fix:不使用OpenAIEmbedding,基于 requests 的嵌入客户端

.env

@@ -7,14 +7,18 @@ MINIO_USE_SSL=False
 MINIO_BASE_PATH=sampledata
 
 # Milvus向量数据库配置信息
-MILVUS_HOST=192.168.92.61
-MILVUS_PORT=19530
-MILVUS_DB=lq_db
+# 开发环境
+# MILVUS_HOST=192.168.92.61
+# MILVUS_PORT=19530
+# 生产环境
+MILVUS_HOST=192.168.92.96
+MILVUS_PORT=31099
+MILVUS_DB=lq_db 
 MILVUS_USER=
 MILVUS_PASSWORD=
 
 DATABASE_URL=mysql+aiomysql://root:Lq123456!@192.168.92.61:13306/lq_db
-# OpenAI Embedding向量模型配置
+#  Embedding向量模型配置
 EMBEDDING_BASE_URL=http://192.168.91.253:9003/v1
 EMBEDDING_MODEL=Qwen3-Embedding-8B
 EMBEDDING_API_KEY=dummy

src/app/config/embeddings.py

@@ -1,26 +1,102 @@
-"""OpenAI Embeddings 配置和客户端获取。"""
+"""MetaX Embeddings 配置和客户端获取。"""
 from __future__ import annotations
 
 from functools import lru_cache
+import time
+from typing import List
 
-from langchain_openai import OpenAIEmbeddings
+import requests
+from requests import HTTPError
+from requests import RequestException
 
 from .setting import settings
 
 
+class MetaXEmbeddings:
+    """基于 requests 的嵌入客户端，兼容项目中现有调用方式。"""
+
+    def __init__(self, base_url: str, model: str, api_key: str, timeout: int = 60) -> None:
+        cleaned_base_url = base_url.rstrip("/")
+        # 允许配置为 .../v1/embeddings，内部统一到 base_url
+        if cleaned_base_url.endswith("/embeddings"):
+            cleaned_base_url = cleaned_base_url[: -len("/embeddings")]
+        self.base_url = cleaned_base_url
+        self.model = model
+        self.api_key = api_key or "dummy"
+        self.timeout = timeout
+        self.endpoint = f"{self.base_url}/embeddings"
+        self.session = requests.Session()
+        self.max_retries = 3
+        self.retry_delay_seconds = 1.5
+        self.max_batch_size = 16
+
+    def _post_embeddings(self, texts: List[str]) -> List[List[float]]:
+        headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {self.api_key}",
+        }
+        payload = {"input": texts, "model": self.model}
+        resp = self.session.post(self.endpoint, headers=headers, json=payload, timeout=self.timeout)
+        resp.raise_for_status()
+        data = resp.json().get("data", [])
+        if len(data) != len(texts):
+            raise RuntimeError(f"Expected {len(texts)} embeddings but got {len(data)}")
+        return [item["embedding"] for item in data]
+
+    def _request_embeddings(self, texts: List[str]) -> List[List[float]]:
+        if not texts:
+            return []
+        vectors: List[List[float]] = []
+        for i in range(0, len(texts), self.max_batch_size):
+            chunk = texts[i : i + self.max_batch_size]
+            last_exc: Exception | None = None
+            for attempt in range(1, self.max_retries + 1):
+                try:
+                    vectors.extend(self._post_embeddings(chunk))
+                    last_exc = None
+                    break
+                except HTTPError as exc:
+                    response = exc.response
+                    status = response.status_code if response is not None else "unknown"
+                    body = ""
+                    if response is not None and response.text:
+                        # 保留后端关键报错信息，避免日志过长
+                        body = response.text.strip().replace("\n", " ")[:500]
+                    last_exc = RuntimeError(
+                        "Embedding request failed: "
+                        f"endpoint={self.endpoint}, model={self.model}, status={status}, body={body!r}"
+                    )
+                except RequestException as exc:
+                    last_exc = RuntimeError(
+                        "Embedding request failed: "
+                        f"endpoint={self.endpoint}, model={self.model}, error={exc!r}"
+                    )
+                if attempt < self.max_retries:
+                    time.sleep(self.retry_delay_seconds * attempt)
+            if last_exc is not None:
+                raise last_exc
+        return vectors
+
+    def _embed(self, text: str) -> List[float]:
+        if not text:
+            return []
+        return self._request_embeddings([text])[0]
+
+    def embed_query(self, text: str) -> List[float]:
+        return self._embed(text)
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        return self._request_embeddings(texts)
+
+
 @lru_cache(maxsize=1)
-def get_embeddings() -> OpenAIEmbeddings:
-    """
-    创建或返回已缓存的 OpenAI Embeddings 客户端。
-    
-    Returns:
-        OpenAIEmbeddings 实例
-    """
+def get_embeddings() -> MetaXEmbeddings:
+    """创建或返回已缓存的 MetaX Embeddings 客户端。"""
     if not settings.EMBEDDING_BASE_URL or not settings.EMBEDDING_MODEL:
         raise ValueError("Embedding configuration is incomplete")
-    
-    return OpenAIEmbeddings(
+
+    return MetaXEmbeddings(
         base_url=settings.EMBEDDING_BASE_URL,
         model=settings.EMBEDDING_MODEL,
         api_key=settings.EMBEDDING_API_KEY,
-    )
+    )