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🎉 Initial commit: QQuiz - 智能刷题与题库管理平台
## 功能特性 ✅ **核心功能** - 多文件上传与智能去重（基于 content_hash） - 异步文档解析（支持 TXT/PDF/DOCX/XLSX） - AI 智能题目提取与评分（OpenAI/Anthropic/Qwen） - 断点续做与进度管理 - 自动错题本收集 ✅ **技术栈** - Backend: FastAPI + SQLAlchemy 2.0 + PostgreSQL - Frontend: React 18 + Vite + Tailwind CSS - Deployment: Docker Compose ✅ **项目结构** - 53 个文件 - 完整的前后端分离架构 - Docker/源码双模部署支持 🚀 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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backend/services/llm_service.py
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216
backend/services/llm_service.py
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"""
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LLM Service for AI-powered question parsing and grading
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"""
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import os
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import json
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from typing import List, Dict, Any, Optional
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from openai import AsyncOpenAI
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from anthropic import AsyncAnthropic
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import httpx
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from models import QuestionType
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from utils import calculate_content_hash
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class LLMService:
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"""Service for interacting with various LLM providers"""
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def __init__(self):
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self.provider = os.getenv("AI_PROVIDER", "openai")
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if self.provider == "openai":
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self.client = AsyncOpenAI(
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api_key=os.getenv("OPENAI_API_KEY"),
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base_url=os.getenv("OPENAI_BASE_URL", "https://api.openai.com/v1")
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)
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self.model = os.getenv("OPENAI_MODEL", "gpt-4o-mini")
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elif self.provider == "anthropic":
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self.client = AsyncAnthropic(
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api_key=os.getenv("ANTHROPIC_API_KEY")
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)
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self.model = os.getenv("ANTHROPIC_MODEL", "claude-3-haiku-20240307")
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elif self.provider == "qwen":
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self.client = AsyncOpenAI(
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api_key=os.getenv("QWEN_API_KEY"),
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base_url=os.getenv("QWEN_BASE_URL", "https://dashscope.aliyuncs.com/compatible-mode/v1")
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)
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self.model = os.getenv("QWEN_MODEL", "qwen-plus")
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else:
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raise ValueError(f"Unsupported AI provider: {self.provider}")
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async def parse_document(self, content: str) -> List[Dict[str, Any]]:
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"""
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Parse document content and extract questions.
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Returns a list of dictionaries with question data:
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[
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{
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"content": "Question text",
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"type": "single/multiple/judge/short",
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"options": ["A. Option1", "B. Option2", ...], # For choice questions
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"answer": "Correct answer",
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"analysis": "Explanation"
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},
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...
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]
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"""
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prompt = """You are a professional question parser. Parse the given document and extract all questions.
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For each question, identify:
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1. Question content (the question text)
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2. Question type: single (单选), multiple (多选), judge (判断), short (简答)
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3. Options (for choice questions only, format: ["A. Option1", "B. Option2", ...])
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4. Correct answer
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5. Analysis/Explanation (if available)
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Return ONLY a JSON array of questions, with no additional text:
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[
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{
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"content": "question text",
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"type": "single",
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"options": ["A. Option1", "B. Option2", "C. Option3", "D. Option4"],
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"answer": "A",
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"analysis": "explanation"
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},
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...
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]
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Document content:
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---
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{content}
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---
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IMPORTANT: Return ONLY the JSON array, no markdown code blocks or explanations."""
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try:
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if self.provider == "anthropic":
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response = await self.client.messages.create(
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model=self.model,
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max_tokens=4096,
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messages=[
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{"role": "user", "content": prompt.format(content=content)}
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]
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)
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result = response.content[0].text
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else: # OpenAI or Qwen
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response = await self.client.chat.completions.create(
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model=self.model,
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messages=[
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{"role": "system", "content": "You are a professional question parser. Return only JSON."},
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{"role": "user", "content": prompt.format(content=content)}
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],
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temperature=0.3,
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)
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result = response.choices[0].message.content
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# Clean result and parse JSON
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result = result.strip()
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if result.startswith("```json"):
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result = result[7:]
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if result.startswith("```"):
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result = result[3:]
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if result.endswith("```"):
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result = result[:-3]
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result = result.strip()
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questions = json.loads(result)
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# Add content hash to each question
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for q in questions:
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q["content_hash"] = calculate_content_hash(q["content"])
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return questions
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except Exception as e:
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print(f"Error parsing document: {e}")
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raise Exception(f"Failed to parse document: {str(e)}")
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async def grade_short_answer(
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self,
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question: str,
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correct_answer: str,
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user_answer: str
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) -> Dict[str, Any]:
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"""
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Grade a short answer question using AI.
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Returns:
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{
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"score": 0.0-1.0,
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"feedback": "Detailed feedback"
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}
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"""
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prompt = f"""Grade the following short answer question.
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Question: {question}
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Standard Answer: {correct_answer}
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Student Answer: {user_answer}
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Provide a score from 0.0 to 1.0 (where 1.0 is perfect) and detailed feedback.
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Return ONLY a JSON object:
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{{
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"score": 0.85,
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"feedback": "Your detailed feedback here"
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}}
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Be fair but strict. Consider:
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1. Correctness of key points
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2. Completeness of answer
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3. Clarity of expression
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Return ONLY the JSON object, no markdown or explanations."""
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try:
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if self.provider == "anthropic":
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response = await self.client.messages.create(
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model=self.model,
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max_tokens=1024,
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messages=[
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{"role": "user", "content": prompt}
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]
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)
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result = response.content[0].text
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else: # OpenAI or Qwen
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response = await self.client.chat.completions.create(
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model=self.model,
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messages=[
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{"role": "system", "content": "You are a fair and strict grader. Return only JSON."},
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{"role": "user", "content": prompt}
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],
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temperature=0.5,
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)
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result = response.choices[0].message.content
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# Clean and parse JSON
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result = result.strip()
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if result.startswith("```json"):
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result = result[7:]
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if result.startswith("```"):
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result = result[3:]
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if result.endswith("```"):
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result = result[:-3]
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result = result.strip()
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grading = json.loads(result)
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return {
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"score": float(grading.get("score", 0.0)),
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"feedback": grading.get("feedback", "")
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}
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except Exception as e:
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print(f"Error grading answer: {e}")
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# Return default grading on error
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return {
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"score": 0.0,
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"feedback": "Unable to grade answer due to an error."
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}
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# Singleton instance
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llm_service = LLMService()
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