Timmy-time-dashboard/src/timmy/session_logger.py

"""Session logging for Timmy - captures interactions, errors, and decisions.

Timmy requested: "I'd love to see a detailed log of all my interactions,
including any mistakes or errors that occur during the session."
"""

import json
import logging
from datetime import date, datetime
from pathlib import Path
from typing import Any

logger = logging.getLogger(__name__)


class SessionLogger:
    """Logs Timmy's interactions to a session file."""

    def __init__(self, logs_dir: str | Path | None = None):
        """Initialize session logger.

        Args:
            logs_dir: Directory for log files. Defaults to /logs in repo root.
        """
        from config import settings

        if logs_dir is None:
            self.logs_dir = Path(settings.repo_root) / "logs"
        else:
            self.logs_dir = Path(logs_dir)

        # Create logs directory if it doesn't exist
        self.logs_dir.mkdir(parents=True, exist_ok=True)

        # Session file path
        self.session_file = self.logs_dir / f"session_{date.today().isoformat()}.jsonl"

        # In-memory buffer
        self._buffer: list[dict] = []

    def record_message(self, role: str, content: str, confidence: float | None = None) -> None:
        """Record a user message.

        Args:
            role: "user" or "timmy"
            content: The message content
            confidence: Optional confidence score (0.0 to 1.0)
        """
        entry = {
            "type": "message",
            "role": role,
            "content": content,
            "timestamp": datetime.now().isoformat(),
        }
        if confidence is not None:
            entry["confidence"] = confidence
        self._buffer.append(entry)

    def record_tool_call(self, tool_name: str, args: dict, result: str) -> None:
        """Record a tool call.

        Args:
            tool_name: Name of the tool called
            args: Arguments passed to the tool
            result: Result from the tool
        """
        # Truncate long results
        result_preview = result[:500] if isinstance(result, str) else str(result)[:500]

        self._buffer.append(
            {
                "type": "tool_call",
                "tool": tool_name,
                "args": args,
                "result": result_preview,
                "timestamp": datetime.now().isoformat(),
            }
        )

    def record_error(self, error: str, context: str | None = None) -> None:
        """Record an error.

        Args:
            error: Error message
            context: Optional context about what was happening
        """
        self._buffer.append(
            {
                "type": "error",
                "error": error,
                "context": context,
                "timestamp": datetime.now().isoformat(),
            }
        )

    def record_decision(self, decision: str, rationale: str | None = None) -> None:
        """Record a decision Timmy made.

        Args:
            decision: What was decided
            rationale: Why that decision was made
        """
        self._buffer.append(
            {
                "type": "decision",
                "decision": decision,
                "rationale": rationale,
                "timestamp": datetime.now().isoformat(),
            }
        )

    def flush(self) -> Path:
        """Flush buffer to disk.

        Returns:
            Path to the session file
        """
        if not self._buffer:
            return self.session_file

        with open(self.session_file, "a") as f:
            for entry in self._buffer:
                f.write(json.dumps(entry) + "\n")

        logger.info("Flushed %d entries to %s", len(self._buffer), self.session_file)
        self._buffer.clear()

        return self.session_file

    def get_session_summary(self) -> dict[str, Any]:
        """Get a summary of the current session.

        Returns:
            Dict with session statistics
        """
        if not self.session_file.exists():
            return {
                "exists": False,
                "entries": 0,
            }

        entries = []
        with open(self.session_file) as f:
            for line in f:
                if line.strip():
                    entries.append(json.loads(line))

        return {
            "exists": True,
            "file": str(self.session_file),
            "entries": len(entries),
            "messages": sum(1 for e in entries if e.get("type") == "message"),
            "tool_calls": sum(1 for e in entries if e.get("type") == "tool_call"),
            "errors": sum(1 for e in entries if e.get("type") == "error"),
            "decisions": sum(1 for e in entries if e.get("type") == "decision"),
        }

    def get_recent_entries(self, limit: int = 50) -> list[dict]:
        """Load recent entries across all session logs.

        Args:
            limit: Maximum number of entries to return.

        Returns:
            List of entries (most recent first).
        """
        entries: list[dict] = []
        log_files = sorted(self.logs_dir.glob("session_*.jsonl"), reverse=True)
        for log_file in log_files:
            if len(entries) >= limit:
                break
            try:
                with open(log_file) as f:
                    lines = [ln for ln in f if ln.strip()]
                for line in reversed(lines):
                    if len(entries) >= limit:
                        break
                    try:
                        entries.append(json.loads(line))
                    except json.JSONDecodeError:
                        continue
            except OSError:
                continue
        return entries

    def search(self, query: str, role: str | None = None, limit: int = 10) -> list[dict]:
        """Search across all session logs for entries matching a query.

        Args:
            query: Case-insensitive substring to search for.
            role: Optional role filter ("user", "timmy", "system").
            limit: Maximum number of results to return.

        Returns:
            List of matching entries (most recent first), each with
            type, timestamp, and relevant content fields.
        """
        query_lower = query.lower()
        matches: list[dict] = []

        # Collect all session files, sorted newest first
        log_files = sorted(self.logs_dir.glob("session_*.jsonl"), reverse=True)

        for log_file in log_files:
            if len(matches) >= limit:
                break
            try:
                with open(log_file) as f:
                    # Read all lines, reverse so newest entries come first
                    lines = [ln for ln in f if ln.strip()]
                for line in reversed(lines):
                    if len(matches) >= limit:
                        break
                    try:
                        entry = json.loads(line)
                    except json.JSONDecodeError:
                        continue

                    # Role filter
                    if role and entry.get("role") != role:
                        continue

                    # Search in text-bearing fields
                    searchable = " ".join(
                        str(entry.get(k, ""))
                        for k in ("content", "error", "decision", "rationale", "result", "tool")
                    ).lower()
                    if query_lower in searchable:
                        entry["_source_file"] = log_file.name
                        matches.append(entry)
            except OSError:
                continue

        return matches


# Global session logger instance
_session_logger: SessionLogger | None = None


def get_session_logger() -> SessionLogger:
    """Get or create the global session logger."""
    global _session_logger
    if _session_logger is None:
        _session_logger = SessionLogger()
    return _session_logger


def get_session_summary() -> dict[str, Any]:
    """Get summary of current session logs.

    Returns:
        Dict with session statistics (entries, messages, errors, etc.)
    """
    logger = get_session_logger()
    return logger.get_session_summary()


def flush_session_logs() -> str:
    """Flush current session logs to disk.

    Returns:
        Path to the log file
    """
    logger = get_session_logger()
    path = logger.flush()
    return str(path)


def session_history(query: str, role: str = "", limit: int = 10) -> str:
    """Search Timmy's past conversation history.

    Find messages, tool calls, errors, and decisions from past sessions
    that match the query. Results are returned most-recent first.

    Args:
        query: What to search for (case-insensitive substring match).
        role: Optional filter by role — "user", "timmy", or "" for all.
        limit: Maximum results to return (default 10).

    Returns:
        Formatted string of matching session entries.
    """
    sl = get_session_logger()
    # Flush buffer first so current session is searchable
    sl.flush()
    results = sl.search(query, role=role or None, limit=limit)
    if not results:
        return f"No session history found matching '{query}'."

    lines = [f"Found {len(results)} result(s) for '{query}':\n"]
    for entry in results:
        ts = entry.get("timestamp", "?")[:19]
        etype = entry.get("type", "?")
        source = entry.get("_source_file", "")

        if etype == "message":
            who = entry.get("role", "?")
            text = entry.get("content", "")[:200]
            lines.append(f"[{ts}] {who}: {text}")
        elif etype == "tool_call":
            tool = entry.get("tool", "?")
            result = entry.get("result", "")[:100]
            lines.append(f"[{ts}] tool:{tool} → {result}")
        elif etype == "error":
            err = entry.get("error", "")[:200]
            lines.append(f"[{ts}] ERROR: {err}")
        elif etype == "decision":
            dec = entry.get("decision", "")[:200]
            lines.append(f"[{ts}] DECIDED: {dec}")
        else:
            lines.append(f"[{ts}] {etype}: {json.dumps(entry)[:200]}")

        if source:
            lines[-1] += f"  ({source})"

    return "\n".join(lines)


# ---------------------------------------------------------------------------
# Confidence threshold used for flagging low-confidence responses
# ---------------------------------------------------------------------------
_LOW_CONFIDENCE_THRESHOLD = 0.5


def self_reflect(limit: int = 30) -> str:
    """Review recent conversations and reflect on Timmy's own behavior.

    Scans past session entries for patterns: low-confidence responses,
    errors, repeated topics, and conversation quality signals.  Returns
    a structured reflection that Timmy can use to improve.

    Args:
        limit: How many recent entries to review (default 30).

    Returns:
        A formatted self-reflection report.
    """
    sl = get_session_logger()
    sl.flush()
    entries = sl.get_recent_entries(limit=limit)

    if not entries:
        return "No conversation history to reflect on yet."

    # Categorize entries
    messages = [e for e in entries if e.get("type") == "message"]
    errors = [e for e in entries if e.get("type") == "error"]
    timmy_msgs = [e for e in messages if e.get("role") == "timmy"]
    user_msgs = [e for e in messages if e.get("role") == "user"]

    # 1. Low-confidence responses
    low_conf = [
        m
        for m in timmy_msgs
        if m.get("confidence") is not None and m["confidence"] < _LOW_CONFIDENCE_THRESHOLD
    ]

    # 2. Identify repeated user topics (simple word frequency)
    topic_counts: dict[str, int] = {}
    for m in user_msgs:
        for word in (m.get("content") or "").lower().split():
            cleaned = word.strip(".,!?\"'()[]")
            if len(cleaned) > 3:
                topic_counts[cleaned] = topic_counts.get(cleaned, 0) + 1
    repeated = sorted(
        ((w, c) for w, c in topic_counts.items() if c >= 3),
        key=lambda x: x[1],
        reverse=True,
    )[:5]

    # Build reflection report
    sections: list[str] = ["## Self-Reflection Report\n"]

    sections.append(
        f"Reviewed {len(entries)} recent entries: "
        f"{len(user_msgs)} user messages, "
        f"{len(timmy_msgs)} responses, "
        f"{len(errors)} errors.\n"
    )

    # Low confidence
    if low_conf:
        sections.append(f"### Low-Confidence Responses ({len(low_conf)})")
        for m in low_conf[:5]:
            ts = (m.get("timestamp") or "?")[:19]
            conf = m.get("confidence", 0)
            text = (m.get("content") or "")[:120]
            sections.append(f"- [{ts}] confidence={conf:.0%}: {text}")
        sections.append("")
    else:
        sections.append(
            "### Low-Confidence Responses\nNone found — all responses above threshold.\n"
        )

    # Errors
    if errors:
        sections.append(f"### Errors ({len(errors)})")
        for e in errors[:5]:
            ts = (e.get("timestamp") or "?")[:19]
            err = (e.get("error") or "")[:120]
            sections.append(f"- [{ts}] {err}")
        sections.append("")
    else:
        sections.append("### Errors\nNo errors recorded.\n")

    # Repeated topics
    if repeated:
        sections.append("### Recurring Topics")
        for word, count in repeated:
            sections.append(f'- "{word}" ({count} mentions)')
        sections.append("")
    else:
        sections.append("### Recurring Topics\nNo strong patterns detected.\n")

    # Actionable summary
    insights: list[str] = []
    if low_conf:
        insights.append("Consider studying topics where confidence was low.")
    if errors:
        insights.append("Review error patterns for recurring infrastructure issues.")
    if repeated:
        top_topic = repeated[0][0]
        insights.append(
            f'User frequently asks about "{top_topic}" — consider deepening knowledge here.'
        )
    if not insights:
        insights.append("Conversations look healthy. Keep up the good work.")

    sections.append("### Insights")
    for insight in insights:
        sections.append(f"- {insight}")

    return "\n".join(sections)