simon
ff911cf6f7
Token Plan 迁移 / 配置热加载:
- configs/llm_models.yaml: 各场景切到 Token Plan(deepseek-v4.1-flash / qwen3.6-flash)
- 新增 configs/runtime_env.py: .env 按 (mtime_ns, size) 热加载并同步 os.environ,
统一 env_get 取值;llm / embedding / vectorstore / mcp / pipeline 改用 env_get
- configs/loader.py / scripts/run_scheduler.py 等配套调整
- 新增 tests/test_hot_reload.py
日报 AI 摘要为空修复(2026-09-25):
- 根因: 推理模型的 reasoning token 与正文共用 max_tokens, 预算 1500 被"思考"
占满 -> text_tokens=0 / finish_reason=length, 摘要静默为空且不重试
- daily_report 场景新增 max_tokens(默认 4000, YAML 保存即热生效);
LLMConfig 支持可选 max_tokens; 分块预算 800 -> 2000
- _llm_call 拆出 _call_once, 正文为空时自动加倍预算重试(上限 16000),
用尽才降级返回空串; 网络异常重试语义不变
- docs/user-guide.md 新增 FAQ; continuation.md 记录本次排查
- 已重跑 2026-09-25 日报(report_id=357)补回 466 字摘要
测试: 相关用例 56 passed(test_hot_reload 12 passed);
ruff 无新增问题; 3 个 crawler 既有失败与本改动无关
112 lines
3.6 KiB
Python
112 lines
3.6 KiB
Python
"""本地 BGE-M3 嵌入实现(可选)。
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需要额外安装本地依赖:
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uv sync --extra local-embedding
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模型 BAAI/bge-m3 首次加载约 2.3 GB(从 HuggingFace 自动下载)。
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默认 1024 维,与 DashScope 兼容。
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"""
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from __future__ import annotations
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import asyncio
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from typing import TYPE_CHECKING
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from loguru import logger
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from .base import AsyncEmbeddingProvider, EmbeddingProvider
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from .models import EmbeddingError
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if TYPE_CHECKING:
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from sentence_transformers import SentenceTransformer
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from configs.loader import load_scene_config
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from configs.runtime_env import env_get
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LOCAL_DEFAULT_MODEL = "BAAI/bge-m3"
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LOCAL_DEFAULT_DIM = 1024
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def _read_env(key: str, default: str | None = None) -> str | None:
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"""读取环境变量（先热加载 .env，改文件后无需重启进程）。"""
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return env_get(key, default)
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def _try_import_st() -> type[SentenceTransformer]:
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try:
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from sentence_transformers import SentenceTransformer # noqa: F811
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except ImportError as e:
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raise EmbeddingError(
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"本地 BGE-M3 后端需要 sentence-transformers,请执行: "
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"uv sync --extra local-embedding"
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) from e
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return SentenceTransformer
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class LocalBGEEmbeddingProvider(EmbeddingProvider):
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"""本地 BGE-M3 同步实现。"""
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name = "local-bge"
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def __init__(
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self,
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*,
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model: str | None = None,
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device: str | None = None,
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normalize: bool = True,
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) -> None:
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st_cls = _try_import_st()
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# 模型优先级:CLI/参数 > YAML scenes.embedding > LOCAL_EMBEDDING_MODEL > 默认值
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# 不读全局 EMBEDDING_MODEL,避免与 DashScope 冲突
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scene_model = load_scene_config("embedding").get("model")
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self.model = (
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model
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or scene_model
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or _read_env("LOCAL_EMBEDDING_MODEL", LOCAL_DEFAULT_MODEL)
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or LOCAL_DEFAULT_MODEL
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)
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self.dim = LOCAL_DEFAULT_DIM
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self._device = device # None -> 让 ST 自选 cpu/cuda
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self._normalize = normalize
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logger.info("加载本地嵌入模型 {} (device={}),首次会下载...", self.model, device or "auto")
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self._st = st_cls(self.model, device=device)
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# 实际维度自检
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actual_dim = self._st.get_sentence_embedding_dimension()
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if actual_dim and actual_dim != self.dim:
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logger.warning(
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"BGE 模型实际维度 {} 与默认 {} 不一致,以实际为准", actual_dim, self.dim
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)
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self.dim = actual_dim
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def embed_batch(self, texts: list[str]) -> list[list[float]]:
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if not texts:
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return []
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try:
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arr = self._st.encode(
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texts,
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normalize_embeddings=self._normalize,
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convert_to_numpy=True,
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show_progress_bar=False,
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)
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except Exception as e: # noqa: BLE001
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raise EmbeddingError(f"BGE-M3 推理失败: {type(e).__name__}: {e}") from e
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return [v.tolist() for v in arr]
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class LocalBGEAsyncEmbeddingProvider(AsyncEmbeddingProvider):
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"""本地 BGE-M3 异步包装。
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sentence-transformers 本身是同步的,这里用 asyncio.to_thread 包一层,
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主要为了让批处理脚本能用统一的 async 接口。
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"""
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name = "local-bge"
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def __init__(self, **kwargs: object) -> None:
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self._sync = LocalBGEEmbeddingProvider(**kwargs) # type: ignore[arg-type]
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self.model = self._sync.model
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self.dim = self._sync.dim
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async def embed_batch(self, texts: list[str]) -> list[list[float]]:
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return await asyncio.to_thread(self._sync.embed_batch, texts)
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