was reachable at startup, False otherwise.
|
||||
|
||||
Note:
|
||||
Returns False if health check hasn't been run yet.
|
||||
Call `check_claude_health()` at startup first.
|
||||
"""
|
||||
return _claude_available is True
|
||||
|
||||
|
||||
def get_model(prefer_cloud: bool | None = None) -> Union[AnthropicModel, OpenAIChatModel]:
|
||||
"""
|
||||
Get the best available model.
|
||||
|
||||
Returns Claude if available and preferred, otherwise Ollama.
|
||||
|
||||
Args:
|
||||
prefer_cloud: Override config.PREFER_CLOUD_BACKEND for this call.
|
||||
If None, uses the config value.
|
||||
|
||||
Returns:
|
||||
PydanticAI model instance (AnthropicModel or OpenAIChatModel).
|
||||
|
||||
Example:
|
||||
>>> model = get_model()
|
||||
>>> agent = Agent(model, system_prompt="...")
|
||||
"""
|
||||
# Determine preference
|
||||
use_cloud = prefer_cloud if prefer_cloud is not None else config.PREFER_CLOUD_BACKEND
|
||||
|
||||
# Use Claude if available and preferred
|
||||
if use_cloud and is_claude_available():
|
||||
logger.debug(
|
||||
"model_selected",
|
||||
backend="claude",
|
||||
model=config.ANTHROPIC_MODEL,
|
||||
)
|
||||
return AnthropicModel(
|
||||
model_name=config.ANTHROPIC_MODEL,
|
||||
api_key=config.ANTHROPIC_API_KEY,
|
||||
)
|
||||
|
||||
# Fall back to Ollama
|
||||
from src.ollama.provider import get_ollama_provider
|
||||
|
||||
logger.debug(
|
||||
"model_selected",
|
||||
backend="ollama",
|
||||
model=config.OLLAMA_DEFAULT_MODEL,
|
||||
reason="fallback" if use_cloud else "preferred_local",
|
||||
)
|
||||
return OpenAIChatModel(
|
||||
model_name=config.OLLAMA_DEFAULT_MODEL,
|
||||
provider=get_ollama_provider(),
|
||||
)
|
||||
|
||||
|
||||
def get_model_info() -> dict:
|
||||
"""
|
||||
Get information about the current model configuration.
|
||||
|
||||
Useful for health checks and debugging.
|
||||
|
||||
Returns:
|
||||
Dict with backend, model name, and availability info.
|
||||
"""
|
||||
use_cloud = config.PREFER_CLOUD_BACKEND and is_claude_available()
|
||||
|
||||
return {
|
||||
"backend": "claude" if use_cloud else "ollama",
|
||||
"model": config.ANTHROPIC_MODEL if use_cloud else config.OLLAMA_DEFAULT_MODEL,
|
||||
"claude_available": is_claude_available(),
|
||||
"claude_configured": bool(config.ANTHROPIC_API_KEY),
|
||||
"prefer_cloud": config.PREFER_CLOUD_BACKEND,
|
||||
}
|
||||
@@ -55,6 +55,7 @@ class ChatCompletionChunkDelta(CustomBaseModel):
|
||||
"""Delta in streaming chunk."""
|
||||
role: str | None = None
|
||||
content: str | None = None
|
||||
reasoning_content: str | None = None # For thinking/reasoning (DeepSeek R1 format)
|
||||
|
||||
|
||||
class ChatCompletionChunkChoice(CustomBaseModel):
|
||||
|
||||
+6
-35
@@ -172,24 +172,9 @@ async def create_chat_completion_stream(
|
||||
|
||||
async for event in stream_generator:
|
||||
if event.event == StreamEventType.REASONING_SUMMARY_DELTA:
|
||||
# Start <think> block if needed
|
||||
if not in_reasoning:
|
||||
yield ChatCompletionChunk(
|
||||
id=completion_id,
|
||||
object=constants.CHAT_COMPLETION_CHUNK_OBJECT,
|
||||
created=created_at,
|
||||
model=request.model,
|
||||
choices=[
|
||||
ChatCompletionChunkChoice(
|
||||
index=0,
|
||||
delta=ChatCompletionChunkDelta(content="<think>\n"),
|
||||
finish_reason=None,
|
||||
)
|
||||
],
|
||||
)
|
||||
in_reasoning = True
|
||||
|
||||
# Stream reasoning delta
|
||||
# Stream reasoning via reasoning_content field (DeepSeek R1 format)
|
||||
# Open WebUI renders this as collapsible thinking block
|
||||
in_reasoning = True
|
||||
yield ChatCompletionChunk(
|
||||
id=completion_id,
|
||||
object=constants.CHAT_COMPLETION_CHUNK_OBJECT,
|
||||
@@ -198,29 +183,15 @@ async def create_chat_completion_stream(
|
||||
choices=[
|
||||
ChatCompletionChunkChoice(
|
||||
index=0,
|
||||
delta=ChatCompletionChunkDelta(content=event.delta),
|
||||
delta=ChatCompletionChunkDelta(reasoning_content=event.delta),
|
||||
finish_reason=None,
|
||||
)
|
||||
],
|
||||
)
|
||||
|
||||
elif event.event == StreamEventType.REASONING_SUMMARY_DONE:
|
||||
# Close <think> block
|
||||
if in_reasoning:
|
||||
yield ChatCompletionChunk(
|
||||
id=completion_id,
|
||||
object=constants.CHAT_COMPLETION_CHUNK_OBJECT,
|
||||
created=created_at,
|
||||
model=request.model,
|
||||
choices=[
|
||||
ChatCompletionChunkChoice(
|
||||
index=0,
|
||||
delta=ChatCompletionChunkDelta(content="</think>\n\n"),
|
||||
finish_reason=None,
|
||||
)
|
||||
],
|
||||
)
|
||||
in_reasoning = False
|
||||
# Signal end of reasoning block (no content needed)
|
||||
in_reasoning = False
|
||||
|
||||
elif event.event == StreamEventType.OUTPUT_TEXT_DELTA:
|
||||
# Stream message content
|
||||
|
||||
@@ -1,337 +0,0 @@
|
||||
"""
|
||||
Performance benchmark storage using Redis.
|
||||
|
||||
Tracks operation timing, tool usage, and recommendation accuracy across sessions.
|
||||
Provides time-series data for performance analysis and optimization.
|
||||
"""
|
||||
import json
|
||||
from datetime import datetime, timezone
|
||||
from typing import Any, Literal, Optional
|
||||
|
||||
import redis.asyncio as redis
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from .config import config
|
||||
from .logging_config import get_logger
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
|
||||
class PerformanceBenchmark(BaseModel):
|
||||
"""
|
||||
Performance benchmark record.
|
||||
|
||||
Stores timing and metadata for operations like Steward analysis,
|
||||
tool calls, and agent execution.
|
||||
"""
|
||||
timestamp: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
|
||||
operation: str # "steward_analysis", "tool_call", "tatlock_execution"
|
||||
duration_seconds: float
|
||||
success: bool
|
||||
|
||||
# Steward-specific fields
|
||||
recommendation_count: Optional[int] = None
|
||||
confidence: Optional[float] = None
|
||||
|
||||
# Tool-specific fields
|
||||
tool_name: Optional[str] = None
|
||||
was_recommended: Optional[bool] = None
|
||||
was_actually_used: Optional[bool] = None
|
||||
|
||||
# Context
|
||||
conversation_id: Optional[str] = None
|
||||
metadata: dict[str, Any] = Field(default_factory=dict)
|
||||
|
||||
def to_redis_dict(self) -> dict[str, Any]:
|
||||
"""Convert to dict suitable for Redis storage."""
|
||||
data = self.model_dump()
|
||||
data["timestamp"] = self.timestamp.isoformat()
|
||||
data["metadata"] = json.dumps(self.metadata)
|
||||
return data
|
||||
|
||||
@classmethod
|
||||
def from_redis_dict(cls, data: dict[str, Any]) -> "PerformanceBenchmark":
|
||||
"""Reconstruct from Redis dict."""
|
||||
data["timestamp"] = datetime.fromisoformat(data["timestamp"])
|
||||
data["metadata"] = json.loads(data.get("metadata", "{}"))
|
||||
return cls(**data)
|
||||
|
||||
|
||||
class BenchmarkStore:
|
||||
"""
|
||||
Redis-backed benchmark storage with automatic expiry.
|
||||
|
||||
Stores performance metrics in time-series format with 30-day retention.
|
||||
Provides querying capabilities for analysis and reporting.
|
||||
"""
|
||||
|
||||
def __init__(self, redis_client: Optional[redis.Redis] = None):
|
||||
"""
|
||||
Initialize benchmark store.
|
||||
|
||||
Args:
|
||||
redis_client: Optional Redis client. If None, creates from config.
|
||||
"""
|
||||
self._client = redis_client
|
||||
self._ttl_days = 30 # 30-day retention
|
||||
|
||||
async def _get_client(self) -> redis.Redis:
|
||||
"""Get or create Redis client."""
|
||||
if self._client is None:
|
||||
self._client = redis.from_url(
|
||||
config.redis_url,
|
||||
encoding="utf-8",
|
||||
decode_responses=True,
|
||||
socket_timeout=config.REDIS_TIMEOUT,
|
||||
socket_connect_timeout=config.REDIS_TIMEOUT,
|
||||
)
|
||||
return self._client
|
||||
|
||||
async def record(self, benchmark: PerformanceBenchmark) -> None:
|
||||
"""
|
||||
Record a performance benchmark.
|
||||
|
||||
Args:
|
||||
benchmark: Performance benchmark to record
|
||||
|
||||
Example:
|
||||
>>> await store.record(PerformanceBenchmark(
|
||||
... operation="steward_analysis",
|
||||
... duration_seconds=1.23,
|
||||
... success=True,
|
||||
... recommendation_count=3,
|
||||
... ))
|
||||
"""
|
||||
if not config.ENABLE_BENCHMARKS:
|
||||
return
|
||||
|
||||
try:
|
||||
client = await self._get_client()
|
||||
|
||||
# Generate key: benchmark:{operation}:{timestamp_ms}
|
||||
timestamp_ms = int(benchmark.timestamp.timestamp() * 1000)
|
||||
key = f"benchmark:{benchmark.operation}:{timestamp_ms}"
|
||||
|
||||
# Store as hash
|
||||
await client.hset(key, mapping=benchmark.to_redis_dict())
|
||||
|
||||
# Set expiry
|
||||
await client.expire(key, self._ttl_days * 24 * 60 * 60)
|
||||
|
||||
# Add to sorted set for time-based queries
|
||||
index_key = f"benchmark_index:{benchmark.operation}"
|
||||
await client.zadd(index_key, {key: timestamp_ms})
|
||||
await client.expire(index_key, self._ttl_days * 24 * 60 * 60)
|
||||
|
||||
logger.debug(
|
||||
"benchmark_recorded",
|
||||
operation=benchmark.operation,
|
||||
duration=benchmark.duration_seconds,
|
||||
success=benchmark.success,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(
|
||||
"benchmark_recording_failed",
|
||||
error=str(e),
|
||||
operation=benchmark.operation,
|
||||
)
|
||||
# Don't fail the request if benchmarking fails
|
||||
|
||||
async def query(
|
||||
self,
|
||||
operation: str,
|
||||
start_time: Optional[datetime] = None,
|
||||
end_time: Optional[datetime] = None,
|
||||
limit: int = 100,
|
||||
) -> list[PerformanceBenchmark]:
|
||||
"""
|
||||
Query benchmarks by operation and time range.
|
||||
|
||||
Args:
|
||||
operation: Operation name to filter by
|
||||
start_time: Start of time range (inclusive)
|
||||
end_time: End of time range (inclusive)
|
||||
limit: Maximum number of results
|
||||
|
||||
Returns:
|
||||
List of benchmarks matching the query
|
||||
|
||||
Example:
|
||||
>>> from datetime import timedelta
|
||||
>>> now = datetime.now(timezone.utc)
|
||||
>>> yesterday = now - timedelta(days=1)
|
||||
>>> benchmarks = await store.query(
|
||||
... "steward_analysis",
|
||||
... start_time=yesterday,
|
||||
... limit=50
|
||||
... )
|
||||
"""
|
||||
if not config.ENABLE_BENCHMARKS:
|
||||
return []
|
||||
|
||||
try:
|
||||
client = await self._get_client()
|
||||
index_key = f"benchmark_index:{operation}"
|
||||
|
||||
# Convert time range to timestamps
|
||||
min_score = (
|
||||
int(start_time.timestamp() * 1000)
|
||||
if start_time
|
||||
else "-inf"
|
||||
)
|
||||
max_score = (
|
||||
int(end_time.timestamp() * 1000)
|
||||
if end_time
|
||||
else "+inf"
|
||||
)
|
||||
|
||||
# Query sorted set
|
||||
keys = await client.zrevrangebyscore(
|
||||
index_key,
|
||||
max_score,
|
||||
min_score,
|
||||
start=0,
|
||||
num=limit,
|
||||
)
|
||||
|
||||
# Fetch benchmark data
|
||||
benchmarks = []
|
||||
for key in keys:
|
||||
data = await client.hgetall(key)
|
||||
if data:
|
||||
benchmarks.append(PerformanceBenchmark.from_redis_dict(data))
|
||||
|
||||
return benchmarks
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
"benchmark_query_failed",
|
||||
error=str(e),
|
||||
operation=operation,
|
||||
)
|
||||
return []
|
||||
|
||||
async def get_statistics(
|
||||
self,
|
||||
operation: str,
|
||||
start_time: Optional[datetime] = None,
|
||||
end_time: Optional[datetime] = None,
|
||||
) -> dict[str, Any]:
|
||||
"""
|
||||
Get aggregate statistics for an operation.
|
||||
|
||||
Args:
|
||||
operation: Operation name
|
||||
start_time: Start of time range
|
||||
end_time: End of time range
|
||||
|
||||
Returns:
|
||||
Dictionary with statistics (count, avg_duration, success_rate, etc.)
|
||||
|
||||
Example:
|
||||
>>> stats = await store.get_statistics("steward_analysis")
|
||||
>>> print(f"Average duration: {stats['avg_duration']}s")
|
||||
>>> print(f"Success rate: {stats['success_rate']}%")
|
||||
"""
|
||||
benchmarks = await self.query(operation, start_time, end_time, limit=1000)
|
||||
|
||||
if not benchmarks:
|
||||
return {
|
||||
"count": 0,
|
||||
"avg_duration": 0.0,
|
||||
"min_duration": 0.0,
|
||||
"max_duration": 0.0,
|
||||
"success_rate": 0.0,
|
||||
}
|
||||
|
||||
durations = [b.duration_seconds for b in benchmarks]
|
||||
successes = sum(1 for b in benchmarks if b.success)
|
||||
|
||||
return {
|
||||
"count": len(benchmarks),
|
||||
"avg_duration": sum(durations) / len(durations),
|
||||
"min_duration": min(durations),
|
||||
"max_duration": max(durations),
|
||||
"success_rate": (successes / len(benchmarks)) * 100,
|
||||
"total_successes": successes,
|
||||
"total_failures": len(benchmarks) - successes,
|
||||
}
|
||||
|
||||
async def get_tool_accuracy(
|
||||
self,
|
||||
start_time: Optional[datetime] = None,
|
||||
end_time: Optional[datetime] = None,
|
||||
) -> dict[str, Any]:
|
||||
"""
|
||||
Analyze tool recommendation accuracy.
|
||||
|
||||
Compares recommended tools vs actually used tools to measure
|
||||
Steward's recommendation precision.
|
||||
|
||||
Args:
|
||||
start_time: Start of time range
|
||||
end_time: End of time range
|
||||
|
||||
Returns:
|
||||
Dictionary with accuracy metrics
|
||||
|
||||
Example:
|
||||
>>> accuracy = await store.get_tool_accuracy()
|
||||
>>> print(f"Precision: {accuracy['precision']}%")
|
||||
"""
|
||||
tool_calls = await self.query("tool_call", start_time, end_time, limit=1000)
|
||||
|
||||
if not tool_calls:
|
||||
return {
|
||||
"total_calls": 0,
|
||||
"recommended_and_used": 0,
|
||||
"recommended_not_used": 0,
|
||||
"not_recommended_but_used": 0,
|
||||
"precision": 0.0,
|
||||
}
|
||||
|
||||
recommended_and_used = sum(
|
||||
1 for b in tool_calls
|
||||
if b.was_recommended and b.was_actually_used
|
||||
)
|
||||
not_recommended_but_used = sum(
|
||||
1 for b in tool_calls
|
||||
if not b.was_recommended and b.was_actually_used
|
||||
)
|
||||
|
||||
total_used = sum(1 for b in tool_calls if b.was_actually_used)
|
||||
precision = (
|
||||
(recommended_and_used / total_used * 100) if total_used > 0 else 0.0
|
||||
)
|
||||
|
||||
return {
|
||||
"total_calls": len(tool_calls),
|
||||
"total_used": total_used,
|
||||
"recommended_and_used": recommended_and_used,
|
||||
"not_recommended_but_used": not_recommended_but_used,
|
||||
"precision": precision,
|
||||
}
|
||||
|
||||
async def close(self) -> None:
|
||||
"""Close Redis connection."""
|
||||
if self._client:
|
||||
await self._client.aclose()
|
||||
self._client = None
|
||||
|
||||
|
||||
# Global benchmark store instance
|
||||
_benchmark_store: Optional[BenchmarkStore] = None
|
||||
|
||||
|
||||
def get_benchmark_store() -> BenchmarkStore:
|
||||
"""
|
||||
Get global benchmark store instance.
|
||||
|
||||
Returns:
|
||||
BenchmarkStore instance
|
||||
"""
|
||||
global _benchmark_store
|
||||
if _benchmark_store is None:
|
||||
_benchmark_store = BenchmarkStore()
|
||||
return _benchmark_store
|
||||
+16
-12
@@ -64,7 +64,21 @@ class Config(BaseSettings):
|
||||
API_PORT: int = Field(default=8000, description="API port")
|
||||
API_PREFIX: str = Field(default="/v1", description="API route prefix")
|
||||
|
||||
# Ollama Configuration
|
||||
# Anthropic Configuration (Claude - preferred backend)
|
||||
ANTHROPIC_API_KEY: str | None = Field(
|
||||
default=None,
|
||||
description="Anthropic API key for Claude access"
|
||||
)
|
||||
ANTHROPIC_MODEL: str = Field(
|
||||
default="claude-sonnet-4-20250514",
|
||||
description="Claude model to use"
|
||||
)
|
||||
PREFER_CLOUD_BACKEND: bool = Field(
|
||||
default=True,
|
||||
description="Prefer Claude over Ollama when available"
|
||||
)
|
||||
|
||||
# Ollama Configuration (local fallback)
|
||||
OLLAMA_HOST: HttpUrl = Field(
|
||||
default="http://localhost:11434",
|
||||
description="Ollama server URL"
|
||||
@@ -101,10 +115,6 @@ class Config(BaseSettings):
|
||||
default=6379,
|
||||
description="Redis server port"
|
||||
)
|
||||
REDIS_BENCHMARK_DB: int = Field(
|
||||
default=6,
|
||||
description="Redis database number for benchmarks"
|
||||
)
|
||||
REDIS_TIMEOUT: int = Field(
|
||||
default=5,
|
||||
description="Redis connection timeout in seconds"
|
||||
@@ -158,7 +168,7 @@ class Config(BaseSettings):
|
||||
description="Ollama model for embeddings"
|
||||
)
|
||||
|
||||
# Redis Memory Database (separate from benchmarks)
|
||||
# Redis Memory Database
|
||||
REDIS_MEMORY_DB: int = Field(
|
||||
default=1,
|
||||
description="Redis database number for memory cache"
|
||||
@@ -173,7 +183,6 @@ class Config(BaseSettings):
|
||||
default=None,
|
||||
description="Logging level (auto-set based on environment if not specified)"
|
||||
)
|
||||
ENABLE_BENCHMARKS: bool = Field(default=True, description="Enable performance benchmarking")
|
||||
|
||||
# User Configuration
|
||||
DEFAULT_USER: str | None = Field(
|
||||
@@ -190,11 +199,6 @@ class Config(BaseSettings):
|
||||
CORS_ALLOW_METHODS: list[str] = ["*"]
|
||||
CORS_ALLOW_HEADERS: list[str] = ["*"]
|
||||
|
||||
@property
|
||||
def redis_url(self) -> str:
|
||||
"""Construct Redis connection URL for benchmarks."""
|
||||
return f"redis://{self.REDIS_HOST}:{self.REDIS_PORT}/{self.REDIS_BENCHMARK_DB}"
|
||||
|
||||
@property
|
||||
def redis_memory_url(self) -> str:
|
||||
"""Construct Redis connection URL for memory cache."""
|
||||
|
||||
@@ -273,6 +273,65 @@ class HouseholdRegistry:
|
||||
|
||||
return tools
|
||||
|
||||
def get_streaming_delegation_tools(self, names: list[str]) -> list[Any]:
|
||||
"""
|
||||
Get streaming delegation wrapper tools for specified capabilities.
|
||||
|
||||
Similar to get_delegation_tools() but returns streaming wrappers
|
||||
that yield butler-perspective think messages during execution.
|
||||
|
||||
These wrappers emit think slugs like:
|
||||
- "Allow me to consult the archives, sir."
|
||||
- "The Librarian has compiled the relevant findings."
|
||||
|
||||
Args:
|
||||
names: List of member names to include
|
||||
|
||||
Returns:
|
||||
List of streaming delegation wrappers and/or raw tools
|
||||
|
||||
Example:
|
||||
>>> tools = registry.get_streaming_delegation_tools(["librarian"])
|
||||
>>> async for chunk in tools[0](task="Search for Docker"):
|
||||
... print(chunk) # Yields think messages then result
|
||||
"""
|
||||
from src.agents.delegation import STREAMING_DELEGATION_WRAPPERS
|
||||
|
||||
tools = []
|
||||
for name in names:
|
||||
member = self._members.get(name)
|
||||
if not member:
|
||||
logger.warning(
|
||||
"household_member_not_found",
|
||||
requested_name=name,
|
||||
available_names=list(self._members.keys()),
|
||||
)
|
||||
continue
|
||||
|
||||
# Check if this member has a streaming delegation wrapper
|
||||
if name in STREAMING_DELEGATION_WRAPPERS and member.agent is not None:
|
||||
tools.append(STREAMING_DELEGATION_WRAPPERS[name])
|
||||
logger.debug(
|
||||
"streaming_delegation_wrapper_added",
|
||||
member=name,
|
||||
)
|
||||
else:
|
||||
# No agent = direct tools (e.g., tatlock_core)
|
||||
tools.extend(member.tools)
|
||||
logger.debug(
|
||||
"raw_tools_added",
|
||||
member=name,
|
||||
tool_count=len(member.tools),
|
||||
)
|
||||
|
||||
logger.info(
|
||||
"streaming_delegation_tools_created",
|
||||
requested_members=names,
|
||||
total_tools=len(tools),
|
||||
)
|
||||
|
||||
return tools
|
||||
|
||||
def list_members(self) -> list[str]:
|
||||
"""
|
||||
List all registered member names.
|
||||
|
||||
@@ -6,7 +6,7 @@ Provides short-term memory storage with TTL:
|
||||
- Recent entities mentioned in conversation
|
||||
- User-scoped with conversation isolation
|
||||
|
||||
Uses Redis DB 2 (separate from benchmarks in DB 1).
|
||||
Uses Redis DB 1.
|
||||
"""
|
||||
import json
|
||||
from typing import Any
|
||||
|
||||
@@ -11,6 +11,7 @@ from src.agents.steward import analyze_request, format_steward_note
|
||||
from src.agents.steward.schemas import StewardRecommendation
|
||||
from src.core.household_registry import get_household_registry
|
||||
from src.core.logging_config import get_logger
|
||||
from src.core.tracing import trace_span, SpanType
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
@@ -93,12 +94,32 @@ async def preprocess_request(
|
||||
conversation_id=conversation_id,
|
||||
)
|
||||
|
||||
# Call Steward with full conversation history
|
||||
recommendation = await analyze_request(
|
||||
enriched_request,
|
||||
conversation_history=conversation_history,
|
||||
conversation_id=conversation_id,
|
||||
)
|
||||
# Call Steward with full conversation history (traced)
|
||||
async with trace_span(
|
||||
"steward_analysis",
|
||||
SpanType.STEWARD,
|
||||
metadata={
|
||||
"request_preview": user_request[:100],
|
||||
"history_length": len(conversation_history),
|
||||
},
|
||||
) as span:
|
||||
recommendation = await analyze_request(
|
||||
enriched_request,
|
||||
conversation_history=conversation_history,
|
||||
conversation_id=conversation_id,
|
||||
)
|
||||
|
||||
# Update span with results
|
||||
if span:
|
||||
span.metadata.update({
|
||||
"recommended_capabilities": recommendation.recommended_capabilities,
|
||||
"complexity": recommendation.estimated_complexity,
|
||||
"has_memory_context": bool(recommendation.memory_context),
|
||||
"has_conversation_context": recommendation.conversation_context.has_previous_context,
|
||||
})
|
||||
span.details["reasoning"] = recommendation.reasoning
|
||||
if recommendation.enriched_query:
|
||||
span.details["enriched_query"] = recommendation.enriched_query
|
||||
|
||||
# Format note for Tatlock (includes conversation context)
|
||||
steward_note = await format_steward_note(recommendation)
|
||||
|
||||
+15
-4
@@ -9,6 +9,7 @@ from src.agents.biographer import register_biographer
|
||||
from src.agents.housekeeper import register_housekeeper
|
||||
from src.agents.librarian import register_librarian
|
||||
from src.agents.tatlock_core import TATLOCK_CORE_CAPABILITY, tatlock_core_tools
|
||||
from src.anthropic.model_selector import check_claude_health, get_model_info
|
||||
from src.core.household_registry import get_household_registry
|
||||
from src.core.logging_config import get_logger
|
||||
|
||||
@@ -81,19 +82,29 @@ def register_household_members():
|
||||
)
|
||||
|
||||
|
||||
def initialize_application():
|
||||
async def initialize_application():
|
||||
"""
|
||||
Initialize the application.
|
||||
|
||||
Performs all startup tasks:
|
||||
1. Register household members
|
||||
2. (Future) Initialize connections
|
||||
3. (Future) Load configuration
|
||||
1. Check Claude API health (for backend selection)
|
||||
2. Register household members
|
||||
3. (Future) Initialize connections
|
||||
|
||||
This should be called once during application startup.
|
||||
"""
|
||||
logger.info("application_initialization_starting")
|
||||
|
||||
# Check Claude API health for backend selection
|
||||
await check_claude_health()
|
||||
model_info = get_model_info()
|
||||
logger.info(
|
||||
"model_backend_configured",
|
||||
backend=model_info["backend"],
|
||||
model=model_info["model"],
|
||||
claude_available=model_info["claude_available"],
|
||||
)
|
||||
|
||||
# Register household members
|
||||
register_household_members()
|
||||
|
||||
|
||||
+32
-46
@@ -1,13 +1,11 @@
|
||||
"""
|
||||
Tool call tracking and benchmarking.
|
||||
Tool call tracking.
|
||||
|
||||
Tracks which tools are recommended by the Steward versus which tools
|
||||
are actually used by Tatlock, recording benchmarks for analysis.
|
||||
are actually used by Tatlock for debugging and analysis.
|
||||
"""
|
||||
from datetime import datetime, timezone
|
||||
from typing import Optional
|
||||
|
||||
from src.core.benchmarks import PerformanceBenchmark, get_benchmark_store
|
||||
from src.core.logging_config import get_logger
|
||||
|
||||
logger = get_logger(__name__)
|
||||
@@ -15,7 +13,7 @@ logger = get_logger(__name__)
|
||||
|
||||
class ToolCallTracker:
|
||||
"""
|
||||
Tracks tool calls for benchmarking and accuracy analysis.
|
||||
Tracks tool calls for accuracy analysis.
|
||||
|
||||
Compares Steward's recommendations with Tatlock's actual tool usage
|
||||
to measure recommendation accuracy.
|
||||
@@ -43,6 +41,20 @@ class ToolCallTracker:
|
||||
conversation_id=conversation_id,
|
||||
)
|
||||
|
||||
def _extract_capability(self, tool_name: str) -> str:
|
||||
"""
|
||||
Extract capability name from tool name.
|
||||
|
||||
Tool names like 'delegate_to_librarian' map to capability 'librarian'.
|
||||
"""
|
||||
if tool_name.startswith("delegate_to_"):
|
||||
return tool_name.replace("delegate_to_", "")
|
||||
return tool_name
|
||||
|
||||
def log_call(self, message: str):
|
||||
"""Log a tool call message (for UI display)."""
|
||||
logger.debug("tool_call_message", message=message)
|
||||
|
||||
async def track_call(self, tool_name: str, duration: float):
|
||||
"""
|
||||
Record a tool call with timing.
|
||||
@@ -56,8 +68,9 @@ class ToolCallTracker:
|
||||
self.actual_calls[tool_name] = []
|
||||
self.actual_calls[tool_name].append(duration)
|
||||
|
||||
# Check if tool was recommended
|
||||
was_recommended = tool_name in self.recommended_capabilities
|
||||
# Check if tool was recommended (normalize tool name to capability)
|
||||
capability = self._extract_capability(tool_name)
|
||||
was_recommended = capability in self.recommended_capabilities
|
||||
|
||||
if not was_recommended:
|
||||
logger.warning(
|
||||
@@ -67,23 +80,6 @@ class ToolCallTracker:
|
||||
recommended=list(self.recommended_capabilities),
|
||||
)
|
||||
|
||||
# Record benchmark to Redis
|
||||
benchmark = PerformanceBenchmark(
|
||||
timestamp=datetime.now(timezone.utc),
|
||||
operation="tool_call",
|
||||
duration_seconds=duration,
|
||||
success=True, # If we got here, the call succeeded
|
||||
tool_name=tool_name,
|
||||
was_recommended=was_recommended,
|
||||
was_actually_used=True,
|
||||
conversation_id=self.conversation_id,
|
||||
metadata={
|
||||
"recommended_capabilities": list(self.recommended_capabilities),
|
||||
},
|
||||
)
|
||||
|
||||
await get_benchmark_store().record(benchmark)
|
||||
|
||||
logger.debug(
|
||||
"tool_call_tracked",
|
||||
tool_name=tool_name,
|
||||
@@ -98,8 +94,12 @@ class ToolCallTracker:
|
||||
Called after Tatlock completes its response to identify
|
||||
tools that were recommended but never used.
|
||||
"""
|
||||
# Normalize actual tool names to capabilities for comparison
|
||||
used_capabilities = {
|
||||
self._extract_capability(tool) for tool in self.actual_calls.keys()
|
||||
}
|
||||
# Find tools that were recommended but not used
|
||||
unused_tools = self.recommended_capabilities - set(self.actual_calls.keys())
|
||||
unused_tools = self.recommended_capabilities - used_capabilities
|
||||
|
||||
if unused_tools:
|
||||
logger.info(
|
||||
@@ -109,24 +109,6 @@ class ToolCallTracker:
|
||||
conversation_id=self.conversation_id,
|
||||
)
|
||||
|
||||
# Record benchmarks for unused recommendations
|
||||
for tool_name in unused_tools:
|
||||
benchmark = PerformanceBenchmark(
|
||||
timestamp=datetime.now(timezone.utc),
|
||||
operation="tool_call",
|
||||
duration_seconds=0.0, # Not used
|
||||
success=True,
|
||||
tool_name=tool_name,
|
||||
was_recommended=True,
|
||||
was_actually_used=False,
|
||||
conversation_id=self.conversation_id,
|
||||
metadata={
|
||||
"recommended_capabilities": list(self.recommended_capabilities),
|
||||
"reason": "recommended_but_unused",
|
||||
},
|
||||
)
|
||||
await get_benchmark_store().record(benchmark)
|
||||
|
||||
# Log summary
|
||||
total_calls = sum(len(durations) for durations in self.actual_calls.values())
|
||||
logger.info(
|
||||
@@ -145,7 +127,11 @@ class ToolCallTracker:
|
||||
Dict with tracking statistics
|
||||
"""
|
||||
total_calls = sum(len(durations) for durations in self.actual_calls.values())
|
||||
unused = self.recommended_capabilities - set(self.actual_calls.keys())
|
||||
# Normalize actual tool names to capabilities for comparison
|
||||
used_capabilities = {
|
||||
self._extract_capability(tool) for tool in self.actual_calls.keys()
|
||||
}
|
||||
unused = self.recommended_capabilities - used_capabilities
|
||||
|
||||
return {
|
||||
"recommended_capabilities": list(self.recommended_capabilities),
|
||||
@@ -154,11 +140,11 @@ class ToolCallTracker:
|
||||
"total_calls": total_calls,
|
||||
"accuracy": {
|
||||
"recommended_and_used": len(
|
||||
self.recommended_capabilities & set(self.actual_calls.keys())
|
||||
self.recommended_capabilities & used_capabilities
|
||||
),
|
||||
"recommended_but_unused": len(unused),
|
||||
"not_recommended_but_used": len(
|
||||
set(self.actual_calls.keys()) - self.recommended_capabilities
|
||||
used_capabilities - self.recommended_capabilities
|
||||
),
|
||||
},
|
||||
}
|
||||
|
||||
@@ -0,0 +1,434 @@
|
||||
"""
|
||||
Lightweight request tracing for local development.
|
||||
|
||||
Captures the full request flow through Tatlock's multi-agent architecture
|
||||
as structured JSON traces for debugging and optimization.
|
||||
|
||||
Enable via DEBUG=true environment variable.
|
||||
|
||||
Traces are written to logs/traces/{trace_id}.json
|
||||
View with logs/traces/viewer.html
|
||||
"""
|
||||
from contextlib import asynccontextmanager
|
||||
from contextvars import ContextVar
|
||||
from dataclasses import dataclass, field
|
||||
from datetime import datetime, timezone
|
||||
from enum import Enum
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
import json
|
||||
import secrets
|
||||
|
||||
from src.core.logging_config import get_logger
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
|
||||
class SpanType(str, Enum):
|
||||
"""Types of traced operations."""
|
||||
ROUTER = "router"
|
||||
STEWARD = "steward"
|
||||
TATLOCK = "tatlock"
|
||||
EXPERT = "expert"
|
||||
TOOL = "tool"
|
||||
|
||||
|
||||
class SpanStatus(str, Enum):
|
||||
"""Span completion status."""
|
||||
OK = "ok"
|
||||
ERROR = "error"
|
||||
|
||||
|
||||
@dataclass
|
||||
class Span:
|
||||
"""A single traced operation."""
|
||||
span_id: str
|
||||
name: str
|
||||
type: SpanType
|
||||
start_time: datetime
|
||||
parent_id: str | None = None
|
||||
end_time: datetime | None = None
|
||||
status: SpanStatus = SpanStatus.OK
|
||||
metadata: dict[str, Any] = field(default_factory=dict)
|
||||
details: dict[str, Any] = field(default_factory=dict)
|
||||
children: list[str] = field(default_factory=list)
|
||||
error: str | None = None
|
||||
|
||||
@property
|
||||
def duration_ms(self) -> float | None:
|
||||
"""Calculate duration in milliseconds."""
|
||||
if self.end_time and self.start_time:
|
||||
return (self.end_time - self.start_time).total_seconds() * 1000
|
||||
return None
|
||||
|
||||
def to_dict(self) -> dict[str, Any]:
|
||||
"""Convert span to dictionary for JSON serialization."""
|
||||
result = {
|
||||
"span_id": self.span_id,
|
||||
"parent_id": self.parent_id,
|
||||
"name": self.name,
|
||||
"type": self.type.value,
|
||||
"start_time": self.start_time.isoformat(),
|
||||
"end_time": self.end_time.isoformat() if self.end_time else None,
|
||||
"duration_ms": round(self.duration_ms, 2) if self.duration_ms else None,
|
||||
"status": self.status.value,
|
||||
"metadata": self.metadata if self.metadata else None,
|
||||
}
|
||||
# Only include non-empty optional fields
|
||||
if self.details:
|
||||
result["details"] = self.details
|
||||
if self.children:
|
||||
result["children"] = self.children
|
||||
if self.error:
|
||||
result["error"] = self.error
|
||||
return {k: v for k, v in result.items() if v is not None}
|
||||
|
||||
|
||||
@dataclass
|
||||
class Trace:
|
||||
"""Complete trace of a request."""
|
||||
trace_id: str
|
||||
conversation_id: str | None
|
||||
user: str
|
||||
timestamp: datetime
|
||||
request: dict[str, Any]
|
||||
spans: list[Span] = field(default_factory=list)
|
||||
response: dict[str, Any] | None = None
|
||||
status: str = "in_progress"
|
||||
|
||||
@property
|
||||
def total_duration_ms(self) -> float | None:
|
||||
"""Calculate total trace duration from span timings."""
|
||||
if not self.spans:
|
||||
return None
|
||||
start = min(s.start_time for s in self.spans)
|
||||
ends = [s.end_time for s in self.spans if s.end_time]
|
||||
if not ends:
|
||||
return None
|
||||
end = max(ends)
|
||||
return (end - start).total_seconds() * 1000
|
||||
|
||||
def to_dict(self) -> dict[str, Any]:
|
||||
"""Convert trace to dictionary for JSON serialization."""
|
||||
return {
|
||||
"trace_id": self.trace_id,
|
||||
"conversation_id": self.conversation_id,
|
||||
"user": self.user,
|
||||
"timestamp": self.timestamp.isoformat(),
|
||||
"total_duration_ms": round(self.total_duration_ms, 2) if self.total_duration_ms else None,
|
||||
"status": self.status,
|
||||
"request": self.request,
|
||||
"response": self.response,
|
||||
"spans": [s.to_dict() for s in self.spans],
|
||||
}
|
||||
|
||||
|
||||
# ContextVar for async-safe trace propagation
|
||||
_current_trace: ContextVar[Trace | None] = ContextVar("current_trace", default=None)
|
||||
_current_span: ContextVar[Span | None] = ContextVar("current_span", default=None)
|
||||
|
||||
|
||||
def tracing_enabled() -> bool:
|
||||
"""Check if tracing is enabled (requires DEBUG=true)."""
|
||||
from src.core.config import config
|
||||
return config.DEBUG
|
||||
|
||||
|
||||
def _generate_id(prefix: str = "") -> str:
|
||||
"""Generate unique ID with optional prefix."""
|
||||
return f"{prefix}{secrets.token_hex(8)}"
|
||||
|
||||
|
||||
def start_trace(
|
||||
conversation_id: str | None,
|
||||
user: str,
|
||||
request: dict[str, Any],
|
||||
) -> Trace | None:
|
||||
"""
|
||||
Start a new trace for a request.
|
||||
|
||||
Args:
|
||||
conversation_id: Conversation identifier
|
||||
user: User identifier
|
||||
request: Request data (should include preview and full)
|
||||
|
||||
Returns:
|
||||
Trace object if tracing enabled, None otherwise
|
||||
"""
|
||||
if not tracing_enabled():
|
||||
return None
|
||||
|
||||
trace = Trace(
|
||||
trace_id=_generate_id("trace_"),
|
||||
conversation_id=conversation_id,
|
||||
user=user,
|
||||
timestamp=datetime.now(timezone.utc),
|
||||
request=request,
|
||||
)
|
||||
_current_trace.set(trace)
|
||||
|
||||
logger.debug("trace_started", trace_id=trace.trace_id, user=user)
|
||||
return trace
|
||||
|
||||
|
||||
def get_current_trace() -> Trace | None:
|
||||
"""Get the current trace from context."""
|
||||
return _current_trace.get()
|
||||
|
||||
|
||||
def get_current_span() -> Span | None:
|
||||
"""Get the current span from context."""
|
||||
return _current_span.get()
|
||||
|
||||
|
||||
def start_span(
|
||||
name: str,
|
||||
span_type: SpanType,
|
||||
metadata: dict[str, Any] | None = None,
|
||||
details: dict[str, Any] | None = None,
|
||||
) -> Span | None:
|
||||
"""
|
||||
Start a new span within the current trace.
|
||||
|
||||
Args:
|
||||
name: Span name (e.g., "steward_analysis")
|
||||
span_type: Type of operation
|
||||
metadata: Quick-access metadata (shown in timeline)
|
||||
details: Expandable details (prompts, full responses)
|
||||
|
||||
Returns:
|
||||
Span object if tracing enabled, None otherwise
|
||||
"""
|
||||
trace = get_current_trace()
|
||||
if not trace:
|
||||
return None
|
||||
|
||||
parent = get_current_span()
|
||||
span = Span(
|
||||
span_id=_generate_id("span_"),
|
||||
name=name,
|
||||
type=span_type,
|
||||
start_time=datetime.now(timezone.utc),
|
||||
parent_id=parent.span_id if parent else None,
|
||||
metadata=metadata or {},
|
||||
details=details or {},
|
||||
)
|
||||
|
||||
# Add to parent's children list
|
||||
if parent:
|
||||
parent.children.append(span.span_id)
|
||||
|
||||
trace.spans.append(span)
|
||||
_current_span.set(span)
|
||||
|
||||
logger.debug(
|
||||
"span_started",
|
||||
span_id=span.span_id,
|
||||
name=name,
|
||||
type=span_type.value,
|
||||
parent_id=span.parent_id,
|
||||
)
|
||||
return span
|
||||
|
||||
|
||||
def end_span(
|
||||
span: Span | None = None,
|
||||
status: SpanStatus = SpanStatus.OK,
|
||||
metadata_update: dict[str, Any] | None = None,
|
||||
details_update: dict[str, Any] | None = None,
|
||||
error: str | None = None,
|
||||
) -> None:
|
||||
"""
|
||||
End a span and restore parent as current.
|
||||
|
||||
Args:
|
||||
span: Span to end (defaults to current span)
|
||||
status: Completion status
|
||||
metadata_update: Additional metadata to merge
|
||||
details_update: Additional details to merge
|
||||
error: Error message if failed
|
||||
"""
|
||||
if span is None:
|
||||
span = get_current_span()
|
||||
if not span:
|
||||
return
|
||||
|
||||
span.end_time = datetime.now(timezone.utc)
|
||||
span.status = status
|
||||
if error:
|
||||
span.error = error
|
||||
span.status = SpanStatus.ERROR
|
||||
if metadata_update:
|
||||
span.metadata.update(metadata_update)
|
||||
if details_update:
|
||||
span.details.update(details_update)
|
||||
|
||||
# Restore parent span as current
|
||||
trace = get_current_trace()
|
||||
if trace and span.parent_id:
|
||||
parent = next((s for s in trace.spans if s.span_id == span.parent_id), None)
|
||||
_current_span.set(parent)
|
||||
else:
|
||||
_current_span.set(None)
|
||||
|
||||
logger.debug(
|
||||
"span_ended",
|
||||
span_id=span.span_id,
|
||||
duration_ms=span.duration_ms,
|
||||
status=status.value,
|
||||
)
|
||||
|
||||
|
||||
def end_trace(
|
||||
response: dict[str, Any] | None = None,
|
||||
status: str = "completed",
|
||||
) -> str | None:
|
||||
"""
|
||||
End the current trace and write to file.
|
||||
|
||||
Args:
|
||||
response: Response data to include
|
||||
status: Final trace status ("completed" or "error")
|
||||
|
||||
Returns:
|
||||
Path to trace file if written, None otherwise
|
||||
"""
|
||||
trace = get_current_trace()
|
||||
if not trace:
|
||||
return None
|
||||
|
||||
trace.response = response
|
||||
trace.status = status
|
||||
|
||||
# Write trace to file
|
||||
trace_path = _write_trace(trace)
|
||||
|
||||
# Clear context
|
||||
_current_trace.set(None)
|
||||
_current_span.set(None)
|
||||
|
||||
logger.info(
|
||||
"trace_completed",
|
||||
trace_id=trace.trace_id,
|
||||
total_duration_ms=round(trace.total_duration_ms, 2) if trace.total_duration_ms else None,
|
||||
span_count=len(trace.spans),
|
||||
path=str(trace_path) if trace_path else None,
|
||||
)
|
||||
|
||||
return str(trace_path) if trace_path else None
|
||||
|
||||
|
||||
def _write_trace(trace: Trace) -> Path | None:
|
||||
"""Write trace to JSON file."""
|
||||
try:
|
||||
# Ensure traces directory exists
|
||||
traces_dir = Path("logs/traces")
|
||||
traces_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Write trace file
|
||||
trace_path = traces_dir / f"{trace.trace_id}.json"
|
||||
with open(trace_path, "w") as f:
|
||||
json.dump(trace.to_dict(), f, indent=2, default=str)
|
||||
|
||||
return trace_path
|
||||
|
||||
except Exception as e:
|
||||
logger.error("trace_write_failed", error=str(e), trace_id=trace.trace_id)
|
||||
return None
|
||||
|
||||
|
||||
@asynccontextmanager
|
||||
async def trace_span(
|
||||
name: str,
|
||||
span_type: SpanType,
|
||||
metadata: dict[str, Any] | None = None,
|
||||
details: dict[str, Any] | None = None,
|
||||
):
|
||||
"""
|
||||
Async context manager for tracing a span.
|
||||
|
||||
Automatically handles start/end timing and error capture.
|
||||
|
||||
Usage:
|
||||
async with trace_span("steward_analysis", SpanType.STEWARD) as span:
|
||||
result = await analyze_request(...)
|
||||
if span:
|
||||
span.metadata["result_count"] = len(result)
|
||||
|
||||
Args:
|
||||
name: Span name
|
||||
span_type: Type of operation
|
||||
metadata: Initial metadata
|
||||
details: Initial details (expandable in viewer)
|
||||
|
||||
Yields:
|
||||
Span object or None if tracing disabled
|
||||
"""
|
||||
span = start_span(name, span_type, metadata, details)
|
||||
try:
|
||||
yield span
|
||||
except Exception as e:
|
||||
end_span(span, SpanStatus.ERROR, error=str(e))
|
||||
raise
|
||||
else:
|
||||
end_span(span, SpanStatus.OK)
|
||||
|
||||
|
||||
def add_tool_spans_from_messages(messages: list[Any], parent_span: Span | None = None) -> None:
|
||||
"""
|
||||
Extract tool calls from PydanticAI result messages and add as child spans.
|
||||
|
||||
Call this after an agent.run() to capture tool-level timing retroactively.
|
||||
Note: Since we don't have actual timing, we estimate based on sequence.
|
||||
|
||||
Args:
|
||||
messages: List from result.new_messages()
|
||||
parent_span: Parent span to attach tool spans to
|
||||
"""
|
||||
trace = get_current_trace()
|
||||
if not trace or not parent_span:
|
||||
return
|
||||
|
||||
# Import PydanticAI message types
|
||||
try:
|
||||
from pydantic_ai.messages import ModelRequest, ModelResponse, ToolCallPart, ToolReturnPart
|
||||
except ImportError:
|
||||
return
|
||||
|
||||
# Track tool calls and their returns
|
||||
tool_calls: dict[str, dict[str, Any]] = {}
|
||||
|
||||
for msg in messages:
|
||||
if isinstance(msg, ModelResponse):
|
||||
for part in msg.parts:
|
||||
if isinstance(part, ToolCallPart):
|
||||
tool_calls[part.tool_call_id] = {
|
||||
"name": part.tool_name,
|
||||
"args": part.args if hasattr(part, 'args') else {},
|
||||
}
|
||||
elif isinstance(msg, ModelRequest):
|
||||
for part in msg.parts:
|
||||
if isinstance(part, ToolReturnPart):
|
||||
if part.tool_call_id in tool_calls:
|
||||
tool_info = tool_calls[part.tool_call_id]
|
||||
# Create a span for this tool call
|
||||
span = Span(
|
||||
span_id=_generate_id("span_"),
|
||||
name=tool_info["name"],
|
||||
type=SpanType.TOOL,
|
||||
start_time=parent_span.start_time, # Approximate
|
||||
end_time=parent_span.end_time or datetime.now(timezone.utc),
|
||||
parent_id=parent_span.span_id,
|
||||
status=SpanStatus.OK,
|
||||
metadata={
|
||||
"tool_name": tool_info["name"],
|
||||
"args_preview": str(tool_info.get("args", {}))[:100],
|
||||
},
|
||||
details={
|
||||
"args": tool_info.get("args", {}),
|
||||
"result": part.content[:2000] if isinstance(part.content, str) else str(part.content)[:2000],
|
||||
},
|
||||
)
|
||||
parent_span.children.append(span.span_id)
|
||||
trace.spans.append(span)
|
||||
@@ -0,0 +1,153 @@
|
||||
"""
|
||||
Trace viewer router.
|
||||
|
||||
Serves the trace viewer UI and trace files when tracing is enabled.
|
||||
Only available when DEBUG=true.
|
||||
"""
|
||||
from pathlib import Path
|
||||
|
||||
from fastapi import APIRouter, HTTPException
|
||||
from fastapi.responses import HTMLResponse, JSONResponse
|
||||
|
||||
from src.core.config import config
|
||||
from src.core.logging_config import get_logger
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
router = APIRouter(prefix="/traces", tags=["traces"])
|
||||
|
||||
TRACES_DIR = Path("logs/traces")
|
||||
VIEWER_PATH = TRACES_DIR / "viewer.html"
|
||||
|
||||
|
||||
def tracing_enabled() -> bool:
|
||||
"""Check if tracing is enabled."""
|
||||
return config.DEBUG
|
||||
|
||||
|
||||
@router.get("", response_class=HTMLResponse)
|
||||
async def get_trace_viewer():
|
||||
"""
|
||||
Serve the trace viewer UI.
|
||||
|
||||
Returns the standalone HTML viewer for browsing traces.
|
||||
"""
|
||||
if not tracing_enabled():
|
||||
raise HTTPException(status_code=404, detail="Tracing not enabled")
|
||||
|
||||
if not VIEWER_PATH.exists():
|
||||
raise HTTPException(status_code=404, detail="Viewer not found")
|
||||
|
||||
return HTMLResponse(content=VIEWER_PATH.read_text())
|
||||
|
||||
|
||||
@router.get("/list")
|
||||
async def list_traces(
|
||||
limit: int = 50,
|
||||
since_minutes: int | None = None,
|
||||
status: str | None = None,
|
||||
search: str | None = None,
|
||||
):
|
||||
"""
|
||||
List available trace files.
|
||||
|
||||
Returns most recent traces first, with basic metadata.
|
||||
|
||||
Args:
|
||||
limit: Maximum number of traces to return (default 50)
|
||||
since_minutes: Only return traces from the last N minutes
|
||||
status: Filter by status (completed, error, streaming)
|
||||
search: Search in request preview text
|
||||
"""
|
||||
if not tracing_enabled():
|
||||
raise HTTPException(status_code=404, detail="Tracing not enabled")
|
||||
|
||||
if not TRACES_DIR.exists():
|
||||
return {"traces": [], "total": 0}
|
||||
|
||||
import json
|
||||
from datetime import datetime, timezone, timedelta
|
||||
|
||||
# Calculate cutoff time if filtering by time
|
||||
cutoff_time = None
|
||||
if since_minutes:
|
||||
cutoff_time = datetime.now(timezone.utc) - timedelta(minutes=since_minutes)
|
||||
|
||||
# Get all trace files, sorted by modification time (newest first)
|
||||
trace_files = sorted(
|
||||
TRACES_DIR.glob("trace_*.json"),
|
||||
key=lambda p: p.stat().st_mtime,
|
||||
reverse=True,
|
||||
)
|
||||
|
||||
traces = []
|
||||
for path in trace_files:
|
||||
if len(traces) >= limit:
|
||||
break
|
||||
|
||||
try:
|
||||
with open(path) as f:
|
||||
data = json.load(f)
|
||||
|
||||
# Parse timestamp for filtering
|
||||
trace_timestamp = data.get("timestamp")
|
||||
if cutoff_time and trace_timestamp:
|
||||
try:
|
||||
ts = datetime.fromisoformat(trace_timestamp.replace('Z', '+00:00'))
|
||||
if ts < cutoff_time:
|
||||
continue
|
||||
except (ValueError, TypeError):
|
||||
pass
|
||||
|
||||
# Filter by status
|
||||
trace_status = data.get("status", "")
|
||||
if status and trace_status != status:
|
||||
continue
|
||||
|
||||
# Filter by search text
|
||||
request_preview = data.get("request", {}).get("input_preview", "")
|
||||
if search and search.lower() not in request_preview.lower():
|
||||
continue
|
||||
|
||||
traces.append({
|
||||
"trace_id": data.get("trace_id"),
|
||||
"timestamp": trace_timestamp,
|
||||
"user": data.get("user"),
|
||||
"status": trace_status,
|
||||
"total_duration_ms": data.get("total_duration_ms"),
|
||||
"span_count": len(data.get("spans", [])),
|
||||
"request_preview": request_preview[:100],
|
||||
})
|
||||
except Exception as e:
|
||||
logger.warning("trace_list_parse_error", path=str(path), error=str(e))
|
||||
|
||||
return {"traces": traces, "total": len(traces)}
|
||||
|
||||
|
||||
@router.get("/{trace_id}")
|
||||
async def get_trace(trace_id: str):
|
||||
"""
|
||||
Get a specific trace by ID.
|
||||
|
||||
Returns the full trace JSON.
|
||||
"""
|
||||
if not tracing_enabled():
|
||||
raise HTTPException(status_code=404, detail="Tracing not enabled")
|
||||
|
||||
# Sanitize trace_id to prevent path traversal
|
||||
if not trace_id.startswith("trace_") or "/" in trace_id or "\\" in trace_id:
|
||||
raise HTTPException(status_code=400, detail="Invalid trace ID")
|
||||
|
||||
trace_path = TRACES_DIR / f"{trace_id}.json"
|
||||
|
||||
if not trace_path.exists():
|
||||
raise HTTPException(status_code=404, detail="Trace not found")
|
||||
|
||||
try:
|
||||
import json
|
||||
with open(trace_path) as f:
|
||||
data = json.load(f)
|
||||
return JSONResponse(content=data)
|
||||
except Exception as e:
|
||||
logger.error("trace_read_error", trace_id=trace_id, error=str(e))
|
||||
raise HTTPException(status_code=500, detail="Failed to read trace")
|
||||
+12
-4
@@ -23,6 +23,7 @@ from src.core.exceptions import AppException
|
||||
from src.core.logging_config import get_logger
|
||||
from src.core.router import router as core_router
|
||||
from src.core.startup import initialize_application
|
||||
from src.core.tracing_router import router as tracing_router
|
||||
from src.models.router import router as models_router
|
||||
from src.responses.router import router as responses_router
|
||||
|
||||
@@ -43,14 +44,16 @@ async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
|
||||
app_name=config.APP_NAME,
|
||||
version=config.APP_VERSION,
|
||||
environment=config.ENVIRONMENT.value,
|
||||
prefer_cloud=config.PREFER_CLOUD_BACKEND,
|
||||
anthropic_model=config.ANTHROPIC_MODEL,
|
||||
ollama_host=str(config.OLLAMA_HOST),
|
||||
ollama_model=config.OLLAMA_DEFAULT_MODEL,
|
||||
redis_url=config.redis_url,
|
||||
redis_url=config.redis_memory_url,
|
||||
log_format=config.log_format,
|
||||
)
|
||||
|
||||
# Initialize application (register household members, etc.)
|
||||
initialize_application()
|
||||
# Initialize application (check Claude health, register household members, etc.)
|
||||
await initialize_application()
|
||||
|
||||
yield
|
||||
|
||||
@@ -90,7 +93,12 @@ def create_application() -> FastAPI:
|
||||
application.include_router(chat_router, prefix=config.API_PREFIX)
|
||||
application.include_router(models_router, prefix=config.API_PREFIX)
|
||||
application.include_router(responses_router, prefix=config.API_PREFIX) # Responses API
|
||||
|
||||
|
||||
# Conditionally include tracing router (only in debug mode)
|
||||
if config.DEBUG:
|
||||
application.include_router(tracing_router)
|
||||
logger.info("tracing_router_enabled")
|
||||
|
||||
return application
|
||||
|
||||
|
||||
|
||||
@@ -0,0 +1,130 @@
|
||||
"""
|
||||
PydanticAI provider for Ollama with message sanitization.
|
||||
|
||||
Ollama's OpenAI-compatible API rejects messages with `content: null`,
|
||||
which PydanticAI sends for assistant messages that only contain tool calls.
|
||||
This provider sanitizes messages to use empty strings instead of null.
|
||||
"""
|
||||
from typing import Any
|
||||
|
||||
from openai import AsyncOpenAI
|
||||
from pydantic_ai.providers.ollama import OllamaProvider
|
||||
|
||||
from src.core.config import config
|
||||
from src.core.logging_config import get_logger
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
|
||||
class TatlockOllamaProvider(OllamaProvider):
|
||||
"""
|
||||
Custom OllamaProvider with message sanitization for Tatlock agents.
|
||||
|
||||
Fixes the 'invalid message content type: <nil>' error that occurs
|
||||
when assistant messages have `content: null` with tool calls.
|
||||
"""
|
||||
|
||||
def __init__(self, base_url: str | None = None):
|
||||
"""
|
||||
Initialize provider with Ollama base URL.
|
||||
|
||||
Args:
|
||||
base_url: Ollama API URL (defaults to config.OLLAMA_HOST/v1)
|
||||
"""
|
||||
if base_url is None:
|
||||
clean_host = str(config.OLLAMA_HOST).rstrip("/")
|
||||
base_url = f"{clean_host}/v1"
|
||||
|
||||
super().__init__(base_url=base_url)
|
||||
|
||||
# Override the client with our sanitized version
|
||||
self._openai_client = _SanitizedAsyncOpenAI(base_url=base_url)
|
||||
|
||||
logger.debug("tatlock_ollama_provider_created", base_url=base_url)
|
||||
|
||||
|
||||
class _SanitizedAsyncOpenAI(AsyncOpenAI):
|
||||
"""AsyncOpenAI client that sanitizes messages before sending."""
|
||||
|
||||
def __init__(self, **kwargs: Any):
|
||||
# Ollama doesn't need an API key
|
||||
super().__init__(api_key="ollama", **kwargs)
|
||||
|
||||
@property
|
||||
def chat(self) -> "_SanitizedChat":
|
||||
"""Return sanitized chat interface."""
|
||||
return _SanitizedChat(self)
|
||||
|
||||
|
||||
class _SanitizedChat:
|
||||
"""Chat interface wrapper with sanitized completions."""
|
||||
|
||||
def __init__(self, client: _SanitizedAsyncOpenAI):
|
||||
self._client = client
|
||||
self._original_chat = AsyncOpenAI.chat.fget(client) # type: ignore
|
||||
|
||||
@property
|
||||
def completions(self) -> "_SanitizedCompletions":
|
||||
"""Return sanitized completions interface."""
|
||||
return _SanitizedCompletions(self._original_chat.completions)
|
||||
|
||||
|
||||
class _SanitizedCompletions:
|
||||
"""Completions wrapper that sanitizes messages before API calls."""
|
||||
|
||||
def __init__(self, original_completions: Any):
|
||||
self._original = original_completions
|
||||
|
||||
async def create(self, **kwargs: Any) -> Any:
|
||||
"""
|
||||
Create chat completion with sanitized messages.
|
||||
|
||||
Converts `content: null` to `content: ""` in assistant messages
|
||||
to prevent Ollama's 'invalid message content type: <nil>' error.
|
||||
"""
|
||||
if "messages" in kwargs:
|
||||
kwargs["messages"] = _sanitize_messages(kwargs["messages"])
|
||||
|
||||
return await self._original.create(**kwargs)
|
||||
|
||||
|
||||
def _sanitize_messages(messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
|
||||
"""
|
||||
Sanitize messages to fix null content issues.
|
||||
|
||||
When an assistant message has tool_calls but no text content,
|
||||
PydanticAI sets content to None. Ollama rejects this.
|
||||
We convert None to empty string.
|
||||
|
||||
Args:
|
||||
messages: List of chat messages
|
||||
|
||||
Returns:
|
||||
Sanitized messages with null content replaced by empty strings
|
||||
"""
|
||||
sanitized = []
|
||||
for msg in messages:
|
||||
msg_copy = dict(msg)
|
||||
|
||||
# Fix null content in assistant messages with tool calls
|
||||
if msg_copy.get("role") == "assistant":
|
||||
if msg_copy.get("content") is None and msg_copy.get("tool_calls"):
|
||||
msg_copy["content"] = ""
|
||||
logger.debug(
|
||||
"sanitized_null_content",
|
||||
tool_call_count=len(msg_copy["tool_calls"]),
|
||||
)
|
||||
|
||||
sanitized.append(msg_copy)
|
||||
|
||||
return sanitized
|
||||
|
||||
|
||||
def get_ollama_provider() -> TatlockOllamaProvider:
|
||||
"""
|
||||
Get a configured Ollama provider for PydanticAI agents.
|
||||
|
||||
Returns:
|
||||
TatlockOllamaProvider configured with sanitization
|
||||
"""
|
||||
return TatlockOllamaProvider()
|
||||
+5
-73
@@ -10,7 +10,6 @@ from sse_starlette.sse import EventSourceResponse
|
||||
from src.responses import service
|
||||
from src.responses.schemas import ResponseRequest, Response
|
||||
from src.core.exceptions import ModelNotFoundError, AppException
|
||||
from src.core.context import current_user, current_conversation, get_default_user
|
||||
from src.core.logging_config import get_logger
|
||||
|
||||
logger = get_logger(__name__)
|
||||
@@ -37,77 +36,16 @@ async def create_response(
|
||||
|
||||
Returns:
|
||||
Response object or SSE stream
|
||||
|
||||
Example non-streaming request:
|
||||
POST /v1/responses
|
||||
{
|
||||
"model": "lorem-tester",
|
||||
"input": [{"role": "user", "content": "Hello"}],
|
||||
"reasoning": {"effort": "medium", "summary": "auto"},
|
||||
"stream": false
|
||||
}
|
||||
|
||||
Example streaming request:
|
||||
POST /v1/responses
|
||||
{
|
||||
"model": "lorem-tester",
|
||||
"input": [{"role": "user", "content": "Hello"}],
|
||||
"stream": true
|
||||
}
|
||||
|
||||
Response format (non-streaming):
|
||||
{
|
||||
"id": "resp_...",
|
||||
"object": "response",
|
||||
"created_at": 1733529600,
|
||||
"model": "lorem-tester",
|
||||
"status": "completed",
|
||||
"output": [
|
||||
{
|
||||
"type": "reasoning",
|
||||
"id": "rs_...",
|
||||
"summary": ["Analyzing...", "Considering..."]
|
||||
},
|
||||
{
|
||||
"type": "message",
|
||||
"id": "msg_...",
|
||||
"role": "assistant",
|
||||
"content": [{"type": "output_text", "text": "Lorem ipsum..."}]
|
||||
}
|
||||
],
|
||||
"usage": {
|
||||
"input_tokens": 10,
|
||||
"output_tokens": 50,
|
||||
"reasoning_tokens": 20,
|
||||
"total_tokens": 80
|
||||
}
|
||||
}
|
||||
|
||||
Streaming format (SSE):
|
||||
event: response.reasoning_summary_text.delta
|
||||
data: {"delta": "Analyzing..."}
|
||||
|
||||
event: response.output_text.delta
|
||||
data: {"delta": "Lorem"}
|
||||
|
||||
event: response.done
|
||||
data: {"response": {...}}
|
||||
"""
|
||||
# Set request context (propagates through all async calls)
|
||||
effective_user = request.user or get_default_user()
|
||||
user_token = current_user.set(effective_user)
|
||||
conv_id = request.metadata.get("conversation_id") if request.metadata else None
|
||||
conv_token = current_conversation.set(conv_id)
|
||||
|
||||
logger.info(
|
||||
"response_request_received",
|
||||
model=request.model,
|
||||
user=effective_user,
|
||||
conversation_id=conv_id,
|
||||
user=request.user,
|
||||
streaming=request.stream,
|
||||
)
|
||||
|
||||
try:
|
||||
# Check if this is a Tatlock request - use Steward preprocessing (Phase 2)
|
||||
# Check if this is a Tatlock request - use Steward preprocessing
|
||||
model_id = request.model
|
||||
if "." in model_id:
|
||||
model_id = model_id.split(".", 1)[1]
|
||||
@@ -116,21 +54,20 @@ async def create_response(
|
||||
|
||||
if request.stream:
|
||||
logger.info("Streaming response requested")
|
||||
|
||||
if use_steward:
|
||||
logger.info("Streaming with Steward preprocessing for Tatlock request")
|
||||
# Use Steward + Tatlock streaming (Milestone 3.5)
|
||||
from src.responses.streaming import StreamingCoordinator
|
||||
coordinator = StreamingCoordinator()
|
||||
return EventSourceResponse(
|
||||
coordinator.stream_response_with_steward(request)
|
||||
)
|
||||
else:
|
||||
# Regular streaming for non-Tatlock models
|
||||
return EventSourceResponse(
|
||||
service.create_response_stream(request)
|
||||
)
|
||||
|
||||
# Use appropriate service method
|
||||
# Non-streaming response
|
||||
if use_steward:
|
||||
logger.info("Using Steward preprocessing for Tatlock request")
|
||||
return await service.create_response_with_steward(request)
|
||||
@@ -148,8 +85,3 @@ async def create_response(
|
||||
except Exception as e:
|
||||
logger.error(f"Unexpected error: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail="Internal server error")
|
||||
|
||||
finally:
|
||||
# Reset context (important for connection reuse)
|
||||
current_user.reset(user_token)
|
||||
current_conversation.reset(conv_token)
|
||||
|
||||
+324
-129
@@ -26,6 +26,8 @@ from src.responses.context import ContextWindow
|
||||
from src.core.preprocessing import preprocess_request
|
||||
from src.core.tool_tracking import ToolCallTracker
|
||||
from src.core.logging_config import get_logger
|
||||
from src.core.tracing import start_trace, end_trace, start_span, SpanType
|
||||
from src.core.context import current_user, current_conversation, get_default_user
|
||||
from src.agents.steward.schemas import StewardRecommendation
|
||||
|
||||
import re
|
||||
@@ -34,6 +36,29 @@ import asyncio
|
||||
logger = get_logger(__name__)
|
||||
|
||||
|
||||
def _extract_user_input(input_data) -> str:
|
||||
"""Extract user input text from request input for tracing."""
|
||||
if isinstance(input_data, str):
|
||||
return input_data
|
||||
elif isinstance(input_data, list) and input_data:
|
||||
last_msg = input_data[-1]
|
||||
if isinstance(last_msg, dict):
|
||||
return last_msg.get("content", str(last_msg))
|
||||
return str(last_msg)
|
||||
return ""
|
||||
|
||||
|
||||
def _extract_response_preview(response: Response) -> str:
|
||||
"""Extract response preview text for tracing."""
|
||||
if response.output:
|
||||
for item in response.output:
|
||||
if hasattr(item, 'content'):
|
||||
for content in item.content:
|
||||
if hasattr(content, 'text'):
|
||||
return content.text[:200]
|
||||
return ""
|
||||
|
||||
|
||||
async def _execute_single_delegation(
|
||||
agent_name: str,
|
||||
task: str,
|
||||
@@ -43,7 +68,7 @@ async def _execute_single_delegation(
|
||||
Execute a single delegation to an agent.
|
||||
|
||||
Args:
|
||||
agent_name: Name of agent (biographer, librarian)
|
||||
agent_name: Name of agent (biographer, librarian, housekeeper)
|
||||
task: Task description
|
||||
tracker: Tool call tracker
|
||||
|
||||
@@ -67,6 +92,13 @@ async def _execute_single_delegation(
|
||||
await tracker.track_call("delegate_to_librarian", duration)
|
||||
return (agent_name, result.output)
|
||||
|
||||
elif agent_name == "housekeeper":
|
||||
from src.agents.delegation import delegate_to_housekeeper
|
||||
result = await delegate_to_housekeeper(task=task)
|
||||
duration = time.time() - start_time
|
||||
await tracker.track_call("delegate_to_housekeeper", duration)
|
||||
return (agent_name, result.output)
|
||||
|
||||
else:
|
||||
return (agent_name, f"Unknown agent: {agent_name}")
|
||||
|
||||
@@ -244,6 +276,68 @@ async def _direct_delegation(
|
||||
return "\n\n".join(results) if results else "I apologize, sir. No delegation results available."
|
||||
|
||||
|
||||
async def _direct_delegation_with_results(
|
||||
user_message: str,
|
||||
recommendation: "StewardRecommendation",
|
||||
tracker: "ToolCallTracker",
|
||||
conversation_id: str,
|
||||
) -> dict:
|
||||
"""
|
||||
Directly delegate to expert agents and return structured results.
|
||||
|
||||
This is the Phase 1 variant of direct delegation that returns results
|
||||
in the same format as TatlockAgent.orchestrate_tool_calls() for
|
||||
consistent Phase 2 synthesis.
|
||||
|
||||
Args:
|
||||
user_message: User's request
|
||||
recommendation: Steward's recommendation
|
||||
tracker: Tool call tracker
|
||||
conversation_id: Conversation ID
|
||||
|
||||
Returns:
|
||||
dict: Orchestration results with expert_results, tool_outputs, etc.
|
||||
"""
|
||||
logger.info(
|
||||
"direct_delegation_with_results",
|
||||
agents=recommendation.recommended_capabilities,
|
||||
conversation_id=conversation_id,
|
||||
)
|
||||
|
||||
expert_results = {}
|
||||
tools_called = []
|
||||
|
||||
for agent in recommendation.recommended_capabilities:
|
||||
try:
|
||||
agent_name, result = await _execute_single_delegation(
|
||||
agent, user_message, tracker
|
||||
)
|
||||
expert_results[agent_name] = result
|
||||
tools_called.append(f"delegate_to_{agent_name}")
|
||||
|
||||
logger.info(
|
||||
"direct_delegation_result",
|
||||
agent=agent_name,
|
||||
result_preview=result[:100] if result else "empty",
|
||||
conversation_id=conversation_id,
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
"direct_delegation_failed",
|
||||
agent=agent,
|
||||
error=str(e),
|
||||
conversation_id=conversation_id,
|
||||
)
|
||||
expert_results[agent] = f"Error: {e}"
|
||||
|
||||
return {
|
||||
"tools_called": tools_called,
|
||||
"expert_results": expert_results,
|
||||
"tool_outputs": {}, # No tool outputs for direct delegation
|
||||
"raw_output": "", # No raw output for direct delegation
|
||||
}
|
||||
|
||||
|
||||
# Global conversation history tracker
|
||||
# In production, this would be backed by a database or Redis
|
||||
_conversation_history = ConversationHistory(max_turns=20)
|
||||
@@ -336,55 +430,99 @@ async def create_response(request: ResponseRequest) -> Response:
|
||||
# Get or generate conversation ID
|
||||
conversation_id = await _conversation_history.get_conversation_id(request)
|
||||
|
||||
# Strip pipeline prefix if present (e.g., "pipeline.model" -> "model")
|
||||
model_id = request.model
|
||||
if "." in model_id:
|
||||
model_id = model_id.split(".", 1)[1]
|
||||
# Set context for tracing
|
||||
effective_user = request.user or get_default_user()
|
||||
current_user.set(effective_user)
|
||||
current_conversation.set(conversation_id)
|
||||
|
||||
# Get agent for model
|
||||
agent = ModelRegistry.get_agent(model_id)
|
||||
# Extract user input for tracing
|
||||
user_input = _extract_user_input(request.input)
|
||||
|
||||
# Collect all output items from agent
|
||||
output_items = []
|
||||
async for item in agent.generate_response(
|
||||
messages=request.input,
|
||||
reasoning=request.reasoning,
|
||||
tools=request.tools,
|
||||
temperature=request.temperature,
|
||||
max_tokens=request.max_output_tokens,
|
||||
stop=request.stop,
|
||||
):
|
||||
output_items.append(item)
|
||||
|
||||
# Convert agent OutputItems to schema OutputItems
|
||||
converted_items = _convert_output_items(output_items)
|
||||
|
||||
# Calculate token usage
|
||||
usage = _calculate_usage(request.input, output_items)
|
||||
|
||||
response = Response(
|
||||
id=f"resp_{generate_id()}",
|
||||
created_at=int(time.time()),
|
||||
model=request.model,
|
||||
status="completed",
|
||||
output=converted_items,
|
||||
usage=usage
|
||||
# Start trace
|
||||
trace = start_trace(
|
||||
conversation_id=conversation_id,
|
||||
user=effective_user,
|
||||
request={
|
||||
"model": request.model,
|
||||
"input_preview": user_input[:200] if user_input else "",
|
||||
"full_input": request.input,
|
||||
"streaming": False,
|
||||
},
|
||||
)
|
||||
|
||||
# Track conversation history (for analytics and future vector memory)
|
||||
await _conversation_history.add_response(conversation_id, response)
|
||||
# Start service span
|
||||
service_span = start_span(
|
||||
"create_response",
|
||||
SpanType.ROUTER,
|
||||
metadata={"model": request.model, "user": effective_user},
|
||||
)
|
||||
|
||||
return response
|
||||
try:
|
||||
# Strip pipeline prefix if present (e.g., "pipeline.model" -> "model")
|
||||
model_id = request.model
|
||||
if "." in model_id:
|
||||
model_id = model_id.split(".", 1)[1]
|
||||
|
||||
# Get agent for model
|
||||
agent = ModelRegistry.get_agent(model_id)
|
||||
|
||||
# Collect all output items from agent
|
||||
output_items = []
|
||||
async for item in agent.generate_response(
|
||||
messages=request.input,
|
||||
reasoning=request.reasoning,
|
||||
tools=request.tools,
|
||||
temperature=request.temperature,
|
||||
max_tokens=request.max_output_tokens,
|
||||
stop=request.stop,
|
||||
):
|
||||
output_items.append(item)
|
||||
|
||||
# Convert agent OutputItems to schema OutputItems
|
||||
converted_items = _convert_output_items(output_items)
|
||||
|
||||
# Calculate token usage
|
||||
usage = _calculate_usage(request.input, output_items)
|
||||
|
||||
response = Response(
|
||||
id=f"resp_{generate_id()}",
|
||||
created_at=int(time.time()),
|
||||
model=request.model,
|
||||
status="completed",
|
||||
output=converted_items,
|
||||
usage=usage
|
||||
)
|
||||
|
||||
# Track conversation history (for analytics and future vector memory)
|
||||
await _conversation_history.add_response(conversation_id, response)
|
||||
|
||||
# End trace with response info
|
||||
response_preview = _extract_response_preview(response)
|
||||
end_trace(
|
||||
response={
|
||||
"output_preview": response_preview,
|
||||
"output_count": len(response.output) if response.output else 0,
|
||||
"status": response.status,
|
||||
},
|
||||
status="completed",
|
||||
)
|
||||
|
||||
return response
|
||||
|
||||
except Exception as e:
|
||||
end_trace(status="error")
|
||||
raise
|
||||
|
||||
|
||||
async def create_response_with_steward(request: ResponseRequest) -> Response:
|
||||
"""
|
||||
Create response using Steward preprocessing (Phase 2 flow).
|
||||
Create response using Steward preprocessing and two-phase Tatlock execution.
|
||||
|
||||
This is the two-tier architecture where:
|
||||
This is the two-tier architecture with two-phase synthesis:
|
||||
1. Steward analyzes the request and recommends capabilities
|
||||
2. Tatlock runs with scoped tools based on recommendations
|
||||
3. Tool usage is tracked for benchmarking
|
||||
2. Phase 1: Tatlock orchestrates tool calls and expert delegations
|
||||
3. Phase 2: Tatlock synthesizes butler-toned response from results
|
||||
4. Tool usage is tracked for analysis
|
||||
|
||||
Args:
|
||||
request: Response request
|
||||
@@ -403,119 +541,176 @@ async def create_response_with_steward(request: ResponseRequest) -> Response:
|
||||
# Get or generate conversation ID
|
||||
conversation_id = await _conversation_history.get_conversation_id(request)
|
||||
|
||||
# Extract user message and conversation history
|
||||
user_message = ""
|
||||
for msg in reversed(request.input):
|
||||
if msg.get("role") == "user":
|
||||
user_message = msg.get("content", "")
|
||||
break
|
||||
# Set context for tracing
|
||||
effective_user = request.user or get_default_user()
|
||||
current_user.set(effective_user)
|
||||
current_conversation.set(conversation_id)
|
||||
|
||||
# Conversation history is all messages except the current one
|
||||
conversation_history = request.input[:-1] if len(request.input) > 1 else []
|
||||
# Extract user input for tracing
|
||||
user_input = _extract_user_input(request.input)
|
||||
|
||||
logger.info(
|
||||
"creating_response_with_steward",
|
||||
user_message_preview=user_message[:100],
|
||||
history_length=len(conversation_history),
|
||||
# Start trace
|
||||
trace = start_trace(
|
||||
conversation_id=conversation_id,
|
||||
user=effective_user,
|
||||
request={
|
||||
"model": request.model,
|
||||
"input_preview": user_input[:200] if user_input else "",
|
||||
"full_input": request.input,
|
||||
"streaming": False,
|
||||
},
|
||||
)
|
||||
|
||||
# Phase 1: Steward preprocessing
|
||||
enriched = await preprocess_request(
|
||||
user_message,
|
||||
conversation_history=conversation_history,
|
||||
conversation_id=conversation_id,
|
||||
# Start service span
|
||||
service_span = start_span(
|
||||
"create_response_with_steward",
|
||||
SpanType.ROUTER,
|
||||
metadata={"model": request.model, "user": effective_user},
|
||||
)
|
||||
|
||||
# Phase 2: Initialize tool tracker
|
||||
tracker = ToolCallTracker(
|
||||
recommended_capabilities=enriched.recommendation.recommended_capabilities,
|
||||
conversation_id=conversation_id,
|
||||
)
|
||||
try:
|
||||
# Extract user message and conversation history
|
||||
user_message = ""
|
||||
for msg in reversed(request.input):
|
||||
if msg.get("role") == "user":
|
||||
user_message = msg.get("content", "")
|
||||
break
|
||||
|
||||
# Phase 3: Check if direct delegation is recommended
|
||||
# If Steward recommends ONLY delegation agents (biographer/librarian),
|
||||
# skip Tatlock and delegate directly
|
||||
delegation_only = all(
|
||||
cap in ("biographer", "librarian")
|
||||
for cap in enriched.recommendation.recommended_capabilities
|
||||
) and enriched.recommendation.recommended_capabilities
|
||||
# Conversation history is all messages except the current one
|
||||
conversation_history = request.input[:-1] if len(request.input) > 1 else []
|
||||
|
||||
if delegation_only:
|
||||
tatlock_response = await _direct_delegation(
|
||||
user_message, enriched.recommendation, tracker, conversation_id
|
||||
logger.info(
|
||||
"creating_response_with_steward",
|
||||
user_message_preview=user_message[:100],
|
||||
history_length=len(conversation_history),
|
||||
conversation_id=conversation_id,
|
||||
)
|
||||
else:
|
||||
# Phase 3a: Run Tatlock with scoped tools
|
||||
|
||||
# Steward preprocessing
|
||||
enriched = await preprocess_request(
|
||||
user_message,
|
||||
conversation_history=conversation_history,
|
||||
conversation_id=conversation_id,
|
||||
)
|
||||
|
||||
# Initialize tool tracker
|
||||
tracker = ToolCallTracker(
|
||||
recommended_capabilities=enriched.recommendation.recommended_capabilities,
|
||||
conversation_id=conversation_id,
|
||||
)
|
||||
|
||||
# Check if direct delegation is recommended
|
||||
# If Steward recommends ONLY delegation agents (biographer/librarian/housekeeper),
|
||||
# we still use two-phase but delegate directly in Phase 1
|
||||
delegation_agents = {"biographer", "librarian", "housekeeper"}
|
||||
delegation_only = all(
|
||||
cap in delegation_agents
|
||||
for cap in enriched.recommendation.recommended_capabilities
|
||||
) and enriched.recommendation.recommended_capabilities
|
||||
|
||||
from src.agents.tatlock import TatlockAgent
|
||||
tatlock = TatlockAgent()
|
||||
|
||||
tatlock_response = await tatlock.run_with_scoped_tools(
|
||||
# Use enriched query (with location/timezone context) if available
|
||||
effective_query = enriched.recommendation.enriched_query or user_message
|
||||
|
||||
if delegation_only:
|
||||
# Direct delegation path - collect results then synthesize
|
||||
orchestration_results = await _direct_delegation_with_results(
|
||||
effective_query, enriched.recommendation, tracker, conversation_id
|
||||
)
|
||||
else:
|
||||
# Phase 1: Orchestrate tool calls
|
||||
orchestration_results = await tatlock.orchestrate_tool_calls(
|
||||
user_message=effective_query,
|
||||
steward_note=enriched.steward_note,
|
||||
scoped_tools=enriched.scoped_tools,
|
||||
message_history=conversation_history,
|
||||
tool_tracker=tracker,
|
||||
)
|
||||
|
||||
# Handle text-based delegation fallback if present
|
||||
if "[DELEGATE:" in orchestration_results.get("raw_output", ""):
|
||||
text_delegation_results = await _handle_text_delegation(
|
||||
orchestration_results["raw_output"], tracker, conversation_id
|
||||
)
|
||||
# Add text delegation results to expert_results
|
||||
if text_delegation_results != orchestration_results["raw_output"]:
|
||||
orchestration_results["expert_results"]["text_delegation"] = text_delegation_results
|
||||
|
||||
# Phase 2: Synthesize butler-toned response from all results
|
||||
tatlock_response = await tatlock.synthesize_from_results(
|
||||
user_message=user_message,
|
||||
steward_note=enriched.steward_note,
|
||||
scoped_tools=enriched.scoped_tools,
|
||||
orchestration_results=orchestration_results,
|
||||
message_history=conversation_history,
|
||||
tool_tracker=tracker,
|
||||
)
|
||||
|
||||
# Phase 3b: Check for text-based delegation fallback
|
||||
# If Tatlock outputs [DELEGATE:...] instead of calling the function,
|
||||
# we parse and execute it here
|
||||
tatlock_response = await _handle_text_delegation(
|
||||
tatlock_response, tracker, conversation_id
|
||||
# Finalize tool tracking
|
||||
await tracker.finalize()
|
||||
|
||||
# Build response output items
|
||||
output_items = []
|
||||
|
||||
# Add Steward reasoning as a reasoning output item
|
||||
output_items.append(ReasoningOutputItem(
|
||||
id=f"reasoning_{generate_id()}",
|
||||
summary=[
|
||||
"🎩 Steward's Analysis:",
|
||||
enriched.steward_reasoning,
|
||||
],
|
||||
status="completed"
|
||||
))
|
||||
|
||||
# Add Tatlock's message
|
||||
output_items.append(MessageOutputItem(
|
||||
id=f"msg_{generate_id()}",
|
||||
role="assistant",
|
||||
content=[OutputTextContent(
|
||||
type="output_text",
|
||||
text=tatlock_response,
|
||||
annotations=[]
|
||||
)],
|
||||
status="completed"
|
||||
))
|
||||
|
||||
# Calculate usage (approximate)
|
||||
usage = _calculate_usage(request.input, output_items)
|
||||
|
||||
response = Response(
|
||||
id=f"resp_{generate_id()}",
|
||||
created_at=int(time.time()),
|
||||
model=request.model,
|
||||
status="completed",
|
||||
output=output_items,
|
||||
usage=usage
|
||||
)
|
||||
|
||||
# Phase 4: Finalize tool tracking
|
||||
await tracker.finalize()
|
||||
# Track conversation history
|
||||
await _conversation_history.add_response(conversation_id, response)
|
||||
|
||||
# Build response output items
|
||||
output_items = []
|
||||
logger.info(
|
||||
"response_with_steward_complete",
|
||||
response_id=response.id,
|
||||
recommended_capabilities=enriched.recommendation.recommended_capabilities,
|
||||
tool_summary=tracker.get_summary(),
|
||||
)
|
||||
|
||||
# Add Steward reasoning as a reasoning output item
|
||||
output_items.append(ReasoningOutputItem(
|
||||
id=f"reasoning_{generate_id()}",
|
||||
summary=[
|
||||
"🎩 Steward's Analysis:",
|
||||
enriched.steward_reasoning,
|
||||
],
|
||||
status="completed"
|
||||
))
|
||||
# End trace with response info
|
||||
response_preview = _extract_response_preview(response)
|
||||
end_trace(
|
||||
response={
|
||||
"output_preview": response_preview,
|
||||
"output_count": len(response.output) if response.output else 0,
|
||||
"status": response.status,
|
||||
},
|
||||
status="completed",
|
||||
)
|
||||
|
||||
# Add Tatlock's message
|
||||
output_items.append(MessageOutputItem(
|
||||
id=f"msg_{generate_id()}",
|
||||
role="assistant",
|
||||
content=[OutputTextContent(
|
||||
type="output_text",
|
||||
text=tatlock_response,
|
||||
annotations=[]
|
||||
)],
|
||||
status="completed"
|
||||
))
|
||||
return response
|
||||
|
||||
# Calculate usage (approximate)
|
||||
usage = _calculate_usage(request.input, output_items)
|
||||
|
||||
response = Response(
|
||||
id=f"resp_{generate_id()}",
|
||||
created_at=int(time.time()),
|
||||
model=request.model,
|
||||
status="completed",
|
||||
output=output_items,
|
||||
usage=usage
|
||||
)
|
||||
|
||||
# Track conversation history
|
||||
await _conversation_history.add_response(conversation_id, response)
|
||||
|
||||
logger.info(
|
||||
"response_with_steward_complete",
|
||||
response_id=response.id,
|
||||
recommended_capabilities=enriched.recommendation.recommended_capabilities,
|
||||
tool_summary=tracker.get_summary(),
|
||||
)
|
||||
|
||||
return response
|
||||
except Exception as e:
|
||||
end_trace(status="error")
|
||||
raise
|
||||
|
||||
|
||||
async def create_response_stream(
|
||||
|
||||
+136
-19
@@ -118,11 +118,12 @@ class StreamingCoordinator:
|
||||
request: "ResponseRequest" # type: ignore # Forward reference
|
||||
) -> AsyncGenerator[StreamEvent, None]:
|
||||
"""
|
||||
Stream response with Steward preprocessing (Phase 2 flow).
|
||||
Stream response with Steward preprocessing and two-phase Tatlock execution.
|
||||
|
||||
Streams in order:
|
||||
1. Steward's analysis as reasoning summary
|
||||
2. Tatlock's response as output text
|
||||
2. Think slugs during expert delegation (butler-perspective messages)
|
||||
3. Synthesized butler-toned response as output text
|
||||
|
||||
Args:
|
||||
request: Response request
|
||||
@@ -130,11 +131,17 @@ class StreamingCoordinator:
|
||||
Yields:
|
||||
StreamEvent: Stream of SSE events
|
||||
"""
|
||||
from src.responses.service import _calculate_usage, generate_id, _conversation_history
|
||||
from src.responses.service import (
|
||||
_calculate_usage,
|
||||
generate_id,
|
||||
_conversation_history,
|
||||
_direct_delegation_with_results,
|
||||
)
|
||||
from src.core.preprocessing import preprocess_request
|
||||
from src.core.tool_tracking import ToolCallTracker
|
||||
from src.responses.schemas import MessageOutputItem, ReasoningOutputItem, OutputTextContent
|
||||
from src.agents.tatlock import TatlockAgent
|
||||
from src.agents.delegation import get_think_message, STREAMING_DELEGATION_WRAPPERS
|
||||
import asyncio
|
||||
|
||||
output_items = []
|
||||
@@ -152,7 +159,7 @@ class StreamingCoordinator:
|
||||
|
||||
conversation_history = request.input[:-1] if len(request.input) > 1 else []
|
||||
|
||||
# Phase 1: Steward preprocessing
|
||||
# Steward preprocessing
|
||||
enriched = await preprocess_request(
|
||||
user_message,
|
||||
conversation_history=conversation_history,
|
||||
@@ -179,31 +186,62 @@ class StreamingCoordinator:
|
||||
)
|
||||
output_items.append(reasoning_item)
|
||||
|
||||
# Phase 2: Initialize tool tracker
|
||||
# Initialize tool tracker
|
||||
tracker = ToolCallTracker(
|
||||
recommended_capabilities=enriched.recommendation.recommended_capabilities,
|
||||
conversation_id=conversation_id,
|
||||
)
|
||||
|
||||
# Phase 3: Stream Tatlock's response with scoped tools
|
||||
tatlock = TatlockAgent()
|
||||
tatlock_response_parts = []
|
||||
# Check if direct delegation is recommended
|
||||
delegation_agents = {"biographer", "librarian", "housekeeper"}
|
||||
delegation_only = all(
|
||||
cap in delegation_agents
|
||||
for cap in enriched.recommendation.recommended_capabilities
|
||||
) and enriched.recommendation.recommended_capabilities
|
||||
|
||||
async for chunk in tatlock.run_with_scoped_tools_stream(
|
||||
tatlock = TatlockAgent()
|
||||
|
||||
if delegation_only:
|
||||
# Direct delegation path with streaming think slugs
|
||||
orchestration_results = await self._stream_direct_delegation(
|
||||
user_message=user_message,
|
||||
recommendation=enriched.recommendation,
|
||||
tracker=tracker,
|
||||
conversation_id=conversation_id,
|
||||
)
|
||||
|
||||
# Stream think slugs that were collected during delegation
|
||||
# Each think message is complete, so we signal done after each
|
||||
for think_msg in orchestration_results.get("think_messages", []):
|
||||
yield ReasoningSummaryDelta(delta=think_msg)
|
||||
yield ReasoningSummaryDone()
|
||||
await asyncio.sleep(0.05)
|
||||
|
||||
else:
|
||||
# Phase 1: Orchestrate tool calls
|
||||
orchestration_results = await tatlock.orchestrate_tool_calls(
|
||||
user_message=user_message,
|
||||
steward_note=enriched.steward_note,
|
||||
scoped_tools=enriched.scoped_tools,
|
||||
message_history=conversation_history,
|
||||
tool_tracker=tracker,
|
||||
)
|
||||
|
||||
# Phase 2: Synthesize butler-toned response
|
||||
tatlock_response = await tatlock.synthesize_from_results(
|
||||
user_message=user_message,
|
||||
steward_note=enriched.steward_note,
|
||||
scoped_tools=enriched.scoped_tools,
|
||||
orchestration_results=orchestration_results,
|
||||
message_history=conversation_history,
|
||||
tool_tracker=tracker,
|
||||
):
|
||||
tatlock_response_parts.append(chunk)
|
||||
yield OutputTextDelta(delta=chunk)
|
||||
)
|
||||
|
||||
# Stream the synthesized response
|
||||
chunk_size = 50
|
||||
for i in range(0, len(tatlock_response), chunk_size):
|
||||
yield OutputTextDelta(delta=tatlock_response[i:i + chunk_size])
|
||||
await asyncio.sleep(0.02)
|
||||
|
||||
yield OutputTextDone()
|
||||
|
||||
# Combine response for output item
|
||||
tatlock_response = "".join(tatlock_response_parts)
|
||||
|
||||
# Add Tatlock message to output items
|
||||
message_item = MessageOutputItem(
|
||||
id=f"msg_{generate_id()}",
|
||||
@@ -217,7 +255,7 @@ class StreamingCoordinator:
|
||||
)
|
||||
output_items.append(message_item)
|
||||
|
||||
# Phase 4: Finalize tool tracking
|
||||
# Finalize tool tracking
|
||||
await tracker.finalize()
|
||||
|
||||
# Calculate usage and build final response
|
||||
@@ -241,6 +279,85 @@ class StreamingCoordinator:
|
||||
# Stream error event
|
||||
yield self._create_error_event(e)
|
||||
|
||||
async def _stream_direct_delegation(
|
||||
self,
|
||||
user_message: str,
|
||||
recommendation: "StewardRecommendation", # type: ignore
|
||||
tracker: "ToolCallTracker", # type: ignore
|
||||
conversation_id: str,
|
||||
) -> dict:
|
||||
"""
|
||||
Execute direct delegation with streaming think messages.
|
||||
|
||||
Collects think messages as delegations execute for streaming to client.
|
||||
|
||||
Args:
|
||||
user_message: User's request
|
||||
recommendation: Steward's recommendation
|
||||
tracker: Tool call tracker
|
||||
conversation_id: Conversation ID
|
||||
|
||||
Returns:
|
||||
dict: Orchestration results with think_messages list
|
||||
"""
|
||||
from src.agents.delegation import (
|
||||
get_think_message,
|
||||
delegate_to_librarian,
|
||||
delegate_to_biographer,
|
||||
delegate_to_housekeeper,
|
||||
)
|
||||
import time as time_module
|
||||
|
||||
expert_results = {}
|
||||
tools_called = []
|
||||
think_messages = []
|
||||
|
||||
for agent in recommendation.recommended_capabilities:
|
||||
# Emit start think message
|
||||
start_msg = get_think_message(agent, user_message, "start")
|
||||
think_messages.append(start_msg + "\n")
|
||||
|
||||
start_time = time_module.time()
|
||||
try:
|
||||
# Execute delegation
|
||||
if agent == "librarian":
|
||||
result = await delegate_to_librarian(task=user_message)
|
||||
elif agent == "biographer":
|
||||
result = await delegate_to_biographer(task=user_message)
|
||||
elif agent == "housekeeper":
|
||||
result = await delegate_to_housekeeper(task=user_message)
|
||||
else:
|
||||
result = None
|
||||
|
||||
duration = time_module.time() - start_time
|
||||
await tracker.track_call(f"delegate_to_{agent}", duration)
|
||||
|
||||
if result and result.success:
|
||||
expert_results[agent] = result.output
|
||||
tools_called.append(f"delegate_to_{agent}")
|
||||
# Emit success think message
|
||||
success_msg = get_think_message(agent, user_message, "success")
|
||||
think_messages.append(success_msg + "\n")
|
||||
else:
|
||||
error_msg = result.error if result else "Unknown error"
|
||||
expert_results[agent] = f"Error: {error_msg}"
|
||||
# Emit error think message
|
||||
error_think = get_think_message(agent, user_message, "error")
|
||||
think_messages.append(error_think + "\n")
|
||||
|
||||
except Exception as e:
|
||||
expert_results[agent] = f"Error: {e}"
|
||||
error_think = get_think_message(agent, user_message, "error")
|
||||
think_messages.append(error_think + "\n")
|
||||
|
||||
return {
|
||||
"tools_called": tools_called,
|
||||
"expert_results": expert_results,
|
||||
"tool_outputs": {},
|
||||
"raw_output": "",
|
||||
"think_messages": think_messages,
|
||||
}
|
||||
|
||||
async def stream_response(
|
||||
self,
|
||||
request: "ResponseRequest" # type: ignore # Forward reference
|
||||
|
||||
@@ -0,0 +1,426 @@
|
||||
"""
|
||||
Tests for Librarian tools.
|
||||
|
||||
Tests the tool functions that wrap the Library-Desk API,
|
||||
including the new web search and content extraction tools.
|
||||
"""
|
||||
|
||||
import pytest
|
||||
from unittest.mock import AsyncMock, MagicMock, patch
|
||||
|
||||
from src.agents.librarian.tools import (
|
||||
search_web,
|
||||
read_url,
|
||||
read_urls_batch,
|
||||
hybrid_search,
|
||||
search_wiki,
|
||||
)
|
||||
from src.agents.librarian.client import (
|
||||
WebSearchResult,
|
||||
WebSearchResponse,
|
||||
ContentExtractionResult,
|
||||
BatchExtractionResponse,
|
||||
)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_client():
|
||||
"""Create a mock LibraryDeskClient."""
|
||||
client = AsyncMock()
|
||||
return client
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Web Search Tests
|
||||
# ============================================================================
|
||||
|
||||
@pytest.mark.unit
|
||||
class TestSearchWeb:
|
||||
"""Tests for search_web tool."""
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_search_web_success(self, mock_client):
|
||||
"""Test successful web search."""
|
||||
mock_response = WebSearchResponse(
|
||||
query="Python async programming",
|
||||
search_type="web",
|
||||
results=[
|
||||
WebSearchResult(
|
||||
title="Async Python Tutorial",
|
||||
url="https://example.com/async",
|
||||
content="Full content about async programming...",
|
||||
snippet="Learn async programming in Python",
|
||||
source="example.com",
|
||||
),
|
||||
WebSearchResult(
|
||||
title="AsyncIO Documentation",
|
||||
url="https://docs.python.org/asyncio",
|
||||
content="Official asyncio docs content...",
|
||||
snippet="Python asyncio library reference",
|
||||
source="docs.python.org",
|
||||
),
|
||||
],
|
||||
total_results=2,
|
||||
search_time_ms=150,
|
||||
sources_summary="**Sources:**\n- example.com\n- docs.python.org",
|
||||
)
|
||||
mock_client.search_web.return_value = mock_response
|
||||
|
||||
with patch(
|
||||
"src.agents.librarian.tools.LibraryDeskClient"
|
||||
) as mock_client_class:
|
||||
mock_client_class.return_value.__aenter__.return_value = mock_client
|
||||
mock_client_class.return_value.__aexit__.return_value = None
|
||||
|
||||
result = await search_web("Python async programming")
|
||||
|
||||
assert "Python async programming" in result
|
||||
assert "Async Python Tutorial" in result
|
||||
assert "https://example.com/async" in result
|
||||
assert "example.com" in result
|
||||
assert "150ms" in result or "2 results" in result
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_search_web_no_results(self, mock_client):
|
||||
"""Test web search with no results."""
|
||||
mock_response = WebSearchResponse(
|
||||
query="nonexistent query xyz123",
|
||||
search_type="web",
|
||||
results=[],
|
||||
total_results=0,
|
||||
search_time_ms=50,
|
||||
)
|
||||
mock_client.search_web.return_value = mock_response
|
||||
|
||||
with patch(
|
||||
"src.agents.librarian.tools.LibraryDeskClient"
|
||||
) as mock_client_class:
|
||||
mock_client_class.return_value.__aenter__.return_value = mock_client
|
||||
mock_client_class.return_value.__aexit__.return_value = None
|
||||
|
||||
result = await search_web("nonexistent query xyz123")
|
||||
|
||||
assert "No results found" in result
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_search_web_error_handling(self, mock_client):
|
||||
"""Test web search error handling."""
|
||||
mock_client.search_web.side_effect = Exception("Connection failed")
|
||||
|
||||
with patch(
|
||||
"src.agents.librarian.tools.LibraryDeskClient"
|
||||
) as mock_client_class:
|
||||
mock_client_class.return_value.__aenter__.return_value = mock_client
|
||||
mock_client_class.return_value.__aexit__.return_value = None
|
||||
|
||||
result = await search_web("test query")
|
||||
|
||||
assert "Error" in result
|
||||
assert "Connection failed" in result
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_search_web_with_news_type(self, mock_client):
|
||||
"""Test web search with news search type."""
|
||||
mock_response = WebSearchResponse(
|
||||
query="latest tech news",
|
||||
search_type="news",
|
||||
results=[
|
||||
WebSearchResult(
|
||||
title="Tech News Today",
|
||||
url="https://news.example.com/tech",
|
||||
snippet="Breaking tech news",
|
||||
source="news.example.com",
|
||||
published_date="2024-01-15",
|
||||
),
|
||||
],
|
||||
total_results=1,
|
||||
search_time_ms=100,
|
||||
)
|
||||
mock_client.search_web.return_value = mock_response
|
||||
|
||||
with patch(
|
||||
"src.agents.librarian.tools.LibraryDeskClient"
|
||||
) as mock_client_class:
|
||||
mock_client_class.return_value.__aenter__.return_value = mock_client
|
||||
mock_client_class.return_value.__aexit__.return_value = None
|
||||
|
||||
result = await search_web("latest tech news", search_type="news")
|
||||
|
||||
assert "Tech News Today" in result
|
||||
mock_client.search_web.assert_called_with(
|
||||
query="latest tech news",
|
||||
limit=10,
|
||||
search_type="news",
|
||||
)
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Read URL Tests
|
||||
# ============================================================================
|
||||
|
||||
@pytest.mark.unit
|
||||
class TestReadUrl:
|
||||
"""Tests for read_url tool."""
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_read_url_success(self, mock_client):
|
||||
"""Test successful URL content extraction."""
|
||||
mock_result = ContentExtractionResult(
|
||||
url="https://example.com/article",
|
||||
title="Great Article Title",
|
||||
content="This is the full article content extracted from the page.",
|
||||
author="John Doe",
|
||||
date="2024-01-10",
|
||||
language="en",
|
||||
success=True,
|
||||
)
|
||||
mock_client.extract_content.return_value = mock_result
|
||||
|
||||
with patch(
|
||||
"src.agents.librarian.tools.LibraryDeskClient"
|
||||
) as mock_client_class:
|
||||
mock_client_class.return_value.__aenter__.return_value = mock_client
|
||||
mock_client_class.return_value.__aexit__.return_value = None
|
||||
|
||||
result = await read_url("https://example.com/article")
|
||||
|
||||
assert "Great Article Title" in result
|
||||
assert "https://example.com/article" in result
|
||||
assert "John Doe" in result
|
||||
assert "full article content" in result
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_read_url_failure(self, mock_client):
|
||||
"""Test URL extraction failure."""
|
||||
mock_result = ContentExtractionResult(
|
||||
url="https://example.com/blocked",
|
||||
success=False,
|
||||
error="403 Forbidden",
|
||||
)
|
||||
mock_client.extract_content.return_value = mock_result
|
||||
|
||||
with patch(
|
||||
"src.agents.librarian.tools.LibraryDeskClient"
|
||||
) as mock_client_class:
|
||||
mock_client_class.return_value.__aenter__.return_value = mock_client
|
||||
mock_client_class.return_value.__aexit__.return_value = None
|
||||
|
||||
result = await read_url("https://example.com/blocked")
|
||||
|
||||
assert "Could not read page" in result
|
||||
assert "403 Forbidden" in result
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_read_url_with_max_length(self, mock_client):
|
||||
"""Test URL extraction with custom max length."""
|
||||
mock_result = ContentExtractionResult(
|
||||
url="https://example.com/long",
|
||||
title="Long Article",
|
||||
content="X" * 10000,
|
||||
success=True,
|
||||
)
|
||||
mock_client.extract_content.return_value = mock_result
|
||||
|
||||
with patch(
|
||||
"src.agents.librarian.tools.LibraryDeskClient"
|
||||
) as mock_client_class:
|
||||
mock_client_class.return_value.__aenter__.return_value = mock_client
|
||||
mock_client_class.return_value.__aexit__.return_value = None
|
||||
|
||||
result = await read_url("https://example.com/long", max_length=2000)
|
||||
|
||||
mock_client.extract_content.assert_called_with(
|
||||
url="https://example.com/long",
|
||||
include_metadata=True,
|
||||
max_length=2000,
|
||||
)
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Batch URL Tests
|
||||
# ============================================================================
|
||||
|
||||
@pytest.mark.unit
|
||||
class TestReadUrlsBatch:
|
||||
"""Tests for read_urls_batch tool."""
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_batch_success(self, mock_client):
|
||||
"""Test successful batch extraction."""
|
||||
mock_response = BatchExtractionResponse(
|
||||
results=[
|
||||
ContentExtractionResult(
|
||||
url="https://example.com/1",
|
||||
title="Article 1",
|
||||
content="Content from article 1",
|
||||
success=True,
|
||||
),
|
||||
ContentExtractionResult(
|
||||
url="https://example.com/2",
|
||||
title="Article 2",
|
||||
content="Content from article 2",
|
||||
success=True,
|
||||
),
|
||||
],
|
||||
total_urls=2,
|
||||
successful=2,
|
||||
failed=0,
|
||||
extraction_time_ms=300,
|
||||
)
|
||||
mock_client.extract_content_batch.return_value = mock_response
|
||||
|
||||
with patch(
|
||||
"src.agents.librarian.tools.LibraryDeskClient"
|
||||
) as mock_client_class:
|
||||
mock_client_class.return_value.__aenter__.return_value = mock_client
|
||||
mock_client_class.return_value.__aexit__.return_value = None
|
||||
|
||||
result = await read_urls_batch([
|
||||
"https://example.com/1",
|
||||
"https://example.com/2",
|
||||
])
|
||||
|
||||
assert "Article 1" in result
|
||||
assert "Article 2" in result
|
||||
assert "2/2" in result or "Extracted 2" in result
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_batch_partial_failure(self, mock_client):
|
||||
"""Test batch extraction with some failures."""
|
||||
mock_response = BatchExtractionResponse(
|
||||
results=[
|
||||
ContentExtractionResult(
|
||||
url="https://example.com/good",
|
||||
title="Good Article",
|
||||
content="Content extracted successfully",
|
||||
success=True,
|
||||
),
|
||||
ContentExtractionResult(
|
||||
url="https://example.com/bad",
|
||||
success=False,
|
||||
error="Connection timeout",
|
||||
),
|
||||
],
|
||||
total_urls=2,
|
||||
successful=1,
|
||||
failed=1,
|
||||
extraction_time_ms=500,
|
||||
)
|
||||
mock_client.extract_content_batch.return_value = mock_response
|
||||
|
||||
with patch(
|
||||
"src.agents.librarian.tools.LibraryDeskClient"
|
||||
) as mock_client_class:
|
||||
mock_client_class.return_value.__aenter__.return_value = mock_client
|
||||
mock_client_class.return_value.__aexit__.return_value = None
|
||||
|
||||
result = await read_urls_batch([
|
||||
"https://example.com/good",
|
||||
"https://example.com/bad",
|
||||
])
|
||||
|
||||
# Should contain successful result
|
||||
assert "Good Article" in result
|
||||
# Should report failure
|
||||
assert "Failed" in result
|
||||
assert "Connection timeout" in result
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Response Model Tests
|
||||
# ============================================================================
|
||||
|
||||
@pytest.mark.unit
|
||||
class TestWebSearchModels:
|
||||
"""Tests for web search response models."""
|
||||
|
||||
def test_web_search_result_model(self):
|
||||
"""Test WebSearchResult model."""
|
||||
result = WebSearchResult(
|
||||
title="Test Title",
|
||||
url="https://example.com",
|
||||
content="Full content here",
|
||||
snippet="Short snippet",
|
||||
source="example.com",
|
||||
published_date="2024-01-15",
|
||||
)
|
||||
|
||||
assert result.title == "Test Title"
|
||||
assert result.url == "https://example.com"
|
||||
assert result.content == "Full content here"
|
||||
assert result.source == "example.com"
|
||||
|
||||
def test_web_search_result_defaults(self):
|
||||
"""Test WebSearchResult default values."""
|
||||
result = WebSearchResult(
|
||||
title="Title",
|
||||
url="https://example.com",
|
||||
)
|
||||
|
||||
assert result.content == ""
|
||||
assert result.snippet == ""
|
||||
assert result.source == ""
|
||||
assert result.published_date is None
|
||||
|
||||
def test_web_search_response_model(self):
|
||||
"""Test WebSearchResponse model."""
|
||||
response = WebSearchResponse(
|
||||
query="test query",
|
||||
search_type="web",
|
||||
results=[
|
||||
WebSearchResult(title="R1", url="https://example.com/1"),
|
||||
WebSearchResult(title="R2", url="https://example.com/2"),
|
||||
],
|
||||
total_results=2,
|
||||
search_time_ms=100,
|
||||
sources_summary="**Sources:** example.com",
|
||||
)
|
||||
|
||||
assert response.query == "test query"
|
||||
assert len(response.results) == 2
|
||||
assert response.total_results == 2
|
||||
|
||||
def test_content_extraction_result_model(self):
|
||||
"""Test ContentExtractionResult model."""
|
||||
result = ContentExtractionResult(
|
||||
url="https://example.com",
|
||||
title="Title",
|
||||
content="Content",
|
||||
author="Author",
|
||||
date="2024-01-01",
|
||||
language="en",
|
||||
success=True,
|
||||
)
|
||||
|
||||
assert result.url == "https://example.com"
|
||||
assert result.success is True
|
||||
assert result.author == "Author"
|
||||
|
||||
def test_content_extraction_failure(self):
|
||||
"""Test ContentExtractionResult for failed extraction."""
|
||||
result = ContentExtractionResult(
|
||||
url="https://example.com",
|
||||
success=False,
|
||||
error="404 Not Found",
|
||||
)
|
||||
|
||||
assert result.success is False
|
||||
assert result.error == "404 Not Found"
|
||||
assert result.content == ""
|
||||
|
||||
def test_batch_extraction_response_model(self):
|
||||
"""Test BatchExtractionResponse model."""
|
||||
response = BatchExtractionResponse(
|
||||
results=[
|
||||
ContentExtractionResult(url="https://1.com", success=True),
|
||||
ContentExtractionResult(url="https://2.com", success=False),
|
||||
],
|
||||
total_urls=2,
|
||||
successful=1,
|
||||
failed=1,
|
||||
extraction_time_ms=500,
|
||||
)
|
||||
|
||||
assert response.total_urls == 2
|
||||
assert response.successful == 1
|
||||
assert response.failed == 1
|
||||
@@ -8,7 +8,7 @@ from unittest.mock import AsyncMock, MagicMock, patch
|
||||
import pytest
|
||||
|
||||
from src.agents.steward.schemas import ConversationContext, StewardRecommendation
|
||||
from src.agents.steward.service import analyze_request, format_steward_note
|
||||
from src.agents.steward.service import analyze_request, format_steward_note, _build_enriched_query
|
||||
from src.core.startup import initialize_application
|
||||
|
||||
|
||||
@@ -199,3 +199,102 @@ class TestFormatStewardNote:
|
||||
|
||||
assert "⚠️ Missing:" in note
|
||||
assert "Advanced research" in note
|
||||
|
||||
|
||||
@pytest.mark.unit
|
||||
class TestBuildEnrichedQuery:
|
||||
"""Tests for _build_enriched_query function."""
|
||||
|
||||
def test_no_enrichment_without_context(self):
|
||||
"""Test no enrichment when memory context is empty."""
|
||||
query = "What's the weather?"
|
||||
result = _build_enriched_query(query, {})
|
||||
|
||||
assert result == query
|
||||
|
||||
def test_enrichment_adds_location(self):
|
||||
"""Test location is appended for weather queries."""
|
||||
query = "What's the weather?"
|
||||
memory_context = {
|
||||
"profile": {"location": "Amsterdam", "timezone": "Europe/Amsterdam"}
|
||||
}
|
||||
|
||||
result = _build_enriched_query(query, memory_context)
|
||||
|
||||
assert "location=Amsterdam" in result
|
||||
assert query in result
|
||||
assert "[User Context:" in result
|
||||
|
||||
def test_no_location_when_specified(self):
|
||||
"""Test location is not appended when already specified."""
|
||||
query = "What's the weather in London?"
|
||||
memory_context = {
|
||||
"profile": {"location": "Amsterdam"}
|
||||
}
|
||||
|
||||
result = _build_enriched_query(query, memory_context)
|
||||
|
||||
# Should not add Amsterdam since location is specified
|
||||
assert result == query
|
||||
|
||||
def test_enrichment_adds_timezone(self):
|
||||
"""Test timezone is appended for time queries."""
|
||||
query = "What time is it?"
|
||||
memory_context = {
|
||||
"profile": {"timezone": "Europe/Amsterdam"}
|
||||
}
|
||||
|
||||
result = _build_enriched_query(query, memory_context)
|
||||
|
||||
assert "timezone=Europe/Amsterdam" in result
|
||||
|
||||
def test_no_timezone_when_specified(self):
|
||||
"""Test timezone is not appended when already specified."""
|
||||
query = "What time is it in UTC?"
|
||||
memory_context = {
|
||||
"profile": {"timezone": "Europe/Amsterdam"}
|
||||
}
|
||||
|
||||
result = _build_enriched_query(query, memory_context)
|
||||
|
||||
assert result == query
|
||||
|
||||
def test_enrichment_adds_temperature_unit(self):
|
||||
"""Test temperature unit is appended for weather queries."""
|
||||
query = "What's the weather?"
|
||||
memory_context = {
|
||||
"profile": {"location": "Amsterdam"},
|
||||
"preferences": {"temperature_unit": "celsius"}
|
||||
}
|
||||
|
||||
result = _build_enriched_query(query, memory_context)
|
||||
|
||||
assert "temperature_unit=celsius" in result
|
||||
|
||||
def test_multiple_context_fields(self):
|
||||
"""Test multiple context fields are appended."""
|
||||
query = "What time and weather today?"
|
||||
memory_context = {
|
||||
"profile": {
|
||||
"location": "Amsterdam",
|
||||
"timezone": "Europe/Amsterdam"
|
||||
},
|
||||
"preferences": {"temperature_unit": "celsius"}
|
||||
}
|
||||
|
||||
result = _build_enriched_query(query, memory_context)
|
||||
|
||||
assert "location=Amsterdam" in result
|
||||
assert "timezone=Europe/Amsterdam" in result
|
||||
assert "temperature_unit=celsius" in result
|
||||
|
||||
def test_no_enrichment_for_unrelated_query(self):
|
||||
"""Test no enrichment for queries that don't need context."""
|
||||
query = "Tell me a joke"
|
||||
memory_context = {
|
||||
"profile": {"location": "Amsterdam", "timezone": "Europe/Amsterdam"}
|
||||
}
|
||||
|
||||
result = _build_enriched_query(query, memory_context)
|
||||
|
||||
assert result == query
|
||||
|
||||
@@ -8,9 +8,14 @@ import pytest
|
||||
from unittest.mock import AsyncMock, patch, MagicMock
|
||||
|
||||
from src.agents.delegation import (
|
||||
ActionType,
|
||||
DelegationTask,
|
||||
DelegationResult,
|
||||
HOUSEHOLD_THINK_MESSAGES,
|
||||
STREAMING_DELEGATION_WRAPPERS,
|
||||
delegate_to_librarian,
|
||||
get_think_message,
|
||||
_detect_action_type,
|
||||
)
|
||||
|
||||
|
||||
@@ -193,3 +198,162 @@ class TestDelegateToLibrarian:
|
||||
result = await delegate_to_librarian(task=original_task)
|
||||
|
||||
assert result.task == original_task
|
||||
|
||||
|
||||
@pytest.mark.unit
|
||||
class TestActionType:
|
||||
"""Tests for the ActionType enum."""
|
||||
|
||||
def test_action_type_values(self):
|
||||
"""Test ActionType enum values."""
|
||||
assert ActionType.RETRIEVE.value == "retrieve"
|
||||
assert ActionType.RESEARCH.value == "research"
|
||||
assert ActionType.CREATE.value == "create"
|
||||
assert ActionType.CONTROL.value == "control"
|
||||
assert ActionType.RECORD.value == "record"
|
||||
|
||||
def test_action_type_is_enum(self):
|
||||
"""Test ActionType is proper enum."""
|
||||
assert len(ActionType) == 5
|
||||
|
||||
|
||||
@pytest.mark.unit
|
||||
class TestHouseholdThinkMessages:
|
||||
"""Tests for HOUSEHOLD_THINK_MESSAGES mapping."""
|
||||
|
||||
def test_librarian_has_messages(self):
|
||||
"""Test librarian has think messages."""
|
||||
assert "librarian" in HOUSEHOLD_THINK_MESSAGES
|
||||
assert ActionType.RETRIEVE in HOUSEHOLD_THINK_MESSAGES["librarian"]
|
||||
assert ActionType.RESEARCH in HOUSEHOLD_THINK_MESSAGES["librarian"]
|
||||
assert ActionType.CREATE in HOUSEHOLD_THINK_MESSAGES["librarian"]
|
||||
|
||||
def test_biographer_has_messages(self):
|
||||
"""Test biographer has think messages."""
|
||||
assert "biographer" in HOUSEHOLD_THINK_MESSAGES
|
||||
assert ActionType.RETRIEVE in HOUSEHOLD_THINK_MESSAGES["biographer"]
|
||||
assert ActionType.RECORD in HOUSEHOLD_THINK_MESSAGES["biographer"]
|
||||
|
||||
def test_housekeeper_has_messages(self):
|
||||
"""Test housekeeper has think messages."""
|
||||
assert "housekeeper" in HOUSEHOLD_THINK_MESSAGES
|
||||
assert ActionType.RETRIEVE in HOUSEHOLD_THINK_MESSAGES["housekeeper"]
|
||||
assert ActionType.CONTROL in HOUSEHOLD_THINK_MESSAGES["housekeeper"]
|
||||
|
||||
def test_messages_have_phases(self):
|
||||
"""Test each action type has start/success/error messages."""
|
||||
for expert, action_types in HOUSEHOLD_THINK_MESSAGES.items():
|
||||
for action_type, messages in action_types.items():
|
||||
assert "start" in messages, f"{expert}/{action_type} missing 'start'"
|
||||
assert "success" in messages, f"{expert}/{action_type} missing 'success'"
|
||||
assert "error" in messages, f"{expert}/{action_type} missing 'error'"
|
||||
|
||||
def test_messages_are_plain_text(self):
|
||||
"""Test messages are plain text (no <think> wrappers - those go to reasoning_content)."""
|
||||
for expert, action_types in HOUSEHOLD_THINK_MESSAGES.items():
|
||||
for action_type, messages in action_types.items():
|
||||
for phase, msg in messages.items():
|
||||
# Messages should NOT have <think> wrappers - they go to reasoning_content field
|
||||
assert "<think>" not in msg, f"{expert}/{action_type}/{phase} should not have <think> wrapper"
|
||||
assert "</think>" not in msg, f"{expert}/{action_type}/{phase} should not have </think> wrapper"
|
||||
# Messages should be non-empty strings
|
||||
assert isinstance(msg, str) and len(msg) > 0, f"{expert}/{action_type}/{phase}"
|
||||
|
||||
|
||||
@pytest.mark.unit
|
||||
class TestDetectActionType:
|
||||
"""Tests for _detect_action_type function."""
|
||||
|
||||
def test_librarian_search_is_retrieve(self):
|
||||
"""Test librarian search tasks are RETRIEVE."""
|
||||
assert _detect_action_type("librarian", "search for Docker info") == ActionType.RETRIEVE
|
||||
assert _detect_action_type("librarian", "find information about CI/CD") == ActionType.RETRIEVE
|
||||
assert _detect_action_type("librarian", "look up Kubernetes docs") == ActionType.RETRIEVE
|
||||
|
||||
def test_librarian_web_search_is_research(self):
|
||||
"""Test librarian web search tasks are RESEARCH."""
|
||||
assert _detect_action_type("librarian", "search the web for news") == ActionType.RESEARCH
|
||||
assert _detect_action_type("librarian", "find online resources") == ActionType.RESEARCH
|
||||
assert _detect_action_type("librarian", "research internet sources") == ActionType.RESEARCH
|
||||
|
||||
def test_librarian_create_is_create(self):
|
||||
"""Test librarian creation tasks are CREATE."""
|
||||
assert _detect_action_type("librarian", "create a wiki page") == ActionType.CREATE
|
||||
assert _detect_action_type("librarian", "write a new article") == ActionType.CREATE
|
||||
assert _detect_action_type("librarian", "add a new entry") == ActionType.CREATE
|
||||
|
||||
def test_biographer_recall_is_retrieve(self):
|
||||
"""Test biographer recall tasks are RETRIEVE."""
|
||||
assert _detect_action_type("biographer", "what car do I drive?") == ActionType.RETRIEVE
|
||||
assert _detect_action_type("biographer", "what is my job?") == ActionType.RETRIEVE
|
||||
|
||||
def test_biographer_record_is_record(self):
|
||||
"""Test biographer record tasks are RECORD."""
|
||||
assert _detect_action_type("biographer", "remember that I work at Acme") == ActionType.RECORD
|
||||
assert _detect_action_type("biographer", "note that my car is a Tesla") == ActionType.RECORD
|
||||
assert _detect_action_type("biographer", "save my preference for dark mode") == ActionType.RECORD
|
||||
|
||||
def test_housekeeper_status_is_retrieve(self):
|
||||
"""Test housekeeper status tasks are RETRIEVE."""
|
||||
assert _detect_action_type("housekeeper", "what devices are in the bedroom?") == ActionType.RETRIEVE
|
||||
assert _detect_action_type("housekeeper", "is the living room light on?") == ActionType.RETRIEVE
|
||||
|
||||
def test_housekeeper_control_is_control(self):
|
||||
"""Test housekeeper control tasks are CONTROL."""
|
||||
assert _detect_action_type("housekeeper", "turn on the lights") == ActionType.CONTROL
|
||||
assert _detect_action_type("housekeeper", "set brightness to 50%") == ActionType.CONTROL
|
||||
assert _detect_action_type("housekeeper", "activate the movie scene") == ActionType.CONTROL
|
||||
assert _detect_action_type("housekeeper", "toggle the fan") == ActionType.CONTROL
|
||||
|
||||
|
||||
@pytest.mark.unit
|
||||
class TestGetThinkMessage:
|
||||
"""Tests for get_think_message function."""
|
||||
|
||||
def test_librarian_retrieve_start(self):
|
||||
"""Test getting librarian retrieve start message."""
|
||||
msg = get_think_message("librarian", "search for Docker", "start")
|
||||
# No <think> wrappers - messages go to reasoning_content field
|
||||
assert "<think>" not in msg
|
||||
assert "archives" in msg.lower() or "consult" in msg.lower()
|
||||
|
||||
def test_librarian_create_success(self):
|
||||
"""Test getting librarian create success message."""
|
||||
msg = get_think_message("librarian", "create a wiki page", "success")
|
||||
assert "<think>" not in msg
|
||||
assert "catalogued" in msg.lower()
|
||||
|
||||
def test_biographer_record_start(self):
|
||||
"""Test getting biographer record start message."""
|
||||
msg = get_think_message("biographer", "remember my preference", "start")
|
||||
assert "<think>" not in msg
|
||||
assert "note" in msg.lower() or "biographer" in msg.lower()
|
||||
|
||||
def test_housekeeper_control_success(self):
|
||||
"""Test getting housekeeper control success message."""
|
||||
msg = get_think_message("housekeeper", "turn on the lights", "success")
|
||||
assert "<think>" not in msg
|
||||
assert "configured" in msg.lower()
|
||||
|
||||
def test_unknown_expert_fallback(self):
|
||||
"""Test unknown expert gets fallback message."""
|
||||
msg = get_think_message("unknown_expert", "some task", "start")
|
||||
assert "<think>" not in msg
|
||||
assert "unknown_expert" in msg.lower()
|
||||
|
||||
|
||||
@pytest.mark.unit
|
||||
class TestStreamingDelegationWrappers:
|
||||
"""Tests for streaming delegation wrapper mapping."""
|
||||
|
||||
def test_streaming_wrappers_exist(self):
|
||||
"""Test streaming wrappers mapping has all experts."""
|
||||
assert "librarian" in STREAMING_DELEGATION_WRAPPERS
|
||||
assert "biographer" in STREAMING_DELEGATION_WRAPPERS
|
||||
assert "housekeeper" in STREAMING_DELEGATION_WRAPPERS
|
||||
|
||||
def test_streaming_wrappers_are_async_generators(self):
|
||||
"""Test streaming wrappers are async generator functions."""
|
||||
import inspect
|
||||
for name, wrapper in STREAMING_DELEGATION_WRAPPERS.items():
|
||||
assert inspect.isasyncgenfunction(wrapper), f"{name} is not an async generator"
|
||||
|
||||
@@ -208,8 +208,8 @@ class TestOrchestrateWithThinkUpdates:
|
||||
):
|
||||
updates.append(update)
|
||||
|
||||
# First update should be think tag about consulting
|
||||
assert any("<think>" in u and "Consulting" in u for u in updates)
|
||||
# First update should be about consulting (no <think> wrappers anymore)
|
||||
assert any("Consulting" in u for u in updates)
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_orchestrate_emits_think_after_delegation(self):
|
||||
@@ -233,8 +233,8 @@ class TestOrchestrateWithThinkUpdates:
|
||||
):
|
||||
updates.append(update)
|
||||
|
||||
# Should have think tag about completion
|
||||
assert any("<think>" in u and "completed" in u for u in updates)
|
||||
# Should have message about completion (no <think> wrappers anymore)
|
||||
assert any("completed" in u for u in updates)
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_orchestrate_yields_expert_output(self):
|
||||
|
||||
+4
-184
@@ -1,17 +1,18 @@
|
||||
"""
|
||||
Tests for Tatlock's permanent tools (calculator, date/time, search).
|
||||
Tests for Tatlock's permanent tools (calculator, date/time).
|
||||
|
||||
Note: Web search has been moved to The Librarian agent.
|
||||
See tests/agents/librarian/test_tools.py for search tests.
|
||||
"""
|
||||
|
||||
import pytest
|
||||
from datetime import datetime
|
||||
from unittest.mock import AsyncMock, patch
|
||||
|
||||
from src.agents.tools import (
|
||||
calculate,
|
||||
get_current_datetime,
|
||||
calculate_time_offset,
|
||||
time_difference,
|
||||
search_web,
|
||||
)
|
||||
|
||||
|
||||
@@ -188,184 +189,3 @@ class TestDateTime:
|
||||
"""Test error handling for invalid dates."""
|
||||
result = time_difference("invalid-date", "now")
|
||||
assert "Error" in result
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Search Tests
|
||||
# ============================================================================
|
||||
|
||||
class TestSearch:
|
||||
"""Tests for web search tool."""
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_search_web_success(self):
|
||||
"""Test successful web search."""
|
||||
mock_response = {
|
||||
"results": [
|
||||
{
|
||||
"title": "Test Result 1",
|
||||
"url": "https://example.com/1",
|
||||
"content": "This is a test result"
|
||||
},
|
||||
{
|
||||
"title": "Test Result 2",
|
||||
"url": "https://example.com/2",
|
||||
"content": "Another test result"
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
with patch("src.agents.tools.httpx.AsyncClient") as mock_client_class:
|
||||
# Create mock response
|
||||
mock_response_obj = type('MockResponse', (), {
|
||||
'status_code': 200,
|
||||
'json': lambda *args, **kwargs: mock_response
|
||||
})()
|
||||
|
||||
# Create mock client with async get method
|
||||
async def mock_get(*args, **kwargs):
|
||||
return mock_response_obj
|
||||
|
||||
mock_client_instance = type('MockClient', (), {
|
||||
'get': mock_get
|
||||
})()
|
||||
|
||||
# Setup async context manager
|
||||
async def mock_aenter(*args, **kwargs):
|
||||
return mock_client_instance
|
||||
|
||||
async def mock_aexit(*args, **kwargs):
|
||||
return None
|
||||
|
||||
mock_client_class.return_value.__aenter__ = mock_aenter
|
||||
mock_client_class.return_value.__aexit__ = mock_aexit
|
||||
|
||||
result = await search_web("test query", num_results=2)
|
||||
|
||||
assert "Test Result 1" in result
|
||||
assert "https://example.com/1" in result
|
||||
assert "Test Result 2" in result
|
||||
assert "https://example.com/2" in result
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_search_web_no_results(self):
|
||||
"""Test web search with no results."""
|
||||
mock_response_data = {"results": []}
|
||||
|
||||
with patch("src.agents.tools.httpx.AsyncClient") as mock_client_class:
|
||||
mock_response_obj = type('MockResponse', (), {
|
||||
'status_code': 200,
|
||||
'json': lambda *args, **kwargs: mock_response_data
|
||||
})()
|
||||
|
||||
async def mock_get(*args, **kwargs):
|
||||
return mock_response_obj
|
||||
|
||||
mock_client_instance = type('MockClient', (), {
|
||||
'get': mock_get
|
||||
})()
|
||||
|
||||
async def mock_aenter(*args, **kwargs):
|
||||
return mock_client_instance
|
||||
|
||||
async def mock_aexit(*args, **kwargs):
|
||||
return None
|
||||
|
||||
mock_client_class.return_value.__aenter__ = mock_aenter
|
||||
mock_client_class.return_value.__aexit__ = mock_aexit
|
||||
|
||||
result = await search_web("test query")
|
||||
|
||||
assert "No results found" in result
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_search_web_connection_error(self):
|
||||
"""Test web search with connection error."""
|
||||
with patch("httpx.AsyncClient") as mock_client:
|
||||
mock_client_instance = AsyncMock()
|
||||
mock_client_instance.get.side_effect = Exception("Connection failed")
|
||||
mock_client.return_value.__aenter__.return_value = mock_client_instance
|
||||
|
||||
result = await search_web("test query")
|
||||
|
||||
assert "Error searching" in result
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_search_web_limits_results(self):
|
||||
"""Test that search limits results to max 10."""
|
||||
mock_response_data = {
|
||||
"results": [
|
||||
{"title": f"Result {i}", "url": f"https://example.com/{i}", "content": "Test"}
|
||||
for i in range(20)
|
||||
]
|
||||
}
|
||||
|
||||
with patch("src.agents.tools.httpx.AsyncClient") as mock_client_class:
|
||||
mock_response_obj = type('MockResponse', (), {
|
||||
'status_code': 200,
|
||||
'json': lambda *args, **kwargs: mock_response_data
|
||||
})()
|
||||
|
||||
async def mock_get(*args, **kwargs):
|
||||
return mock_response_obj
|
||||
|
||||
mock_client_instance = type('MockClient', (), {
|
||||
'get': mock_get
|
||||
})()
|
||||
|
||||
async def mock_aenter(*args, **kwargs):
|
||||
return mock_client_instance
|
||||
|
||||
async def mock_aexit(*args, **kwargs):
|
||||
return None
|
||||
|
||||
mock_client_class.return_value.__aenter__ = mock_aenter
|
||||
mock_client_class.return_value.__aexit__ = mock_aexit
|
||||
|
||||
result = await search_web("test query", num_results=15)
|
||||
|
||||
# Should only return 10 results (max limit)
|
||||
result_count = result.count("URL:")
|
||||
assert result_count == 10
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_search_web_formats_results(self):
|
||||
"""Test that search results are properly formatted."""
|
||||
mock_response_data = {
|
||||
"results": [
|
||||
{
|
||||
"title": "Test Title",
|
||||
"url": "https://example.com",
|
||||
"content": "Test content description"
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
with patch("src.agents.tools.httpx.AsyncClient") as mock_client_class:
|
||||
mock_response_obj = type('MockResponse', (), {
|
||||
'status_code': 200,
|
||||
'json': lambda *args, **kwargs: mock_response_data
|
||||
})()
|
||||
|
||||
async def mock_get(*args, **kwargs):
|
||||
return mock_response_obj
|
||||
|
||||
mock_client_instance = type('MockClient', (), {
|
||||
'get': mock_get
|
||||
})()
|
||||
|
||||
async def mock_aenter(*args, **kwargs):
|
||||
return mock_client_instance
|
||||
|
||||
async def mock_aexit(*args, **kwargs):
|
||||
return None
|
||||
|
||||
mock_client_class.return_value.__aenter__ = mock_aenter
|
||||
mock_client_class.return_value.__aexit__ = mock_aexit
|
||||
|
||||
result = await search_web("test query")
|
||||
|
||||
# Check formatting
|
||||
assert "1. Test Title" in result
|
||||
assert "URL: https://example.com" in result
|
||||
assert "Test content description" in result
|
||||
|
||||
@@ -4,7 +4,7 @@ Tests for chat completions streaming wrapper.
|
||||
Tests that the wrapper correctly:
|
||||
- Wraps Responses API
|
||||
- Enables reasoning automatically
|
||||
- Converts reasoning to <think> tags
|
||||
- Streams reasoning via reasoning_content field (DeepSeek R1 format)
|
||||
- Streams both reasoning and content
|
||||
"""
|
||||
import json
|
||||
@@ -17,7 +17,7 @@ from src.chat import constants
|
||||
@pytest.mark.unit
|
||||
@pytest.mark.asyncio
|
||||
async def test_streaming_wrapper_enables_reasoning(async_client: AsyncClient):
|
||||
"""Test that streaming wrapper automatically enables reasoning."""
|
||||
"""Test that streaming wrapper automatically enables reasoning via reasoning_content."""
|
||||
request_data = {
|
||||
"model": "lorem-tester",
|
||||
"messages": [
|
||||
@@ -27,7 +27,7 @@ async def test_streaming_wrapper_enables_reasoning(async_client: AsyncClient):
|
||||
}
|
||||
|
||||
chunks_received = []
|
||||
think_tags_found = False
|
||||
reasoning_content_found = False
|
||||
|
||||
async with async_client.stream(
|
||||
"POST",
|
||||
@@ -51,12 +51,12 @@ async def test_streaming_wrapper_enables_reasoning(async_client: AsyncClient):
|
||||
chunk = json.loads(data_str)
|
||||
chunks_received.append(chunk)
|
||||
|
||||
# Check for <think> tags in delta content
|
||||
# Check for reasoning_content in delta (DeepSeek R1 format)
|
||||
if "choices" in chunk and len(chunk["choices"]) > 0:
|
||||
delta = chunk["choices"][0].get("delta", {})
|
||||
content = delta.get("content")
|
||||
if content and ("<think>" in content or "</think>" in content):
|
||||
think_tags_found = True
|
||||
reasoning = delta.get("reasoning_content")
|
||||
if reasoning:
|
||||
reasoning_content_found = True
|
||||
|
||||
except json.JSONDecodeError:
|
||||
pass
|
||||
@@ -64,14 +64,14 @@ async def test_streaming_wrapper_enables_reasoning(async_client: AsyncClient):
|
||||
# Should have received chunks
|
||||
assert len(chunks_received) > 0
|
||||
|
||||
# Should have found <think> tags (reasoning enabled automatically)
|
||||
assert think_tags_found, "Expected <think> tags in streaming output"
|
||||
# Should have found reasoning_content (reasoning enabled automatically)
|
||||
assert reasoning_content_found, "Expected reasoning_content in streaming output"
|
||||
|
||||
|
||||
@pytest.mark.unit
|
||||
@pytest.mark.asyncio
|
||||
async def test_streaming_wrapper_reasoning_before_content(async_client: AsyncClient):
|
||||
"""Test that reasoning (<think> tags) comes before actual content."""
|
||||
"""Test that reasoning_content comes before regular content."""
|
||||
request_data = {
|
||||
"model": "lorem-tester",
|
||||
"messages": [
|
||||
@@ -80,10 +80,7 @@ async def test_streaming_wrapper_reasoning_before_content(async_client: AsyncCli
|
||||
"stream": True
|
||||
}
|
||||
|
||||
all_content = []
|
||||
found_think_opening = False
|
||||
found_think_closing = False
|
||||
found_content_after_think = False
|
||||
chunk_types = [] # Track order: 'reasoning' or 'content'
|
||||
|
||||
async with async_client.stream(
|
||||
"POST",
|
||||
@@ -106,28 +103,22 @@ async def test_streaming_wrapper_reasoning_before_content(async_client: AsyncCli
|
||||
chunk = json.loads(data_str)
|
||||
if "choices" in chunk and len(chunk["choices"]) > 0:
|
||||
delta = chunk["choices"][0].get("delta", {})
|
||||
content = delta.get("content", "")
|
||||
if content:
|
||||
all_content.append(content)
|
||||
reasoning = delta.get("reasoning_content")
|
||||
content = delta.get("content")
|
||||
|
||||
if "<think>" in content:
|
||||
found_think_opening = True
|
||||
if "</think>" in content:
|
||||
found_think_closing = True
|
||||
# Content after closing think tag
|
||||
if found_think_closing and content.strip() and "<think>" not in content and "</think>" not in content:
|
||||
found_content_after_think = True
|
||||
if reasoning:
|
||||
chunk_types.append("reasoning")
|
||||
if content:
|
||||
chunk_types.append("content")
|
||||
|
||||
except json.JSONDecodeError:
|
||||
pass
|
||||
|
||||
# Verify ordering
|
||||
full_text = "".join(all_content)
|
||||
if found_think_opening and found_think_closing:
|
||||
# Reasoning should come before main content
|
||||
think_start = full_text.index("<think>")
|
||||
think_end = full_text.index("</think>")
|
||||
assert think_start < think_end, "Opening <think> should come before closing </think>"
|
||||
# Verify reasoning comes before content
|
||||
if "reasoning" in chunk_types and "content" in chunk_types:
|
||||
first_reasoning = chunk_types.index("reasoning")
|
||||
first_content = chunk_types.index("content")
|
||||
assert first_reasoning < first_content, "reasoning_content should come before content"
|
||||
|
||||
|
||||
@pytest.mark.unit
|
||||
|
||||
@@ -1,351 +0,0 @@
|
||||
"""
|
||||
Tests for benchmark storage.
|
||||
|
||||
Tests performance tracking, Redis storage, and analytics features.
|
||||
"""
|
||||
import json
|
||||
from datetime import datetime, timedelta, timezone
|
||||
from unittest.mock import AsyncMock, MagicMock, patch
|
||||
|
||||
import pytest
|
||||
|
||||
from src.core.benchmarks import (
|
||||
BenchmarkStore,
|
||||
PerformanceBenchmark,
|
||||
get_benchmark_store,
|
||||
)
|
||||
|
||||
|
||||
class TestPerformanceBenchmark:
|
||||
"""Test PerformanceBenchmark model."""
|
||||
|
||||
def test_benchmark_creation(self):
|
||||
"""Test creating a performance benchmark."""
|
||||
benchmark = PerformanceBenchmark(
|
||||
operation="steward_analysis",
|
||||
duration_seconds=1.23,
|
||||
success=True,
|
||||
recommendation_count=3,
|
||||
)
|
||||
|
||||
assert benchmark.operation == "steward_analysis"
|
||||
assert benchmark.duration_seconds == 1.23
|
||||
assert benchmark.success is True
|
||||
assert benchmark.recommendation_count == 3
|
||||
assert isinstance(benchmark.timestamp, datetime)
|
||||
|
||||
def test_benchmark_with_tool_fields(self):
|
||||
"""Test benchmark with tool-specific fields."""
|
||||
benchmark = PerformanceBenchmark(
|
||||
operation="tool_call",
|
||||
duration_seconds=0.5,
|
||||
success=True,
|
||||
tool_name="calculate",
|
||||
was_recommended=True,
|
||||
was_actually_used=True,
|
||||
)
|
||||
|
||||
assert benchmark.tool_name == "calculate"
|
||||
assert benchmark.was_recommended is True
|
||||
assert benchmark.was_actually_used is True
|
||||
|
||||
def test_benchmark_to_redis_dict(self):
|
||||
"""Test conversion to Redis dict."""
|
||||
benchmark = PerformanceBenchmark(
|
||||
operation="test_op",
|
||||
duration_seconds=1.0,
|
||||
success=True,
|
||||
metadata={"key": "value"},
|
||||
)
|
||||
|
||||
redis_dict = benchmark.to_redis_dict()
|
||||
assert redis_dict["operation"] == "test_op"
|
||||
assert redis_dict["duration_seconds"] == 1.0
|
||||
assert redis_dict["success"] is True
|
||||
assert isinstance(redis_dict["timestamp"], str)
|
||||
assert isinstance(redis_dict["metadata"], str)
|
||||
|
||||
def test_benchmark_from_redis_dict(self):
|
||||
"""Test reconstruction from Redis dict."""
|
||||
now = datetime.now(timezone.utc)
|
||||
redis_dict = {
|
||||
"timestamp": now.isoformat(),
|
||||
"operation": "test_op",
|
||||
"duration_seconds": 1.5,
|
||||
"success": True,
|
||||
"metadata": json.dumps({"test": "data"}),
|
||||
"recommendation_count": None,
|
||||
"confidence": None,
|
||||
"tool_name": None,
|
||||
"was_recommended": None,
|
||||
"was_actually_used": None,
|
||||
"conversation_id": None,
|
||||
}
|
||||
|
||||
benchmark = PerformanceBenchmark.from_redis_dict(redis_dict)
|
||||
assert benchmark.operation == "test_op"
|
||||
assert benchmark.duration_seconds == 1.5
|
||||
assert benchmark.metadata == {"test": "data"}
|
||||
|
||||
|
||||
class TestBenchmarkStore:
|
||||
"""Test BenchmarkStore functionality."""
|
||||
|
||||
@pytest.fixture
|
||||
def mock_redis(self):
|
||||
"""Create mock Redis client."""
|
||||
mock = AsyncMock()
|
||||
mock.hset = AsyncMock()
|
||||
mock.expire = AsyncMock()
|
||||
mock.zadd = AsyncMock()
|
||||
mock.zrevrangebyscore = AsyncMock(return_value=[])
|
||||
mock.hgetall = AsyncMock(return_value={})
|
||||
mock.aclose = AsyncMock()
|
||||
return mock
|
||||
|
||||
@pytest.fixture
|
||||
def store(self, mock_redis):
|
||||
"""Create benchmark store with mock Redis."""
|
||||
return BenchmarkStore(redis_client=mock_redis)
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_record_benchmark(self, store, mock_redis):
|
||||
"""Test recording a benchmark."""
|
||||
benchmark = PerformanceBenchmark(
|
||||
operation="test_op",
|
||||
duration_seconds=1.0,
|
||||
success=True,
|
||||
)
|
||||
|
||||
await store.record(benchmark)
|
||||
|
||||
# Verify Redis calls
|
||||
mock_redis.hset.assert_called_once()
|
||||
mock_redis.expire.assert_called()
|
||||
mock_redis.zadd.assert_called_once()
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_record_benchmark_disabled(self, mock_redis):
|
||||
"""Test recording when benchmarks are disabled."""
|
||||
with patch("src.core.benchmarks.config.ENABLE_BENCHMARKS", False):
|
||||
store = BenchmarkStore(redis_client=mock_redis)
|
||||
benchmark = PerformanceBenchmark(
|
||||
operation="test_op",
|
||||
duration_seconds=1.0,
|
||||
success=True,
|
||||
)
|
||||
|
||||
await store.record(benchmark)
|
||||
|
||||
# Should not call Redis
|
||||
mock_redis.hset.assert_not_called()
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_record_benchmark_handles_errors(self, store, mock_redis):
|
||||
"""Test recording handles Redis errors gracefully."""
|
||||
mock_redis.hset.side_effect = Exception("Redis error")
|
||||
|
||||
benchmark = PerformanceBenchmark(
|
||||
operation="test_op",
|
||||
duration_seconds=1.0,
|
||||
success=True,
|
||||
)
|
||||
|
||||
# Should not raise exception
|
||||
await store.record(benchmark)
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_query_benchmarks(self, store, mock_redis):
|
||||
"""Test querying benchmarks."""
|
||||
# Setup mock data
|
||||
now = datetime.now(timezone.utc)
|
||||
mock_key = f"benchmark:test_op:{int(now.timestamp() * 1000)}"
|
||||
mock_redis.zrevrangebyscore.return_value = [mock_key]
|
||||
|
||||
# Mock hgetall to return proper data
|
||||
mock_redis.hgetall.return_value = {
|
||||
"timestamp": now.isoformat(),
|
||||
"operation": "test_op",
|
||||
"duration_seconds": 1.5, # Numeric, not string
|
||||
"success": True,
|
||||
"metadata": "{}",
|
||||
"recommendation_count": None,
|
||||
"confidence": None,
|
||||
"tool_name": None,
|
||||
"was_recommended": None,
|
||||
"was_actually_used": None,
|
||||
"conversation_id": None,
|
||||
}
|
||||
|
||||
results = await store.query("test_op", limit=10)
|
||||
|
||||
assert len(results) == 1
|
||||
assert results[0].operation == "test_op"
|
||||
mock_redis.zrevrangebyscore.assert_called_once()
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_query_with_time_range(self, store, mock_redis):
|
||||
"""Test querying with time range."""
|
||||
now = datetime.now(timezone.utc)
|
||||
start_time = now - timedelta(hours=1)
|
||||
end_time = now
|
||||
|
||||
await store.query("test_op", start_time=start_time, end_time=end_time)
|
||||
|
||||
# Verify time range was converted to timestamps
|
||||
call_args = mock_redis.zrevrangebyscore.call_args
|
||||
assert call_args is not None
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_query_disabled_benchmarks(self, mock_redis):
|
||||
"""Test querying when benchmarks are disabled."""
|
||||
with patch("src.core.benchmarks.config.ENABLE_BENCHMARKS", False):
|
||||
store = BenchmarkStore(redis_client=mock_redis)
|
||||
results = await store.query("test_op")
|
||||
assert results == []
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_query_handles_errors(self, store, mock_redis):
|
||||
"""Test query handles errors gracefully."""
|
||||
mock_redis.zrevrangebyscore.side_effect = Exception("Redis error")
|
||||
|
||||
results = await store.query("test_op")
|
||||
assert results == []
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_get_statistics(self, store, mock_redis):
|
||||
"""Test getting statistics."""
|
||||
# Setup mock data with multiple benchmarks
|
||||
now = datetime.now(timezone.utc)
|
||||
mock_keys = [
|
||||
f"benchmark:test_op:{int((now - timedelta(seconds=i)).timestamp() * 1000)}"
|
||||
for i in range(3)
|
||||
]
|
||||
mock_redis.zrevrangebyscore.return_value = mock_keys
|
||||
|
||||
# Return different durations and success values
|
||||
benchmarks_data = [
|
||||
{"duration_seconds": "1.0", "success": "True"},
|
||||
{"duration_seconds": "2.0", "success": "True"},
|
||||
{"duration_seconds": "3.0", "success": "False"},
|
||||
]
|
||||
|
||||
async def mock_hgetall(key):
|
||||
idx = mock_keys.index(key)
|
||||
data = benchmarks_data[idx]
|
||||
return {
|
||||
"timestamp": now.isoformat(),
|
||||
"operation": "test_op",
|
||||
"duration_seconds": float(data["duration_seconds"]),
|
||||
"success": data["success"] == "True",
|
||||
"metadata": "{}",
|
||||
"recommendation_count": None,
|
||||
"confidence": None,
|
||||
"tool_name": None,
|
||||
"was_recommended": None,
|
||||
"was_actually_used": None,
|
||||
"conversation_id": None,
|
||||
}
|
||||
|
||||
mock_redis.hgetall.side_effect = mock_hgetall
|
||||
|
||||
stats = await store.get_statistics("test_op")
|
||||
|
||||
assert stats["count"] == 3
|
||||
assert stats["avg_duration"] == 2.0 # (1 + 2 + 3) / 3
|
||||
assert stats["min_duration"] == 1.0
|
||||
assert stats["max_duration"] == 3.0
|
||||
assert stats["success_rate"] == pytest.approx(66.67, rel=0.01)
|
||||
assert stats["total_successes"] == 2
|
||||
assert stats["total_failures"] == 1
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_get_statistics_empty(self, store, mock_redis):
|
||||
"""Test statistics with no data."""
|
||||
mock_redis.zrevrangebyscore.return_value = []
|
||||
|
||||
stats = await store.get_statistics("test_op")
|
||||
|
||||
assert stats["count"] == 0
|
||||
assert stats["avg_duration"] == 0.0
|
||||
assert stats["success_rate"] == 0.0
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_get_tool_accuracy(self, store, mock_redis):
|
||||
"""Test tool accuracy calculation."""
|
||||
# Setup mock data
|
||||
now = datetime.now(timezone.utc)
|
||||
mock_keys = [
|
||||
f"benchmark:tool_call:{int((now - timedelta(seconds=i)).timestamp() * 1000)}"
|
||||
for i in range(4)
|
||||
]
|
||||
mock_redis.zrevrangebyscore.return_value = mock_keys
|
||||
|
||||
# Different combinations of recommended/used
|
||||
tool_data = [
|
||||
{"was_recommended": "True", "was_actually_used": "True"}, # Good
|
||||
{"was_recommended": "True", "was_actually_used": "True"}, # Good
|
||||
{"was_recommended": "False", "was_actually_used": "True"}, # Missed
|
||||
{"was_recommended": "True", "was_actually_used": "False"}, # Not used
|
||||
]
|
||||
|
||||
async def mock_hgetall(key):
|
||||
idx = mock_keys.index(key)
|
||||
data = tool_data[idx]
|
||||
return {
|
||||
"timestamp": now.isoformat(),
|
||||
"operation": "tool_call",
|
||||
"duration_seconds": 1.0,
|
||||
"success": True,
|
||||
"metadata": "{}",
|
||||
"recommendation_count": None,
|
||||
"confidence": None,
|
||||
"tool_name": "test_tool",
|
||||
"conversation_id": None,
|
||||
"was_recommended": data["was_recommended"] == "True",
|
||||
"was_actually_used": data["was_actually_used"] == "True",
|
||||
}
|
||||
|
||||
mock_redis.hgetall.side_effect = mock_hgetall
|
||||
|
||||
accuracy = await store.get_tool_accuracy()
|
||||
|
||||
assert accuracy["total_calls"] == 4
|
||||
assert accuracy["total_used"] == 3
|
||||
assert accuracy["recommended_and_used"] == 2
|
||||
assert accuracy["not_recommended_but_used"] == 1
|
||||
assert accuracy["precision"] == pytest.approx(66.67, rel=0.01)
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_get_tool_accuracy_empty(self, store, mock_redis):
|
||||
"""Test tool accuracy with no data."""
|
||||
mock_redis.zrevrangebyscore.return_value = []
|
||||
|
||||
accuracy = await store.get_tool_accuracy()
|
||||
|
||||
assert accuracy["total_calls"] == 0
|
||||
assert accuracy["precision"] == 0.0
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_close(self, store, mock_redis):
|
||||
"""Test closing the store."""
|
||||
await store.close()
|
||||
mock_redis.aclose.assert_called_once()
|
||||
|
||||
# Client should be None after close
|
||||
assert store._client is None
|
||||
|
||||
|
||||
class TestGlobalBenchmarkStore:
|
||||
"""Test global benchmark store instance."""
|
||||
|
||||
def test_get_benchmark_store(self):
|
||||
"""Test getting global store instance."""
|
||||
store = get_benchmark_store()
|
||||
assert isinstance(store, BenchmarkStore)
|
||||
|
||||
def test_get_benchmark_store_singleton(self):
|
||||
"""Test store is singleton."""
|
||||
store1 = get_benchmark_store()
|
||||
store2 = get_benchmark_store()
|
||||
assert store1 is store2
|
||||
@@ -0,0 +1,101 @@
|
||||
"""
|
||||
Tests for tool call tracking.
|
||||
|
||||
Tests capability extraction and recommendation matching.
|
||||
"""
|
||||
from unittest.mock import AsyncMock, patch
|
||||
|
||||
import pytest
|
||||
|
||||
from src.core.tool_tracking import ToolCallTracker
|
||||
|
||||
|
||||
class TestToolCallTracker:
|
||||
"""Test ToolCallTracker functionality."""
|
||||
|
||||
def test_extract_capability_delegation_tool(self):
|
||||
"""Test extracting capability from delegation tool name."""
|
||||
tracker = ToolCallTracker(recommended_capabilities=["librarian"])
|
||||
|
||||
assert tracker._extract_capability("delegate_to_librarian") == "librarian"
|
||||
assert tracker._extract_capability("delegate_to_biographer") == "biographer"
|
||||
assert tracker._extract_capability("delegate_to_housekeeper") == "housekeeper"
|
||||
|
||||
def test_extract_capability_non_delegation_tool(self):
|
||||
"""Test that non-delegation tools return unchanged."""
|
||||
tracker = ToolCallTracker(recommended_capabilities=[])
|
||||
|
||||
assert tracker._extract_capability("calculate") == "calculate"
|
||||
assert tracker._extract_capability("search_web") == "search_web"
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_track_call_recognizes_delegation_as_recommended(self):
|
||||
"""Test that delegate_to_X is recognized when X is recommended."""
|
||||
tracker = ToolCallTracker(
|
||||
recommended_capabilities=["librarian", "biographer"]
|
||||
)
|
||||
|
||||
with patch("src.core.tool_tracking.get_benchmark_store") as mock_store:
|
||||
mock_store.return_value.record = AsyncMock()
|
||||
|
||||
await tracker.track_call("delegate_to_librarian", 1.0)
|
||||
|
||||
# Should NOT log warning since librarian was recommended
|
||||
call_args = mock_store.return_value.record.call_args
|
||||
benchmark = call_args[0][0]
|
||||
assert benchmark.was_recommended is True
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_track_call_detects_not_recommended(self):
|
||||
"""Test that unrecommended tools are flagged."""
|
||||
tracker = ToolCallTracker(
|
||||
recommended_capabilities=["librarian"]
|
||||
)
|
||||
|
||||
with patch("src.core.tool_tracking.get_benchmark_store") as mock_store:
|
||||
mock_store.return_value.record = AsyncMock()
|
||||
|
||||
await tracker.track_call("delegate_to_housekeeper", 1.0)
|
||||
|
||||
call_args = mock_store.return_value.record.call_args
|
||||
benchmark = call_args[0][0]
|
||||
assert benchmark.was_recommended is False
|
||||
|
||||
def test_get_summary_with_delegation_tools(self):
|
||||
"""Test summary correctly maps delegation tools to capabilities."""
|
||||
tracker = ToolCallTracker(
|
||||
recommended_capabilities=["librarian", "biographer"]
|
||||
)
|
||||
tracker.actual_calls = {
|
||||
"delegate_to_librarian": [1.0, 2.0],
|
||||
"delegate_to_housekeeper": [0.5], # Not recommended
|
||||
}
|
||||
|
||||
summary = tracker.get_summary()
|
||||
|
||||
assert summary["accuracy"]["recommended_and_used"] == 1 # librarian
|
||||
assert summary["accuracy"]["recommended_but_unused"] == 1 # biographer
|
||||
assert summary["accuracy"]["not_recommended_but_used"] == 1 # housekeeper
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_finalize_with_delegation_tools(self):
|
||||
"""Test finalize correctly identifies unused recommendations."""
|
||||
tracker = ToolCallTracker(
|
||||
recommended_capabilities=["librarian", "biographer"]
|
||||
)
|
||||
tracker.actual_calls = {
|
||||
"delegate_to_librarian": [1.0],
|
||||
}
|
||||
|
||||
with patch("src.core.tool_tracking.get_benchmark_store") as mock_store:
|
||||
mock_store.return_value.record = AsyncMock()
|
||||
|
||||
await tracker.finalize()
|
||||
|
||||
# Should record benchmark for unused biographer
|
||||
assert mock_store.return_value.record.called
|
||||
call_args = mock_store.return_value.record.call_args
|
||||
benchmark = call_args[0][0]
|
||||
assert benchmark.tool_name == "biographer"
|
||||
assert benchmark.was_recommended is True
|
||||
assert benchmark.was_actually_used is False
|
||||
@@ -8,8 +8,8 @@ These tests hit the actual running server and test the full stack:
|
||||
- Response formatting
|
||||
"""
|
||||
import pytest
|
||||
import pytest_asyncio
|
||||
import httpx
|
||||
import asyncio
|
||||
from typing import AsyncGenerator
|
||||
|
||||
# Test server base URL (assumes server is running on localhost:8777 via ./wakeup.sh)
|
||||
@@ -17,15 +17,7 @@ BASE_URL = "http://localhost:8777"
|
||||
API_TIMEOUT = 120.0 # 120 second timeout for LLM calls
|
||||
|
||||
|
||||
@pytest.fixture(scope="module")
|
||||
def event_loop():
|
||||
"""Create event loop for async tests."""
|
||||
loop = asyncio.get_event_loop_policy().new_event_loop()
|
||||
yield loop
|
||||
loop.close()
|
||||
|
||||
|
||||
@pytest.fixture(scope="module")
|
||||
@pytest_asyncio.fixture(loop_scope="module", scope="module")
|
||||
async def client() -> AsyncGenerator[httpx.AsyncClient, None]:
|
||||
"""HTTP client for making requests."""
|
||||
async with httpx.AsyncClient(base_url=BASE_URL, timeout=API_TIMEOUT) as client:
|
||||
|
||||
@@ -43,7 +43,7 @@ LOG_FILE="$LOGS_DIR/server.log"
|
||||
echo -e "${YELLOW}Logs will be written to: ${LOG_FILE}${NC}"
|
||||
|
||||
# Start the server
|
||||
echo -e "${GREEN}Starting uvicorn server on http://localhost:8777${NC}"
|
||||
echo -e "${GREEN}Starting uvicorn server on http://tower-of-joy:8777${NC}"
|
||||
echo -e "${YELLOW}Press Ctrl+C to stop the server${NC}"
|
||||
echo ""
|
||||
|
||||
|
||||
Reference in New Issue
Block a user