OpenAI Codex Code 생성 열 시트
제품정보
OpenAI Codex는 수십 개의 프로그래밍 언어에서 코드를 이해하고 생성 할 수있는 자연 언어를 번역하는 강력한 AI 시스템입니다. GPT-3 아키텍처에 내장되어 공공 코드, Codex Power GitHub Copilot의 수십억 개의 라인에 훈련되고 고급 코드 완료, 생성 및 설명 기능을 제공합니다. 자연적인 언어 묘사에서, 기능, 종류 및 전체적인 신청을 이해하는 것을 excels.
· ** 참고**: OpenAI Codex API는 3월 2023일 이전에 공개되었습니다. 이 가이드는 GPT-3.5/GPT-4 모델에 대한 과거 사용 및 마이그레이션을 포함합니다.
현재 모델로 마이그레이션
GPT-3.5/GPT-4 코드 생성
카지노사이트
Legacy Codex API 사용 (역사적 참조)
카지노사이트
현대 Code Generation Setup
파이썬 SDK 제품 설명
#!/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:
```{언어}
{partial_code}에 대하여
Continue the code logically and maintain the existing style and patterns. """
try:
response = self.client.chat.completions.create(
model=model,
messages=[
\\\\{"role": "user", "content": prompt\\\\}
],
max_tokens=1000,
temperature=0.1
)
return response.choices[0].message.content
except Exception as e:
return f"Error completing code: \\\\{e\\\\}"
def explain_code(self, code: str, language: str = "python") -> str:
"""Explain existing code"""
prompt = f"""
Explain this \\{language\\} code in detail:
이름 *
Provide: 1. High-level overview 2. Line-by-line explanation of complex parts 3. Purpose and functionality 4. Potential improvements """
try:
response = self.client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[
\\\\{"role": "user", "content": prompt\\\\}
],
max_tokens=1500,
temperature=0.1
)
return response.choices[0].message.content
except Exception as e:
return f"Error explaining code: \\\\{e\\\\}"
def fix_code(self, buggy_code: str, error_message: str = None,
language: str = "python") -> str:
"""Fix buggy code"""
prompt = f"""
Fix this \\{language\\} code that has issues:
벌레잡기
__CODE_BLOCK_5_{ 언어}
이름 *
카지노사이트
## Language-Specific Code 생성
### Python 개발
카지노사이트
### JavaScript/TypeScript 개발
카지노사이트
### 바로가기
카지노사이트
## 고급 코드 생성 기술
### Context-Aware 세대
```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:
```{언어}
이름 *
카지노사이트
### Multi-Step Code 생성
```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:
```{ 언어}
{이전_code}
Build upon this existing code. """
prompt = f"""
\\{context\\}
Implement this specific step: \\{step\\}
Provide complete, working \\{language\\} code that: - Implements only this step - Integrates with previous code (if any) - Includes proper error handling - Follows best practices - Includes comments explaining the implementation """
try:
response = self.client.chat.completions.create(
model="gpt-4",
messages=[
\\\\{"role": "user", "content": prompt\\\\}
],
max_tokens=2000,
temperature=0.1
)
return response.choices[0].message.content
except Exception as e:
return f"Error implementing step: \\\\{e\\\\}"
def generate_complete_solution(self, requirement: str,
language: str = "python") -> Dict:
"""Generate complete solution using multi-step approach"""
print(f"Planning implementation for: \\\\{requirement\\\\}")
# Step 1: Create implementation plan
steps = self.plan_implementation(requirement)
print(f"Implementation plan created with \\\\{len(steps)\\\\} steps")
# Step 2: Implement each step
complete_code = ""
step_implementations = []
for i, step in enumerate(steps, 1):
print(f"Implementing step \\\\{i\\\\}/\\\\{len(steps)\\\\}: \\\\{step[:50]\\\\}...")
step_code = self.implement_step(step, complete_code, language)
step_implementations.append(\\\\{
"step": step,
"code": step_code,
"step_number": i
\\\\})
# Accumulate code for next step
complete_code += f"\n\n# Step \\\\{i\\\\}: \\\\{step\\\\}\n\\\\{step_code\\\\}"
# Step 3: Review and optimize complete solution
optimized_code = self.optimize_complete_solution(complete_code, language)
return \\\\{
"requirement": requirement,
"language": language,
"plan": steps,
"step_implementations": step_implementations,
"complete_code": complete_code,
"optimized_code": optimized_code
\\\\}
def optimize_complete_solution(self, code: str, language: str = "python") -> str:
"""Optimize the complete solution"""
prompt = f"""
Review and optimize this complete \\{language\\} solution:
```{언어} 이름 * 카지노사이트
IDE 및 Editor 통합
VS 코드 통합
카지노사이트
Vim/Neovim 플러그인
카지노사이트
Emacs 통합
카지노사이트
명령줄 도구
CLI 코드 생성기
카지노사이트
모범 사례 및 최적화
Code Generation에 대한 신속한 엔지니어링
오프화이트
Code 품질 검증
카지노사이트
문제 해결 및 일반적인 문제
API 마이그레이션 문제
오프화이트
성능 최적화
카지노사이트
자료 및 문서
공식 자료
미화 가이드
커뮤니티 리소스
도구 및 확장
이 속임수 시트는 현대 AI 코드 생성 도구를 사용하여 deprecated OpenAI Codex에 대한 교체를 제공합니다. 생산 사용 전에 AI-generated 코드를 테스트하고 API 키 관리를위한 보안 모범 사례를 따르십시오. 필수