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Add notebook executor as service function and add codeact agent. (#231)
--------- Co-authored-by: wenhao <[email protected]>
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examples/conversation_with_codeact_agent/codeact_agent.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,166 @@ | ||
| # -*- coding: utf-8 -*- | ||
| # pylint: disable=C0301 | ||
| """An agent class that implements the CodeAct agent. | ||
| This agent can execute code interactively as actions. | ||
| More details can be found at the paper of codeact agent | ||
| https://arxiv.org/abs/2402.01030 | ||
| and the original repo of codeact https://github.com/xingyaoww/code-act | ||
| """ | ||
| from agentscope.agents import AgentBase | ||
| from agentscope.message import Msg | ||
| from agentscope.service import ( | ||
| ServiceResponse, | ||
| ServiceExecStatus, | ||
| NoteBookExecutor, | ||
| ) | ||
| from agentscope.parsers import RegexTaggedContentParser | ||
|
|
||
| SYSTEM_MESSAGE = """ | ||
| You are a helpful assistant that gives helpful, detailed, and polite answers to the user's questions. | ||
| You should interact with the interactive Python (Jupyter Notebook) environment and receive the corresponding output when needed. The code written by assistant should be enclosed using <execute> tag, for example: <execute> print('Hello World!') </execute>. | ||
| You should attempt fewer things at a time instead of putting too much code in one <execute> block. You can install packages through PIP by <execute> !pip install [package needed] </execute> and should always import packages and define variables before starting to use them. | ||
| You should stop <execute> and provide an answer when they have already obtained the answer from the execution result. Whenever possible, execute the code for the user using <execute> instead of providing it. | ||
| Your response should be concise, but do express their thoughts. Always write the code in <execute> block to execute them. | ||
| You should not ask for the user's input unless necessary. Solve the task on your own and leave no unanswered questions behind. | ||
| You should do every thing by your self. | ||
| """ # noqa | ||
|
|
||
| EXAMPLE_MESSAGE = """ | ||
| Additionally, you are provided with the following code available: | ||
| {example_code} | ||
| The above code is already available in your interactive Python (Jupyter Notebook) environment, allowing you to directly use these variables and functions without needing to redeclare them. | ||
| """ # noqa | ||
|
|
||
|
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| class CodeActAgent(AgentBase): | ||
| """ | ||
| The implementation of CodeAct-agent. | ||
| The agent can execute code interactively as actions. | ||
| More details can be found at the paper of codeact agent | ||
| https://arxiv.org/abs/2402.01030 | ||
| and the original repo of codeact https://github.com/xingyaoww/code-act | ||
| """ | ||
|
|
||
| def __init__( | ||
| self, | ||
| name: str, | ||
| model_config_name: str, | ||
| example_code: str = "", | ||
| ) -> None: | ||
| """ | ||
| Initialize the CodeActAgent. | ||
| Args: | ||
| name(`str`): | ||
| The name of the agent. | ||
| model_config_name(`str`): | ||
| The name of the model configuration. | ||
| example_code(Optional`str`): | ||
| The example code to be executed bewfore the interaction. | ||
| You can import reference libs, define variables | ||
| and functions to be called. For example: | ||
| ```python | ||
| from agentscope.service import bing_search | ||
| import os | ||
| api_key = "{YOUR_BING_API_KEY}" | ||
| def search(question: str): | ||
| return bing_search(question, api_key=api_key, num_results=3).content | ||
| ``` | ||
| """ # noqa | ||
| super().__init__( | ||
| name=name, | ||
| model_config_name=model_config_name, | ||
| ) | ||
| self.n_max_executions = 5 | ||
| self.example_code = example_code | ||
| self.code_executor = NoteBookExecutor() | ||
|
|
||
| sys_msg = Msg(name="system", role="system", content=SYSTEM_MESSAGE) | ||
| example_msg = Msg( | ||
| name="user", | ||
| role="user", | ||
| content=EXAMPLE_MESSAGE.format(example_code=self.example_code), | ||
| ) | ||
|
|
||
| self.memory.add(sys_msg) | ||
|
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| if self.example_code != "": | ||
| code_execution_result = self.code_executor.run_code_on_notebook( | ||
| self.example_code, | ||
| ) | ||
| code_exec_msg = self.handle_code_result( | ||
| code_execution_result, | ||
| "Example Code excuted: ", | ||
| ) | ||
| self.memory.add(example_msg) | ||
| self.memory.add(code_exec_msg) | ||
| self.speak(code_exec_msg) | ||
|
|
||
| self.parser = RegexTaggedContentParser(try_parse_json=False) | ||
|
|
||
| def handle_code_result( | ||
| self, | ||
| code_execution_result: ServiceResponse, | ||
| content_pre_sring: str = "", | ||
| ) -> Msg: | ||
| """return the message from code result""" | ||
| code_exec_content = content_pre_sring | ||
| if code_execution_result.status == ServiceExecStatus.SUCCESS: | ||
| code_exec_content += "Excution Successful:\n" | ||
| else: | ||
| code_exec_content += "Excution Failed:\n" | ||
| code_exec_content += "Execution Output:\n" + str( | ||
| code_execution_result.content, | ||
| ) | ||
| return Msg(name="user", role="user", content=code_exec_content) | ||
|
|
||
| def reply(self, x: Msg = None) -> Msg: | ||
| """The reply function that implements the codeact agent.""" | ||
|
|
||
| self.memory.add(x) | ||
|
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||
| excution_count = 0 | ||
| while ( | ||
| self.memory.get_memory(1)[-1].role == "user" | ||
| and excution_count < self.n_max_executions | ||
| ): | ||
| prompt = self.model.format(self.memory.get_memory()) | ||
| model_res = self.model(prompt) | ||
| msg_res = Msg( | ||
| name=self.name, | ||
| content=model_res.text, | ||
| role="assistant", | ||
| ) | ||
| self.memory.add(msg_res) | ||
| self.speak(msg_res) | ||
| res = self.parser.parse(model_res) | ||
| code = res.parsed.get("execute") | ||
| if code is not None: | ||
| code = code.strip() | ||
| code_execution_result = ( | ||
| self.code_executor.run_code_on_notebook(code) | ||
| ) | ||
| excution_count += 1 | ||
| code_exec_msg = self.handle_code_result(code_execution_result) | ||
| self.memory.add(code_exec_msg) | ||
| self.speak(code_exec_msg) | ||
|
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||
| if excution_count == self.n_max_executions: | ||
| assert self.memory.get_memory(1)[-1].role == "user" | ||
| code_max_exec_msg = Msg( | ||
| name="assitant", | ||
| role="assistant", | ||
| content=( | ||
| "I have reached the maximum number " | ||
| f"of executions ({self.n_max_executions=}). " | ||
| "Can you assist me or ask me another question?" | ||
| ), | ||
| ) | ||
| self.memory.add(code_max_exec_msg) | ||
| self.speak(code_max_exec_msg) | ||
| return code_max_exec_msg | ||
|
|
||
| return msg_res |
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