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feat:deepsearch agent
This commit is contained in:
aries_ckt
parent
1efcd1b887
commit
a54eefefa2
@ -0,0 +1,198 @@
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import json
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import logging
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from typing import Optional, List
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from dbgpt.agent import Action, ActionOutput, AgentResource, Resource, ResourceType
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from dbgpt.util.json_utils import parse_or_raise_error
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from ...resource.tool.base import BaseTool, ToolParameter
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from ...util.react_parser import ReActOutputParser, ReActStep
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from .tool_action import ToolAction, run_tool
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from dbgpt._private.pydantic import BaseModel, Field, model_to_dict
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logger = logging.getLogger(__name__)
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class DeepSearchModel(BaseModel):
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"""Chart item model."""
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status: str = Field(
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...,
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description="The status of the current action, can be split_query, summary, or reflection.",
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)
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tools: List[dict] = Field(
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default_factory=list,
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description="List of tools to be used in the action.",
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)
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intention: str = Field(
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...,
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description="The intention of the current action, describing what you want to achieve.",
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)
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sub_queries: List[str] = Field(
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default_factory=list,
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description="List of sub-queries generated from the current action.",
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)
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def to_dict(self):
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"""Convert to dict."""
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return model_to_dict(self)
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class DeepSearchAction(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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@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 None
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@classmethod
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def parse_action(
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cls,
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ai_message: str,
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default_action: "ReActAction",
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resource: Optional[Resource] = None,
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**kwargs,
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) -> Optional["ReActAction"]:
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"""Parse the action from the message.
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If you want skip the action, return None.
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"""
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return default_action
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async def run(
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self,
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ai_message: str,
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resource: Optional[AgentResource] = None,
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rely_action_out: Optional[ActionOutput] = None,
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need_vis_render: bool = True,
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**kwargs,
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) -> ActionOutput:
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"""Perform the action."""
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try:
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action_param: DeepSearchModel = self._input_convert(
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ai_message, DeepSearchModel
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)
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except Exception as e:
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logger.exception(str(e))
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return ActionOutput(
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is_exe_success=False,
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content="The requested correctly structured answer could not be found.",
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)
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if action_param.status == "split_query":
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sub_queries = action_param.sub_queries
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# execute knowledge search
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if not action_param.tools:
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return ActionOutput(
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is_exe_success=False,
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content="No tools available for knowledge search.",
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)
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if action_param.tools:
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for tool in action_param.tools:
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if tool.get("tool_type") == "KnowledgeRetrieve":
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knowledge_args = action_param.get("args", {})
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if not knowledge_args:
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return ActionOutput(
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is_exe_success=False,
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content="No arguments provided for knowledge search.",
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)
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act_out = await self.knowledge_retrieve(
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sub_queries,
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knowledge_args,
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self.resource,
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)
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# if "parser" in kwargs and isinstance(kwargs["parser"], ReActOutputParser):
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# parser = kwargs["parser"]
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# else:
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# parser = ReActOutputParser()
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# steps = parser.parse(ai_message)
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# if len(steps) != 1:
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# raise ValueError("Only one action is allowed each time.")
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# step = steps[0]
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# act_out = await self._do_run(ai_message, step, need_vis_render=need_vis_render)
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# if not act_out.action:
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# act_out.action = step.action
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# if step.thought:
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# act_out.thoughts = step.thought
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# if (
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# not act_out.action_input
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# and step.action_input
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# and isinstance(step.action_input, str)
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# ):
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# act_out.action_input = step.action_input
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return act_out
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async def knowledge_retrieve(
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self, sub_queries: List[str], knowledge_args: List[str], resource: Resource
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) -> ActionOutput:
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"""Perform knowledge retrieval."""
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query_context_map = {}
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for query in sub_queries:
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resource_prompt, resource_reference = await resource.get_prompt(
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lang=self.language, question=query
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)
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query_context_map[query] = resource_prompt
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action_output = ActionOutput(
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is_exe_success=True,
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content="\n".join([
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f"{query}:{context}" for query, context in query_context_map.items()]
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),
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view="\n".join([
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f"{query}:{context}" for query, context in query_context_map.items()]
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),
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observations=query_context_map,
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)
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return action_output
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async def _do_run(
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self,
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ai_message: str,
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parsed_step: ReActStep,
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need_vis_render: bool = True,
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) -> ActionOutput:
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"""Perform the action."""
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tool_args = {}
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name = parsed_step.action
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action_input = parsed_step.action_input
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action_input_str = action_input
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if not name:
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terminal_content = str(action_input_str if action_input_str else ai_message)
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return ActionOutput(
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is_exe_success=True,
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content=terminal_content,
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observations=terminal_content,
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terminate=True,
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)
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try:
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# Try to parse the action input to dict
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if action_input and isinstance(action_input, str):
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tool_args = parse_or_raise_error(action_input)
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elif isinstance(action_input, dict) or isinstance(action_input, list):
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tool_args = action_input
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action_input_str = json.dumps(action_input, ensure_ascii=False)
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except json.JSONDecodeError:
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if parsed_step.action == "terminate":
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tool_args = {"output": action_input}
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logger.warning(f"Failed to parse the args: {action_input}")
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act_out = await run_tool(
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name,
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tool_args,
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self.resource,
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self.render_protocol,
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need_vis_render=need_vis_render,
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raw_tool_input=action_input_str,
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)
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if not act_out.action_input:
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act_out.action_input = action_input_str
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return act_out
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426
packages/dbgpt-core/src/dbgpt/agent/expand/deep_search.py
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426
packages/dbgpt-core/src/dbgpt/agent/expand/deep_search.py
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@ -0,0 +1,426 @@
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import json
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import logging
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from datetime import datetime
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from typing import Any, Dict, List, Optional, Type, Union
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from dbgpt._private.pydantic import Field
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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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Resource,
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ResourceType,
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StructuredAgentMemoryFragment,
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)
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from dbgpt.agent.core.role import AgentRunMode
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from dbgpt.agent.resource import BaseTool, ResourcePack, ToolPack
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from dbgpt.agent.util.react_parser import ReActOutputParser
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from dbgpt.util.configure import DynConfig
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from .actions.deep_search_action import DeepSearchAction
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from ...core import ModelMessageRoleType
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from .actions.react_action import ReActAction, Terminate
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from ...util.tracer import root_tracer
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logger = logging.getLogger(__name__)
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_DEEPSEARCH_GOAL = """Answer the following questions or solve the tasks by \
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selecting the right search tools.
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"""
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# _DEEPSEARCH_SYSTEM_TEMPLATE = """
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# You are a DeepSearch Assistant. Your task is to answer questions or solve problems by utilizing a combination of knowledge retrieve tools and search tools. You should break down the task, search for information, reflect on the results, and synthesize a comprehensive answer.
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#
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# <AVAILABLE TOOLS>
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# 1. Knowledge Tools: Query the internal knowledge base for information. \n {{knowledge_tools}}
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# 2. WebSearch Tools: Perform an internet search for up-to-date or additional information. \n {{search_tools}}
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# 3. Summarize: Summarize and synthesize information from multiple sources.
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# </AVAILABLE TOOLS>
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#
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# # PROCESS #
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# 1. Analyze the task and create a search plan.
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# 2. Use one or more tools to gather information.
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# 3. Reflect on the gathered information and determine if it's sufficient to answer the question.
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# 4. If the information is insufficient, revise your plan and continue searching.
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# 5. Once you have enough information, synthesize a final answer.
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#
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# <RESPONSE FORMAT>
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# For each step in your process, your response should contain:
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# 1. Analysis of the current state and reasoning for your next action (prefix "Thought: ").
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# 2. One or more tool uses, each containing:
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# - Tool name (prefix "Tool: ").
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# - Tool input (prefix "Input: ").
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# 3. After receiving tool output, a reflection on the information (prefix "Reflection: ").
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# </RESPONSE FORMAT>
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#
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# <EXAMPLE>
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# Human: Who won the Nobel Prize in Literature in 2022?
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#
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# DeepSearch:
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# Thought: To answer this question, I need to search for recent information about the Nobel Prize in Literature. I'll start with a web search as it's likely to have the most up-to-date information.
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#
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# Tool: WebSearch
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# Input: Nobel Prize in Literature 2022 winner
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# </EXAMPLE>
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#
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# <TASK>
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# Please Solve this task:
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# {{ question }}
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# </TASK>
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#
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# The current time is: {{ now_time }}.
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# """
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_DEEPSEARCH_SYSTEM_TEMPLATE = """
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你是一个深度搜索助手。你的任务是你将用户原始问题一个或者多个子问题，并且给出可用知识库工具和搜索工具来回答问题或解决问题。
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<可用工具>
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1. KnowledgeRetrieve: 查询内部知识库以获取信息。\n可用知识库: {{knowledge_tools}}
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2. WebSearch: 进行互联网搜索以获取最新或额外信息。\n 可用搜索工具: {{search_tools}}
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3. 总结: 对多个来源的信息进行总结和综合。
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</可用工具>
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<流程>
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1. 分析任务并创建搜索计划。
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2. 选择使用一个或多个工具收集信息。
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</流程>
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<回复格式>
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严格按以下JSON格式输出，确保可直接解析：
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{
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"status": "split_query (拆解搜索计划) | summary (仅当可用知识可以回答用户问题) | reflection (反思) "
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"tools": [{
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"tool_type": "工具类型"
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"args": "args1",
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}],
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"intention": "当前你的意图,
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"sub_queries": [],
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}
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</回复格式>
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<示例>
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人类: 谁在2022年获得了诺贝尔文学奖？
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深度搜索:
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思考: 要回答这个问题,我需要搜索关于2022年诺贝尔文学奖的最新信息。我会从网络搜索开始,因为这可能会有最新的信息。
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<可用工具>
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1. KnowledgeRetrieve: 查询内部知识库以获取信息。\n可用知识库: {{knowledge_tools}}
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2. WebSearch: 进行互联网搜索以获取最新或额外信息。\n 可用搜索工具: {{search_tools}}
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3. 总结: 对多个来源的信息进行总结和综合。
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</可用工具>
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工具类型: KnowledgeRetrieve
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工具参数:
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: 2022年诺贝尔文学奖得主
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返回
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{
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"status": "split_query"
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"tools"?: [{
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"tool_type": "KnowledgeRetrieve"
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"args": "knowledge_name",
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}],
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"intention": "你的拆解意图,
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"sub_queries": [],
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}
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</示例>
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<任务>
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请解决这个任务:
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{{ question }}
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</任务>
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当前时间是: {{ now_time }}。
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"""
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_DEEPSEARCH_USER_TEMPLATE = """"""
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_REACT_WRITE_MEMORY_TEMPLATE = """\
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{% if question %}Question: {{ question }} {% endif %}
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{% if thought %}Thought: {{ thought }} {% endif %}
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{% if action %}Action: {{ action }} {% endif %}
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{% if action_input %}Action Input: {{ action_input }} {% endif %}
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{% if observation %}Observation: {{ observation }} {% endif %}
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"""
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class DeepSearchAgent(ConversableAgent):
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max_retry_count: int = 15
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run_mode: AgentRunMode = AgentRunMode.LOOP
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profile: ProfileConfig = ProfileConfig(
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name=DynConfig(
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"DeepSearchAssistant",
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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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"DeepSearchAssistant",
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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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_DEEPSEARCH_GOAL,
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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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system_prompt_template=_DEEPSEARCH_SYSTEM_TEMPLATE,
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user_prompt_template=_DEEPSEARCH_USER_TEMPLATE,
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write_memory_template=_REACT_WRITE_MEMORY_TEMPLATE,
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)
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parser: ReActOutputParser = Field(default_factory=ReActOutputParser)
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def __init__(self, **kwargs):
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"""Init indicator AssistantAgent."""
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super().__init__(**kwargs)
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self._init_actions([DeepSearchAction, Terminate])
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# async def _a_init_reply_message(
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# self,
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# received_message: AgentMessage,
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# rely_messages: Optional[List[AgentMessage]] = None,
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# ) -> AgentMessage:
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# reply_message = super()._init_reply_message(received_message, rely_messages)
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#
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# tool_packs = ToolPack.from_resource(self.resource)
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# action_space = []
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# action_space_names = []
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# action_space_simple_desc = []
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# if tool_packs:
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# tool_pack = tool_packs[0]
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# for tool in tool_pack.sub_resources:
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# tool_desc, _ = await tool.get_prompt(lang=self.language)
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# action_space_names.append(tool.name)
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# action_space.append(tool_desc)
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# if isinstance(tool, BaseTool):
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# tool_simple_desc = tool.description
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# else:
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# tool_simple_desc = tool.get_prompt()
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# action_space_simple_desc.append(f"{tool.name}: {tool_simple_desc}")
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# else:
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# for action in self.actions:
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# action_space_names.append(action.name)
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# action_space.append(action.get_action_description())
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# # self.actions
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# reply_message.context = {
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# "max_steps": self.max_retry_count,
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# "action_space": "\n".join(action_space),
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# "action_space_names": ", ".join(action_space_names),
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# "action_space_simple_desc": "\n".join(action_space_simple_desc),
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# }
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# return reply_message
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async def preload_resource(self) -> None:
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await super().preload_resource()
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self._check_and_add_terminate()
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async def generate_resource_variables(
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self, resource_prompt: Optional[str] = None
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) -> Dict[str, Any]:
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"""Generate the resource variables."""
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out_schema: Optional[str] = ""
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if self.actions and len(self.actions) > 0:
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out_schema = self.actions[0].ai_out_schema
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if not resource_prompt:
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resource_prompt = ""
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now_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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return {
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"knowledge_tools": resource_prompt,
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"search_tools": "",
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"out_schema": out_schema,
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"now_time": now_time,
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}
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def _check_and_add_terminate(self):
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if not self.resource:
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return
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_is_has_terminal = False
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|
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def _has_terminal(r: Resource):
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nonlocal _is_has_terminal
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if r.type() == ResourceType.Tool and isinstance(r, Terminate):
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_is_has_terminal = True
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return r
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_has_add_terminal = False
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def _add_terminate(r: Resource):
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nonlocal _has_add_terminal
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if not _has_add_terminal and isinstance(r, ResourcePack):
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terminal = Terminate()
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r._resources[terminal.name] = terminal
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_has_add_terminal = True
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return r
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self.resource.apply(apply_func=_has_terminal)
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if not _is_has_terminal:
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# Add terminal action to the resource
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self.resource.apply(apply_pack_func=_add_terminate)
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|
||||
async def load_resource(self, question: str, is_retry_chat: bool = False):
|
||||
"""Load agent bind resource."""
|
||||
abilities = []
|
||||
if self.resource:
|
||||
def _remove_tool(r: Resource):
|
||||
if r.type() == ResourceType.Tool:
|
||||
return None
|
||||
return r
|
||||
|
||||
# Remove all tools from the resource
|
||||
# We will handle tools separately
|
||||
|
||||
if isinstance(self.resource, ResourcePack):
|
||||
for resource in self.resource.sub_resources:
|
||||
from dbgpt_serve.agent.resource.knowledge import \
|
||||
KnowledgeSpaceRetrieverResource
|
||||
if isinstance(resource, KnowledgeSpaceRetrieverResource):
|
||||
abilities.append({
|
||||
"knowledge_name": resource.retriever_name,
|
||||
"knowledge_desc": resource.retriever_desc,
|
||||
})
|
||||
else:
|
||||
from dbgpt_serve.agent.resource.knowledge import KnowledgeSpaceRetrieverResource
|
||||
if isinstance(self.resource, KnowledgeSpaceRetrieverResource):
|
||||
abilities.append({
|
||||
"knowledge_name": self.resource.retriever_name,
|
||||
"knowledge_desc": self.resource.retriever_desc,
|
||||
})
|
||||
|
||||
# new_resource = self.resource.apply(apply_func=_remove_tool)
|
||||
# if new_resource:
|
||||
# resource_prompt, resource_reference = await new_resource.get_prompt(
|
||||
# lang=self.language, question=question
|
||||
# )
|
||||
# return resource_prompt, resource_reference
|
||||
return json.dumps(abilities, ensure_ascii=False), []
|
||||
|
||||
def prepare_act_param(
|
||||
self,
|
||||
received_message: Optional[AgentMessage],
|
||||
sender: Agent,
|
||||
rely_messages: Optional[List[AgentMessage]] = None,
|
||||
**kwargs,
|
||||
) -> Dict[str, Any]:
|
||||
"""Prepare the parameters for the act method."""
|
||||
return {
|
||||
"parser": self.parser,
|
||||
}
|
||||
|
||||
async def act(
|
||||
self,
|
||||
message: AgentMessage,
|
||||
sender: Agent,
|
||||
reviewer: Optional[Agent] = None,
|
||||
is_retry_chat: bool = False,
|
||||
last_speaker_name: Optional[str] = None,
|
||||
**kwargs,
|
||||
) -> ActionOutput:
|
||||
"""Perform actions."""
|
||||
last_out: Optional[ActionOutput] = None
|
||||
for i, action in enumerate(self.actions):
|
||||
if not message:
|
||||
raise ValueError("The message content is empty!")
|
||||
|
||||
with root_tracer.start_span(
|
||||
"agent.act.run",
|
||||
metadata={
|
||||
"message": message,
|
||||
"sender": sender.name if sender else None,
|
||||
"recipient": self.name,
|
||||
"reviewer": reviewer.name if reviewer else None,
|
||||
"rely_action_out": last_out.to_dict() if last_out else None,
|
||||
"conv_uid": self.not_null_agent_context.conv_id,
|
||||
"action_index": i,
|
||||
"total_action": len(self.actions),
|
||||
},
|
||||
) as span:
|
||||
ai_message = message.content if message.content else ""
|
||||
# real_action = action.parse_action(
|
||||
# ai_message, default_action=action, **kwargs
|
||||
# )
|
||||
# if real_action is None:
|
||||
# continue
|
||||
|
||||
last_out = await action.run(
|
||||
ai_message=message.content if message.content else "",
|
||||
resource=None,
|
||||
rely_action_out=last_out,
|
||||
**kwargs,
|
||||
)
|
||||
span.metadata["action_out"] = last_out.to_dict() if last_out else None
|
||||
if not last_out:
|
||||
raise ValueError("Action should return value！")
|
||||
return last_out
|
||||
|
||||
@property
|
||||
def memory_fragment_class(self) -> Type[AgentMemoryFragment]:
|
||||
"""Return the memory fragment class."""
|
||||
return StructuredAgentMemoryFragment
|
||||
|
||||
async def read_memories(
|
||||
self,
|
||||
observation: str,
|
||||
) -> Union[str, List["AgentMessage"]]:
|
||||
memories = await self.memory.read(observation)
|
||||
not_json_memories = []
|
||||
messages = []
|
||||
structured_memories = []
|
||||
for m in memories:
|
||||
if m.raw_observation:
|
||||
try:
|
||||
mem_dict = json.loads(m.raw_observation)
|
||||
if isinstance(mem_dict, dict):
|
||||
structured_memories.append(mem_dict)
|
||||
elif isinstance(mem_dict, list):
|
||||
structured_memories.extend(mem_dict)
|
||||
else:
|
||||
raise ValueError("Invalid memory format.")
|
||||
except Exception:
|
||||
not_json_memories.append(m.raw_observation)
|
||||
|
||||
for mem_dict in structured_memories:
|
||||
question = mem_dict.get("question")
|
||||
thought = mem_dict.get("thought")
|
||||
action = mem_dict.get("action")
|
||||
action_input = mem_dict.get("action_input")
|
||||
observation = mem_dict.get("observation")
|
||||
if question:
|
||||
messages.append(
|
||||
AgentMessage(
|
||||
content=f"Question: {question}",
|
||||
role=ModelMessageRoleType.HUMAN,
|
||||
)
|
||||
)
|
||||
ai_content = []
|
||||
if thought:
|
||||
ai_content.append(f"Thought: {thought}")
|
||||
if action:
|
||||
ai_content.append(f"Action: {action}")
|
||||
if action_input:
|
||||
ai_content.append(f"Action Input: {action_input}")
|
||||
messages.append(
|
||||
AgentMessage(
|
||||
content="\n".join(ai_content),
|
||||
role=ModelMessageRoleType.AI,
|
||||
)
|
||||
)
|
||||
|
||||
if observation:
|
||||
messages.append(
|
||||
AgentMessage(
|
||||
content=f"Observation: {observation}",
|
||||
role=ModelMessageRoleType.HUMAN,
|
||||
)
|
||||
)
|
||||
|
||||
if not messages and not_json_memories:
|
||||
messages.append(
|
||||
AgentMessage(
|
||||
content="\n".join(not_json_memories),
|
||||
role=ModelMessageRoleType.HUMAN,
|
||||
)
|
||||
)
|
||||
return messages
|
||||
@ -95,6 +95,12 @@ class KnowledgeSpaceRetrieverResource(RetrieverResource):
|
||||
"""Return the retriever desc."""
|
||||
return self._retriever_desc
|
||||
|
||||
# @property
|
||||
# def abilities(self) -> List[dict]:
|
||||
# abilities = []
|
||||
# if self.reso
|
||||
# return abilities
|
||||
|
||||
@classmethod
|
||||
def resource_parameters_class(
|
||||
cls, **kwargs
|
||||
|
||||
@ -182,7 +182,7 @@ class KnowledgeSpaceRetriever(BaseRetriever):
|
||||
logger.info("Starting Full Text retrieval")
|
||||
return await self.full_text_retrieve(query, self._top_k, filters)
|
||||
elif self._retrieve_mode == RetrieverStrategy.Tree.value:
|
||||
logger.info("Starting Doc Tree retrieval")
|
||||
logger.ianfo("Starting Doc Tree retrieval")
|
||||
return await self.tree_index_retrieve(query, self._top_k, filters)
|
||||
elif self._retrieve_mode == RetrieverStrategy.HYBRID.value:
|
||||
logger.info("Starting Hybrid retrieval")
|
||||
|
||||
Loading…
Reference in New Issue
Block a user