code cleaning

Author: chenweize1998

TO-DO-fix.txt

@@ -7,12 +7,14 @@
 from .base import BaseAgent
 from agentverse.agents.simulation_agent.conversation import ConversationAgent
 from agentverse.agents.simulation_agent.tool import ToolAgent
-from agentverse.agents.simulation_agent.prisoner_dilemma import PoliceAgent, PrisonerAgent
+from agentverse.agents.simulation_agent.prisoner_dilemma import (
+    PoliceAgent,
+    PrisonerAgent,
+)
 
 from agentverse.agents.tasksolving_agent.role_assigner import RoleAssignerAgent
 from agentverse.agents.tasksolving_agent.critic import CriticAgent
 from agentverse.agents.tasksolving_agent.evaluator import EvaluatorAgent
 from agentverse.agents.tasksolving_agent.solver import SolverAgent
 from agentverse.agents.tasksolving_agent.manager import ManagerAgent
 from agentverse.agents.tasksolving_agent.executor import ExecutorAgent
-from agentverse.agents.tasksolving_agent.executor_fc import ExecutorAgent_fc

agentverse/agents/__init__.py

@@ -87,11 +87,6 @@ async def astep(
                 logger.error(e)
                 logger.warn("Retrying...")
                 continue
-        # if parsed_response is None:
-        #     return AgentCriticism(True, "LLM failed.", self)
-        # parsed_response = AgentCriticism(
-        #     parsed_response.is_agree, parsed_response.criticism, self
-        # )
 
         if parsed_response is None:
             logger.error(f"{self.name} failed to generate valid response.")

agentverse/agents/tasksolving_agent/critic.py

@@ -8,7 +8,6 @@
 from string import Template
 from typing import TYPE_CHECKING, List, Tuple
 
-# from agentverse.environments import PipelineEnvironment
 from agentverse.message import Message
 
 from agentverse.agents import agent_registry

agentverse/agents/tasksolving_agent/executor_fc.py

@@ -13,8 +13,6 @@
 from agentverse.agents import agent_registry
 from agentverse.agents.base import BaseAgent
 
-# from agentverse.environments import PipelineEnvironment
-
 
 logger = get_logger()
 
@@ -25,8 +23,6 @@ def step(
         self, advice: str, task_description: str, cnt_critic_agents: int
     ) -> RoleAssignerMessage:
         logger.debug("", self.name, Fore.MAGENTA)
-        # prompt = self._fill_prompt_template(advice, task_description, cnt_critic_agents)
-        # logger.debug(f"Prompt:\n{prompt}", "Role Assigner", Fore.CYAN)
         prepend_prompt, append_prompt = self.get_all_prompts(
             advice=advice,
             task_description=task_description,

agentverse/agents/tasksolving_agent/manager.py

@@ -52,7 +52,6 @@ def add_tool_response(
     def add_message_to_all_agents(
         self, agents: List[BaseAgent], message: Message
     ) -> bool:
-        print(message.receiver)
         if "all" in message.receiver:
             # If receiver is all, then add the message to all agents
             for agent in agents:

agentverse/agents/tasksolving_agent/role_assigner.py

@@ -20,18 +20,6 @@
 
 @EnvironmentRegistry.register("task-basic")
 class BasicEnvironment(BaseEnvironment):
-    """
-    A basic environment implementing the logic of conversation.
-
-    Args:
-        agents: List of agents
-        rule: Rule for the environment
-        max_loop_rounds: Maximum number of loop rounds number
-        cnt_turn: Current round number
-        last_messages: Messages from last turn
-        rule_params: Variables set by the rule
-    """
-
     rule: TasksolvingRule
     agents: Dict[Enum, Union[BaseAgent, List[BaseAgent]]] = None
 
@@ -60,9 +48,7 @@ def __init__(self, **kwargs):
     async def step(
         self, advice: str = "No advice yet.", previous_plan: str = "No solution yet."
     ) -> List[Message]:
-        # advice = "No advice yet."
         result = ""
-        # previous_plan = "No solution yet."
         logs = []
 
         logger.info(f"Loop Round {self.cnt_turn}")
@@ -80,11 +66,6 @@ async def step(
         plan: List[SolverMessage] = await self.rule.decision_making(
             self.task_description, self.agents, previous_plan, advice
         )
-        # Although plan may be a list in some cases, all the cases we currently consider
-        # only have one plan, so we just take the first element.
-        # TODO: make it more general
-        # plan = plan[0].content
-        # plan = [p.content for p in plan]
         flatten_plan = "\n".join([p.content for p in plan])
         logs.append({"module": "Decision Maker", "content": flatten_plan})
         logger.info("", f"Decision Plan:\n{flatten_plan}", Fore.YELLOW)

agentverse/environments/simulation_env/rules/updater/basic.py

@@ -20,7 +20,7 @@ class VerticalSolverFirstDecisionMaker(BaseDecisionMaker):
     Discuss in a vertical manner.
     """
 
-    name: str = "vertical"
+    name: str = "vertical-sovler-first"
     max_inner_turns: int = 3
 
     async def astep(

agentverse/environments/tasksolving_env/basic.py

@@ -89,10 +89,10 @@ def prepare_task_config(task):
             agent_configs["tool_memory"] = load_memory(agent_configs["tool_memory"])
         llm = load_llm(agent_configs.get("llm", "text-davinci-003"))
         agent_configs["llm"] = llm
-        #agent_configs["tools"] = load_tools(agent_configs.get("tools", []))
+        # agent_configs["tools"] = load_tools(agent_configs.get("tools", []))
 
         # Build the output parser
-        output_parser_config = agent_configs.get("output_parser", {})
+        output_parser_config = agent_configs.get("output_parser", {"type": "dummy"})
         if output_parser_config.get("type", None) == "role_assigner":
             output_parser_config["cnt_critic_agents"] = task_config.get(
                 "cnt_critic_agents", 0

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

@@ -2,6 +2,7 @@
 import json
 import ast
 import os
+from aiohttp import ClientSession
 from typing import Dict, List, Optional, Union
 from tenacity import retry, stop_after_attempt, wait_exponential
 
@@ -23,9 +24,9 @@
     logging.warning("openai package is not installed")
 else:
     openai.api_key = os.environ.get("OPENAI_API_KEY")
-    openai.proxy = os.environ.get("http_proxy")
-    if openai.proxy is None:
-        openai.proxy = os.environ.get("HTTP_PROXY")
+    # openai.proxy = os.environ.get("http_proxy")
+    # if openai.proxy is None:
+    #     openai.proxy = os.environ.get("HTTP_PROXY")
     if openai.api_key is None:
         logging.warning(
             "OpenAI API key is not set. Please set the environment variable OPENAI_API_KEY"
@@ -114,9 +115,6 @@ def generate_response(
         append_prompt: str = "",
         functions: List[dict] = [],
     ) -> LLMResult:
-        # logger.debug(prepend_prompt)
-        # logger.debug(history)
-        # logger.debug(append_prompt)
         messages = self.construct_messages(prepend_prompt, history, append_prompt)
         logger.log_prompt(messages)
 
@@ -126,9 +124,6 @@ def generate_response(
                 response = openai.ChatCompletion.create(
                     messages=messages,
                     functions=functions,
-                    # function_call="auto",
-                    # function_call={"name": "run_code"},
-                    # stream=True,
                     **self.args.dict(),
                 )
                 if response["choices"][0]["message"].get("function_call") is not None:
@@ -180,23 +175,18 @@ async def agenerate_response(
         append_prompt: str = "",
         functions: List[dict] = [],
     ) -> LLMResult:
-        # logger.debug(prepend_prompt)
-        # logger.debug(history)
-        # logger.debug(append_prompt)
         messages = self.construct_messages(prepend_prompt, history, append_prompt)
         logger.log_prompt(messages)
 
         try:
-            # Execute function call
             if functions != []:
-                response = await openai.ChatCompletion.acreate(
-                    messages=messages,
-                    functions=functions,
-                    # function_call="auto",
-                    # function_call={"name": "run_code"},
-                    # stream=True,
-                    **self.args.dict(),
-                )
+                async with ClientSession(trust_env=True) as session:
+                    openai.aiosession.set(session)
+                    response = await openai.ChatCompletion.acreate(
+                        messages=messages,
+                        functions=functions,
+                        **self.args.dict(),
+                    )
                 if response["choices"][0]["message"].get("function_call") is not None:
                     function_name = response["choices"][0]["message"]["function_call"][
                         "name"
@@ -256,10 +246,12 @@ async def agenerate_response(
                     )
 
             else:
-                response = await openai.ChatCompletion.acreate(
-                    messages=messages,
-                    **self.args.dict(),
-                )
+                async with ClientSession(trust_env=True) as session:
+                    openai.aiosession.set(session)
+                    response = await openai.ChatCompletion.acreate(
+                        messages=messages,
+                        **self.args.dict(),
+                    )
                 return LLMResult(
                     content=response["choices"][0]["message"]["content"],
                     send_tokens=response["usage"]["prompt_tokens"],