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在当前branch 清除其他无关的actions
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stellahsr
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commit
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13 changed files with 0 additions and 914 deletions
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@ -12,16 +12,12 @@ from metagpt.actions.action_output import ActionOutput
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from metagpt.actions.add_requirement import BossRequirement
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from metagpt.actions.debug_error import DebugError
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from metagpt.actions.design_api import WriteDesign
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from metagpt.actions.design_api_review import DesignReview
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from metagpt.actions.design_filenames import DesignFilenames
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from metagpt.actions.project_management import AssignTasks, WriteTasks
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from metagpt.actions.research import CollectLinks, WebBrowseAndSummarize, ConductResearch
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from metagpt.actions.run_code import RunCode
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from metagpt.actions.search_and_summarize import SearchAndSummarize
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from metagpt.actions.write_code import WriteCode
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from metagpt.actions.write_code_review import WriteCodeReview
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from metagpt.actions.write_prd import WritePRD
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from metagpt.actions.write_prd_review import WritePRDReview
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from metagpt.actions.write_test import WriteTest
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@ -30,10 +26,7 @@ class ActionType(Enum):
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ADD_REQUIREMENT = BossRequirement
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WRITE_PRD = WritePRD
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WRITE_PRD_REVIEW = WritePRDReview
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WRITE_DESIGN = WriteDesign
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DESIGN_REVIEW = DesignReview
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DESIGN_FILENAMES = DesignFilenames
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WRTIE_CODE = WriteCode
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WRITE_CODE_REVIEW = WriteCodeReview
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WRITE_TEST = WriteTest
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@ -42,9 +35,6 @@ class ActionType(Enum):
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WRITE_TASKS = WriteTasks
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ASSIGN_TASKS = AssignTasks
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SEARCH_AND_SUMMARIZE = SearchAndSummarize
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COLLECT_LINKS = CollectLinks
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WEB_BROWSE_AND_SUMMARIZE = WebBrowseAndSummarize
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CONDUCT_RESEARCH = ConductResearch
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__all__ = [
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@ -1,37 +0,0 @@
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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@Time : 2023/5/19 12:01
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@Author : alexanderwu
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@File : analyze_dep_libs.py
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"""
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from metagpt.actions import Action
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PROMPT = """You are an AI developer, trying to write a program that generates code for users based on their intentions.
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For the user's prompt:
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---
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The API is: {prompt}
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---
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We decide the generated files are: {filepaths_string}
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Now that we have a file list, we need to understand the shared dependencies they have.
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Please list and briefly describe the shared contents between the files we are generating, including exported variables,
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data patterns, id names of all DOM elements that javascript functions will use, message names and function names.
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Focus only on the names of shared dependencies, do not add any other explanations.
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"""
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class AnalyzeDepLibs(Action):
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def __init__(self, name, context=None, llm=None):
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super().__init__(name, context, llm)
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self.desc = "Analyze the runtime dependencies of the program based on the context"
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async def run(self, requirement, filepaths_string):
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# prompt = f"Below is the product requirement document (PRD):\n\n{prd}\n\n{PROMPT}"
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prompt = PROMPT.format(prompt=requirement, filepaths_string=filepaths_string)
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design_filenames = await self._aask(prompt)
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return design_filenames
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@ -1,53 +0,0 @@
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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@Time : 2023/6/9 22:22
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@Author : Leo Xiao
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@File : azure_tts.py
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"""
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from azure.cognitiveservices.speech import AudioConfig, SpeechConfig, SpeechSynthesizer
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from metagpt.actions.action import Action
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from metagpt.config import Config
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class AzureTTS(Action):
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def __init__(self, name, context=None, llm=None):
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super().__init__(name, context, llm)
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self.config = Config()
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# Parameters reference: https://learn.microsoft.com/zh-cn/azure/cognitive-services/speech-service/language-support?tabs=tts#voice-styles-and-roles
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def synthesize_speech(self, lang, voice, role, text, output_file):
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subscription_key = self.config.get('AZURE_TTS_SUBSCRIPTION_KEY')
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region = self.config.get('AZURE_TTS_REGION')
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speech_config = SpeechConfig(
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subscription=subscription_key, region=region)
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speech_config.speech_synthesis_voice_name = voice
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audio_config = AudioConfig(filename=output_file)
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synthesizer = SpeechSynthesizer(
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speech_config=speech_config,
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audio_config=audio_config)
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# if voice=="zh-CN-YunxiNeural":
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ssml_string = f"""
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<speak version='1.0' xmlns='http://www.w3.org/2001/10/synthesis' xml:lang='{lang}' xmlns:mstts='http://www.w3.org/2001/mstts'>
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<voice name='{voice}'>
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<mstts:express-as style='affectionate' role='{role}'>
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{text}
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</mstts:express-as>
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</voice>
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</speak>
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"""
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synthesizer.speak_ssml_async(ssml_string).get()
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if __name__ == "__main__":
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azure_tts = AzureTTS("azure_tts")
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azure_tts.synthesize_speech(
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"zh-CN",
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"zh-CN-YunxiNeural",
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"Boy",
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"Hello, I am Kaka",
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"output.wav")
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@ -1,65 +0,0 @@
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from pathlib import Path
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import traceback
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from metagpt.actions.write_code import WriteCode
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from metagpt.logs import logger
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from metagpt.schema import Message
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from metagpt.utils.highlight import highlight
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CLONE_PROMPT = """
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*context*
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Please convert the function code ```{source_code}``` into the the function format: ```{template_func}```.
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*Please Write code based on the following list and context*
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1. Write code start with ```, and end with ```.
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2. Please implement it in one function if possible, except for import statements. for exmaple:
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```python
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import pandas as pd
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def run(*args) -> pd.DataFrame:
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...
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```
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3. Do not use public member functions that do not exist in your design.
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4. The output function name, input parameters and return value must be the same as ```{template_func}```.
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5. Make sure the results before and after the code conversion are required to be exactly the same.
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6. Don't repeat my context in your replies.
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7. Return full results, for example, if the return value has df.head(), please return df.
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8. If you must use a third-party package, use the most popular ones, for example: pandas, numpy, ta, ...
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"""
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class CloneFunction(WriteCode):
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def __init__(self, name="CloneFunction", context: list[Message] = None, llm=None):
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super().__init__(name, context, llm)
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def _save(self, code_path, code):
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if isinstance(code_path, str):
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code_path = Path(code_path)
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code_path.parent.mkdir(parents=True, exist_ok=True)
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code_path.write_text(code)
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logger.info(f"Saving Code to {code_path}")
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async def run(self, template_func: str, source_code: str) -> str:
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"""将source_code转换成template_func一样的入参和返回类型"""
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prompt = CLONE_PROMPT.format(source_code=source_code, template_func=template_func)
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logger.info(f"query for CloneFunction: \n {prompt}")
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code = await self.write_code(prompt)
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logger.info(f'CloneFunction code is \n {highlight(code)}')
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return code
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def run_function_code(func_code: str, func_name: str, *args, **kwargs):
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"""Run function code from string code."""
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try:
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locals_ = {}
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exec(func_code, locals_)
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func = locals_[func_name]
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return func(*args, **kwargs), ""
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except Exception:
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return "", traceback.format_exc()
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def run_function_script(code_script_path: str, func_name: str, *args, **kwargs):
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"""Run function code from script."""
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if isinstance(code_script_path, str):
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code_path = Path(code_script_path)
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code = code_path.read_text(encoding='utf-8')
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return run_function_code(code, func_name, *args, **kwargs)
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@ -1,22 +0,0 @@
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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@Time : 2023/5/11 19:31
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@Author : alexanderwu
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@File : design_api_review.py
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"""
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from metagpt.actions.action import Action
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class DesignReview(Action):
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def __init__(self, name, context=None, llm=None):
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super().__init__(name, context, llm)
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async def run(self, prd, api_design):
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prompt = f"Here is the Product Requirement Document (PRD):\n\n{prd}\n\nHere is the list of APIs designed " \
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f"based on this PRD:\n\n{api_design}\n\nPlease review whether this API design meets the requirements" \
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f" of the PRD, and whether it complies with good design practices."
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api_review = await self._aask(prompt)
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return api_review
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@ -1,29 +0,0 @@
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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@Time : 2023/5/19 11:50
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@Author : alexanderwu
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@File : design_filenames.py
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"""
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from metagpt.actions import Action
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from metagpt.logs import logger
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PROMPT = """You are an AI developer, trying to write a program that generates code for users based on their intentions.
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When given their intentions, provide a complete and exhaustive list of file paths needed to write the program for the user.
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Only list the file paths you will write and return them as a Python string list.
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Do not add any other explanations, just return a Python string list."""
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class DesignFilenames(Action):
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def __init__(self, name, context=None, llm=None):
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super().__init__(name, context, llm)
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self.desc = "Based on the PRD, consider system design, and carry out the basic design of the corresponding " \
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"APIs, data structures, and database tables. Please give your design, feedback clearly and in detail."
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async def run(self, prd):
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prompt = f"The following is the Product Requirement Document (PRD):\n\n{prd}\n\n{PROMPT}"
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design_filenames = await self._aask(prompt)
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logger.debug(prompt)
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logger.debug(design_filenames)
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return design_filenames
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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@Time : 2023/9/12 17:45
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@Author : fisherdeng
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@File : detail_mining.py
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"""
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from metagpt.actions import Action, ActionOutput
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from metagpt.logs import logger
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PROMPT_TEMPLATE = """
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##TOPIC
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{topic}
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##RECORD
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{record}
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##Format example
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{format_example}
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-----
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Task: Refer to the "##TOPIC" (discussion objectives) and "##RECORD" (discussion records) to further inquire about the details that interest you, within a word limit of 150 words.
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Special Note 1: Your intention is solely to ask questions without endorsing or negating any individual's viewpoints.
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Special Note 2: This output should only include the topic "##OUTPUT". Do not add, remove, or modify the topic. Begin the output with '##OUTPUT', followed by an immediate line break, and then proceed to provide the content in the specified format as outlined in the "##Format example" section.
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Special Note 3: The output should be in the same language as the input.
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"""
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FORMAT_EXAMPLE = """
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##
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##OUTPUT
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...(Please provide the specific details you would like to inquire about here.)
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##
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##
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"""
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OUTPUT_MAPPING = {
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"OUTPUT": (str, ...),
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}
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class DetailMining(Action):
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"""This class allows LLM to further mine noteworthy details based on specific "##TOPIC"(discussion topic) and "##RECORD" (discussion records), thereby deepening the discussion.
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"""
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def __init__(self, name="", context=None, llm=None):
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super().__init__(name, context, llm)
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async def run(self, topic, record) -> ActionOutput:
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prompt = PROMPT_TEMPLATE.format(topic=topic, record=record, format_example=FORMAT_EXAMPLE)
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rsp = await self._aask_v1(prompt, "detail_mining", OUTPUT_MAPPING)
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return rsp
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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@Time : 2023/9/13 12:26
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@Author : femto Zheng
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@File : execute_task.py
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"""
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from metagpt.actions import Action
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from metagpt.schema import Message
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class ExecuteTask(Action):
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def __init__(self, name="ExecuteTask", context: list[Message] = None, llm=None):
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super().__init__(name, context, llm)
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def run(self, *args, **kwargs):
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pass
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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@Time : 2023/9/19 15:02
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@Author : DevXiaolan
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@File : prepare_interview.py
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"""
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from metagpt.actions import Action
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PROMPT_TEMPLATE = """
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# Context
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{context}
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## Format example
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---
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Q1: question 1 here
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References:
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- point 1
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- point 2
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Q2: question 2 here...
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---
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-----
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Role: You are an interviewer of our company who is well-knonwn in frontend or backend develop;
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Requirement: Provide a list of questions for the interviewer to ask the interviewee, by reading the resume of the interviewee in the context.
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Attention: Provide as markdown block as the format above, at least 10 questions.
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"""
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# prepare for a interview
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class PrepareInterview(Action):
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def __init__(self, name, context=None, llm=None):
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super().__init__(name, context, llm)
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async def run(self, context):
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prompt = PROMPT_TEMPLATE.format(context=context)
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question_list = await self._aask_v1(prompt)
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return question_list
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@ -1,278 +0,0 @@
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#!/usr/bin/env python
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from __future__ import annotations
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import asyncio
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import json
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from typing import Callable
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from pydantic import parse_obj_as
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from metagpt.actions import Action
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from metagpt.config import CONFIG
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from metagpt.logs import logger
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from metagpt.tools.search_engine import SearchEngine
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from metagpt.tools.web_browser_engine import WebBrowserEngine, WebBrowserEngineType
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from metagpt.utils.common import OutputParser
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from metagpt.utils.text import generate_prompt_chunk, reduce_message_length
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LANG_PROMPT = "Please respond in {language}."
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RESEARCH_BASE_SYSTEM = """You are an AI critical thinker research assistant. Your sole purpose is to write well \
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written, critically acclaimed, objective and structured reports on the given text."""
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RESEARCH_TOPIC_SYSTEM = "You are an AI researcher assistant, and your research topic is:\n#TOPIC#\n{topic}"
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SEARCH_TOPIC_PROMPT = """Please provide up to 2 necessary keywords related to your research topic for Google search. \
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Your response must be in JSON format, for example: ["keyword1", "keyword2"]."""
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SUMMARIZE_SEARCH_PROMPT = """### Requirements
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1. The keywords related to your research topic and the search results are shown in the "Search Result Information" section.
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2. Provide up to {decomposition_nums} queries related to your research topic base on the search results.
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3. Please respond in the following JSON format: ["query1", "query2", "query3", ...].
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### Search Result Information
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{search_results}
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"""
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COLLECT_AND_RANKURLS_PROMPT = """### Topic
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{topic}
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### Query
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{query}
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### The online search results
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{results}
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### Requirements
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Please remove irrelevant search results that are not related to the query or topic. Then, sort the remaining search results \
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based on the link credibility. If two results have equal credibility, prioritize them based on the relevance. Provide the
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ranked results' indices in JSON format, like [0, 1, 3, 4, ...], without including other words.
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"""
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WEB_BROWSE_AND_SUMMARIZE_PROMPT = '''### Requirements
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1. Utilize the text in the "Reference Information" section to respond to the question "{query}".
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2. If the question cannot be directly answered using the text, but the text is related to the research topic, please provide \
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a comprehensive summary of the text.
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3. If the text is entirely unrelated to the research topic, please reply with a simple text "Not relevant."
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4. Include all relevant factual information, numbers, statistics, etc., if available.
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### Reference Information
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{content}
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'''
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CONDUCT_RESEARCH_PROMPT = '''### Reference Information
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{content}
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### Requirements
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Please provide a detailed research report in response to the following topic: "{topic}", using the information provided \
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above. The report must meet the following requirements:
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- Focus on directly addressing the chosen topic.
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- Ensure a well-structured and in-depth presentation, incorporating relevant facts and figures where available.
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- Present data and findings in an intuitive manner, utilizing feature comparative tables, if applicable.
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- The report should have a minimum word count of 2,000 and be formatted with Markdown syntax following APA style guidelines.
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- Include all source URLs in APA format at the end of the report.
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'''
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class CollectLinks(Action):
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"""Action class to collect links from a search engine."""
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def __init__(
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self,
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name: str = "",
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*args,
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rank_func: Callable[[list[str]], None] | None = None,
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||||
**kwargs,
|
||||
):
|
||||
super().__init__(name, *args, **kwargs)
|
||||
self.desc = "Collect links from a search engine."
|
||||
self.search_engine = SearchEngine()
|
||||
self.rank_func = rank_func
|
||||
|
||||
async def run(
|
||||
self,
|
||||
topic: str,
|
||||
decomposition_nums: int = 4,
|
||||
url_per_query: int = 4,
|
||||
system_text: str | None = None,
|
||||
) -> dict[str, list[str]]:
|
||||
"""Run the action to collect links.
|
||||
|
||||
Args:
|
||||
topic: The research topic.
|
||||
decomposition_nums: The number of search questions to generate.
|
||||
url_per_query: The number of URLs to collect per search question.
|
||||
system_text: The system text.
|
||||
|
||||
Returns:
|
||||
A dictionary containing the search questions as keys and the collected URLs as values.
|
||||
"""
|
||||
system_text = system_text if system_text else RESEARCH_TOPIC_SYSTEM.format(topic=topic)
|
||||
keywords = await self._aask(SEARCH_TOPIC_PROMPT, [system_text])
|
||||
try:
|
||||
keywords = OutputParser.extract_struct(keywords, list)
|
||||
keywords = parse_obj_as(list[str], keywords)
|
||||
except Exception as e:
|
||||
logger.exception(f"fail to get keywords related to the research topic \"{topic}\" for {e}")
|
||||
keywords = [topic]
|
||||
results = await asyncio.gather(*(self.search_engine.run(i, as_string=False) for i in keywords))
|
||||
|
||||
def gen_msg():
|
||||
while True:
|
||||
search_results = "\n".join(f"#### Keyword: {i}\n Search Result: {j}\n" for (i, j) in zip(keywords, results))
|
||||
prompt = SUMMARIZE_SEARCH_PROMPT.format(decomposition_nums=decomposition_nums, search_results=search_results)
|
||||
yield prompt
|
||||
remove = max(results, key=len)
|
||||
remove.pop()
|
||||
if len(remove) == 0:
|
||||
break
|
||||
prompt = reduce_message_length(gen_msg(), self.llm.model, system_text, CONFIG.max_tokens_rsp)
|
||||
logger.debug(prompt)
|
||||
queries = await self._aask(prompt, [system_text])
|
||||
try:
|
||||
queries = OutputParser.extract_struct(queries, list)
|
||||
queries = parse_obj_as(list[str], queries)
|
||||
except Exception as e:
|
||||
logger.exception(f"fail to break down the research question due to {e}")
|
||||
queries = keywords
|
||||
ret = {}
|
||||
for query in queries:
|
||||
ret[query] = await self._search_and_rank_urls(topic, query, url_per_query)
|
||||
return ret
|
||||
|
||||
async def _search_and_rank_urls(self, topic: str, query: str, num_results: int = 4) -> list[str]:
|
||||
"""Search and rank URLs based on a query.
|
||||
|
||||
Args:
|
||||
topic: The research topic.
|
||||
query: The search query.
|
||||
num_results: The number of URLs to collect.
|
||||
|
||||
Returns:
|
||||
A list of ranked URLs.
|
||||
"""
|
||||
max_results = max(num_results * 2, 6)
|
||||
results = await self.search_engine.run(query, max_results=max_results, as_string=False)
|
||||
_results = "\n".join(f"{i}: {j}" for i, j in zip(range(max_results), results))
|
||||
prompt = COLLECT_AND_RANKURLS_PROMPT.format(topic=topic, query=query, results=_results)
|
||||
logger.debug(prompt)
|
||||
indices = await self._aask(prompt)
|
||||
try:
|
||||
indices = OutputParser.extract_struct(indices, list)
|
||||
assert all(isinstance(i, int) for i in indices)
|
||||
except Exception as e:
|
||||
logger.exception(f"fail to rank results for {e}")
|
||||
indices = list(range(max_results))
|
||||
results = [results[i] for i in indices]
|
||||
if self.rank_func:
|
||||
results = self.rank_func(results)
|
||||
return [i["link"] for i in results[:num_results]]
|
||||
|
||||
|
||||
class WebBrowseAndSummarize(Action):
|
||||
"""Action class to explore the web and provide summaries of articles and webpages."""
|
||||
def __init__(
|
||||
self,
|
||||
*args,
|
||||
browse_func: Callable[[list[str]], None] | None = None,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(*args, **kwargs)
|
||||
if CONFIG.model_for_researcher_summary:
|
||||
self.llm.model = CONFIG.model_for_researcher_summary
|
||||
self.web_browser_engine = WebBrowserEngine(
|
||||
engine=WebBrowserEngineType.CUSTOM if browse_func else None,
|
||||
run_func=browse_func,
|
||||
)
|
||||
self.desc = "Explore the web and provide summaries of articles and webpages."
|
||||
|
||||
async def run(
|
||||
self,
|
||||
url: str,
|
||||
*urls: str,
|
||||
query: str,
|
||||
system_text: str = RESEARCH_BASE_SYSTEM,
|
||||
) -> dict[str, str]:
|
||||
"""Run the action to browse the web and provide summaries.
|
||||
|
||||
Args:
|
||||
url: The main URL to browse.
|
||||
urls: Additional URLs to browse.
|
||||
query: The research question.
|
||||
system_text: The system text.
|
||||
|
||||
Returns:
|
||||
A dictionary containing the URLs as keys and their summaries as values.
|
||||
"""
|
||||
contents = await self.web_browser_engine.run(url, *urls)
|
||||
if not urls:
|
||||
contents = [contents]
|
||||
|
||||
summaries = {}
|
||||
prompt_template = WEB_BROWSE_AND_SUMMARIZE_PROMPT.format(query=query, content="{}")
|
||||
for u, content in zip([url, *urls], contents):
|
||||
content = content.inner_text
|
||||
chunk_summaries = []
|
||||
for prompt in generate_prompt_chunk(content, prompt_template, self.llm.model, system_text, CONFIG.max_tokens_rsp):
|
||||
logger.debug(prompt)
|
||||
summary = await self._aask(prompt, [system_text])
|
||||
if summary == "Not relevant.":
|
||||
continue
|
||||
chunk_summaries.append(summary)
|
||||
|
||||
if not chunk_summaries:
|
||||
summaries[u] = None
|
||||
continue
|
||||
|
||||
if len(chunk_summaries) == 1:
|
||||
summaries[u] = chunk_summaries[0]
|
||||
continue
|
||||
|
||||
content = "\n".join(chunk_summaries)
|
||||
prompt = WEB_BROWSE_AND_SUMMARIZE_PROMPT.format(query=query, content=content)
|
||||
summary = await self._aask(prompt, [system_text])
|
||||
summaries[u] = summary
|
||||
return summaries
|
||||
|
||||
|
||||
class ConductResearch(Action):
|
||||
"""Action class to conduct research and generate a research report."""
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
if CONFIG.model_for_researcher_report:
|
||||
self.llm.model = CONFIG.model_for_researcher_report
|
||||
|
||||
async def run(
|
||||
self,
|
||||
topic: str,
|
||||
content: str,
|
||||
system_text: str = RESEARCH_BASE_SYSTEM,
|
||||
) -> str:
|
||||
"""Run the action to conduct research and generate a research report.
|
||||
|
||||
Args:
|
||||
topic: The research topic.
|
||||
content: The content for research.
|
||||
system_text: The system text.
|
||||
|
||||
Returns:
|
||||
The generated research report.
|
||||
"""
|
||||
prompt = CONDUCT_RESEARCH_PROMPT.format(topic=topic, content=content)
|
||||
logger.debug(prompt)
|
||||
self.llm.auto_max_tokens = True
|
||||
return await self._aask(prompt, [system_text])
|
||||
|
||||
|
||||
def get_research_system_text(topic: str, language: str):
|
||||
"""Get the system text for conducting research.
|
||||
|
||||
Args:
|
||||
topic: The research topic.
|
||||
language: The language for the system text.
|
||||
|
||||
Returns:
|
||||
The system text for conducting research.
|
||||
"""
|
||||
return " ".join((RESEARCH_TOPIC_SYSTEM.format(topic=topic), LANG_PROMPT.format(language=language)))
|
||||
|
|
@ -1,214 +0,0 @@
|
|||
"""Code Docstring Generator.
|
||||
|
||||
This script provides a tool to automatically generate docstrings for Python code. It uses the specified style to create
|
||||
docstrings for the given code and system text.
|
||||
|
||||
Usage:
|
||||
python3 -m metagpt.actions.write_docstring <filename> [--overwrite] [--style=<docstring_style>]
|
||||
|
||||
Arguments:
|
||||
filename The path to the Python file for which you want to generate docstrings.
|
||||
|
||||
Options:
|
||||
--overwrite If specified, overwrite the original file with the code containing docstrings.
|
||||
--style=<docstring_style> Specify the style of the generated docstrings.
|
||||
Valid values: 'google', 'numpy', or 'sphinx'.
|
||||
Default: 'google'
|
||||
|
||||
Example:
|
||||
python3 -m metagpt.actions.write_docstring startup.py --overwrite False --style=numpy
|
||||
|
||||
This script uses the 'fire' library to create a command-line interface. It generates docstrings for the given Python code using
|
||||
the specified docstring style and adds them to the code.
|
||||
"""
|
||||
import ast
|
||||
from typing import Literal
|
||||
|
||||
from metagpt.actions.action import Action
|
||||
from metagpt.utils.common import OutputParser
|
||||
from metagpt.utils.pycst import merge_docstring
|
||||
|
||||
PYTHON_DOCSTRING_SYSTEM = '''### Requirements
|
||||
1. Add docstrings to the given code following the {style} style.
|
||||
2. Replace the function body with an Ellipsis object(...) to reduce output.
|
||||
3. If the types are already annotated, there is no need to include them in the docstring.
|
||||
4. Extract only class, function or the docstrings for the module parts from the given Python code, avoiding any other text.
|
||||
|
||||
### Input Example
|
||||
```python
|
||||
def function_with_pep484_type_annotations(param1: int) -> bool:
|
||||
return isinstance(param1, int)
|
||||
|
||||
class ExampleError(Exception):
|
||||
def __init__(self, msg: str):
|
||||
self.msg = msg
|
||||
```
|
||||
|
||||
### Output Example
|
||||
```python
|
||||
{example}
|
||||
```
|
||||
'''
|
||||
|
||||
# https://www.sphinx-doc.org/en/master/usage/extensions/napoleon.html
|
||||
|
||||
PYTHON_DOCSTRING_EXAMPLE_GOOGLE = '''
|
||||
def function_with_pep484_type_annotations(param1: int) -> bool:
|
||||
"""Example function with PEP 484 type annotations.
|
||||
|
||||
Extended description of function.
|
||||
|
||||
Args:
|
||||
param1: The first parameter.
|
||||
|
||||
Returns:
|
||||
The return value. True for success, False otherwise.
|
||||
"""
|
||||
...
|
||||
|
||||
class ExampleError(Exception):
|
||||
"""Exceptions are documented in the same way as classes.
|
||||
|
||||
The __init__ method was documented in the class level docstring.
|
||||
|
||||
Args:
|
||||
msg: Human readable string describing the exception.
|
||||
|
||||
Attributes:
|
||||
msg: Human readable string describing the exception.
|
||||
"""
|
||||
...
|
||||
'''
|
||||
|
||||
PYTHON_DOCSTRING_EXAMPLE_NUMPY = '''
|
||||
def function_with_pep484_type_annotations(param1: int) -> bool:
|
||||
"""
|
||||
Example function with PEP 484 type annotations.
|
||||
|
||||
Extended description of function.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
param1
|
||||
The first parameter.
|
||||
|
||||
Returns
|
||||
-------
|
||||
bool
|
||||
The return value. True for success, False otherwise.
|
||||
"""
|
||||
...
|
||||
|
||||
class ExampleError(Exception):
|
||||
"""
|
||||
Exceptions are documented in the same way as classes.
|
||||
|
||||
The __init__ method was documented in the class level docstring.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
msg
|
||||
Human readable string describing the exception.
|
||||
|
||||
Attributes
|
||||
----------
|
||||
msg
|
||||
Human readable string describing the exception.
|
||||
"""
|
||||
...
|
||||
'''
|
||||
|
||||
PYTHON_DOCSTRING_EXAMPLE_SPHINX = '''
|
||||
def function_with_pep484_type_annotations(param1: int) -> bool:
|
||||
"""Example function with PEP 484 type annotations.
|
||||
|
||||
Extended description of function.
|
||||
|
||||
:param param1: The first parameter.
|
||||
:type param1: int
|
||||
|
||||
:return: The return value. True for success, False otherwise.
|
||||
:rtype: bool
|
||||
"""
|
||||
...
|
||||
|
||||
class ExampleError(Exception):
|
||||
"""Exceptions are documented in the same way as classes.
|
||||
|
||||
The __init__ method was documented in the class level docstring.
|
||||
|
||||
:param msg: Human-readable string describing the exception.
|
||||
:type msg: str
|
||||
"""
|
||||
...
|
||||
'''
|
||||
|
||||
_python_docstring_style = {
|
||||
"google": PYTHON_DOCSTRING_EXAMPLE_GOOGLE.strip(),
|
||||
"numpy": PYTHON_DOCSTRING_EXAMPLE_NUMPY.strip(),
|
||||
"sphinx": PYTHON_DOCSTRING_EXAMPLE_SPHINX.strip(),
|
||||
}
|
||||
|
||||
|
||||
class WriteDocstring(Action):
|
||||
"""This class is used to write docstrings for code.
|
||||
|
||||
Attributes:
|
||||
desc: A string describing the action.
|
||||
"""
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.desc = "Write docstring for code."
|
||||
|
||||
async def run(
|
||||
self, code: str,
|
||||
system_text: str = PYTHON_DOCSTRING_SYSTEM,
|
||||
style: Literal["google", "numpy", "sphinx"] = "google",
|
||||
) -> str:
|
||||
"""Writes docstrings for the given code and system text in the specified style.
|
||||
|
||||
Args:
|
||||
code: A string of Python code.
|
||||
system_text: A string of system text.
|
||||
style: A string specifying the style of the docstring. Can be 'google', 'numpy', or 'sphinx'.
|
||||
|
||||
Returns:
|
||||
The Python code with docstrings added.
|
||||
"""
|
||||
system_text = system_text.format(style=style, example=_python_docstring_style[style])
|
||||
simplified_code = _simplify_python_code(code)
|
||||
documented_code = await self._aask(f"```python\n{simplified_code}\n```", [system_text])
|
||||
documented_code = OutputParser.parse_python_code(documented_code)
|
||||
return merge_docstring(code, documented_code)
|
||||
|
||||
|
||||
def _simplify_python_code(code: str) -> None:
|
||||
"""Simplifies the given Python code by removing expressions and the last if statement.
|
||||
|
||||
Args:
|
||||
code: A string of Python code.
|
||||
|
||||
Returns:
|
||||
The simplified Python code.
|
||||
"""
|
||||
code_tree = ast.parse(code)
|
||||
code_tree.body = [i for i in code_tree.body if not isinstance(i, ast.Expr)]
|
||||
if isinstance(code_tree.body[-1], ast.If):
|
||||
code_tree.body.pop()
|
||||
return ast.unparse(code_tree)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import fire
|
||||
|
||||
async def run(filename: str, overwrite: bool = False, style: Literal["google", "numpy", "sphinx"] = "google"):
|
||||
with open(filename) as f:
|
||||
code = f.read()
|
||||
code = await WriteDocstring().run(code, style=style)
|
||||
if overwrite:
|
||||
with open(filename, "w") as f:
|
||||
f.write(code)
|
||||
return code
|
||||
|
||||
fire.Fire(run)
|
||||
|
|
@ -1,28 +0,0 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
@Time : 2023/5/11 17:45
|
||||
@Author : alexanderwu
|
||||
@File : write_prd_review.py
|
||||
"""
|
||||
from metagpt.actions.action import Action
|
||||
|
||||
|
||||
class WritePRDReview(Action):
|
||||
def __init__(self, name, context=None, llm=None):
|
||||
super().__init__(name, context, llm)
|
||||
self.prd = None
|
||||
self.desc = "Based on the PRD, conduct a PRD Review, providing clear and detailed feedback"
|
||||
self.prd_review_prompt_template = """
|
||||
Given the following Product Requirement Document (PRD):
|
||||
{prd}
|
||||
|
||||
As a project manager, please review it and provide your feedback and suggestions.
|
||||
"""
|
||||
|
||||
async def run(self, prd):
|
||||
self.prd = prd
|
||||
prompt = self.prd_review_prompt_template.format(prd=self.prd)
|
||||
review = await self._aask(prompt)
|
||||
return review
|
||||
|
||||
|
|
@ -1,68 +0,0 @@
|
|||
#!/usr/bin/env python3
|
||||
# _*_ coding: utf-8 _*_
|
||||
"""
|
||||
@Time : 2023/9/4 15:40:40
|
||||
@Author : Stitch-z
|
||||
@File : tutorial_assistant.py
|
||||
@Describe : Actions of the tutorial assistant, including writing directories and document content.
|
||||
"""
|
||||
|
||||
from typing import Dict
|
||||
|
||||
from metagpt.actions import Action
|
||||
from metagpt.prompts.tutorial_assistant import DIRECTORY_PROMPT, CONTENT_PROMPT
|
||||
from metagpt.utils.common import OutputParser
|
||||
|
||||
|
||||
class WriteDirectory(Action):
|
||||
"""Action class for writing tutorial directories.
|
||||
|
||||
Args:
|
||||
name: The name of the action.
|
||||
language: The language to output, default is "Chinese".
|
||||
"""
|
||||
|
||||
def __init__(self, name: str = "", language: str = "Chinese", *args, **kwargs):
|
||||
super().__init__(name, *args, **kwargs)
|
||||
self.language = language
|
||||
|
||||
async def run(self, topic: str, *args, **kwargs) -> Dict:
|
||||
"""Execute the action to generate a tutorial directory according to the topic.
|
||||
|
||||
Args:
|
||||
topic: The tutorial topic.
|
||||
|
||||
Returns:
|
||||
the tutorial directory information, including {"title": "xxx", "directory": [{"dir 1": ["sub dir 1", "sub dir 2"]}]}.
|
||||
"""
|
||||
prompt = DIRECTORY_PROMPT.format(topic=topic, language=self.language)
|
||||
resp = await self._aask(prompt=prompt)
|
||||
return OutputParser.extract_struct(resp, dict)
|
||||
|
||||
|
||||
class WriteContent(Action):
|
||||
"""Action class for writing tutorial content.
|
||||
|
||||
Args:
|
||||
name: The name of the action.
|
||||
directory: The content to write.
|
||||
language: The language to output, default is "Chinese".
|
||||
"""
|
||||
|
||||
def __init__(self, name: str = "", directory: str = "", language: str = "Chinese", *args, **kwargs):
|
||||
super().__init__(name, *args, **kwargs)
|
||||
self.language = language
|
||||
self.directory = directory
|
||||
|
||||
async def run(self, topic: str, *args, **kwargs) -> str:
|
||||
"""Execute the action to write document content according to the directory and topic.
|
||||
|
||||
Args:
|
||||
topic: The tutorial topic.
|
||||
|
||||
Returns:
|
||||
The written tutorial content.
|
||||
"""
|
||||
prompt = CONTENT_PROMPT.format(topic=topic, language=self.language, directory=self.directory)
|
||||
return await self._aask(prompt=prompt)
|
||||
|
||||
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Add table
Add a link
Reference in a new issue