微调Autogen代码结构

crazy_functions/agent_fns/autogen_general.py

@@ -0,0 +1,76 @@
+from toolbox import CatchException, update_ui, gen_time_str, trimmed_format_exc, ProxyNetworkActivate
+from toolbox import report_execption, get_log_folder, update_ui_lastest_msg, Singleton
+from crazy_functions.agent_fns.pipe import PluginMultiprocessManager, PipeCom
+import time
+
+
+class AutoGenGeneral(PluginMultiprocessManager):
+
+    def gpt_academic_print_override(self, user_proxy, message, sender):
+        # ⭐⭐ 子进程执行
+        self.child_conn.send(PipeCom("show", sender.name + '\n\n---\n\n' + message['content']))
+
+    def gpt_academic_get_human_input(self, user_proxy, message):
+        # ⭐⭐ 子进程执行
+        patience = 300
+        begin_waiting_time = time.time()
+        self.child_conn.send(PipeCom("interact", message))
+        while True:
+            time.sleep(0.5)
+            if self.child_conn.poll(): 
+                wait_success = True
+                break
+            if time.time() - begin_waiting_time > patience:
+                self.child_conn.send(PipeCom("done", ""))
+                wait_success = False
+                break
+        if wait_success:
+            return self.child_conn.recv().content
+        else:
+            raise TimeoutError("等待用户输入超时")
+
+    def define_agents(self):
+        raise NotImplementedError
+
+    def do_audogen(self, input):
+        # ⭐⭐ 子进程执行
+        input = input.content
+        with ProxyNetworkActivate("AutoGen"):
+            from autogen import AssistantAgent, UserProxyAgent
+            config_list = [{
+                'model': self.llm_kwargs['llm_model'], 
+                'api_key': self.llm_kwargs['api_key'],
+            },]
+            autogen_work_dir = get_log_folder('autogen')
+            code_execution_config={"work_dir": autogen_work_dir, "use_docker":True}
+            agents = self.define_agents()
+            user_proxy = None
+            assistant = None
+            for agent_kwargs in agents:
+                agent_cls = agent_kwargs.pop('cls')
+                kwargs = {
+                    'llm_config':{
+                        "config_list": config_list,
+                    },
+                    'code_execution_config':code_execution_config
+                }
+                kwargs.update(agent_kwargs)
+                agent_handle = agent_cls(**kwargs)
+                agent_handle._print_received_message = lambda a,b: self.gpt_academic_print_override(agent_kwargs, a, b)
+                if agent_kwargs['name'] == 'user_proxy':
+                    agent_handle.get_human_input = lambda a: self.gpt_academic_get_human_input(user_proxy, a)
+                    user_proxy = agent_handle
+                if agent_kwargs['name'] == 'assistant': assistant = agent_handle
+            try:
+                if user_proxy is None or assistant is None: raise Exception("用户代理或助理代理未定义")
+                user_proxy.initiate_chat(assistant, message=input)
+            except Exception as e:
+                tb_str = '```\n' + trimmed_format_exc() + '```'
+                self.child_conn.send(PipeCom("done", "AutoGen 执行失败: \n\n" + tb_str))
+
+    def subprocess_worker(self, child_conn):
+        # ⭐⭐ 子进程执行
+        self.child_conn = child_conn
+        while True:
+            msg = self.child_conn.recv() # PipeCom
+            self.do_audogen(msg)