725 lines
28 KiB
Python
725 lines
28 KiB
Python
"""
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代码Prompt管理模块
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用于生成和管理代码相关问答的专属Prompt模板
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支持基于代码意图分类的动态Prompt选择和生成
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"""
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import json
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import re
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import sys
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import os
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from typing import Dict, Any, Optional, List, Tuple
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from enum import Enum
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from loguru import logger
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# 添加项目根目录到 Python 模块搜索路径
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current_dir = os.path.dirname(os.path.abspath(__file__))
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project_root = os.path.dirname(current_dir)
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if project_root not in sys.path:
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sys.path.insert(0, project_root)
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from utils.query_processor import CodeIntentCategory, PromptTemplateType
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from utils.prompt import (
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CODE_EXPLANATION_TEMPLATE,
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CODE_DEBUGGING_TEMPLATE,
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CODE_GENERATION_TEMPLATE,
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ALGORITHM_EXPLANATION_TEMPLATE,
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CODE_OPTIMIZATION_TEMPLATE,
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GENERAL_QA_TEMPLATE,
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)
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class CodePromptManager:
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"""代码Prompt管理类"""
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def __init__(self):
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"""
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初始化代码Prompt管理器
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"""
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self._templates = self._load_templates()
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self._context_cache: Dict[str, List[Dict[str, str]]] = {}
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logger.info("代码Prompt管理器初始化完成")
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def _load_templates(self) -> Dict[PromptTemplateType, str]:
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"""
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加载Prompt模板
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Returns:
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Dict[PromptTemplateType, str]: Prompt模板字典
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"""
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return {
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PromptTemplateType.CODE_EXPLANATION: CODE_EXPLANATION_TEMPLATE,
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PromptTemplateType.CODE_DEBUGGING: CODE_DEBUGGING_TEMPLATE,
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PromptTemplateType.CODE_GENERATION: CODE_GENERATION_TEMPLATE,
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PromptTemplateType.ALGORITHM_EXPLANATION: ALGORITHM_EXPLANATION_TEMPLATE,
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PromptTemplateType.CODE_OPTIMIZATION: CODE_OPTIMIZATION_TEMPLATE,
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PromptTemplateType.GENERAL_QA: GENERAL_QA_TEMPLATE
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}
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def _map_intent_to_prompt_type(self, intent_category: str) -> PromptTemplateType:
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"""
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将代码意图分类映射到Prompt类型
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Args:
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intent_category: 代码意图分类（字符串）
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Returns:
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PromptTemplateType: 对应的Prompt类型
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"""
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mapping = {
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# 代码解释与逻辑类 -> CODE_EXPLANATION
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"logic_explanation": PromptTemplateType.CODE_EXPLANATION,
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"entity_introduction": PromptTemplateType.CODE_EXPLANATION,
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"code_structure": PromptTemplateType.CODE_EXPLANATION,
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# 代码生成与实现类 -> CODE_GENERATION
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"code_generation": PromptTemplateType.CODE_GENERATION,
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"boilerplate_implementation": PromptTemplateType.CODE_GENERATION,
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# 调试、优化与理论类
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"error_debugging": PromptTemplateType.CODE_DEBUGGING,
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"code_optimization": PromptTemplateType.CODE_OPTIMIZATION,
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"algorithm_theory": PromptTemplateType.ALGORITHM_EXPLANATION,
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# 非代码问题 -> GENERAL_QA
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"general_technical": PromptTemplateType.GENERAL_QA,
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"non_technical": PromptTemplateType.GENERAL_QA,
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"unknown": PromptTemplateType.GENERAL_QA
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}
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return mapping.get(intent_category, PromptTemplateType.GENERAL_QA)
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def _build_conversation_history(self, history: Optional[Any]) -> str:
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"""
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构建对话历史字符串
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Args:
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history: 对话历史，可以是字符串或字典列表
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Returns:
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str: 格式化的对话历史
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"""
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if not history:
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return "无"
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# 如果是字符串，直接返回
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if isinstance(history, str):
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return history
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# 如果是字典列表，格式化为字符串
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if isinstance(history, list):
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history_str = []
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for item in history:
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if isinstance(item, dict):
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role = item.get('role', 'user')
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content = item.get('content', '')
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if role == 'user':
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history_str.append(f"用户: {content}")
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else:
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history_str.append(f"助手: {content}")
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return "\n".join(history_str)
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# 其他类型，转换为字符串
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return str(history)
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def _extract_code_from_context(self, code_context: str) -> str:
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"""
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从上下文中提取代码
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Args:
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code_context: 代码上下文
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Returns:
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str: 提取的代码
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"""
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if not code_context:
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return "无"
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# 尝试提取代码块
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code_blocks = re.findall(r'```[\w]*\n[\s\S]*?```', code_context)
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if code_blocks:
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# 提取所有代码块并合并
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extracted_code = []
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for block in code_blocks:
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# 提取语言标记
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lang_match = re.match(r'```([\w]*)\n', block)
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language = lang_match.group(1) if lang_match else ""
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# 去除代码块标记
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code = re.sub(r'```[\w]*\n|```', '', block)
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code = code.strip()
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if code:
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if language:
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extracted_code.append(f"语言: {language}\n{code}")
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else:
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extracted_code.append(code)
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return "\n\n".join(extracted_code)
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# 如果没有代码块标记，尝试提取看起来像代码的部分
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# 查找连续的多行代码（以缩进或常见代码关键字开头）
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lines = code_context.split('\n')
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code_lines = []
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in_code = False
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for line in lines:
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# 检查是否是代码行
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line_stripped = line.strip()
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if (line_stripped and
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(line.startswith(' ') or line.startswith('\t') or # 缩进
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line_stripped.startswith('def ') or line_stripped.startswith('class ') or # Python关键字
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line_stripped.startswith('import ') or line_stripped.startswith('from ') or # 导入
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line_stripped.startswith('if ') or line_stripped.startswith('for ') or # 控制流
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line_stripped.startswith('while ') or line_stripped.startswith('try ') or
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line_stripped.startswith('except ') or line_stripped.startswith('finally ') or
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line_stripped.startswith('return ') or line_stripped.startswith('print(') or
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line_stripped.startswith('// ') or line_stripped.startswith('# ') or # 注释
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line_stripped.endswith(';') or # 分号结尾（如Java、C++等）
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line_stripped.startswith('{') or line_stripped.startswith('}') or # 大括号
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re.match(r'^[\w_]+\s*=\s*', line_stripped) or # 变量赋值
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re.match(r'^[\w_]+\s*\(.*\)\s*\{{?', line_stripped))): # 函数定义
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code_lines.append(line)
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in_code = True
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elif in_code and line.strip() == '':
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# 保留代码中的空行
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code_lines.append(line)
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elif in_code and len(code_lines) > 3:
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# 如果已经收集了多行代码，并且遇到非代码行，停止收集
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break
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else:
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# 非代码行，重置
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code_lines = []
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in_code = False
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if len(code_lines) > 3:
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return "\n".join(code_lines)
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return code_context
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def _format_code_block(self, code: str, language: str = "") -> str:
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"""
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格式化代码块，提高显示质量
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Args:
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code: 代码内容
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language: 代码语言
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Returns:
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str: 格式化的代码块
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"""
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if not code:
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return ""
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# 添加语言标记
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lang_tag = language if language else ""
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# 确保代码块格式正确
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formatted_code = f"```{lang_tag}\n{code}\n```"
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return formatted_code
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def _build_enhanced_context(self, retrieved_results: List[Dict[str, Any]], intent_result: Optional[Dict[str, Any]]) -> str:
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"""
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根据意图和检索结果构建增强的上下文
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Args:
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retrieved_results: 检索结果列表，每个元素包含 id、text 和 metadata
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intent_result: 意图识别结果
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Returns:
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str: 增强的上下文
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"""
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if not retrieved_results:
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return "未找到相关参考信息"
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context_parts = []
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intent = intent_result.get('intent', '') if intent_result else ''
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for result in retrieved_results:
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metadata = result.get('metadata', {})
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text = result.get('text', '')
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result_id = result.get('id', 1)
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# 提取所有 metadata 字段
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func_id = metadata.get('func_id', '')
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func_name = metadata.get('func_name', '')
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class_name = metadata.get('class_name', 'None')
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file_path = metadata.get('file_path', '')
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lang = metadata.get('lang', '')
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params = metadata.get('params', 0)
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return_type = metadata.get('return_type', 'None')
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docstring = metadata.get('docstring', '')
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start_line = metadata.get('start_line', '')
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end_line = metadata.get('end_line', '')
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repo_id = metadata.get('repo_id', '')
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branch = metadata.get('branch', '')
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func_body = metadata.get('func_body', '')
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# 根据意图构建不同的上下文
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if intent == "code_understanding":
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# 代码理解意图，强调语言、函数名、类名、参数、返回类型和函数体
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context_part = f"【参考信息{result_id}】这是由{lang}实现的函数{func_name}"
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if class_name and class_name != "None":
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context_part += f"，属于{class_name}类"
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context_part += f"，它接收{params}个参数，返回类型为{return_type}"
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if docstring:
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context_part += f"。函数说明：{docstring}"
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context_part += f"\n文件路径：{file_path}，位置：{start_line}-{end_line}\n"
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context_part += f"具体实现：\n{func_body}\n"
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context_part += f"仓库：{repo_id}，分支：{branch}\n"
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context_part += f"原始文本：\n{text}"
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elif intent == "code_modification":
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# 代码修改意图，强调文件路径、位置和函数体
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context_part = f"【参考信息{result_id}】需要修改的代码位于文件：{file_path}，位置：{start_line}-{end_line}"
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context_part += f"\n函数名：{func_name}"
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if class_name and class_name != "None":
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context_part += f"（{class_name}类）"
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context_part += f"，由{lang}实现\n"
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context_part += f"函数签名：接收{params}个参数，返回类型为{return_type}\n"
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if docstring:
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context_part += f"函数说明：{docstring}\n"
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context_part += f"具体实现：\n{func_body}\n"
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context_part += f"仓库：{repo_id}，分支：{branch}\n"
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context_part += f"原始文本：\n{text}"
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elif intent == "functionality_question":
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# 功能询问意图，强调函数名、文档、参数和返回类型
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context_part = f"【参考信息{result_id}】函数{func_name}"
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if class_name and class_name != "None":
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context_part += f"（{class_name}类）"
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context_part += f"的功能说明：\n{docstring}\n"
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context_part += f"由{lang}实现，接收{params}个参数，返回类型为{return_type}\n"
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context_part += f"文件路径：{file_path}，位置：{start_line}-{end_line}\n"
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context_part += f"具体实现：\n{func_body}\n"
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context_part += f"仓库：{repo_id}，分支：{branch}\n"
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context_part += f"原始文本：\n{text}"
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else:
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# 其他意图，综合所有信息
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context_part = f"【参考信息{result_id}】（来源：{file_path}）"
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context_part += f"\n函数：{func_name}"
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if class_name and class_name != "None":
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context_part += f"（{class_name}类）"
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context_part += f"，语言：{lang}\n"
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context_part += f"参数：{params}个，返回类型：{return_type}\n"
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if docstring:
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context_part += f"说明：{docstring}\n"
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context_part += f"位置：{start_line}-{end_line}\n"
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context_part += f"仓库：{repo_id}，分支：{branch}\n"
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context_part += f"实现：\n{func_body}\n"
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context_part += f"原始文本：\n{text}"
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context_parts.append(context_part)
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return "\n\n".join(context_parts)
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def generate_prompt(
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self,
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user_query: str,
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intent_category: CodeIntentCategory,
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code_context: Optional[str] = None,
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conversation_history: Optional[List[Dict[str, str]]] = None,
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error_message: Optional[str] = None,
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target_language: Optional[str] = None,
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user_requirement: Optional[str] = None
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) -> str:
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"""
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生成代码专用Prompt
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Args:
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user_query: 用户问题
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intent_category: 代码意图分类
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code_context: 代码上下文
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conversation_history: 对话历史
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error_message: 错误信息（仅Bug修复场景）
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target_language: 目标编程语言（仅代码生成场景）
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user_requirement: 用户需求（仅代码生成场景）
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Returns:
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str: 生成的Prompt
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"""
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try:
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logger.info(f"生成代码Prompt，意图分类: {intent_category}")
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# 映射意图到Prompt类型
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prompt_type = self._map_intent_to_prompt_type(intent_category)
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logger.info(f"选择Prompt类型: {prompt_type}")
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# 获取对应模板
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template = self._templates.get(prompt_type)
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if not template:
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logger.warning(f"未找到对应Prompt模板: {prompt_type}")
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template = self._templates[PromptTemplateType.GENERAL_QA]
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# 准备参数
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params = {
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"user_query": user_query,
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"code_context": code_context or "无",
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"conversation_history": self._build_conversation_history(conversation_history),
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"error_message": error_message or "无",
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"target_language": target_language or "根据上下文判断",
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"user_requirement": user_requirement or user_query,
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"algorithm_code": self._extract_code_from_context(code_context) if code_context else "无"
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}
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# 填充模板
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prompt = template
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for key, value in params.items():
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placeholder = f"{{{key}}}"
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prompt = prompt.replace(placeholder, value)
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logger.info(f"Prompt生成完成，长度: {len(prompt)}字符")
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return prompt
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except Exception as e:
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logger.error(f"生成Prompt失败: {e}")
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# 返回通用模板
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return self._templates[PromptTemplateType.GENERAL_QA].format(
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user_query=user_query,
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code_context=code_context or "无",
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conversation_history=self._build_conversation_history(conversation_history),
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error_message="无",
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target_language="根据上下文判断",
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user_requirement=user_query,
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algorithm_code="无"
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)
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def generate_dynamic_prompt(
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self,
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user_query: str,
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intent_result: Optional[Dict[str, Any]] = None,
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code_context: Optional[str] = None,
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conversation_history: Optional[List[Dict[str, str]]] = None,
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**kwargs
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) -> str:
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"""
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生成动态Prompt（基于意图识别结果）
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Args:
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user_query: 用户问题
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intent_result: 意图识别结果
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code_context: 代码上下文
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conversation_history: 对话历史
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**kwargs: 其他参数
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Returns:
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str: 生成的动态Prompt
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"""
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try:
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if intent_result:
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# 从意图结果中提取分类
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category_str = intent_result.get('category', 'unknown')
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# 直接使用category_str，因为CodeIntentCategory是一个普通的类，不是枚举类型
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intent_category = category_str
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else:
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# 默认使用通用分类
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intent_category = CodeIntentCategory.UNKNOWN
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# 提取其他参数
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error_message = kwargs.get('error_message')
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target_language = kwargs.get('target_language')
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user_requirement = kwargs.get('user_requirement')
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retrieved_results = kwargs.get('retrieved_results', [])
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# 根据意图和 retrieved_results 构建增强的上下文
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enhanced_context = code_context
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if retrieved_results:
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enhanced_context = self._build_enhanced_context(retrieved_results, intent_result)
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# 生成Prompt
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return self.generate_prompt(
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user_query=user_query,
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intent_category=intent_category,
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code_context=enhanced_context,
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conversation_history=conversation_history,
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error_message=error_message,
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target_language=target_language,
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user_requirement=user_requirement
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)
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except Exception as e:
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logger.error(f"生成动态Prompt失败: {e}")
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# 返回通用Prompt
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return self._templates[PromptTemplateType.GENERAL_QA].format(
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user_query=user_query,
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code_context=code_context or "无",
|
||
conversation_history=self._build_conversation_history(conversation_history),
|
||
error_message="无",
|
||
target_language="根据上下文判断",
|
||
user_requirement=user_query,
|
||
algorithm_code="无"
|
||
)
|
||
|
||
def optimize_prompt(
|
||
self,
|
||
prompt: str,
|
||
max_length: int = 4000,
|
||
preserve_structure: bool = True
|
||
) -> str:
|
||
"""
|
||
优化Prompt长度
|
||
|
||
Args:
|
||
prompt: 原始Prompt
|
||
max_length: 最大长度
|
||
preserve_structure: 是否保留结构
|
||
|
||
Returns:
|
||
str: 优化后的Prompt
|
||
"""
|
||
if len(prompt) <= max_length:
|
||
return prompt
|
||
|
||
logger.warning(f"Prompt过长 ({len(prompt)} > {max_length})，需要优化")
|
||
|
||
if preserve_structure:
|
||
# 保留结构，只优化内容部分
|
||
# 1. 保留角色设定和核心指令
|
||
# 2. 精简分析要求
|
||
# 3. 缩短代码上下文
|
||
|
||
# 提取角色设定和核心指令
|
||
role_match = re.search(r'# 角色设定[\s\S]*?# 核心指令[\s\S]*?\n', prompt)
|
||
if role_match:
|
||
role_section = role_match.group(0)
|
||
else:
|
||
role_section = ""
|
||
|
||
# 提取分析要求
|
||
req_match = re.search(r'# 分析要求[\s\S]*?(?=# |$)', prompt)
|
||
if req_match:
|
||
req_section = req_match.group(0)
|
||
# 精简分析要求
|
||
req_lines = req_section.split('\n')
|
||
# 只保留前3条要求
|
||
req_section = '\n'.join(req_lines[:4]) # 保留标题和前3条
|
||
else:
|
||
req_section = ""
|
||
|
||
# 提取其他部分
|
||
rest_match = re.search(r'# (代码上下文|错误信息|对话历史|用户问题|输出格式)[\s\S]*$', prompt)
|
||
if rest_match:
|
||
rest_section = rest_match.group(0)
|
||
# 缩短代码上下文
|
||
if '# 代码上下文' in rest_section:
|
||
code_match = re.search(r'# 代码上下文[\s\S]*?(?=# |$)', rest_section)
|
||
if code_match:
|
||
code_section = code_match.group(0)
|
||
# 只保留前500个字符
|
||
if len(code_section) > 600:
|
||
code_lines = code_section.split('\n')
|
||
if len(code_lines) > 3:
|
||
# 保留标题和前几行
|
||
code_section = '\n'.join(code_lines[:2]) + '\n...\n(代码已截断)'
|
||
rest_section = rest_section.replace(code_match.group(0), code_section)
|
||
else:
|
||
rest_section = ""
|
||
|
||
optimized = role_section + '\n' + req_section + '\n' + rest_section
|
||
|
||
if len(optimized) > max_length:
|
||
# 进一步缩短
|
||
optimized = optimized[:max_length - 3] + '...'
|
||
|
||
else:
|
||
# 直接截断
|
||
optimized = prompt[:max_length - 3] + '...'
|
||
|
||
logger.info(f"Prompt优化完成，长度: {len(optimized)}字符")
|
||
return optimized
|
||
|
||
def save_prompt_template(
|
||
self,
|
||
template_type: PromptTemplateType,
|
||
template_content: str,
|
||
description: Optional[str] = None
|
||
) -> bool:
|
||
"""
|
||
保存自定义Prompt模板
|
||
|
||
Args:
|
||
template_type: Prompt类型
|
||
template_content: 模板内容
|
||
description: 模板描述
|
||
|
||
Returns:
|
||
bool: 保存是否成功
|
||
"""
|
||
try:
|
||
# 这里可以扩展为持久化存储
|
||
# 目前只是在内存中更新
|
||
self._templates[template_type] = template_content
|
||
logger.info(f"保存Prompt模板成功: {template_type.value}")
|
||
return True
|
||
except Exception as e:
|
||
logger.error(f"保存Prompt模板失败: {e}")
|
||
return False
|
||
|
||
def get_prompt_template(self, template_type: PromptTemplateType) -> Optional[str]:
|
||
"""
|
||
获取Prompt模板
|
||
|
||
Args:
|
||
template_type: Prompt类型
|
||
|
||
Returns:
|
||
Optional[str]: 模板内容
|
||
"""
|
||
return self._templates.get(template_type)
|
||
|
||
def list_available_templates(self) -> List[Dict[str, Any]]:
|
||
"""
|
||
列出可用的Prompt模板
|
||
|
||
Returns:
|
||
List[Dict[str, Any]]: 模板列表
|
||
"""
|
||
templates = []
|
||
for template_type, content in self._templates.items():
|
||
templates.append({
|
||
"type": template_type.value,
|
||
"name": template_type.name,
|
||
"length": len(content),
|
||
"sample": content[:100] + "..." if len(content) > 100 else content
|
||
})
|
||
return templates
|
||
|
||
|
||
# 全局Prompt管理器实例
|
||
_prompt_manager = None
|
||
|
||
def get_prompt_manager() -> CodePromptManager:
|
||
"""
|
||
获取全局Prompt管理器实例
|
||
|
||
Returns:
|
||
CodePromptManager: Prompt管理器实例
|
||
"""
|
||
global _prompt_manager
|
||
if _prompt_manager is None:
|
||
_prompt_manager = CodePromptManager()
|
||
return _prompt_manager
|
||
|
||
|
||
def generate_code_prompt(
|
||
user_query: str,
|
||
intent_category: CodeIntentCategory,
|
||
code_context: Optional[str] = None,
|
||
conversation_history: Optional[List[Dict[str, str]]] = None,
|
||
**kwargs
|
||
) -> str:
|
||
"""
|
||
生成代码专用Prompt
|
||
|
||
Args:
|
||
user_query: 用户问题
|
||
intent_category: 代码意图分类
|
||
code_context: 代码上下文
|
||
conversation_history: 对话历史
|
||
**kwargs: 其他参数
|
||
|
||
Returns:
|
||
str: 生成的Prompt
|
||
"""
|
||
manager = get_prompt_manager()
|
||
return manager.generate_prompt(
|
||
user_query=user_query,
|
||
intent_category=intent_category,
|
||
code_context=code_context,
|
||
conversation_history=conversation_history,
|
||
**kwargs
|
||
)
|
||
|
||
|
||
def generate_dynamic_code_prompt(
|
||
user_query: str,
|
||
intent_result: Optional[Dict[str, Any]] = None,
|
||
code_context: Optional[str] = None,
|
||
conversation_history: Optional[List[Dict[str, str]]] = None,
|
||
**kwargs
|
||
) -> str:
|
||
"""
|
||
生成动态代码Prompt
|
||
|
||
Args:
|
||
user_query: 用户问题
|
||
intent_result: 意图识别结果
|
||
code_context: 代码上下文
|
||
conversation_history: 对话历史
|
||
**kwargs: 其他参数
|
||
|
||
Returns:
|
||
str: 生成的动态Prompt
|
||
"""
|
||
manager = get_prompt_manager()
|
||
return manager.generate_dynamic_prompt(
|
||
user_query=user_query,
|
||
intent_result=intent_result,
|
||
code_context=code_context,
|
||
conversation_history=conversation_history,
|
||
**kwargs
|
||
)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
"""测试代码"""
|
||
import asyncio
|
||
from utils.code_intent import CodeIntentDetector
|
||
|
||
async def test_prompt_generation():
|
||
"""测试Prompt生成"""
|
||
print("=" * 80)
|
||
print("测试代码Prompt生成")
|
||
print("=" * 80)
|
||
|
||
# 初始化管理器
|
||
manager = CodePromptManager()
|
||
detector = CodeIntentDetector()
|
||
|
||
# 测试用例
|
||
test_cases = [
|
||
{
|
||
"query": "这个函数是做什么的？如何使用它？",
|
||
"code": "def calculate_factorial(n):\n if n <= 1:\n return 1\n return n * calculate_factorial(n-1)",
|
||
"category": CodeIntentCategory.ENTITY_INTRODUCTION
|
||
},
|
||
{
|
||
"query": "为什么会报语法错误？",
|
||
"code": "for i in range(10)\n print(i)",
|
||
"error": "SyntaxError: invalid syntax",
|
||
"category": CodeIntentCategory.ERROR_DEBUGGING
|
||
},
|
||
{
|
||
"query": "如何实现快速排序算法？",
|
||
"category": CodeIntentCategory.CODE_GENERATION
|
||
},
|
||
{
|
||
"query": "如何优化这段代码的性能？",
|
||
"code": "def slow_function():\n result = []\n for i in range(100000):\n result.append(i * 2)\n return result",
|
||
"category": CodeIntentCategory.CODE_OPTIMIZATION
|
||
}
|
||
]
|
||
|
||
for i, test_case in enumerate(test_cases):
|
||
print(f"\n测试用例 {i+1}: {test_case['query']}")
|
||
print("-" * 60)
|
||
|
||
# 生成Prompt
|
||
prompt = manager.generate_prompt(
|
||
user_query=test_case['query'],
|
||
intent_category=test_case['category'],
|
||
code_context=test_case.get('code'),
|
||
error_message=test_case.get('error')
|
||
)
|
||
|
||
# 打印结果
|
||
print(f"Prompt类型: {manager._map_intent_to_prompt_type(test_case['category']).value}")
|
||
print(f"Prompt长度: {len(prompt)}字符")
|
||
print("\nPrompt内容:")
|
||
print(prompt[:300] + "..." if len(prompt) > 300 else prompt)
|
||
print("-" * 60)
|
||
|
||
print("\n" + "=" * 80)
|
||
print("测试完成")
|
||
print("=" * 80)
|
||
|
||
asyncio.run(test_prompt_generation())
|