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chore: archive ml-db-consolidation change and sync specs

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- Archived change to openspec/changes/archive/2026-02-17-ml-db-consolidation/
- Created new postgres-data-layer spec with PostgreSQL connection, schema definitions, Drizzle migrations, npm deps, and SQLite migration requirements
- Updated docker-deployment spec: Docker Compose now PostgreSQL-based (postgres dependency, ml-data volume, DATABASE_URL); env vars updated (DATABASE_URL added, DATABASE_PATH removed); database persistence updated to PostgreSQL volumes; health check updated to PostgreSQL
- Updated ml-training spec: added database name scenario (candle_annotator) and new direct annotation data access requirement

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
This commit is contained in:
Marko Djordjevic 2026-02-17 18:22:28 +01:00
parent 0e8dcc6707
commit 38df874255
10 changed files with 532 additions and 31 deletions
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23
openspec/specs/ml-training/spec.md
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@ -84,6 +84,29 @@ The system SHALL store training run metadata in the PostgreSQL database. Each tr
- **WHEN** the system queries training runs
- **THEN** it returns records from PostgreSQL ordered by created_at descending
#### Scenario: Database name updated
- **WHEN** the ML service connects to PostgreSQL
- **THEN** it connects to the `candle_annotator` database (not `ml_db`)
### Requirement: Direct annotation data access
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.
#### Scenario: Query candle data for training
- **WHEN** the ML training pipeline needs OHLC data for a chart
- **THEN** it queries the `candles` table in PostgreSQL filtered by `chart_id`, ordered by `time`
#### Scenario: Query span annotations for labels
- **WHEN** the ML training pipeline needs labeled spans for training
- **THEN** it queries the `span_annotations` table in PostgreSQL filtered by `chart_id` and optionally by `source`
#### Scenario: No CSV/JSON export required
- **WHEN** the ML training pipeline starts
- **THEN** it does not require pre-exported CSV or JSON files — all data is read from PostgreSQL
#### Scenario: Shared database connection
- **WHEN** the ML service reads candle/annotation data
- **THEN** it uses the same PostgreSQL connection (same database, same credentials) as for `training_runs`
### Requirement: Pipeline config logging
The system SHALL log the full pipeline YAML config as an MLflow artifact with each training run. This config SHALL be used during inference to replicate the exact preprocessing steps.