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Quality Service (Unified)

File: src/cofounder_agent/modules/content/quality_service.py Tested by: src/cofounder_agent/tests/unit/services/test_quality_service.py Last reviewed: 2026-04-30

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What it does

UnifiedQualityService.evaluate(content, context, method) runs a single pass of the seven-criteria quality framework — clarity, accuracy, completeness, relevance, SEO, readability, engagement — against generated content and returns a QualityAssessment with the per-dimension scores, an overall score (0-100), a pass/fail flag, and a list of refinement suggestions. This service is the quality scoring layer (was content quality score in the legacy pipeline). It complements MultiModelQA (adversarial reviewer fan-out) and content_validator (programmatic hard rules) — quality_service produces a numeric grade with feedback, the others produce pass/fail decisions. Three evaluation modes:

• PATTERN_BASED (default) — fast, deterministic. Heuristics delegated to services.quality_scorers (per-dimension functions), plus in-service artifact detection (photo metadata, leaked image-gen prompt prompts, raw HTML), LLM pattern detection (cliché openers, buzzwords, filler phrases, generic transitions, repetitive starters, listicle titles, hedging, exclamation spam, formulaic structure), and Flesch-Kincaid grade-level scoring. No LLM calls.
• LLM_BASED — sends content + dimension JSON schema to the injected llm_client, parses the response. Falls back to PATTERN_BASED if no client is configured, the response has no parseable JSON, or the call errors.
• HYBRID — runs both, averages the dimension scores 50/50. Falls back to pattern-only if LLM is unavailable or itself fell back to pattern (avoids double-counting).

The service also tracks running statistics (total_evaluations, passing_count, failing_count, average_score) for the lifetime of the instance.

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Public API

• UnifiedQualityService(database_service=None, qa_agent=None, llm_client=None, *, site_config) — constructor. site_config is required (keyword-only, DI’d); the rest are optional — only database_service is needed for persistence and only llm_client for LLM_BASED / HYBRID.
• await qs.evaluate(content, context=None, method=EvaluationMethod.PATTERN_BASED, store_result=True) -> QualityAssessment — main entry point. context may include topic, keywords, audience, target_length, task_id, content_id. When store_result=True and database_service is set, persists to the quality_evaluations table via database_service.create_quality_evaluation(...).
• qs.detect_truncation(content) -> bool — static helper; returns True if the LLM appears to have hit its output token limit. Truncated content cannot pass regardless of overall score.
• qs.flesch_kincaid_grade_level(text) -> float — static helper; delegates to quality_scorers.flesch_kincaid_grade_level.
• qs.get_statistics() -> dict — running counters and pass-rate.
• Module factories:
  • get_quality_service(database_service=None, llm_client=None, *, site_config)
  • get_content_quality_service(...) — backward-compat alias.
• Backward-compat aliases:
  • ContentQualityService = UnifiedQualityService (class alias).
• Re-exported types (so callers don’t need to import from quality_models):
  • EvaluationMethod (PATTERN_BASED, LLM_BASED, HYBRID)
  • QualityAssessment, QualityDimensions, QualityScore, RefinementType

QualityAssessment shape (from services.quality_models):

• dimensions: QualityDimensions — seven 0-100 scores
• overall_score: float — 0-100 (after artifact + LLM-pattern penalties)
• passing: bool — overall_score >= qa_pass_threshold AND not truncated
• feedback: str — human-readable summary
• suggestions: list[str] — refinement hints
• evaluation_method: EvaluationMethod
• content_length: int, word_count: int
• flesch_kincaid_grade_level: float
• truncation_detected: bool

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Configuration

Pipeline-wide thresholds are loaded from app_settings via quality_scorers.qa_cfg() (called as _qa_cfg() inside this service). Every threshold has a sensible default — see quality_scorers.py for the full list. The most-touched ones:

• qa_pass_threshold (default 70.0) — overall-score cut-off for passing=True.
• qa_critical_floor (default 50.0) — minimum-dimension floor (if clarity/readability/relevance falls below this the overall is capped at that value).
• qa_artifact_penalty_per (default 5.0) — points subtracted per artifact category found (photo metadata, image-gen prompt leak, etc.).
• qa_artifact_penalty_max (default 20.0) — total artifact-penalty cap.
• qa_fk_target_min / qa_fk_target_max (defaults 8.0 / 12.0) — Flesch-Kincaid grade-level acceptance band.

LLM-pattern detection (_score_llm_patterns) — the bulk of the DB-tunable surface, all under the qa_llm_* prefix. Toggle the entire detector with qa_llm_patterns_enabled (default True). Per-pattern thresholds (selected — see quality_service.py lines 587-615 for the full set):

• qa_llm_buzzword_warn_threshold (3) / qa_llm_buzzword_fail_threshold (5)
• qa_llm_buzzword_penalty_per (0.5) / qa_llm_buzzword_max_penalty (5.0)
• qa_llm_filler_warn_threshold (2) / qa_llm_filler_fail_threshold (4)
• qa_llm_opener_penalty (5.0) — cliché AI opener
• qa_llm_transition_penalty_per (1.0) / qa_llm_transition_min_count (2)
• qa_llm_listicle_title_penalty (2.0)
• qa_llm_hedge_ratio_threshold (0.02) / qa_llm_hedge_penalty (2.0)
• qa_llm_repetitive_starter_penalty_per (1.0) / qa_llm_repetitive_min_count (3)
• qa_llm_formulaic_structure_penalty (2.0) / qa_llm_formulaic_min_avg_words (50) / qa_llm_formulaic_variance (0.2)
• qa_llm_exclamation_threshold (5) / qa_llm_exclamation_penalty_per (0.3)

Per-dimension scoring tunables (clarity word-per-sentence bands, accuracy citation bonuses, completeness word-count step function, relevance keyword-density gates, SEO/engagement baselines) all live under the qa_* prefix in quality_scorers.qa_cfg().

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Dependencies

• Reads from:
  • services.quality_scorers — every per-dimension scorer plus the qa_cfg() config loader.
  • services.quality_models — EvaluationMethod, QualityAssessment, QualityDimensions, QualityScore, RefinementType types.
  • services.site_config — indirectly via quality_scorers.qa_cfg() and directly inside _score_llm_patterns() for the qa_llm_* keys.
  • Injected llm_client (when method != PATTERN_BASED).
• Writes to:
  • quality_evaluations table — only when database_service is injected and store_result=True. Persistence is best-effort; any exception is logged at error and swallowed (the assessment still returns to the caller).
• External APIs: none directly. The injected llm_client is what talks to Ollama/cloud.
• Sister-service callers:
  • modules.content.stages.quality_evaluation — the quality_evaluation graph_def node (moved from services/stages/ to modules/content/stages/ during the 2026-06-04 content-module migration).
  • main.py — constructed at startup as UnifiedQualityService() via the modules.content.api thin-adapter boundary.

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Failure modes

• evaluate() raises — outer try/except catches anything from the per-method branches, logs [_evaluate] Evaluation failed: <e> at ERROR with traceback, returns a stub assessment (all 5.0/10, passing False, evaluator UnifiedQualityService-Error). Pipeline keeps going.
• LLM client returns malformed JSON — _evaluate_llm_based catches JSONDecodeError/KeyError/TypeError/ValueError and falls back to PATTERN_BASED. The LLM call itself can throw — also caught, also fallback.
• No llm_client and LLM_BASED requested — logs warning, returns a PATTERN_BASED assessment.
• HYBRID with LLM fallback — if _evaluate_llm_based returned a PATTERN_BASED result (because of one of the failures above), hybrid returns the pattern result alone (no double-weighting).
• Truncated content — _evaluate_pattern_based always sets passing=False regardless of score, and inserts an explicit truncation suggestion at the top of the suggestions list. The score itself is NOT zeroed — the dimensions reflect what’s there.
• Persistence failure — _store_evaluation catches all exceptions, logs at error. Caller sees a successful assessment; the row just isn’t there. No retry.
• Missing task_id / content_id in context — _store_evaluation logs at debug and returns without writing. (Without an ID the row has nothing to FK against; silent skip is the right call.)
• qa_llm_patterns_enabled = false — entire LLM-pattern detector short-circuits to (0.0, []). Score is unaffected by buzzwords, filler, etc. Useful when validating a deliberately-stylized post.

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Common ops

• Lower the pass bar for a genre that scores low for legitimate reasons (e.g. very short news posts): poindexter settings set qa_pass_threshold 60
• Disable buzzword penalties temporarily: poindexter settings set qa_llm_buzzword_penalty_per 0 (or the nuclear option: qa_llm_patterns_enabled false).
• Inspect recent quality evaluations: SELECT created_at, overall_score, passing, evaluation_method FROM quality_evaluations ORDER BY created_at DESC LIMIT 50;
• Find LLM-pattern-heavy posts — search the suggestions JSON column on quality_evaluations for "AI writing pattern" to see how often the writer falls back on slop patterns by category.
• Run a one-off evaluation in the REPL:
  import asyncio
  from modules.content.quality_service import UnifiedQualityService, EvaluationMethod
  qs = UnifiedQualityService()
  result = asyncio.run(qs.evaluate("# Post\n\nBody...", context={"topic": "fastapi"}))
  print(result.overall_score, result.passing, result.suggestions)
  
• Debug “why did this pass with a 50?” — check the truncation flag AND the FK grade-level vs target band; the suggestions list usually spells out the mid-tier reasons.

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See also

• docs/architecture/services/multi_model_qa.md — companion adversarial reviewer; uses quality scoring as one of several inputs.
• docs/architecture/services/content_validator.md — companion programmatic hard-rule layer (no scoring; pure pass/fail).
• docs/architecture/anti-hallucination.md — full QA pipeline picture.
• services.quality_scorers — per-dimension scoring functions and the qa_cfg() settings dictionary.
• services.quality_models — data classes for assessments, dimensions, evaluation methods, refinement types.