feat:增加AI生成资料接口
This commit is contained in:
@@ -1,4 +1,4 @@
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study_prompt = """
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question_prompt = """
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你是一位专业的出题专家，擅长根据学习材料生成高质量的客观题（单选题和多选题）。
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请根据下方【学习材料】生成 5～10 道题目，严格遵守以下要求：
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@@ -51,3 +51,34 @@ study_prompt = """
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}
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]
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"""
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knowledge_prompt = """
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你是一位资深教学与教研专家，擅长将各类专业知识系统化梳理，转化为结构清晰、易懂好记的学习资料，适用于不同基础的学习者。
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请根据下方【学科】·【模块】·【名称】生成一份高质量学习资料，**以 Markdown 格式输出**，用于系统学习与复习。
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要求如下：
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1. 全文控制在 **500~800字**（可根据内容复杂度±10%），语言准确、条理清晰，适合打印或电子阅读。
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2. Markdown 结构必须包含以下一级或二级标题：
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- ## 模块概述
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（定义、作用、学习意义，可简要说明该模块在主题中的地位）
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- ## 核心知识点
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（分点讲解，重点概念用 **粗体** 标注）
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- ## 对比与辨析
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（对比易混淆概念）
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- ## 记忆口诀 / 核心规律
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（简洁押韵或逻辑清晰的归纳，便于记忆）
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- ## 本章小结
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（总结本模块最核心的要点）
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3. 内容需适配当前主题的属性：
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- 理工类：突出公式、定理、推导过程、解题思路
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- 医学类：突出机制、适应症、禁忌症、注意事项
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- 文科 / 法商 / 管理类：突出概念界定、逻辑框架、典型案例
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4. 写作风格：
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- 严谨、客观，贴近国内主流教材表述
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- 避免口语化（如“同学们”“大家注意啦”）
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- 不假设学习者是在校大学生，兼顾自学者与在职进修人员
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5. 输出要求：
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- 仅输出 Markdown 正文，不要包含 `<html>`、JSON 或其他格式
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- 不要在开头重复本提示词内容
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"""
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@@ -1,9 +1,14 @@
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from langchain.agents import create_agent
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from agent.model import model
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from agent.prompt import study_prompt
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from agent.prompt import question_prompt, knowledge_prompt
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study_agent = create_agent(
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question_agent = create_agent(
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model=model,
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system_prompt=study_prompt
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system_prompt=question_prompt
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)
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knowledge_agent = create_agent(
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model=model,
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system_prompt=knowledge_prompt
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)
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77
main.py
77
main.py
@@ -1,19 +1,7 @@
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from typing import List
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from fastapi import FastAPI, UploadFile, File, Depends, Form
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from sqlalchemy.ext.asyncio import AsyncSession
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from fastapi import FastAPI
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from starlette.middleware.cors import CORSMiddleware
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from starlette.responses import StreamingResponse
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from database import get_db
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from schemas.agent import QueryRequest
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from schemas.library import LibraryFileRequest, LibraryFileResponse
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from schemas.mistake import MistakeResponse, MistakeRequest
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from schemas.practice import PracticeRecordResponse, PracticeRecordRequest
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from service import library_service, practice_service, mistake_service
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from service.agent_service import query_agent
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from storage import upload_rustfs
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from ocr import ocr_from_url
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from routers import routers
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app = FastAPI(title="AI Study Service")
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@@ -24,62 +12,5 @@ app.add_middleware(
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allow_headers=["*"],
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)
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@app.post("/library/upload")
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def upload(file: UploadFile = File(...), filename: str = Form(...)):
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url = upload_rustfs(file.file, filename)
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return ocr_from_url(url)
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@app.post("/library/question")
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def generate(query: QueryRequest):
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"""流式对话"""
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return StreamingResponse(
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query_agent(query),
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media_type="text/event-stream"
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)
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@app.get("/library/files", response_model=List[LibraryFileResponse])
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async def list_files(db: AsyncSession = Depends(get_db)):
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return await library_service.list_files(db)
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@app.get("/library/files/{id}", response_model=LibraryFileResponse)
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async def get_file(id: int, db: AsyncSession = Depends(get_db)):
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return await library_service.get_file(db, id)
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@app.post("/library/files", response_model=bool)
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async def add_file(data: LibraryFileRequest, db: AsyncSession = Depends(get_db)):
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return await library_service.add_file(db, data)
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@app.put("/library/files/{id}", response_model=bool)
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async def edit_file(id: int, data: LibraryFileRequest, db: AsyncSession = Depends(get_db)):
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return await library_service.edit_file(db, id, data)
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@app.get("/practice/record/{id}", response_model=List[PracticeRecordResponse])
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async def get_record(id: int, db: AsyncSession = Depends(get_db)):
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return await practice_service.get_record(db, id)
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@app.post("/practice/record", response_model=bool)
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async def add_record(data: PracticeRecordRequest, db: AsyncSession = Depends(get_db)):
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return await practice_service.add_record(db, data)
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@app.get("/mistake", response_model=List[MistakeResponse])
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async def list_mistakes(db: AsyncSession = Depends(get_db)):
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return await mistake_service.list_mistakes(db)
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@app.post("/mistake", response_model=bool)
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async def list_mistakes(data: list[MistakeRequest], db: AsyncSession = Depends(get_db)):
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return await mistake_service.add_mistakes(db, data)
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@app.delete("/mistake/{id}", response_model=bool)
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async def remove_mistake(id: int, db: AsyncSession = Depends(get_db)):
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return await mistake_service.remove_mistake(db, id)
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for router in routers:
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app.include_router(router)
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9
routers/__init__.py
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9
routers/__init__.py
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@@ -0,0 +1,9 @@
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from .library import router as library_router
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from .practice import router as practice_router
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from .mistake import router as mistake_router
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routers = [
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library_router,
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practice_router,
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mistake_router,
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]
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55
routers/library.py
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55
routers/library.py
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@@ -0,0 +1,55 @@
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from typing import List
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from fastapi import APIRouter, Depends, File, Form, UploadFile
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from sqlalchemy.ext.asyncio import AsyncSession
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from starlette.responses import StreamingResponse
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from database import get_db
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from schemas.library import LibraryFileRequest, LibraryFileResponse
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from schemas.agent import QueryQuestionRequest, QueryKnowledgeRequest
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from service import library_service, agent_service
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from storage import upload_rustfs
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from ocr import ocr_from_url
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router = APIRouter(prefix="/library", tags=["Library"])
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@router.post("/upload")
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def upload(file: UploadFile = File(...), filename: str = Form(...)):
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url = upload_rustfs(file.file, filename)
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return ocr_from_url(url)
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@router.post("/question")
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def generate_question(query: QueryQuestionRequest):
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return StreamingResponse(
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agent_service.query_question_agent(query),
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media_type="text/event-stream",
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)
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@router.post("/knowledge")
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def generate_knowledge(query: QueryKnowledgeRequest):
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return StreamingResponse(
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agent_service.query_knowledge_agent(query),
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media_type="text/event-stream",
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)
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@router.get("/files", response_model=List[LibraryFileResponse])
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async def list_files(db: AsyncSession = Depends(get_db)):
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return await library_service.list_files(db)
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@router.get("/files/{file_id}", response_model=LibraryFileResponse)
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async def get_file(file_id: int, db: AsyncSession = Depends(get_db)):
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return await library_service.get_file(db, file_id)
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@router.post("/files", response_model=bool)
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async def add_file(data: LibraryFileRequest, db: AsyncSession = Depends(get_db)):
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return await library_service.add_file(db, data)
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@router.put("/files/{file_id}", response_model=bool)
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async def edit_file(file_id: int, data: LibraryFileRequest, db: AsyncSession = Depends(get_db)):
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return await library_service.edit_file(db, file_id, data)
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24
routers/mistake.py
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24
routers/mistake.py
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@@ -0,0 +1,24 @@
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from typing import List
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from fastapi import APIRouter, Depends
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from sqlalchemy.ext.asyncio import AsyncSession
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from database import get_db
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from schemas.mistake import MistakeResponse, MistakeRequest
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from service import mistake_service
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router = APIRouter(prefix="/mistake", tags=["Mistake"])
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@router.get("", response_model=List[MistakeResponse])
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async def list_mistakes(db: AsyncSession = Depends(get_db)):
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return await mistake_service.list_mistakes(db)
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@router.post("", response_model=bool)
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async def add_mistakes(data: list[MistakeRequest], db: AsyncSession = Depends(get_db)):
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return await mistake_service.add_mistakes(db, data)
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@router.delete("/{mistake_id}", response_model=bool)
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async def remove_mistake(mistake_id: int, db: AsyncSession = Depends(get_db)):
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return await mistake_service.remove_mistake(db, mistake_id)
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19
routers/practice.py
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19
routers/practice.py
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from typing import List
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from fastapi import APIRouter, Depends
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from sqlalchemy.ext.asyncio import AsyncSession
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from database import get_db
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from schemas.practice import PracticeRecordResponse, PracticeRecordRequest
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from service import practice_service
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router = APIRouter(prefix="/practice", tags=["Practice"])
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@router.get("/record/{user_id}", response_model=List[PracticeRecordResponse])
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async def get_record(user_id: int, db: AsyncSession = Depends(get_db)):
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return await practice_service.get_record(db, user_id)
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@router.post("/record", response_model=bool)
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async def add_record(data: PracticeRecordRequest, db: AsyncSession = Depends(get_db), ):
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return await practice_service.add_record(db, data)
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@@ -1,5 +1,11 @@
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from pydantic import BaseModel
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class QueryRequest(BaseModel):
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class QueryQuestionRequest(BaseModel):
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message: str
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class QueryKnowledgeRequest(BaseModel):
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subject: str
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module: str
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name: str
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@@ -1,15 +1,32 @@
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from langchain_core.messages import AIMessageChunk, HumanMessage
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from agent.study import study_agent
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from schemas.agent import QueryRequest
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from agent.study import question_agent, knowledge_agent
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from schemas.agent import QueryQuestionRequest, QueryKnowledgeRequest
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def query_agent(query: QueryRequest):
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def query_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 study_agent.stream(
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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 query_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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