forked from Rockachopa/Timmy-time-dashboard
Kimi Agent
3afb62afb7
Co-authored-by: Kimi Agent <kimi@timmy.local> Co-committed-by: Kimi Agent <kimi@timmy.local>
435 lines
14 KiB
Python
435 lines
14 KiB
Python
"""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)
|