refactor: decople the agent impl part and introduce some helper context method to call llm
This commit is contained in:
Soulter
@@ -40,6 +40,13 @@ class BaseAgentRunner(T.Generic[TContext]):
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"""Process a single step of the agent."""
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...
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@abc.abstractmethod
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async def step_until_done(
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self, max_step: int
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) -> T.AsyncGenerator[AgentResponse, None]:
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"""Process steps until the agent is done."""
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...
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@abc.abstractmethod
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def done(self) -> bool:
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"""Check if the agent has completed its task.
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@@ -177,6 +177,16 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
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)
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self.req.append_tool_calls_result(tool_calls_result)
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async def step_until_done(
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self, max_step: int
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) -> T.AsyncGenerator[AgentResponse, None]:
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"""Process steps until the agent is done."""
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step_count = 0
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while not self.done() and step_count < max_step:
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step_count += 1
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async for resp in self.step():
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yield resp
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async def _handle_function_tools(
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self,
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req: ProviderRequest,
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@@ -1,14 +1,14 @@
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from dataclasses import dataclass
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from astrbot.core.agent.run_context import ContextWrapper
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from astrbot.core.platform.astr_message_event import AstrMessageEvent
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from astrbot.core.provider import Provider
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from astrbot.core.provider.entities import ProviderRequest
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@dataclass
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class AstrAgentContext:
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provider: Provider
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first_provider_request: ProviderRequest
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curr_provider_request: ProviderRequest
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streaming: bool
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event: AstrMessageEvent
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AgentContextWrapper = ContextWrapper[AstrAgentContext]
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@@ -0,0 +1,36 @@
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from typing import Any
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from mcp.types import CallToolResult
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from astrbot.core.agent.hooks import BaseAgentRunHooks
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from astrbot.core.agent.run_context import ContextWrapper
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from astrbot.core.agent.tool import FunctionTool
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from astrbot.core.astr_agent_context import AstrAgentContext
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from astrbot.core.pipeline.context_utils import call_event_hook
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from astrbot.core.star.star_handler import EventType
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class MainAgentHooks(BaseAgentRunHooks[AstrAgentContext]):
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async def on_agent_done(self, run_context, llm_response):
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# 执行事件钩子
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await call_event_hook(
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run_context.context.event,
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EventType.OnLLMResponseEvent,
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llm_response,
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)
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async def on_tool_end(
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self,
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run_context: ContextWrapper[AstrAgentContext],
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tool: FunctionTool[Any],
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tool_args: dict | None,
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tool_result: CallToolResult | None,
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):
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run_context.context.event.clear_result()
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class EmptyAgentHooks(BaseAgentRunHooks[AstrAgentContext]):
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pass
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MAIN_AGENT_HOOKS = MainAgentHooks()
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@@ -0,0 +1,77 @@
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import traceback
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from collections.abc import AsyncGenerator
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from astrbot.core import logger
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from astrbot.core.agent.runners.tool_loop_agent_runner import ToolLoopAgentRunner
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from astrbot.core.astr_agent_context import AstrAgentContext
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from astrbot.core.message.message_event_result import (
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MessageChain,
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MessageEventResult,
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ResultContentType,
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)
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AgentRunner = ToolLoopAgentRunner[AstrAgentContext]
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async def run_agent(
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agent_runner: AgentRunner,
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max_step: int = 30,
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show_tool_use: bool = True,
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stream_to_general: bool = False,
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) -> AsyncGenerator[MessageChain | None, None]:
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step_idx = 0
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astr_event = agent_runner.run_context.context.event
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while step_idx < max_step:
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step_idx += 1
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try:
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async for resp in agent_runner.step():
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if astr_event.is_stopped():
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return
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if resp.type == "tool_call_result":
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msg_chain = resp.data["chain"]
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if msg_chain.type == "tool_direct_result":
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# tool_direct_result 用于标记 llm tool 需要直接发送给用户的内容
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resp.data["chain"].type = "tool_call_result"
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await astr_event.send(resp.data["chain"])
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continue
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# 对于其他情况，暂时先不处理
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continue
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elif resp.type == "tool_call":
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if agent_runner.streaming:
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# 用来标记流式响应需要分节
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yield MessageChain(chain=[], type="break")
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if show_tool_use or astr_event.get_platform_name() == "webchat":
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resp.data["chain"].type = "tool_call"
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await astr_event.send(resp.data["chain"])
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continue
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if stream_to_general and resp.type == "streaming_delta":
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continue
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if stream_to_general or not agent_runner.streaming:
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content_typ = (
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ResultContentType.LLM_RESULT
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if resp.type == "llm_result"
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else ResultContentType.GENERAL_RESULT
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)
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astr_event.set_result(
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MessageEventResult(
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chain=resp.data["chain"].chain,
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result_content_type=content_typ,
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),
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)
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yield
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astr_event.clear_result()
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elif resp.type == "streaming_delta":
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yield resp.data["chain"] # MessageChain
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if agent_runner.done():
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break
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except Exception as e:
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logger.error(traceback.format_exc())
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err_msg = f"\n\nAstrBot 请求失败。\n错误类型: {type(e).__name__}\n错误信息: {e!s}\n\n请在控制台查看和分享错误详情。\n"
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if agent_runner.streaming:
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yield MessageChain().message(err_msg)
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else:
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astr_event.set_result(MessageEventResult().message(err_msg))
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return
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@@ -0,0 +1,301 @@
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import asyncio
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import inspect
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import traceback
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import typing as T
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import mcp
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from astrbot import logger
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from astrbot.core.agent.handoff import HandoffTool
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from astrbot.core.agent.hooks import BaseAgentRunHooks
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from astrbot.core.agent.mcp_client import MCPTool
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from astrbot.core.agent.run_context import ContextWrapper
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from astrbot.core.agent.tool import FunctionTool, ToolSet
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from astrbot.core.agent.tool_executor import BaseFunctionToolExecutor
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from astrbot.core.astr_agent_context import AstrAgentContext
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from astrbot.core.message.message_event_result import (
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CommandResult,
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MessageChain,
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MessageEventResult,
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)
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from astrbot.core.provider.entities import ProviderRequest
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from astrbot.core.provider.register import llm_tools
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from .astr_agent_context import AgentContextWrapper
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from .astr_agent_run_util import AgentRunner, run_agent
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class FunctionToolExecutor(BaseFunctionToolExecutor[AstrAgentContext]):
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@classmethod
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async def execute(cls, tool, run_context, **tool_args):
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"""执行函数调用。
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Args:
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event (AstrMessageEvent): 事件对象, 当 origin 为 local 时必须提供。
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**kwargs: 函数调用的参数。
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Returns:
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AsyncGenerator[None | mcp.types.CallToolResult, None]
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"""
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if isinstance(tool, HandoffTool):
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async for r in cls._execute_handoff(tool, run_context, **tool_args):
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yield r
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return
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elif isinstance(tool, MCPTool):
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async for r in cls._execute_mcp(tool, run_context, **tool_args):
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yield r
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return
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else:
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async for r in cls._execute_local(tool, run_context, **tool_args):
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yield r
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return
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@classmethod
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async def _execute_handoff(
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cls,
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tool: HandoffTool,
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run_context: ContextWrapper[AstrAgentContext],
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**tool_args,
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):
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input_ = tool_args.get("input", "agent")
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agent_runner = AgentRunner()
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# make toolset for the agent
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tools = tool.agent.tools
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if tools:
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toolset = ToolSet()
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for t in tools:
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if isinstance(t, str):
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_t = llm_tools.get_func(t)
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if _t:
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toolset.add_tool(_t)
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elif isinstance(t, FunctionTool):
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toolset.add_tool(t)
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else:
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toolset = None
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request = ProviderRequest(
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prompt=input_,
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system_prompt=tool.description or "",
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image_urls=[], # 暂时不传递原始 agent 的上下文
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contexts=[], # 暂时不传递原始 agent 的上下文
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func_tool=toolset,
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)
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astr_agent_ctx = AstrAgentContext(
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provider=run_context.context.provider,
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event=run_context.context.event,
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)
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event = run_context.context.event
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logger.debug(f"正在将任务委托给 Agent: {tool.agent.name}, input: {input_}")
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await event.send(
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MessageChain().message("✨ 正在将任务委托给 Agent: " + tool.agent.name),
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)
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await agent_runner.reset(
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provider=run_context.context.provider,
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request=request,
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run_context=AgentContextWrapper(
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context=astr_agent_ctx,
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tool_call_timeout=run_context.tool_call_timeout,
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),
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tool_executor=FunctionToolExecutor(),
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agent_hooks=tool.agent.run_hooks or BaseAgentRunHooks[AstrAgentContext](),
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)
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async for _ in run_agent(agent_runner, 15, True):
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pass
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if agent_runner.done():
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llm_response = agent_runner.get_final_llm_resp()
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if not llm_response:
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text_content = mcp.types.TextContent(
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type="text",
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text=f"error when deligate task to {tool.agent.name}",
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)
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yield mcp.types.CallToolResult(content=[text_content])
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return
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logger.debug(
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f"Agent {tool.agent.name} 任务完成, response: {llm_response.completion_text}",
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)
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result = (
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f"Agent {tool.agent.name} respond with: {llm_response.completion_text}\n\n"
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"Note: If the result is error or need user provide more information, please provide more information to the agent(you can ask user for more information first)."
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)
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text_content = mcp.types.TextContent(
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type="text",
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text=result,
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)
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yield mcp.types.CallToolResult(content=[text_content])
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else:
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text_content = mcp.types.TextContent(
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type="text",
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text=f"error when deligate task to {tool.agent.name}",
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)
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yield mcp.types.CallToolResult(content=[text_content])
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return
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@classmethod
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async def _execute_local(
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cls,
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tool: FunctionTool,
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run_context: ContextWrapper[AstrAgentContext],
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**tool_args,
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):
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event = run_context.context.event
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if not event:
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raise ValueError("Event must be provided for local function tools.")
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is_override_call = False
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for ty in type(tool).mro():
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if "call" in ty.__dict__ and ty.__dict__["call"] is not FunctionTool.call:
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is_override_call = True
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break
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# 检查 tool 下有没有 run 方法
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if not tool.handler and not hasattr(tool, "run") and not is_override_call:
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raise ValueError("Tool must have a valid handler or override 'run' method.")
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awaitable = None
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method_name = ""
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if tool.handler:
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awaitable = tool.handler
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method_name = "decorator_handler"
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elif is_override_call:
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awaitable = tool.call
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method_name = "call"
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elif hasattr(tool, "run"):
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awaitable = getattr(tool, "run")
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method_name = "run"
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if awaitable is None:
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raise ValueError("Tool must have a valid handler or override 'run' method.")
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wrapper = call_local_llm_tool(
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context=run_context,
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handler=awaitable,
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method_name=method_name,
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**tool_args,
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)
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while True:
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try:
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resp = await asyncio.wait_for(
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anext(wrapper),
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timeout=run_context.tool_call_timeout,
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)
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if resp is not None:
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if isinstance(resp, mcp.types.CallToolResult):
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yield resp
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else:
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text_content = mcp.types.TextContent(
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type="text",
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text=str(resp),
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)
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yield mcp.types.CallToolResult(content=[text_content])
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else:
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# NOTE: Tool 在这里直接请求发送消息给用户
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# TODO: 是否需要判断 event.get_result() 是否为空?
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# 如果为空,则说明没有发送消息给用户,并且返回值为空,将返回一个特殊的 TextContent,其内容如"工具没有返回内容"
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if res := run_context.context.event.get_result():
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if res.chain:
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try:
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await event.send(
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MessageChain(
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chain=res.chain,
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type="tool_direct_result",
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)
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)
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except Exception as e:
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logger.error(
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f"Tool 直接发送消息失败: {e}",
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exc_info=True,
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)
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yield None
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except asyncio.TimeoutError:
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raise Exception(
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f"tool {tool.name} execution timeout after {run_context.tool_call_timeout} seconds.",
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)
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except StopAsyncIteration:
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break
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@classmethod
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async def _execute_mcp(
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cls,
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tool: FunctionTool,
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run_context: ContextWrapper[AstrAgentContext],
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**tool_args,
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):
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res = await tool.call(run_context, **tool_args)
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if not res:
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return
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yield res
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|
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async def call_local_llm_tool(
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context: ContextWrapper[AstrAgentContext],
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handler: T.Callable[..., T.Awaitable[T.Any]],
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method_name: str,
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*args,
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**kwargs,
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) -> T.AsyncGenerator[T.Any, None]:
|
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"""执行本地 LLM 工具的处理函数并处理其返回结果"""
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ready_to_call = None # 一个协程或者异步生成器
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|
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trace_ = None
|
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event = context.context.event
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|
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try:
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if method_name == "run" or method_name == "decorator_handler":
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ready_to_call = handler(event, *args, **kwargs)
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elif method_name == "call":
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ready_to_call = handler(context, *args, **kwargs)
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else:
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raise ValueError(f"未知的方法名: {method_name}")
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except ValueError as e:
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logger.error(f"调用本地 LLM 工具时出错: {e}", exc_info=True)
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except TypeError:
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logger.error("处理函数参数不匹配，请检查 handler 的定义。", exc_info=True)
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except Exception as e:
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trace_ = traceback.format_exc()
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logger.error(f"调用本地 LLM 工具时出错: {e}\n{trace_}")
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if not ready_to_call:
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return
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if inspect.isasyncgen(ready_to_call):
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_has_yielded = False
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try:
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async for ret in ready_to_call:
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# 这里逐步执行异步生成器, 对于每个 yield 返回的 ret, 执行下面的代码
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# 返回值只能是 MessageEventResult 或者 None（无返回值）
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_has_yielded = True
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if isinstance(ret, (MessageEventResult, CommandResult)):
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# 如果返回值是 MessageEventResult, 设置结果并继续
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event.set_result(ret)
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yield
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else:
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# 如果返回值是 None, 则不设置结果并继续
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# 继续执行后续阶段
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yield ret
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if not _has_yielded:
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# 如果这个异步生成器没有执行到 yield 分支
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||||
yield
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||||
except Exception as e:
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logger.error(f"Previous Error: {trace_}")
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raise e
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elif inspect.iscoroutine(ready_to_call):
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# 如果只是一个协程, 直接执行
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ret = await ready_to_call
|
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if isinstance(ret, (MessageEventResult, CommandResult)):
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||||
event.set_result(ret)
|
||||
yield
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||||
else:
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||||
yield ret
|
||||
@@ -0,0 +1,9 @@
|
||||
from __future__ import annotations
|
||||
|
||||
|
||||
class AstrBotError(Exception):
|
||||
"""Base exception for all AstrBot errors."""
|
||||
|
||||
|
||||
class ProviderNotFoundError(AstrBotError):
|
||||
"""Raised when a specified provider is not found."""
|
||||
@@ -3,7 +3,7 @@ from dataclasses import dataclass
|
||||
from astrbot.core.config import AstrBotConfig
|
||||
from astrbot.core.star import PluginManager
|
||||
|
||||
from .context_utils import call_event_hook, call_handler, call_local_llm_tool
|
||||
from .context_utils import call_event_hook, call_handler
|
||||
|
||||
|
||||
@dataclass
|
||||
@@ -15,4 +15,3 @@ class PipelineContext:
|
||||
astrbot_config_id: str
|
||||
call_handler = call_handler
|
||||
call_event_hook = call_event_hook
|
||||
call_local_llm_tool = call_local_llm_tool
|
||||
|
||||
@@ -3,8 +3,6 @@ import traceback
|
||||
import typing as T
|
||||
|
||||
from astrbot import logger
|
||||
from astrbot.core.agent.run_context import ContextWrapper
|
||||
from astrbot.core.astr_agent_context import AstrAgentContext
|
||||
from astrbot.core.message.message_event_result import CommandResult, MessageEventResult
|
||||
from astrbot.core.platform.astr_message_event import AstrMessageEvent
|
||||
from astrbot.core.star.star import star_map
|
||||
@@ -107,66 +105,3 @@ async def call_event_hook(
|
||||
return True
|
||||
|
||||
return event.is_stopped()
|
||||
|
||||
|
||||
async def call_local_llm_tool(
|
||||
context: ContextWrapper[AstrAgentContext],
|
||||
handler: T.Callable[..., T.Awaitable[T.Any]],
|
||||
method_name: str,
|
||||
*args,
|
||||
**kwargs,
|
||||
) -> T.AsyncGenerator[T.Any, None]:
|
||||
"""执行本地 LLM 工具的处理函数并处理其返回结果"""
|
||||
ready_to_call = None # 一个协程或者异步生成器
|
||||
|
||||
trace_ = None
|
||||
|
||||
event = context.context.event
|
||||
|
||||
try:
|
||||
if method_name == "run" or method_name == "decorator_handler":
|
||||
ready_to_call = handler(event, *args, **kwargs)
|
||||
elif method_name == "call":
|
||||
ready_to_call = handler(context, *args, **kwargs)
|
||||
else:
|
||||
raise ValueError(f"未知的方法名: {method_name}")
|
||||
except ValueError as e:
|
||||
logger.error(f"调用本地 LLM 工具时出错: {e}", exc_info=True)
|
||||
except TypeError:
|
||||
logger.error("处理函数参数不匹配，请检查 handler 的定义。", exc_info=True)
|
||||
except Exception as e:
|
||||
trace_ = traceback.format_exc()
|
||||
logger.error(f"调用本地 LLM 工具时出错: {e}\n{trace_}")
|
||||
|
||||
if not ready_to_call:
|
||||
return
|
||||
|
||||
if inspect.isasyncgen(ready_to_call):
|
||||
_has_yielded = False
|
||||
try:
|
||||
async for ret in ready_to_call:
|
||||
# 这里逐步执行异步生成器, 对于每个 yield 返回的 ret, 执行下面的代码
|
||||
# 返回值只能是 MessageEventResult 或者 None（无返回值）
|
||||
_has_yielded = True
|
||||
if isinstance(ret, (MessageEventResult, CommandResult)):
|
||||
# 如果返回值是 MessageEventResult, 设置结果并继续
|
||||
event.set_result(ret)
|
||||
yield
|
||||
else:
|
||||
# 如果返回值是 None, 则不设置结果并继续
|
||||
# 继续执行后续阶段
|
||||
yield ret
|
||||
if not _has_yielded:
|
||||
# 如果这个异步生成器没有执行到 yield 分支
|
||||
yield
|
||||
except Exception as e:
|
||||
logger.error(f"Previous Error: {trace_}")
|
||||
raise e
|
||||
elif inspect.iscoroutine(ready_to_call):
|
||||
# 如果只是一个协程, 直接执行
|
||||
ret = await ready_to_call
|
||||
if isinstance(ret, (MessageEventResult, CommandResult)):
|
||||
event.set_result(ret)
|
||||
yield
|
||||
else:
|
||||
yield ret
|
||||
|
||||
@@ -3,20 +3,10 @@
|
||||
import asyncio
|
||||
import copy
|
||||
import json
|
||||
import traceback
|
||||
from collections.abc import AsyncGenerator
|
||||
from typing import Any
|
||||
|
||||
from mcp.types import CallToolResult
|
||||
|
||||
from astrbot.core import logger
|
||||
from astrbot.core.agent.handoff import HandoffTool
|
||||
from astrbot.core.agent.hooks import BaseAgentRunHooks
|
||||
from astrbot.core.agent.mcp_client import MCPTool
|
||||
from astrbot.core.agent.run_context import ContextWrapper
|
||||
from astrbot.core.agent.runners.tool_loop_agent_runner import ToolLoopAgentRunner
|
||||
from astrbot.core.agent.tool import FunctionTool, ToolSet
|
||||
from astrbot.core.agent.tool_executor import BaseFunctionToolExecutor
|
||||
from astrbot.core.agent.tool import ToolSet
|
||||
from astrbot.core.astr_agent_context import AstrAgentContext
|
||||
from astrbot.core.conversation_mgr import Conversation
|
||||
from astrbot.core.message.components import Image
|
||||
@@ -31,328 +21,19 @@ from astrbot.core.provider.entities import (
|
||||
LLMResponse,
|
||||
ProviderRequest,
|
||||
)
|
||||
from astrbot.core.provider.register import llm_tools
|
||||
from astrbot.core.star.session_llm_manager import SessionServiceManager
|
||||
from astrbot.core.star.star_handler import EventType, star_map
|
||||
from astrbot.core.utils.metrics import Metric
|
||||
from astrbot.core.utils.session_lock import session_lock_manager
|
||||
|
||||
from ...context import PipelineContext, call_event_hook, call_local_llm_tool
|
||||
from ....astr_agent_context import AgentContextWrapper
|
||||
from ....astr_agent_hooks import MAIN_AGENT_HOOKS
|
||||
from ....astr_agent_run_util import AgentRunner, run_agent
|
||||
from ....astr_agent_tool_exec import FunctionToolExecutor
|
||||
from ...context import PipelineContext, call_event_hook
|
||||
from ..stage import Stage
|
||||
from ..utils import inject_kb_context
|
||||
|
||||
try:
|
||||
import mcp
|
||||
except (ModuleNotFoundError, ImportError):
|
||||
logger.warning("警告: 缺少依赖库 'mcp'，将无法使用 MCP 服务。")
|
||||
|
||||
|
||||
AgentContextWrapper = ContextWrapper[AstrAgentContext]
|
||||
AgentRunner = ToolLoopAgentRunner[AstrAgentContext]
|
||||
|
||||
|
||||
class FunctionToolExecutor(BaseFunctionToolExecutor[AstrAgentContext]):
|
||||
@classmethod
|
||||
async def execute(cls, tool, run_context, **tool_args):
|
||||
"""执行函数调用。
|
||||
|
||||
Args:
|
||||
event (AstrMessageEvent): 事件对象, 当 origin 为 local 时必须提供。
|
||||
**kwargs: 函数调用的参数。
|
||||
|
||||
Returns:
|
||||
AsyncGenerator[None | mcp.types.CallToolResult, None]
|
||||
|
||||
"""
|
||||
if isinstance(tool, HandoffTool):
|
||||
async for r in cls._execute_handoff(tool, run_context, **tool_args):
|
||||
yield r
|
||||
return
|
||||
|
||||
elif isinstance(tool, MCPTool):
|
||||
async for r in cls._execute_mcp(tool, run_context, **tool_args):
|
||||
yield r
|
||||
return
|
||||
|
||||
else:
|
||||
async for r in cls._execute_local(tool, run_context, **tool_args):
|
||||
yield r
|
||||
return
|
||||
|
||||
@classmethod
|
||||
async def _execute_handoff(
|
||||
cls,
|
||||
tool: HandoffTool,
|
||||
run_context: ContextWrapper[AstrAgentContext],
|
||||
**tool_args,
|
||||
):
|
||||
input_ = tool_args.get("input", "agent")
|
||||
agent_runner = AgentRunner()
|
||||
|
||||
# make toolset for the agent
|
||||
tools = tool.agent.tools
|
||||
if tools:
|
||||
toolset = ToolSet()
|
||||
for t in tools:
|
||||
if isinstance(t, str):
|
||||
_t = llm_tools.get_func(t)
|
||||
if _t:
|
||||
toolset.add_tool(_t)
|
||||
elif isinstance(t, FunctionTool):
|
||||
toolset.add_tool(t)
|
||||
else:
|
||||
toolset = None
|
||||
|
||||
request = ProviderRequest(
|
||||
prompt=input_,
|
||||
system_prompt=tool.description or "",
|
||||
image_urls=[], # 暂时不传递原始 agent 的上下文
|
||||
contexts=[], # 暂时不传递原始 agent 的上下文
|
||||
func_tool=toolset,
|
||||
)
|
||||
astr_agent_ctx = AstrAgentContext(
|
||||
provider=run_context.context.provider,
|
||||
first_provider_request=run_context.context.first_provider_request,
|
||||
curr_provider_request=request,
|
||||
streaming=run_context.context.streaming,
|
||||
event=run_context.context.event,
|
||||
)
|
||||
|
||||
event = run_context.context.event
|
||||
|
||||
logger.debug(f"正在将任务委托给 Agent: {tool.agent.name}, input: {input_}")
|
||||
await event.send(
|
||||
MessageChain().message("✨ 正在将任务委托给 Agent: " + tool.agent.name),
|
||||
)
|
||||
|
||||
await agent_runner.reset(
|
||||
provider=run_context.context.provider,
|
||||
request=request,
|
||||
run_context=AgentContextWrapper(
|
||||
context=astr_agent_ctx,
|
||||
tool_call_timeout=run_context.tool_call_timeout,
|
||||
),
|
||||
tool_executor=FunctionToolExecutor(),
|
||||
agent_hooks=tool.agent.run_hooks or BaseAgentRunHooks[AstrAgentContext](),
|
||||
streaming=run_context.context.streaming,
|
||||
)
|
||||
|
||||
async for _ in run_agent(agent_runner, 15, True):
|
||||
pass
|
||||
|
||||
if agent_runner.done():
|
||||
llm_response = agent_runner.get_final_llm_resp()
|
||||
|
||||
if not llm_response:
|
||||
text_content = mcp.types.TextContent(
|
||||
type="text",
|
||||
text=f"error when deligate task to {tool.agent.name}",
|
||||
)
|
||||
yield mcp.types.CallToolResult(content=[text_content])
|
||||
return
|
||||
|
||||
logger.debug(
|
||||
f"Agent {tool.agent.name} 任务完成, response: {llm_response.completion_text}",
|
||||
)
|
||||
|
||||
result = (
|
||||
f"Agent {tool.agent.name} respond with: {llm_response.completion_text}\n\n"
|
||||
"Note: If the result is error or need user provide more information, please provide more information to the agent(you can ask user for more information first)."
|
||||
)
|
||||
|
||||
text_content = mcp.types.TextContent(
|
||||
type="text",
|
||||
text=result,
|
||||
)
|
||||
yield mcp.types.CallToolResult(content=[text_content])
|
||||
else:
|
||||
text_content = mcp.types.TextContent(
|
||||
type="text",
|
||||
text=f"error when deligate task to {tool.agent.name}",
|
||||
)
|
||||
yield mcp.types.CallToolResult(content=[text_content])
|
||||
return
|
||||
|
||||
@classmethod
|
||||
async def _execute_local(
|
||||
cls,
|
||||
tool: FunctionTool,
|
||||
run_context: ContextWrapper[AstrAgentContext],
|
||||
**tool_args,
|
||||
):
|
||||
event = run_context.context.event
|
||||
if not event:
|
||||
raise ValueError("Event must be provided for local function tools.")
|
||||
|
||||
is_override_call = False
|
||||
for ty in type(tool).mro():
|
||||
if "call" in ty.__dict__ and ty.__dict__["call"] is not FunctionTool.call:
|
||||
is_override_call = True
|
||||
break
|
||||
|
||||
# 检查 tool 下有没有 run 方法
|
||||
if not tool.handler and not hasattr(tool, "run") and not is_override_call:
|
||||
raise ValueError("Tool must have a valid handler or override 'run' method.")
|
||||
|
||||
awaitable = None
|
||||
method_name = ""
|
||||
if tool.handler:
|
||||
awaitable = tool.handler
|
||||
method_name = "decorator_handler"
|
||||
elif is_override_call:
|
||||
awaitable = tool.call
|
||||
method_name = "call"
|
||||
elif hasattr(tool, "run"):
|
||||
awaitable = getattr(tool, "run")
|
||||
method_name = "run"
|
||||
if awaitable is None:
|
||||
raise ValueError("Tool must have a valid handler or override 'run' method.")
|
||||
|
||||
wrapper = call_local_llm_tool(
|
||||
context=run_context,
|
||||
handler=awaitable,
|
||||
method_name=method_name,
|
||||
**tool_args,
|
||||
)
|
||||
while True:
|
||||
try:
|
||||
resp = await asyncio.wait_for(
|
||||
anext(wrapper),
|
||||
timeout=run_context.tool_call_timeout,
|
||||
)
|
||||
if resp is not None:
|
||||
if isinstance(resp, mcp.types.CallToolResult):
|
||||
yield resp
|
||||
else:
|
||||
text_content = mcp.types.TextContent(
|
||||
type="text",
|
||||
text=str(resp),
|
||||
)
|
||||
yield mcp.types.CallToolResult(content=[text_content])
|
||||
else:
|
||||
# NOTE: Tool 在这里直接请求发送消息给用户
|
||||
# TODO: 是否需要判断 event.get_result() 是否为空?
|
||||
# 如果为空,则说明没有发送消息给用户,并且返回值为空,将返回一个特殊的 TextContent,其内容如"工具没有返回内容"
|
||||
if res := run_context.context.event.get_result():
|
||||
if res.chain:
|
||||
try:
|
||||
await event.send(
|
||||
MessageChain(
|
||||
chain=res.chain,
|
||||
type="tool_direct_result",
|
||||
)
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"Tool 直接发送消息失败: {e}",
|
||||
exc_info=True,
|
||||
)
|
||||
yield None
|
||||
except asyncio.TimeoutError:
|
||||
raise Exception(
|
||||
f"tool {tool.name} execution timeout after {run_context.tool_call_timeout} seconds.",
|
||||
)
|
||||
except StopAsyncIteration:
|
||||
break
|
||||
|
||||
@classmethod
|
||||
async def _execute_mcp(
|
||||
cls,
|
||||
tool: FunctionTool,
|
||||
run_context: ContextWrapper[AstrAgentContext],
|
||||
**tool_args,
|
||||
):
|
||||
res = await tool.call(run_context, **tool_args)
|
||||
if not res:
|
||||
return
|
||||
yield res
|
||||
|
||||
|
||||
class MainAgentHooks(BaseAgentRunHooks[AstrAgentContext]):
|
||||
async def on_agent_done(self, run_context, llm_response):
|
||||
# 执行事件钩子
|
||||
await call_event_hook(
|
||||
run_context.context.event,
|
||||
EventType.OnLLMResponseEvent,
|
||||
llm_response,
|
||||
)
|
||||
|
||||
async def on_tool_end(
|
||||
self,
|
||||
run_context: ContextWrapper[AstrAgentContext],
|
||||
tool: FunctionTool[Any],
|
||||
tool_args: dict | None,
|
||||
tool_result: CallToolResult | None,
|
||||
):
|
||||
run_context.context.event.clear_result()
|
||||
|
||||
|
||||
MAIN_AGENT_HOOKS = MainAgentHooks()
|
||||
|
||||
|
||||
async def run_agent(
|
||||
agent_runner: AgentRunner,
|
||||
max_step: int = 30,
|
||||
show_tool_use: bool = True,
|
||||
stream_to_general: bool = False,
|
||||
) -> AsyncGenerator[MessageChain, None]:
|
||||
step_idx = 0
|
||||
astr_event = agent_runner.run_context.context.event
|
||||
while step_idx < max_step:
|
||||
step_idx += 1
|
||||
try:
|
||||
async for resp in agent_runner.step():
|
||||
if astr_event.is_stopped():
|
||||
return
|
||||
if resp.type == "tool_call_result":
|
||||
msg_chain = resp.data["chain"]
|
||||
if msg_chain.type == "tool_direct_result":
|
||||
# tool_direct_result 用于标记 llm tool 需要直接发送给用户的内容
|
||||
resp.data["chain"].type = "tool_call_result"
|
||||
await astr_event.send(resp.data["chain"])
|
||||
continue
|
||||
# 对于其他情况，暂时先不处理
|
||||
continue
|
||||
elif resp.type == "tool_call":
|
||||
if agent_runner.streaming:
|
||||
# 用来标记流式响应需要分节
|
||||
yield MessageChain(chain=[], type="break")
|
||||
if show_tool_use or astr_event.get_platform_name() == "webchat":
|
||||
resp.data["chain"].type = "tool_call"
|
||||
await astr_event.send(resp.data["chain"])
|
||||
continue
|
||||
|
||||
if stream_to_general and resp.type == "streaming_delta":
|
||||
continue
|
||||
|
||||
if stream_to_general or not agent_runner.streaming:
|
||||
content_typ = (
|
||||
ResultContentType.LLM_RESULT
|
||||
if resp.type == "llm_result"
|
||||
else ResultContentType.GENERAL_RESULT
|
||||
)
|
||||
astr_event.set_result(
|
||||
MessageEventResult(
|
||||
chain=resp.data["chain"].chain,
|
||||
result_content_type=content_typ,
|
||||
),
|
||||
)
|
||||
yield
|
||||
astr_event.clear_result()
|
||||
elif resp.type == "streaming_delta":
|
||||
yield resp.data["chain"] # MessageChain
|
||||
if agent_runner.done():
|
||||
break
|
||||
|
||||
except Exception as e:
|
||||
logger.error(traceback.format_exc())
|
||||
err_msg = f"\n\nAstrBot 请求失败。\n错误类型: {type(e).__name__}\n错误信息: {e!s}\n\n请在控制台查看和分享错误详情。\n"
|
||||
if agent_runner.streaming:
|
||||
yield MessageChain().message(err_msg)
|
||||
else:
|
||||
astr_event.set_result(MessageEventResult().message(err_msg))
|
||||
return
|
||||
|
||||
|
||||
class LLMRequestSubStage(Stage):
|
||||
async def initialize(self, ctx: PipelineContext) -> None:
|
||||
@@ -569,6 +250,9 @@ class LLMRequestSubStage(Stage):
|
||||
logger.debug("LLM 响应为空，不保存记录。")
|
||||
return
|
||||
|
||||
if req.contexts is None:
|
||||
req.contexts = []
|
||||
|
||||
# 历史上下文
|
||||
messages = copy.deepcopy(req.contexts)
|
||||
# 这一轮对话请求的用户输入
|
||||
@@ -644,7 +328,9 @@ class LLMRequestSubStage(Stage):
|
||||
req.contexts = json.loads(req.conversation.history)
|
||||
|
||||
else:
|
||||
req = ProviderRequest(prompt="", image_urls=[])
|
||||
req = ProviderRequest()
|
||||
req.prompt = ""
|
||||
req.image_urls = []
|
||||
if sel_model := event.get_extra("selected_model"):
|
||||
req.model = sel_model
|
||||
if self.provider_wake_prefix and not event.message_str.startswith(
|
||||
@@ -681,15 +367,14 @@ class LLMRequestSubStage(Stage):
|
||||
req.contexts = json.loads(req.contexts)
|
||||
|
||||
# truncate contexts to fit max length
|
||||
req.contexts = self._truncate_contexts(req.contexts)
|
||||
if req.contexts:
|
||||
req.contexts = self._truncate_contexts(req.contexts)
|
||||
self._fix_messages(req.contexts)
|
||||
|
||||
# session_id
|
||||
if not req.session_id:
|
||||
req.session_id = event.unified_msg_origin
|
||||
|
||||
# fix messages
|
||||
req.contexts = self._fix_messages(req.contexts)
|
||||
|
||||
# check provider modalities, if provider does not support image/tool_use, clear them in request.
|
||||
self._modalities_fix(provider, req)
|
||||
|
||||
@@ -710,9 +395,6 @@ class LLMRequestSubStage(Stage):
|
||||
)
|
||||
astr_agent_ctx = AstrAgentContext(
|
||||
provider=provider,
|
||||
first_provider_request=req,
|
||||
curr_provider_request=req,
|
||||
streaming=streaming_response,
|
||||
event=event,
|
||||
)
|
||||
await agent_runner.reset(
|
||||
|
||||
@@ -66,9 +66,9 @@ class ToolCallsResult:
|
||||
|
||||
@dataclass
|
||||
class ProviderRequest:
|
||||
prompt: str
|
||||
prompt: str | None = None
|
||||
"""提示词"""
|
||||
session_id: str = ""
|
||||
session_id: str | None = ""
|
||||
"""会话 ID"""
|
||||
image_urls: list[str] = field(default_factory=list)
|
||||
"""图片 URL 列表"""
|
||||
|
||||
@@ -5,6 +5,12 @@ from typing import Any
|
||||
|
||||
from deprecated import deprecated
|
||||
|
||||
from astrbot.core.agent.hooks import BaseAgentRunHooks
|
||||
from astrbot.core.agent.message import Message
|
||||
from astrbot.core.agent.runners.tool_loop_agent_runner import ToolLoopAgentRunner
|
||||
from astrbot.core.agent.tool import ToolSet
|
||||
from astrbot.core.astr_agent_context import AgentContextWrapper, AstrAgentContext
|
||||
from astrbot.core.astr_agent_tool_exec import FunctionToolExecutor
|
||||
from astrbot.core.astrbot_config_mgr import AstrBotConfigManager
|
||||
from astrbot.core.config.astrbot_config import AstrBotConfig
|
||||
from astrbot.core.conversation_mgr import ConversationManager
|
||||
@@ -13,10 +19,10 @@ from astrbot.core.knowledge_base.kb_mgr import KnowledgeBaseManager
|
||||
from astrbot.core.message.message_event_result import MessageChain
|
||||
from astrbot.core.persona_mgr import PersonaManager
|
||||
from astrbot.core.platform import Platform
|
||||
from astrbot.core.platform.astr_message_event import MessageSesion
|
||||
from astrbot.core.platform.astr_message_event import AstrMessageEvent, MessageSesion
|
||||
from astrbot.core.platform.manager import PlatformManager
|
||||
from astrbot.core.platform_message_history_mgr import PlatformMessageHistoryManager
|
||||
from astrbot.core.provider.entities import ProviderType
|
||||
from astrbot.core.provider.entities import LLMResponse, ProviderRequest, ProviderType
|
||||
from astrbot.core.provider.func_tool_manager import FunctionTool, FunctionToolManager
|
||||
from astrbot.core.provider.manager import ProviderManager
|
||||
from astrbot.core.provider.provider import (
|
||||
@@ -31,6 +37,7 @@ from astrbot.core.star.filter.platform_adapter_type import (
|
||||
PlatformAdapterType,
|
||||
)
|
||||
|
||||
from ..exceptions import ProviderNotFoundError
|
||||
from .filter.command import CommandFilter
|
||||
from .filter.regex import RegexFilter
|
||||
from .star import StarMetadata, star_map, star_registry
|
||||
@@ -75,6 +82,139 @@ class Context:
|
||||
self.astrbot_config_mgr = astrbot_config_mgr
|
||||
self.kb_manager = knowledge_base_manager
|
||||
|
||||
async def llm_generate(
|
||||
self,
|
||||
*,
|
||||
chat_provider_id: str,
|
||||
prompt: str | None = None,
|
||||
image_urls: list[str] | None = None,
|
||||
tools: ToolSet | None = None,
|
||||
system_prompt: str | None = None,
|
||||
contexts: list[Message] | list[dict] | None = None,
|
||||
**kwargs: Any,
|
||||
) -> LLMResponse:
|
||||
"""Call the LLM to generate a response. The method will not automatically execute tool calls. If you want to use tool calls, please use `tool_loop_agent()`.
|
||||
|
||||
.. versionadded:: 4.5.7 (sdk)
|
||||
|
||||
Args:
|
||||
chat_provider_id: The chat provider ID to use.
|
||||
prompt: The prompt to send to the LLM, if `contexts` and `prompt` are both provided, `prompt` will be appended as the last user message
|
||||
image_urls: List of image URLs to include in the prompt, if `contexts` and `prompt` are both provided, `image_urls` will be appended to the last user message
|
||||
tools: ToolSet of tools available to the LLM
|
||||
system_prompt: System prompt to guide the LLM's behavior, if provided, it will always insert as the first system message in the context
|
||||
contexts: context messages for the LLM
|
||||
**kwargs: Additional keyword arguments for LLM generation, OpenAI compatible
|
||||
|
||||
Raises:
|
||||
ChatProviderNotFoundError: If the specified chat provider ID is not found
|
||||
Exception: For other errors during LLM generation
|
||||
"""
|
||||
prov = await self.provider_manager.get_provider_by_id(chat_provider_id)
|
||||
if not prov or not isinstance(prov, Provider):
|
||||
raise ProviderNotFoundError(f"Provider {chat_provider_id} not found")
|
||||
llm_resp = await prov.text_chat(
|
||||
prompt=prompt,
|
||||
image_urls=image_urls,
|
||||
func_tool=tools,
|
||||
contexts=contexts,
|
||||
system_prompt=system_prompt,
|
||||
**kwargs,
|
||||
)
|
||||
return llm_resp
|
||||
|
||||
async def tool_loop_agent(
|
||||
self,
|
||||
*,
|
||||
event: AstrMessageEvent,
|
||||
chat_provider_id: str,
|
||||
prompt: str | None = None,
|
||||
image_urls: list[str] | None = None,
|
||||
tools: ToolSet | None = None,
|
||||
system_prompt: str | None = None,
|
||||
contexts: list[Message] | list[dict] | None = None,
|
||||
max_steps: int = 30,
|
||||
tool_call_timeout: int = 60,
|
||||
**kwargs: Any,
|
||||
) -> LLMResponse:
|
||||
"""Run an agent loop that allows the LLM to call tools iteratively until a final answer is produced.
|
||||
|
||||
Args:
|
||||
chat_provider_id: The chat provider ID to use.
|
||||
prompt: The prompt to send to the LLM, if `contexts` and `prompt` are both provided, `prompt` will be appended as the last user message
|
||||
image_urls: List of image URLs to include in the prompt, if `contexts` and `prompt` are both provided, `image_urls` will be appended to the last user message
|
||||
tools: ToolSet of tools available to the LLM
|
||||
system_prompt: System prompt to guide the LLM's behavior, if provided, it will always insert as the first system message in the context
|
||||
contexts: context messages for the LLM
|
||||
max_steps: Maximum number of tool calls before stopping the loop
|
||||
**kwargs: Additional keyword arguments for LLM generation, OpenAI compatible
|
||||
|
||||
Returns:
|
||||
The final LLMResponse after tool calls are completed.
|
||||
|
||||
Raises:
|
||||
ChatProviderNotFoundError: If the specified chat provider ID is not found
|
||||
Exception: For other errors during LLM generation
|
||||
"""
|
||||
prov = await self.provider_manager.get_provider_by_id(chat_provider_id)
|
||||
if not prov or not isinstance(prov, Provider):
|
||||
raise ProviderNotFoundError(f"Provider {chat_provider_id} not found")
|
||||
|
||||
context_ = []
|
||||
for msg in contexts or []:
|
||||
if isinstance(msg, Message):
|
||||
context_.append(msg.model_dump())
|
||||
else:
|
||||
context_.append(msg)
|
||||
|
||||
request = ProviderRequest(
|
||||
prompt=prompt,
|
||||
image_urls=image_urls,
|
||||
func_tool=tools,
|
||||
contexts=context_,
|
||||
system_prompt=system_prompt,
|
||||
)
|
||||
astr_agent_ctx = AstrAgentContext(
|
||||
provider=prov,
|
||||
event=event,
|
||||
)
|
||||
agent_runner = ToolLoopAgentRunner()
|
||||
tool_executor = FunctionToolExecutor()
|
||||
await agent_runner.reset(
|
||||
provider=prov,
|
||||
request=request,
|
||||
run_context=AgentContextWrapper(
|
||||
context=astr_agent_ctx,
|
||||
tool_call_timeout=tool_call_timeout,
|
||||
),
|
||||
tool_executor=tool_executor,
|
||||
agent_hooks=kwargs.get(
|
||||
"agent_hooks", BaseAgentRunHooks[AstrAgentContext]()
|
||||
),
|
||||
streaming=kwargs.get("stream", False),
|
||||
)
|
||||
async for _ in agent_runner.step_until_done(max_steps):
|
||||
pass
|
||||
llm_resp = agent_runner.get_final_llm_resp()
|
||||
if not llm_resp:
|
||||
raise Exception("Agent did not produce a final LLM response")
|
||||
return llm_resp
|
||||
|
||||
async def get_current_chat_provider_id(self, umo: str) -> str:
|
||||
"""Get the ID of the currently used chat provider.
|
||||
|
||||
Args:
|
||||
umo(str): unified_message_origin value, if provided and user has enabled provider session isolation, the provider preferred by that session will be used.
|
||||
|
||||
Raises:
|
||||
ProviderNotFoundError: If the specified chat provider is not found
|
||||
|
||||
"""
|
||||
prov = self.get_using_provider(umo)
|
||||
if not prov:
|
||||
raise ProviderNotFoundError("Provider not found")
|
||||
return prov.meta().id
|
||||
|
||||
def get_registered_star(self, star_name: str) -> StarMetadata | None:
|
||||
"""根据插件名获取插件的 Metadata"""
|
||||
for star in star_registry:
|
||||
|
||||
Reference in New Issue
Block a user