Soulter
8a0f865af1
* feat: enhance tool call handling and UI integration for tool calls render - Added support for tool call messages in the agent runner and webchat event handling. - Implemented JSON message component for structured tool call data. - Updated chat route to save tool call information in message history. - Enhanced frontend to display tool call details in a collapsible format, including status and results. - Introduced elapsed time tracking for ongoing tool calls in the chat interface. * fix: improve message handling in agent run utility and tool loop runner - Refactored message sending logic in `astr_agent_run_util.py` to use `msg_chain` directly for better clarity. - Added a check in `tool_loop_agent_runner.py` to ensure `tool_call_result_blocks` is not empty before yielding the last tool call result, preventing potential errors. * refactor: enhance message structure and UI for chat components - Updated message handling in `MessageList.vue` to support structured message parts, including plain text, images, audio, and files. - Improved the `Chat.vue` component styles for better visual consistency. - Refactored message parsing logic in `useMessages.ts` to accommodate new message formats and ensure proper rendering of embedded content. - Removed deprecated tool call handling from the message structure, streamlining the message display process. * chore: ruff format * feat: implement agent statistics tracking and display in chat - Added `AgentStats` and `TokenUsage` data classes to track agent performance metrics. - Enhanced `ToolLoopAgentRunner` to collect and update agent statistics during execution. - Integrated agent statistics sending to webchat for real-time updates. - Updated chat route to save and display agent statistics in message history. - Improved frontend components to visualize agent statistics, including token usage and duration metrics. * fix: improve message handling in Telegram event and agent run utility - Updated message sending logic in `astr_agent_run_util.py` to send the correct message chain for tool calls. - Enhanced `tg_event.py` to edit messages during streaming breaks, improving message management and user experience. - Added error handling for message editing failures to ensure robustness. * chore: ruff format
116 lines
4.6 KiB
Python
116 lines
4.6 KiB
Python
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.components import Json
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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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from astrbot.core.provider.entities import LLMResponse
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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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show_reasoning: 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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await astr_event.send(msg_chain)
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continue
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if astr_event.get_platform_id() == "webchat":
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await astr_event.send(msg_chain)
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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 astr_event.get_platform_name() == "webchat":
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await astr_event.send(resp.data["chain"])
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elif show_tool_use:
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json_comp = resp.data["chain"].chain[0]
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if isinstance(json_comp, Json):
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m = f"🔨 调用工具: {json_comp.data.get('name')}"
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else:
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m = "🔨 调用工具..."
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chain = MessageChain(type="tool_call").message(m)
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await astr_event.send(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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chain = resp.data["chain"]
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if chain.type == "reasoning" and not show_reasoning:
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# display the reasoning content only when configured
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continue
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yield resp.data["chain"] # MessageChain
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if agent_runner.done():
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# send agent stats to webchat
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if astr_event.get_platform_name() == "webchat":
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await astr_event.send(
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MessageChain(
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type="agent_stats",
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chain=[Json(data=agent_runner.stats.to_dict())],
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)
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)
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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请在平台日志查看和分享错误详情。\n"
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error_llm_response = LLMResponse(
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role="err",
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completion_text=err_msg,
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)
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try:
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await agent_runner.agent_hooks.on_agent_done(
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agent_runner.run_context, error_llm_response
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)
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except Exception:
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logger.exception("Error in on_agent_done hook")
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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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