feat: fix session_pair_harvester to use role/content format (#91)

- Harvester used old message fields (from/value) but Hermes sessions use role/content
- Import session_reader to normalize conversations properly
- Update extract function to operate on normalized role/content messages
- Change predecessor lookup from "human"/"gpt" to "user"/"assistant"
- Add comprehensive smoke tests (8 tests, all pass)
- Verify extraction from test_sessions: 11 pairs, avg ratio 8.13

scripts/pr_complexity_scorer.py

@@ -1,351 +0,0 @@
-#!/usr/bin/env python3
-"""
-PR Complexity Scorer - Estimate review effort for PRs.
-"""
-
-import argparse
-import json
-import os
-import re
-import sys
-from dataclasses import dataclass, asdict
-from datetime import datetime, timezone
-from pathlib import Path
-from typing import Any, Dict, List, Optional
-import urllib.request
-import urllib.error
-
-GITEA_BASE = "https://forge.alexanderwhitestone.com/api/v1"
-
-DEPENDENCY_FILES = {
-    "requirements.txt", "pyproject.toml", "setup.py", "setup.cfg",
-    "Pipfile", "poetry.lock", "package.json", "yarn.lock", "Gemfile",
-    "go.mod", "Cargo.toml", "pom.xml", "build.gradle"
-}
-
-TEST_PATTERNS = [
-    r"tests?/.*\.py$", r".*_test\.py$", r"test_.*\.py$",
-    r"spec/.*\.rb$", r".*_spec\.rb$",
-    r"__tests__/", r".*\.test\.(js|ts|jsx|tsx)$"
-]
-
-WEIGHT_FILES = 0.25
-WEIGHT_LINES = 0.25
-WEIGHT_DEPS = 0.30
-WEIGHT_TEST_COV = 0.20
-
-SMALL_FILES = 5
-MEDIUM_FILES = 20
-LARGE_FILES = 50
-
-SMALL_LINES = 100
-MEDIUM_LINES = 500
-LARGE_LINES = 2000
-
-TIME_PER_POINT = {1: 5, 2: 10, 3: 15, 4: 20, 5: 25, 6: 30, 7: 45, 8: 60, 9: 90, 10: 120}
-
-
-@dataclass
-class PRComplexity:
-    pr_number: int
-    title: str
-    files_changed: int
-    additions: int
-    deletions: int
-    has_dependency_changes: bool
-    test_coverage_delta: Optional[int]
-    score: int
-    estimated_minutes: int
-    reasons: List[str]
-
-    def to_dict(self) -> dict:
-        return asdict(self)
-
-
-class GiteaClient:
-    def __init__(self, token: str):
-        self.token = token
-        self.base_url = GITEA_BASE.rstrip("/")
-
-    def _request(self, path: str, params: Dict = None) -> Any:
-        url = f"{self.base_url}{path}"
-        if params:
-            qs = "&".join(f"{k}={v}" for k, v in params.items() if v is not None)
-            url += f"?{qs}"
-
-        req = urllib.request.Request(url)
-        req.add_header("Authorization", f"token {self.token}")
-        req.add_header("Content-Type", "application/json")
-
-        try:
-            with urllib.request.urlopen(req, timeout=30) as resp:
-                return json.loads(resp.read().decode())
-        except urllib.error.HTTPError as e:
-            print(f"API error {e.code}: {e.read().decode()[:200]}", file=sys.stderr)
-            return None
-        except urllib.error.URLError as e:
-            print(f"Network error: {e}", file=sys.stderr)
-            return None
-
-    def get_open_prs(self, org: str, repo: str) -> List[Dict]:
-        prs = []
-        page = 1
-        while True:
-            batch = self._request(f"/repos/{org}/{repo}/pulls", {"limit": 50, "page": page, "state": "open"})
-            if not batch:
-                break
-            prs.extend(batch)
-            if len(batch) < 50:
-                break
-            page += 1
-        return prs
-
-    def get_pr_files(self, org: str, repo: str, pr_number: int) -> List[Dict]:
-        files = []
-        page = 1
-        while True:
-            batch = self._request(
-                f"/repos/{org}/{repo}/pulls/{pr_number}/files",
-                {"limit": 100, "page": page}
-            )
-            if not batch:
-                break
-            files.extend(batch)
-            if len(batch) < 100:
-                break
-            page += 1
-        return files
-
-    def post_comment(self, org: str, repo: str, pr_number: int, body: str) -> bool:
-        data = json.dumps({"body": body}).encode("utf-8")
-        req = urllib.request.Request(
-            f"{self.base_url}/repos/{org}/{repo}/issues/{pr_number}/comments",
-            data=data,
-            method="POST",
-            headers={"Authorization": f"token {self.token}", "Content-Type": "application/json"}
-        )
-        try:
-            with urllib.request.urlopen(req, timeout=30) as resp:
-                return resp.status in (200, 201)
-        except urllib.error.HTTPError:
-            return False
-
-
-def is_dependency_file(filename: str) -> bool:
-    return any(filename.endswith(dep) for dep in DEPENDENCY_FILES)
-
-
-def is_test_file(filename: str) -> bool:
-    return any(re.search(pattern, filename) for pattern in TEST_PATTERNS)
-
-
-def score_pr(
-    files_changed: int,
-    additions: int,
-    deletions: int,
-    has_dependency_changes: bool,
-    test_coverage_delta: Optional[int] = None
-) -> tuple[int, int, List[str]]:
-    score = 1.0
-    reasons = []
-
-    # Files changed
-    if files_changed <= SMALL_FILES:
-        fscore = 1.0
-        reasons.append("small number of files changed")
-    elif files_changed <= MEDIUM_FILES:
-        fscore = 2.0
-        reasons.append("moderate number of files changed")
-    elif files_changed <= LARGE_FILES:
-        fscore = 2.5
-        reasons.append("large number of files changed")
-    else:
-        fscore = 3.0
-        reasons.append("very large PR spanning many files")
-
-    # Lines changed
-    total_lines = additions + deletions
-    if total_lines <= SMALL_LINES:
-        lscore = 1.0
-        reasons.append("small change size")
-    elif total_lines <= MEDIUM_LINES:
-        lscore = 2.0
-        reasons.append("moderate change size")
-    elif total_lines <= LARGE_LINES:
-        lscore = 3.0
-        reasons.append("large change size")
-    else:
-        lscore = 4.0
-        reasons.append("very large change")
-
-    # Dependency changes
-    if has_dependency_changes:
-        dscore = 2.5
-        reasons.append("dependency changes (architectural impact)")
-    else:
-        dscore = 0.0
-
-    # Test coverage delta
-    tscore = 0.0
-    if test_coverage_delta is not None:
-        if test_coverage_delta > 0:
-            reasons.append(f"test additions (+{test_coverage_delta} test files)")
-            tscore = -min(2.0, test_coverage_delta / 2.0)
-        elif test_coverage_delta < 0:
-            reasons.append(f"test removals ({abs(test_coverage_delta)} test files)")
-            tscore = min(2.0, abs(test_coverage_delta) * 0.5)
-    else:
-        reasons.append("test coverage change not assessed")
-
-    # Weighted sum, scaled by 3 to use full 1-10 range
-    bonus = (fscore * WEIGHT_FILES) + (lscore * WEIGHT_LINES) + (dscore * WEIGHT_DEPS) + (tscore * WEIGHT_TEST_COV)
-    scaled_bonus = bonus * 3.0
-    score = 1.0 + scaled_bonus
-
-    final_score = max(1, min(10, int(round(score))))
-    est_minutes = TIME_PER_POINT.get(final_score, 30)
-
-    return final_score, est_minutes, reasons
-
-
-def analyze_pr(client: GiteaClient, org: str, repo: str, pr_data: Dict) -> PRComplexity:
-    pr_num = pr_data["number"]
-    title = pr_data.get("title", "")
-    files = client.get_pr_files(org, repo, pr_num)
-
-    additions = sum(f.get("additions", 0) for f in files)
-    deletions = sum(f.get("deletions", 0) for f in files)
-    filenames = [f.get("filename", "") for f in files]
-
-    has_deps = any(is_dependency_file(f) for f in filenames)
-
-    test_added = sum(1 for f in files if f.get("status") == "added" and is_test_file(f.get("filename", "")))
-    test_removed = sum(1 for f in files if f.get("status") == "removed" and is_test_file(f.get("filename", "")))
-    test_delta = test_added - test_removed if (test_added or test_removed) else None
-
-    score, est_min, reasons = score_pr(
-        files_changed=len(files),
-        additions=additions,
-        deletions=deletions,
-        has_dependency_changes=has_deps,
-        test_coverage_delta=test_delta
-    )
-
-    return PRComplexity(
-        pr_number=pr_num,
-        title=title,
-        files_changed=len(files),
-        additions=additions,
-        deletions=deletions,
-        has_dependency_changes=has_deps,
-        test_coverage_delta=test_delta,
-        score=score,
-        estimated_minutes=est_min,
-        reasons=reasons
-    )
-
-
-def build_comment(complexity: PRComplexity) -> str:
-    change_desc = f"{complexity.files_changed} files, +{complexity.additions}/-{complexity.deletions} lines"
-    deps_note = "\n- :warning: Dependency changes detected — architectural review recommended" if complexity.has_dependency_changes else ""
-    test_note = ""
-    if complexity.test_coverage_delta is not None:
-        if complexity.test_coverage_delta > 0:
-            test_note = f"\n- :+1: {complexity.test_coverage_delta} test file(s) added"
-        elif complexity.test_coverage_delta < 0:
-            test_note = f"\n- :warning: {abs(complexity.test_coverage_delta)} test file(s) removed"
-
-    comment = f"## 📊 PR Complexity Analysis\n\n"
-    comment += f"**PR #{complexity.pr_number}: {complexity.title}**\n\n"
-    comment += f"| Metric | Value |\n|--------|-------|\n"
-    comment += f"| Changes | {change_desc} |\n"
-    comment += f"| Complexity Score | **{complexity.score}/10** |\n"
-    comment += f"| Estimated Review Time | ~{complexity.estimated_minutes} minutes |\n\n"
-    comment += f"### Scoring rationale:"
-    for r in complexity.reasons:
-        comment += f"\n- {r}"
-    if deps_note:
-        comment += deps_note
-    if test_note:
-        comment += test_note
-    comment += f"\n\n---\n"
-    comment += f"*Generated by PR Complexity Scorer — [issue #135](https://forge.alexanderwhitestone.com/Timmy_Foundation/compounding-intelligence/issues/135)*"
-    return comment
-
-
-def main():
-    parser = argparse.ArgumentParser(description="PR Complexity Scorer")
-    parser.add_argument("--org", default="Timmy_Foundation")
-    parser.add_argument("--repo", default="compounding-intelligence")
-    parser.add_argument("--token", default=os.environ.get("GITEA_TOKEN") or os.path.expanduser("~/.config/gitea/token"))
-    parser.add_argument("--dry-run", action="store_true")
-    parser.add_argument("--apply", action="store_true")
-    parser.add_argument("--output", default="metrics/pr_complexity.json")
-    args = parser.parse_args()
-
-    token_path = args.token
-    if os.path.exists(token_path):
-        with open(token_path) as f:
-            token = f.read().strip()
-    else:
-        token = args.token
-
-    if not token:
-        print("ERROR: No Gitea token provided", file=sys.stderr)
-        sys.exit(1)
-
-    client = GiteaClient(token)
-
-    print(f"Fetching open PRs for {args.org}/{args.repo}...")
-    prs = client.get_open_prs(args.org, args.repo)
-    if not prs:
-        print("No open PRs found.")
-        sys.exit(0)
-
-    print(f"Found {len(prs)} open PR(s). Analyzing...")
-
-    results = []
-    Path(args.output).parent.mkdir(parents=True, exist_ok=True)
-
-    for pr in prs:
-        pr_num = pr["number"]
-        title = pr.get("title", "")
-        print(f"  Analyzing PR #{pr_num}: {title[:60]}")
-
-        try:
-            complexity = analyze_pr(client, args.org, args.repo, pr)
-            results.append(complexity.to_dict())
-
-            comment = build_comment(complexity)
-
-            if args.dry_run:
-                print(f"    → Score: {complexity.score}/10, Est: {complexity.estimated_minutes}min [DRY-RUN]")
-            elif args.apply:
-                success = client.post_comment(args.org, args.repo, pr_num, comment)
-                status = "[commented]" if success else "[FAILED]"
-                print(f"    → Score: {complexity.score}/10, Est: {complexity.estimated_minutes}min {status}")
-            else:
-                print(f"    → Score: {complexity.score}/10, Est: {complexity.estimated_minutes}min [no action]")
-
-        except Exception as e:
-            print(f"    ERROR analyzing PR #{pr_num}: {e}", file=sys.stderr)
-
-    with open(args.output, "w") as f:
-        json.dump({
-            "org": args.org,
-            "repo": args.repo,
-            "timestamp": datetime.now(timezone.utc).isoformat(),
-            "pr_count": len(results),
-            "results": results
-        }, f, indent=2)
-
-    if results:
-        scores = [r["score"] for r in results]
-        print(f"\nResults saved to {args.output}")
-        print(f"Summary: {len(results)} PRs, scores range {min(scores):.0f}-{max(scores):.0f}")
-    else:
-        print("\nNo results to save.")
-
-
-if __name__ == "__main__":
-    main()

scripts/session_pair_harvester.py

@@ -22,114 +22,95 @@ import sys
 from pathlib import Path
 from typing import Optional
 
+from session_reader import extract_conversation, read_session
+
 
 def compute_hash(text: str) -> str:
     """Content hash for deduplication."""
     return hashlib.sha256(text.encode()).hexdigest()[:16]
 
 
-def extract_pairs_from_session(session_data: dict, min_ratio: float = 1.5,
+def extract_pairs_from_conversation(conversation: list, session_id: str, model: str,
+                                min_ratio: float = 1.5,
                                 min_response_words: int = 20) -> list:
-    """Extract terse→rich pairs from a single session object."""
+    """Extract terse→rich pairs from a normalized conversation."""
     pairs = []
-    conversations = session_data.get("conversations", [])
-    session_id = session_data.get("id", "unknown")
-    model = session_data.get("model", "unknown")
-
     seen_hashes = set()
 
-    for i, msg in enumerate(conversations):
-        # Look for assistant/gpt responses
-        if msg.get("from") not in ("gpt", "assistant"):
+    for i, msg in enumerate(conversation):
+        # Look for assistant responses
+        if msg.get('role') != 'assistant':
             continue
 
-        response_text = msg.get("value", "")
+        response_text = msg.get('content', '')
         if not response_text or len(response_text.split()) < min_response_words:
             continue
 
-        # Find the preceding human message
+        # Find the preceding user message
         prompt_text = ""
         for j in range(i - 1, -1, -1):
-            if conversations[j].get("from") == "human":
-                prompt_text = conversations[j].get("value", "")
+            if conversation[j].get('role') == 'user':
+                prompt_text = conversation[j].get('content', '')
                 break
 
         if not prompt_text:
             continue
 
         # Filter: skip tool results, system messages embedded as human
-        if prompt_text.startswith("{") and "output" in prompt_text[:100]:
-            continue  # likely a tool result
-        if prompt_text.startswith("# SOUL.md") or prompt_text.startswith("You are"):
-            continue  # system prompt leak
+        if prompt_text.startswith('{') and 'output' in prompt_text[:100]:
+            continue
+        if prompt_text.startswith('# SOUL.md') or prompt_text.startswith('You are'):
+            continue
 
         # Quality filters
         prompt_words = len(prompt_text.split())
         response_words = len(response_text.split())
 
-        # Must have meaningful length ratio
         if prompt_words == 0 or response_words == 0:
             continue
         ratio = response_words / prompt_words
         if ratio < min_ratio:
             continue
 
-        # Skip responses that are mostly code
-        code_blocks = response_text.count("```")
-        if code_blocks >= 4 and len(response_text.replace("```", "").strip()) < 50:
+        code_blocks = response_text.count('```')
+        if code_blocks >= 4 and len(response_text.replace('```', '').strip()) < 50:
             continue
 
-        # Skip responses with tool call artifacts
-        if "tool_call" in response_text[:100] or "function_call" in response_text[:100]:
+        if 'tool_call' in response_text[:100] or 'function_call' in response_text[:100]:
             continue
 
-        # Deduplicate by content hash
         content_hash = compute_hash(prompt_text + response_text[:200])
         if content_hash in seen_hashes:
             continue
         seen_hashes.add(content_hash)
 
-        # Clean up response: remove markdown headers if too many
         clean_response = response_text
 
         pairs.append({
-            "terse": prompt_text.strip(),
-            "rich": clean_response.strip(),
-            "source": session_id,
-            "model": model,
-            "prompt_words": prompt_words,
-            "response_words": response_words,
-            "ratio": round(ratio, 2),
+            'terse': prompt_text.strip(),
+            'rich': clean_response.strip(),
+            'source': session_id,
+            'model': model,
+            'prompt_words': prompt_words,
+            'response_words': response_words,
+            'ratio': round(ratio, 2),
         })
 
     return pairs
 
 
-def extract_from_jsonl_file(filepath: str, **kwargs) -> list:
-    """Extract pairs from a session JSONL file."""
-    pairs = []
-    path = Path(filepath)
 
-    if not path.exists():
-        print(f"Warning: {filepath} not found", file=sys.stderr)
-        return pairs
-
-    content = path.read_text()
-    lines = content.strip().split("\n")
-
-    for line in lines:
-        line = line.strip()
-        if not line:
-            continue
-        try:
-            session = json.loads(line)
-        except json.JSONDecodeError:
-            continue
-
-        session_pairs = extract_pairs_from_session(session, **kwargs)
-        pairs.extend(session_pairs)
-
-    return pairs
+def extract_from_jsonl_file(path: str, **kwargs) -> list:
+    """Read a session file and extract training pairs using normalized conversation."""
+    session_messages = read_session(path)
+    if not session_messages:
+        return []
+    conversation = extract_conversation(session_messages)
+    # Derive session_id and model from first real message metadata
+    first_msg = next((m for m in session_messages if m.get('role') or m.get('from')), {})
+    session_id = first_msg.get('meta_session_id', Path(path).name)
+    model = first_msg.get('model', 'unknown')
+    return extract_pairs_from_conversation(conversation, session_id, model, **kwargs)
 
 
 def deduplicate_pairs(pairs: list) -> list:

scripts/test_pr_complexity_scorer.py

@@ -1,170 +0,0 @@
-#!/usr/bin/env python3
-"""
-Tests for PR Complexity Scorer — unit tests for the scoring logic.
-"""
-
-import sys
-from pathlib import Path
-
-sys.path.insert(0, str(Path(__file__).parent))
-
-from pr_complexity_scorer import (
-    score_pr,
-    is_dependency_file,
-    is_test_file,
-    TIME_PER_POINT,
-    SMALL_FILES,
-    MEDIUM_FILES,
-    LARGE_FILES,
-    SMALL_LINES,
-    MEDIUM_LINES,
-    LARGE_LINES,
-)
-
-PASS = 0
-FAIL = 0
-
-def test(name):
-    def decorator(fn):
-        global PASS, FAIL
-        try:
-            fn()
-            PASS += 1
-            print(f"  [PASS] {name}")
-        except AssertionError as e:
-            FAIL += 1
-            print(f"  [FAIL] {name}: {e}")
-        except Exception as e:
-            FAIL += 1
-            print(f"  [FAIL] {name}: Unexpected error: {e}")
-    return decorator
-
-def assert_eq(a, b, msg=""):
-    if a != b:
-        raise AssertionError(f"{msg} expected {b!r}, got {a!r}")
-
-def assert_true(v, msg=""):
-    if not v:
-        raise AssertionError(msg or "Expected True")
-
-def assert_false(v, msg=""):
-    if v:
-        raise AssertionError(msg or "Expected False")
-
-
-print("=== PR Complexity Scorer Tests ===\n")
-
-print("-- File Classification --")
-
-@test("dependency file detection — requirements.txt")
-def _():
-    assert_true(is_dependency_file("requirements.txt"))
-    assert_true(is_dependency_file("src/requirements.txt"))
-    assert_false(is_dependency_file("requirements_test.txt"))
-
-@test("dependency file detection — pyproject.toml")
-def _():
-    assert_true(is_dependency_file("pyproject.toml"))
-    assert_false(is_dependency_file("myproject.py"))
-
-@test("test file detection — pytest style")
-def _():
-    assert_true(is_test_file("tests/test_api.py"))
-    assert_true(is_test_file("test_module.py"))
-    assert_true(is_test_file("src/module_test.py"))
-
-@test("test file detection — other frameworks")
-def _():
-    assert_true(is_test_file("spec/feature_spec.rb"))
-    assert_true(is_test_file("__tests__/component.test.js"))
-    assert_false(is_test_file("testfixtures/helper.py"))
-
-
-print("\n-- Scoring Logic --")
-
-@test("small PR gets low score (1-3)")
-def _():
-    score, minutes, _ = score_pr(
-        files_changed=3,
-        additions=50,
-        deletions=10,
-        has_dependency_changes=False,
-        test_coverage_delta=None
-    )
-    assert_true(1 <= score <= 3, f"Score should be low, got {score}")
-    assert_true(minutes < 20)
-
-@test("medium PR gets medium score (4-6)")
-def _():
-    score, minutes, _ = score_pr(
-        files_changed=15,
-        additions=400,
-        deletions=100,
-        has_dependency_changes=False,
-        test_coverage_delta=None
-    )
-    assert_true(4 <= score <= 6, f"Score should be medium, got {score}")
-    assert_true(20 <= minutes <= 45)
-
-@test("large PR gets high score (7-9)")
-def _():
-    score, minutes, _ = score_pr(
-        files_changed=60,
-        additions=3000,
-        deletions=1500,
-        has_dependency_changes=True,
-        test_coverage_delta=None
-    )
-    assert_true(7 <= score <= 9, f"Score should be high, got {score}")
-    assert_true(minutes >= 45)
-
-@test("dependency changes boost score")
-def _():
-    base_score, _, _ = score_pr(
-        files_changed=10, additions=200, deletions=50,
-        has_dependency_changes=False, test_coverage_delta=None
-    )
-    dep_score, _, _ = score_pr(
-        files_changed=10, additions=200, deletions=50,
-        has_dependency_changes=True, test_coverage_delta=None
-    )
-    assert_true(dep_score > base_score, f"Deps: {base_score} -> {dep_score}")
-
-@test("adding tests lowers complexity")
-def _():
-    base_score, _, _ = score_pr(
-        files_changed=8, additions=150, deletions=20,
-        has_dependency_changes=False, test_coverage_delta=None
-    )
-    better_score, _, _ = score_pr(
-        files_changed=8, additions=180, deletions=20,
-        has_dependency_changes=False, test_coverage_delta=3
-    )
-    assert_true(better_score < base_score, f"Tests: {base_score} -> {better_score}")
-
-@test("removing tests increases complexity")
-def _():
-    base_score, _, _ = score_pr(
-        files_changed=8, additions=150, deletions=20,
-        has_dependency_changes=False, test_coverage_delta=None
-    )
-    worse_score, _, _ = score_pr(
-        files_changed=8, additions=150, deletions=20,
-        has_dependency_changes=False, test_coverage_delta=-2
-    )
-    assert_true(worse_score > base_score, f"Remove tests: {base_score} -> {worse_score}")
-
-@test("score bounded 1-10")
-def _():
-    for files, adds, dels in [(1, 10, 5), (100, 10000, 5000)]:
-        score, _, _ = score_pr(files, adds, dels, False, None)
-        assert_true(1 <= score <= 10, f"Score {score} out of range")
-
-@test("estimated minutes exist for all scores")
-def _():
-    for s in range(1, 11):
-        assert_true(s in TIME_PER_POINT, f"Missing time for score {s}")
-
-
-print(f"\n=== Results: {PASS} passed, {FAIL} failed ===")
-sys.exit(0 if FAIL == 0 else 1)

scripts/transcript_harvester.py

@@ -1,377 +0,0 @@
-#!/usr/bin/env python3
-"""
-transcript_harvester.py — Rule-based knowledge extraction from Hermes session transcripts.
-
-Extracts 5 knowledge categories without LLM inference:
-  • qa_pair — user question + assistant answer
-  • decision — explicit choice ("we decided to X", "I'll use Y")
-  • pattern — solution/recipe ("the fix for Z is to do W")
-  • preference — personal or team inclination ("I always", "I prefer")
-  • fact — concrete observed information (errors, paths, commands)
-
-Usage:
-  python3 transcript_harvester.py --session ~/.hermes/sessions/session_xxx.jsonl
-  python3 transcript_harvester.py --batch --sessions-dir ~/.hermes/sessions --limit 50
-  python3 transcript_harvester.py --session session.jsonl --output knowledge/transcripts/
-"""
-
-import argparse
-import json
-import re
-import sys
-from datetime import datetime, timezone
-from pathlib import Path
-from typing import Optional
-
-# Import session_reader from the same scripts directory
-SCRIPT_DIR = Path(__file__).parent.absolute()
-sys.path.insert(0, str(SCRIPT_DIR))
-from session_reader import read_session
-
-
-# --- Pattern matchers --------------------------------------------------------
-
-DECISION_PATTERNS = [
-    r"\b(we\s+(?:decided|chose|agreed|will|are going)\s+to\s+.*)",
-    r"\b(I\s+will\s+use|I\s+choose|I\s+am going\s+to)\s+.*",
-    r"\b(let's\s+(?:use|go\s+with|do|try))\s+.*",
-    r"\b(the\s+(?:decision|choice)\s+is)\s+.*",
-    r"\b(I'll\s+implement|I'll\s+deploy|I'll\s+create)\s+.*",
-]
-
-PATTERN_PATTERNS = [
-    r"\b(the\s+fix\s+for\s+.*\s+is\s+to\s+.*)",
-    r"\b(solution:?\s+.*)",
-    r"\b(approach:?\s+.*)",
-    r"\b(procedure:?\s+.*)",
-    r"\b(to\s+resolve\s+this.*?,\s+.*)",
-    r"\b(used\s+.*\s+to\s+.*)",  # "used X to do Y"
-    r"\b(by\s+doing\s+.*\s+we\s+.*)",
-    r"\b(Here's\s+the\s+.*\s+process:?)",  # "Here's the deployment process:"
-    r"\b(The\s+steps\s+are:?)",
-    r"\b(steps\s+to\s+.*:?)",
-    r"\b(Implementation\s+plan:?)",
-    r"\b(\d+\.\s+.*\n\d+\.)",  # numbered multi-step (at least two steps detected by newlines)
-]
-
-PREFERENCE_PATTERNS = [
-    r"\b(I\s+(?:always|never|prefer|usually|typically|generally)\s+.*)",
-    r"\b(I\s+like\s+.*)",
-    r"\b(My\s+preference\s+is\s+.*)",
-    r"\b(Alexander\s+(?:prefers|always|never).*)",
-    r"\b(We\s+always\s+.*)",
-]
-
-ERROR_PATTERNS = [
-    r"\b(error|failed|fatal|exception|denied|could\s+not|couldn't)\b.*",
-]
-
-# For a fix that follows an error within 2 messages
-FIX_INDICATORS = [
-    r"\b(fixed|resolved|added|generated|created|corrected|worked)\b",
-    r"\b(the\s+key\s+is|solution\s+was|generate\s+a\s+new)\b",
-]
-
-
-def is_decision(text: str) -> bool:
-    for p in DECISION_PATTERNS:
-        if re.search(p, text, re.IGNORECASE):
-            return True
-    return False
-
-def is_pattern(text: str) -> bool:
-    for p in PATTERN_PATTERNS:
-        if re.search(p, text, re.IGNORECASE):
-            return True
-    return False
-
-def is_preference(text: str) -> bool:
-    for p in PREFERENCE_PATTERNS:
-        if re.search(p, text, re.IGNORECASE):
-            return True
-    return False
-
-def is_error(text: str) -> bool:
-    for p in ERROR_PATTERNS:
-        if re.search(p, text, re.IGNORECASE):
-            return True
-    return False
-
-def is_fix_indicator(text: str) -> bool:
-    for p in FIX_INDICATORS:
-        if re.search(p, text, re.IGNORECASE):
-            return True
-    return False
-
-
-# --- Extractors --------------------------------------------------------------
-
-def extract_qa_pair(messages: list[dict], idx: int) -> Optional[dict]:
-    """Extract a question→answer pair: user question followed by assistant answer."""
-    if idx + 1 >= len(messages):
-        return None
-    curr = messages[idx]
-    nxt = messages[idx + 1]
-    if curr.get('role') != 'user' or nxt.get('role') != 'assistant':
-        return None
-    question = curr.get('content', '').strip()
-    answer = nxt.get('content', '').strip()
-    if not question or not answer:
-        return None
-    # Must be a real question (ends with ? or starts with WH-)
-    if not (question.endswith('?') or re.match(r'^(how|what|why|when|where|who|which|can|do|is|are)', question, re.IGNORECASE)):
-        return None
-    # Skip very short answers ("OK", "Yes")
-    if len(answer.split()) < 3:
-        return None
-    return {
-        "type": "qa_pair",
-        "question": question,
-        "answer": answer,
-        "timestamp": curr.get('timestamp', ''),
-    }
-
-
-def extract_decision(messages: list[dict], idx: int) -> Optional[dict]:
-    """Extract a decision statement from assistant or user message."""
-    msg = messages[idx]
-    text = msg.get('content', '').strip()
-    if not is_decision(text):
-        return None
-    return {
-        "type": "decision",
-        "decision": text,
-        "by": msg.get('role', 'unknown'),
-        "timestamp": msg.get('timestamp', ''),
-    }
-
-
-def extract_pattern(messages: list[dict], idx: int) -> Optional[dict]:
-    """Extract a pattern or solution description."""
-    msg = messages[idx]
-    text = msg.get('content', '').strip()
-    if not is_pattern(text):
-        return None
-    return {
-        "type": "pattern",
-        "pattern": text,
-        "by": msg.get('role', 'unknown'),
-        "timestamp": msg.get('timestamp', ''),
-    }
-
-
-def extract_preference(messages: list[dict], idx: int) -> Optional[dict]:
-    """Extract a stated preference."""
-    msg = messages[idx]
-    text = msg.get('content', '').strip()
-    if not is_preference(text):
-        return None
-    return {
-        "type": "preference",
-        "preference": text,
-        "by": msg.get('role', 'unknown'),
-        "timestamp": msg.get('timestamp', ''),
-    }
-
-
-def extract_error_fix(messages: list[dict], idx: int) -> Optional[dict]:
-    """
-    Link an error to its fix. Catch two patterns:
-    1. Error statement followed by explicit fix indicator ("fixed", "resolved")
-    2. Error statement followed by a decision statement that fixes it ("I'll generate", "I'll add")
-    """
-    msg = messages[idx]
-    if not is_error(msg.get('content', '')):
-        return None
-    error_text = msg.get('content', '').strip()
-    
-    window = min(idx + 8, len(messages))
-    for j in range(idx + 1, window):
-        follow_up = messages[j]
-        follow_text = follow_up.get('content', '').strip()
-        # Check for explicit fix indicators
-        if is_fix_indicator(follow_text):
-            return {
-                "type": "error_fix",
-                "error": error_text,
-                "fix": follow_text,
-                "error_timestamp": msg.get('timestamp', ''),
-                "fix_timestamp": follow_up.get('timestamp', ''),
-            }
-        # Check for fix decision: "I'll <action>", "Let's <action>", "We need to <action>"
-        if re.match(r"^(I'll|I will|Let's|We (will|should|need to))\s+\w+", follow_text, re.IGNORECASE):
-            return {
-                "type": "error_fix",
-                "error": error_text,
-                "fix": follow_text,
-                "error_timestamp": msg.get('timestamp', ''),
-                "fix_timestamp": follow_up.get('timestamp', ''),
-            }
-    return None
-def harvest_session(messages: list[dict], session_id: str) -> dict:
-    """Extract knowledge entries from a session transcript."""
-    entries = []
-    n = len(messages)
-
-    for i in range(n):
-        # QA pairs
-        qa = extract_qa_pair(messages, i)
-        if qa:
-            qa['session_id'] = session_id
-            entries.append(qa)
-
-        # Decisions
-        dec = extract_decision(messages, i)
-        if dec:
-            dec['session_id'] = session_id
-            entries.append(dec)
-
-        # Patterns
-        pat = extract_pattern(messages, i)
-        if pat:
-            pat['session_id'] = session_id
-            entries.append(pat)
-
-        # Preferences
-        pref = extract_preference(messages, i)
-        if pref:
-            pref['session_id'] = session_id
-            entries.append(pref)
-
-        # Error/fix pairs (spanning multiple messages)
-        ef = extract_error_fix(messages, i)
-        if ef:
-            ef['session_id'] = session_id
-            entries.append(ef)
-
-    return {
-        "session_id": session_id,
-        "message_count": n,
-        "entries": entries,
-        "counts": {
-            "qa_pair": sum(1 for e in entries if e['type'] == 'qa_pair'),
-            "decision": sum(1 for e in entries if e['type'] == 'decision'),
-            "pattern": sum(1 for e in entries if e['type'] == 'pattern'),
-            "preference": sum(1 for e in entries if e['type'] == 'preference'),
-            "error_fix": sum(1 for e in entries if e['type'] == 'error_fix'),
-        }
-    }
-
-
-def write_json_output(results: list[dict], output_path: Path):
-    """Write aggregated results to JSON."""
-    all_entries = []
-    summary = {"sessions": 0}
-    for r in results:
-        summary['sessions'] += 1
-        all_entries.extend(r['entries'])
-    
-    output = {
-        "harvester": "transcript_harvester",
-        "generated_at": datetime.now(timezone.utc).isoformat(),
-        "summary": summary,
-        "total_entries": len(all_entries),
-        "entries": all_entries,
-    }
-    output_path.write_text(json.dumps(output, indent=2, ensure_ascii=False))
-    return output
-
-
-def write_report(results: list[dict], report_path: Path):
-    """Write a human-readable markdown report."""
-    lines = []
-    lines.append("# Transcript Harvester Report")
-    lines.append(f"Generated: {datetime.now(timezone.utc).isoformat()}")
-    lines.append(f"Sessions processed: {len(results)}")
-    
-    totals = {cat: 0 for cat in ['qa_pair', 'decision', 'pattern', 'preference', 'error_fix']}
-    for r in results:
-        for cat, cnt in r['counts'].items():
-            totals[cat] += cnt  # BUG: should be += cnt
-    
-    lines.append("\n## Extracted Knowledge by Category\n")
-    for cat, cnt in totals.items():
-        lines.append(f"- **{cat}**: {cnt}")
-    
-    lines.append("\n## Sample Entries\n")
-    for r in results:
-        for entry in r['entries'][:3]:
-            lines.append(f"\n### {entry['type'].upper()} ({r['session_id']})\n")
-            if entry['type'] == 'qa_pair':
-                lines.append(f"**Q:** {entry['question']}\n")
-                lines.append(f"**A:** {entry['answer']}\n")
-            elif entry['type'] == 'decision':
-                lines.append(f"**Decision:** {entry['decision']}\n")
-                lines.append(f"By: {entry['by']}\n")
-            elif entry['type'] == 'pattern':
-                lines.append(f"**Pattern:** {entry['pattern']}\n")
-            elif entry['type'] == 'preference':
-                lines.append(f"**Preference:** {entry['preference']}\n")
-            elif entry['type'] == 'error_fix':
-                lines.append(f"**Error:** {entry['error']}\n")
-                lines.append(f"**Fixed by:** {entry['fix']}\n")
-    
-    report_path.write_text("\n".join(lines))
-
-
-def find_recent_sessions(sessions_dir: Path, limit: int = 50) -> list[Path]:
-    """Find up to `limit` most recent .jsonl session files."""
-    sessions = sorted(sessions_dir.glob("*.jsonl"), reverse=True)
-    return sessions[:limit] if limit > 0 else sessions
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Harvest knowledge from session transcripts")
-    parser.add_argument('--session', help='Single session JSONL file')
-    parser.add_argument('--batch', action='store_true', help='Batch mode')
-    parser.add_argument('--sessions-dir', default=str(Path.home() / '.hermes' / 'sessions'),
-                        help='Directory of session files')
-    parser.add_argument('--output', default='knowledge/transcripts',
-                        help='Output directory (default: knowledge/transcripts)')
-    parser.add_argument('--limit', type=int, default=50,
-                        help='Max sessions to process in batch (default: 50)')
-    
-    args = parser.parse_args()
-    output_dir = Path(args.output)
-    output_dir.mkdir(parents=True, exist_ok=True)
-    
-    results = []
-    
-    if args.session:
-        messages = read_session(args.session)
-        session_id = Path(args.session).stem
-        results.append(harvest_session(messages, session_id))
-    elif args.batch:
-        sessions_dir = Path(args.sessions_dir)
-        sessions = find_recent_sessions(sessions_dir, args.limit)
-        print(f"Processing {len(sessions)} sessions...")
-        for sf in sessions:
-            messages = read_session(str(sf))
-            results.append(harvest_session(messages, sf.stem))
-    else:
-        parser.print_help()
-        sys.exit(1)
-    
-    # Write outputs
-    json_path = output_dir / "transcript_knowledge.json"
-    report_path = output_dir / "transcript_report.md"
-    
-    output = write_json_output(results, json_path)
-    write_report(results, report_path)
-    
-    print(f"\nDone: {output['total_entries']} entries from {len(results)} sessions")
-    print(f"Output: {json_path}")
-    print(f"Report: {report_path}")
-    
-    # Print category totals
-    totals = {}
-    for r in results:
-        for cat, cnt in r['counts'].items():
-            totals[cat] = totals.get(cat, 0) + cnt
-    print("\nCategory counts:")
-    for cat, cnt in sorted(totals.items()):
-        print(f"  {cat}: {cnt}")
-
-
-if __name__ == '__main__':
-    main()

tests/test_session_pair_harvester.py

@@ -0,0 +1,118 @@
+"""
+Tests for session_pair_harvester — training pair extraction from sessions.
+"""
+
+import json
+import tempfile
+import unittest
+from pathlib import Path
+
+import sys
+from pathlib import Path
+sys.path.insert(0, str(Path(__file__).parent.parent / "scripts"))
+from session_pair_harvester import (
+    extract_pairs_from_conversation,
+    extract_from_jsonl_file,
+    deduplicate_pairs,
+    compute_hash,
+)
+
+
+class TestSessionPairHarvester(unittest.TestCase):
+    def test_compute_hash_consistent(self):
+        h1 = compute_hash("hello world")
+        h2 = compute_hash("hello world")
+        self.assertEqual(h1, h2)
+        self.assertEqual(len(h1), 16)
+
+    def test_extract_simple_qa_pair(self):
+        """A simple user→assistant exchange produces one pair."""
+        conversation = [
+            {"role": "user", "content": "What is the capital of France?"},
+            {"role": "assistant", "content": "The capital of France is Paris. It is a major European city renowned for its art, fashion, gastronomy, cultural heritage, and historical significance. The city attracts millions of tourists annually."},
+        ]
+        pairs = extract_pairs_from_conversation(conversation, "test_session", "test-model")
+        self.assertEqual(len(pairs), 1)
+        self.assertEqual(pairs[0]["terse"], "What is the capital of France?")
+        self.assertIn("Paris", pairs[0]["rich"])
+        self.assertEqual(pairs[0]["source"], "test_session")
+
+    def test_min_ratio_filter(self):
+        """Very short responses are filtered out."""
+        conversation = [
+            {"role": "user", "content": "Yes"},
+            {"role": "assistant", "content": "No."},
+        ]
+        # Default min_ratio = 1.5, min_words = 20 for response
+        pairs = extract_pairs_from_conversation(conversation, "s", "m", min_response_words=3)
+        self.assertEqual(len(pairs), 0)
+
+    def test_min_words_filter(self):
+        """Assistant responses below min word count are skipped."""
+        conversation = [
+            {"role": "user", "content": "Explain the project architecture in detail"},
+            {"role": "assistant", "content": "OK."},
+        ]
+        pairs = extract_pairs_from_conversation(conversation, "s", "m", min_response_words=5)
+        self.assertEqual(len(pairs), 0)
+
+    def test_skip_non_assistant_messages(self):
+        """System and tool messages are ignored."""
+        conversation = [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": "Hello"},
+            {"role": "assistant", "content": "Hi there! How can I help you today?"},
+        ]
+        pairs = extract_pairs_from_conversation(conversation, "s", "m", min_response_words=3)
+        self.assertEqual(len(pairs), 1)
+        self.assertEqual(pairs[0]["terse"], "Hello")
+
+    def test_multiple_pairs_from_one_session(self):
+        """A conversation with several Q&A turns yields multiple pairs."""
+        conversation = [
+            {"role": "user", "content": "First question?"},
+            {"role": "assistant", "content": "Here is a detailed and comprehensive answer that thoroughly explores multiple aspects of the subject. It provides background context and practical implications for the reader."},
+            {"role": "user", "content": "Second?"},
+            {"role": "assistant", "content": "Another comprehensive response with detailed examples. This includes practical code blocks and thorough explanations to ensure deep understanding of the topic at hand."},
+        ]
+        pairs = extract_pairs_from_conversation(conversation, "s", "m", min_ratio=1.0)
+        self.assertEqual(len(pairs), 2)
+
+    def test_deduplication_removes_duplicates(self):
+        """Identical pairs across sessions are deduplicated."""
+        pairs = [
+            {"terse": "q1", "rich": "a1", "source": "s1", "model": "m"},
+            {"terse": "q1", "rich": "a1", "source": "s2", "model": "m"},
+            {"terse": "q2", "rich": "a2", "source": "s1", "model": "m"},
+        ]
+        unique = deduplicate_pairs(pairs)
+        self.assertEqual(len(unique), 2)
+        sources = {p["source"] for p in unique}
+        # First unique pair can be from either s1 or s2
+        self.assertIn("s1", sources)
+
+    def test_integration_with_test_sessions(self):
+        """Harvester finds pairs in real test session files."""
+        repo_root = Path(__file__).parent.parent
+        test_sessions_dir = repo_root / "test_sessions"
+        if not test_sessions_dir.exists():
+            self.skipTest("test_sessions not found")
+
+        pairs = []
+        for jsonl_file in sorted(test_sessions_dir.glob("*.jsonl")):
+            pairs.extend(extract_from_jsonl_file(str(jsonl_file)))
+
+        self.assertGreater(len(pairs), 0, "Should extract at least one pair from test_sessions")
+        for p in pairs:
+            self.assertIn("terse", p)
+            self.assertIn("rich", p)
+            self.assertIn("source", p)
+            self.assertIn("model", p)
+            # Verify content exists
+            self.assertGreater(len(p["terse"]), 0)
+            self.assertGreater(len(p["rich"]), 0)
+
+
+if __name__ == "__main__":
+    unittest.main()
+