feat: training data pipeline — knowledge entries → JSONL training pairs

Add scripts/knowledge_to_training_pairs.py which reads quality-gated
knowledge entries from knowledge/index.json and emits terse→rich
training pairs in JSONL format.

Features:
- Derives terse queries from facts via category-aware heuristics
- Configurable quality filters: min-confidence, model-filter, date range
- Output includes domain, source_confidence, source_model
- Smoke tests added in tests/test_knowledge_to_training_pairs.py

Deliverables for #199:
1. Pipeline script: scripts/knowledge_to_training_pairs.py
2. End-to-end: knowledge/index.json → training_pairs.jsonl (or custom JSONL)
3. Config: min-confidence, model-filter, after/before date filters
4. Test: 9 smoke tests covering conversion, filtering, and end-to-end run

Closes #199

scripts/knowledge_to_training_pairs.py

@@ -0,0 +1,255 @@
+#!/usr/bin/env python3
+"""
+knowledge_to_training_pairs.py — Convert quality-gated knowledge entries into training pairs.
+
+Reads knowledge/index.json (or a custom JSONL of entries), applies quality filters,
+and emits terse→rich training pairs in JSONL format for model fine-tuning.
+
+Usage:
+    python3 scripts/knowledge_to_training_pairs.py \
+        --input knowledge/index.json \
+        --output training_pairs.jsonl \
+        --min-confidence 0.7 \
+        --model-filter claude-sonnet,gpt-4 \
+        --after 2026-01-01
+
+Input entry format (from index.json facts):
+    {
+        "id": "hermes-agent:pitfall:001",
+        "fact": "deploy-crons.py leaves jobs in mixed model format",
+        "category": "pitfall",
+        "domain": "hermes-agent",
+        "confidence": 0.95,
+        ...
+    }
+
+Output training pair format:
+    {
+        "terse": "How do I handle deploy-crons.py mixed model format?",
+        "rich": "deploy-crons.py leaves jobs in mixed model format.",
+        "domain": "hermes-agent",
+        "source_confidence": 0.95,
+        "source_model": "unknown"
+    }
+"""
+
+import argparse
+import json
+import os
+import sys
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Optional
+
+
+def fact_to_terse(fact: str, category: str, domain: str) -> str:
+    """
+    Derive a short user query from a knowledge fact.
+
+    Strategy:
+    - Pitfalls → "How do I avoid/handle/fix <fact excerpt>?"
+    - Patterns  → "What's the recommended way to <pattern core>?"
+    - Tool quirks → "How does <tool> behave in <context>?"
+    - Facts → "What should I know about <fact excerpt>?"
+    - Questions → "What is the answer to: <fact>?"
+    """
+    fact_lower = fact.lower()
+    # Extract a concise excerpt (first sentence or 80 chars)
+    excerpt = fact.split('. ')[0] if '. ' in fact else fact[:80]
+
+    if category == "pitfall":
+        verbs = ["avoid", "handle", "fix", "prevent"]
+        # pick verb based on fact wording
+        if "trigger" in fact_lower or "cause" in fact_lower:
+            verb = "avoid"
+        elif "broken" in fact_lower or "fails" in fact_lower:
+            verb = "fix"
+        else:
+            verb = "handle"
+        return f"How do I {verb} {excerpt.rstrip('.')}?"
+    elif category == "pattern":
+        return f"What's the recommended way to {excerpt.rstrip('.')}?"
+    elif category == "tool-quirk":
+        # Try to extract tool name
+        tool = fact.split()[0] if fact.split() else domain
+        return f"How does {tool} behave in this context?"
+    elif category == "question":
+        return f"What is the answer to: {excerpt}?"
+    else:  # fact or unknown
+        return f"What should I know about {excerpt.rstrip('.')}?"
+
+
+def parse_date(date_str: Optional[str]) -> Optional[datetime]:
+    """Parse ISO date string to datetime, or return None."""
+    if not date_str:
+        return None
+    try:
+        return datetime.fromisoformat(date_str.replace("Z", "+00:00"))
+    except ValueError:
+        return None
+
+
+def load_knowledge_index(path: str) -> list[dict]:
+    """Load knowledge facts from index.json (or plain JSONL of entries)."""
+    p = Path(path)
+    if not p.exists():
+        print(f"ERROR: Knowledge input not found: {path}", file=sys.stderr)
+        sys.exit(1)
+
+    with open(p) as f:
+        data = json.load(f)
+
+    # index.json format: {"facts": [...], ...}
+    if isinstance(data, dict) and "facts" in data:
+        return data["facts"]
+    # JSONL format: one entry per line
+    if isinstance(data, list):
+        return data
+    # Plain file with JSON array
+    print(f"ERROR: Unrecognized input format in {path}", file=sys.stderr)
+    sys.exit(1)
+
+
+def filter_entries(entries: list[dict],
+                   min_confidence: float = 0.0,
+                   model_filter: Optional[list[str]] = None,
+                   after: Optional[datetime] = None,
+                   before: Optional[datetime] = None) -> list[dict]:
+    """Apply quality and provenance filters."""
+    filtered = []
+    for entry in entries:
+        # Confidence filter (entry confidence)
+        conf = entry.get("confidence", 0.0)
+        if conf < min_confidence:
+            continue
+
+        # Model filter: if specified, entry's model must be in the list
+        if model_filter:
+            entry_model = entry.get("model", entry.get("provenance", {}).get("model", "unknown"))
+            if entry_model not in model_filter:
+                continue
+
+        # Date filter: use last_confirmed or first_seen or harvested_at
+        entry_date = None
+        for field in ("last_confirmed", "first_seen", "harvested_at"):
+            if field in entry:
+                entry_date = parse_date(entry[field])
+                if entry_date:
+                    break
+        if after and entry_date and entry_date < after:
+            continue
+        if before and entry_date and entry_date > before:
+            continue
+
+        filtered.append(entry)
+    return filtered
+
+
+def entry_to_pair(entry: dict) -> dict:
+    """Convert a knowledge entry into a training pair."""
+    fact = entry.get("fact", "").strip()
+    if not fact:
+        return None
+
+    category = entry.get("category", "fact")
+    domain = entry.get("domain", "global")
+
+    terse = fact_to_terse(fact, category, domain)
+    rich = fact
+    source_confidence = round(entry.get("confidence", 0.0), 4)
+    source_model = entry.get("model", entry.get("provenance", {}).get("model", "unknown"))
+
+    return {
+        "terse": terse,
+        "rich": rich,
+        "domain": domain,
+        "source_confidence": source_confidence,
+        "source_model": source_model,
+    }
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Knowledge entries → training pairs")
+    parser.add_argument("--input", "-i", default="knowledge/index.json",
+                        help="Input knowledge index or JSONL (default: knowledge/index.json)")
+    parser.add_argument("--output", "-o", default="training_pairs.jsonl",
+                        help="Output JSONL file")
+    parser.add_argument("--min-confidence", type=float, default=0.5,
+                        help="Minimum entry confidence to include (0.0-1.0, default: 0.5)")
+    parser.add_argument("--model-filter",
+                        help="Comma-separated list of source models to include")
+    parser.add_argument("--after",
+                        help="Include entries last_confirmed/first_seen on or after this date (YYYY-MM-DD)")
+    parser.add_argument("--before",
+                        help="Include entries last_confirmed/first_seen on or before this date (YYYY-MM-DD)")
+    parser.add_argument("--dry-run", action="store_true",
+                        help="Print sample pairs and stats without writing")
+    args = parser.parse_args()
+
+    # Load
+    entries = load_knowledge_index(args.input)
+    print(f"Loaded {len(entries)} entries from {args.input}", file=sys.stderr)
+
+    # Parse filters
+    model_list = args.model_filter.split(",") if args.model_filter else None
+    after_dt = parse_date(args.after) if args.after else None
+    before_dt = parse_date(args.before) if args.before else None
+
+    # Filter
+    kept = filter_entries(
+        entries,
+        min_confidence=args.min_confidence,
+        model_filter=model_list,
+        after=after_dt,
+        before=before_dt,
+    )
+    print(f"After filtering: {len(kept)} / {len(entries)} entries", file=sys.stderr)
+
+    # Convert
+    pairs = []
+    for entry in kept:
+        pair = entry_to_pair(entry)
+        if pair:
+            pairs.append(pair)
+
+    # Stats
+    if pairs:
+        avg_conf = sum(p["source_confidence"] for p in pairs) / len(pairs)
+        domains = {}
+        models = {}
+        for p in pairs:
+            domains[p["domain"]] = domains.get(p["domain"], 0) + 1
+            models[p["source_model"]] = models.get(p["source_model"], 0) + 1
+    else:
+        avg_conf = 0.0
+        domains = {}
+        models = {}
+
+    stats = {
+        "input_entries": len(entries),
+        "after_filter": len(kept),
+        "pairs_generated": len(pairs),
+        "avg_confidence": round(avg_conf, 4),
+        "domains": domains,
+        "source_models": models,
+    }
+    print(json.dumps(stats, indent=2), file=sys.stderr)
+
+    if args.dry_run:
+        print("\nSample pairs:", file=sys.stderr)
+        for p in pairs[:3]:
+            print(json.dumps(p, ensure_ascii=False), file=sys.stderr)
+        return
+
+    # Write JSONL
+    out_path = Path(args.output)
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+    with open(out_path, "w", encoding="utf-8") as f:
+        for pair in pairs:
+            f.write(json.dumps(pair, ensure_ascii=False) + "\n")
+
+    print(f"\nWrote {len(pairs)} training pairs to {out_path}", file=sys.stderr)
+
+
+if __name__ == "__main__":
+    main()

tests/test_knowledge_to_training_pairs.py

@@ -0,0 +1,174 @@
+#!/usr/bin/env python3
+"""
+Smoke tests for knowledge_to_training_pairs.py
+
+Tests:
+  - Output is valid JSONL
+  - Each line has required fields (terse, rich, domain, source_confidence, source_model)
+  - Confidence values are in [0,1]
+  - Terse is non-empty and reasonably short (< 200 chars)
+  - Rich matches the original fact
+"""
+
+import json
+import sys
+import os
+import tempfile
+from pathlib import Path
+
+# Add scripts dir to path for imports
+SCRIPT_DIR = Path(__file__).parent.parent / "scripts"
+sys.path.insert(0, str(SCRIPT_DIR))
+
+from knowledge_to_training_pairs import (
+    fact_to_terse,
+    filter_entries,
+    entry_to_pair,
+    parse_date,
+)
+
+
+def test_fact_to_terse_pitfall():
+    fact = "deploy-crons.py leaves jobs in mixed model format"
+    category = "pitfall"
+    domain = "hermes-agent"
+    terse = fact_to_terse(fact, category, domain)
+    assert terse.startswith("How do I")
+    assert "?" in terse
+    assert len(terse) < 150
+    print("PASS: test_fact_to_terse_pitfall")
+
+
+def test_fact_to_terse_fact():
+    fact = "Python is a high-level programming language"
+    terse = fact_to_terse(fact, "fact", "global")
+    assert terse.startswith("What should I know about")
+    assert "?" in terse
+    print("PASS: test_fact_to_terse_fact")
+
+
+def test_fact_to_terse_pattern():
+    fact = "Use sparse checkout for large repos"
+    terse = fact_to_terse(fact, "pattern", "devops")
+    assert "recommended way" in terse or "best way" in terse
+    print("PASS: test_fact_to_terse_pattern")
+
+
+def test_entry_to_pair_structure():
+    entry = {
+        "id": "test:001",
+        "fact": "Test fact text.",
+        "category": "fact",
+        "domain": "test-domain",
+        "confidence": 0.85,
+        "model": "test-model",
+    }
+    pair = entry_to_pair(entry)
+    assert pair is not None
+    assert "terse" in pair
+    assert "rich" in pair
+    assert "domain" in pair
+    assert "source_confidence" in pair
+    assert "source_model" in pair
+    assert pair["rich"] == "Test fact text."
+    assert pair["domain"] == "test-domain"
+    assert 0.0 <= pair["source_confidence"] <= 1.0
+    print("PASS: test_entry_to_pair_structure")
+
+
+def test_filter_by_confidence():
+    entries = [
+        {"fact": "A", "confidence": 0.9},
+        {"fact": "B", "confidence": 0.4},
+        {"fact": "C", "confidence": 0.6},
+    ]
+    filtered = filter_entries(entries, min_confidence=0.5)
+    assert len(filtered) == 2
+    assert all(e["confidence"] >= 0.5 for e in filtered)
+    print("PASS: test_filter_by_confidence")
+
+
+def test_filter_by_model():
+    entries = [
+        {"fact": "A", "model": "claude-sonnet"},
+        {"fact": "B", "model": "gpt-4"},
+        {"fact": "C", "model": "unknown"},
+    ]
+    filtered = filter_entries(entries, model_filter=["claude-sonnet", "gpt-4"])
+    assert len(filtered) == 2
+    assert all(e["model"] in ("claude-sonnet", "gpt-4") for e in filtered)
+    print("PASS: test_filter_by_model")
+
+
+def test_filter_by_date():
+    entries = [
+        {"fact": "A", "last_confirmed": "2026-04-10"},
+        {"fact": "B", "last_confirmed": "2026-03-01"},
+        {"fact": "C", "first_seen": "2026-04-15"},
+    ]
+    after_dt = parse_date("2026-04-01")
+    filtered = filter_entries(entries, after=after_dt)
+    assert len(filtered) == 2
+    print("PASS: test_filter_by_date")
+
+
+def test_end_to_end_jsonl_output():
+    """Integration test: run the script and verify JSONL validity."""
+    import subprocess
+
+    repo_dir = SCRIPT_DIR.parent
+    result = subprocess.run(
+        ["python3", "scripts/knowledge_to_training_pairs.py", "--dry-run"],
+        capture_output=True, text=True, cwd=repo_dir
+    )
+    assert result.returncode == 0
+    stderr = result.stderr.strip()
+
+    # The stats JSON object is at the top of stderr. Find its bounds via brace matching.
+    start = stderr.find('{')
+    assert start >= 0, "Stats JSON not found in stderr"
+    stderr_sub = stderr[start:]
+
+    depth = 0
+    end = 0
+    for i, ch in enumerate(stderr_sub):
+        if ch == '{':
+            depth += 1
+        elif ch == '}':
+            depth -= 1
+            if depth == 0:
+                end = i + 1
+                break
+    assert end > 0, "Unterminated JSON in stderr"
+
+    stats = json.loads(stderr_sub[:end])
+    assert stats["input_entries"] > 0
+    assert stats["pairs_generated"] > 0
+    print("PASS: test_end_to_end_jsonl_output")
+
+
+def test_terse_length_constraint():
+    """Terse should be reasonably short for training."""
+    # Sample facts from actual knowledge
+    test_facts = [
+        ("deploy-crons.py leaves jobs in mixed model format", "pitfall", "hermes-agent"),
+        ("Cron jobs with blank fallback_model fields trigger warnings", "pitfall", "hermes-agent"),
+        ("Use the Gitea REST API when clone times out", "pattern", "devops"),
+    ]
+    for fact, cat, domain in test_facts:
+        terse = fact_to_terse(fact, cat, domain)
+        assert len(terse) < 200, f"Terse too long ({len(terse)}): {terse}"
+    print("PASS: test_terse_length_constraint")
+
+
+if __name__ == "__main__":
+    test_fact_to_terse_pitfall()
+    test_fact_to_terse_fact()
+    test_fact_to_terse_pattern()
+    test_entry_to_pair_structure()
+    test_filter_by_confidence()
+    test_filter_by_model()
+    test_filter_by_date()
+    test_end_to_end_jsonl_output()
+    test_terse_length_constraint()
+    print("\nAll smoke tests passed.")