添加默认问题回答

api.py

 from collections import Counter
 from contextlib import asynccontextmanager
-from datetime import datetime
 
 import uvicorn
 from fastapi import FastAPI, Request, APIRouter
@@ -13,7 +12,6 @@ from config.system import config
 from model.entity.AiChatPermissionEntity import AiChatPermissionEntity
 from model.entity.AiChatRecommendQuestionEntity import AiChatRecommendQuestionEntity
 from model.entity.AiChatRecordEntity import AiChatRecordEntity
-from model.param.AiChatRecordParam import AiChatRecordParam
 from model.vo.ResultVo import ResultVo
 
 
@@ -96,21 +94,6 @@ async def chat(request: Request, message: str):
                                media_type="text/event-stream", headers={"Cache-Control": "no-cache"})
 
 
-@router.post("/chat/record", description="记录AI回答")
-async def record_chat(chat_record_param: AiChatRecordParam):
-    await db_util.update(
-        AiChatRecordEntity,
-        {
-            "id": chat_record_param.id
-        },
-        {
-            "answer": chat_record_param.answer,
-            "answer_time": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-        }
-    )
-    return ResultVo()
-
-
 if __name__ == "__main__":
     cors = config.get("cors", {})
     api.add_middleware(CORSMiddleware, allow_origins=cors.get("allow_origins", ["*"]),

client/client.py

@@ -15,9 +15,24 @@ from pydantic import AnyUrl
 from config.database import db_util
 from config.logger import logger
 from config.system import config
+from model.entity.AiChatRecommendQuestionEntity import AiChatRecommendQuestionEntity
 from model.entity.AiChatRecordEntity import AiChatRecordEntity
 
 
+async def record_answer(_id, _completion_answer):
+    """AI回答完成后，异步更新回答数据"""
+    await db_util.update(
+        AiChatRecordEntity,
+        {
+            "id": _id
+        },
+        {
+            "answer": _completion_answer,
+            "answer_time": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        }
+    )
+
+
 class McpClient:
     def __init__(self):
         self.customer_resource: [] = None  # 客户资源
@@ -132,6 +147,20 @@ class McpClient:
         chat_record_entity = await db_util.add(chat_record_entity)
 
         logger.info(f"--> user origin query, message: {message}, customer_id: {customer_id}")
+
+        db_question = await db_util.get_by_filter(
+            AiChatRecommendQuestionEntity,
+            AiChatRecommendQuestionEntity.question.__eq__(message),
+        )
+        if db_question and len(db_question) > 0 and (default_answer := db_question[0].default_answer):
+            # 固定问题，模拟AI回答
+            step = 2  # 两个字符两个字符的输出
+            for i in range(0, len(default_answer), step):
+                await asyncio.sleep(0.05)  # 50毫秒延迟
+                yield json.dumps({"content": default_answer[i:i + step]})
+            yield "[DONE]"
+            asyncio.create_task(record_answer(chat_record_entity.id, default_answer))
+        else:
             messages = [
                 {"role": "system", "content": self.default_system_prompt},
                 {"role": "system", "content": self.deal_customer_permission(customer_id)},
@@ -182,11 +211,15 @@ class McpClient:
                 tool_choice="auto",
                 stream=True
             )
+            completion_answer = ""
             async for chunk in ai_stream_response:
                 if chunk.choices[0].finish_reason == "stop":
-                yield f"[DONE]-{chat_record_entity.id}"
+                    asyncio.create_task(record_answer(chat_record_entity.id, completion_answer))
+                    yield "[DONE]"
                 else:
-                yield json.dumps({"content": chunk.choices[0].delta.content})
+                    content = chunk.choices[0].delta.content
+                    completion_answer += content
+                    yield json.dumps({"content": content})
 
 
 mcp_client_instance = McpClient()