Initial commit

app/core/agent/agent_chat.py

@@ -0,0 +1,109 @@
+import asyncio
+import os
+import time
+
+from typing import Dict, Any, List
+
+from app.core.logging_config import get_business_logger
+from app.schemas.prompt_schema import  render_prompt_message, PromptMessageRole
+from app.services.api_resquests_server import messages_type, write_messages
+from app.services.agent_server import ChatRequest, tool_memory, create_dynamic_agent, tool_Retrieval
+
+logger = get_business_logger()
+class Agent_chat:
+    def __init__(self,config_data: dict):
+        self.prompt_message = render_prompt_message(
+            config_data.template_str,
+            PromptMessageRole.USER,
+            config_data.params
+        )
+        self.prompt = self.prompt_message.get_text_content()
+        self.model_configs = config_data.model_configs
+        self.history_memory = config_data.history_memory
+        self.knowledge_base = config_data.knowledge_base
+        logger.info(f"渲染结果：{self.prompt_message.get_text_content()}" )
+
+    async def run_agent(self,agent, end_user_id:str, user_prompt:str, model_name:str):
+        response = agent.invoke(
+            {
+                "messages": [
+                    {
+                        "role": "user",
+                        "content": user_prompt
+                    }
+                ]
+            },
+            {"configurable": {"thread_id": f'{model_name}_{end_user_id}'}},
+        )
+        outputs = []
+        for msg in response["messages"]:
+            if hasattr(msg, "tool_calls") and msg.tool_calls:
+                outputs.append({
+                    "role": "assistant",
+                    "tool_calls": [
+                        {"name": t["name"], "arguments": t["args"]}
+                        for t in msg.tool_calls
+                    ]
+                })
+            elif hasattr(msg, "content") and msg.content:
+                outputs.append({
+                    "role": msg.__class__.__name__.lower().replace("message", ""),
+                    "content": msg.content
+                })
+        ai_messages=[msg['content'] for msg in outputs if msg["role"] == "ai"]
+        return {"model_name": model_name, "end_user_id": end_user_id, "response": ai_messages}
+
+    async def chat(self,req: ChatRequest) -> Dict[str, Any]:
+
+        end_user_id = req.end_user_id  # 用 user_id 作为对话线程标识
+        start=time.time()
+        user_prompt = req.message
+
+        '''判断是都写入redis数据库'''
+        messags_type = await messages_type(req.message,end_user_id)
+        messags_type=messags_type['data']
+        if messags_type=='question':
+            writer_result=await write_messages(f'{end_user_id}', req.message)
+            logger.info(f'判断类型写入耗时：{time.time() - start},{writer_result}')
+
+
+
+        '''history_memory'''
+
+        if self.history_memory==True:
+            tool_result =await tool_memory(req)
+            if tool_result!='' :tool_result=tool_result['data']
+            if tool_result!='' :self.prompt=self.prompt+f''',历史消息：{tool_result},结合历史消息'''
+            logger.info(f"记忆科学消耗时间：{time.time()-start},工具调用结果:{tool_result}")
+
+        '''baidu'''
+
+
+        '''knowledge_base'''
+        if self.knowledge_base == True:
+            retrieval_result=await tool_Retrieval(req)
+            retrieval_knowledge = [i['page_content'] for i in retrieval_result['data']]
+            retrieval_knowledge=','.join(retrieval_knowledge)
+            logger.info(f"检索消耗时间：{time.time()-start},{retrieval_knowledge}")
+            if retrieval_knowledge!='' :self.prompt=self.prompt+f",知识库检索内容：{retrieval_knowledge},结合检索结果"
+        self.prompt=self.prompt+f'给出最合适的答案，确保答案的完整性，只保留用户的问题的回答，不额外输出提示语'
+        logger.info(f"用户输入：{user_prompt}")
+        logger.info(f"系统prompt：{self.prompt}")
+
+        AGENTS = {
+            cfg["name"]: await create_dynamic_agent(cfg["name"], cfg["moder_id"], self.prompt, req.token)
+            for cfg in self.model_configs
+        }
+        tasks=[
+            self.run_agent(agent, end_user_id, user_prompt, model_name)
+            for model_name, agent in AGENTS.items()
+        ]
+        # 并行运行
+        results = await asyncio.gather(*tasks)
+
+        result=[]
+
+        for i in results:
+            result.append(i)
+        chat_result=(f"最终耗时：{time.time()-start},{result}")
+        return chat_result