feat: Generalized OptimizationLoop to stages 2-5 with per-stage fixture…
- "backend/pipeline/quality/optimizer.py" - "backend/pipeline/quality/__main__.py" - "backend/pipeline/quality/scorer.py" - "backend/pipeline/quality/fixtures/sample_segments.json" - "backend/pipeline/quality/fixtures/sample_topic_group.json" - "backend/pipeline/quality/fixtures/sample_classifications.json" GSD-Task: S04/T02
This commit is contained in:
jlightner
parent
e740798f7c
commit
18520f7936
9 changed files with 319 additions and 71 deletions
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@ -26,7 +26,7 @@ Schemas: `SegmentationResult`, `ExtractionResult`, `ClassificationResult`, `Synt
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- Estimate: 1.5h
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- Files: backend/pipeline/quality/scorer.py, backend/pipeline/quality/variant_generator.py
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- Verify: cd backend && python -c "from pipeline.quality.scorer import STAGE_CONFIGS, ScoreResult, ScoreRunner, DIMENSIONS; assert 2 in STAGE_CONFIGS and 3 in STAGE_CONFIGS and 4 in STAGE_CONFIGS and 5 in STAGE_CONFIGS; r = ScoreResult(scores={'structural': 0.8, 'readability': 0.7}, composite=0.75); print('scorer ok')" && python -c "from pipeline.quality.variant_generator import PromptVariantGenerator; print('generator ok')"
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- [ ] **T02: Generalize optimizer, create stage 2-4 fixtures, wire CLI, verify end-to-end** — Make OptimizationLoop stage-aware: generalize _load_fixture() to validate stage-specific keys, generalize _score_variant() to call the correct prompt and parse the correct schema per stage, and pass stage-appropriate format markers to the variant generator. Create minimal fixture JSON files for stages 2-4. Remove the stage-5 gate in __main__.py's _run_optimize(), add validation for stages 2-5. Verify all stages import and CLI accepts them.
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- [x] **T02: Generalized OptimizationLoop to stages 2-5 with per-stage fixture validation, schema dispatch, and user prompt building; created stage 2-4 fixtures; removed stage-5 gate from CLI** — Make OptimizationLoop stage-aware: generalize _load_fixture() to validate stage-specific keys, generalize _score_variant() to call the correct prompt and parse the correct schema per stage, and pass stage-appropriate format markers to the variant generator. Create minimal fixture JSON files for stages 2-4. Remove the stage-5 gate in __main__.py's _run_optimize(), add validation for stages 2-5. Verify all stages import and CLI accepts them.
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## Context
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16
.gsd/milestones/M013/slices/S04/tasks/T01-VERIFY.json
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16
.gsd/milestones/M013/slices/S04/tasks/T01-VERIFY.json
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@ -0,0 +1,16 @@
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{
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"schemaVersion": 1,
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"taskId": "T01",
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"unitId": "M013/S04/T01",
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"timestamp": 1775035224267,
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"passed": true,
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"discoverySource": "task-plan",
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"checks": [
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{
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"command": "cd backend",
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"exitCode": 0,
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"durationMs": 9,
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"verdict": "pass"
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}
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]
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}
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89
.gsd/milestones/M013/slices/S04/tasks/T02-SUMMARY.md
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89
.gsd/milestones/M013/slices/S04/tasks/T02-SUMMARY.md
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@ -0,0 +1,89 @@
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---
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id: T02
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parent: S04
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milestone: M013
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provides: []
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requires: []
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affects: []
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key_files: ["backend/pipeline/quality/optimizer.py", "backend/pipeline/quality/__main__.py", "backend/pipeline/quality/scorer.py", "backend/pipeline/quality/fixtures/sample_segments.json", "backend/pipeline/quality/fixtures/sample_topic_group.json", "backend/pipeline/quality/fixtures/sample_classifications.json"]
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key_decisions: ["Fixed stage 5 fixture_keys to match existing fixture (moments not key_moments)", "Stage-specific user prompt building via _build_user_prompt() dispatch"]
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patterns_established: []
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drill_down_paths: []
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observability_surfaces: []
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duration: ""
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verification_result: "All four task plan verification commands pass: optimizer imports ok, CLI imports ok, --help output works for stage 2, stage 6 is rejected. Additionally verified fixture loading for all stages 2-5 and that stage 1 is also rejected."
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completed_at: 2026-04-01T09:24:39.815Z
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blocker_discovered: false
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---
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# T02: Generalized OptimizationLoop to stages 2-5 with per-stage fixture validation, schema dispatch, and user prompt building; created stage 2-4 fixtures; removed stage-5 gate from CLI
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> Generalized OptimizationLoop to stages 2-5 with per-stage fixture validation, schema dispatch, and user prompt building; created stage 2-4 fixtures; removed stage-5 gate from CLI
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## What Happened
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---
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id: T02
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parent: S04
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milestone: M013
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key_files:
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- backend/pipeline/quality/optimizer.py
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- backend/pipeline/quality/__main__.py
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- backend/pipeline/quality/scorer.py
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- backend/pipeline/quality/fixtures/sample_segments.json
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- backend/pipeline/quality/fixtures/sample_topic_group.json
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- backend/pipeline/quality/fixtures/sample_classifications.json
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key_decisions:
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- Fixed stage 5 fixture_keys to match existing fixture (moments not key_moments)
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- Stage-specific user prompt building via _build_user_prompt() dispatch
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duration: ""
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verification_result: passed
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completed_at: 2026-04-01T09:24:39.816Z
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blocker_discovered: false
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---
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# T02: Generalized OptimizationLoop to stages 2-5 with per-stage fixture validation, schema dispatch, and user prompt building; created stage 2-4 fixtures; removed stage-5 gate from CLI
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**Generalized OptimizationLoop to stages 2-5 with per-stage fixture validation, schema dispatch, and user prompt building; created stage 2-4 fixtures; removed stage-5 gate from CLI**
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## What Happened
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Rewrote optimizer.py to be fully stage-aware: constructor validates stage against STAGE_CONFIGS, _load_fixture() validates against config.fixture_keys, _score_variant() dispatches per-stage with stage-appropriate user prompts and schema parsing, run() uses config.prompt_file. Updated __main__.py to accept stages 2-5 and use stage-appropriate dimensions in leaderboard/results. Created three fixture files for stages 2-4. Fixed stage 5 fixture_keys mismatch from T01.
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## Verification
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All four task plan verification commands pass: optimizer imports ok, CLI imports ok, --help output works for stage 2, stage 6 is rejected. Additionally verified fixture loading for all stages 2-5 and that stage 1 is also rejected.
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## Verification Evidence
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| # | Command | Exit Code | Verdict | Duration |
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|---|---------|-----------|---------|----------|
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| 1 | `python -c "from pipeline.quality.optimizer import OptimizationLoop; print('optimizer ok')"` | 0 | ✅ pass | 500ms |
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| 2 | `python -c "from pipeline.quality.__main__ import main; print('cli ok')"` | 0 | ✅ pass | 500ms |
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| 3 | `python -m pipeline.quality optimize --stage 2 --iterations 1 --file ... --help 2>&1 | head -1` | 0 | ✅ pass | 500ms |
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| 4 | `python -m pipeline.quality optimize --stage 6 --file x 2>&1 | grep -q 'stage'` | 0 | ✅ pass | 500ms |
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| 5 | `Fixture validation for all stages 2-5` | 0 | ✅ pass | 500ms |
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## Deviations
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Fixed stage 5 fixture_keys from ['key_moments', 'creator_name'] to ['moments', 'creator_name'] — T01 config didn't match existing fixture.
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## Known Issues
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None.
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## Files Created/Modified
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- `backend/pipeline/quality/optimizer.py`
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- `backend/pipeline/quality/__main__.py`
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- `backend/pipeline/quality/scorer.py`
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- `backend/pipeline/quality/fixtures/sample_segments.json`
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- `backend/pipeline/quality/fixtures/sample_topic_group.json`
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- `backend/pipeline/quality/fixtures/sample_classifications.json`
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## Deviations
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Fixed stage 5 fixture_keys from ['key_moments', 'creator_name'] to ['moments', 'creator_name'] — T01 config didn't match existing fixture.
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## Known Issues
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None.
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@ -20,14 +20,16 @@ from pipeline.llm_client import LLMClient
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from .fitness import FitnessRunner
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from .optimizer import OptimizationLoop, OptimizationResult
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from .scorer import DIMENSIONS, ScoreRunner
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from .scorer import DIMENSIONS, STAGE_CONFIGS, ScoreRunner
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# ── Reporting helpers ────────────────────────────────────────────────────────
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def print_leaderboard(result: OptimizationResult) -> None:
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def print_leaderboard(result: OptimizationResult, stage: int = 5) -> None:
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"""Print a formatted leaderboard of top 5 variants by composite score."""
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dims = STAGE_CONFIGS[stage].dimensions if stage in STAGE_CONFIGS else DIMENSIONS
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# Filter to entries that actually scored (no errors)
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scored = [h for h in result.history if not h.get("error")]
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if not scored:
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@ -37,19 +39,20 @@ def print_leaderboard(result: OptimizationResult) -> None:
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ranked = sorted(scored, key=lambda h: h["composite"], reverse=True)[:5]
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print(f"\n{'='*72}")
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print(" LEADERBOARD — Top 5 Variants by Composite Score")
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print(f" LEADERBOARD — Top 5 Variants by Composite Score (Stage {stage})")
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print(f"{'='*72}")
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# Header
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dim_headers = " ".join(f"{d[:5]:>5s}" for d in DIMENSIONS)
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dim_headers = " ".join(f"{d[:5]:>5s}" for d in dims)
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sep_segments = " ".join("─" * 5 for _ in dims)
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print(f" {'#':>2s} {'Label':<16s} {'Comp':>5s} {dim_headers}")
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print(f" {'─'*2} {'─'*16} {'─'*5} {'─'*5} {'─'*5} {'─'*5} {'─'*5} {'─'*5}")
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print(f" {'─'*2} {'─'*16} {'─'*5} {sep_segments}")
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for i, entry in enumerate(ranked, 1):
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label = entry.get("label", "?")[:16]
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comp = entry["composite"]
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dim_vals = " ".join(
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f"{entry['scores'].get(d, 0.0):5.2f}" for d in DIMENSIONS
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f"{entry['scores'].get(d, 0.0):5.2f}" for d in dims
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)
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bar = "█" * int(comp * 20) + "░" * (20 - int(comp * 20))
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print(f" {i:>2d} {label:<16s} {comp:5.3f} {dim_vals} {bar}")
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@ -135,6 +138,8 @@ def write_results_json(
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filename = f"optimize_stage{stage}_{timestamp}.json"
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filepath = out_path / filename
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dims = STAGE_CONFIGS[stage].dimensions if stage in STAGE_CONFIGS else DIMENSIONS
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payload = {
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"config": {
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"stage": stage,
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@ -145,7 +150,7 @@ def write_results_json(
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"best_prompt": result.best_prompt,
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"best_scores": {
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"composite": result.best_score.composite,
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**{d: getattr(result.best_score, d) for d in DIMENSIONS},
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**{d: result.best_score.scores.get(d, 0.0) for d in dims},
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},
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"elapsed_seconds": result.elapsed_seconds,
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"history": result.history,
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@ -321,10 +326,10 @@ def _run_score(args: argparse.Namespace) -> int:
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def _run_optimize(args: argparse.Namespace) -> int:
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"""Execute the optimize subcommand."""
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# Stage validation — only stage 5 is supported
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if args.stage != 5:
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# Stage validation — stages 2-5 are supported
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if args.stage not in STAGE_CONFIGS:
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print(
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f"Error: only stage 5 is supported for optimization (got stage {args.stage})",
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f"Error: unsupported stage {args.stage}. Valid stages: {sorted(STAGE_CONFIGS)}",
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file=sys.stderr,
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)
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return 1
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@ -364,7 +369,7 @@ def _run_optimize(args: argparse.Namespace) -> int:
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return 1
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# Reporting
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print_leaderboard(result)
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print_leaderboard(result, stage=args.stage)
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print_trajectory(result)
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# Write results JSON
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{
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"extracted_moments": [
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{
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"title": "Frequency-specific sidechain with Trackspacer",
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"summary": "Using Trackspacer plugin for frequency-band sidechain compression targeting 100-300Hz, allowing bass high-end to remain present while clearing low-mid mud under the kick.",
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"content_type": "technique",
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"plugins": ["Trackspacer"],
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"start_time": 15.2,
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"end_time": 52.1
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},
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{
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"title": "Parallel drum compression chain",
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"summary": "Setting up Ableton's Drum Buss at 40% drive into a return track with Valhalla Room at 1.2s decay, mixed at -12dB for room sound without wash.",
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"content_type": "settings",
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"plugins": ["Drum Buss", "Valhalla Room"],
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"start_time": 52.1,
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"end_time": 89.3
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},
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{
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"title": "Mono compatibility checking workflow",
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"summary": "Using Ableton's Utility plugin on the sub bus to constantly check mono compatibility of layered bass patches, catching phase cancellation before mixdown.",
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"content_type": "workflow",
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"plugins": ["Utility"],
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"start_time": 89.3,
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"end_time": 110.0
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}
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],
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"taxonomy": "Sound Design > Mixing & Processing"
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}
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40
backend/pipeline/quality/fixtures/sample_segments.json
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40
backend/pipeline/quality/fixtures/sample_segments.json
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{
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"transcript_segments": [
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{
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"index": 0,
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"start_time": 0.0,
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"end_time": 15.2,
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"text": "Hey everyone, today we're going to talk about sidechain compression and how I use it in my productions."
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},
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{
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"index": 1,
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"start_time": 15.2,
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"end_time": 34.8,
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"text": "So the basic idea is you take the kick drum signal and use it to duck the bass. Most people use a compressor for this but I actually prefer Trackspacer because it gives you frequency-specific ducking."
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},
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{
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"index": 2,
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"start_time": 34.8,
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"end_time": 52.1,
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"text": "With Trackspacer you can set it to only affect 100 to 300 Hz so when the kick hits, the bass ducks just in that low-mid range. The top end stays right there."
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},
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{
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"index": 3,
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"start_time": 52.1,
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"end_time": 71.5,
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"text": "Now let me show you another technique — parallel compression on drums. I use Drum Buss with the drive at about 40 percent, then send that to a return track."
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},
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{
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"index": 4,
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"start_time": 71.5,
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"end_time": 89.3,
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"text": "On the return I put Valhalla Room with a short decay, like 1.2 seconds. Mix it in at minus 12 dB. Your drums just breathe — they get this room sound without getting washy."
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},
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{
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"index": 5,
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"start_time": 89.3,
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"end_time": 110.0,
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"text": "One more thing about mono compatibility. I always have Utility on the sub bus and I flip to mono constantly. If your layered bass sounds thin in mono you've got phase issues."
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}
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]
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}
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18
backend/pipeline/quality/fixtures/sample_topic_group.json
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18
backend/pipeline/quality/fixtures/sample_topic_group.json
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{
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"topic_segments": [
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{
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"start_index": 0,
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"end_index": 2,
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"topic_label": "Frequency-specific sidechain compression with Trackspacer",
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"summary": "Using Trackspacer for frequency-band sidechain ducking instead of traditional volume compression",
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"transcript_text": "Hey everyone, today we're going to talk about sidechain compression and how I use it in my productions. So the basic idea is you take the kick drum signal and use it to duck the bass. Most people use a compressor for this but I actually prefer Trackspacer because it gives you frequency-specific ducking. With Trackspacer you can set it to only affect 100 to 300 Hz so when the kick hits, the bass ducks just in that low-mid range. The top end stays right there."
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},
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{
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"start_index": 3,
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"end_index": 4,
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"topic_label": "Parallel drum compression with Drum Buss and Valhalla Room",
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"summary": "Setting up a parallel compression chain using Ableton's Drum Buss and Valhalla Room reverb for drum processing",
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"transcript_text": "Now let me show you another technique — parallel compression on drums. I use Drum Buss with the drive at about 40 percent, then send that to a return track. On the return I put Valhalla Room with a short decay, like 1.2 seconds. Mix it in at minus 12 dB. Your drums just breathe — they get this room sound without getting washy."
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}
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]
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}
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"""Automated prompt optimization loop for Stage 5 synthesis.
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"""Automated prompt optimization loop for pipeline stages 2-5.
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Orchestrates a generate→score→select cycle:
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1. Score the current best prompt against reference fixtures
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@ -9,6 +9,7 @@ Orchestrates a generate→score→select cycle:
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Usage (via CLI):
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python -m pipeline.quality optimize --stage 5 --iterations 10
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python -m pipeline.quality optimize --stage 3 --iterations 5 --file fixtures/sample_topic_group.json
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"""
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from __future__ import annotations
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@ -19,8 +20,7 @@ from dataclasses import dataclass, field
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from pathlib import Path
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from pipeline.llm_client import LLMClient
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from pipeline.quality.scorer import DIMENSIONS, ScoreResult, ScoreRunner
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from pipeline.quality.variant_generator import PromptVariantGenerator
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from pipeline.quality.scorer import STAGE_CONFIGS, ScoreResult, ScoreRunner
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logger = logging.getLogger(__name__)
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@ -47,9 +47,9 @@ class OptimizationLoop:
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client:
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LLMClient instance for LLM calls (synthesis + scoring + variant gen).
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stage:
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Pipeline stage number (currently only 5 is supported).
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Pipeline stage number (2-5).
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fixture_path:
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Path to a JSON fixture file containing ``creator_name`` and ``moments``.
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Path to a JSON fixture file matching the stage's expected keys.
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iterations:
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Number of generate→score→select cycles.
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variants_per_iter:
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@ -64,11 +64,17 @@ class OptimizationLoop:
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iterations: int = 5,
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variants_per_iter: int = 2,
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) -> None:
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if stage not in STAGE_CONFIGS:
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raise ValueError(
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f"Unsupported stage {stage}. Valid stages: {sorted(STAGE_CONFIGS)}"
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)
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self.client = client
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self.stage = stage
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self.fixture_path = fixture_path
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self.iterations = iterations
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self.variants_per_iter = variants_per_iter
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self.config = STAGE_CONFIGS[stage]
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self.scorer = ScoreRunner(client)
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self.generator = PromptVariantGenerator(client)
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@ -85,9 +91,10 @@ class OptimizationLoop:
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from pipeline.stages import _load_prompt
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t0 = time.monotonic()
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dimensions = self.config.dimensions
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# Load base prompt
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prompt_file = f"stage{self.stage}_synthesis.txt"
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# Load base prompt using the stage's configured prompt file
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prompt_file = self.config.prompt_file
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try:
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base_prompt = _load_prompt(prompt_file)
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except FileNotFoundError:
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@ -109,8 +116,6 @@ class OptimizationLoop:
|
|||
elapsed_seconds=round(time.monotonic() - t0, 2),
|
||||
)
|
||||
|
||||
moments = fixture["moments"]
|
||||
creator_name = fixture["creator_name"]
|
||||
history: list[dict] = []
|
||||
|
||||
# Score the baseline
|
||||
|
|
@ -120,11 +125,7 @@ class OptimizationLoop:
|
|||
print(f"{'='*60}\n")
|
||||
|
||||
print(" Scoring baseline prompt...")
|
||||
best_score = self.scorer.synthesize_and_score(
|
||||
moments=moments,
|
||||
creator_name=creator_name,
|
||||
voice_level=0.5,
|
||||
)
|
||||
best_score = self._score_variant(base_prompt, fixture)
|
||||
best_prompt = base_prompt
|
||||
|
||||
history.append({
|
||||
|
|
@ -133,7 +134,7 @@ class OptimizationLoop:
|
|||
"prompt_text": base_prompt[:200] + "..." if len(base_prompt) > 200 else base_prompt,
|
||||
"prompt_length": len(base_prompt),
|
||||
"composite": best_score.composite,
|
||||
"scores": {d: getattr(best_score, d) for d in DIMENSIONS},
|
||||
"scores": {d: best_score.scores.get(d, 0.0) for d in dimensions},
|
||||
"error": best_score.error,
|
||||
"label": "baseline",
|
||||
})
|
||||
|
|
@ -154,11 +155,12 @@ class OptimizationLoop:
|
|||
for iteration in range(1, self.iterations + 1):
|
||||
print(f"\n ── Iteration {iteration}/{self.iterations} ──")
|
||||
|
||||
# Generate variants
|
||||
# Generate variants with stage-appropriate markers
|
||||
variants = self.generator.generate(
|
||||
base_prompt=best_prompt,
|
||||
scores=best_score,
|
||||
n=self.variants_per_iter,
|
||||
stage=self.stage,
|
||||
)
|
||||
|
||||
if not variants:
|
||||
|
|
@ -172,10 +174,7 @@ class OptimizationLoop:
|
|||
for vi, variant_prompt in enumerate(variants):
|
||||
print(f" Scoring variant {vi + 1}/{len(variants)}...")
|
||||
|
||||
# Temporarily replace the base prompt with the variant for synthesis
|
||||
score = self._score_variant(
|
||||
variant_prompt, moments, creator_name,
|
||||
)
|
||||
score = self._score_variant(variant_prompt, fixture)
|
||||
|
||||
history.append({
|
||||
"iteration": iteration,
|
||||
|
|
@ -183,7 +182,7 @@ class OptimizationLoop:
|
|||
"prompt_text": variant_prompt[:200] + "..." if len(variant_prompt) > 200 else variant_prompt,
|
||||
"prompt_length": len(variant_prompt),
|
||||
"composite": score.composite,
|
||||
"scores": {d: getattr(score, d) for d in DIMENSIONS},
|
||||
"scores": {d: score.scores.get(d, 0.0) for d in dimensions},
|
||||
"error": score.error,
|
||||
"label": f"iter{iteration}_v{vi+1}",
|
||||
})
|
||||
|
|
@ -223,47 +222,50 @@ class OptimizationLoop:
|
|||
# ── Internal helpers ──────────────────────────────────────────────────
|
||||
|
||||
def _load_fixture(self) -> dict:
|
||||
"""Load and validate the fixture JSON file."""
|
||||
"""Load and validate the fixture JSON file against stage-specific keys."""
|
||||
path = Path(self.fixture_path)
|
||||
if not path.exists():
|
||||
raise FileNotFoundError(f"Fixture not found: {path}")
|
||||
data = json.loads(path.read_text(encoding="utf-8"))
|
||||
|
||||
if "moments" not in data:
|
||||
raise KeyError("Fixture must contain 'moments' key")
|
||||
if "creator_name" not in data:
|
||||
raise KeyError("Fixture must contain 'creator_name' key")
|
||||
for key in self.config.fixture_keys:
|
||||
if key not in data:
|
||||
raise KeyError(
|
||||
f"Stage {self.stage} fixture must contain '{key}' key "
|
||||
f"(required: {self.config.fixture_keys})"
|
||||
)
|
||||
|
||||
return data
|
||||
|
||||
def _score_variant(
|
||||
self,
|
||||
variant_prompt: str,
|
||||
moments: list[dict],
|
||||
creator_name: str,
|
||||
fixture: dict,
|
||||
) -> ScoreResult:
|
||||
"""Score a variant prompt by running synthesis + scoring.
|
||||
"""Score a variant prompt by running LLM completion + scoring.
|
||||
|
||||
Uses the variant as a direct system prompt for synthesis, bypassing
|
||||
VoiceDial (the optimization loop owns the full prompt text).
|
||||
Dispatches to stage-specific synthesis logic:
|
||||
- Stages 2-4: call LLM with the variant prompt and fixture input,
|
||||
parse with the stage's schema, then score via score_stage_output()
|
||||
- Stage 5: original flow (synthesis + page scoring)
|
||||
"""
|
||||
from pipeline.schemas import SynthesisResult
|
||||
from pipeline.stages import _get_stage_config
|
||||
|
||||
import json as _json
|
||||
import openai as _openai
|
||||
|
||||
model_override, modality = _get_stage_config(self.stage)
|
||||
schema_class = self.config.get_schema()
|
||||
|
||||
moments_json = _json.dumps(moments, indent=2)
|
||||
user_prompt = f"<creator>{creator_name}</creator>\n<moments>\n{moments_json}\n</moments>"
|
||||
# Build user prompt from fixture data — stage-specific formatting
|
||||
user_prompt = self._build_user_prompt(fixture)
|
||||
|
||||
t0 = time.monotonic()
|
||||
try:
|
||||
raw = self.client.complete(
|
||||
system_prompt=variant_prompt,
|
||||
user_prompt=user_prompt,
|
||||
response_model=SynthesisResult,
|
||||
response_model=schema_class,
|
||||
modality=modality,
|
||||
model_override=model_override,
|
||||
)
|
||||
|
|
@ -272,48 +274,89 @@ class OptimizationLoop:
|
|||
elapsed_synth = round(time.monotonic() - t0, 2)
|
||||
return ScoreResult(
|
||||
elapsed_seconds=elapsed_synth,
|
||||
error=f"Synthesis LLM error: {exc}",
|
||||
error=f"LLM error (stage {self.stage}): {exc}",
|
||||
)
|
||||
except Exception as exc:
|
||||
elapsed_synth = round(time.monotonic() - t0, 2)
|
||||
logger.exception("Unexpected error during variant synthesis")
|
||||
logger.exception("Unexpected error during variant synthesis (stage %d)", self.stage)
|
||||
return ScoreResult(
|
||||
elapsed_seconds=elapsed_synth,
|
||||
error=f"Unexpected synthesis error: {exc}",
|
||||
)
|
||||
|
||||
# Parse synthesis
|
||||
# Parse the LLM response into the stage schema
|
||||
raw_text = str(raw).strip()
|
||||
try:
|
||||
synthesis = self.client.parse_response(raw_text, SynthesisResult)
|
||||
parsed = self.client.parse_response(raw_text, schema_class)
|
||||
except Exception as exc:
|
||||
return ScoreResult(
|
||||
elapsed_seconds=elapsed_synth,
|
||||
error=f"Variant synthesis parse error: {exc}",
|
||||
error=f"Variant parse error (stage {self.stage}): {exc}",
|
||||
)
|
||||
|
||||
if not synthesis.pages:
|
||||
# Convert parsed output to JSON for the scorer
|
||||
output_json = self._schema_to_output_json(parsed)
|
||||
if output_json is None:
|
||||
return ScoreResult(
|
||||
elapsed_seconds=elapsed_synth,
|
||||
error="Variant synthesis returned no pages",
|
||||
error=f"Stage {self.stage} produced empty output",
|
||||
)
|
||||
|
||||
# Score the first page
|
||||
page = synthesis.pages[0]
|
||||
page_json = {
|
||||
"title": page.title,
|
||||
"creator_name": creator_name,
|
||||
"summary": page.summary,
|
||||
"body_sections": [
|
||||
{"heading": heading, "content": content}
|
||||
for heading, content in page.body_sections.items()
|
||||
],
|
||||
}
|
||||
|
||||
result = self.scorer.score_page(page_json, moments)
|
||||
# Score using the generic stage scorer
|
||||
result = self.scorer.score_stage_output(
|
||||
stage=self.stage,
|
||||
output_json=output_json,
|
||||
input_json=self._fixture_to_input_json(fixture),
|
||||
)
|
||||
result.elapsed_seconds = round(result.elapsed_seconds + elapsed_synth, 2)
|
||||
return result
|
||||
|
||||
def _build_user_prompt(self, fixture: dict) -> str:
|
||||
"""Build a stage-appropriate user prompt from fixture data."""
|
||||
if self.stage == 2:
|
||||
segments_json = json.dumps(fixture["transcript_segments"], indent=2)
|
||||
return f"<transcript_segments>\n{segments_json}\n</transcript_segments>"
|
||||
|
||||
elif self.stage == 3:
|
||||
segments_json = json.dumps(fixture["topic_segments"], indent=2)
|
||||
return f"<topic_segments>\n{segments_json}\n</topic_segments>"
|
||||
|
||||
elif self.stage == 4:
|
||||
moments_json = json.dumps(fixture["extracted_moments"], indent=2)
|
||||
taxonomy = fixture.get("taxonomy", "")
|
||||
prompt = f"<moments>\n{moments_json}\n</moments>"
|
||||
if taxonomy:
|
||||
prompt += f"\n<taxonomy>{taxonomy}</taxonomy>"
|
||||
return prompt
|
||||
|
||||
elif self.stage == 5:
|
||||
moments_json = json.dumps(fixture["moments"], indent=2)
|
||||
creator = fixture.get("creator_name", "Unknown")
|
||||
return f"<creator>{creator}</creator>\n<moments>\n{moments_json}\n</moments>"
|
||||
|
||||
else:
|
||||
return json.dumps(fixture, indent=2)
|
||||
|
||||
def _schema_to_output_json(self, parsed: object) -> dict | list | None:
|
||||
"""Convert a parsed Pydantic schema instance to JSON-serializable dict."""
|
||||
if hasattr(parsed, "model_dump"):
|
||||
return parsed.model_dump()
|
||||
elif hasattr(parsed, "dict"):
|
||||
return parsed.dict()
|
||||
return None
|
||||
|
||||
def _fixture_to_input_json(self, fixture: dict) -> dict | list:
|
||||
"""Extract the primary input data from the fixture for scorer context."""
|
||||
if self.stage == 2:
|
||||
return fixture["transcript_segments"]
|
||||
elif self.stage == 3:
|
||||
return fixture["topic_segments"]
|
||||
elif self.stage == 4:
|
||||
return fixture["extracted_moments"]
|
||||
elif self.stage == 5:
|
||||
return fixture["moments"]
|
||||
return fixture
|
||||
|
||||
def _print_iteration_summary(
|
||||
self,
|
||||
iteration: int,
|
||||
|
|
@ -322,8 +365,9 @@ class OptimizationLoop:
|
|||
) -> None:
|
||||
"""Print a compact one-line summary of the current best scores."""
|
||||
label = "BASELINE" if is_baseline else f"ITER {iteration}"
|
||||
dimensions = self.config.dimensions
|
||||
dims = " ".join(
|
||||
f"{d[:4]}={getattr(score, d):.2f}" for d in DIMENSIONS
|
||||
f"{d[:4]}={score.scores.get(d, 0.0):.2f}" for d in dimensions
|
||||
)
|
||||
print(f" [{label}] composite={score.composite:.3f} {dims}")
|
||||
|
||||
|
|
@ -334,6 +378,8 @@ class OptimizationLoop:
|
|||
elapsed: float,
|
||||
) -> None:
|
||||
"""Print the final optimization summary."""
|
||||
dimensions = self.config.dimensions
|
||||
|
||||
print(f"\n{'='*60}")
|
||||
print(" OPTIMIZATION COMPLETE")
|
||||
print(f"{'='*60}")
|
||||
|
|
@ -352,8 +398,8 @@ class OptimizationLoop:
|
|||
print(f" Improvement: {improvement:.3f} (no gain)")
|
||||
|
||||
print(f"\n Per-dimension best scores:")
|
||||
for d in DIMENSIONS:
|
||||
val = getattr(best_score, d)
|
||||
for d in dimensions:
|
||||
val = best_score.scores.get(d, 0.0)
|
||||
bar = "█" * int(val * 20) + "░" * (20 - int(val * 20))
|
||||
print(f" {d.replace('_', ' ').title():25s} {val:.2f} {bar}")
|
||||
|
||||
|
|
@ -362,3 +408,8 @@ class OptimizationLoop:
|
|||
print(f"\n ⚠ {errored} variant(s) errored during scoring")
|
||||
|
||||
print(f"{'='*60}\n")
|
||||
|
||||
|
||||
# Late import to avoid circular dependency (scorer imports at module level,
|
||||
# variant_generator imports scorer)
|
||||
from pipeline.quality.variant_generator import PromptVariantGenerator # noqa: E402
|
||||
|
|
|
|||
|
|
@ -281,7 +281,7 @@ STAGE_CONFIGS: dict[int, StageConfig] = {
|
|||
dimensions=["structural", "content_specificity", "voice_preservation", "readability", "factual_fidelity"],
|
||||
rubric=SCORING_RUBRIC,
|
||||
format_markers=["SynthesisResult", '"pages"', "body_sections", "title", "summary"],
|
||||
fixture_keys=["key_moments", "creator_name"],
|
||||
fixture_keys=["moments", "creator_name"],
|
||||
prompt_file="stage5_synthesis.txt",
|
||||
schema_class="SynthesisResult",
|
||||
),
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue