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candle-annotator/openspec/changes/archive/2026-02-17-ml-db-consolidation/specs/ml-training/spec.md
Marko Djordjevic 38df874255 chore: archive ml-db-consolidation change and sync specs 
- 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>
2026-02-17 18:22:28 +01:00

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MODIFIED Requirements

Requirement: PostgreSQL training metadata storage

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).

Scenario: Store training run record

  • WHEN a training run completes successfully
  • THEN the system inserts a record into the PostgreSQL training_runs table with the run metadata

Scenario: Query training history

  • 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)

ADDED Requirements

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