fix: make to_message asynchronous to accelerate compressing chat hist…

…ory of multiple agents

agentverse/agents/tasksolving_agent/critic.py

@@ -88,7 +88,7 @@ async def astep(
 
         max_send_token -= prompt_token
 
-        history = self.memory.to_messages(
+        history = await self.memory.to_messages(
             self.name,
             start_index=-self.max_history,
             max_send_token=max_send_token,

agentverse/agents/tasksolving_agent/evaluator.py

@@ -26,6 +26,17 @@ def step(
         task_description: str,
         all_role_description: str,
     ) -> EvaluatorMessage:
+        pass
+        # return parsed_response
+
+    async def astep(
+        self,
+        solution: str,
+        result: str,
+        task_description: str,
+        all_role_description: str,
+    ) -> EvaluatorMessage:
+        """Asynchronous version of step"""
         logger.debug("", self.name, Fore.MAGENTA)
         prepend_prompt, append_prompt, prompt_token = self.get_all_prompts(
             solution=solution,
@@ -49,15 +60,15 @@ def step(
 
         max_send_token -= prompt_token
 
-        history = self.memory.to_messages(
+        history = await self.memory.to_messages(
             self.name,
             max_send_token=max_send_token,
             model=model_name,
         )
         parsed_response = None
         for i in range(self.max_retry):
             try:
-                response = self.llm.generate_response(
+                response = await self.llm.agenerate_response(
                     prepend_prompt, history, append_prompt
                 )
                 parsed_response = self.output_parser.parse(response)
@@ -78,11 +89,6 @@ def step(
             advice=parsed_response[1] if parsed_response is not None else "",
         )
         return message
-        # return parsed_response
-
-    async def astep(self, solution: str) -> EvaluatorMessage:
-        """Asynchronous version of step"""
-        pass
 
     def _fill_prompt_template(self, solution: str, task_description: str) -> str:
         """Fill the placeholders in the prompt template

agentverse/agents/tasksolving_agent/executor.py

@@ -23,72 +23,7 @@ class ExecutorAgent(BaseAgent):
     def step(
         self, task_description: str, solution: str, tools: List[dict] = [], **kwargs
     ) -> ExecutorMessage:
-        logger.debug("", self.name, Fore.MAGENTA)
-        prepend_prompt, append_prompt, prompt_token = self.get_all_prompts(
-            task_description=task_description,
-            solution=solution,
-            agent_name=self.name,
-            **kwargs,
-        )
-
-        model_name = self.llm.args.model
-
-        if model_name.startswith("gpt-3.5-turbo"):
-            tokens_per_message = 4
-        else:
-            tokens_per_message = 3
-
-        max_send_token = self.llm.send_token_limit(model_name)
-        if len(prepend_prompt) > 0:
-            max_send_token -= tokens_per_message
-        if (len(append_prompt)) > 0:
-            max_send_token -= tokens_per_message
-
-        max_send_token -= prompt_token
-
-        history = self.memory.to_messages(
-            self.name,
-            start_index=-self.max_history,
-            max_send_token=max_send_token,
-            model=model_name,
-        )
-        parsed_response = None
-        for i in range(self.max_retry):
-            try:
-                response = self.llm.generate_response(
-                    prepend_prompt, history, append_prompt, tools
-                )
-                parsed_response = self.output_parser.parse(response)
-                break
-            except (KeyboardInterrupt, bdb.BdbQuit):
-                raise
-            except Exception as e:
-                logger.error(e)
-                logger.warn("Retrying...")
-                continue
-
-        if parsed_response is None:
-            logger.error(f"{self.name} failed to generate valid response.")
-        if isinstance(parsed_response, AgentFinish):
-            message = ExecutorMessage(
-                content=parsed_response.return_values["output"],
-                sender=self.name,
-                sender_agent=self,
-            )
-        elif isinstance(parsed_response, AgentAction):
-            message = ExecutorMessage(
-                content=parsed_response.log,
-                sender=self.name,
-                sender_agent=self,
-                tool_name=parsed_response.tool,
-                tool_input=parsed_response.tool_input,
-            )
-        else:
-            raise ValueError(
-                f"Error response type: {type(parsed_response)}. Only support \
-                    AgentFinish and AgentAction. Modify your output parser."
-            )
-        return message
+        pass
 
     async def astep(
         self, task_description: str, solution: str, tools: List[dict] = [], **kwargs
@@ -116,7 +51,7 @@ async def astep(
 
         max_send_token -= prompt_token
 
-        history = self.memory.to_messages(
+        history = await self.memory.to_messages(
             self.name,
             start_index=-self.max_history,
             max_send_token=max_send_token,

agentverse/agents/tasksolving_agent/role_assigner.py

@@ -22,6 +22,12 @@ class RoleAssignerAgent(BaseAgent):
     def step(
         self, advice: str, task_description: str, cnt_critic_agents: int
     ) -> RoleAssignerMessage:
+        pass
+
+    async def astep(
+        self, advice: str, task_description: str, cnt_critic_agents: int
+    ) -> RoleAssignerMessage:
+        """Asynchronous version of step"""
         logger.debug("", self.name, Fore.MAGENTA)
         prepend_prompt, append_prompt, prompt_token = self.get_all_prompts(
             advice=advice,
@@ -44,13 +50,13 @@ def step(
 
         max_send_token -= prompt_token
 
-        history = self.memory.to_messages(
+        history = await self.memory.to_messages(
             self.name, max_send_token=max_send_token, model=model_name
         )
         parsed_response = None
         for i in range(self.max_retry):
             try:
-                response = self.llm.generate_response(
+                response = await self.llm.agenerate_response(
                     prepend_prompt, history, append_prompt
                 )
                 parsed_response = self.output_parser.parse(response)
@@ -76,10 +82,6 @@ def step(
         )
         return message
 
-    async def astep(self, env_description: str = "") -> RoleAssignerMessage:
-        """Asynchronous version of step"""
-        pass
-
     def _fill_prompt_template(
         self, advice, task_description: str, cnt_critic_agents: int
     ) -> str:

agentverse/agents/tasksolving_agent/solver.py

@@ -25,6 +25,12 @@ class SolverAgent(BaseAgent):
     def step(
         self, former_solution: str, advice: str, task_description: str = "", **kwargs
     ) -> SolverMessage:
+        pass
+
+    async def astep(
+        self, former_solution: str, advice: str, task_description: str = "", **kwargs
+    ) -> SolverMessage:
+        """Asynchronous version of step"""
         logger.debug("", self.name, Fore.MAGENTA)
         # prompt = self._fill_prompt_template(
         #     former_solution, critic_opinions, advice, task_description
@@ -52,7 +58,7 @@ def step(
 
         max_send_token -= prompt_token
 
-        history = self.memory.to_messages(
+        history = await self.memory.to_messages(
             self.name,
             start_index=-self.max_history,
             max_send_token=max_send_token,
@@ -61,7 +67,7 @@ def step(
         parsed_response = None
         for i in range(self.max_retry):
             try:
-                response = self.llm.generate_response(
+                response = await self.llm.agenerate_response(
                     prepend_prompt, history, append_prompt
                 )
                 parsed_response = self.output_parser.parse(response)
@@ -85,10 +91,6 @@ def step(
         )
         return message
 
-    async def astep(self, env_description: str = "") -> SolverMessage:
-        """Asynchronous version of step"""
-        pass
-
     def _fill_prompt_template(
         self,
         former_solution: str,

agentverse/environments/tasksolving_env/basic.py

@@ -51,7 +51,7 @@ async def step(
         logger.info(f"Loop Round {self.cnt_turn}")
 
         # ================== EXPERT RECRUITMENT ==================
-        agents = self.rule.role_assign(
+        agents = await self.rule.role_assign(
             self.task_description, self.agents, self.cnt_turn, advice
         )
         description = "\n".join([agent.role_description for agent in agents])
@@ -79,7 +79,7 @@ async def step(
         # ================== EXECUTION ==================
 
         # ================== EVALUATION ==================
-        score, advice = self.rule.evaluate(
+        score, advice = await self.rule.evaluate(
             self.task_description, self.agents, plan, result
         )
         logs.append(

agentverse/environments/tasksolving_env/rules/base.py

@@ -68,7 +68,7 @@ def build_components(config: Dict, registry):
             **kwargs,
         )
 
-    def role_assign(
+    async def role_assign(
         self,
         task_description: str,
         agents: List[BaseAgent],
@@ -79,7 +79,7 @@ def role_assign(
         if self.role_assign_only_once and cnt_turn > 0:
             agents = [agents[AGENT_TYPES.SOLVER]] + agents[AGENT_TYPES.CRITIC]
         else:
-            agents = self.role_assigner.step(
+            agents = await self.role_assigner.astep(
                 role_assigner=agents[AGENT_TYPES.ROLE_ASSIGNMENT],
                 group_members=[agents[AGENT_TYPES.SOLVER]] + agents[AGENT_TYPES.CRITIC],
                 advice=advice,
@@ -137,7 +137,7 @@ async def execute(
             agents[AGENT_TYPES.SOLVER].add_message_to_memory(results)
         return results
 
-    def evaluate(
+    async def evaluate(
         self,
         task_description: str,
         agents: List[BaseAgent],
@@ -162,7 +162,7 @@ def evaluate(
         #         logger.error("Bad response from human evaluator!")
         #     return ([comprehensiveness, detailedness, feasibility, novelty], advice)
         # else:
-        evaluation = self.evaluator.step(
+        evaluation = await self.evaluator.astep(
             agent=agents[AGENT_TYPES.EVALUATION],
             solution=solution,
             result=result,

agentverse/environments/tasksolving_env/rules/decision_maker/brainstorming.py

@@ -53,7 +53,7 @@ async def astep(
                 Fore.YELLOW,
             )
 
-        result = agents[0].step(previous_plan, advice, task_description)
+        result = await agents[0].astep(previous_plan, advice, task_description)
         for agent in agents:
             agent.memory.reset()
         self.broadcast_messages(

agentverse/environments/tasksolving_env/rules/decision_maker/central.py

@@ -47,7 +47,7 @@ async def astep(
             ),
         )
         agents[1].add_message_to_memory([result])
-        result = agents[0].step(
+        result = await agents[0].astep(
             previous_plan, advice, task_description, chat_record=result.content
         )
         return [result]

agentverse/environments/tasksolving_env/rules/decision_maker/concurrent.py

@@ -59,7 +59,7 @@ async def astep(
             last_reviews = nonempty_reviews
 
         agents[0].add_message_to_memory(last_reviews)
-        result = agents[0].step(previous_plan, advice, task_description)
+        result = await agents[0].astep(previous_plan, advice, task_description)
         # agents[0].add_message_to_memory([result])
         self.broadcast_messages(agents, [result])
         return [result]

agentverse/environments/tasksolving_env/rules/decision_maker/dynamic.py

@@ -56,7 +56,7 @@ async def astep(
             #    Fore.YELLOW,
             # )
 
-            previous_sentence = manager.step(
+            previous_sentence = await manager.astep(
                 previous_plan, review, advice, task_description, previous_sentence
             )
             reviews.append(previous_sentence)
@@ -76,7 +76,7 @@ async def astep(
                 nonempty_reviews.append(review)
         agents[0].add_message_to_memory(nonempty_reviews)
 
-        result = agents[0].step(previous_plan, advice, task_description)
+        result = await agents[0].astep(previous_plan, advice, task_description)
 
         return [result]
 

agentverse/environments/tasksolving_env/rules/decision_maker/horizontal.py

@@ -50,7 +50,7 @@ async def astep(
                 Fore.YELLOW,
             )
 
-        result = agents[0].step(previous_plan, advice, task_description)
+        result = await agents[0].astep(previous_plan, advice, task_description)
         return [result]
 
     def reset(self):

agentverse/environments/tasksolving_env/rules/decision_maker/horizontal_tool.py

@@ -77,7 +77,9 @@ async def astep(
             if end_flag:
                 break
 
-        result: SolverMessage = agents[0].step(previous_plan, advice, task_description)
+        result: SolverMessage = await agents[0].astep(
+            previous_plan, advice, task_description
+        )
         result_list = []
         for res in result.content:
             res_tmp = deepcopy(result)

agentverse/environments/tasksolving_env/rules/decision_maker/vertical.py

@@ -50,7 +50,7 @@ async def astep(
             if not review.is_agree and review.content != "":
                 nonempty_reviews.append(review)
         agents[0].add_message_to_memory(nonempty_reviews)
-        result = agents[0].step(previous_plan, advice, task_description)
+        result = await agents[0].astep(previous_plan, advice, task_description)
         agents[0].add_message_to_memory([result])
         return [result]
 

agentverse/environments/tasksolving_env/rules/decision_maker/vertical_solver_first.py

@@ -38,7 +38,7 @@ async def astep(
             self.broadcast_messages(
                 agents, [Message(content=advice, sender="Evaluator")]
             )
-        previous_plan = agents[0].step(previous_plan, advice, task_description)
+        previous_plan = await agents[0].astep(previous_plan, advice, task_description)
         self.broadcast_messages(agents, [previous_plan])
         logger.info("", f"Initial Plan:\n{previous_plan.content}", Fore.BLUE)
         for i in range(self.max_inner_turns):
@@ -65,7 +65,7 @@ async def astep(
                 logger.info("", "Consensus Reached!.", Fore.GREEN)
                 break
             self.broadcast_messages(agents, nonempty_reviews)
-            previous_plan = agents[0].step(previous_plan, advice, task_description)
+            previous_plan = await agents[0].astep(previous_plan, advice, task_description)
             logger.info("", f"Updated Plan:\n{previous_plan.content}", Fore.BLUE)
             self.broadcast_messages(agents, [previous_plan])
         result = previous_plan

agentverse/environments/tasksolving_env/rules/evaluator/base.py

@@ -20,7 +20,7 @@ class BaseEvaluator(BaseModel):
     """
 
     @abstractmethod
-    def step(
+    def astep(
         self,
         agent: EvaluatorAgent,
         solution: List[SolverMessage],