587 lines
23 KiB
Python
587 lines
23 KiB
Python
"""
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LLM服务 - 代码分析核心服务
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支持中英文双语输出
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"""
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import json
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import re
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import logging
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from typing import Dict, Any, Optional
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from .types import LLMConfig, LLMProvider, LLMMessage, LLMRequest, DEFAULT_MODELS
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from .factory import LLMFactory
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from app.core.config import settings
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logger = logging.getLogger(__name__)
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class LLMService:
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"""LLM服务类"""
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def __init__(self, user_config: Optional[Dict[str, Any]] = None):
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"""
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初始化LLM服务
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Args:
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user_config: 用户配置字典，包含llmConfig字段
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"""
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self._config: Optional[LLMConfig] = None
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self._user_config = user_config or {}
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@property
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def config(self) -> LLMConfig:
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"""获取LLM配置（优先使用用户配置，然后使用系统配置）"""
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if self._config is None:
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user_llm_config = self._user_config.get('llmConfig', {})
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# 优先使用用户配置的provider，否则使用系统配置
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provider_str = user_llm_config.get('llmProvider') or getattr(settings, 'LLM_PROVIDER', 'openai')
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provider = self._parse_provider(provider_str)
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# 获取API Key - 优先级：用户配置 > 系统通用配置 > 系统平台专属配置
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api_key = (
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user_llm_config.get('llmApiKey') or
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getattr(settings, 'LLM_API_KEY', '') or
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self._get_provider_api_key_from_user_config(provider, user_llm_config) or
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self._get_provider_api_key(provider)
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)
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# 获取Base URL
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base_url = (
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user_llm_config.get('llmBaseUrl') or
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getattr(settings, 'LLM_BASE_URL', None) or
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self._get_provider_base_url(provider)
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)
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# 获取模型
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model = (
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user_llm_config.get('llmModel') or
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getattr(settings, 'LLM_MODEL', '') or
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DEFAULT_MODELS.get(provider, 'gpt-4o-mini')
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)
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# 获取超时时间（用户配置是毫秒，系统配置是秒）
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timeout_ms = user_llm_config.get('llmTimeout')
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if timeout_ms:
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# 用户配置是毫秒，转换为秒
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timeout = int(timeout_ms / 1000) if timeout_ms > 1000 else int(timeout_ms)
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else:
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# 系统配置是秒
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timeout = int(getattr(settings, 'LLM_TIMEOUT', 150))
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# 获取温度
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temperature = user_llm_config.get('llmTemperature') if user_llm_config.get('llmTemperature') is not None else float(getattr(settings, 'LLM_TEMPERATURE', 0.1))
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# 获取最大token数
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max_tokens = user_llm_config.get('llmMaxTokens') or int(getattr(settings, 'LLM_MAX_TOKENS', 4096))
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self._config = LLMConfig(
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provider=provider,
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api_key=api_key,
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model=model,
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base_url=base_url,
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timeout=timeout,
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temperature=temperature,
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max_tokens=max_tokens,
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)
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return self._config
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def _get_provider_api_key_from_user_config(self, provider: LLMProvider, user_llm_config: Dict[str, Any]) -> Optional[str]:
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"""从用户配置中获取平台专属API Key"""
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provider_key_map = {
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LLMProvider.OPENAI: 'openaiApiKey',
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LLMProvider.GEMINI: 'geminiApiKey',
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LLMProvider.CLAUDE: 'claudeApiKey',
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LLMProvider.QWEN: 'qwenApiKey',
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LLMProvider.DEEPSEEK: 'deepseekApiKey',
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LLMProvider.ZHIPU: 'zhipuApiKey',
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LLMProvider.MOONSHOT: 'moonshotApiKey',
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LLMProvider.BAIDU: 'baiduApiKey',
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LLMProvider.MINIMAX: 'minimaxApiKey',
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LLMProvider.DOUBAO: 'doubaoApiKey',
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}
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key_name = provider_key_map.get(provider)
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if key_name:
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return user_llm_config.get(key_name)
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return None
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def _get_provider_api_key(self, provider: LLMProvider) -> str:
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"""根据提供商获取API Key"""
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provider_key_map = {
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LLMProvider.OPENAI: 'OPENAI_API_KEY',
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LLMProvider.GEMINI: 'GEMINI_API_KEY',
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LLMProvider.CLAUDE: 'CLAUDE_API_KEY',
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LLMProvider.QWEN: 'QWEN_API_KEY',
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LLMProvider.DEEPSEEK: 'DEEPSEEK_API_KEY',
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LLMProvider.ZHIPU: 'ZHIPU_API_KEY',
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LLMProvider.MOONSHOT: 'MOONSHOT_API_KEY',
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LLMProvider.BAIDU: 'BAIDU_API_KEY',
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LLMProvider.MINIMAX: 'MINIMAX_API_KEY',
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LLMProvider.DOUBAO: 'DOUBAO_API_KEY',
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LLMProvider.OLLAMA: None, # Ollama 不需要 API Key
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}
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key_name = provider_key_map.get(provider)
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if key_name:
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return getattr(settings, key_name, '') or ''
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return 'ollama' # Ollama的默认值
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def _get_provider_base_url(self, provider: LLMProvider) -> Optional[str]:
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"""根据提供商获取Base URL"""
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if provider == LLMProvider.OPENAI:
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return getattr(settings, 'OPENAI_BASE_URL', None)
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elif provider == LLMProvider.OLLAMA:
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return getattr(settings, 'OLLAMA_BASE_URL', 'http://localhost:11434/v1')
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return None
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def _parse_provider(self, provider_str: str) -> LLMProvider:
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"""解析provider字符串"""
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provider_map = {
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'gemini': LLMProvider.GEMINI,
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'openai': LLMProvider.OPENAI,
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'claude': LLMProvider.CLAUDE,
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'qwen': LLMProvider.QWEN,
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'deepseek': LLMProvider.DEEPSEEK,
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'zhipu': LLMProvider.ZHIPU,
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'moonshot': LLMProvider.MOONSHOT,
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'baidu': LLMProvider.BAIDU,
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'minimax': LLMProvider.MINIMAX,
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'doubao': LLMProvider.DOUBAO,
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'ollama': LLMProvider.OLLAMA,
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}
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return provider_map.get(provider_str.lower(), LLMProvider.OPENAI)
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def _get_output_language(self) -> str:
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"""获取输出语言配置（优先使用用户配置）"""
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user_other_config = self._user_config.get('otherConfig', {})
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return user_other_config.get('outputLanguage') or getattr(settings, 'OUTPUT_LANGUAGE', 'zh-CN')
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def _build_system_prompt(self, is_chinese: bool) -> str:
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"""构建系统提示词（支持中英文）"""
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schema = """{
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"issues": [
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{
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"type": "security|bug|performance|style|maintainability",
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"severity": "critical|high|medium|low",
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"title": "string",
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"description": "string",
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"suggestion": "string",
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"line": 1,
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"column": 1,
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"code_snippet": "string",
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"ai_explanation": "string",
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"xai": {
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"what": "string",
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"why": "string",
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"how": "string",
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"learn_more": "string(optional)"
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}
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}
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],
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"quality_score": 0-100,
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"summary": {
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"total_issues": number,
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"critical_issues": number,
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"high_issues": number,
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"medium_issues": number,
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"low_issues": number
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},
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"metrics": {
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"complexity": 0-100,
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"maintainability": 0-100,
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"security": 0-100,
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"performance": 0-100
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}
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}"""
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if is_chinese:
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return f"""⚠️⚠️⚠️ 只输出JSON，禁止输出其他任何格式！禁止markdown！禁止文本分析！⚠️⚠️⚠️
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你是一个专业的代码审计助手。你的任务是分析代码并返回严格符合JSON Schema的结果。
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【最重要】输出格式要求：
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1. 必须只输出纯JSON对象，从{{开始，到}}结束
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2. 禁止在JSON前后添加任何文字、说明、markdown标记
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3. 禁止输出```json或###等markdown语法
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4. 如果是文档文件（如README），也必须以JSON格式输出分析结果
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【内容要求】：
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1. 所有文本内容必须统一使用简体中文
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2. JSON字符串值中的特殊字符必须正确转义（换行用\\n，双引号用\\"，反斜杠用\\\\）
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3. code_snippet字段必须使用\\n表示换行
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请从以下维度全面分析代码：
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- 编码规范和代码风格
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- 潜在的 Bug 和逻辑错误
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- 性能问题和优化建议
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- 安全漏洞和风险
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- 可维护性和可读性
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- 最佳实践和设计模式
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输出格式必须严格符合以下 JSON Schema：
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{schema}
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注意：
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- title: 问题的简短标题（中文）
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- description: 详细描述问题（中文）
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- suggestion: 具体的修复建议（中文）
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- line: 问题所在的行号（从1开始计数，必须准确对应代码中的行号）
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- column: 问题所在的列号（从1开始计数，指向问题代码的起始位置）
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- code_snippet: 包含问题的代码片段（建议包含问题行及其前后1-2行作为上下文，保持原始缩进格式）
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- ai_explanation: AI 的深入解释（中文）
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- xai.what: 这是什么问题（中文）
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- xai.why: 为什么会有这个问题（中文）
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- xai.how: 如何修复这个问题（中文）
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【重要】关于行号和代码片段：
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1. line 必须是问题代码的行号！！！代码左侧有"行号|"标注，例如"25| const x = 1"表示第25行，line字段必须填25
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2. column 是问题代码在该行中的起始列位置（从1开始，不包括"行号|"前缀部分）
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3. code_snippet 应该包含问题代码及其上下文（前后各1-2行），去掉"行号|"前缀，保持原始代码的缩进
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4. 如果代码片段包含多行，必须使用 \\n 表示换行符（这是JSON的要求）
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5. 如果无法确定准确的行号，不要填写line和column字段（不要填0）
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【严格禁止】：
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- 禁止在任何字段中使用英文，所有内容必须是简体中文
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- 禁止在JSON字符串值中使用真实换行符，必须用\\n转义
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- 禁止输出markdown代码块标记（如```json）
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⚠️ 重要提醒：line字段必须从代码左侧的行号标注中读取，不要猜测或填0！"""
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else:
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return f"""⚠️⚠️⚠️ OUTPUT JSON ONLY! NO OTHER FORMAT! NO MARKDOWN! NO TEXT ANALYSIS! ⚠️⚠️⚠️
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You are a professional code auditing assistant. Your task is to analyze code and return results in strict JSON Schema format.
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【MOST IMPORTANT】Output format requirements:
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1. MUST output pure JSON object only, starting with {{ and ending with }}
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2. NO text, explanation, or markdown markers before or after JSON
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3. NO ```json or ### markdown syntax
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4. Even for document files (like README), output analysis in JSON format
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【Content requirements】:
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1. All text content MUST be in English ONLY
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2. Special characters in JSON strings must be properly escaped (\\n for newlines, \\" for quotes, \\\\ for backslashes)
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3. code_snippet field MUST use \\n for newlines
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Please comprehensively analyze the code from the following dimensions:
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- Coding standards and code style
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- Potential bugs and logical errors
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- Performance issues and optimization suggestions
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- Security vulnerabilities and risks
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- Maintainability and readability
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- Best practices and design patterns
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The output format MUST strictly conform to the following JSON Schema:
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{schema}
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Note:
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- title: Brief title of the issue (in English)
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- description: Detailed description of the issue (in English)
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- suggestion: Specific fix suggestions (in English)
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- line: Line number where the issue occurs (1-indexed, must accurately correspond to the line in the code)
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- column: Column number where the issue starts (1-indexed, pointing to the start position of the problematic code)
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- code_snippet: Code snippet containing the issue (should include the problem line plus 1-2 lines before and after for context, preserve original indentation)
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- ai_explanation: AI's in-depth explanation (in English)
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- xai.what: What is this issue (in English)
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- xai.why: Why does this issue exist (in English)
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- xai.how: How to fix this issue (in English)
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【IMPORTANT】About line numbers and code snippets:
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1. 'line' MUST be the line number from code!!! Code has "lineNumber|" prefix, e.g. "25| const x = 1" means line 25, you MUST set line to 25
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2. 'column' is the starting column position in that line (1-indexed, excluding the "lineNumber|" prefix)
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3. 'code_snippet' should include the problematic code with context (1-2 lines before/after), remove "lineNumber|" prefix, preserve indentation
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4. If code snippet has multiple lines, use \\n for newlines (JSON requirement)
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5. If you cannot determine the exact line number, do NOT fill line and column fields (don't use 0)
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【STRICTLY PROHIBITED】:
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- NO Chinese characters in any field - English ONLY
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- NO real newline characters in JSON string values - must use \\n
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- NO markdown code block markers (like ```json)
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⚠️ CRITICAL: Read line numbers from the "lineNumber|" prefix on the left of each code line. Do NOT guess or use 0!"""
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async def analyze_code(self, code: str, language: str) -> Dict[str, Any]:
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"""
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分析代码并返回结构化问题
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支持中英文输出
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"""
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# 获取输出语言配置
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output_language = self._get_output_language()
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is_chinese = output_language == 'zh-CN'
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# 添加行号帮助LLM定位问题
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code_with_lines = '\n'.join(
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f"{i+1}| {line}" for i, line in enumerate(code.split('\n'))
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)
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# 构建系统提示词
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system_prompt = self._build_system_prompt(is_chinese)
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# 构建用户提示词
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if is_chinese:
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user_prompt = f"""编程语言: {language}
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⚠️ 代码已标注行号（格式：行号| 代码内容），请根据行号准确填写 line 字段！
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请分析以下代码:
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{code_with_lines}"""
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else:
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user_prompt = f"""Programming Language: {language}
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⚠️ Code is annotated with line numbers (format: lineNumber| code), please fill the 'line' field accurately based on these numbers!
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Please analyze the following code:
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{code_with_lines}"""
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try:
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adapter = LLMFactory.create_adapter(self.config)
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request = LLMRequest(
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messages=[
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LLMMessage(role="system", content=system_prompt),
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LLMMessage(role="user", content=user_prompt)
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],
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temperature=0.1,
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)
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response = await adapter.complete(request)
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content = response.content
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# 检查响应内容是否为空
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if not content or not content.strip():
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logger.warning(f"LLM返回空响应 - Provider: {self.config.provider.value}, Model: {self.config.model}")
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logger.warning(f"响应详情 - Finish Reason: {response.finish_reason}, Usage: {response.usage}")
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return self._get_default_response()
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# 尝试从响应中提取JSON
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result = self._parse_json(content)
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return result
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except Exception as e:
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logger.error(f"LLM Analysis failed: {e}", exc_info=True)
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logger.error(f"Provider: {self.config.provider.value}, Model: {self.config.model}")
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return self._get_default_response()
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def _parse_json(self, text: str) -> Dict[str, Any]:
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"""从LLM响应中解析JSON（增强版）"""
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# 检查输入是否为空
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if not text or not text.strip():
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logger.warning("LLM响应内容为空，无法解析JSON")
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return self._get_default_response()
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def clean_text(s: str) -> str:
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"""清理文本中的控制字符"""
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# 移除BOM和零宽字符
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s = s.replace('\ufeff', '').replace('\u200b', '').replace('\u200c', '').replace('\u200d', '')
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return s
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def fix_json_format(s: str) -> str:
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"""修复常见的JSON格式问题"""
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s = s.strip()
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# 移除尾部逗号
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s = re.sub(r',(\s*[}\]])', r'\1', s)
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# 修复未转义的换行符（在字符串值中）
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s = re.sub(r':\s*"([^"]*)\n([^"]*)"', r': "\1\\n\2"', s)
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return s
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def aggressive_fix_json(s: str) -> str:
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"""激进的JSON修复：尝试修复更多格式问题"""
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s = clean_text(s)
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s = s.strip()
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# 找到第一个 { 和最后一个 }
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start_idx = s.find('{')
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if start_idx == -1:
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raise ValueError("No JSON object found")
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# 尝试找到最后一个 }
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last_brace = s.rfind('}')
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if last_brace > start_idx:
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s = s[start_idx:last_brace + 1]
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# 修复常见的JSON问题
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# 1. 移除尾部逗号
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s = re.sub(r',(\s*[}\]])', r'\1', s)
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# 2. 修复单引号为双引号（仅在键名中，小心处理）
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s = re.sub(r"'(\w+)'\s*:", r'"\1":', s)
|
||
# 3. 修复未转义的控制字符（在字符串值中，但不在键名中）
|
||
# 只移除不在引号内的控制字符，或未转义的换行符/制表符
|
||
lines = []
|
||
in_string = False
|
||
escape_next = False
|
||
for char in s:
|
||
if escape_next:
|
||
escape_next = False
|
||
lines.append(char)
|
||
continue
|
||
if char == '\\':
|
||
escape_next = True
|
||
lines.append(char)
|
||
continue
|
||
if char == '"':
|
||
in_string = not in_string
|
||
lines.append(char)
|
||
continue
|
||
# 如果在字符串外，移除控制字符；如果在字符串内，保留（假设已转义）
|
||
if not in_string and ord(char) < 32 and char not in ['\n', '\t', '\r']:
|
||
continue # 跳过控制字符
|
||
lines.append(char)
|
||
s = ''.join(lines)
|
||
|
||
return s
|
||
|
||
# 尝试多种方式解析
|
||
attempts = [
|
||
# 1. 直接解析
|
||
lambda: json.loads(text),
|
||
# 2. 清理后解析
|
||
lambda: json.loads(fix_json_format(clean_text(text))),
|
||
# 3. 从markdown代码块提取
|
||
lambda: self._extract_from_markdown(text),
|
||
# 4. 智能提取JSON对象
|
||
lambda: self._extract_json_object(clean_text(text)),
|
||
# 5. 修复截断的JSON
|
||
lambda: self._fix_truncated_json(clean_text(text)),
|
||
# 6. 激进修复后解析
|
||
lambda: json.loads(aggressive_fix_json(text)),
|
||
]
|
||
|
||
last_error = None
|
||
for i, attempt in enumerate(attempts):
|
||
try:
|
||
result = attempt()
|
||
if result and isinstance(result, dict):
|
||
if i > 0:
|
||
logger.info(f"✅ JSON解析成功（方法 {i + 1}/{len(attempts)}）")
|
||
return result
|
||
except Exception as e:
|
||
last_error = e
|
||
if i == 0:
|
||
logger.debug(f"直接解析失败，尝试其他方法... {e}")
|
||
|
||
# 所有尝试都失败
|
||
logger.warning("⚠️ 无法解析LLM响应为JSON")
|
||
logger.warning(f"原始内容长度: {len(text)} 字符")
|
||
logger.warning(f"原始内容（前500字符）: {text[:500]}")
|
||
logger.warning(f"原始内容（后500字符）: {text[-500:] if len(text) > 500 else text}")
|
||
if last_error:
|
||
logger.warning(f"最后错误: {type(last_error).__name__}: {str(last_error)}")
|
||
return self._get_default_response()
|
||
|
||
def _extract_from_markdown(self, text: str) -> Dict[str, Any]:
|
||
"""从markdown代码块提取JSON"""
|
||
match = re.search(r'```(?:json)?\s*(\{[\s\S]*?\})\s*```', text)
|
||
if match:
|
||
return json.loads(match.group(1))
|
||
raise ValueError("No markdown code block found")
|
||
|
||
def _extract_json_object(self, text: str) -> Dict[str, Any]:
|
||
"""智能提取JSON对象"""
|
||
start_idx = text.find('{')
|
||
if start_idx == -1:
|
||
raise ValueError("No JSON object found")
|
||
|
||
# 考虑字符串内的花括号和转义字符
|
||
brace_count = 0
|
||
bracket_count = 0
|
||
in_string = False
|
||
escape_next = False
|
||
end_idx = -1
|
||
|
||
for i in range(start_idx, len(text)):
|
||
char = text[i]
|
||
|
||
if escape_next:
|
||
escape_next = False
|
||
continue
|
||
|
||
if char == '\\':
|
||
escape_next = True
|
||
continue
|
||
|
||
if char == '"' and not escape_next:
|
||
in_string = not in_string
|
||
continue
|
||
|
||
if not in_string:
|
||
if char == '{':
|
||
brace_count += 1
|
||
elif char == '}':
|
||
brace_count -= 1
|
||
if brace_count == 0 and bracket_count == 0:
|
||
end_idx = i + 1
|
||
break
|
||
elif char == '[':
|
||
bracket_count += 1
|
||
elif char == ']':
|
||
bracket_count -= 1
|
||
|
||
if end_idx == -1:
|
||
# 如果找不到完整的JSON，尝试使用最后一个 }
|
||
last_brace = text.rfind('}')
|
||
if last_brace > start_idx:
|
||
end_idx = last_brace + 1
|
||
else:
|
||
raise ValueError("Incomplete JSON object")
|
||
|
||
json_str = text[start_idx:end_idx]
|
||
# 修复格式问题
|
||
json_str = re.sub(r',(\s*[}\]])', r'\1', json_str)
|
||
# 尝试修复未闭合的括号
|
||
open_braces = json_str.count('{') - json_str.count('}')
|
||
open_brackets = json_str.count('[') - json_str.count(']')
|
||
if open_braces > 0:
|
||
json_str += '}' * open_braces
|
||
if open_brackets > 0:
|
||
json_str += ']' * open_brackets
|
||
|
||
return json.loads(json_str)
|
||
|
||
def _fix_truncated_json(self, text: str) -> Dict[str, Any]:
|
||
"""修复截断的JSON"""
|
||
start_idx = text.find('{')
|
||
if start_idx == -1:
|
||
raise ValueError("Cannot fix truncated JSON")
|
||
|
||
json_str = text[start_idx:]
|
||
|
||
# 计算缺失的闭合符号
|
||
open_braces = json_str.count('{')
|
||
close_braces = json_str.count('}')
|
||
open_brackets = json_str.count('[')
|
||
close_brackets = json_str.count(']')
|
||
|
||
# 补全缺失的闭合符号
|
||
json_str += ']' * max(0, open_brackets - close_brackets)
|
||
json_str += '}' * max(0, open_braces - close_braces)
|
||
|
||
# 修复格式
|
||
json_str = re.sub(r',(\s*[}\]])', r'\1', json_str)
|
||
return json.loads(json_str)
|
||
|
||
def _get_default_response(self) -> Dict[str, Any]:
|
||
"""返回默认响应"""
|
||
return {
|
||
"issues": [],
|
||
"quality_score": 80,
|
||
"summary": {
|
||
"total_issues": 0,
|
||
"critical_issues": 0,
|
||
"high_issues": 0,
|
||
"medium_issues": 0,
|
||
"low_issues": 0
|
||
},
|
||
"metrics": {
|
||
"complexity": 80,
|
||
"maintainability": 80,
|
||
"security": 80,
|
||
"performance": 80
|
||
}
|
||
}
|
||
|
||
|
||
# 全局服务实例
|
||
llm_service = LLMService()
|