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331d74059f
1. SummarizeCode动作：用于基于代码进行总结，思考bug、逻辑、todo
2. CodeReview动作优化：目前强制要求回答问题，有更高的成功率了
2. 数据结构
1. Document的标准化：Env->Repo->Document，其中Document/Asset/Code都只用Document
1. 原用于检索的Document改为IndexableDocument
2. Repo结构引入：用于Document装载与元数据装载
3. RepoParser引入：写了一个简单的AST parser（后续可能要换tree-sitter），给出了整库symbol
3. 配置优化
1. 默认更换为gpt-4-1106-preview，以获得最好的效果与成本
2. 提供~/.metagpt作为配置最高优先级目录，从中读取config.yaml
3. workspace可以灵活指定了，在config中配置
4. metagpt作为默认命令行，而非python startup.py
1. 使用新的METAGPT_ROOT生成方式，而非寻找git，以便cli安装
2. 命令行由fire换为了typer，它会带来相对更好的体验
3. project_name可以灵活指定了，在metagpt命令行输入中配置
5. 其他
1. BossRequirement -> UserRequirement
2. 大量错误文本的修正，增加了可读性
3. 中量提示词优化，稍微提升了一些准确率
4. 暂时屏蔽了LongtermMemory相关逻辑，这个逻辑底层调用了langchain的FAISS，会带来~5秒加载耗时
5. 修复了安装包中的部分描述错误
83 lines
2.8 KiB
Python
83 lines
2.8 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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@Time : 2023/9/13 12:36
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@Author : femto Zheng
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@File : sk_agent.py
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"""
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import asyncio
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from semantic_kernel.core_skills import FileIOSkill, MathSkill, TextSkill, TimeSkill
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from semantic_kernel.planning import SequentialPlanner
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# from semantic_kernel.planning import SequentialPlanner
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from semantic_kernel.planning.action_planner.action_planner import ActionPlanner
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from metagpt.actions import UserRequirement
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from metagpt.const import SKILL_DIRECTORY
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from metagpt.roles.sk_agent import SkAgent
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from metagpt.schema import Message
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from metagpt.tools.search_engine import SkSearchEngine
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async def main():
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# await basic_planner_example()
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# await action_planner_example()
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# await sequential_planner_example()
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await basic_planner_web_search_example()
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async def basic_planner_example():
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task = """
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Tomorrow is Valentine's day. I need to come up with a few date ideas. She speaks French so write it in French.
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Convert the text to uppercase"""
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role = SkAgent()
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# let's give the agent some skills
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role.import_semantic_skill_from_directory(SKILL_DIRECTORY, "SummarizeSkill")
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role.import_semantic_skill_from_directory(SKILL_DIRECTORY, "WriterSkill")
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role.import_skill(TextSkill(), "TextSkill")
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# using BasicPlanner
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await role.run(Message(content=task, cause_by=UserRequirement))
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async def sequential_planner_example():
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task = """
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Tomorrow is Valentine's day. I need to come up with a few date ideas. She speaks French so write it in French.
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Convert the text to uppercase"""
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role = SkAgent(planner_cls=SequentialPlanner)
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# let's give the agent some skills
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role.import_semantic_skill_from_directory(SKILL_DIRECTORY, "SummarizeSkill")
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role.import_semantic_skill_from_directory(SKILL_DIRECTORY, "WriterSkill")
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role.import_skill(TextSkill(), "TextSkill")
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# using BasicPlanner
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await role.run(Message(content=task, cause_by=UserRequirement))
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async def basic_planner_web_search_example():
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task = """
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Question: Who made the 1989 comic book, the film version of which Jon Raymond Polito appeared in?"""
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role = SkAgent()
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role.import_skill(SkSearchEngine(), "WebSearchSkill")
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# role.import_semantic_skill_from_directory(skills_directory, "QASkill")
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await role.run(Message(content=task, cause_by=UserRequirement))
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async def action_planner_example():
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role = SkAgent(planner_cls=ActionPlanner)
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# let's give the agent 4 skills
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role.import_skill(MathSkill(), "math")
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role.import_skill(FileIOSkill(), "fileIO")
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role.import_skill(TimeSkill(), "time")
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role.import_skill(TextSkill(), "text")
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task = "What is the sum of 110 and 990?"
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await role.run(Message(content=task, cause_by=UserRequirement)) # it will choose mathskill.Add
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if __name__ == "__main__":
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asyncio.run(main())
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