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feat(agents): add ReActAgent (#2420)
Co-authored-by: dongzhancai1 <dongzhancai1@jd.com>
This commit is contained in:
Cooper
GitHub
parent
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commit
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89
examples/agents/react_agent_example.py
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89
examples/agents/react_agent_example.py
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import asyncio
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import logging
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import os
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import sys
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from typing_extensions import Annotated, Doc
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from dbgpt.agent import AgentContext, AgentMemory, LLMConfig, UserProxyAgent
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from dbgpt.agent.expand.react_agent import ReActAgent
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from dbgpt.agent.resource import ToolPack, tool
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logging.basicConfig(
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stream=sys.stdout,
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level=logging.INFO,
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format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
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)
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@tool
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def terminate(
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final_answer: Annotated[str, Doc("final literal answer about the goal")],
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) -> str:
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"""When the goal achieved, this tool must be called."""
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return final_answer
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@tool
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def simple_calculator(first_number: int, second_number: int, operator: str) -> float:
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"""Simple calculator tool. Just support +, -, *, /."""
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if isinstance(first_number, str):
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first_number = int(first_number)
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if isinstance(second_number, str):
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second_number = int(second_number)
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if operator == "+":
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return first_number + second_number
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elif operator == "-":
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return first_number - second_number
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elif operator == "*":
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return first_number * second_number
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elif operator == "/":
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return first_number / second_number
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else:
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raise ValueError(f"Invalid operator: {operator}")
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@tool
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def count_directory_files(path: Annotated[str, Doc("The directory path")]) -> int:
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"""Count the number of files in a directory."""
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if not os.path.isdir(path):
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raise ValueError(f"Invalid directory path: {path}")
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return len(os.listdir(path))
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async def main():
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from dbgpt.model.proxy.llms.siliconflow import SiliconFlowLLMClient
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llm_client = SiliconFlowLLMClient(
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model_alias="Qwen/Qwen2-7B-Instruct",
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)
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agent_memory = AgentMemory()
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agent_memory.gpts_memory.init(conv_id="test456")
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context: AgentContext = AgentContext(conv_id="test456", gpts_app_name="ReAct")
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tools = ToolPack([simple_calculator, count_directory_files, terminate])
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user_proxy = await UserProxyAgent().bind(agent_memory).bind(context).build()
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tool_engineer = (
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await ReActAgent(end_action_name="terminate", max_steps=10)
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.bind(context)
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.bind(LLMConfig(llm_client=llm_client))
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.bind(agent_memory)
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.bind(tools)
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.build()
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)
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await user_proxy.initiate_chat(
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recipient=tool_engineer,
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reviewer=user_proxy,
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message="Calculate the product of 10 and 99, Count the number of files in /tmp， answer in Chinese.",
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)
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# dbgpt-vis message infos
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print(await agent_memory.gpts_memory.app_link_chat_message("test456"))
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if __name__ == "__main__":
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asyncio.run(main())
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@ -0,0 +1,54 @@
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import json
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import logging
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from typing import Optional
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from dbgpt.agent import ResourceType
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from dbgpt.agent.expand.actions.tool_action import ToolAction, ToolInput
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from dbgpt.vis import Vis, VisPlugin
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logger = logging.getLogger(__name__)
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class ReActAction(ToolAction):
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"""ReAct action class."""
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def __init__(self, **kwargs):
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"""Tool action init."""
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super().__init__(**kwargs)
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self._render_protocol = VisPlugin()
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@property
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def resource_need(self) -> Optional[ResourceType]:
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"""Return the resource type needed for the action."""
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return ResourceType.Tool
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@property
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def render_protocol(self) -> Optional[Vis]:
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"""Return the render protocol."""
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return self._render_protocol
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@property
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def out_model_type(self):
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"""Return the output model type."""
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return ToolInput
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@property
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def ai_out_schema(self) -> Optional[str]:
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"""Return the AI output schema."""
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out_put_schema = {
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"Thought": "Summary of thoughts to the user",
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"Action": {
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"tool_name": "The name of a tool that can be used to answer "
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"the current"
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"question or solve the current task.",
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"args": {
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"arg name1": "arg value1",
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"arg name2": "arg value2",
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},
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},
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}
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return f"""Please response in the following json format:
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{json.dumps(out_put_schema, indent=2, ensure_ascii=False)}
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Make sure the response is correct json and can be parsed by Python json.loads.
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"""
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329
packages/dbgpt-core/src/dbgpt/agent/expand/react_agent.py
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329
packages/dbgpt-core/src/dbgpt/agent/expand/react_agent.py
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import json
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import logging
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from typing import Any, List, Optional, Tuple
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from dbgpt.agent import (
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ActionOutput,
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Agent,
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AgentMemoryFragment,
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AgentMessage,
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ConversableAgent,
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ProfileConfig,
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ResourceType,
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)
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from dbgpt.agent.expand.actions.react_action import ReActAction
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from dbgpt.core import ModelMessageRoleType
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from dbgpt.util.configure import DynConfig
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from dbgpt.util.json_utils import find_json_objects
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logger = logging.getLogger(__name__)
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_REACT_SYSTEM_TEMPLATE = """\
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You are a {{ role }}, {% if name %}named {{ name }}.
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{% endif %}your goal is {% if is_retry_chat %}{{ retry_goal }}
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{% else %}{{ goal }}
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{% endif %}.\
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At the same time, please strictly abide by the constraints and specifications
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in the "IMPORTANT REMINDER" below.
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{% if resource_prompt %}\
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# ACTION SPACE #
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{{ resource_prompt }}
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{% endif %}
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{% if expand_prompt %}\
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{{ expand_prompt }}
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{% endif %}\
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# IMPORTANT REMINDER #
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The current time is:{{now_time}}.
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{% if constraints %}\
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{% for constraint in constraints %}\
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{{ loop.index }}. {{ constraint }}
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{% endfor %}\
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{% endif %}\
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{% if is_retry_chat %}\
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{% if retry_constraints %}\
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{% for retry_constraint in retry_constraints %}\
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{{ loop.index }}. {{ retry_constraint }}
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{% endfor %}\
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{% endif %}\
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{% else %}\
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{% endif %}\
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{% if examples %}\
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# EXAMPLE INTERACTION #
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You can refer to the following examples:
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{{ examples }}\
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{% endif %}\
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{% if most_recent_memories %}\
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# History of Solving Task#
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{{ most_recent_memories }}\
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{% endif %}\
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# RESPONSE FORMAT #
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{% if out_schema %} {{ out_schema }} {% endif %}\
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################### TASK ###################
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Please solve the task:
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"""
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_REACT_WRITE_MEMORY_TEMPLATE = """\
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{% if question %}Question: {{ question }} {% endif %}
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{% if assistant %}Assistant: {{ assistant }} {% endif %}
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{% if observation %}Observation: {{ observation }} {% endif %}
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"""
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class ReActAgent(ConversableAgent):
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end_action_name: str = DynConfig(
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"terminate",
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category="agent",
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key="dbgpt_agent_expand_plugin_assistant_agent_end_action_name",
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)
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max_steps: int = DynConfig(
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10,
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category="agent",
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key="dbgpt_agent_expand_plugin_assistant_agent_max_steps",
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)
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profile: ProfileConfig = ProfileConfig(
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name=DynConfig(
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"ReAct",
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category="agent",
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key="dbgpt_agent_expand_plugin_assistant_agent_name",
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),
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role=DynConfig(
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"ToolMaster",
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category="agent",
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key="dbgpt_agent_expand_plugin_assistant_agent_role",
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),
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goal=DynConfig(
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"Read and understand the tool information given in the action space "
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"below to understand their capabilities and how to use them,and choosing "
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"the right tool to solve the task",
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category="agent",
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key="dbgpt_agent_expand_plugin_assistant_agent_goal",
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),
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constraints=DynConfig(
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[
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"Achieve the goal step by step."
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"Each step, please read the parameter definition of the tool carefully "
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"and extract the specific parameters required to execute the tool "
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"from the user's goal.",
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"information in json format according to the following required format."
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"If there is an example, please refer to the sample format output.",
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"each step, you can only select one tool in action space.",
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],
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category="agent",
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key="dbgpt_agent_expand_plugin_assistant_agent_constraints",
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),
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system_prompt_template=_REACT_SYSTEM_TEMPLATE,
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write_memory_template=_REACT_WRITE_MEMORY_TEMPLATE,
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)
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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self._init_actions([ReActAction])
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async def review(self, message: Optional[str], censored: Agent) -> Tuple[bool, Any]:
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"""Review the message based on the censored message."""
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try:
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json_obj = find_json_objects(message)
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if len(json_obj) == 0:
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raise ValueError(
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"No correct json object found in the message。"
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"Please strictly output JSON in the defined "
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"format, and only one action can be ouput each time. "
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)
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return True, json_obj[0]
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except Exception as e:
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logger.error(f"review error: {e}")
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raise e
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def validate_action(self, action_name: str) -> bool:
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tools = self.resource.get_resource_by_type(ResourceType.Tool)
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for tool in tools:
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if tool.name == action_name:
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return True
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raise ValueError(f"{action_name} is not in the action space.")
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async def generate_reply(
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self,
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received_message: AgentMessage,
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sender: Agent,
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reviewer: Optional[Agent] = None,
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rely_messages: Optional[List[AgentMessage]] = None,
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historical_dialogues: Optional[List[AgentMessage]] = None,
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is_retry_chat: bool = False,
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last_speaker_name: Optional[str] = None,
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**kwargs,
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) -> AgentMessage:
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"""Generate a reply based on the received messages."""
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try:
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logger.info(
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f"generate agent reply!sender={sender}, "
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f"rely_messages_len={rely_messages}"
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)
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self.validate_action(self.end_action_name)
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observation = AgentMessage(content="please start!")
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reply_message: AgentMessage = self._init_reply_message(
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received_message=received_message
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)
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thinking_messages, resource_info = await self._load_thinking_messages(
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received_message=observation,
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sender=sender,
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rely_messages=rely_messages,
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historical_dialogues=historical_dialogues,
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context=reply_message.get_dict_context(),
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is_retry_chat=is_retry_chat,
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)
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# attach current task to system prompt
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thinking_messages[0].content = (
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thinking_messages[0].content + "\n" + received_message.content
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)
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done = False
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max_steps = self.max_steps
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await self.write_memories(
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question=received_message.content,
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ai_message="",
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)
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while not done and max_steps > 0:
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ai_message = ""
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try:
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# 1. thinking
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llm_reply, model_name = await self.thinking(
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thinking_messages, sender
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)
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reply_message.model_name = model_name
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reply_message.resource_info = resource_info
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ai_message = llm_reply
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thinking_messages.append(
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AgentMessage(role=ModelMessageRoleType.AI, content=llm_reply)
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)
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approve, json_obj = await self.review(llm_reply, self)
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logger.info(f"jons_obj: {json_obj}")
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action = json_obj["Action"]
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thought = json_obj["Thought"]
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action.update({"thought": thought})
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reply_message.content = json.dumps(action, ensure_ascii=False)
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tool_name = action["tool_name"]
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self.validate_action(tool_name)
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# 2. act
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act_extent_param = self.prepare_act_param(
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received_message=received_message,
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sender=sender,
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rely_messages=rely_messages,
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historical_dialogues=historical_dialogues,
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)
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act_out: ActionOutput = await self.act(
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message=reply_message,
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sender=sender,
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reviewer=reviewer,
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is_retry_chat=is_retry_chat,
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last_speaker_name=last_speaker_name,
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**act_extent_param,
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)
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if act_out:
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reply_message.action_report = act_out
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# 3. obs
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check_pass, reason = await self.verify(
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reply_message, sender, reviewer
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)
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done = tool_name == self.end_action_name and check_pass
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if check_pass:
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logger.info(f"Observation:{act_out.content}")
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thinking_messages.append(
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AgentMessage(
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role=ModelMessageRoleType.HUMAN,
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content=f"Observation: {tool_name} "
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f"output:{act_out.content}\n",
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)
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)
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await self.write_memories(
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question="",
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ai_message=ai_message,
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action_output=act_out,
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check_pass=check_pass,
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)
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else:
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observation = f"Observation: {reason}"
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logger.info(f"Observation:{observation}")
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thinking_messages.append(
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AgentMessage(
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role=ModelMessageRoleType.HUMAN, content=observation
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)
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)
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await self.write_memories(
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question="",
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ai_message=ai_message,
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check_pass=check_pass,
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check_fail_reason=reason,
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)
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max_steps -= 1
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except Exception as e:
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fail_reason = (
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f"Observation: Exception occurs：({type(e).__name__}){e}."
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)
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logger.error(fail_reason)
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thinking_messages.append(
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AgentMessage(
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role=ModelMessageRoleType.HUMAN, content=fail_reason
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)
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)
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await self.write_memories(
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question="",
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ai_message=ai_message,
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check_pass=False,
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check_fail_reason=fail_reason,
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)
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reply_message.success = done
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await self.adjust_final_message(True, reply_message)
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return reply_message
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except Exception as e:
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logger.exception("Generate reply exception!")
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err_message = AgentMessage(content=str(e))
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err_message.success = False
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return err_message
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async def write_memories(
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self,
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question: str,
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ai_message: str,
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action_output: Optional[ActionOutput] = None,
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check_pass: bool = True,
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check_fail_reason: Optional[str] = None,
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) -> None:
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"""Write the memories to the memory.
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We suggest you to override this method to save the conversation to memory
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according to your needs.
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Args:
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question(str): The question received.
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ai_message(str): The AI message, LLM output.
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action_output(ActionOutput): The action output.
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check_pass(bool): Whether the check pass.
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check_fail_reason(str): The check fail reason.
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"""
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observation = ""
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if action_output and action_output.observations:
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observation = action_output.observations
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elif check_fail_reason:
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observation = check_fail_reason
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memory_map = {
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"question": question,
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"assistant": ai_message,
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"observation": observation,
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}
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write_memory_template = self.write_memory_template
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memory_content = self._render_template(write_memory_template, **memory_map)
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fragment = AgentMemoryFragment(memory_content)
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await self.memory.write(fragment)
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