Michael Chihlas
da93ae55c3
Wraps each static system prompt in a single-block list so Phase 0.1's AnthropicProvider applies cache_control: ephemeral automatically (policy α, first block gets marked when no caller-authored cache_control is present). Call sites: - ai_tree_generator.scaffold_branches: SCAFFOLD_SYSTEM_PROMPT (~1k tokens) - ai_tree_generator.generate_branch_detail: BRANCH_DETAIL_SYSTEM_PROMPT (~2.5k tokens with few-shot example); retries inside the same function re-read the cached block instead of paying full input cost on each attempt - kb_conversion.convert_document: TROUBLESHOOTING or PROCEDURAL prompt (each caches independently by text content) - ai_fix.generate_fixes: FIX_SYSTEM_PROMPT on first attempt + corrective retry - script_builder.send_message: SYSTEM_PROMPT_TEMPLATE (per-session language substitution — same-language sessions share cache entries) Each edit includes an inline comment explaining why the block is cacheable (stable-constant, retry-reuse, per-language variant) so a future dev can see the intent at the cache_control marker site. script_builder history caching deliberately deferred — per Phase 0.1 decision (option i), AnthropicProvider does not automatically cache the message list. If script_builder's growing 20-message history turns out to be a visible cost driver via the anthropic.cache telemetry, route that caller through the 0.4 chat wrapper which handles history caching. No runtime verification from code-server; cache-hit behavior will be confirmed against the new dev environment when it's up, per the inline TODO(phase0-verify) in ai_provider.py. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Troubleshooting Decision Tree - Backend API
FastAPI backend for the Troubleshooting Decision Tree application.
Quick Start
1. Set up Python environment
cd backend
python -m venv venv
# Windows
venv\Scripts\activate
# macOS/Linux
source venv/bin/activate
pip install -r requirements.txt
2. Start PostgreSQL database
Using Docker:
docker-compose up -d
Or install PostgreSQL locally and create a database:
CREATE DATABASE decision_tree;
3. Configure environment
Copy the example env file and update as needed:
cp .env.example .env
4. Run database migrations
alembic upgrade head
5. Start the server
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000
The API will be available at:
- API: http://localhost:8000
- Docs: http://localhost:8000/api/docs
- ReDoc: http://localhost:8000/api/redoc
API Endpoints
Authentication
POST /api/v1/auth/register- Register new userPOST /api/v1/auth/login- Login (form data)POST /api/v1/auth/login/json- Login (JSON body)POST /api/v1/auth/refresh- Refresh tokenGET /api/v1/auth/me- Get current userPOST /api/v1/auth/logout- Logout
Trees
GET /api/v1/trees- List all treesGET /api/v1/trees/categories- List categoriesGET /api/v1/trees/search?q=query- Search treesGET /api/v1/trees/{id}- Get specific treePOST /api/v1/trees- Create tree (engineer/admin)PUT /api/v1/trees/{id}- Update tree (engineer/admin)DELETE /api/v1/trees/{id}- Delete tree (admin)
Sessions
GET /api/v1/sessions- List user's sessionsGET /api/v1/sessions/{id}- Get specific sessionPOST /api/v1/sessions- Start new sessionPUT /api/v1/sessions/{id}- Update sessionPOST /api/v1/sessions/{id}/complete- Complete sessionPOST /api/v1/sessions/{id}/export- Export session
Development
Create new migration
alembic revision --autogenerate -m "description"
Run migrations
alembic upgrade head
Rollback migration
alembic downgrade -1
Project Structure
backend/
├── alembic/ # Database migrations
│ └── versions/
├── app/
│ ├── api/
│ │ ├── endpoints/ # API route handlers
│ │ ├── deps.py # Dependencies (auth, etc.)
│ │ └── router.py # Main router
│ ├── core/
│ │ ├── config.py # Settings
│ │ ├── database.py # DB connection
│ │ └── security.py # JWT, password hashing
│ ├── models/ # SQLAlchemy models
│ ├── schemas/ # Pydantic schemas
│ └── main.py # FastAPI app
├── tests/
├── alembic.ini
├── docker-compose.yml
├── requirements.txt
└── README.md