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Timmy-time-dashboard/timmy_automations/retrain/trajectory_exporter.py

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"""Trajectory exporter — reads session JSONL logs and extracts conversation trajectories.
A trajectory is a coherent sequence of messages + tool calls that form
a single task attempt. Each trajectory becomes one training example.
Refs: #1105
"""
from __future__ import annotations
import json
import logging
from dataclasses import dataclass, field
from datetime import UTC, datetime, timedelta
from pathlib import Path
from typing import Any
logger = logging.getLogger(__name__)
_LOGS_DIR_DEFAULT = "logs"
_SESSION_GLOB = "session_*.jsonl"
@dataclass
class Trajectory:
"""A single conversation trajectory extracted from session logs."""
session_date: str
started_at: str
ended_at: str
messages: list[dict[str, Any]] = field(default_factory=list)
tool_calls: list[dict[str, Any]] = field(default_factory=list)
errors: list[dict[str, Any]] = field(default_factory=list)
decisions: list[dict[str, Any]] = field(default_factory=list)
@property
def message_count(self) -> int:
return len(self.messages)
@property
def tool_call_count(self) -> int:
return len(self.tool_calls)
@property
def error_count(self) -> int:
return len(self.errors)
@property
def has_successful_tool_call(self) -> bool:
"""True if any tool call succeeded (no error entry follows it)."""
return self.tool_call_count > 0 and self.error_count == 0
@property
def is_multi_step(self) -> bool:
"""True if this trajectory involved multiple turns with tool use."""
return self.message_count >= 2 and self.tool_call_count >= 1
def to_chat_format(self) -> list[dict[str, str]]:
"""Convert trajectory to chat-format messages for training.
Interleaves messages and tool-call results as assistant/tool turns.
"""
chat: list[dict[str, str]] = []
# Merge all entries by timestamp and emit in order
all_entries = sorted(
self.messages + self.tool_calls + self.decisions,
key=lambda e: e.get("timestamp", ""),
)
for entry in all_entries:
etype = entry.get("type")
if etype == "message":
role = "user" if entry.get("role") == "user" else "assistant"
content = entry.get("content", "")
if content:
chat.append({"role": role, "content": content})
elif etype == "tool_call":
tool = entry.get("tool", "unknown")
result = entry.get("result", "")
chat.append(
{
"role": "assistant",
"content": f"[tool:{tool}] {result}",
}
)
elif etype == "decision":
decision = entry.get("decision", "")
if decision:
chat.append({"role": "assistant", "content": f"[decided] {decision}"})
return chat
class TrajectoryExporter:
"""Reads session JSONL logs and yields Trajectory objects for a date range."""
def __init__(self, logs_dir: str | Path | None = None, repo_root: str | Path | None = None):
if repo_root is None:
repo_root = Path(__file__).resolve().parent.parent.parent
self._repo_root = Path(repo_root)
if logs_dir is None:
self._logs_dir = self._repo_root / _LOGS_DIR_DEFAULT
else:
self._logs_dir = Path(logs_dir)
def export_week(self, weeks_ago: int = 0) -> list[Trajectory]:
"""Export all trajectories from the specified week.
Args:
weeks_ago: 0 = current week, 1 = last week, etc.
Returns:
List of Trajectory objects extracted from session logs.
"""
now = datetime.now(tz=UTC)
# Week boundaries: MonSun
days_since_monday = now.weekday()
week_start = (now - timedelta(days=days_since_monday + 7 * weeks_ago)).replace(
hour=0, minute=0, second=0, microsecond=0
)
week_end = week_start + timedelta(days=7)
logger.info(
"Exporting trajectories for week %s%s",
week_start.date().isoformat(),
week_end.date().isoformat(),
)
trajectories: list[Trajectory] = []
log_files = sorted(self._logs_dir.glob(_SESSION_GLOB))
for log_file in log_files:
# Parse date from filename: session_YYYY-MM-DD.jsonl
try:
date_str = log_file.stem.removeprefix("session_")
file_date = datetime.strptime(date_str, "%Y-%m-%d").replace(tzinfo=UTC)
except ValueError:
logger.debug("Skipping non-date session file: %s", log_file.name)
continue
if not (week_start <= file_date < week_end):
continue
file_trajectories = self._extract_from_file(log_file)
trajectories.extend(file_trajectories)
logger.info(
"Extracted %d trajectories from %s", len(file_trajectories), log_file.name
)
logger.info("Total trajectories exported: %d", len(trajectories))
return trajectories
def _extract_from_file(self, log_file: Path) -> list[Trajectory]:
"""Parse a single session JSONL file into trajectories.
Groups entries into trajectories by finding natural conversation
boundaries (gaps of inactivity or topic shifts in the message stream).
"""
entries: list[dict[str, Any]] = []
try:
with open(log_file) as f:
for line in f:
line = line.strip()
if not line:
continue
try:
entries.append(json.loads(line))
except json.JSONDecodeError:
logger.debug("Skipping malformed JSON line in %s", log_file.name)
except OSError as exc:
logger.warning("Could not read %s: %s", log_file, exc)
return []
if not entries:
return []
date_str = log_file.stem.removeprefix("session_")
return self._segment_trajectories(entries, date_str)
def _segment_trajectories(
self, entries: list[dict[str, Any]], session_date: str
) -> list[Trajectory]:
"""Split a flat list of session entries into discrete trajectories.
Segmentation rule: start a new trajectory when:
- A user message follows a Timmy message (new conversation turn)
- More than 5 minutes have elapsed between entries
This produces training examples that are coherent task attempts.
"""
if not entries:
return []
trajectories: list[Trajectory] = []
current_entries: list[dict[str, Any]] = []
prev_ts: datetime | None = None
_SEGMENT_GAP_MINUTES = 5
def _flush() -> None:
if current_entries:
traj = _build_trajectory(current_entries, session_date)
if traj.message_count > 0:
trajectories.append(traj)
for entry in entries:
ts_raw = entry.get("timestamp", "")
try:
ts = datetime.fromisoformat(ts_raw.replace("Z", "+00:00"))
except (ValueError, AttributeError):
ts = None
# Time-gap segmentation
if ts and prev_ts and (ts - prev_ts).total_seconds() > _SEGMENT_GAP_MINUTES * 60:
_flush()
current_entries = []
# New-turn segmentation: user message after assistant turn
etype = entry.get("type")
erole = entry.get("role")
if etype == "message" and erole == "user" and current_entries:
# Check if previous non-error entry was a Timmy message
for prev in reversed(current_entries):
if prev.get("type") == "message":
if prev.get("role") == "timmy":
_flush()
current_entries = []
break
current_entries.append(entry)
if ts:
prev_ts = ts
_flush()
return trajectories
def _build_trajectory(entries: list[dict[str, Any]], session_date: str) -> Trajectory:
"""Build a Trajectory from a flat list of entries."""
messages = [e for e in entries if e.get("type") == "message"]
tool_calls = [e for e in entries if e.get("type") == "tool_call"]
errors = [e for e in entries if e.get("type") == "error"]
decisions = [e for e in entries if e.get("type") == "decision"]
timestamps = [e.get("timestamp", "") for e in entries if e.get("timestamp")]
started_at = min(timestamps) if timestamps else ""
ended_at = max(timestamps) if timestamps else ""
return Trajectory(
session_date=session_date,
started_at=started_at,
ended_at=ended_at,
messages=messages,
tool_calls=tool_calls,
errors=errors,
decisions=decisions,
)