215 lines
9.2 KiB
Python
215 lines
9.2 KiB
Python
"""
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LLM 调用节点模块
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负责调用大语言模型并处理响应
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"""
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import time
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from typing import Any, Dict
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from langchain_core.language_models import BaseChatModel
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from langchain_core.messages import AIMessage
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# 本地模块
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from ...main_graph.state import MainGraphState
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from ...agent.prompts import create_system_prompt
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from ...utils.logging import log_state_change
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from backend.app.logger import debug, info, error
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def create_dynamic_llm_call_node(chat_services: Dict[str, BaseChatModel], tools: list):
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"""
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工厂函数：创建动态 LLM 调用节点（根据 state.current_model 选择模型）
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Args:
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chat_services: 模型名称 -> ChatModel 实例 的字典
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tools: 工具列表（llm_call 不使用工具，只负责回答）
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Returns:
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异步节点函数
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"""
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# llm_call 节点不使用工具，只负责生成回答
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# 直接使用原始模型，不绑定工具
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models = chat_services
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# 预构建 prompt（不带工具描述）
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prompt = create_system_prompt()
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from langchain_core.runnables.config import RunnableConfig
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async def call_llm(state: MainGraphState, config: RunnableConfig) -> Dict[str, Any]:
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"""
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LLM 调用节点（动态选择模型）
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Args:
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state: 当前对话状态
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config: LangChain/LangGraph 自动注入的配置，包含 callbacks 等信息
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Returns:
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更新后的状态字典
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"""
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log_state_change("llm_call", state, "进入")
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memory_context = getattr(state, "memory_context", "暂无用户信息")
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start_time = time.time()
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# 关键修复：如果 state.final_result 已经存在（比如子图执行完），直接返回
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if state.final_result:
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info(f"[llm_call] 检测到已有最终结果，直接返回: {state.final_result[:100]}...")
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elapsed_time = time.time() - start_time
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return {
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"final_result": state.final_result,
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"success": True,
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"current_phase": "done",
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"llm_calls": getattr(state, 'llm_calls', 0) + 1,
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"last_elapsed_time": elapsed_time,
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"turns_since_last_summary": getattr(state, 'turns_since_last_summary', 0) + 1,
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}
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# 动态选择模型
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model_name = getattr(state, "current_model", "")
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if not model_name or model_name not in models:
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# 回退到第一个可用模型
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fallback_name = next(iter(models.keys()))
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info(f"[llm_call] 模型 '{model_name}' 不可用，回退到 '{fallback_name}'")
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model_name = fallback_name
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llm = models[model_name]
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info(f"[llm_call] 使用模型（无工具）: {model_name}")
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try:
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# 添加上下文到消息
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messages_with_context = list(state.messages)
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info(f"[llm_call] 原始消息数量: {len(messages_with_context)}")
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for i, msg in enumerate(messages_with_context):
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msg_type = getattr(msg, 'type', 'unknown')
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msg_content = getattr(msg, 'content', '')[:100] if hasattr(msg, 'content') else str(msg)[:100]
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info(f"[llm_call] msg[{i}] type={msg_type}, content={repr(msg_content)}")
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if state.rag_context:
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from langchain_core.messages import SystemMessage
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rag_system_msg = SystemMessage(content=f"以下是检索到的相关信息：\n{state.rag_context}")
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inserted = False
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for i, msg in enumerate(messages_with_context):
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if msg.type == "human":
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messages_with_context.insert(i, rag_system_msg)
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inserted = True
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break
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if not inserted:
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messages_with_context.insert(0, rag_system_msg)
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info(f"[llm_call] RAG上下文已添加，长度: {len(state.rag_context)}")
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# 恢复为：手动进行 astream，并将所有的 chunk 拼接成最终的 response 返回。
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# LangGraph 会自动监听这期间产生的所有 token。
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chain = prompt | llm
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chunks = []
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info(f"[llm_call] 开始调用 LLM astream...")
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async for chunk in chain.astream(
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{
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"messages": messages_with_context,
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"memory_context": memory_context
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},
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config=config
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):
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chunks.append(chunk)
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info(f"[llm_call] LLM astream 完成，共收到 {len(chunks)} 个 chunks,info:{chunks[0].content[:50]}...{chunks[-1].content[:50]}")
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# 将所有 chunk 合并成最终的 AIMessage
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if chunks:
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response = chunks[0].content
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for chunk in chunks[1:]:
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response = response + chunk.content
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# 将所有 chunk 合并成最终的 AIMessage
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if chunks:
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response = chunks[0]
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for chunk in chunks[1:]:
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response = response + chunk
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else:
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response = AIMessage(content="")
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info(f"[llm_call] ⚠️ 警告: 没有收到任何 chunks！")
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elapsed_time = time.time() - start_time
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# 提取 token 用量（兼容不同 LLM 提供商的元数据格式）
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token_usage = {}
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input_tokens = 0
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output_tokens = 0
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# 尝试从 response_metadata 中提取
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if hasattr(response, 'response_metadata') and response.response_metadata:
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meta = response.response_metadata
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if 'token_usage' in meta:
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token_usage = meta['token_usage']
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elif 'usage' in meta:
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token_usage = meta['usage']
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# 尝试从 additional_kwargs 中提取
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if not token_usage and hasattr(response, 'additional_kwargs'):
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add_kwargs = response.additional_kwargs
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if 'llm_output' in add_kwargs and 'token_usage' in add_kwargs['llm_output']:
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token_usage = add_kwargs['llm_output']['token_usage']
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# 提取具体的 token 数值
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if token_usage:
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input_tokens = token_usage.get('prompt_tokens', token_usage.get('input_tokens', 0))
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output_tokens = token_usage.get('completion_tokens', token_usage.get('output_tokens', 0))
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# 打印 LLM 的完整输出
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debug("\n" + "="*80)
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debug(f"📥 [LLM输出] 模型: {model_name} 返回的完整响应:")
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debug(f" 消息类型: {response.type.upper()}")
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debug(f" 内容长度: {len(str(response.content))} 字符")
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debug("-"*80)
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debug(f"{response.content}")
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# 打印响应统计信息
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info(f"⏱️ [LLM统计] 调用耗时: {elapsed_time:.2f}秒")
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info(f"📊 [LLM统计] Token用量: 输入={input_tokens}, 输出={output_tokens}, 总计={input_tokens + output_tokens}")
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if token_usage:
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debug(f"📋 [LLM统计] 详细用量: {token_usage}")
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debug("="*80 + "\n")
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result = {
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"messages": [response],
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"llm_calls": getattr(state, 'llm_calls', 0) + 1,
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"last_token_usage": token_usage,
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"last_elapsed_time": elapsed_time,
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"turns_since_last_summary": getattr(state, 'turns_since_last_summary', 0) + 1,
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"final_result": response.content,
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"success": True,
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"current_phase": "done",
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"current_model": model_name # 记录实际使用的模型
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}
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log_state_change("llm_call", state, "离开")
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return result
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except Exception as e:
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elapsed_time = time.time() - start_time
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error(f"\n❌ [LLM错误] 模型 {model_name} 调用失败 (耗时: {elapsed_time:.2f}秒)")
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error(f" 错误类型: {type(e).__name__}")
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error(f" 错误信息: {str(e)}")
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import traceback
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error(f"📋 堆栈: {traceback.format_exc()}")
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debug("="*80 + "\n")
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# 返回一个友好的错误消息
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error_response = AIMessage(
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content="抱歉，模型暂时无法响应，可能是网络超时或服务繁忙，请稍后再试。"
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)
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error_result = {
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"messages": [error_response],
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"llm_calls": getattr(state, 'llm_calls', 0),
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"last_token_usage": {},
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"last_elapsed_time": elapsed_time,
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"turns_since_last_summary": getattr(state, 'turns_since_last_summary', 0) + 1,
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"final_result": "抱歉，模型暂时无法响应，可能是网络超时或服务繁忙，请稍后再试。",
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"success": False,
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"current_phase": "done",
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"current_model": model_name
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}
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log_state_change("llm_call", state, "离开（异常）")
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return error_result
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return call_llm
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