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disentangle planner and tool module, optimize tool module, add react mode
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yzlin
parent
0a2273c7a0
commit
0116de01b9
20 changed files with 554 additions and 354 deletions
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@ -7,168 +7,57 @@
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from __future__ import annotations
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import json
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from typing import Tuple
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from metagpt.actions import Action
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from metagpt.logs import logger
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from metagpt.prompts.mi.ml_action import MODEL_TRAIN_EXAMPLE, USE_ML_TOOLS_EXAMPLE
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from metagpt.prompts.mi.write_analysis_code import (
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CHECK_DATA_PROMPT,
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DEBUG_REFLECTION_EXAMPLE,
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INTERPRETER_SYSTEM_MSG,
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REFLECTION_PROMPT,
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REFLECTION_SYSTEM_MSG,
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STRUCTUAL_PROMPT,
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TOOL_RECOMMENDATION_PROMPT,
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)
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from metagpt.schema import Message, Plan
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from metagpt.tools import TOOL_REGISTRY
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from metagpt.tools.tool_registry import validate_tool_names
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from metagpt.tools.tool_type import ToolType
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from metagpt.utils.common import CodeParser, process_message, remove_comments
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class WriteCodeWithTools(Action):
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"""Write code with help of local available tools. Choose tools first, then generate code to use the tools"""
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use_tools: bool = True
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# selected tools to choose from, listed by their names. An empty list means selection from all tools.
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selected_tools: list[str] = []
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def _get_tools_by_type(self, tool_type: str) -> dict:
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"""
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Retreive tools by tool type from registry, but filtered by pre-selected tool list
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Args:
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tool_type (str): Tool type to retrieve from the registry
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Returns:
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dict: A dict of tool name to Tool object, representing available tools under the type
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"""
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candidate_tools = TOOL_REGISTRY.get_tools_by_type(tool_type)
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if self.selected_tools:
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candidate_tool_names = set(self.selected_tools) & candidate_tools.keys()
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candidate_tools = {tool_name: candidate_tools[tool_name] for tool_name in candidate_tool_names}
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return candidate_tools
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async def _recommend_tool(
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self,
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task: str,
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available_tools: dict,
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) -> dict:
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"""
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Recommend tools for the specified task.
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Args:
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task (str): the task to recommend tools for
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available_tools (dict): the available tools description
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Returns:
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dict: schemas of recommended tools for the specified task
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"""
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prompt = TOOL_RECOMMENDATION_PROMPT.format(
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current_task=task,
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available_tools=available_tools,
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)
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rsp = await self._aask(prompt)
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rsp = CodeParser.parse_code(block=None, text=rsp)
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recommend_tools = json.loads(rsp)
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logger.info(f"Recommended tools: \n{recommend_tools}")
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# Parses and validates the recommended tools, for LLM might hallucinate and recommend non-existing tools
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valid_tools = validate_tool_names(recommend_tools, return_tool_object=True)
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tool_schemas = {tool.name: tool.schemas for tool in valid_tools}
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return tool_schemas
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async def _prepare_tools(self, plan: Plan) -> Tuple[dict, str, str]:
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"""Prepare tool schemas and usage instructions according to current task
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Args:
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plan (Plan): The overall plan containing task information.
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Returns:
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Tuple[dict, str, str]: A tool schemas ({tool_name: tool_schema_dict}), a usage prompt for the type of tools selected, and examples of using the tools
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"""
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if not self.use_tools:
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return {}, "", ""
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# find tool type from task type through exact match, can extend to retrieval in the future
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tool_type = plan.current_task.task_type
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# prepare tool-type-specific instruction
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tool_type_usage_prompt = (
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TOOL_REGISTRY.get_tool_type(tool_type).usage_prompt if TOOL_REGISTRY.has_tool_type(tool_type) else ""
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)
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# ML-specific tool usage examples
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examples = ""
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if plan.current_task.task_type in [
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ToolType.DATA_PREPROCESS.type_name,
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ToolType.FEATURE_ENGINEERING.type_name,
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]:
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examples = USE_ML_TOOLS_EXAMPLE
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elif plan.current_task.task_type in [ToolType.MODEL_TRAIN.type_name]:
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examples = MODEL_TRAIN_EXAMPLE
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# prepare schemas of available tools
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tool_schemas = {}
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available_tools = self._get_tools_by_type(tool_type)
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if available_tools:
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available_tools = {tool_name: tool.schemas["description"] for tool_name, tool in available_tools.items()}
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tool_schemas = await self._recommend_tool(plan.current_task.instruction, available_tools)
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return tool_schemas, tool_type_usage_prompt, examples
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async def _debug_with_reflection(self, context: list[Message], working_memory: list[Message]):
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reflection_prompt = REFLECTION_PROMPT.format(
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debug_example=DEBUG_REFLECTION_EXAMPLE,
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context=context,
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previous_impl=working_memory,
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)
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# print(reflection_prompt)
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system_prompt = "You are an AI Python assistant. You will be given your previous implementation code of a task, runtime error results, and a hint to change the implementation appropriately. Write your full implementation "
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rsp = await self._aask(reflection_prompt, system_msgs=[system_prompt])
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rsp = await self._aask(reflection_prompt, system_msgs=[REFLECTION_SYSTEM_MSG])
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reflection = json.loads(CodeParser.parse_code(block=None, text=rsp))
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return reflection["improved_impl"]
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async def run(
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self,
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plan: Plan,
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working_memory: list[Message] = [],
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user_requirement: str,
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plan_status: str = "",
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tool_info: str = "",
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working_memory: list[Message] = None,
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use_reflection: bool = False,
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**kwargs,
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) -> str:
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# prepare tool schemas and tool-type-specific instruction
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tool_schemas, tool_type_usage_prompt, examples = await self._prepare_tools(plan=plan)
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# necessary components to be used in prompt
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finished_tasks = plan.get_finished_tasks()
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code_written = [remove_comments(task.code) for task in finished_tasks]
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code_written = "\n\n".join(code_written)
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task_results = [task.result for task in finished_tasks]
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task_results = "\n\n".join(task_results)
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# structure prompt
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structual_prompt = STRUCTUAL_PROMPT.format(
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user_requirement=plan.goal,
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code_written=code_written,
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task_results=task_results,
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current_task=plan.current_task.instruction,
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tool_type_usage_prompt=tool_type_usage_prompt,
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tool_schemas=tool_schemas,
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examples=examples,
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user_requirement=user_requirement,
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plan_status=plan_status,
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tool_info=tool_info,
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)
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working_memory = working_memory or []
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context = [Message(content=structual_prompt, role="user")] + working_memory
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context = process_message(context)
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# temp = context + working_memory
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# print(*temp, sep="***\n\n***")
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# LLM call
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if not use_reflection:
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rsp = await self.llm.aask(context, **kwargs)
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rsp = await self.llm.aask(context, system_msgs=[INTERPRETER_SYSTEM_MSG], **kwargs)
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code = CodeParser.parse_code(block=None, text=rsp)
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else:
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@ -13,7 +13,7 @@ from typing import Tuple
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from metagpt.actions import Action
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from metagpt.logs import logger
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from metagpt.schema import Message, Plan, Task
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from metagpt.tools import TOOL_REGISTRY
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from metagpt.strategy.task_type import TaskType
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from metagpt.utils.common import CodeParser
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@ -43,7 +43,7 @@ class WritePlan(Action):
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async def run(self, context: list[Message], max_tasks: int = 5, use_tools: bool = False) -> str:
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task_type_desc = "\n".join(
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[f"- **{tool_type.name}**: {tool_type.desc}" for tool_type in TOOL_REGISTRY.get_tool_types().values()]
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[f"- **{tt.type_name}**: {tt.value.desc}" for tt in TaskType]
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) # task type are binded with tool type now, should be improved in the future
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prompt = self.PROMPT_TEMPLATE.format(
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context="\n".join([str(ct) for ct in context]), max_tasks=max_tasks, task_type_desc=task_type_desc
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