generated from kgod/ai-review-template
hypandClaude
ae2d9b128d
自内部仓库剥离深度优化与 RAG 知识库后的交付版本： - Builder 对话式简历生成（FSM + 意图路由 LLM 兜底增强） - 条目级轻度优化：事实覆盖门禁 + STAR/bullet 修复链，功能/简介/成果与技术栈同级保护 - 简历导入：DOCX/PDF 解析、结构归一、手机号脱敏 - PostgreSQL 运行时 + Alembic 迁移链 Co-Authored-By: Claude <noreply@anthropic.com>
75 lines
2.7 KiB
Python
75 lines
2.7 KiB
Python
"""SSE transport for observable Builder chat turns."""
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from __future__ import annotations
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import json
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import logging
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from collections.abc import Callable, Iterator
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from queue import Queue
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from threading import Thread
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from typing import Any
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from fastapi.responses import StreamingResponse
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from .fsm import FSMError
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from .models import ActionResponse
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BuilderOperation = Callable[[], ActionResponse]
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PHASE_LABELS = {
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"suggesting_next": "正在判断下一步建议",
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"structuring": "正在整理信息",
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"checking_gaps": "正在检查可补充的信息",
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"rewriting": "正在生成候选改写",
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"saving": "正在写入简历",
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}
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def stream_builder_message(operation: BuilderOperation) -> StreamingResponse:
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events: Queue[tuple[str, dict[str, Any]] | None] = Queue()
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def emit(event: str, data: dict[str, Any] | None = None) -> None:
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events.put((event, data or {}))
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def worker() -> None:
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try:
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result = operation()
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_emit_statuses(emit, tuple(result.builder_stream_phases))
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for chunk in _chunks(str((result.turn.content if result.turn else "") or "")):
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emit("delta", {"text": chunk})
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emit("complete", result.model_dump(mode="json"))
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except FSMError as exc:
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emit("error", {"code": exc.code, "message": exc.message, "status_code": exc.status_code})
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except Exception as exc: # pragma: no cover - defensive transport boundary
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logging.getLogger(__name__).exception("builder SSE operation failed")
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emit("error", {"code": "builder_stream_failed", "message": "Resume assistant stream failed. Please retry.", "status_code": 502, "reason_code": type(exc).__name__})
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finally:
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events.put(None)
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def generate() -> Iterator[str]:
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thread = Thread(target=worker, name="resume-builder-sse", daemon=True)
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thread.start()
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while True:
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item = events.get()
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if item is None:
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break
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event, data = item
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yield _frame(event, data)
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return StreamingResponse(generate(), media_type="text/event-stream", headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"})
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def _emit_statuses(emit: Callable[[str, dict[str, Any]], None], phases: tuple[str, ...]) -> None:
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for phase in phases:
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emit("status", {"phase": phase, "label": PHASE_LABELS[phase]})
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def _chunks(text: str, size: int = 24) -> Iterator[str]:
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if not text:
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return
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for index in range(0, len(text), size):
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yield text[index : index + size]
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def _frame(event: str, data: dict[str, Any]) -> str:
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return f"event: {event}\ndata: {json.dumps(data, ensure_ascii=False, separators=(',', ':'))}\n\n" |