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https://github.com/jxxghp/MoviePilot.git
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fix(agent): 基于langgraph_step过滤中间步骤思考文本，抽离ThinkTagStripper类
- 利用metadata中的langgraph_step检测工具调用前的中间步骤，非VERBOSE模式下 自动reset清除模型输出的计划/推理文本（如NEXT STEPS、tool call描述等） - 将<think>标签流式剥离逻辑抽离为独立的_ThinkTagStripper类，简化主流程
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jxxghp
@@ -36,6 +36,79 @@ class AgentChain(ChainBase):
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pass
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class _ThinkTagStripper:
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"""
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流式剥离 <think>...</think> 标签的辅助类。
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维护内部缓冲区，处理标签跨 token 边界被截断的情况。
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"""
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def __init__(self):
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self.buffer = ""
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self.in_think_tag = False
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def reset(self):
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"""重置状态"""
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self.buffer = ""
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self.in_think_tag = False
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def process(self, text: str, on_output: Callable[[str], None]):
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"""
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将新文本送入处理，剥离 <think> 标签后通过 on_output 回调输出。
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:param text: 新增的文本片段
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:param on_output: 输出回调，接收过滤后的文本
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:return: 本次调用是否通过 on_output 输出了内容
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"""
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self.buffer += text
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emitted = False
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while self.buffer:
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if not self.in_think_tag:
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start_idx = self.buffer.find("<think>")
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if start_idx != -1:
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if start_idx > 0:
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on_output(self.buffer[:start_idx])
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emitted = True
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self.in_think_tag = True
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self.buffer = self.buffer[start_idx + 7:]
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else:
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# 检查是否以 <think> 的不完整前缀结尾
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partial_match = False
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for i in range(6, 0, -1):
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if self.buffer.endswith("<think>"[:i]):
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if len(self.buffer) > i:
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on_output(self.buffer[:-i])
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emitted = True
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self.buffer = self.buffer[-i:]
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partial_match = True
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break
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if not partial_match:
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on_output(self.buffer)
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emitted = True
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self.buffer = ""
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else:
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end_idx = self.buffer.find("</think>")
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if end_idx != -1:
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self.in_think_tag = False
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self.buffer = self.buffer[end_idx + 8:]
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else:
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# 检查是否以 </think> 的不完整前缀结尾
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partial_match = False
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for i in range(7, 0, -1):
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if self.buffer.endswith("</think>"[:i]):
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self.buffer = self.buffer[-i:]
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partial_match = True
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break
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if not partial_match:
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self.buffer = ""
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break
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return emitted
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def flush(self, on_output: Callable[[str], None]):
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"""流式结束时，输出缓冲区中剩余的非思考内容"""
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if self.buffer and not self.in_think_tag:
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on_output(self.buffer)
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self.buffer = ""
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class MoviePilotAgent:
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"""
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MoviePilot AI智能体（基于 LangChain v1 + LangGraph）
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@@ -218,8 +291,11 @@ class MoviePilotAgent:
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:param config: Agent 运行配置
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:param on_token: 收到有效 token 时的回调
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"""
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in_think_tag = False
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buffer = ""
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stripper = _ThinkTagStripper()
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# 非VERBOSE模式下，跟踪当前langgraph_step以检测中间步骤的模型输出
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# 当模型在工具调用之前输出的"计划/思考"文本，会在检测到tool_call时被清除
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current_model_step = -1
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has_emitted_in_step = False
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async for chunk in agent.astream(
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messages,
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@@ -230,59 +306,41 @@ class MoviePilotAgent:
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):
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if chunk["type"] == "messages":
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token, metadata = chunk["data"]
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if (
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token
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and hasattr(token, "tool_call_chunks")
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and not token.tool_call_chunks
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):
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# 跳过模型思考/推理内容（如 DeepSeek R1 的 reasoning_content）
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additional = getattr(token, "additional_kwargs", None)
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if additional and additional.get("reasoning_content"):
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continue
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if token.content:
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# content 可能是字符串或内容块列表，过滤掉思考类型的块
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content = self._extract_text_content(token.content)
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if content:
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buffer += content
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while buffer:
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if not in_think_tag:
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start_idx = buffer.find("<think>")
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if start_idx != -1:
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if start_idx > 0:
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on_token(buffer[:start_idx])
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in_think_tag = True
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buffer = buffer[start_idx + 7:]
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else:
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# 检查是否以 <think> 的前缀结尾
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partial_match = False
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for i in range(6, 0, -1):
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if buffer.endswith("<think>"[:i]):
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if len(buffer) > i:
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on_token(buffer[:-i])
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buffer = buffer[-i:]
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partial_match = True
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break
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if not partial_match:
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on_token(buffer)
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buffer = ""
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else:
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end_idx = buffer.find("</think>")
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if end_idx != -1:
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in_think_tag = False
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buffer = buffer[end_idx + 8:]
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else:
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# 检查是否以 </think> 的前缀结尾
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partial_match = False
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for i in range(7, 0, -1):
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if buffer.endswith("</think>"[:i]):
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buffer = buffer[-i:]
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partial_match = True
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break
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if not partial_match:
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buffer = ""
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if not token or not hasattr(token, "tool_call_chunks"):
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continue
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if buffer and not in_think_tag:
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on_token(buffer)
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# 获取当前步骤信息
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step = metadata.get("langgraph_step", -1) if metadata else -1
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if token.tool_call_chunks:
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# 检测到工具调用token：说明当前步骤是中间步骤
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# 非VERBOSE模式下，清除该步骤之前输出的"计划/思考"文本
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if not settings.AI_AGENT_VERBOSE and has_emitted_in_step:
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self.stream_handler.reset()
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stripper.reset()
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has_emitted_in_step = False
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continue
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# 以下处理纯文本token（tool_call_chunks为空）
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# 检测步骤变化，重置步骤内emit跟踪
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if step != current_model_step:
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current_model_step = step
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has_emitted_in_step = False
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# 跳过模型思考/推理内容（如 DeepSeek R1 的 reasoning_content）
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additional = getattr(token, "additional_kwargs", None)
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if additional and additional.get("reasoning_content"):
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continue
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if token.content:
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# content 可能是字符串或内容块列表，过滤掉思考类型的块
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content = self._extract_text_content(token.content)
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if content:
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if stripper.process(content, on_token):
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has_emitted_in_step = True
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stripper.flush(on_token)
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async def _execute_agent(self, messages: List[BaseMessage]):
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"""
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