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https://github.com/handsomezhuzhu/QQuiz.git
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feat: 添加 Gemini 支持和 AI 参考答案生成功能
主要功能： - 🎯 新增 Google Gemini AI 提供商支持 - 原生 PDF 理解能力（最多1000页） - 完整保留图片、表格、公式等内容 - 支持自定义 Base URL（用于代理/中转服务） - 🤖 实现 AI 参考答案自动生成 - 当题目缺少答案时自动调用 AI 生成参考答案 - 支持单选、多选、判断、简答等所有题型 - 答案标记为"AI参考答案："便于识别 - 🔧 优化文档解析功能 - 改进中文 Prompt 提高识别准确度 - 自动修复 JSON 中的控制字符（换行符等） - 智能题目类型验证和自动转换（proof→short等） - 增加超时时间和重试机制 - 🎨 完善管理后台配置界面 - 新增 Gemini 配置区域 - 突出显示 PDF 原生支持特性 - 为其他提供商添加"仅文本"警告 - 支持 Gemini Base URL 自定义 技术改进： - 添加 google-genai 依赖 - 实现异步 API 调用适配 - 完善错误处理和日志输出 - 统一配置管理和数据库存储 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
@@ -67,10 +67,87 @@ async def check_upload_limits(user_id: int, file_size: int, db: AsyncSession):
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)
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async def generate_ai_reference_answer(
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llm_service,
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question_content: str,
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question_type: str,
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options: Optional[List[str]] = None
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) -> str:
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"""
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Generate an AI reference answer for a question without a provided answer.
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Args:
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llm_service: LLM service instance
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question_content: The question text
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question_type: Type of question (single, multiple, judge, short)
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options: Question options (for choice questions)
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Returns:
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Generated answer text
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"""
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# Build prompt based on question type
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if question_type in ["single", "multiple"] and options:
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options_text = "\n".join(options)
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prompt = f"""这是一道{
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'单选题' if question_type == 'single' else '多选题'
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}，但文档中没有提供答案。请根据题目内容，推理出最可能的正确答案。
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题目：{question_content}
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选项：
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{options_text}
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请只返回你认为正确的选项字母（如 A 或 AB），不要有其他解释。如果无法确定，请返回"无法确定"。"""
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elif question_type == "judge":
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prompt = f"""这是一道判断题，但文档中没有提供答案。请根据题目内容，判断正误。
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题目：{question_content}
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请只返回"对"或"错"，不要有其他解释。如果无法确定，请返回"无法确定"。"""
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else: # short answer
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prompt = f"""这是一道简答题，但文档中没有提供答案。请根据题目内容，给出一个简洁的参考答案（50字以内）。
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题目：{question_content}
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请直接返回答案内容，不要有"答案："等前缀。如果无法回答，请返回"无法确定"。"""
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# Generate answer using LLM
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if llm_service.provider == "gemini":
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import asyncio
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def _generate():
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return llm_service.client.models.generate_content(
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model=llm_service.model,
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contents=prompt
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)
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response = await asyncio.to_thread(_generate)
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return response.text.strip()
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elif llm_service.provider == "anthropic":
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response = await llm_service.client.messages.create(
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model=llm_service.model,
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max_tokens=256,
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messages=[{"role": "user", "content": prompt}]
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)
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return response.content[0].text.strip()
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else: # OpenAI or Qwen
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response = await llm_service.client.chat.completions.create(
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model=llm_service.model,
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messages=[
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{"role": "system", "content": "You are a helpful assistant that provides concise answers."},
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{"role": "user", "content": prompt}
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],
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temperature=0.7,
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max_tokens=256
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)
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return response.choices[0].message.content.strip()
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async def process_questions_with_dedup(
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exam_id: int,
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questions_data: List[dict],
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db: AsyncSession
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db: AsyncSession,
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llm_service=None
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) -> ParseResult:
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"""
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Process parsed questions with deduplication logic.
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@@ -79,6 +156,7 @@ async def process_questions_with_dedup(
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exam_id: Target exam ID
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questions_data: List of question dicts from LLM parsing
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db: Database session
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llm_service: LLM service instance for generating AI answers
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Returns:
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ParseResult with statistics
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@@ -86,6 +164,7 @@ async def process_questions_with_dedup(
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total_parsed = len(questions_data)
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duplicates_removed = 0
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new_added = 0
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ai_answers_generated = 0
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# Get existing content hashes for this exam
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result = await db.execute(
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@@ -101,13 +180,40 @@ async def process_questions_with_dedup(
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duplicates_removed += 1
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continue
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# Handle missing answers - generate AI reference answer
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answer = q_data.get("answer")
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if (answer is None or answer == "null" or answer == "") and llm_service:
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print(f"[Question] Generating AI reference answer for: {q_data['content'][:50]}...", flush=True)
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try:
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# Convert question type to string if it's not already
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q_type = q_data["type"]
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if hasattr(q_type, 'value'):
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q_type = q_type.value
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elif isinstance(q_type, str):
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q_type = q_type.lower()
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ai_answer = await generate_ai_reference_answer(
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llm_service,
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q_data["content"],
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q_type,
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q_data.get("options")
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)
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answer = f"AI参考答案：{ai_answer}"
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ai_answers_generated += 1
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print(f"[Question] ✅ AI answer generated: {ai_answer[:50]}...", flush=True)
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except Exception as e:
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print(f"[Question] ⚠️ Failed to generate AI answer: {e}", flush=True)
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answer = "（答案未提供）"
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elif answer is None or answer == "null" or answer == "":
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answer = "（答案未提供）"
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# Create new question
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new_question = Question(
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exam_id=exam_id,
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content=q_data["content"],
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type=q_data["type"],
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options=q_data.get("options"),
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answer=q_data["answer"],
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answer=answer,
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analysis=q_data.get("analysis"),
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content_hash=content_hash
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)
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@@ -117,11 +223,15 @@ async def process_questions_with_dedup(
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await db.commit()
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message = f"Parsed {total_parsed} questions, removed {duplicates_removed} duplicates, added {new_added} new questions"
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if ai_answers_generated > 0:
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message += f", generated {ai_answers_generated} AI reference answers"
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return ParseResult(
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total_parsed=total_parsed,
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duplicates_removed=duplicates_removed,
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new_added=new_added,
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message=f"Parsed {total_parsed} questions, removed {duplicates_removed} duplicates, added {new_added} new questions"
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message=message
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)
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@@ -145,27 +255,54 @@ async def async_parse_and_save(
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exam.status = ExamStatus.PROCESSING
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await db.commit()
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# Parse document
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print(f"[Exam {exam_id}] Parsing document: {filename}")
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text_content = await document_parser.parse_file(file_content, filename)
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if not text_content or len(text_content.strip()) < 10:
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raise Exception("Document appears to be empty or too short")
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# Load LLM configuration from database
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llm_config = await load_llm_config(db)
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llm_service = LLMService(config=llm_config)
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# Parse questions using LLM
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print(f"[Exam {exam_id}] Calling LLM to extract questions...")
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questions_data = await llm_service.parse_document(text_content)
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# Check if file is PDF and provider is Gemini
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is_pdf = filename.lower().endswith('.pdf')
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is_gemini = llm_config.get('ai_provider') == 'gemini'
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print(f"[Exam {exam_id}] Parsing document: {filename}")
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print(f"[Exam {exam_id}] File type: {'PDF' if is_pdf else 'Text-based'}", flush=True)
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print(f"[Exam {exam_id}] AI Provider: {llm_config.get('ai_provider')}", flush=True)
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try:
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if is_pdf and is_gemini:
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# Use Gemini's native PDF processing
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print(f"[Exam {exam_id}] Using Gemini native PDF processing", flush=True)
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print(f"[Exam {exam_id}] PDF file size: {len(file_content)} bytes", flush=True)
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questions_data = await llm_service.parse_document_with_pdf(file_content, filename)
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else:
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# Extract text first, then parse
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if is_pdf:
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print(f"[Exam {exam_id}] ⚠️ Warning: Using text extraction for PDF (provider does not support native PDF)", flush=True)
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print(f"[Exam {exam_id}] Extracting text from document...", flush=True)
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text_content = await document_parser.parse_file(file_content, filename)
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if not text_content or len(text_content.strip()) < 10:
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raise Exception("Document appears to be empty or too short")
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print(f"[Exam {exam_id}] Text content length: {len(text_content)} chars", flush=True)
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print(f"[Exam {exam_id}] Document content preview:\n{text_content[:500]}\n{'...' if len(text_content) > 500 else ''}", flush=True)
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print(f"[Exam {exam_id}] Calling LLM to extract questions...", flush=True)
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questions_data = await llm_service.parse_document(text_content)
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except Exception as parse_error:
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print(f"[Exam {exam_id}] ⚠️ Parse error details: {type(parse_error).__name__}", flush=True)
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print(f"[Exam {exam_id}] ⚠️ Parse error message: {str(parse_error)}", flush=True)
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import traceback
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print(f"[Exam {exam_id}] ⚠️ Full traceback:\n{traceback.format_exc()}", flush=True)
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raise
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if not questions_data:
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raise Exception("No questions found in document")
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# Process questions with deduplication
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# Process questions with deduplication and AI answer generation
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print(f"[Exam {exam_id}] Processing questions with deduplication...")
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parse_result = await process_questions_with_dedup(exam_id, questions_data, db)
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parse_result = await process_questions_with_dedup(exam_id, questions_data, db, llm_service)
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# Update exam status and total questions
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result = await db.execute(select(Exam).where(Exam.id == exam_id))
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