5f70f13da3
- Remove better-sqlite3, add pg driver - Convert schema to PostgreSQL types (serial, timestamp, boolean, jsonb) - Generate fresh PostgreSQL migrations - Update database connection layer with pg.Pool - Fix all API routes: remove JSON.parse/stringify, use native timestamps and booleans - Update drizzle.config.ts and .env.example for PostgreSQL
37 lines
2 KiB
Markdown
37 lines
2 KiB
Markdown
## MODIFIED Requirements
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### Requirement: PostgreSQL training metadata storage
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The system SHALL store training run metadata in the PostgreSQL database. Each training run record SHALL include: run_id (MLflow run ID), model_type, experiment_name, pipeline_config_hash, dataset_version, metrics summary (JSON), status, and timestamps (created_at, completed_at).
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#### Scenario: Store training run record
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- **WHEN** a training run completes successfully
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- **THEN** the system inserts a record into the PostgreSQL `training_runs` table with the run metadata
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#### Scenario: Query training history
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- **WHEN** the system queries training runs
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- **THEN** it returns records from PostgreSQL ordered by created_at descending
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#### Scenario: Database name updated
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- **WHEN** the ML service connects to PostgreSQL
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- **THEN** it connects to the `candle_annotator` database (not `ml_db`)
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## ADDED Requirements
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### Requirement: Direct annotation data access
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The ML service SHALL read candle and annotation data directly from PostgreSQL instead of requiring CSV/JSON file exports. The ML service SHALL query the `candles`, `annotations`, `span_annotations`, and `charts` tables for training data.
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#### Scenario: Query candle data for training
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- **WHEN** the ML training pipeline needs OHLC data for a chart
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- **THEN** it queries the `candles` table in PostgreSQL filtered by `chart_id`, ordered by `time`
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#### Scenario: Query span annotations for labels
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- **WHEN** the ML training pipeline needs labeled spans for training
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- **THEN** it queries the `span_annotations` table in PostgreSQL filtered by `chart_id` and optionally by `source`
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#### Scenario: No CSV/JSON export required
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- **WHEN** the ML training pipeline starts
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- **THEN** it does not require pre-exported CSV or JSON files — all data is read from PostgreSQL
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#### Scenario: Shared database connection
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- **WHEN** the ML service reads candle/annotation data
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- **THEN** it uses the same PostgreSQL connection (same database, same credentials) as for `training_runs`
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