Python implements Ollama's prompt word generation and optimization

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
         = {
            'mistral': 'mistral',
            'llama2': 'llama2',
            'neural-chat': 'neural-chat'
        }

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: {}
        - Domain: {}
        - Requirements: {', '.join()}
        
        Generate a structured prompt that includes:
        1. Context setting
        2. Specific requirements
        3. Output format
        4. Constraints
        5. Examples (if applicable)
        """

        response = (
            self.api_url,
            json={
                "model": self.model_name,
                "prompt": prompt,
                "stream": False
            }
        )
        
        return ()["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 = (
            self.api_url,
            json={
                "model": self.model_name,
                "prompt": prompt,
                "stream": False
            }
        )
        
        return ()["response"]

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
        """

async def main():
    # Initialize the service    prompt_service = PromptGenerationService(model_name='mistral')
    
    # Example of code generation prompt word    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("Code Generation Prompt Word:", code_prompt)
    
    # Example of document generation prompt word    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("Document generation prompt word:", doc_prompt)

    # Prompt word optimization example    original_prompt = "Write a React component"
    optimized_prompt = await prompt_service.optimize_prompt(original_prompt)
    print("Optimized prompt words:", optimized_prompt)

if __name__ == "__main__":
    import asyncio
    (main())

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:
        # Simple prompt word verification        return len(()) > 0

    @staticmethod
    def enhance_prompt(prompt: str) -> str:
        # Add common prompt words to enhance        return f"""
        {prompt}
        
        Additional requirements:
        - Provide clear and detailed explanations
        - Include practical examples
        - Consider edge cases
        - Follow best practices
        """

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 :
            raise ModelConnectionError("Unable to connect to Ollama service")
        except Exception as e:
            raise PromptGenerationError(f"Prompt word generation error: {str(e)}")
    return wrapper

class Config:
    MODELS = {
        'mistral': {
            'name': 'mistral',
            'description': 'Fast, lightweight prompt word generation',
            'parameters': {
                'temperature': 0.7,
                'max_tokens': 2000
            }
        },
        'llama2': {
            'name': 'llama2',
            'description': 'Complex, detailed prompt word requirements',
            'parameters': {
                'temperature': 0.8,
                'max_tokens': 4000
            }
        },
        'neural-chat': {
            'name': 'neural-chat',
            'description': 'Interactive Prompt Word Optimization',
            'parameters': {
                'temperature': 0.9,
                'max_tokens': 3000
            }
        }
    }