# SQLite Migration Guide for Model Learning

**Status**: Planned for Node 24.15.0 upgrade
**Current**: JSON-based storage (model-learner.js, self-report-fixer.js)
**Target**: Native `node:sqlite` integration

## Why SQLite?

1. **Zero dependencies**: Node 24+ has built-in `node:sqlite` (no package install)
2. **Queryable**: SQL joins with UOK's `llm_task_outcomes` table for unified learning database
3. **Transactional**: Atomic outcome recording prevents partial state corruption
4. **Performant**: Indexes on (task_type, model_id) for per-task-type ranking queries
5. **Durable**: WAL mode ensures data survives crashes

## Current State (Node 20)

### JSON-Based Storage
- `model-learner.js`: `.sf/model-performance.json` (nested object hierarchy)
  ```json
  {
    "execute-task": {
      "gpt-4o": {
        "successes": 42,
        "failures": 3,
        "successRate": 0.93
      }
    }
  }
  ```
- `self-report-fixer.js`: Stateless (no persistent storage)
- `triage-self-feedback.js`: Reads/writes `REQUIREMENTS.md`, `ARCHITECTURE.md`

### Pain Points
- Entire file read/write on every outcome (O(n) latency)
- No queryable schema (must load all data, filter in-memory)
- No transactions (partial failures possible)
- No natural joins with UOK database

## SQLite Schema (Target)

### Table 1: model_outcomes
Raw event log for every model outcome.

```sql
CREATE TABLE model_outcomes (
  id INTEGER PRIMARY KEY AUTOINCREMENT,
  task_type TEXT NOT NULL,           -- "execute-task", "plan-slice", etc.
  model_id TEXT NOT NULL,             -- "gpt-4o", "claude-opus", etc.
  success INTEGER NOT NULL,           -- 1 = success, 0 = failure
  timeout INTEGER NOT NULL DEFAULT 0, -- 1 = timed out, 0 = normal
  tokens_used INTEGER NOT NULL DEFAULT 0,
  cost_usd REAL NOT NULL DEFAULT 0.0,
  timestamp TEXT NOT NULL,            -- ISO 8601
  FOREIGN KEY (task_type, model_id) REFERENCES model_stats(task_type, model_id)
);

CREATE INDEX idx_outcomes_task_model ON model_outcomes(task_type, model_id);
CREATE INDEX idx_outcomes_timestamp ON model_outcomes(timestamp DESC);
```

### Table 2: model_stats
Aggregated per-task-per-model statistics (updated atomically with each outcome).

```sql
CREATE TABLE model_stats (
  id INTEGER PRIMARY KEY AUTOINCREMENT,
  task_type TEXT NOT NULL,
  model_id TEXT NOT NULL,
  successes INTEGER NOT NULL DEFAULT 0,
  failures INTEGER NOT NULL DEFAULT 0,
  timeouts INTEGER NOT NULL DEFAULT 0,
  total_tokens INTEGER NOT NULL DEFAULT 0,
  total_cost REAL NOT NULL DEFAULT 0.0,
  last_used TEXT,                    -- ISO 8601 timestamp of last outcome
  UNIQUE(task_type, model_id)
);

CREATE INDEX idx_stats_task_model ON model_stats(task_type, model_id);
```

## Migration Steps

### Phase 1: Refactor `ModelPerformanceTracker` (model-learner.js)

**Before** (JSON):
```javascript
recordOutcome(taskType, modelId, outcome) {
  if (!this.data[taskType]) this.data[taskType] = {};
  if (!this.data[taskType][modelId]) {
    this.data[taskType][modelId] = { successes: 0, failures: 0, ... };
  }
  const stats = this.data[taskType][modelId];
  if (outcome.success) stats.successes += 1;
  else stats.failures += 1;
  this._save(); // Entire file rewrite
}
```

**After** (SQLite):
```javascript
recordOutcome(taskType, modelId, outcome) {
  this.db.exec("BEGIN");
  
  // Insert event
  const insertStmt = this.db.prepare(`
    INSERT INTO model_outcomes (task_type, model_id, success, timeout, ...)
    VALUES (?, ?, ?, ?, ...)
  `);
  insertStmt.run(taskType, modelId, outcome.success ? 1 : 0, ...);
  
  // Upsert stats
  const updateStmt = this.db.prepare(`
    INSERT INTO model_stats (task_type, model_id, successes, ...)
    VALUES (?, ?, ?, ...)
    ON CONFLICT(task_type, model_id) DO UPDATE SET
      successes = successes + ?,
      failures = failures + ?,
      ...
  `);
  updateStmt.run(...);
  
  this.db.exec("COMMIT");
}
```

**Benefits**:
- O(1) outcome recording (single INSERT)
- Atomic transaction (both tables updated together)
- No full-file rewrite

### Phase 2: Update Query Methods

**getRankedModels** → SQL SELECT with ORDER BY

```javascript
getRankedModels(taskType, minSamples = 3) {
  const query = this.db.prepare(`
    SELECT model_id, successes, failures, total_tokens, total_cost, last_used
    FROM model_stats
    WHERE task_type = ? AND (successes + failures) >= ?
    ORDER BY (CAST(successes AS FLOAT) / (successes + failures)) DESC
  `);
  return query.all(taskType, minSamples).map(row => ({
    modelId: row.model_id,
    successRate: row.successes / (row.successes + row.failures),
    ...
  }));
}
```

### Phase 3: Integrate with UOK Database (Optional)

If UOK stores outcomes in its database, consider a **federated schema**:
- Keep model_learner SQLite database separate (`.sf/model-performance.db`)
- OR: Create view in UOK database that joins with UOK's `llm_task_outcomes`

```sql
-- In UOK database:
CREATE VIEW model_performance AS
SELECT 
  outcome.task_type,
  outcome.model_id,
  COUNT(CASE WHEN outcome.success = 1 THEN 1 END) as successes,
  COUNT(CASE WHEN outcome.success = 0 THEN 1 END) as failures,
  SUM(outcome.tokens_used) as total_tokens,
  SUM(outcome.cost_usd) as total_cost
FROM llm_task_outcomes outcome
GROUP BY outcome.task_type, outcome.model_id;
```

### Phase 4: Data Migration (JSON → SQLite)

Create migration function in constructor:

```javascript
_initDb() {
  const db = new DatabaseSync(this.dbPath);
  // ... create tables ...
  
  // Migrate existing JSON data
  if (existsSync(this.oldJsonPath)) {
    const jsonData = JSON.parse(readFileSync(this.oldJsonPath, 'utf-8'));
    this._migrateFromJson(db, jsonData);
    // After migration: delete old JSON or archive
  }
  
  return db;
}

_migrateFromJson(db, jsonData) {
  db.exec("BEGIN");
  
  for (const [taskType, models] of Object.entries(jsonData)) {
    for (const [modelId, stats] of Object.entries(models)) {
      const insertStmt = db.prepare(`
        INSERT INTO model_stats 
        (task_type, model_id, successes, failures, timeouts, total_tokens, total_cost, last_used)
        VALUES (?, ?, ?, ?, ?, ?, ?, ?)
      `);
      insertStmt.run(
        taskType, modelId,
        stats.successes, stats.failures, stats.timeouts || 0,
        stats.totalTokens, stats.totalCost, stats.lastUsed
      );
    }
  }
  
  db.exec("COMMIT");
}
```

## Testing Strategy

### Unit Tests (No Changes Needed)
Existing tests in `model-learner.test.ts` should pass unchanged:
- `recordOutcome()` API remains the same
- `getRankedModels()` returns same shape
- `shouldDemote()`, `getABTestCandidates()` unchanged

### Integration Tests (Add SQLite-Specific)
```typescript
test("persists to SQLite database", () => {
  const learner = new ModelLearner(basePath);
  learner.recordOutcome("execute-task", "gpt-4o", { success: true, tokensUsed: 100 });
  
  // Verify record in model_outcomes table
  const query = learner.tracker.db.prepare(`
    SELECT COUNT(*) as count FROM model_outcomes 
    WHERE task_type = ? AND model_id = ?
  `);
  const result = query.get("execute-task", "gpt-4o");
  expect(result.count).toBe(1);
});

test("transactions are atomic", () => {
  // Simulate failure during upsert
  // Verify both INSERT and UPDATE succeed or both rollback
});
```

## Timeline

1. **When Node 24.15.0 becomes standard** (6-8 weeks)
   - Update `.nvmrc`, `package.json` engines
   - Enable snap to run Node 24
   
2. **Migration PR** (2 days of work)
   - Refactor `ModelPerformanceTracker` class
   - Add migration function
   - Test with existing unit tests
   
3. **Rollout** (1 day)
   - Deploy with backward-compatible JSON→SQLite auto-migration
   - Monitor for edge cases
   - Archive old JSON files after 1 week

## Backward Compatibility

- **Auto-migrate**: On first run with Node 24, detect `.sf/model-performance.json` and import to SQLite
- **Keep JSON**: Don't delete old JSON file immediately (keep for 1 week as backup)
- **Graceful fallback**: If SQLite init fails, log error and fall back to JSON (degraded mode)

## Future Opportunities

Once SQLite is in place:

1. **Dashboard**: Query performance metrics
   ```sql
   SELECT model_id, 
     ROUND(100.0 * successes / (successes + failures), 1) as success_rate,
     total_tokens, total_cost
   FROM model_stats
   WHERE task_type = ?
   ORDER BY success_rate DESC;
   ```

2. **Trend analysis**: Model performance over time
   ```sql
   SELECT DATE(timestamp) as day, model_id, COUNT(*) as attempts,
     SUM(success) as wins, 
     ROUND(100.0 * SUM(success) / COUNT(*), 1) as daily_success_rate
   FROM model_outcomes
   WHERE task_type = ? AND timestamp > date('now', '-30 days')
   GROUP BY day, model_id
   ORDER BY day DESC;
   ```

3. **A/B testing**: Compare challenger vs incumbent in detail
   ```sql
   SELECT 
     model_id,
     COUNT(*) as trials,
     SUM(success) as wins,
     ROUND(AVG(tokens_used), 0) as avg_tokens,
     ROUND(AVG(cost_usd), 4) as avg_cost
   FROM model_outcomes
   WHERE task_type = ? AND timestamp > ?
   GROUP BY model_id;
   ```

4. **Federated learning**: Export performance data for cross-project analysis
   ```sql
   SELECT * FROM model_stats
   WHERE successes + failures >= 10  -- High-confidence entries only
   ORDER BY success_rate DESC;
   ```

## References

- Node.js `node:sqlite` docs: https://nodejs.org/api/sqlite.html
- UOK `llm_task_outcomes` schema: See `docs/dev/UOK-SELF-EVOLUTION.md`
- SQLite WAL mode: https://www.sqlite.org/wal.html