1. 基础环境配置
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import requests import json from typing import List, Dict, Optional from dataclasses import dataclass
@dataclass class PromptContext: task: str domain: str requirements: List[str]
class OllamaService: def __init__(self, base_url: str = "http://localhost:11434"): self.base_url = base_url self.models = { 'mistral': 'mistral', 'llama2': 'llama2', 'neural-chat': 'neural-chat' } |
2. 核心功能实现
2.1 提示词生成服务
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class PromptGenerationService: def __init__(self, model_name: str = 'mistral'): self.model_name = model_name self.api_url = "http://localhost:11434/api/generate"
async def generate_prompt(self, context: PromptContext) -> str: prompt = f""" Task: Create a detailed prompt for the following context: - Task Type: {context.task} - Domain: {context.domain} - Requirements: {', '.join(context.requirements)}
Generate a structured prompt that includes: 1. Context setting 2. Specific requirements 3. Output format 4. Constraints 5. Examples (if applicable) """
response = requests.post( self.api_url, json={ "model": self.model_name, "prompt": prompt, "stream": False } )
return response.json()["response"]
async def optimize_prompt(self, original_prompt: str) -> Dict: prompt = f""" Analyze and optimize the following prompt: "{original_prompt}"
Provide: 1. Improved version 2. Explanation of changes 3. Potential variations """
response = requests.post( self.api_url, json={ "model": self.model_name, "prompt": prompt, "stream": False } )
return response.json()["response"] |
2.2 提示词模板管理
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class PromptTemplates: @staticmethod def get_code_review_template(code: str) -> str: return f""" Analyze the following code: [code]
Provide: 1. Code quality assessment 2. Potential improvements 3. Security concerns 4. Performance optimization """
@staticmethod def get_documentation_template(component: str) -> str: return f""" Generate documentation for: {component}
Include: 1. Overview 2. API reference 3. Usage examples 4. Best practices """
@staticmethod def get_refactoring_template(code: str) -> str: return f""" Suggest refactoring for: [code]
Consider: 1. Design patterns 2. Clean code principles 3. Performance impact 4. Maintainability """ |
3. 使用示例
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async def main(): # 初始化服务 prompt_service = PromptGenerationService(model_name='mistral')
# 代码生成提示词示例 code_context = PromptContext( task='code_generation', domain='web_development', requirements=[ 'React component', 'TypeScript', 'Material UI', 'Form handling' ] )
code_prompt = await prompt_service.generate_prompt(code_context) print("代码生成提示词：", code_prompt)
# 文档生成提示词示例 doc_context = PromptContext( task='documentation', domain='API_reference', requirements=[ 'OpenAPI format', 'Examples included', 'Error handling', 'Authentication details' ] )
doc_prompt = await prompt_service.generate_prompt(doc_context) print("文档生成提示词：", doc_prompt)
# 提示词优化示例 original_prompt = "写一个React组件" optimized_prompt = await prompt_service.optimize_prompt(original_prompt) print("优化后的提示词：", optimized_prompt)
if __name__ == "__main__": import asyncio asyncio.run(main()) |
4. 工具类实现
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class PromptUtils: @staticmethod def format_requirements(requirements: List[str]) -> str: return "\n".join([f"- {req}" for req in requirements])
@staticmethod def validate_prompt(prompt: str) -> bool: # 简单的提示词验证 return len(prompt.strip()) > 0
@staticmethod def enhance_prompt(prompt: str) -> str: # 添加通用的提示词增强 return f""" {prompt}
Additional requirements: - Provide clear and detailed explanations - Include practical examples - Consider edge cases - Follow best practices """ |
5. 错误处理
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class PromptGenerationError(Exception): pass
class ModelConnectionError(Exception): pass
def handle_api_errors(func): async def wrapper(*args, **kwargs): try: return await func(*args, **kwargs) except requests.exceptions.ConnectionError: raise ModelConnectionError("无法连接到Ollama服务") except Exception as e: raise PromptGenerationError(f"提示词生成错误: {str(e)}") return wrapper |
6. 配置管理
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class Config: MODELS = { 'mistral': { 'name': 'mistral', 'description': '快速、轻量级提示词生成', 'parameters': { 'temperature': 0.7, 'max_tokens': 2000 } }, 'llama2': { 'name': 'llama2', 'description': '复杂、详细的提示词需求', 'parameters': { 'temperature': 0.8, 'max_tokens': 4000 } }, 'neural-chat': { 'name': 'neural-chat', 'description': '交互式提示词优化', 'parameters': { 'temperature': 0.9, 'max_tokens': 3000 } } } |
使用这个Python实现，你可以：
- 生成结构化的提示词
- 优化现有提示词
- 使用预定义模板
- 处理各种场景的提示词需求
主要优点：
- 面向对象的设计
- 异步支持
- 错误处理
- 类型提示
- 配置管理
- 模块化结构
这个实现可以作为一个基础框架，根据具体需求进行扩展和定制。
