feat: 知识库小重构

src/chat/knowledge/llm_client.py

@@ -0,0 +1,45 @@
+from openai import OpenAI
+
+
+class LLMMessage:
+    def __init__(self, role, content):
+        self.role = role
+        self.content = content
+
+    def to_dict(self):
+        return {"role": self.role, "content": self.content}
+
+
+class LLMClient:
+    """LLM客户端，对应一个API服务商"""
+
+    def __init__(self, url, api_key):
+        self.client = OpenAI(
+            base_url=url,
+            api_key=api_key,
+        )
+
+    def send_chat_request(self, model, messages):
+        """发送对话请求，等待返回结果"""
+        response = self.client.chat.completions.create(model=model, messages=messages, stream=False)
+        if hasattr(response.choices[0].message, "reasoning_content"):
+            # 有单独的推理内容块
+            reasoning_content = response.choices[0].message.reasoning_content
+            content = response.choices[0].message.content
+        else:
+            # 无单独的推理内容块
+            response = response.choices[0].message.content.split("<think>")[-1].split("</think>")
+            # 如果有推理内容，则分割推理内容和内容
+            if len(response) == 2:
+                reasoning_content = response[0]
+                content = response[1]
+            else:
+                reasoning_content = None
+                content = response[0]
+
+        return reasoning_content, content
+
+    def send_embedding_request(self, model, text):
+        """发送嵌入请求，等待返回结果"""
+        text = text.replace("\n", " ")
+        return self.client.embeddings.create(input=[text], model=model).data[0].embedding