mirror of
https://github.com/handsomezhuzhu/QQuiz.git
synced 2026-02-20 12:00:14 +00:00
handsomezhuzhu
f403eacb9d
核心改进： - 🔄 完全重写 Gemini 实现方式 - 移除 google-genai SDK 依赖 - 改用 httpx 直接调用 Gemini REST API - 完全控制请求 URL 和参数 - 🌐 完美支持自定义 Base URL - 支持代理服务（如 https://load.zhuzihan.com/proxy/gemini-self） - 支持 API Key 轮训中转服务 - 兼容标准 Gemini API 格式（v1beta/models/{model}:generateContent） - 📄 保留完整的 PDF 处理能力 - 使用 inline_data 格式（base64 编码） - 原生 PDF 理解（最多 1000 页） - 完整保留图片、表格、公式等视觉元素 - ⚡ 优化性能和稳定性 - 统一使用 httpx.AsyncClient - 120 秒超时配置 - 连接池管理（max_keepalive_connections=5） - 完善的错误处理和日志输出 技术细节： - 移除依赖：google-genai==1.0.0 - 请求格式：标准 Gemini REST API - 响应解析：直接从 JSON 提取 candidates[0].content.parts[0].text - PDF 上传：inline_data with base64 encoding 影响范围： - 文本内容解析 ✅ - PDF 文档解析 ✅ - 简答题评分 ✅ - AI 参考答案生成 ✅ 🎉 现在 Gemini 可完美配合用户自建的代理/轮训服务使用！ 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
703 lines
29 KiB
Python
703 lines
29 KiB
Python
"""
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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, config: Optional[Dict[str, str]] = None):
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"""
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Initialize LLM Service with optional configuration.
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If config is not provided, falls back to environment variables.
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Args:
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config: Dictionary with keys like 'ai_provider', 'openai_api_key', etc.
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"""
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# Get provider from config or environment
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self.provider = (config or {}).get("ai_provider") or os.getenv("AI_PROVIDER", "openai")
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if self.provider == "openai":
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api_key = (config or {}).get("openai_api_key") or os.getenv("OPENAI_API_KEY")
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base_url = (config or {}).get("openai_base_url") or os.getenv("OPENAI_BASE_URL", "https://api.openai.com/v1")
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self.model = (config or {}).get("openai_model") or os.getenv("OPENAI_MODEL", "gpt-4o-mini")
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if not api_key:
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raise ValueError("OpenAI API key not configured")
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self.client = AsyncOpenAI(
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api_key=api_key,
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base_url=base_url,
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timeout=120.0, # 增加超时时间到 120 秒
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max_retries=3 # 自动重试 3 次
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)
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# Log configuration for debugging
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print(f"[LLM Config] Provider: OpenAI", flush=True)
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print(f"[LLM Config] Base URL: {base_url}", flush=True)
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print(f"[LLM Config] Model: {self.model}", flush=True)
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print(f"[LLM Config] API Key: {api_key[:10]}...{api_key[-4:] if len(api_key) > 14 else 'xxxx'}", flush=True)
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elif self.provider == "anthropic":
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api_key = (config or {}).get("anthropic_api_key") or os.getenv("ANTHROPIC_API_KEY")
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self.model = (config or {}).get("anthropic_model") or os.getenv("ANTHROPIC_MODEL", "claude-3-haiku-20240307")
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if not api_key:
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raise ValueError("Anthropic API key not configured")
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self.client = AsyncAnthropic(
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api_key=api_key
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)
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elif self.provider == "qwen":
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api_key = (config or {}).get("qwen_api_key") or os.getenv("QWEN_API_KEY")
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base_url = (config or {}).get("qwen_base_url") or os.getenv("QWEN_BASE_URL", "https://dashscope.aliyuncs.com/compatible-mode/v1")
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self.model = (config or {}).get("qwen_model") or os.getenv("QWEN_MODEL", "qwen-plus")
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if not api_key:
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raise ValueError("Qwen API key not configured")
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self.client = AsyncOpenAI(
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api_key=api_key,
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base_url=base_url,
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timeout=120.0, # 增加超时时间到 120 秒
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max_retries=3 # 自动重试 3 次
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)
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elif self.provider == "gemini":
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api_key = (config or {}).get("gemini_api_key") or os.getenv("GEMINI_API_KEY")
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base_url = (config or {}).get("gemini_base_url") or os.getenv("GEMINI_BASE_URL")
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self.model = (config or {}).get("gemini_model") or os.getenv("GEMINI_MODEL", "gemini-2.0-flash-exp")
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if not api_key:
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raise ValueError("Gemini API key not configured")
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# Store Gemini configuration for REST API calls
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self.gemini_api_key = api_key
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self.gemini_base_url = base_url or "https://generativelanguage.googleapis.com"
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# Create httpx client for REST API calls (instead of SDK)
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self.client = httpx.AsyncClient(
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timeout=120.0,
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limits=httpx.Limits(max_keepalive_connections=5, max_connections=10)
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)
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# Log configuration for debugging
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print(f"[LLM Config] Provider: Gemini (REST API)", flush=True)
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print(f"[LLM Config] Model: {self.model}", flush=True)
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print(f"[LLM Config] Base URL: {self.gemini_base_url}", flush=True)
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print(f"[LLM Config] API Key: {api_key[:10]}...{api_key[-4:] if len(api_key) > 14 else 'xxxx'}", flush=True)
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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 = """你是一个专业的试题解析专家。请仔细分析下面的文档内容，提取其中的所有试题。
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请注意：
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- 文档中可能包含中文或英文题目
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- 题目可能有多种格式，请灵活识别
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- 即使格式不标准，也请尽量提取题目内容
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- 如果文档只是普通文章而没有题目，请返回空数组 []
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对于每道题目，请识别：
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1. 题目内容 (完整的题目文字)
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2. 题目类型（**只能**使用以下4种类型之一）：
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- single：单选题
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- multiple：多选题
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- judge：判断题
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- short：简答题（包括问答题、计算题、证明题、填空题等所有非选择题）
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3. 选项 (仅针对选择题，格式: ["A. 选项1", "B. 选项2", ...])
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4. 正确答案 (请仔细查找文档中的答案。如果确实没有答案，可以填 null)
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5. 解析/说明 (如果有的话)
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**重要**：题目类型必须是 single、multiple、judge、short 之一，不要使用其他类型名称！
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返回格式：请**只返回** JSON 数组，不要有任何其他文字或 markdown 代码块：
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[
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{{
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"content": "题目内容",
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"type": "single",
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"options": ["A. 选项1", "B. 选项2", "C. 选项3", "D. 选项4"],
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"answer": "A",
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"analysis": "解析说明"
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}},
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...
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]
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文档内容：
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---
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{content}
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---
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重要提示：
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- 仔细阅读文档内容
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- 识别所有看起来像试题的内容
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- 如果文档中没有题目（比如只是普通文章），返回 []
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- **只返回 JSON 数组**，不要包含 ```json 或其他标记"""
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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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elif self.provider == "gemini":
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# Gemini uses REST API
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print(f"[Gemini Text] Calling Gemini REST API with model: {self.model}", flush=True)
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url = f"{self.gemini_base_url}/v1beta/models/{self.model}:generateContent"
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headers = {"Content-Type": "application/json"}
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params = {"key": self.gemini_api_key}
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payload = {
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"contents": [{
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"parts": [{"text": prompt.format(content=content)}]
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}]
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}
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response = await self.client.post(url, headers=headers, params=params, json=payload)
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response.raise_for_status()
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response_data = response.json()
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# Extract text from response
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result = response_data["candidates"][0]["content"]["parts"][0]["text"]
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print(f"[Gemini Text] API call completed", flush=True)
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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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# Log original response for debugging
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import sys
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print(f"[LLM Raw Response] Length: {len(result)} chars", flush=True)
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print(f"[LLM Raw Response] First 300 chars:\n{result[:300]}", flush=True)
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print(f"[LLM Raw Response] Last 200 chars:\n{result[-200:]}", flush=True)
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sys.stdout.flush()
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# Clean result and parse JSON
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result = result.strip()
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# Remove markdown code blocks
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if result.startswith("```json"):
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result = result[7:]
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elif 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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# Try to find JSON array if there's extra text
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if not result.startswith('['):
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# Find the first '[' character
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start_idx = result.find('[')
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if start_idx != -1:
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print(f"[JSON Cleanup] Found '[' at position {start_idx}, extracting array...")
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result = result[start_idx:]
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else:
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print(f"[JSON Error] No '[' found in response!")
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raise Exception("LLM response does not contain a JSON array")
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if not result.endswith(']'):
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# Find the last ']' character
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end_idx = result.rfind(']')
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if end_idx != -1:
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print(f"[JSON Cleanup] Found last ']' at position {end_idx}")
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result = result[:end_idx + 1]
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result = result.strip()
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# Log the cleaned result for debugging
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print(f"[LLM Cleaned JSON] Length: {len(result)} chars")
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print(f"[LLM Cleaned JSON] First 300 chars:\n{result[:300]}")
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try:
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questions = json.loads(result)
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except json.JSONDecodeError as je:
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print(f"[JSON Error] Failed to parse JSON at line {je.lineno}, column {je.colno}")
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print(f"[JSON Error] Error: {je.msg}")
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# If error is about control characters, try to fix them
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if "control character" in je.msg.lower() or "invalid \\escape" in je.msg.lower():
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print(f"[JSON Cleanup] Attempting to fix control characters...", flush=True)
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# Fix unescaped control characters in JSON string values
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import re
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def fix_string_value(match):
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"""Fix control characters inside a JSON string value"""
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string_content = match.group(1)
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# Escape control characters
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string_content = string_content.replace('\n', '\\n')
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string_content = string_content.replace('\r', '\\r')
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string_content = string_content.replace('\t', '\\t')
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string_content = string_content.replace('\b', '\\b')
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string_content = string_content.replace('\f', '\\f')
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return f'"{string_content}"'
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# Match string values in JSON
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# Pattern matches: "..." (handles escaped quotes and backslashes)
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# (?:[^"\\]|\\.)* means: either non-quote-non-backslash OR backslash-followed-by-anything, repeated
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fixed_result = re.sub(r'"((?:[^"\\]|\\.)*)"', fix_string_value, result)
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print(f"[JSON Cleanup] Retrying with fixed control characters...", flush=True)
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try:
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questions = json.loads(fixed_result)
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print(f"[JSON Cleanup] ✅ Successfully parsed after fixing control characters!", flush=True)
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except json.JSONDecodeError as je2:
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print(f"[JSON Error] Still failed after fix: {je2.msg}", flush=True)
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# Print context around the error
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lines = result.split('\n')
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if je.lineno <= len(lines):
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start = max(0, je.lineno - 3)
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end = min(len(lines), je.lineno + 2)
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print(f"[JSON Error] Context (lines {start+1}-{end}):")
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for i in range(start, end):
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marker = " >>> " if i == je.lineno - 1 else " "
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print(f"{marker}{i+1}: {lines[i]}")
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raise Exception(f"Invalid JSON format from LLM: {je.msg} at line {je.lineno}")
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else:
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# Print context around the error
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lines = result.split('\n')
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if je.lineno <= len(lines):
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start = max(0, je.lineno - 3)
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end = min(len(lines), je.lineno + 2)
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print(f"[JSON Error] Context (lines {start+1}-{end}):")
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for i in range(start, end):
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marker = " >>> " if i == je.lineno - 1 else " "
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print(f"{marker}{i+1}: {lines[i]}")
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raise Exception(f"Invalid JSON format from LLM: {je.msg} at line {je.lineno}")
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# Validate that we got a list
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if not isinstance(questions, list):
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raise Exception(f"Expected a list of questions, got {type(questions)}")
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if len(questions) == 0:
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raise Exception("No questions found in the parsed result")
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# Validate and fix question types
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valid_types = {"single", "multiple", "judge", "short"}
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type_mapping = {
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"proof": "short",
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"essay": "short",
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"calculation": "short",
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"fill": "short",
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"填空": "short",
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"证明": "short",
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"计算": "short",
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"问答": "short",
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"单选": "single",
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"多选": "multiple",
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"判断": "judge",
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"简答": "short"
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}
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# Add content hash and validate types
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for q in questions:
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if "content" not in q:
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print(f"[Warning] Question missing 'content' field: {q}")
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continue
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# Validate and fix question type
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q_type = q.get("type", "short")
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if isinstance(q_type, str):
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q_type_lower = q_type.lower()
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if q_type_lower not in valid_types:
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# Try to map to valid type
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if q_type_lower in type_mapping:
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old_type = q_type
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q["type"] = type_mapping[q_type_lower]
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print(f"[Type Fix] Changed '{old_type}' to '{q['type']}' for question: {q['content'][:50]}...", flush=True)
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else:
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# Default to short answer
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print(f"[Type Fix] Unknown type '{q_type}', defaulting to 'short' for question: {q['content'][:50]}...", flush=True)
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q["type"] = "short"
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else:
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q["type"] = q_type_lower
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else:
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q["type"] = "short"
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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] Document parsing failed: {str(e)}")
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raise Exception(f"Failed to parse document: {str(e)}")
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async def parse_document_with_pdf(self, pdf_bytes: bytes, filename: str) -> List[Dict[str, Any]]:
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"""
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Parse PDF document using Gemini's native PDF understanding.
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Only works with Gemini provider.
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Args:
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pdf_bytes: PDF file content as bytes
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filename: Original filename for logging
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Returns:
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List of question dictionaries
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"""
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if self.provider != "gemini":
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raise ValueError("PDF parsing is only supported with Gemini provider")
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prompt = """你是一个专业的试题解析专家。请仔细分析这个 PDF 文档，提取其中的所有试题。
|
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|
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请注意：
|
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- PDF 中可能包含中文或英文题目
|
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- 题目可能有多种格式，请灵活识别
|
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- 即使格式不标准，也请尽量提取题目内容
|
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- 题目内容如果包含代码或换行，请将换行符替换为空格或\\n
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|
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对于每道题目，请识别：
|
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1. 题目内容 (完整的题目文字，如果有代码请保持在一行或用\\n表示换行)
|
||
2. 题目类型（**只能**使用以下4种类型之一）：
|
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- single：单选题
|
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- multiple：多选题
|
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- judge：判断题
|
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- short：简答题（包括问答题、计算题、证明题、填空题等所有非选择题）
|
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3. 选项 (仅针对选择题，格式: ["A. 选项1", "B. 选项2", ...])
|
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4. 正确答案 (请仔细查找文档中的答案。如果确实没有答案，可以填 null)
|
||
5. 解析/说明 (如果有的话)
|
||
|
||
**重要**：题目类型必须是 single、multiple、judge、short 之一，不要使用其他类型名称！
|
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|
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返回格式要求：
|
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1. **必须**返回一个完整的 JSON 数组（以 [ 开始，以 ] 结束）
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2. **不要**返回 JSONL 格式（每行一个 JSON 对象）
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3. **不要**包含 markdown 代码块标记（```json 或 ```）
|
||
4. **不要**包含任何解释性文字
|
||
|
||
正确的格式示例：
|
||
[
|
||
{{
|
||
"content": "题目内容",
|
||
"type": "single",
|
||
"options": ["A. 选项1", "B. 选项2", "C. 选项3", "D. 选项4"],
|
||
"answer": "A",
|
||
"analysis": "解析说明"
|
||
}},
|
||
{{
|
||
"content": "第二道题",
|
||
"type": "judge",
|
||
"options": [],
|
||
"answer": "对",
|
||
"analysis": null
|
||
}}
|
||
]
|
||
|
||
重要提示：
|
||
- 请仔细查看 PDF 的每一页
|
||
- 识别所有看起来像试题的内容
|
||
- 如果找不到明确的选项，可以根据上下文推断题目类型
|
||
- 题目内容中的换行请用\\n或空格替换，确保 JSON 格式正确
|
||
- **只返回一个 JSON 数组**，不要包含其他任何内容"""
|
||
|
||
try:
|
||
print(f"[Gemini PDF] Processing PDF: {filename}", flush=True)
|
||
print(f"[Gemini PDF] File size: {len(pdf_bytes)} bytes", flush=True)
|
||
|
||
# Use Gemini's native PDF processing via REST API
|
||
import base64
|
||
|
||
# Encode PDF to base64
|
||
pdf_base64 = base64.b64encode(pdf_bytes).decode('utf-8')
|
||
print(f"[Gemini PDF] PDF encoded to base64: {len(pdf_base64)} chars", flush=True)
|
||
|
||
# Build REST API request
|
||
url = f"{self.gemini_base_url}/v1beta/models/{self.model}:generateContent"
|
||
headers = {"Content-Type": "application/json"}
|
||
params = {"key": self.gemini_api_key}
|
||
payload = {
|
||
"contents": [{
|
||
"parts": [
|
||
{"inline_data": {"mime_type": "application/pdf", "data": pdf_base64}},
|
||
{"text": prompt}
|
||
]
|
||
}]
|
||
}
|
||
|
||
print(f"[Gemini PDF] Calling Gemini REST API with model: {self.model}", flush=True)
|
||
response = await self.client.post(url, headers=headers, params=params, json=payload)
|
||
response.raise_for_status()
|
||
print(f"[Gemini PDF] API call completed", flush=True)
|
||
|
||
response_data = response.json()
|
||
|
||
# Extract text from response
|
||
result = response_data["candidates"][0]["content"]["parts"][0]["text"]
|
||
print(f"[Gemini PDF] Response retrieved, checking content...", flush=True)
|
||
|
||
# Log original response for debugging
|
||
import sys
|
||
print(f"[LLM Raw Response] Length: {len(result)} chars", flush=True)
|
||
print(f"[LLM Raw Response] First 300 chars:\n{result[:300]}", flush=True)
|
||
print(f"[LLM Raw Response] Last 200 chars:\n{result[-200:]}", flush=True)
|
||
sys.stdout.flush()
|
||
|
||
# Clean result and parse JSON (same as text method)
|
||
result = result.strip()
|
||
|
||
# Remove markdown code blocks
|
||
if result.startswith("```json"):
|
||
result = result[7:]
|
||
elif result.startswith("```"):
|
||
result = result[3:]
|
||
|
||
if result.endswith("```"):
|
||
result = result[:-3]
|
||
|
||
result = result.strip()
|
||
|
||
# Try to find JSON array if there's extra text
|
||
if not result.startswith('['):
|
||
start_idx = result.find('[')
|
||
if start_idx != -1:
|
||
print(f"[JSON Cleanup] Found '[' at position {start_idx}, extracting array...", flush=True)
|
||
result = result[start_idx:]
|
||
else:
|
||
print(f"[JSON Error] No '[' found in response!", flush=True)
|
||
raise Exception("LLM response does not contain a JSON array")
|
||
|
||
if not result.endswith(']'):
|
||
end_idx = result.rfind(']')
|
||
if end_idx != -1:
|
||
print(f"[JSON Cleanup] Found last ']' at position {end_idx}", flush=True)
|
||
result = result[:end_idx + 1]
|
||
|
||
result = result.strip()
|
||
|
||
# Log the cleaned result for debugging
|
||
print(f"[LLM Cleaned JSON] Length: {len(result)} chars", flush=True)
|
||
print(f"[LLM Cleaned JSON] First 300 chars:\n{result[:300]}", flush=True)
|
||
|
||
try:
|
||
questions = json.loads(result)
|
||
except json.JSONDecodeError as je:
|
||
print(f"[JSON Error] Failed to parse JSON at line {je.lineno}, column {je.colno}", flush=True)
|
||
print(f"[JSON Error] Error: {je.msg}", flush=True)
|
||
# Print context around the error
|
||
lines = result.split('\n')
|
||
if je.lineno <= len(lines):
|
||
start = max(0, je.lineno - 3)
|
||
end = min(len(lines), je.lineno + 2)
|
||
print(f"[JSON Error] Context (lines {start+1}-{end}):", flush=True)
|
||
for i in range(start, end):
|
||
marker = " >>> " if i == je.lineno - 1 else " "
|
||
print(f"{marker}{i+1}: {lines[i]}", flush=True)
|
||
raise Exception(f"Invalid JSON format from LLM: {je.msg} at line {je.lineno}")
|
||
|
||
# Validate that we got a list
|
||
if not isinstance(questions, list):
|
||
raise Exception(f"Expected a list of questions, got {type(questions)}")
|
||
|
||
if len(questions) == 0:
|
||
# Provide more helpful error message
|
||
print(f"[Gemini PDF] ⚠️ Gemini returned empty array - PDF may not contain recognizable questions", flush=True)
|
||
print(f"[Gemini PDF] 💡 Trying to get Gemini's explanation...", flush=True)
|
||
|
||
# Ask Gemini what it saw in the PDF using REST API
|
||
explanation_payload = {
|
||
"contents": [{
|
||
"parts": [
|
||
{"inline_data": {"mime_type": "application/pdf", "data": pdf_base64}},
|
||
{"text": "Please describe what you see in this PDF document. What is the main content? Are there any questions, exercises, or test items? Respond in Chinese."}
|
||
]
|
||
}]
|
||
}
|
||
|
||
explanation_response = await self.client.post(url, headers=headers, params=params, json=explanation_payload)
|
||
explanation_response.raise_for_status()
|
||
explanation_data = explanation_response.json()
|
||
explanation = explanation_data["candidates"][0]["content"]["parts"][0]["text"]
|
||
print(f"[Gemini PDF] 📄 Gemini sees: {explanation[:500]}...", flush=True)
|
||
|
||
raise Exception(f"No questions found in PDF. Gemini's description: {explanation[:200]}...")
|
||
|
||
# Validate and fix question types
|
||
valid_types = {"single", "multiple", "judge", "short"}
|
||
type_mapping = {
|
||
"proof": "short",
|
||
"essay": "short",
|
||
"calculation": "short",
|
||
"fill": "short",
|
||
"填空": "short",
|
||
"证明": "short",
|
||
"计算": "short",
|
||
"问答": "short",
|
||
"单选": "single",
|
||
"多选": "multiple",
|
||
"判断": "judge",
|
||
"简答": "short"
|
||
}
|
||
|
||
# Add content hash and validate types
|
||
for q in questions:
|
||
if "content" not in q:
|
||
print(f"[Warning] Question missing 'content' field: {q}", flush=True)
|
||
continue
|
||
|
||
# Validate and fix question type
|
||
q_type = q.get("type", "short")
|
||
if isinstance(q_type, str):
|
||
q_type_lower = q_type.lower()
|
||
if q_type_lower not in valid_types:
|
||
# Try to map to valid type
|
||
if q_type_lower in type_mapping:
|
||
old_type = q_type
|
||
q["type"] = type_mapping[q_type_lower]
|
||
print(f"[Type Fix] Changed '{old_type}' to '{q['type']}' for question: {q['content'][:50]}...", flush=True)
|
||
else:
|
||
# Default to short answer
|
||
print(f"[Type Fix] Unknown type '{q_type}', defaulting to 'short' for question: {q['content'][:50]}...", flush=True)
|
||
q["type"] = "short"
|
||
else:
|
||
q["type"] = q_type_lower
|
||
else:
|
||
q["type"] = "short"
|
||
|
||
q["content_hash"] = calculate_content_hash(q["content"])
|
||
|
||
print(f"[Gemini PDF] Successfully extracted {len(questions)} questions", flush=True)
|
||
return questions
|
||
|
||
except Exception as e:
|
||
print(f"[Error] PDF parsing failed: {str(e)}", flush=True)
|
||
raise Exception(f"Failed to parse PDF document: {str(e)}")
|
||
|
||
async def grade_short_answer(
|
||
self,
|
||
question: str,
|
||
correct_answer: str,
|
||
user_answer: str
|
||
) -> Dict[str, Any]:
|
||
"""
|
||
Grade a short answer question using AI.
|
||
|
||
Returns:
|
||
{
|
||
"score": 0.0-1.0,
|
||
"feedback": "Detailed feedback"
|
||
}
|
||
"""
|
||
prompt = f"""Grade the following short answer question.
|
||
|
||
Question: {question}
|
||
|
||
Standard Answer: {correct_answer}
|
||
|
||
Student Answer: {user_answer}
|
||
|
||
Provide a score from 0.0 to 1.0 (where 1.0 is perfect) and detailed feedback.
|
||
|
||
Return ONLY a JSON object:
|
||
{{
|
||
"score": 0.85,
|
||
"feedback": "Your detailed feedback here"
|
||
}}
|
||
|
||
Be fair but strict. Consider:
|
||
1. Correctness of key points
|
||
2. Completeness of answer
|
||
3. Clarity of expression
|
||
|
||
Return ONLY the JSON object, no markdown or explanations."""
|
||
|
||
try:
|
||
if self.provider == "anthropic":
|
||
response = await self.client.messages.create(
|
||
model=self.model,
|
||
max_tokens=1024,
|
||
messages=[
|
||
{"role": "user", "content": prompt}
|
||
]
|
||
)
|
||
result = response.content[0].text
|
||
elif self.provider == "gemini":
|
||
# Gemini uses REST API
|
||
url = f"{self.gemini_base_url}/v1beta/models/{self.model}:generateContent"
|
||
headers = {"Content-Type": "application/json"}
|
||
params = {"key": self.gemini_api_key}
|
||
payload = {
|
||
"contents": [{
|
||
"parts": [{"text": prompt}]
|
||
}]
|
||
}
|
||
|
||
response = await self.client.post(url, headers=headers, params=params, json=payload)
|
||
response.raise_for_status()
|
||
response_data = response.json()
|
||
result = response_data["candidates"][0]["content"]["parts"][0]["text"]
|
||
else: # OpenAI or Qwen
|
||
response = await self.client.chat.completions.create(
|
||
model=self.model,
|
||
messages=[
|
||
{"role": "system", "content": "You are a fair and strict grader. Return only JSON."},
|
||
{"role": "user", "content": prompt}
|
||
],
|
||
temperature=0.5,
|
||
)
|
||
result = response.choices[0].message.content
|
||
|
||
# Clean and parse JSON
|
||
result = result.strip()
|
||
if result.startswith("```json"):
|
||
result = result[7:]
|
||
if result.startswith("```"):
|
||
result = result[3:]
|
||
if result.endswith("```"):
|
||
result = result[:-3]
|
||
result = result.strip()
|
||
|
||
grading = json.loads(result)
|
||
return {
|
||
"score": float(grading.get("score", 0.0)),
|
||
"feedback": grading.get("feedback", "")
|
||
}
|
||
|
||
except Exception as e:
|
||
print(f"Error grading answer: {e}")
|
||
# Return default grading on error
|
||
return {
|
||
"score": 0.0,
|
||
"feedback": "Unable to grade answer due to an error."
|
||
}
|
||
|
||
|
||
# Singleton instance
|
||
llm_service = LLMService()
|