289e0d506f
`_get_tool_usage()` merged `tool_name` rows and assistant `tool_calls` JSON with a GLOBAL per-tool max. That is right inside one session (both columns describe the same call) but wrong across sessions: a gateway session recording `tool_name` only plus a CLI session recording `tool_calls` only for the same tool reported 1 use instead of 2. Group both queries by (session_id, tool_name), reconcile with max per session, then sum across sessions. Port of PR #9896 by @MonkeyLeeT onto the `_scoped` query layout; one invariant test covering disjoint sessions AND a paired session. Fixes #9814
587 lines
35 KiB
Python
587 lines
35 KiB
Python
"""Session Insights Engine: aggregates the SQLite state DB into usage insights (tokens, cost estimates, tool/skill
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usage, activity, model/platform breakdowns). ``InsightsEngine(db).generate(days=30)`` → ``format_terminal(report)``."""
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import json
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import sqlite3
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import time
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from collections import Counter, defaultdict
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from datetime import datetime
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from decimal import Decimal
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from typing import Any, Dict, List, Optional
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from agent.usage_pricing import CanonicalUsage, estimate_usage_cost, format_cost_label, format_duration_compact, has_known_pricing
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from hermes_cli.timefmt import coerce_epoch
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_TOKEN_KEYS = ("input_tokens", "output_tokens", "cache_read_tokens", "cache_write_tokens")
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_SKILL_TOOLS = {"skill_view", "skill_manage"}
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def _fmt_est_cost(est_cost: float) -> str:
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"""Shared label helper so sub-cent totals render at 4dp, not "~$0.00".
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Routes through ``format_cost_label`` so sub-cent aggregates render at 4dp instead of collapsing to
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"~$0.00" (#79220 bug class — the same dishonesty this module's cost buckets exist to fix, #77223).
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"""
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return format_cost_label(Decimal(str(est_cost)))
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def _estimate_cost(session_or_model: Dict[str, Any] | str, input_tokens: int = 0, output_tokens: int = 0, *, cache_read_tokens: int = 0,
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cache_write_tokens: int = 0, provider: Optional[str] = None, base_url: Optional[str] = None) -> tuple[float, str]:
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"""Estimate the USD cost for a session row or a model/token tuple."""
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if isinstance(session_or_model, dict):
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s = session_or_model
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model = s.get("model") or ""
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usage = CanonicalUsage(**{k: s.get(k) or 0 for k in _TOKEN_KEYS})
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provider, base_url = s.get("billing_provider"), s.get("billing_base_url")
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else:
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model = session_or_model or ""
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usage = CanonicalUsage(input_tokens, output_tokens, cache_read_tokens, cache_write_tokens)
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result = estimate_usage_cost(model, usage, provider=provider, base_url=base_url)
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return float(result.amount_usd or 0.0), result.status
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def _bar_chart(values: List[int], max_width: int = 20) -> List[str]:
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peak = max(values) if values else 1
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return ["" for _ in values] if peak == 0 else ["█" * max(1, int(v / peak * max_width)) if v > 0 else "" for v in values]
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def _short_model(model: Optional[str]) -> str:
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"""Display name: strip the provider prefix; empty → "unknown"."""
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return (model or "unknown").split("/")[-1]
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def _parse_json(raw: Any, kind: type) -> Any:
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"""JSON-decode *raw* when it is a string; the value if it is a *kind*, else None."""
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try:
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if isinstance(raw, str):
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raw = json.loads(raw)
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except (json.JSONDecodeError, TypeError):
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return None
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return raw if isinstance(raw, kind) else None
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def _iter_functions(raw_calls: Any):
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"""Yield the ``function`` dict of every well-formed entry in a tool_calls column."""
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for call in _parse_json(raw_calls, list) or []:
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if isinstance(call, dict):
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yield call.get("function", {})
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def _hour12(hr: int) -> str:
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return f"{hr % 12 or 12}{'AM' if hr < 12 else 'PM'}"
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def _day(ts: Any) -> str:
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return datetime.fromtimestamp(ts).strftime("%b %d") if ts and (ts := coerce_epoch(ts)) else "?"
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def _scoped(before: str, after: str = "", *, src: str = " AND s.source = ?") -> tuple[str, str]:
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"""(unfiltered, source-filtered) query pair sharing one body. Built once at class definition,
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so no runtime value can alter query structure."""
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return before + after, before + src + after
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class InsightsEngine:
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"""Analyzes session history from a SessionDB (or raw sqlite3 connection)."""
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_SESSION_COLS = ("id, source, model, started_at, ended_at, "
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"message_count, tool_call_count, input_tokens, output_tokens, "
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"cache_read_tokens, cache_write_tokens, billing_provider, "
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"billing_base_url, billing_mode, estimated_cost_usd, "
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"actual_cost_usd, cost_status, cost_source, api_call_count")
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_GET_SESSIONS_ALL, _GET_SESSIONS_WITH_SOURCE = _scoped(
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f"SELECT {_SESSION_COLS} FROM sessions WHERE started_at >= ?",
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" ORDER BY started_at DESC",
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src=" AND source = ?",
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)
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# ``INDEXED BY`` pins the partial index so the plan is deterministic on a
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# fresh state.db (before ANALYZE) for both branches; without it the
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# source-filtered probe falls back to idx_messages_session_active and scans
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# each session's non-tool-call rows. The pin is a HARD dependency (SQLite
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# raises ``no such index``): read-only opens skip ``_init_schema``, so an
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# older writer's DB may lack it — ``__init__`` probes once and falls back
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# to the unpinned variants (identical rows, optimizer-chosen plan).
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_MESSAGES_ASSISTANT_CALLS_INDEX = "idx_messages_assistant_calls_by_session"
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_ASSISTANT_CALLS = (
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f" FROM messages m INDEXED BY {_MESSAGES_ASSISTANT_CALLS_INDEX}"
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" JOIN sessions s ON s.id = m.session_id"
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" WHERE s.started_at >= ?"
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)
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_GET_TOOL_CALLS_ALL, _GET_TOOL_CALLS_WITH_SOURCE = _scoped(
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"SELECT m.session_id, m.tool_calls" + _ASSISTANT_CALLS,
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" AND m.role = 'assistant' AND m.tool_calls IS NOT NULL",
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)
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_GET_SKILL_CALLS_ALL, _GET_SKILL_CALLS_WITH_SOURCE = _scoped(
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"SELECT m.tool_calls, m.timestamp" + _ASSISTANT_CALLS,
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" AND m.role = 'assistant' AND m.tool_calls IS NOT NULL"
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" AND (instr(m.tool_calls, 'skill_view') > 0"
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" OR instr(m.tool_calls, 'skill_manage') > 0)",
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)
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_GET_TOOL_NAMES_ALL, _GET_TOOL_NAMES_WITH_SOURCE = _scoped(
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"""SELECT m.session_id, m.tool_name, COUNT(*) as count
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FROM messages m
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JOIN sessions s ON s.id = m.session_id
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WHERE s.started_at >= ?""",
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"""
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AND m.role = 'tool' AND m.tool_name IS NOT NULL
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GROUP BY m.session_id, m.tool_name""",
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)
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_GET_MESSAGE_STATS_ALL, _GET_MESSAGE_STATS_WITH_SOURCE = _scoped(
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"""SELECT
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COUNT(*) as total_messages,
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SUM(CASE WHEN m.role = 'user' THEN 1 ELSE 0 END) as user_messages,
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SUM(CASE WHEN m.role = 'assistant' THEN 1 ELSE 0 END) as assistant_messages,
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SUM(CASE WHEN m.role = 'tool' THEN 1 ELSE 0 END) as tool_messages
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FROM messages m
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JOIN sessions s ON s.id = m.session_id
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WHERE s.started_at >= ?""",
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)
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_GET_MODEL_USAGE_ALL, _GET_MODEL_USAGE_WITH_SOURCE = _scoped(
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"SELECT u.session_id, u.model, u.billing_provider, u.billing_base_url,"
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" u.api_call_count, u.input_tokens, u.output_tokens,"
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" u.cache_read_tokens, u.cache_write_tokens, u.reasoning_tokens,"
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" u.estimated_cost_usd, u.actual_cost_usd, u.cost_status,"
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" u.cost_source, u.billing_mode"
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" FROM session_model_usage u"
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" JOIN sessions s ON s.id = u.session_id"
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" WHERE s.started_at >= ?",
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)
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_PINNED = ("_GET_TOOL_CALLS", "_GET_SKILL_CALLS")
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def __init__(self, db):
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self.db = db
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self._conn = db._conn
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try:
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self._has_assistant_calls_index = bool(self._conn.execute(
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"SELECT 1 FROM sqlite_master WHERE type='index' AND name=?", (self._MESSAGES_ASSISTANT_CALLS_INDEX,)).fetchone())
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except sqlite3.Error:
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self._has_assistant_calls_index = False
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if not self._has_assistant_calls_index:
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strip = f" INDEXED BY {self._MESSAGES_ASSISTANT_CALLS_INDEX}"
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for base in self._PINNED:
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for suffix in ("_ALL", "_WITH_SOURCE"):
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setattr(self, base + suffix, getattr(self, base + suffix).replace(strip, ""))
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def _query(self, base: str, cutoff: float, source: Optional[str]) -> list:
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"""Rows of ``<base>_WITH_SOURCE`` or ``<base>_ALL`` (instance attrs, so the unpinned fallback applies)."""
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sql, params = (getattr(self, base + "_WITH_SOURCE"), (cutoff, source)) if source else (getattr(self, base + "_ALL"), (cutoff,))
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return self._conn.execute(sql, params).fetchall()
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def generate(self, days: int = 30, source: str = None) -> Dict[str, Any]:
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"""Generate a complete insights report for the last ``days`` days, optionally filtered by source platform."""
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cutoff = time.time() - (days * 86400)
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# Drain the SessionDB's async accounting queue so counters are exact
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# (self.db may be a raw sqlite3 connection in tests — guard).
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flush = getattr(self.db, "flush_token_counts", None)
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if callable(flush):
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flush()
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sessions = self._get_sessions(cutoff, source)
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tool_usage = self._get_tool_usage(cutoff, source)
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skill_usage = self._get_skill_usage(cutoff, source)
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message_stats = self._get_message_stats(cutoff, source)
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if not sessions:
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return {"days": days, "source_filter": source, "empty": True, "overview": {}, "models": [], "platforms": [], "tools": [],
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"skills": self._compute_skill_breakdown([]), "activity": {}, "top_sessions": []}
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models = self._compute_model_breakdown(sessions, cutoff, source)
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return {
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"days": days, "source_filter": source, "empty": False, "generated_at": time.time(),
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"overview": self._compute_overview(sessions, message_stats, models),
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"models": models,
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"platforms": self._compute_platform_breakdown(sessions),
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"tools": self._compute_tool_breakdown(tool_usage),
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"skills": self._compute_skill_breakdown(skill_usage),
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"activity": self._compute_activity_patterns(sessions),
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"top_sessions": self._compute_top_sessions(sessions),
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}
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def get_usage_breakdown(self, days: int = 30, source: str = None) -> Dict[str, Any]:
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"""Analytics-usage payload (tools + skills) without a full generate(); the
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instr()-prefiltered skill query loads only skill_view/skill_manage messages."""
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cutoff = time.time() - (days * 86400)
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return {"tools": self._compute_tool_breakdown(self._get_tool_usage(cutoff, source)),
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"skills": self._compute_skill_breakdown(self._get_skill_usage(cutoff, source))}
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# ------------------------------------------------------------------ SQL
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def _get_sessions(self, cutoff: float, source: str = None) -> List[Dict]:
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# Coerce the two epoch columns once at load: one corrupt/TEXT cell must degrade to "unknown"
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# for that session, never abort the whole report (#99959).
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rows = [dict(row) for row in self._query("_GET_SESSIONS", cutoff, source)]
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for row in rows:
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for col in ("started_at", "ended_at"):
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row[col] = coerce_epoch(row.get(col), session_id=row.get("id"), field=col)
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return rows
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def _get_tool_usage(self, cutoff: float, source: str = None) -> List[Dict]:
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"""Tool call counts from two sources: ``tool_name`` on 'tool' rows (set
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by the gateway) and ``tool_calls`` JSON on assistant rows (covers CLI,
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where tool_name is not populated). The two views are reconciled PER
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SESSION (max — they describe the same calls), then summed across
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sessions: a global max dropped every call from a session that only
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carried the other representation (#9814)."""
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by_session_tool = Counter()
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for row in self._query("_GET_TOOL_NAMES", cutoff, source):
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by_session_tool[(row["session_id"], row["tool_name"])] += row["count"]
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calls_by_session_tool = Counter()
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for row in self._query("_GET_TOOL_CALLS", cutoff, source):
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try:
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names = filter(None, (fn.get("name") for fn in _iter_functions(row["tool_calls"])))
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calls_by_session_tool.update((row["session_id"], name) for name in names)
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except (TypeError, AttributeError):
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continue
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tool_counts = Counter()
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for key in set(by_session_tool) | set(calls_by_session_tool):
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tool_counts[key[1]] += max(by_session_tool.get(key, 0), calls_by_session_tool.get(key, 0))
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return [{"tool_name": name, "count": count} for name, count in tool_counts.most_common()]
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def _get_skill_usage(self, cutoff: float, source: str = None) -> List[Dict]:
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"""Extract per-skill usage from assistant tool calls."""
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skill_counts: Dict[str, Dict[str, Any]] = {}
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for row in self._query("_GET_SKILL_CALLS", cutoff, source):
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timestamp = row["timestamp"]
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for func in _iter_functions(row["tool_calls"]):
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tool_name = func.get("name")
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if tool_name not in _SKILL_TOOLS:
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continue
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skill_name = (_parse_json(func.get("arguments"), dict) or {}).get("name")
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if not isinstance(skill_name, str) or not skill_name.strip():
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continue
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entry = skill_counts.setdefault(skill_name, {"skill": skill_name, "view_count": 0, "manage_count": 0, "last_used_at": None})
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entry["view_count" if tool_name == "skill_view" else "manage_count"] += 1
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if timestamp is not None and (entry["last_used_at"] is None or timestamp > entry["last_used_at"]):
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entry["last_used_at"] = timestamp
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return list(skill_counts.values())
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def _get_message_stats(self, cutoff: float, source: str = None) -> Dict:
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rows = self._query("_GET_MESSAGE_STATS", cutoff, source)
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return dict(rows[0]) if rows else {"total_messages": 0, "user_messages": 0, "assistant_messages": 0, "tool_messages": 0}
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def _get_model_usage(self, cutoff: float, source: str = None) -> List[Dict]:
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"""Per-model usage rows; [] when the table is missing (older DB) so the caller falls back to the per-session aggregate."""
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try:
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return [dict(row) for row in self._query("_GET_MODEL_USAGE", cutoff, source)]
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except sqlite3.OperationalError:
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return []
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# -------------------------------------------------------------- Compute
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def _compute_overview(self, sessions: List[Dict], message_stats: Dict, models: Optional[List[Dict]] = None) -> Dict:
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# Per-model breakdown includes auxiliary usage rows (vision/compression/
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# titles) plus reconciled residuals, while session counters carry
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# main-loop usage only — sum the breakdown when available so overview
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# totals match the per-model table and aux spend isn't undercounted.
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rows = models or sessions
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total_input, total_output, total_cache_read, total_cache_write = (sum(int(r.get(k) or 0) for r in rows) for k in _TOKEN_KEYS)
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total_tokens = total_input + total_output + total_cache_read + total_cache_write
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total_tool_calls = sum(s.get("tool_call_count") or 0 for s in sessions)
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total_messages = sum(s.get("message_count") or 0 for s in sessions)
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total_cost = actual_cost = 0.0
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models_with_pricing, models_without_pricing, status_counts = set(), set(), Counter()
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for s in sessions:
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model = s.get("model") or ""
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estimated, status = _estimate_cost(s)
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total_cost += estimated
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actual_cost += s.get("actual_cost_usd") or 0.0
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status_counts[status] += 1
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known = has_known_pricing(model, s.get("billing_provider"), s.get("billing_base_url"))
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(models_with_pricing if known else models_without_pricing).add(_short_model(model))
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if models:
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total_cost = sum(float(m.get("cost") or 0.0) for m in models)
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# Guard against negative durations from clock drift.
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durations = [s["ended_at"] - s["started_at"] for s in sessions
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if s.get("started_at") and s.get("ended_at") and s["ended_at"] > s["started_at"]]
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started = [s["started_at"] for s in sessions if s.get("started_at")]
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n = len(sessions)
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return {
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"total_sessions": n, "total_messages": total_messages, "total_tool_calls": total_tool_calls,
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"total_input_tokens": total_input, "total_output_tokens": total_output,
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"total_cache_read_tokens": total_cache_read, "total_cache_write_tokens": total_cache_write,
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"total_tokens": total_tokens, "estimated_cost": total_cost, "actual_cost": actual_cost,
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"total_hours": sum(durations) / 3600 if durations else 0,
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"avg_session_duration": sum(durations) / len(durations) if durations else 0,
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"avg_messages_per_session": total_messages / n if sessions else 0,
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"avg_tokens_per_session": total_tokens / n if sessions else 0,
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"user_messages": message_stats.get("user_messages") or 0,
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"assistant_messages": message_stats.get("assistant_messages") or 0,
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"tool_messages": message_stats.get("tool_messages") or 0,
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"date_range_start": min(started) if started else None,
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"date_range_end": max(started) if started else None,
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"models_with_pricing": sorted(models_with_pricing),
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"models_without_pricing": sorted(models_without_pricing),
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"unknown_cost_sessions": status_counts["unknown"],
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"included_cost_sessions": status_counts["included"],
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}
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def _compute_model_breakdown(self, sessions: List[Dict], cutoff: float, source: str = None) -> List[Dict]:
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"""Tokens/cost per model from session_model_usage, so a session that
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switched models via ``/model`` splits across every model it used.
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Sessions without per-model rows (pre-table data) fall back to their
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single recorded aggregate. Tool calls aren't tied to an API call, so
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they stay attributed to the session's recorded model."""
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count_keys = _TOKEN_KEYS + ("reasoning_tokens", "api_call_count")
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model_data = defaultdict(lambda: {"sessions": set(), **dict.fromkeys(_TOKEN_KEYS, 0), "reasoning_tokens": 0, "total_tokens": 0,
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"api_calls": 0, "tool_calls": 0, "cost": 0.0, "actual_cost": 0.0})
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def _accumulate(model, provider, base_url, session_id, counts: Dict[str, int], *,
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stored_cost=None, actual_cost=None, cost_status=None):
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model = model or "unknown"
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d: Dict[str, Any] = model_data[_short_model(model)]
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d["sessions"].add(session_id)
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for key in _TOKEN_KEYS + ("reasoning_tokens",):
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d[key] += counts[key]
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d["total_tokens"] += sum(counts[k] for k in _TOKEN_KEYS)
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d["api_calls"] += counts["api_call_count"]
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if stored_cost is None:
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estimate, status = _estimate_cost(model, counts["input_tokens"], counts["output_tokens"], cache_read_tokens=counts["cache_read_tokens"],
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cache_write_tokens=counts["cache_write_tokens"], provider=provider or None, base_url=base_url)
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else:
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estimate, status = float(stored_cost or 0.0), cost_status or "unknown"
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d["cost"] += estimate
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d["actual_cost"] += float(actual_cost or 0.0)
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d["cost_status"] = status
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d["has_pricing"] = has_known_pricing(model, provider or None, base_url) or d.get("has_pricing", False)
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usage_totals = defaultdict(lambda: dict.fromkeys(count_keys, 0) | {"estimated_cost_usd": 0.0, "actual_cost_usd": 0.0})
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for r in self._get_model_usage(cutoff, source):
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totals: Dict[str, Any] = usage_totals[r["session_id"]]
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counts = {key: r[key] or 0 for key in count_keys}
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for key in count_keys:
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totals[key] += counts[key]
|
|
totals["estimated_cost_usd"] += r["estimated_cost_usd"] or 0.0
|
|
totals["actual_cost_usd"] += r["actual_cost_usd"] or 0.0
|
|
_accumulate(r["model"], r["billing_provider"], r.get("billing_base_url"), r["session_id"], counts,
|
|
stored_cost=r["estimated_cost_usd"] if r.get("cost_status") or r.get("cost_source") else None,
|
|
actual_cost=r["actual_cost_usd"], cost_status=r.get("cost_status"))
|
|
# Reconcile against the aggregate row: covers legacy sessions,
|
|
# interrupted migrations, and absolute cumulative updates without
|
|
# double-counting already-attributed route deltas.
|
|
for s in sessions:
|
|
totals = usage_totals[s["id"]]
|
|
residual = {k: max(0, (s.get(k) or 0) - totals[k]) for k in _TOKEN_KEYS + ("api_call_count",)}
|
|
residual["reasoning_tokens"] = 0
|
|
residual_cost = max(0.0, float(s.get("estimated_cost_usd") or 0.0) - totals["estimated_cost_usd"])
|
|
residual_actual = max(0.0, float(s.get("actual_cost_usd") or 0.0) - totals["actual_cost_usd"])
|
|
if any(residual.values()) or residual_cost or residual_actual:
|
|
_accumulate(s.get("model"), s.get("billing_provider"), s.get("billing_base_url"), s["id"], residual,
|
|
stored_cost=residual_cost, actual_cost=residual_actual, cost_status=s.get("cost_status"))
|
|
for s in sessions:
|
|
if s.get("tool_call_count"):
|
|
model_data[_short_model(s.get("model"))]["tool_calls"] += s["tool_call_count"]
|
|
# Models seen only via tool-call attribution never hit _accumulate —
|
|
# default has_pricing/cost_status so the output shape is uniform for JSON consumers.
|
|
defaults = (("has_pricing", False), ("cost_status", "unknown"))
|
|
result = [{"model": model, **data, "sessions": len(data["sessions"]), **{k: v for k, v in defaults if k not in data}}
|
|
for model, data in model_data.items()]
|
|
return sorted(result, key=lambda x: (x["total_tokens"], x["sessions"]), reverse=True)
|
|
|
|
def _compute_platform_breakdown(self, sessions: List[Dict]) -> List[Dict]:
|
|
platform_data = defaultdict(lambda: {"sessions": 0, "messages": 0, **dict.fromkeys(_TOKEN_KEYS, 0), "total_tokens": 0, "tool_calls": 0})
|
|
for s in sessions:
|
|
d = platform_data[s.get("source") or "unknown"]
|
|
d["sessions"] += 1
|
|
d["messages"] += s.get("message_count") or 0
|
|
for k in _TOKEN_KEYS:
|
|
d[k] += s.get(k) or 0
|
|
d["total_tokens"] += s.get(k) or 0
|
|
d["tool_calls"] += s.get("tool_call_count") or 0
|
|
return sorted(({"platform": platform, **data} for platform, data in platform_data.items()), key=lambda x: x["sessions"], reverse=True)
|
|
|
|
def _compute_tool_breakdown(self, tool_usage: List[Dict]) -> List[Dict]:
|
|
"""Ranked tool list with percentages."""
|
|
total_calls = sum(t["count"] for t in tool_usage)
|
|
return [{"tool": t["tool_name"], "count": t["count"], "percentage": (t["count"] / total_calls * 100) if total_calls else 0} for t in tool_usage]
|
|
|
|
def _compute_skill_breakdown(self, skill_usage: List[Dict]) -> Dict[str, Any]:
|
|
"""Per-skill usage → summary + ranked list."""
|
|
total_skill_loads = sum(s["view_count"] for s in skill_usage)
|
|
total_skill_edits = sum(s["manage_count"] for s in skill_usage)
|
|
total_skill_actions = total_skill_loads + total_skill_edits
|
|
top_skills = [{
|
|
"skill": skill["skill"], "view_count": skill["view_count"], "manage_count": skill["manage_count"], "total_count": total_count,
|
|
"percentage": (total_count / total_skill_actions * 100) if total_skill_actions else 0, "last_used_at": skill.get("last_used_at"),
|
|
} for skill in skill_usage for total_count in (skill["view_count"] + skill["manage_count"],)]
|
|
top_skills.sort(key=lambda s: (s["total_count"], s["view_count"], s["manage_count"], s["last_used_at"] or 0, s["skill"]), reverse=True)
|
|
return {
|
|
"summary": {"total_skill_loads": total_skill_loads, "total_skill_edits": total_skill_edits,
|
|
"total_skill_actions": total_skill_actions, "distinct_skills_used": len(skill_usage)},
|
|
"top_skills": top_skills,
|
|
}
|
|
|
|
def _compute_activity_patterns(self, sessions: List[Dict]) -> Dict:
|
|
"""Activity by day of week, hour, and active-day streak."""
|
|
day_counts, hour_counts, daily_counts = Counter(), Counter(), Counter() # weekday (0=Monday), hour, "YYYY-MM-DD"
|
|
for s in sessions:
|
|
ts = s.get("started_at")
|
|
if not ts:
|
|
continue
|
|
dt = datetime.fromtimestamp(ts)
|
|
day_counts[dt.weekday()] += 1
|
|
hour_counts[dt.hour] += 1
|
|
daily_counts[dt.strftime("%Y-%m-%d")] += 1
|
|
day_names = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]
|
|
day_breakdown = [{"day": day_names[i], "count": day_counts.get(i, 0)} for i in range(7)]
|
|
hour_breakdown = [{"hour": i, "count": hour_counts.get(i, 0)} for i in range(24)]
|
|
max_streak = 0
|
|
if daily_counts:
|
|
dates = [datetime.strptime(d, "%Y-%m-%d") for d in sorted(daily_counts)]
|
|
current_streak = max_streak = 1
|
|
for prev, cur in zip(dates, dates[1:]):
|
|
current_streak = current_streak + 1 if (cur - prev).days == 1 else 1
|
|
max_streak = max(max_streak, current_streak)
|
|
return {"by_day": day_breakdown, "by_hour": hour_breakdown, "busiest_day": max(day_breakdown, key=lambda x: x["count"]),
|
|
"busiest_hour": max(hour_breakdown, key=lambda x: x["count"]), "active_days": len(daily_counts), "max_streak": max_streak}
|
|
|
|
_TOP_METRICS = (
|
|
("Most messages", lambda s: s.get("message_count") or 0, "{} msgs"),
|
|
("Most tokens", lambda s: (s.get("input_tokens") or 0) + (s.get("output_tokens") or 0), "{:,} tokens"),
|
|
("Most tool calls", lambda s: s.get("tool_call_count") or 0, "{} calls"),
|
|
)
|
|
|
|
def _compute_top_sessions(self, sessions: List[Dict]) -> List[Dict]:
|
|
"""Notable sessions (longest, most messages, most tokens, most tool calls)."""
|
|
top = []
|
|
timed = [s for s in sessions if s.get("started_at") and s.get("ended_at")]
|
|
if timed:
|
|
longest = max(timed, key=lambda s: s["ended_at"] - s["started_at"])
|
|
top.append({"label": "Longest session", "session_id": longest["id"][:16],
|
|
"value": format_duration_compact(longest["ended_at"] - longest["started_at"]), "date": _day(longest["started_at"])})
|
|
for label, metric, fmt in self._TOP_METRICS:
|
|
best = max(sessions, key=metric)
|
|
value = metric(best)
|
|
if value > 0:
|
|
top.append({"label": label, "session_id": best["id"][:16], "value": fmt.format(value), "date": _day(best.get("started_at"))})
|
|
return top
|
|
|
|
# ------------------------------------------------------------- Formatting
|
|
|
|
@staticmethod
|
|
def _section(title: str) -> List[str]:
|
|
return [f" {title}", " " + "─" * 56]
|
|
|
|
@staticmethod
|
|
def _cost_lines(o: Dict, templates: tuple) -> List[str]:
|
|
"""One formatted line per non-zero cost bucket (estimated, included, unknown)."""
|
|
# Cost breakdown — surface the three buckets so subscription-included and unknown-cost sessions are
|
|
# visible instead of silently collapsing to $0. See #77223.
|
|
est_cost = o.get("estimated_cost", 0.0)
|
|
values = (_fmt_est_cost(est_cost) if est_cost > 0 else "", o.get("included_cost_sessions", 0), o.get("unknown_cost_sessions", 0))
|
|
return [tpl.format(v) for tpl, v in zip(templates, values) if v]
|
|
|
|
def format_terminal(self, report: Dict) -> str:
|
|
"""Format the insights report for terminal display (CLI)."""
|
|
if report.get("empty"):
|
|
src = f" (source: {report['source_filter']})" if report.get("source_filter") else ""
|
|
return f" No sessions found in the last {report.get('days', 30)} days{src}."
|
|
o = report["overview"]
|
|
period_label = f"Last {report['days']} days"
|
|
if report.get("source_filter"):
|
|
period_label += f" ({report['source_filter']})"
|
|
padding = 58 - len(period_label) - 2
|
|
left_pad = padding // 2
|
|
lines = [
|
|
"",
|
|
" ╔══════════════════════════════════════════════════════════╗",
|
|
" ║ 📊 Hermes Insights ║",
|
|
f" ║{' ' * left_pad} {period_label} {' ' * (padding - left_pad)}║",
|
|
" ╚══════════════════════════════════════════════════════════╝",
|
|
"",
|
|
]
|
|
if (start := coerce_epoch(o.get("date_range_start"))) is not None and (end := coerce_epoch(o.get("date_range_end"))) is not None:
|
|
start_str = datetime.fromtimestamp(start).strftime("%b %d, %Y")
|
|
end_str = datetime.fromtimestamp(end).strftime("%b %d, %Y")
|
|
lines += [f" Period: {start_str} — {end_str}", ""]
|
|
lines += self._section("📋 Overview") + [
|
|
f" Sessions: {o['total_sessions']:<12} Messages: {o['total_messages']:,}",
|
|
f" Tool calls: {o['total_tool_calls']:<12,} User messages: {o['user_messages']:,}",
|
|
f" Input tokens: {o['total_input_tokens']:<12,} Output tokens: {o['total_output_tokens']:,}",
|
|
f" Total tokens: {o['total_tokens']:,}",
|
|
]
|
|
if o["total_hours"] > 0:
|
|
lines.append(f" Active time: ~{format_duration_compact(o['total_hours'] * 3600):<11} Avg session: ~{format_duration_compact(o['avg_session_duration'])}")
|
|
lines += [f" Avg msgs/session: {o['avg_messages_per_session']:.1f}", ""]
|
|
# Cost buckets: show included/unknown sessions instead of collapsing to $0.
|
|
cost_lines = self._cost_lines(o, (" Estimated: {}", " Included: {} session(s) (subscription — no provider invoice)",
|
|
" Unknown: {} session(s) (no pricing data)"))
|
|
if cost_lines:
|
|
lines += self._section("💰 Cost") + cost_lines + [""]
|
|
if report["models"]:
|
|
lines += self._section("🤖 Models Used") + [f" {'Model':<30} {'Sessions':>8} {'Tokens':>12}"]
|
|
lines += [f" {m['model'][:28]:<30} {m['sessions']:>8} {m['total_tokens']:>12,}" for m in report["models"]] + [""]
|
|
platforms = report["platforms"]
|
|
if len(platforms) > 1 or (platforms and platforms[0]["platform"] != "cli"):
|
|
lines += self._section("📱 Platforms") + [f" {'Platform':<14} {'Sessions':>8} {'Messages':>10} {'Tokens':>14}"]
|
|
lines += [f" {p['platform']:<14} {p['sessions']:>8} {p['messages']:>10,} {p['total_tokens']:>14,}" for p in platforms] + [""]
|
|
if report["tools"]:
|
|
lines += self._section("🔧 Top Tools") + [f" {'Tool':<28} {'Calls':>8} {'%':>8}"]
|
|
lines += [f" {t['tool']:<28} {t['count']:>8,} {t['percentage']:>7.1f}%" for t in report["tools"][:15]]
|
|
if len(report["tools"]) > 15:
|
|
lines.append(f" ... and {len(report['tools']) - 15} more tools")
|
|
lines.append("")
|
|
skills = report.get("skills", {})
|
|
top_skills = skills.get("top_skills", [])
|
|
if top_skills:
|
|
lines += self._section("🧠 Top Skills") + [f" {'Skill':<28} {'Loads':>7} {'Edits':>7} {'Last used':>11}"]
|
|
for skill in top_skills[:10]:
|
|
last_used = _day(skill.get("last_used_at")) if skill.get("last_used_at") else "—"
|
|
lines.append(f" {skill['skill'][:28]:<28} {skill['view_count']:>7,} {skill['manage_count']:>7,} {last_used:>11}")
|
|
summary = skills.get("summary", {})
|
|
lines += [f" Distinct skills: {summary.get('distinct_skills_used', 0)} Loads: {summary.get('total_skill_loads', 0):,} "
|
|
f"Edits: {summary.get('total_skill_edits', 0):,}", ""]
|
|
act = report.get("activity", {})
|
|
if act.get("by_day"):
|
|
lines += self._section("📅 Activity Patterns")
|
|
bars = _bar_chart([d["count"] for d in act["by_day"]], max_width=15)
|
|
lines += [f" {d['day']} {bar:<15} {d['count']}" for bar, d in zip(bars, act["by_day"])] + [""]
|
|
busy_hours = [h for h in sorted(act["by_hour"], key=lambda x: x["count"], reverse=True) if h["count"] > 0][:5]
|
|
if busy_hours:
|
|
hour_strs = [f"{_hour12(h['hour'])} ({h['count']})" for h in busy_hours]
|
|
lines.append(f" Peak hours: {', '.join(hour_strs)}")
|
|
if act.get("active_days"):
|
|
lines.append(f" Active days: {act['active_days']}")
|
|
if act.get("max_streak") and act["max_streak"] > 1:
|
|
lines.append(f" Best streak: {act['max_streak']} consecutive days")
|
|
lines.append("")
|
|
if report.get("top_sessions"):
|
|
lines += self._section("🏆 Notable Sessions")
|
|
lines += [f" {ts['label']:<20} {ts['value']:<18} ({ts['date']}, {ts['session_id']})" for ts in report["top_sessions"]] + [""]
|
|
return "\n".join(lines)
|
|
|
|
def format_gateway(self, report: Dict) -> str:
|
|
"""Format the insights report for gateway/messaging (shorter)."""
|
|
if report.get("empty"):
|
|
return f"No sessions found in the last {report.get('days', 30)} days."
|
|
o = report["overview"]
|
|
lines = [
|
|
f"📊 **Hermes Insights** — Last {report['days']} days\n",
|
|
f"**Sessions:** {o['total_sessions']} | **Messages:** {o['total_messages']:,} | **Tool calls:** {o['total_tool_calls']:,}",
|
|
f"**Tokens:** {o['total_tokens']:,} (in: {o['total_input_tokens']:,} / out: {o['total_output_tokens']:,})",
|
|
]
|
|
if o["total_hours"] > 0:
|
|
lines.append(f"**Active time:** ~{format_duration_compact(o['total_hours'] * 3600)} | **Avg session:** ~{format_duration_compact(o['avg_session_duration'])}")
|
|
lines.append("")
|
|
cost_parts = self._cost_lines(o, ("{} estimated", "{} included (subscription)", "{} unknown"))
|
|
if cost_parts:
|
|
lines += [f"**Cost:** {' | '.join(cost_parts)}", ""]
|
|
if report["models"]:
|
|
lines += ["**🤖 Models:**"] + [f" {m['model'][:25]} — {m['sessions']} sessions, {m['total_tokens']:,} tokens" for m in report["models"][:5]] + [""]
|
|
if len(report["platforms"]) > 1:
|
|
lines += ["**📱 Platforms:**"] + [f" {p['platform']} — {p['sessions']} sessions, {p['messages']:,} msgs" for p in report["platforms"]] + [""]
|
|
if report["tools"]:
|
|
lines += ["**🔧 Top Tools:**"] + [f" {t['tool']} — {t['count']:,} calls ({t['percentage']:.1f}%)" for t in report["tools"][:8]] + [""]
|
|
skills = report.get("skills", {})
|
|
if skills.get("top_skills"):
|
|
lines.append("**🧠 Top Skills:**")
|
|
for skill in skills["top_skills"][:5]:
|
|
suffix = f", last used {_day(skill['last_used_at'])}" if skill.get("last_used_at") else ""
|
|
lines.append(f" {skill['skill']} — {skill['view_count']:,} loads, {skill['manage_count']:,} edits{suffix}")
|
|
lines.append("")
|
|
act = report.get("activity", {})
|
|
if act.get("busiest_day") and act.get("busiest_hour"):
|
|
lines.append(f"**📅 Busiest:** {act['busiest_day']['day']}s ({act['busiest_day']['count']} sessions), {_hour12(act['busiest_hour']['hour'])} ({act['busiest_hour']['count']} sessions)")
|
|
if act.get("active_days"):
|
|
lines.append(f"**Active days:** {act['active_days']}")
|
|
if act.get("max_streak", 0) > 1:
|
|
lines.append(f"**Best streak:** {act['max_streak']} consecutive days")
|
|
return "\n".join(lines)
|