Commit Graph

3 Commits

Author SHA1 Message Date
8e7e80807b refactor: remove config_path from API interface
Makes config_path an internal server detail rather than an API parameter.

Changes:
- Remove config_path from SimulateTriggerRequest
- Add config_path parameter to create_app() with default
- Store in app.state.config_path for internal use
- Update trigger endpoint to use internal config path
- Change missing config error from 400 to 500 (server error)

API calls now only need to specify date_range (and optionally models):
  POST /simulate/trigger
  {"date_range": ["2025-01-16"]}

The server uses configs/default_config.json by default.
This simplifies the API and hides implementation details from clients.
2025-10-31 15:18:56 -04:00
ec2a37e474 feat: use enabled field from config to determine which models run
Changed the API to respect the 'enabled' field in model configurations,
rather than requiring models to be explicitly specified in API requests.

Changes:
- Make 'models' parameter optional in POST /simulate/trigger
- If models not provided, read config and use enabled models
- If models provided, use as explicit override (for testing)
- Raise error if no enabled models found and none specified
- Update response message to show model count

Behavior:
- Default: Only runs models with "enabled": true in config
- Override: Can still specify models in request for manual testing
- Safety: Prevents accidental execution of disabled/expensive models

Example before (required):
  POST /simulate/trigger
  {"config_path": "...", "date_range": [...], "models": ["gpt-4"]}

Example after (optional):
  POST /simulate/trigger
  {"config_path": "...", "date_range": [...]}
  # Uses models where enabled: true

This makes the config file the source of truth for which models
should run, while still allowing ad-hoc overrides for testing.
2025-10-31 15:12:11 -04:00
fb9583b374 feat: transform to REST API service with SQLite persistence (v0.3.0)
Major architecture transformation from batch-only to API service with
database persistence for Windmill integration.

## REST API Implementation
- POST /simulate/trigger - Start simulation jobs
- GET /simulate/status/{job_id} - Monitor job progress
- GET /results - Query results with filters (job_id, date, model)
- GET /health - Service health checks

## Database Layer
- SQLite persistence with 6 tables (jobs, job_details, positions,
  holdings, reasoning_logs, tool_usage)
- Foreign key constraints with cascade deletes
- Replaces JSONL file storage

## Backend Components
- JobManager: Job lifecycle management with concurrency control
- RuntimeConfigManager: Thread-safe isolated runtime configs
- ModelDayExecutor: Single model-day execution engine
- SimulationWorker: Date-sequential, model-parallel orchestration

## Testing
- 102 unit and integration tests (85% coverage)
- Database: 98% coverage
- Job manager: 98% coverage
- API endpoints: 81% coverage
- Pydantic models: 100% coverage
- TDD approach throughout

## Docker Deployment
- Dual-mode: API server (persistent) + batch (one-time)
- Health checks with 30s interval
- Volume persistence for database and logs
- Separate entrypoints for each mode

## Validation Tools
- scripts/validate_docker_build.sh - Build validation
- scripts/test_api_endpoints.sh - Complete API testing
- scripts/test_batch_mode.sh - Batch mode validation
- DOCKER_API.md - Deployment guide
- TESTING_GUIDE.md - Testing procedures

## Configuration
- API_PORT environment variable (default: 8080)
- Backwards compatible with existing configs
- FastAPI, uvicorn, pydantic>=2.0 dependencies

Co-Authored-By: AI Assistant <noreply@example.com>
2025-10-31 11:47:10 -04:00