memoria/hooks/pre-compact.py

"""
PreCompact hook - captures conversation transcript before auto-compaction.

When Claude Code's context window fills up, it auto-compacts (summarizes and
discards detail). This hook fires BEFORE that happens, extracting conversation
context and spawning flush.py to extract knowledge that would otherwise
be lost to summarization.

The hook itself does NO API calls - only local file I/O for speed (<10s).
"""

from __future__ import annotations

import json
import logging
import os
import re
import subprocess
import sys
from datetime import datetime, timezone
from pathlib import Path

# Recursion guard
if os.environ.get("CLAUDE_INVOKED_BY"):
    sys.exit(0)

ROOT = Path(__file__).resolve().parent.parent
SCRIPTS_DIR = ROOT / "scripts"
STATE_DIR = SCRIPTS_DIR

logging.basicConfig(
    filename=str(SCRIPTS_DIR / "flush.log"),
    level=logging.INFO,
    format="%(asctime)s %(levelname)s [pre-compact] %(message)s",
    datefmt="%Y-%m-%d %H:%M:%S",
)

MAX_TURNS = 30
MAX_CONTEXT_CHARS = 15_000
MIN_TURNS_TO_FLUSH = 5


def extract_conversation_context(transcript_path: Path) -> tuple[str, int]:
    """Read JSONL transcript and extract last ~N conversation turns as markdown."""
    turns: list[str] = []

    with open(transcript_path, encoding="utf-8") as f:
        for line in f:
            line = line.strip()
            if not line:
                continue
            try:
                entry = json.loads(line)
            except json.JSONDecodeError:
                continue

            msg = entry.get("message", {})
            if isinstance(msg, dict):
                role = msg.get("role", "")
                content = msg.get("content", "")
            else:
                role = entry.get("role", "")
                content = entry.get("content", "")

            if role not in ("user", "assistant"):
                continue

            if isinstance(content, list):
                text_parts = []
                for block in content:
                    if isinstance(block, dict) and block.get("type") == "text":
                        text_parts.append(block.get("text", ""))
                    elif isinstance(block, str):
                        text_parts.append(block)
                content = "\n".join(text_parts)

            if isinstance(content, str) and content.strip():
                label = "User" if role == "user" else "Assistant"
                turns.append(f"**{label}:** {content.strip()}\n")

    recent = turns[-MAX_TURNS:]
    context = "\n".join(recent)

    if len(context) > MAX_CONTEXT_CHARS:
        context = context[-MAX_CONTEXT_CHARS:]
        boundary = context.find("\n**")
        if boundary > 0:
            context = context[boundary + 1 :]

    return context, len(recent)


def main() -> None:
    # Read hook input from stdin
    try:
        raw_input = sys.stdin.read()
        try:
            hook_input: dict = json.loads(raw_input)
        except json.JSONDecodeError:
            fixed_input = re.sub(r'(?<!\\)\\(?!["\\])', r'\\\\', raw_input)
            hook_input = json.loads(fixed_input)
    except (json.JSONDecodeError, ValueError, EOFError) as e:
        logging.error("Failed to parse stdin: %s", e)
        return

    session_id = hook_input.get("session_id", "unknown")
    transcript_path_str = hook_input.get("transcript_path", "")

    logging.info("PreCompact fired: session=%s", session_id)

    # transcript_path can be empty (known Claude Code bug #13668)
    if not transcript_path_str or not isinstance(transcript_path_str, str):
        logging.info("SKIP: no transcript path")
        return

    transcript_path = Path(transcript_path_str)
    if not transcript_path.exists():
        logging.info("SKIP: transcript missing: %s", transcript_path_str)
        return

    # Extract conversation context in the hook
    try:
        context, turn_count = extract_conversation_context(transcript_path)
    except Exception as e:
        logging.error("Context extraction failed: %s", e)
        return

    if not context.strip():
        logging.info("SKIP: empty context")
        return

    if turn_count < MIN_TURNS_TO_FLUSH:
        logging.info("SKIP: only %d turns (min %d)", turn_count, MIN_TURNS_TO_FLUSH)
        return

    # Write context to a temp file for the background process
    timestamp = datetime.now(timezone.utc).astimezone().strftime("%Y%m%d-%H%M%S")
    context_file = STATE_DIR / f"flush-context-{session_id}-{timestamp}.md"
    context_file.write_text(context, encoding="utf-8")

    # Spawn flush.py as a background process
    flush_script = SCRIPTS_DIR / "flush.py"

    cmd = [
        "uv",
        "run",
        "--directory",
        str(ROOT),
        "python",
        str(flush_script),
        str(context_file),
        session_id,
    ]

    creation_flags = subprocess.CREATE_NO_WINDOW if sys.platform == "win32" else 0

    try:
        subprocess.Popen(
            cmd,
            stdout=subprocess.DEVNULL,
            stderr=subprocess.DEVNULL,
            creationflags=creation_flags,
        )
        logging.info("Spawned flush.py for session %s (%d turns, %d chars)", session_id, turn_count, len(context))
    except Exception as e:
        logging.error("Failed to spawn flush.py: %s", e)


if __name__ == "__main__":
    main()