56 lines
2.1 KiB
Python
56 lines
2.1 KiB
Python
from langchain_core.messages import AIMessageChunk, HumanMessage
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from agent.study import question_agent, knowledge_agent, exam_agent
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from schemas.agent import QueryQuestionRequest, QueryKnowledgeRequest, QueryExamRequest
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def generate_question_agent(query: QueryQuestionRequest):
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try:
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user_msg = f"学习内容：\n{query.message}"
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# 流式调用Agent
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for chunk, metadata in question_agent.stream(
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{"messages": [HumanMessage(content=user_msg)]},
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stream_mode="messages"
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):
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if isinstance(chunk, AIMessageChunk):
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if isinstance(chunk, AIMessageChunk) and chunk.content:
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yield chunk.content
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except Exception as e:
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print(f"\n[错误]: {str(e)}")
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yield "信息检索失败，请重新输入问题提问"
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def generate_knowledge_agent(query: QueryKnowledgeRequest):
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try:
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user_msg = f"学科：{query.subject}，模块：{query.module}， 名称：{query.name}"
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# 流式调用Agent
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for chunk, metadata in knowledge_agent.stream(
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{"messages": [HumanMessage(content=user_msg)]},
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stream_mode="messages"
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):
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if isinstance(chunk, AIMessageChunk):
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if isinstance(chunk, AIMessageChunk) and chunk.content:
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yield chunk.content
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except Exception as e:
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print(f"\n[错误]: {str(e)}")
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yield "信息检索失败，请重新输入问题提问"
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def generate_exam_agent(query: QueryExamRequest):
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try:
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user_msg = f"学科：{query.subject}，题目总数：{query.total}"
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# 流式调用Agent
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for chunk, metadata in exam_agent.stream(
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{"messages": [HumanMessage(content=user_msg)]},
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stream_mode="messages"
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):
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if isinstance(chunk, AIMessageChunk):
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if isinstance(chunk, AIMessageChunk) and chunk.content:
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yield chunk.content
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except Exception as e:
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print(f"\n[错误]: {str(e)}")
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yield "信息检索失败，请重新输入问题提问"
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