Files
planet/backend/app/services/bgp_ai_brief_store.py

161 lines
4.7 KiB
Python

from __future__ import annotations
import json
from dataclasses import dataclass
from datetime import UTC, datetime
from pathlib import Path
from typing import Any
from uuid import uuid4
from app.core.config import ROOT_DIR
from app.schemas.ai import BGPBriefRecordResponse, BGPBriefRecordSummary, SituationalAnalysisResponse
_BRIEF_STORAGE_DIR = ROOT_DIR / "data" / "ai" / "bgp-briefs"
_METADATA_PREFIX = "<!-- planet-bgp-brief-meta "
_METADATA_SUFFIX = " -->"
_BRIEF_TITLE = "BGP AI 简报"
@dataclass(slots=True)
class _StoredBrief:
id: str
title: str
provider: str
model: str
request_id: str | None
generated_at: str
content_markdown: str
facts: list[str]
context: dict[str, Any]
path: Path
def _ensure_storage_dir() -> Path:
_BRIEF_STORAGE_DIR.mkdir(parents=True, exist_ok=True)
return _BRIEF_STORAGE_DIR
def _build_metadata_line(metadata: dict[str, Any]) -> str:
return f"{_METADATA_PREFIX}{json.dumps(metadata, ensure_ascii=False)}{_METADATA_SUFFIX}"
def _parse_brief_file(path: Path) -> _StoredBrief | None:
try:
raw_text = path.read_text(encoding="utf-8")
except OSError:
return None
first_line, separator, remainder = raw_text.partition("\n")
if not separator or not first_line.startswith(_METADATA_PREFIX) or not first_line.endswith(_METADATA_SUFFIX):
return None
metadata_payload = first_line[len(_METADATA_PREFIX) : -len(_METADATA_SUFFIX)]
try:
metadata = json.loads(metadata_payload)
except json.JSONDecodeError:
return None
return _StoredBrief(
id=str(metadata.get("id") or path.stem),
title=str(metadata.get("title") or _BRIEF_TITLE),
provider=str(metadata.get("provider") or "-"),
model=str(metadata.get("model") or "-"),
request_id=metadata.get("request_id"),
generated_at=str(metadata.get("generated_at") or datetime.fromtimestamp(path.stat().st_mtime, UTC).isoformat()),
content_markdown=remainder.lstrip("\n"),
facts=list(metadata.get("facts") or []),
context=dict(metadata.get("context") or {}),
path=path,
)
def list_bgp_brief_records(limit: int = 50) -> list[BGPBriefRecordSummary]:
storage_dir = _ensure_storage_dir()
records: list[_StoredBrief] = []
for path in storage_dir.glob("*.md"):
parsed = _parse_brief_file(path)
if parsed is not None:
records.append(parsed)
records.sort(key=lambda item: item.generated_at, reverse=True)
return [
BGPBriefRecordSummary(
id=item.id,
title=item.title,
provider=item.provider,
model=item.model,
request_id=item.request_id,
generated_at=item.generated_at,
)
for item in records[: max(limit, 1)]
]
def get_bgp_brief_record(brief_id: str) -> BGPBriefRecordResponse | None:
path = _ensure_storage_dir() / f"{brief_id}.md"
parsed = _parse_brief_file(path)
if parsed is None:
return None
return BGPBriefRecordResponse(
id=parsed.id,
title=parsed.title,
provider=parsed.provider,
model=parsed.model,
request_id=parsed.request_id,
generated_at=parsed.generated_at,
content_markdown=parsed.content_markdown,
facts=parsed.facts,
context=parsed.context,
)
def get_latest_bgp_brief_record() -> BGPBriefRecordResponse | None:
summaries = list_bgp_brief_records(limit=1)
if not summaries:
return None
return get_bgp_brief_record(summaries[0].id)
def save_bgp_brief_record(
analysis: SituationalAnalysisResponse,
*,
request_id: str | None,
facts: list[str] | None = None,
context: dict[str, Any] | None = None,
generated_at: datetime | None = None,
) -> BGPBriefRecordResponse:
created_at = generated_at or datetime.now(UTC)
brief_id = f"{created_at.strftime('%Y%m%dT%H%M%SZ')}-{uuid4().hex[:8]}"
path = _ensure_storage_dir() / f"{brief_id}.md"
metadata = {
"id": brief_id,
"title": _BRIEF_TITLE,
"provider": analysis.provider,
"model": analysis.model,
"request_id": request_id,
"generated_at": created_at.isoformat(),
"facts": facts or [],
"context": context or {},
}
markdown_text = f"{_build_metadata_line(metadata)}\n\n{analysis.content.rstrip()}\n"
path.write_text(markdown_text, encoding="utf-8")
return BGPBriefRecordResponse(
id=brief_id,
title=_BRIEF_TITLE,
provider=analysis.provider,
model=analysis.model,
request_id=request_id,
generated_at=created_at.isoformat(),
content_markdown=analysis.content,
facts=facts or [],
context=context or {},
)