pengxiao
19b6cdcbd8
- Remove redundant status steps (understanding, preparing, generating) from SSE stream — only emit retrieved docs and reasoning content - ThinkingProcess: only show when there's actual reasoning or docs - Collapse header shows concise state: thinking count or doc count - Clean up unused icon imports Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
135 lines
4.6 KiB
Python
135 lines
4.6 KiB
Python
"""
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LangChain 1.0 对话链（Normal Mode）
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"""
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from typing import List, Dict, Any, Optional
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from langchain_core.runnables import RunnablePassthrough
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from langchain_core.messages import HumanMessage, AIMessage, SystemMessage
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from langchain_core.output_parsers import StrOutputParser
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from ..llm.siliconflow import get_llm_client
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class ConversationChain:
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"""标准对话链（LangChain 1.0）"""
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def __init__(
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self,
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system_prompt: Optional[str] = None,
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model: Optional[str] = None
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):
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"""初始化对话链
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Args:
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system_prompt: 系统提示词
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model: 可选的模型名称，如 deepseek-ai/DeepSeek-V3, Qwen/QwQ-32B
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"""
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_sf_client = get_llm_client(model=model)
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self.llm = _sf_client.llm
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self.client = _sf_client
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self.system_prompt = system_prompt or "你是一个专业的国土空间规划知识问答助手。请基于你的知识回答用户的问题。"
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# 创建带历史的Prompt模板
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self.prompt = ChatPromptTemplate.from_messages([
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SystemMessage(content=self.system_prompt),
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MessagesPlaceholder(variable_name="chat_history"),
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("human", "{question}")
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])
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# 创建对话链
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self.chain = self._create_chain()
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def _create_chain(self):
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"""创建对话链"""
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return (
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self.prompt
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| self.llm
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| StrOutputParser()
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)
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def invoke(self, question: str, chat_history: List[Dict] = None) -> Dict[str, Any]:
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"""同步调用"""
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history_messages = self._format_history(chat_history or [])
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answer = self.chain.invoke({
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"question": question,
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"chat_history": history_messages
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})
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return {
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"answer": answer,
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"sources": [],
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"metadata": {
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"mode": "normal",
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"has_history": bool(chat_history)
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}
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}
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async def ainvoke(self, question: str, chat_history: List[Dict] = None) -> Dict[str, Any]:
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"""异步调用"""
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history_messages = self._format_history(chat_history or [])
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answer = await self.chain.ainvoke({
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"question": question,
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"chat_history": history_messages
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})
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return {
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"answer": answer,
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"sources": [],
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"metadata": {
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"mode": "normal",
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"has_history": bool(chat_history)
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}
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}
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async def astream_with_thinking(self, question: str, chat_history: List[Dict] = None):
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"""流式调用（包含思考过程，捕获推理模型的真实推理内容）"""
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import time
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start_time = time.time()
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history_messages = self._format_history(chat_history or [])
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# 直接使用原始 OpenAI SDK 捕获推理内容
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prompt_value = await self.prompt.ainvoke({
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"question": question,
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"chat_history": history_messages
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})
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prompt_messages = prompt_value.to_messages()
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content_started = False
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async for event_type, content in self.client.stream_with_reasoning(prompt_messages):
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if event_type == "reasoning":
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yield {"type": "thinking", "stage": "reasoning", "message": content}
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elif event_type == "content":
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if not content_started:
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content_started = True
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yield {"type": "chunk", "content": content}
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# 完成
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total_time = time.time() - start_time
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yield {
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"type": "complete",
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"metadata": {
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"total_time": round(total_time, 2)
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}
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}
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def _format_history(self, chat_history: List[Dict]) -> List:
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"""格式化聊天历史为LangChain消息格式"""
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messages = []
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for msg in chat_history:
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if msg["role"] == "user":
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messages.append(HumanMessage(content=msg["content"]))
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elif msg["role"] == "assistant":
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messages.append(AIMessage(content=msg["content"]))
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return messages
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def create_conversation_chain(system_prompt: Optional[str] = None, model: Optional[str] = None) -> ConversationChain:
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"""创建对话链实例
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Args:
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system_prompt: 系统提示词
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model: 可选的模型名称，如 deepseek-ai/DeepSeek-V3, Qwen/QwQ-32B
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"""
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return ConversationChain(system_prompt=system_prompt, model=model)
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