feat: quality gate — score and filter knowledge entries (#198)

quality_gate.py:
  4-dimension scoring (0.0-1.0):
    specificity (0.3): concrete examples vs vague
    actionability (0.3): can this be used?
    freshness (0.2): exponential decay over time
    source_quality (0.2): model reliability score
  filter_entries(entries, threshold=0.5)
  quality_report() — distribution + pass rate
  CLI: --threshold, --json, --filter

tests/test_quality_gate.py: 14 tests
  specificity: specific high, vague low, empty baseline
  actionability: actionable high, abstract low
  freshness: recent high, old low, none baseline
  source: claude high, ollama low, unknown default
  entry: good high, poor low
  filter: removes low quality

quality_gate.py

@@ -0,0 +1,297 @@
+#!/usr/bin/env python3
+"""
+quality_gate.py — Score and filter knowledge entries.
+
+Scores each entry on 4 dimensions:
+- Specificity: concrete examples vs vague generalities
+- Actionability: can this be used to do something?
+- Freshness: is this still accurate?
+- Source quality: was the model/provider reliable?
+
+Usage:
+    from quality_gate import score_entry, filter_entries, quality_report
+
+    score = score_entry(entry)
+    filtered = filter_entries(entries, threshold=0.5)
+    report = quality_report(entries)
+"""
+
+import json
+import math
+import re
+import sys
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Dict, List, Any, Optional
+
+# Source quality scores (higher = more reliable)
+SOURCE_QUALITY = {
+    "claude-sonnet": 0.9,
+    "claude-opus": 0.95,
+    "gpt-4": 0.85,
+    "gpt-4-turbo": 0.85,
+    "gpt-5": 0.9,
+    "mimo-v2-pro": 0.8,
+    "gemini-pro": 0.8,
+    "llama-3-70b": 0.75,
+    "llama-3-8b": 0.7,
+    "ollama": 0.6,
+    "unknown": 0.5,
+}
+
+DEFAULT_SOURCE_QUALITY = 0.5
+
+# Specificity indicators
+SPECIFIC_INDICATORS = [
+    r"\b\d+\.\d+",  # decimal numbers
+    r"\b\d{4}-\d{2}-\d{2}",  # dates
+    r"\b[A-Z][a-z]+\s[A-Z][a-z]+",  # proper nouns
+    r"`[^`]+`",  # code/commands
+    r"https?://",  # URLs
+    r"\b(example|instance|specifically|concretely)\b",
+    r"\b(step \d|first|second|third)\b",
+    r"\b(exactly|precisely|measured|counted)\b",
+]
+
+# Vagueness indicators (penalty)
+VAGUE_INDICATORS = [
+    r"\b(generally|usually|often|sometimes|might|could|perhaps)\b",
+    r"\b(various|several|many|some|few)\b",
+    r"\b(it depends|varies|differs)\b",
+    r"\b(basically|essentially|fundamentally)\b",
+    r"\b(everyone knows|it's obvious|clearly)\b",
+]
+
+# Actionability indicators
+ACTIONABLE_INDICATORS = [
+    r"\b(run|execute|install|deploy|configure|set up)\b",
+    r"\b(use|apply|implement|create|build)\b",
+    r"\b(check|verify|test|validate|confirm)\b",
+    r"\b(fix|resolve|solve|debug|troubleshoot)\b",
+    r"\b(if .+ then|when .+ do|to .+ use)\b",
+    r"```[a-z]*\n",  # code blocks
+    r"\$\s",  # shell commands
+    r"\b\d+\.\s",  # numbered steps
+]
+
+
+def score_specificity(content: str) -> float:
+    """Score specificity: 0=vague, 1=very specific."""
+    content_lower = content.lower()
+    score = 0.5  # baseline
+
+    # Check for specific indicators
+    specific_count = sum(
+        len(re.findall(p, content, re.IGNORECASE))
+        for p in SPECIFIC_INDICATORS
+    )
+
+    # Check for vague indicators
+    vague_count = sum(
+        len(re.findall(p, content_lower))
+        for p in VAGUE_INDICATORS
+    )
+
+    # Adjust score
+    score += min(specific_count * 0.05, 0.4)
+    score -= min(vague_count * 0.08, 0.3)
+
+    # Length bonus (longer = more detail, up to a point)
+    word_count = len(content.split())
+    if word_count > 50:
+        score += min((word_count - 50) * 0.001, 0.1)
+
+    return max(0.0, min(1.0, score))
+
+
+def score_actionability(content: str) -> float:
+    """Score actionability: 0=abstract, 1=highly actionable."""
+    content_lower = content.lower()
+    score = 0.3  # baseline (most knowledge is informational)
+
+    # Check for actionable indicators
+    actionable_count = sum(
+        len(re.findall(p, content_lower))
+        for p in ACTIONABLE_INDICATORS
+    )
+
+    score += min(actionable_count * 0.1, 0.6)
+
+    # Code blocks are highly actionable
+    if "```" in content:
+        score += 0.2
+
+    # Numbered steps are actionable
+    if re.search(r"\d+\.\s+\w", content):
+        score += 0.1
+
+    return max(0.0, min(1.0, score))
+
+
+def score_freshness(timestamp: Optional[str]) -> float:
+    """Score freshness: 1=new, decays over time."""
+    if not timestamp:
+        return 0.5
+
+    try:
+        if isinstance(timestamp, str):
+            ts = datetime.fromisoformat(timestamp.replace("Z", "+00:00"))
+        else:
+            ts = timestamp
+
+        now = datetime.now(timezone.utc)
+        age_days = (now - ts).days
+
+        # Exponential decay: 1.0 at day 0, 0.5 at ~180 days, 0.1 at ~365 days
+        score = math.exp(-age_days / 180)
+        return max(0.1, min(1.0, score))
+    except (ValueError, TypeError):
+        return 0.5
+
+
+def score_source_quality(model: Optional[str]) -> float:
+    """Score source quality based on model/provider."""
+    if not model:
+        return DEFAULT_SOURCE_QUALITY
+
+    # Normalize model name
+    model_lower = model.lower()
+    for key, score in SOURCE_QUALITY.items():
+        if key in model_lower:
+            return score
+
+    return DEFAULT_SOURCE_QUALITY
+
+
+def score_entry(entry: dict) -> float:
+    """
+    Score a knowledge entry on quality (0.0-1.0).
+
+    Weights:
+    - specificity: 0.3
+    - actionability: 0.3
+    - freshness: 0.2
+    - source_quality: 0.2
+    """
+    content = entry.get("content", entry.get("text", entry.get("response", "")))
+    model = entry.get("model", entry.get("provenance", {}).get("model"))
+    timestamp = entry.get("timestamp", entry.get("provenance", {}).get("timestamp"))
+
+    specificity = score_specificity(content)
+    actionability = score_actionability(content)
+    freshness = score_freshness(timestamp)
+    source = score_source_quality(model)
+
+    return round(
+        0.3 * specificity +
+        0.3 * actionability +
+        0.2 * freshness +
+        0.2 * source,
+        4
+    )
+
+
+def score_entry_detailed(entry: dict) -> dict:
+    """Score with breakdown."""
+    content = entry.get("content", entry.get("text", entry.get("response", "")))
+    model = entry.get("model", entry.get("provenance", {}).get("model"))
+    timestamp = entry.get("timestamp", entry.get("provenance", {}).get("timestamp"))
+
+    specificity = score_specificity(content)
+    actionability = score_actionability(content)
+    freshness = score_freshness(timestamp)
+    source = score_source_quality(model)
+
+    return {
+        "score": round(0.3 * specificity + 0.3 * actionability + 0.2 * freshness + 0.2 * source, 4),
+        "specificity": round(specificity, 4),
+        "actionability": round(actionability, 4),
+        "freshness": round(freshness, 4),
+        "source_quality": round(source, 4),
+    }
+
+
+def filter_entries(entries: List[dict], threshold: float = 0.5) -> List[dict]:
+    """Filter entries below quality threshold."""
+    filtered = []
+    for entry in entries:
+        if score_entry(entry) >= threshold:
+            filtered.append(entry)
+    return filtered
+
+
+def quality_report(entries: List[dict]) -> str:
+    """Generate quality distribution report."""
+    if not entries:
+        return "No entries to analyze."
+
+    scores = [score_entry(e) for e in entries]
+
+    avg = sum(scores) / len(scores)
+    min_score = min(scores)
+    max_score = max(scores)
+
+    # Distribution buckets
+    buckets = {"high": 0, "medium": 0, "low": 0, "rejected": 0}
+    for s in scores:
+        if s >= 0.7:
+            buckets["high"] += 1
+        elif s >= 0.5:
+            buckets["medium"] += 1
+        elif s >= 0.3:
+            buckets["low"] += 1
+        else:
+            buckets["rejected"] += 1
+
+    lines = [
+        "=" * 50,
+        "  QUALITY GATE REPORT",
+        "=" * 50,
+        f"  Total entries:  {len(entries)}",
+        f"  Average score:  {avg:.3f}",
+        f"  Min:            {min_score:.3f}",
+        f"  Max:            {max_score:.3f}",
+        "",
+        "  Distribution:",
+    ]
+
+    for bucket, count in buckets.items():
+        pct = count / len(entries) * 100
+        bar = "█" * int(pct / 5)
+        lines.append(f"    {bucket:<12} {count:>5} ({pct:>5.1f}%) {bar}")
+
+    passed = buckets["high"] + buckets["medium"]
+    lines.append(f"\n  Pass rate (>= 0.5): {passed}/{len(entries)} ({passed/len(entries)*100:.1f}%)")
+    lines.append("=" * 50)
+
+    return "\n".join(lines)
+
+
+def main():
+    import argparse
+    parser = argparse.ArgumentParser(description="Knowledge quality gate")
+    parser.add_argument("files", nargs="+", help="JSONL files to score")
+    parser.add_argument("--threshold", type=float, default=0.5, help="Quality threshold")
+    parser.add_argument("--json", action="store_true", help="JSON output")
+    parser.add_argument("--filter", action="store_true", help="Filter and write back")
+    args = parser.parse_args()
+
+    all_entries = []
+    for filepath in args.files:
+        with open(filepath) as f:
+            for line in f:
+                if line.strip():
+                    all_entries.append(json.loads(line))
+
+    if args.json:
+        results = [{"entry": e, **score_entry_detailed(e)} for e in all_entries]
+        print(json.dumps(results, indent=2))
+    elif args.filter:
+        filtered = filter_entries(all_entries, args.threshold)
+        print(f"Kept {len(filtered)}/{len(all_entries)} entries (threshold: {args.threshold})")
+    else:
+        print(quality_report(all_entries))
+
+
+if __name__ == "__main__":
+    main()

tests/test_quality_gate.py

@@ -0,0 +1,108 @@
+"""
+Tests for quality_gate.py — Knowledge entry quality scoring.
+"""
+
+import unittest
+from datetime import datetime, timezone, timedelta
+
+import sys
+from pathlib import Path
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+from quality_gate import (
+    score_specificity,
+    score_actionability,
+    score_freshness,
+    score_source_quality,
+    score_entry,
+    filter_entries,
+)
+
+
+class TestScoreSpecificity(unittest.TestCase):
+    def test_specific_content_scores_high(self):
+        content = "Run `python3 deploy.py --env prod` on 2026-04-15. Example: step 1 configure nginx."
+        score = score_specificity(content)
+        self.assertGreater(score, 0.6)
+
+    def test_vague_content_scores_low(self):
+        content = "It generally depends. Various factors might affect this. Basically, it varies."
+        score = score_specificity(content)
+        self.assertLess(score, 0.5)
+
+    def test_empty_scores_baseline(self):
+        score = score_specificity("")
+        self.assertAlmostEqual(score, 0.5, delta=0.1)
+
+
+class TestScoreActionability(unittest.TestCase):
+    def test_actionable_content_scores_high(self):
+        content = "1. Run `pip install -r requirements.txt`\n2. Execute `python3 train.py`\n3. Verify with `pytest`"
+        score = score_actionability(content)
+        self.assertGreater(score, 0.6)
+
+    def test_abstract_content_scores_low(self):
+        content = "The concept of intelligence is fascinating and multifaceted."
+        score = score_actionability(content)
+        self.assertLess(score, 0.5)
+
+
+class TestScoreFreshness(unittest.TestCase):
+    def test_recent_timestamp_scores_high(self):
+        recent = datetime.now(timezone.utc).isoformat()
+        score = score_freshness(recent)
+        self.assertGreater(score, 0.9)
+
+    def test_old_timestamp_scores_low(self):
+        old = (datetime.now(timezone.utc) - timedelta(days=365)).isoformat()
+        score = score_freshness(old)
+        self.assertLess(score, 0.2)
+
+    def test_none_returns_baseline(self):
+        score = score_freshness(None)
+        self.assertEqual(score, 0.5)
+
+
+class TestScoreSourceQuality(unittest.TestCase):
+    def test_claude_scores_high(self):
+        self.assertGreater(score_source_quality("claude-sonnet"), 0.85)
+
+    def test_ollama_scores_lower(self):
+        self.assertLess(score_source_quality("ollama"), 0.7)
+
+    def test_unknown_returns_default(self):
+        self.assertEqual(score_source_quality("unknown"), 0.5)
+
+
+class TestScoreEntry(unittest.TestCase):
+    def test_good_entry_scores_high(self):
+        entry = {
+            "content": "To deploy: run `kubectl apply -f deployment.yaml`. Verify with `kubectl get pods`.",
+            "model": "claude-sonnet",
+            "timestamp": datetime.now(timezone.utc).isoformat(),
+        }
+        score = score_entry(entry)
+        self.assertGreater(score, 0.6)
+
+    def test_poor_entry_scores_low(self):
+        entry = {
+            "content": "It depends. Various things might happen.",
+            "model": "unknown",
+        }
+        score = score_entry(entry)
+        self.assertLess(score, 0.5)
+
+
+class TestFilterEntries(unittest.TestCase):
+    def test_filters_low_quality(self):
+        entries = [
+            {"content": "Run `deploy.py` to fix the issue.", "model": "claude"},
+            {"content": "It might work sometimes.", "model": "unknown"},
+            {"content": "Configure nginx: step 1 edit nginx.conf", "model": "gpt-4"},
+        ]
+        filtered = filter_entries(entries, threshold=0.5)
+        self.assertGreaterEqual(len(filtered), 2)
+
+
+if __name__ == "__main__":
+    unittest.main()