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import json
import ast
import openai
from string import Template
from colorama import Fore
from aiohttp import ClientSession
from copy import deepcopy
from typing import TYPE_CHECKING, Any, List, Tuple
from agentverse.agents import ExecutorAgent
from agentverse.message import Message, ExecutorMessage, SolverMessage
from agentverse.logging import logger
from . import BaseExecutor, executor_registry
import asyncio
url = "http://127.0.0.1:8080"
# url = "http://8.217.97.110:8080"
SUMMARIZE_PROMPT = """Here is the text gathered from a webpage, and a question you need to answer from the webpage.
-- Webpage --
${webpage}
-- Question --
${question}
Now summarize the webpage to answer the question. If the question cannot be answer from the webpage, return the summarization of the webpage."""
@executor_registry.register("tool-using")
class ToolUsingExecutor(BaseExecutor):
num_agents: int = 3
max_tool_call_times: int = 10
tools: List[dict] = []
tool_names: List[str] = []
tool_config: str = None
cookies: dict = {}
tool_retrieval: bool = False
real_execution_agents: dict = {}
agent_names: List[str] = []
# tool_description: str
def __init__(self, *args, **kwargs):
assert kwargs.get("tool_config", None) is not None
with open(kwargs.get("tool_config"), "r") as f:
tools_dict = json.load(f)
tools = tools_dict["tools_json"]
tool_names = [t["name"] for t in tools]
# For each tool, we manually add a "thought" argument to achieve
# chain-of-thought in OpenAI's function call.
for t in tools:
properties = t["parameters"]["properties"]
thought = {
"thought": {
"type": "string",
"description": "Your internal reasoning and thoughts on the task, and how you plan to solve it based on the current attempts.",
}
}
thought.update(properties)
t["parameters"]["properties"] = thought
t["parameters"]["required"].insert(0, "thought")
super().__init__(
tools=tools,
tool_names=tool_names,
# tool_description=tool_description,
*args,
**kwargs,
)
async def astep(
self,
agent: ExecutorAgent,
task_description: str,
plans: List[SolverMessage],
*args,
**kwargs,
):
plan_this_turn = {}
agent_name_this_turn = []
for i in range(len(plans)):
name = plans[i].content.split("-")[0].strip()
if name not in self.real_execution_agents:
self.real_execution_agents[name] = deepcopy(agent)
self.real_execution_agents[name].name = name
self.agent_names.append(name)
plan_this_turn[name] = plans[i].content.split("-")[1].strip()
agent_name_this_turn.append(name)
# agents = [deepcopy(agent) for _ in range(len(plans))]
if self.tool_retrieval:
# We retrieve 5 related tools for each agent
tools_and_cookies = await asyncio.gather(
*[
self.retrieve_tools(plan_this_turn[name], self.tools)
for name in agent_name_this_turn
]
)
tools = {
name: t[0] for name, t in zip(agent_name_this_turn, tools_and_cookies)
}
cookies = {
name: t[1] for name, t in zip(agent_name_this_turn, tools_and_cookies)
}
self.update_cookies(cookies)
else:
# We just use the tools that are provided in the config file
tools = {name: self.tools for name in agent_name_this_turn}
# Record the indices of agents that have finished their tasks
# so that they will not be called again
finished_agent_names = set()
# result = ["" for _ in range(len(plan_this_turn))]
result = {name: "" for name in agent_name_this_turn}
for current_turn in range(self.max_tool_call_times):
if len(finished_agent_names) == len(agent_name_this_turn):
# All agents have finished their tasks. Break the loop.
break
# Filter out agents that have finished and gather tool actions for the rest
tool_calls = []
active_agents_names = [
name
for name in agent_name_this_turn
if name not in finished_agent_names
]
for name in active_agents_names:
if current_turn == self.max_tool_call_times - 1:
tool = [t for t in tools[name] if t["name"] == "submit_task"]
else:
tool = tools[name]
tool_calls.append(
self.real_execution_agents[name].astep(
task_description,
plan_this_turn[name],
tool,
current_turn=current_turn + 1,
)
)
# Use asyncio.gather to run astep concurrently
tool_call_decisions = await asyncio.gather(*tool_calls)
for name, tool_call_result in zip(active_agents_names, tool_call_decisions):
self.real_execution_agents[name].add_message_to_memory(
[tool_call_result]
)
# Actually call the tool and get the observation
tool_responses = await asyncio.gather(
*[
ToolUsingExecutor.call_tool(
tool.tool_name,
tool.tool_input,
self.cookies.get(name, None),
)
for name, tool in zip(active_agents_names, tool_call_decisions)
]
)
# Update each agent's memory and check if they have finished
cookies = {}
for name, response in zip(active_agents_names, tool_responses):
observation = response["observation"]
is_finish = response["is_finish"]
cookies[name] = response["cookies"]
self.real_execution_agents[name].add_message_to_memory([observation])
logger.info(
f"\nTool: {observation.tool_name}\nTool Input: {observation.tool_input}\nObservation: {observation.content}",
name,
Fore.YELLOW,
)
if is_finish:
finished_agent_names.add(name)
result[name] = observation.content
self.update_cookies(cookies)
message_result = []
for name, conclusion in result.items():
if conclusion != "":
message_result.append(
ExecutorMessage(
content=f"[{name}]: My execution result:\n{conclusion}",
sender=name,
)
)
return message_result
def update_cookies(self, cookies: dict):
for name, cookie in cookies.items():
self.cookies[name] = cookie
@classmethod
async def retrieve_tools(
cls, plan: SolverMessage, curr_tools: List = [], cookies=None
):
async with ClientSession(cookies=cookies) as session:
if cookies is None:
async with session.post(f"{url}/get_cookie", timeout=30) as response:
cookies = response.cookies
session.cookie_jar.update_cookies(cookies)
await response.text()
# Sometimes the toolserver's docker container is not ready yet
# So we need to wait for a while
await asyncio.sleep(10)
async with session.post(
f"{url}/retrieving_tools", json={"question": plan.content, "top_k": 5}
) as response:
retrieved_tools = await response.json()
retrieved_tools = ast.literal_eval(retrieved_tools)
tools = deepcopy(curr_tools)
existed_tool_names = set([t["name"] for t in tools])
# Add the retrieved tools into the final tools
for tool in retrieved_tools["tools_json"]:
if tool["name"] not in existed_tool_names:
existed_tool_names.add(tool["name"])
tools.append(tool)
return tools, cookies
@classmethod
async def call_tool(cls, command: str, arguments: dict, cookies=None):
async def _summarize_webpage(webpage, question):
summarize_prompt = Template(SUMMARIZE_PROMPT).safe_substitute(
webpage=webpage, question=question
)
for _ in range(3):
try:
response = await openai.ChatCompletion.acreate(
messages=[{"role": "user", "content": summarize_prompt}],
model="gpt-3.5-turbo-16k",
)
except:
continue
return response["choices"][0]["message"]["content"]
if command == "submit_task":
return {
"observation": ExecutorMessage(
content=f"Task Status: {arguments['status']}\nConclusion: {arguments['conclusion']}",
sender="function",
tool_name=command,
tool_input=arguments,
),
"is_finish": True,
"cookies": cookies,
}
if command == "":
return {
"observation": ExecutorMessage(
content=f"The function calling format is incorrect.",
sender="function",
tool_name=command,
tool_input=arguments,
),
"is_finish": False,
"cookies": cookies,
}
for i in range(3):
try:
async with ClientSession(cookies=cookies) as session:
if cookies is None:
async with session.post(
f"{url}/get_cookie", timeout=30
) as response:
cookies = response.cookies
session.cookie_jar.update_cookies(cookies)
await response.text()
# Sometimes the toolserver's docker container is not ready yet
# So we need to wait for a while
await asyncio.sleep(10)
payload_arguments = deepcopy(arguments)
if "thought" in payload_arguments:
del payload_arguments["thought"]
payload = {
"tool_name": command,
"arguments": payload_arguments,
}
# async with ClientSession() as session:
async with session.post(
f"{url}/execute_tool",
json=payload,
headers={
"toolbench_key": "p5ZASSLBO0EknAQLE5ecNZ7kq5i1YfY9eoWUXNxL3TM6lXwdXs"
},
timeout=30,
) as response:
content = await response.text()
if command == "WebEnv_browse_website":
content = await _summarize_webpage(
content, arguments["question"]
)
message = ExecutorMessage(
content=content,
sender="function",
tool_name=command,
tool_input=arguments,
)
# async with session.post(
# f"{url}/release_session", timeout=30
# ) as response:
# await response.text()
break
except Exception as e:
message = ExecutorMessage(
content="Failed to call the tool. Exception: " + str(e),
sender="function",
tool_name=command,
tool_input=arguments,
)
continue
return {"observation": message, "is_finish": False, "cookies": cookies}
def broadcast_messages(self, agents, messages) -> None:
for agent in agents:
agent.add_message_to_memory(messages)
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