78 lines
3.5 KiB
Python
78 lines
3.5 KiB
Python
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import hashlib
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import math
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from dataclasses import dataclass
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import httpx
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from app.core.config import settings
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from app.core.exceptions import AppError
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@dataclass(frozen=True)
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class EmbeddingUsage:
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"""Embedding 调用指标：记录批量向量化的模型和 token 用量。"""
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model: str
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total_tokens: int | None = None
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class OpenAICompatibleEmbeddingClient:
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"""Embedding Adapter：封装 OpenAI-compatible embeddings 接口，并提供稳定 mock。"""
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@property
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def is_mock_mode(self) -> bool:
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"""模式判断：没有 API Key 或显式 mock provider 时使用确定性本地向量。"""
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return settings.embedding_provider.lower() == "mock" or not settings.embedding_api_key
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async def embed_texts(self, texts: list[str]) -> tuple[list[list[float]], EmbeddingUsage]:
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"""文本向量化：对文本批量生成 embedding，返回与输入顺序一致的向量列表。"""
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if not texts:
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return [], EmbeddingUsage(model=settings.embedding_model, total_tokens=0)
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if self.is_mock_mode:
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return [self._mock_vector(text) for text in texts], EmbeddingUsage(model=f"mock-{settings.embedding_model}")
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try:
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async with httpx.AsyncClient(timeout=settings.embedding_timeout_seconds) as client:
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resp = await client.post(
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self._embeddings_url(),
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headers={"Authorization": f"Bearer {settings.embedding_api_key}"},
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json={"model": settings.embedding_model, "input": texts},
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)
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resp.raise_for_status()
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payload = resp.json()
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except (httpx.TimeoutException, httpx.HTTPError, ValueError) as exc:
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raise AppError("EMBEDDING_CALL_FAILED", "embedding service call failed", 502) from exc
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try:
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vectors = [item["embedding"] for item in sorted(payload["data"], key=lambda item: item.get("index", 0))]
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self._validate_vectors(vectors)
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usage = payload.get("usage") or {}
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return vectors, EmbeddingUsage(model=settings.embedding_model, total_tokens=usage.get("total_tokens"))
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except (KeyError, TypeError, ValueError) as exc:
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raise AppError("EMBEDDING_RESPONSE_INVALID", "embedding response format invalid", 502) from exc
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def _embeddings_url(self) -> str:
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"""接口地址：兼容 base URL 和完整 /embeddings URL 两种写法。"""
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base_url = settings.embedding_base_url.rstrip("/")
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if base_url.endswith("/embeddings"):
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return base_url
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return f"{base_url}/embeddings"
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def _validate_vectors(self, vectors: list[list[float]]) -> None:
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"""向量校验：确保向量维度与 Milvus collection 维度一致。"""
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for vector in vectors:
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if len(vector) != settings.embedding_dim:
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raise ValueError(f"embedding dimension mismatch: {len(vector)} != {settings.embedding_dim}")
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def _mock_vector(self, text: str) -> list[float]:
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"""Mock向量：基于文本哈希生成稳定归一化向量，便于本地和CI测试。"""
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values: list[float] = []
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seed = hashlib.sha256(f"{settings.embedding_model}:{text}".encode("utf-8")).digest()
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current = seed
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while len(values) < settings.embedding_dim:
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current = hashlib.sha256(current).digest()
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values.extend((byte / 127.5) - 1.0 for byte in current)
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vector = values[: settings.embedding_dim]
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norm = math.sqrt(sum(item * item for item in vector)) or 1.0
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return [item / norm for item in vector]
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