62 lines
2.0 KiB
Python
62 lines
2.0 KiB
Python
"""
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LangGraph 节点日志工具模块
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提供状态流转追踪和 LLM 输入输出打印功能
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"""
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from app.config import ENABLE_GRAPH_TRACE
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from app.logger import debug, info
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def log_state_change(node_name: str, state: dict, prefix: str = "进入"):
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"""
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记录状态变化日志
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Args:
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node_name: 节点名称
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state: 当前状态
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prefix: 日志前缀（"进入" 或 "离开"）
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"""
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from app.logger import info
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messages = state.get("messages", [])
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msg_count = len(messages)
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last_msg = messages[-1] if messages else None
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last_info = ""
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if last_msg:
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# 兼容 dict 和对象两种格式
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if isinstance(last_msg, dict):
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content_preview = str(last_msg.get("content", ""))[:10].replace("\n", " ")
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msg_type = last_msg.get("type", "unknown")
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else:
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content_preview = str(last_msg.content)[:10].replace("\n", " ")
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msg_type = getattr(last_msg, 'type', 'unknown')
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last_info = f"{msg_type.upper()}: {content_preview}"
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info(f"🔄 [{node_name}] {prefix} | 消息数:{msg_count} | 最后一条:{last_info}")
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def print_llm_input(prompt_value):
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"""
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RunnableLambda 回调函数：打印格式化后发送给 LLM 的完整消息
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Args:
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prompt_value: ChatPromptValue 对象，包含格式化后的消息列表
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Returns:
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原样返回 prompt_value，不影响链式调用
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"""
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if not ENABLE_GRAPH_TRACE:
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return prompt_value
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messages = prompt_value.messages # ChatPromptValue 提供 .messages 属性
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debug("\n" + "=" * 80)
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debug("📤 [LLM输入] 格式化后发送给大模型的完整消息:")
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debug(f" 总消息数: {len(messages)}")
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debug("-" * 80)
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for i, msg in enumerate(messages):
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content_preview = str(msg.content) # 完整输出
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debug(f" [{i}] {msg.type.upper():10s}: {content_preview}")
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debug("\n" + "=" * 80 + "\n")
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return prompt_value
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