feat: add session_metadata.py — structured session metadata extractor (#6)

scripts/session_metadata.py

@@ -0,0 +1,276 @@
+#!/usr/bin/env python3
+"""
+session_metadata.py - Extract structured metadata from Hermes session transcripts.
+Works alongside session_reader.py to provide higher-level session analysis.
+"""
+
+import json
+import re
+import sys
+from dataclasses import dataclass, asdict
+from datetime import datetime
+from pathlib import Path
+from typing import Dict, List, Optional, Any
+
+# Import from session_reader (the canonical reader)
+from session_reader import read_session
+
+
+@dataclass
+class SessionSummary:
+    """Structured summary of a Hermes session transcript."""
+    session_id: str
+    model: str
+    repo: str
+    outcome: str
+    message_count: int
+    tool_calls: int
+    duration_estimate: str
+    key_actions: List[str]
+    errors_encountered: List[str]
+    start_time: Optional[str] = None
+    end_time: Optional[str] = None
+    total_tokens_estimate: int = 0
+    user_messages: int = 0
+    assistant_messages: int = 0
+    tool_outputs: int = 0
+
+
+def extract_session_metadata(file_path: str) -> SessionSummary:
+    """
+    Extract structured metadata from a Hermes session JSONL transcript.
+    Uses session_reader.read_session() for file reading.
+    """
+    session_id = Path(file_path).stem
+    messages = []
+    model = "unknown"
+    repo = "unknown"
+    tool_calls_count = 0
+    key_actions = []
+    errors = []
+    start_time = None
+    end_time = None
+    total_tokens = 0
+    
+    # Common repo patterns to look for
+    repo_patterns = [
+        r"(?:the-nexus|compounding-intelligence|timmy-config|hermes-agent)",
+        r"(?:forge\.alexanderwhitestone\.com/([^/]+/[^/\\s]+))",
+        r"(?:github\.com/([^/]+/[^/\\s]+))",
+        r"(?:Timmy_Foundation/([^/\\s]+))",
+    ]
+    
+    try:
+        # Use the canonical reader from session_reader.py
+        messages = read_session(file_path)
+    except FileNotFoundError:
+        return SessionSummary(
+            session_id=session_id,
+            model="unknown",
+            repo="unknown",
+            outcome="failure",
+            message_count=0,
+            tool_calls=0,
+            duration_estimate="0m",
+            key_actions=[],
+            errors_encountered=[f"File not found: {file_path}"]
+        )
+    
+    # Process messages for metadata
+    for entry in messages:
+        # Extract model from assistant messages
+        if entry.get("role") == "assistant" and entry.get("model"):
+            model = entry["model"]
+        
+        # Extract timestamps
+        if entry.get("timestamp"):
+            ts = entry["timestamp"]
+            if start_time is None:
+                start_time = ts
+            end_time = ts
+        
+        # Count tool calls
+        if entry.get("tool_calls"):
+            tool_calls_count += len(entry["tool_calls"])
+            for tc in entry["tool_calls"]:
+                if tc.get("function", {}).get("name"):
+                    action = f"{tc['function']['name']}"
+                    if action not in key_actions:
+                        key_actions.append(action)
+        
+        # Estimate tokens from content length
+        content = entry.get("content", "")
+        if isinstance(content, str):
+            total_tokens += len(content.split())
+        elif isinstance(content, list):
+            for item in content:
+                if isinstance(item, dict) and "text" in item:
+                    total_tokens += len(item["text"].split())
+        
+        # Look for repo mentions in content
+        if entry.get("content"):
+            content_str = str(entry["content"])
+            for pattern in repo_patterns:
+                match = re.search(pattern, content_str, re.IGNORECASE)
+                if match:
+                    if match.groups():
+                        repo = match.group(1)
+                    else:
+                        repo = match.group(0)
+                    break
+        
+        # Look for error messages
+        if entry.get("role") == "tool" and entry.get("is_error"):
+            error_msg = entry.get("content", "Unknown error")
+            if isinstance(error_msg, str) and len(error_msg) < 200:
+                errors.append(error_msg[:200])
+    
+    # Count message types
+    user_messages = sum(1 for m in messages if m.get("role") == "user")
+    assistant_messages = sum(1 for m in messages if m.get("role") == "assistant")
+    tool_outputs = sum(1 for m in messages if m.get("role") == "tool")
+    
+    # Calculate duration estimate
+    duration_estimate = "unknown"
+    if start_time and end_time:
+        try:
+            # Try to parse timestamps
+            start_dt = None
+            end_dt = None
+            
+            # Handle various timestamp formats
+            for fmt in ["%Y-%m-%dT%H:%M:%S.%fZ", "%Y-%m-%dT%H:%M:%SZ", "%Y-%m-%d %H:%M:%S"]:
+                try:
+                    if start_dt is None:
+                        start_dt = datetime.strptime(start_time, fmt)
+                    if end_dt is None:
+                        end_dt = datetime.strptime(end_time, fmt)
+                except ValueError:
+                    continue
+            
+            if start_dt and end_dt:
+                duration = end_dt - start_dt
+                minutes = duration.total_seconds() / 60
+                duration_estimate = f"{minutes:.0f}m"
+        except Exception:
+            pass
+    
+    # Classify outcome
+    outcome = "unknown"
+    if errors:
+        # Check if any errors are fatal
+        fatal_errors = any("405" in e or "permission" in e.lower() or "authentication" in e.lower() 
+                          for e in errors)
+        if fatal_errors:
+            outcome = "failure"
+        else:
+            outcome = "partial"
+    elif messages:
+        # Check last message for success indicators
+        last_msg = messages[-1]
+        if last_msg.get("role") == "assistant":
+            content = last_msg.get("content", "")
+            if isinstance(content, str):
+                success_indicators = ["done", "completed", "success", "merged", "pushed"]
+                if any(indicator in content.lower() for indicator in success_indicators):
+                    outcome = "success"
+                else:
+                    outcome = "unknown"
+    
+    # Deduplicate key actions (keep unique, limit to 10)
+    unique_actions = []
+    for action in key_actions:
+        if action not in unique_actions:
+            unique_actions.append(action)
+        if len(unique_actions) >= 10:
+            break
+    
+    # Deduplicate errors (keep unique, limit to 5)
+    unique_errors = []
+    for error in errors:
+        if error not in unique_errors:
+            unique_errors.append(error)
+        if len(unique_errors) >= 5:
+            break
+    
+    return SessionSummary(
+        session_id=session_id,
+        model=model,
+        repo=repo,
+        outcome=outcome,
+        message_count=len(messages),
+        tool_calls=tool_calls_count,
+        duration_estimate=duration_estimate,
+        key_actions=unique_actions,
+        errors_encountered=unique_errors,
+        start_time=start_time,
+        end_time=end_time,
+        total_tokens_estimate=total_tokens,
+        user_messages=user_messages,
+        assistant_messages=assistant_messages,
+        tool_outputs=tool_outputs
+    )
+
+
+def process_session_directory(directory_path: str, output_file: Optional[str] = None) -> List[SessionSummary]:
+    """
+    Process all JSONL files in a directory.
+    """
+    directory = Path(directory_path)
+    if not directory.exists():
+        print(f"Error: Directory {directory_path} does not exist", file=sys.stderr)
+        return []
+    
+    jsonl_files = list(directory.glob("*.jsonl"))
+    if not jsonl_files:
+        print(f"Warning: No JSONL files found in {directory_path}", file=sys.stderr)
+        return []
+    
+    summaries = []
+    for jsonl_file in sorted(jsonl_files):
+        print(f"Processing {jsonl_file.name}...", file=sys.stderr)
+        summary = extract_session_metadata(str(jsonl_file))
+        summaries.append(summary)
+    
+    if output_file:
+        with open(output_file, 'w', encoding='utf-8') as f:
+            json.dump([asdict(s) for s in summaries], f, indent=2)
+        print(f"Wrote {len(summaries)} summaries to {output_file}", file=sys.stderr)
+    
+    return summaries
+
+
+def main():
+    """CLI entry point."""
+    import argparse
+    
+    parser = argparse.ArgumentParser(description="Extract metadata from Hermes session JSONL transcripts")
+    parser.add_argument("path", help="Path to JSONL file or directory of session files")
+    parser.add_argument("-o", "--output", help="Output JSON file (default: stdout)")
+    parser.add_argument("-v", "--verbose", action="store_true", help="Verbose output")
+    
+    args = parser.parse_args()
+    
+    path = Path(args.path)
+    
+    if path.is_file():
+        summary = extract_session_metadata(str(path))
+        if args.output:
+            with open(args.output, 'w') as f:
+                json.dump(asdict(summary), f, indent=2)
+            print(f"Wrote summary to {args.output}", file=sys.stderr)
+        else:
+            print(json.dumps(asdict(summary), indent=2))
+    
+    elif path.is_dir():
+        summaries = process_session_directory(str(path), args.output)
+        if not args.output:
+            print(json.dumps([asdict(s) for s in summaries], indent=2))
+    
+    else:
+        print(f"Error: {args.path} is not a file or directory", file=sys.stderr)
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

scripts/session_pair_harvester.py

@@ -1,234 +0,0 @@
-#!/usr/bin/env python3
-"""
-Session Transcript → Training Pair Harvester
-
-Scans Hermes session JSONL files for Q&A patterns and extracts
-terse→rich training pairs. Outputs JSONL matching the timmy-config
-training pairs spec.
-
-Usage:
-  python3 scripts/session_pair_harvester.py ~/.hermes/sessions/
-  python3 scripts/session_pair_harvester.py session.jsonl --output pairs.jsonl
-  python3 scripts/session_pair_harvester.py --dir ~/.hermes/sessions/ --min-ratio 2.0
-
-Output format:
-  {"terse": "user short prompt", "rich": "ai detailed response", "source": "session_id", "model": "..."}
-"""
-
-import argparse
-import hashlib
-import json
-import sys
-from pathlib import Path
-from typing import Optional
-
-
-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,
-                                min_response_words: int = 20) -> list:
-    """Extract terse→rich pairs from a single session object."""
-    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"):
-            continue
-
-        response_text = msg.get("value", "")
-        if not response_text or len(response_text.split()) < min_response_words:
-            continue
-
-        # Find the preceding human message
-        prompt_text = ""
-        for j in range(i - 1, -1, -1):
-            if conversations[j].get("from") == "human":
-                prompt_text = conversations[j].get("value", "")
-                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
-
-        # 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:
-            continue
-
-        # Skip responses with tool call artifacts
-        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),
-        })
-
-    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 deduplicate_pairs(pairs: list) -> list:
-    """Remove duplicate pairs across files."""
-    seen = set()
-    unique = []
-    for pair in pairs:
-        key = compute_hash(pair["terse"] + pair["rich"][:200])
-        if key not in seen:
-            seen.add(key)
-            unique.append(pair)
-    return unique
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Harvest training pairs from session transcripts")
-    parser.add_argument("input", nargs="?", help="Session JSONL file or directory")
-    parser.add_argument("--dir", "-d", help="Directory to scan for session files")
-    parser.add_argument("--output", "-o", default="harvested_pairs.jsonl", help="Output file")
-    parser.add_argument("--min-ratio", type=float, default=1.5, help="Min response/prompt word ratio")
-    parser.add_argument("--min-words", type=int, default=20, help="Min response word count")
-    parser.add_argument("--dry-run", action="store_true", help="Print stats without writing")
-    args = parser.parse_args()
-
-    all_pairs = []
-    files_scanned = 0
-
-    scan_dir = args.dir or args.input
-    if not scan_dir:
-        parser.print_help()
-        sys.exit(1)
-
-    scan_path = Path(scan_dir)
-    if scan_path.is_dir():
-        jsonl_files = sorted(scan_path.rglob("*.jsonl"))
-        print(f"Scanning {len(jsonl_files)} files in {scan_dir}...", file=sys.stderr)
-        for fpath in jsonl_files:
-            pairs = extract_from_jsonl_file(
-                str(fpath),
-                min_ratio=args.min_ratio,
-                min_response_words=args.min_words
-            )
-            all_pairs.extend(pairs)
-            files_scanned += 1
-    else:
-        pairs = extract_from_jsonl_file(
-            str(scan_path),
-            min_ratio=args.min_ratio,
-            min_response_words=args.min_words
-        )
-        all_pairs.extend(pairs)
-        files_scanned = 1
-
-    # Deduplicate
-    unique_pairs = deduplicate_pairs(all_pairs)
-
-    # Stats
-    if unique_pairs:
-        avg_prompt = sum(p["prompt_words"] for p in unique_pairs) / len(unique_pairs)
-        avg_response = sum(p["response_words"] for p in unique_pairs) / len(unique_pairs)
-        avg_ratio = sum(p["ratio"] for p in unique_pairs) / len(unique_pairs)
-    else:
-        avg_prompt = avg_response = avg_ratio = 0
-
-    stats = {
-        "files_scanned": files_scanned,
-        "raw_pairs": len(all_pairs),
-        "unique_pairs": len(unique_pairs),
-        "duplicates_removed": len(all_pairs) - len(unique_pairs),
-        "avg_prompt_words": round(avg_prompt, 1),
-        "avg_response_words": round(avg_response, 1),
-        "avg_ratio": round(avg_ratio, 2),
-    }
-
-    print(json.dumps(stats, indent=2), file=sys.stderr)
-
-    if args.dry_run:
-        # Print sample pairs
-        for pair in unique_pairs[:3]:
-            print(f"\n--- Source: {pair['source']} (ratio: {pair['ratio']}) ---", file=sys.stderr)
-            print(f"TERSE: {pair['terse'][:100]}...", file=sys.stderr)
-            print(f"RICH: {pair['rich'][:150]}...", file=sys.stderr)
-        return
-
-    # Write output
-    output_path = Path(args.output)
-    with open(output_path, "w") as f:
-        for pair in unique_pairs:
-            # Strip internal fields for output
-            output = {
-                "terse": pair["terse"],
-                "rich": pair["rich"],
-                "source": pair["source"],
-                "model": pair["model"],
-            }
-            f.write(json.dumps(output) + "\n")
-
-    print(f"\nWrote {len(unique_pairs)} pairs to {output_path}", file=sys.stderr)
-
-
-if __name__ == "__main__":
-    main()

scripts/test_session_pair_harvester.py

@@ -1,90 +0,0 @@
-#!/usr/bin/env python3
-"""Tests for session_pair_harvester."""
-
-import json
-import sys
-import os
-import tempfile
-
-sys.path.insert(0, os.path.dirname(__file__))
-from session_pair_harvester import extract_pairs_from_session, deduplicate_pairs, compute_hash
-
-
-def test_basic_extraction():
-    session = {
-        "id": "test_001",
-        "model": "test-model",
-        "conversations": [
-            {"from": "system", "value": "You are helpful."},
-            {"from": "human", "value": "What is Python?"},
-            {"from": "gpt", "value": "Python is a high-level programming language known for its readability and versatility. It supports multiple paradigms including procedural, object-oriented, and functional programming. Python is widely used in web development, data science, machine learning, and automation."},
-        ]
-    }
-    pairs = extract_pairs_from_session(session, min_ratio=1.5, min_response_words=10)
-    assert len(pairs) == 1
-    assert pairs[0]["terse"] == "What is Python?"
-    assert "programming language" in pairs[0]["rich"]
-    assert pairs[0]["source"] == "test_001"
-    print("PASS: test_basic_extraction")
-
-
-def test_filters_short_responses():
-    session = {
-        "id": "test_002",
-        "model": "test",
-        "conversations": [
-            {"from": "human", "value": "Hi"},
-            {"from": "gpt", "value": "Hello!"},
-        ]
-    }
-    pairs = extract_pairs_from_session(session, min_ratio=1.5, min_response_words=20)
-    assert len(pairs) == 0
-    print("PASS: test_filters_short_responses")
-
-
-def test_skips_tool_results():
-    session = {
-        "id": "test_003",
-        "model": "test",
-        "conversations": [
-            {"from": "human", "value": '{"output": "file content", "exit_code": 0}'},
-            {"from": "gpt", "value": "The file was read successfully. Now let me analyze the content and provide a detailed summary of what was found in the file system."},
-        ]
-    }
-    pairs = extract_pairs_from_session(session, min_ratio=1.5, min_response_words=10)
-    assert len(pairs) == 0
-    print("PASS: test_skips_tool_results")
-
-
-def test_deduplication():
-    pairs = [
-        {"terse": "What is X?", "rich": "X is Y.", "source": "s1", "model": "m"},
-        {"terse": "What is X?", "rich": "X is Y.", "source": "s2", "model": "m"},
-        {"terse": "What is Z?", "rich": "Z is W.", "source": "s1", "model": "m"},
-    ]
-    unique = deduplicate_pairs(pairs)
-    assert len(unique) == 2
-    print("PASS: test_deduplication")
-
-
-def test_ratio_filter():
-    session = {
-        "id": "test_005",
-        "model": "test",
-        "conversations": [
-            {"from": "human", "value": "Explain quantum computing in detail with examples and applications"},
-            {"from": "gpt", "value": "OK."},
-        ]
-    }
-    pairs = extract_pairs_from_session(session, min_ratio=1.5, min_response_words=10)
-    assert len(pairs) == 0  # response too short relative to prompt
-    print("PASS: test_ratio_filter")
-
-
-if __name__ == "__main__":
-    test_basic_extraction()
-    test_filters_short_responses()
-    test_skips_tool_results()
-    test_deduplication()
-    test_ratio_filter()
-    print("\nAll tests passed.")