341 lines
11 KiB
Python
341 lines
11 KiB
Python
"""Frictionless diagnose service — NL time extraction + layered log search.
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This module is the public interface for the diagnose package.
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Full implementation lives here so that patch("app.services.diagnose._HAS_DATEPARSER")
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and patch("app.services.diagnose._search_dates") continue to target the correct
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namespace, preserving backward compatibility with existing tests.
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The verbatim original is preserved in legacy.py for reference.
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"""
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from __future__ import annotations
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import asyncio
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import dataclasses
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import logging
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import os
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import re
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from collections.abc import AsyncGenerator
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from datetime import datetime, timedelta, timezone
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from pathlib import Path
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from typing import Any
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from app.context.retriever import retrieve_context, format_context_block
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from app.services.llm import summarize
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from app.services.search import SearchResult, entries_in_window, search
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logger = logging.getLogger(__name__)
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try:
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from dateparser.search import search_dates as _search_dates # type: ignore[import]
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_HAS_DATEPARSER = True
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except ImportError:
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_search_dates = None # type: ignore[assignment]
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_HAS_DATEPARSER = False
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_RELATIVE_RE = re.compile(
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r"\b(?:last|past)\s+(?:(?P<n>\d+)|(?P<approx>a\s+few|few|couple(?:\s+of)?|several))?\s*(?P<unit>minute|hour|day|week)s?\b",
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re.IGNORECASE,
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)
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_RELATIVE_UNITS = {"minute": 1, "hour": 60, "day": 1440, "week": 10080}
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# Fuzzy quantifiers map to a reasonable span so "last few hours" → 3h window
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_APPROX_N = 3
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def _relative_window(match: re.Match) -> tuple[str, str]:
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"""Convert a relative time match to (since_iso, until_iso)."""
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n_str = match.group("n")
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approx = match.group("approx")
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unit = match.group("unit").lower()
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n = int(n_str) if n_str else (_APPROX_N if approx else 1)
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minutes = n * _RELATIVE_UNITS[unit]
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return _last_n_minutes(minutes), _now_iso()
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def parse_time_window(query: str) -> tuple[str | None, str | None, str]:
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"""Extract a time window from a natural-language query string.
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Returns (since_iso, until_iso, keywords) where keywords is the query with
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the matched time phrase stripped. Falls back to last-60-min window.
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"""
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# Handle relative expressions first ("last hour", "past 30 minutes", etc.)
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# dateparser misinterprets these as absolute times.
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m = _RELATIVE_RE.search(query)
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if m:
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since, until = _relative_window(m)
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keywords = re.sub(r"\s{2,}", " ", query[: m.start()] + query[m.end() :]).strip()
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return since, until, keywords or query
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if _HAS_DATEPARSER and _search_dates is not None:
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# Tell dateparser what timezone the user is in so "3:35 am" means local time.
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# PREFER_DAY_OF_MONTH is unused here but PREFER_DATES_FROM=past ensures
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# "3:35 am" resolves to the most recent past occurrence, not a future one.
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local_offset = datetime.now().astimezone().utcoffset()
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offset_h = int((local_offset.total_seconds() if local_offset else 0) / 3600)
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tz_str = f"UTC{'+' if offset_h >= 0 else ''}{offset_h}"
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try:
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results = _search_dates(
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query,
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languages=["en"],
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settings={
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"PREFER_DATES_FROM": "past",
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"TIMEZONE": tz_str,
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"RETURN_AS_TIMEZONE_AWARE": True,
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},
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)
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except Exception as e:
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logger.warning(
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"dateparser failed (%s) on query %r — falling back to 60-min window",
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type(e).__name__,
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query,
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)
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results = None
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if results:
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phrase, dt = results[0]
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if dt.tzinfo is None:
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dt = dt.replace(tzinfo=timezone.utc)
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else:
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dt = dt.astimezone(
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timezone.utc
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) # normalise to UTC for SQLite string compare
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since = (dt - timedelta(minutes=30)).isoformat()
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until = (dt + timedelta(minutes=30)).isoformat()
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keywords = re.sub(r"\s{2,}", " ", query.replace(phrase, " ").strip())
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return since, until, keywords or query
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return _last_n_minutes(60), _now_iso(), query
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def diagnose(
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db_path: Path,
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query: str,
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since: str | None = None,
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until: str | None = None,
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source_filter: str | None = None,
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llm_url: str | None = None,
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llm_model: str | None = None,
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llm_api_key: str | None = None,
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) -> dict[str, Any]:
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"""Run layered log search with NL time extraction. Returns summary + entries."""
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time_detected = since is not None and until is not None
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if not time_detected:
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parsed_since, parsed_until, keywords = parse_time_window(query)
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since = since or parsed_since
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until = until or parsed_until
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time_detected = keywords != query
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else:
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keywords = query
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keyword_hits = search(
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db_path,
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query=keywords,
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since=since,
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until=until,
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source_filter=source_filter,
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limit=150,
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or_mode=True,
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)
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window_hits = entries_in_window(
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db_path,
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since=since,
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until=until,
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source_filter=source_filter,
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limit=50,
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per_source_cap=15,
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)
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seen: set[str] = set()
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merged: list[SearchResult] = []
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for r in keyword_hits + window_hits:
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if r.entry_id not in seen:
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seen.add(r.entry_id)
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merged.append(r)
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combined = sorted(merged, key=lambda r: (r.timestamp_iso or "\xff", r.sequence))[
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:200
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]
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by_severity: dict[str, int] = {"CRITICAL": 0, "ERROR": 0, "WARN": 0, "INFO": 0}
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by_source: dict[str, int] = {}
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for r in combined:
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sev = (r.severity or "INFO").upper()
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if sev in by_severity:
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by_severity[sev] += 1
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by_source[r.source_id] = by_source.get(r.source_id, 0) + 1
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reasoning: str | None = None
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if llm_url and llm_model:
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reasoning = summarize(
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query, combined, llm_url=llm_url, llm_model=llm_model, api_key=llm_api_key
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)
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return {
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"summary": {
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"total": len(combined),
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"window_start": since,
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"window_end": until,
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"time_detected": time_detected,
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"by_severity": by_severity,
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"by_source": by_source,
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},
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"reasoning": reasoning,
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"entries": combined,
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}
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async def diagnose_stream(
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db_path: Path,
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query: str,
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since: str | None = None,
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until: str | None = None,
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source_filter: str | None = None,
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llm_url: str | None = None,
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llm_model: str | None = None,
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llm_api_key: str | None = None,
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) -> AsyncGenerator[dict[str, Any], None]:
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"""Async generator yielding SSE event dicts for the diagnose pipeline.
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Yields events in order:
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{"type":"status","message":"…"} — pipeline progress
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{"type":"summary","data":{…}} — window + severity counts (fast, from DB)
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{"type":"entries","data":[…]} — log entries (fast, from DB)
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{"type":"reasoning","text":"…"} — LLM analysis (slow, optional)
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{"type":"done"}
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"""
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keywords = query.strip()
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source_browse = not keywords and source_filter is not None
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if source_browse:
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# No keyword — browsing a source directly. Use 24h window; skip FTS entirely.
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yield {"type": "status", "message": f"Loading {source_filter}…"}
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since = since or _last_n_minutes(60 * 24)
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until = until or _now_iso()
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time_detected = False
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else:
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yield {"type": "status", "message": "Parsing time window…"}
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time_detected = since is not None and until is not None
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if not time_detected:
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parsed_since, parsed_until, keywords = await asyncio.to_thread(
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parse_time_window, query
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)
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since = since or parsed_since
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until = until or parsed_until
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time_detected = keywords != query
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yield {"type": "status", "message": "Loading environment context…"}
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ctx = await asyncio.to_thread(lambda: retrieve_context(db_path, query))
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context_block = format_context_block(ctx)
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yield {
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"type": "context",
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"facts": ctx.facts,
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"chunks": ctx.chunks,
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}
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yield {"type": "status", "message": "Searching logs…"}
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if source_browse:
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keyword_hits: list[SearchResult] = []
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window_hits = await asyncio.to_thread(
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lambda: entries_in_window(
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db_path,
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since,
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until,
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source_filter=source_filter,
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limit=200,
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)
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)
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else:
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keyword_hits, window_hits = await asyncio.gather(
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asyncio.to_thread(
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lambda: search(
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db_path,
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keywords,
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source_filter=source_filter,
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since=since,
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until=until,
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limit=150,
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or_mode=True,
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)
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),
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asyncio.to_thread(
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lambda: entries_in_window(
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db_path,
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since,
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until,
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source_filter=source_filter,
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limit=50,
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per_source_cap=15,
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)
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),
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)
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seen: set[str] = set()
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merged: list[SearchResult] = []
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for r in keyword_hits + window_hits:
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if r.entry_id not in seen:
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seen.add(r.entry_id)
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merged.append(r)
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combined = sorted(merged, key=lambda r: (r.timestamp_iso or "\xff", r.sequence))[
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:200
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]
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by_severity: dict[str, int] = {"CRITICAL": 0, "ERROR": 0, "WARN": 0, "INFO": 0}
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by_source: dict[str, int] = {}
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for r in combined:
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sev = (r.severity or "INFO").upper()
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if sev in by_severity:
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by_severity[sev] += 1
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by_source[r.source_id] = by_source.get(r.source_id, 0) + 1
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yield {
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"type": "summary",
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"data": {
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"total": len(combined),
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"window_start": since,
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"window_end": until,
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"time_detected": time_detected,
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"by_severity": by_severity,
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"by_source": by_source,
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},
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}
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yield {"type": "entries", "data": [dataclasses.asdict(r) for r in combined]}
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if llm_url and llm_model and combined:
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yield {"type": "status", "message": "Analyzing with LLM…"}
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reasoning = await asyncio.to_thread(
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lambda: summarize(
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query,
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combined,
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llm_url,
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llm_model,
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llm_api_key,
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context_block=context_block,
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)
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)
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if reasoning:
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yield {"type": "reasoning", "text": reasoning}
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yield {"type": "done"}
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def _now_iso() -> str:
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return datetime.now(timezone.utc).isoformat()
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def _last_n_minutes(n: int) -> str:
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return (datetime.now(timezone.utc) - timedelta(minutes=n)).isoformat()
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__all__ = [
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"diagnose",
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"diagnose_stream",
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"parse_time_window",
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]
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# Feature flag for Task 6
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MULTI_AGENT_ENABLED = (
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os.getenv("TURNSTONE_MULTI_AGENT_DIAGNOSE", "false").lower() == "true"
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)
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