Foglio di Cheat Generazione Codice OpenAI Codex
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## Panoramica
OpenAI Codex è un potente sistema AI che traduce il linguaggio naturale in codice, capace di comprendere e generare codice in dozzine di linguaggi di programmazione. Costruito su architettura GPT-3 e addestrato su miliardi di linee di codice pubblico, poteri Codex GitHub Copilot e fornisce funzionalità avanzate di completamento del codice, generazione e spiegazione. eccelle nel contesto di comprensione, generando funzioni, classi e intere applicazioni da descrizioni di lingua naturale.
> ⚠️ **Nota**: OpenAI Codex API è stato deprecato nel marzo 2023. Questa guida copre l'utilizzo storico e la migrazione ai modelli GPT-3.5/GPT-4 per le attività di generazione di codice.
## Migrazione ai modelli attuali
### GPT-3.5/GPT-4 per la generazione di codici
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### Legacy Codex API Utilizzo (riferimento storico)
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## Setup di generazione di codice moderno
### Python SDK Configurazione
#!/usr/bin/env python3
# modern-codex-replacement.py
import openai
import os
import json
from typing import List, Dict, Optional
from datetime import datetime
class ModernCodeGenerator:
def __init__(self, api_key: str = None):
self.client = openai.OpenAI(
api_key=api_key or os.getenv("OPENAI_API_KEY")
)
self.conversation_history = []
def generate_code(self, prompt: str, language: str = "python",
model: str = "gpt-3.5-turbo") -> str:
"""Generate code using modern OpenAI models"""
system_prompt = f"""
You are an expert \\\\{language\\\\} developer. Generate clean, efficient,
and well-documented code that follows best practices. Include:
- Proper error handling
- Type hints (where applicable)
- Comprehensive docstrings
- Security considerations
- Performance optimizations
"""
try:
response = self.client.chat.completions.create(
model=model,
messages=[
\\\\{"role": "system", "content": system_prompt\\\\},
\\\\{"role": "user", "content": prompt\\\\}
],
max_tokens=2000,
temperature=0.1,
top_p=1.0,
frequency_penalty=0.0,
presence_penalty=0.0
)
generated_code = response.choices[0].message.content
# Store in conversation history
self.conversation_history.append(\\\\{
"prompt": prompt,
"language": language,
"model": model,
"response": generated_code,
"timestamp": datetime.now().isoformat()
\\\\})
return generated_code
except Exception as e:
return f"Error generating code: \\\\{e\\\\}"
def complete_code(self, partial_code: str, language: str = "python",
model: str = "gpt-3.5-turbo") -> str:
"""Complete partial code snippets"""
prompt = f"""
Complete this \\\\{language\\\\} code snippet. Provide only the missing parts:
```{language}
{partial_code}
{code}
Traduzione:
{code}
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## Generazione del codice linguistico
### Python Development
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### Sviluppo JavaScript/TypeScript
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### Sviluppo
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## Tecniche di generazione di codici avanzate
### Generazione di Context-Aware
```python
#!/usr/bin/env python3
# context-aware-generation.py
import openai
import os
import ast
from typing import List, Dict
class ContextAwareGenerator:
def __init__(self):
self.client = openai.OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
self.project_context = \\\\{\\\\}
def analyze_codebase(self, directory: str) -> Dict:
"""Analyze existing codebase for context"""
context = \\\\{
"languages": set(),
"frameworks": set(),
"patterns": set(),
"dependencies": set(),
"file_structure": \\\\{\\\\}
\\\\}
# Analyze Python files
for root, dirs, files in os.walk(directory):
for file in files:
if file.endswith('.py'):
file_path = os.path.join(root, file)
try:
with open(file_path, 'r') as f:
content = f.read()
# Parse AST for imports and patterns
tree = ast.parse(content)
for node in ast.walk(tree):
if isinstance(node, ast.Import):
for alias in node.names:
context["dependencies"].add(alias.name)
elif isinstance(node, ast.ImportFrom):
if node.module:
context["dependencies"].add(node.module)
context["languages"].add("python")
# Detect frameworks
if "flask" in content.lower():
context["frameworks"].add("Flask")
if "django" in content.lower():
context["frameworks"].add("Django")
if "fastapi" in content.lower():
context["frameworks"].add("FastAPI")
except Exception as e:
print(f"Error analyzing \\\\{file_path\\\\}: \\\\{e\\\\}")
self.project_context = context
return context
def generate_with_context(self, prompt: str, language: str = "python") -> str:
"""Generate code with project context"""
context_info = ""
if self.project_context:
context_info = f"""
Project Context:
- Languages: \\\\{', '.join(self.project_context.get('languages', []))\\\\}
- Frameworks: \\\\{', '.join(self.project_context.get('frameworks', []))\\\\}
- Key Dependencies: \\\\{', '.join(list(self.project_context.get('dependencies', []))[:10])\\\\}
Please generate code that fits with this existing codebase.
"""
full_prompt = f"\\\\{context_info\\\\}\n\nRequest: \\\\{prompt\\\\}"
try:
response = self.client.chat.completions.create(
model="gpt-4",
messages=[
\\\\{
"role": "system",
"content": f"You are an expert \\\\{language\\\\} developer working on an existing project. Generate code that integrates well with the existing codebase."
\\\\},
\\\\{"role": "user", "content": full_prompt\\\\}
],
max_tokens=2000,
temperature=0.1
)
return response.choices[0].message.content
except Exception as e:
return f"Error generating contextual code: \\\\{e\\\\}"
def suggest_refactoring(self, code: str, language: str = "python") -> str:
"""Suggest refactoring based on project context"""
context_info = ""
if self.project_context:
frameworks = ', '.join(self.project_context.get('frameworks', []))
if frameworks:
context_info = f"This project uses \\\\{frameworks\\\\}. "
prompt = f"""
\\\\{context_info\\\\}Analyze this \\\\{language\\\\} code and suggest refactoring:
```{language}
{code}
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### Generazione di codice multi-Step
```python
#!/usr/bin/env python3
# multi-step-generation.py
import openai
import os
from typing import List, Dict
class MultiStepGenerator:
def __init__(self):
self.client = openai.OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
self.generation_steps = []
def plan_implementation(self, requirement: str) -> List[str]:
"""Break down complex requirements into implementation steps"""
prompt = f"""
Break down this software requirement into detailed implementation steps:
Requirement: \\\\{requirement\\\\}
Provide a step-by-step implementation plan with:
1. Architecture decisions
2. Component breakdown
3. Implementation order
4. Dependencies between components
5. Testing strategy
Format as a numbered list of specific, actionable steps.
"""
try:
response = self.client.chat.completions.create(
model="gpt-4",
messages=[
\\\\{"role": "user", "content": prompt\\\\}
],
max_tokens=1500,
temperature=0.1
)
plan = response.choices[0].message.content
# Extract steps (simple parsing)
steps = []
for line in plan.split('\n'):
if line.strip() and (line.strip()[0].isdigit() or line.strip().startswith('-')):
steps.append(line.strip())
self.generation_steps = steps
return steps
except Exception as e:
return [f"Error creating plan: \\\\{e\\\\}"]
def implement_step(self, step: str, previous_code: str = "",
language: str = "python") -> str:
"""Implement a specific step"""
context = ""
if previous_code:
context = f"""
Previous implementation:
```{language}
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