deploy: auto commit server changes 2026-06-11 20:55:54
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@@ -84,7 +84,7 @@ def vector_norm(vector: Iterable[float]) -> float:
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total = 0.0
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total = 0.0
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for value in vector:
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for value in vector:
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total += float(value) * float(value)
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total += float(value) * float(value)
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return math.sqrt(total) if total > 0 else 0.0
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return round(math.sqrt(total), 8) if total > 0 else 0.0
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def json_loads_maybe(value: Any, default: Any) -> Any:
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def json_loads_maybe(value: Any, default: Any) -> Any:
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@@ -532,44 +532,49 @@ class EmbeddingClient:
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self.base_url = (os.environ.get("EMBEDDING_BASE_URL") or DEFAULT_EMBEDDING_BASE_URL).rstrip("/")
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self.base_url = (os.environ.get("EMBEDDING_BASE_URL") or DEFAULT_EMBEDDING_BASE_URL).rstrip("/")
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self.model = os.environ.get("EMBEDDING_MODEL") or DEFAULT_EMBEDDING_MODEL
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self.model = os.environ.get("EMBEDDING_MODEL") or DEFAULT_EMBEDDING_MODEL
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self.dimension = int(os.environ.get("EMBEDDING_DIM") or DEFAULT_EMBEDDING_DIM)
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self.dimension = int(os.environ.get("EMBEDDING_DIM") or DEFAULT_EMBEDDING_DIM)
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self.batch_size = max(1, int(os.environ.get("EMBEDDING_BATCH_SIZE") or 10))
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async def embed(self, chunks: List[str]) -> Dict[str, Any]:
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async def embed(self, chunks: List[str]) -> Dict[str, Any]:
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if not self.api_key or not self.base_url or not self.model:
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if not self.api_key or not self.base_url or not self.model:
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return {"success": False, "error": "Embedding API配置未完成", "embeddings": []}
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return {"success": False, "error": "Embedding API配置未完成", "embeddings": []}
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payload: Dict[str, Any] = {"model": self.model, "input": chunks}
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if self.dimension > 0:
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payload["dimensions"] = self.dimension
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start = time.monotonic()
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start = time.monotonic()
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embeddings: List[List[float]] = []
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tokens = 0
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try:
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try:
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async with aiohttp.ClientSession(timeout=aiohttp.ClientTimeout(total=60)) as session:
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async with aiohttp.ClientSession(timeout=aiohttp.ClientTimeout(total=60)) as session:
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async with session.post(
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for offset in range(0, len(chunks), self.batch_size):
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self.base_url + "/v1/embeddings",
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batch = chunks[offset:offset + self.batch_size]
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json=payload,
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payload: Dict[str, Any] = {"model": self.model, "input": batch}
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headers={"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"},
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if self.dimension > 0:
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ssl=False,
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payload["dimensions"] = self.dimension
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) as resp:
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async with session.post(
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body = await resp.text()
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self.base_url + "/v1/embeddings",
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try:
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json=payload,
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data = json.loads(body)
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headers={"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"},
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except Exception:
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ssl=False,
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data = {}
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) as resp:
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if resp.status != 200 or not isinstance(data.get("data"), list):
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body = await resp.text()
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message = ((data.get("error") or {}).get("message") if isinstance(data, dict) else "") or f"HTTP {resp.status}"
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try:
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return {"success": False, "error": message, "embeddings": [], "cost_ms": int((time.monotonic() - start) * 1000)}
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data = json.loads(body)
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embeddings = []
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except Exception:
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for row in data.get("data") or []:
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data = {}
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embedding = row.get("embedding") if isinstance(row, dict) else None
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if resp.status != 200 or not isinstance(data.get("data"), list):
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if isinstance(embedding, list) and embedding:
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message = ((data.get("error") or {}).get("message") if isinstance(data, dict) else "") or f"HTTP {resp.status}"
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embeddings.append([float(value) for value in embedding])
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return {"success": False, "error": message, "embeddings": [], "cost_ms": int((time.monotonic() - start) * 1000)}
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return {
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for row in data.get("data") or []:
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"success": bool(embeddings),
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embedding = row.get("embedding") if isinstance(row, dict) else None
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"error": "" if embeddings else "Embedding结果为空",
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if isinstance(embedding, list) and embedding:
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"embeddings": embeddings,
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embeddings.append([float(value) for value in embedding])
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"model": self.model,
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tokens += int(((data.get("usage") or {}).get("total_tokens") or 0) if isinstance(data, dict) else 0)
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"dimension": len(embeddings[0]) if embeddings else 0,
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return {
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"tokens": int(((data.get("usage") or {}).get("total_tokens") or 0) if isinstance(data, dict) else 0),
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"success": len(embeddings) == len(chunks),
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"cost_ms": int((time.monotonic() - start) * 1000),
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"error": "" if len(embeddings) == len(chunks) else "Embedding结果数量不匹配",
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}
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"embeddings": embeddings,
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"model": self.model,
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"dimension": len(embeddings[0]) if embeddings else 0,
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"tokens": tokens,
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"cost_ms": int((time.monotonic() - start) * 1000),
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}
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except Exception as exc:
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except Exception as exc:
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return {"success": False, "error": f"Embedding请求失败: {exc}", "embeddings": []}
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return {"success": False, "error": f"Embedding请求失败: {exc}", "embeddings": []}
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