5a11d8f0ee
* refactor: LLM response handling with reasoning content - Added a `show_reasoning` parameter to `run_agent` to control the display of reasoning content. - Updated `LLMResponse` to include a `reasoning_content` field for storing reasoning text. - Modified `WebChatMessageEvent` to handle and send reasoning content in streaming responses. - Implemented reasoning extraction in various provider sources (e.g., OpenAI, Gemini). - Updated the chat interface to display reasoning content in a collapsible format. - Removed the deprecated `thinking_filter` package and its associated logic. - Updated localization files to include new reasoning-related strings. * feat: add Groq chat completion provider and associated configurations * Update astrbot/core/provider/sources/gemini_source.py Co-authored-by: sourcery-ai[bot] <58596630+sourcery-ai[bot]@users.noreply.github.com> --------- Co-authored-by: sourcery-ai[bot] <58596630+sourcery-ai[bot]@users.noreply.github.com>
81 lines
3.2 KiB
Python
81 lines
3.2 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.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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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(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:
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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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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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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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