Generate, debug, and optimize code with AI models across multiple programming languages
import requests def generate_code(prompt, language="python", model="gpt-4o"): """Generate code based on natural language description""" headers = { "Authorization": "Bearer YOUR_API_KEY", "Content-Type": "application/json" } data = { "model": model, "messages": [ {"role": "system", "content": f"You are an expert {language} programmer."}, {"role": "user", "content": prompt} ], "temperature": 0.7 } response = requests.post( "https://api.anyapi.ai/v1/chat/completions", headers=headers, json=data ) return response.json()["choices"][0]["message"]["content"] # Example usage generated_sql = generate_code( "Create a SQL query to find top 5 customers by total purchase amount", language="sql" ) print(generated_sql)
async function generateCode(prompt, language = 'javascript', model = 'gpt-4o') { const response = await fetch('https://api.anyapi.ai/v1/chat/completions', { method: 'POST', headers: { 'Authorization': 'Bearer YOUR_API_KEY', 'Content-Type': 'application/json' }, body: JSON.stringify({ model: model, messages: [ { role: 'system', content: `You are an expert ${language} programmer.` }, { role: 'user', content: prompt } ], temperature: 0.7 }) }); const data = await response.json(); return data.choices[0].message.content; } // Example usage const generatedCode = await generateCode( 'Create a function to validate email addresses', 'javascript' ); console.log(generatedCode);
class CodeAssistant: def __init__(self, api_key): self.api_key = api_key def complete_code(self, partial_code, language="python"): """Complete partial code with context""" prompt = f"Complete this {language} code:\n\n```{language}\n{partial_code}\n```" # API call to generate completion # Returns completed code pass
def debug_code(code, error_message, language="python"): """Debug code that's producing an error""" prompt = f""" Debug this {language} code that's producing an error: Error: {error_message} Code: ```{language} {code}
## Best Practices ### 1. Clear Prompts - Be specific about requirements - Include input/output examples - Specify coding standards ### 2. Language-Specific Context - Mention the programming language - Include framework/library versions - Specify target environment ### 3. Iterative Refinement - Start with basic implementation - Refine with additional requirements - Test and improve incrementally ## Model Recommendations | Task | Recommended Model | Why | |------|------------------|-----| | Complex Algorithms | GPT-4o | Best reasoning capabilities | | Quick Scripts | GPT-4o-mini | Fast and cost-effective | | Code Reviews | Claude 3.5 Sonnet | Excellent analysis | | Documentation | Claude 3.5 Sonnet | Clear explanations | ## Getting Started <CardGroup cols={2}> <Card title="Quick Start" icon="rocket" href="/quickstart/setting-up"> Start generating code with AI </Card> <Card title="API Reference" icon="book" href="/api-reference/text-models/overview"> Explore text generation models </Card> <Card title="Use Cases" icon="lightbulb" href="/use-cases/create-assistant"> See coding examples in action </Card> <Card title="Function Calling" icon="function" href="/capabilities/function-calling"> Integrate with development tools </Card> </CardGroup>