feat: 批量生成文本embedding，优化兴趣匹配计算逻辑，支持消息兴趣值的批量更新

src/llm_models/utils_model.py

@@ -1111,15 +1111,15 @@ class LLMRequest:
 
             return response.content or "", (response.reasoning_content or "", model_info.name, response.tool_calls)
 
-    async def get_embedding(self, embedding_input: str) -> tuple[list[float], str]:
+    async def get_embedding(self, embedding_input: str | list[str]) -> tuple[list[float] | list[list[float]], str]:
         """
-        获取嵌入向量。
+        获取嵌入向量，支持批量文本
 
         Args:
-            embedding_input (str): 获取嵌入的目标
+            embedding_input (str | list[str]): 需要生成嵌入的文本或文本列表
 
         Returns:
-            (Tuple[List[float], str]): (嵌入向量，使用的模型名称)
+            (Tuple[Union[List[float], List[List[float]]], str]): 嵌入结果及使用的模型名称
         """
         start_time = time.time()
         response, model_info = await self._strategy.execute_with_failover(
@@ -1128,10 +1128,25 @@ class LLMRequest:
 
         await self._record_usage(model_info, response.usage, time.time() - start_time, "/embeddings")
 
-        if not response.embedding:
+        if response.embedding is None:
             raise RuntimeError("获取embedding失败")
 
-        return response.embedding, model_info.name
+        embeddings = response.embedding
+        is_batch_request = isinstance(embedding_input, list)
+
+        if is_batch_request:
+            if not isinstance(embeddings, list):
+                raise RuntimeError("获取embedding失败，批量结果格式异常")
+
+            if embeddings and not isinstance(embeddings[0], list):
+                embeddings = [embeddings]  # type: ignore[list-item]
+
+            return embeddings, model_info.name
+
+        if isinstance(embeddings, list) and embeddings and isinstance(embeddings[0], list):
+            return embeddings[0], model_info.name
+
+        return embeddings, model_info.name
 
     async def _record_usage(self, model_info: ModelInfo, usage: UsageRecord | None, time_cost: float, endpoint: str):
         """