OpenAI Codex コード生成チートシート

概要

OpenAI Codexは、自然言語からコードに変換する強力なAIシステムで、数十のプログラミング言語でコードを理解し、生成できます。GPT-3アーキテクチャに基づき、公開されているコードの何十億行にも及ぶデータで学習されたCodexは、GitHub Copilotを支え、高度なコード補完、生成、説明機能を提供します。コンテキストを理解し、自然言語の説明から関数、クラス、さらには完全なアプリケーションを生成することに優れています。

⚠️ 注意: OpenAI Codex APIは2023年3月に廃止されました。このガイドは歴史的な使用方法と、コード生成タスクのためのGPT-3.5/GPT-4モデルへの移行について説明します。

現行モデルへの移行

コード生成のためのGPT-3.5/GPT-4

# Install OpenAI Python library
pip install openai

# Basic setup for code generation with current models
import openai

openai.api_key = "your-api-key-here"

# Use GPT-3.5-turbo or GPT-4 for code generation
response = openai.ChatCompletion.create(
    model="gpt-3.5-turbo",  # or "gpt-4"
    messages=[
        \\\\{
            "role": "system",
            "content": "You are an expert programmer. Generate clean, efficient, and well-documented code."
        \\\\},
        \\\\{
            "role": "user",
            "content": "Write a Python function to implement binary search"
        \\\\}
    ],
    max_tokens=1000,
    temperature=0.1  # Lower temperature for more deterministic code
)

print(response.choices[0].message.content)

レガシーCodex API の使用（歴史的参照）

# Historical Codex API usage (deprecated)
import openai

openai.api_key = "your-api-key-here"

# Codex completion (no longer available)
response = openai.Completion.create(
    engine="code-davinci-002",  # Deprecated
    prompt="# Function to calculate factorial\ndef factorial(n):",
    max_tokens=150,
    temperature=0,
    stop=["#", "\n\n"]
)

print(response.choices[0].text)

モダンなコード生成セットアップ

Pythonの SDK 設定

#!/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}

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:

{code}

Provide:

High-level overview
Line-by-line explanation of complex parts
Purpose and functionality

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:

{buggy_code}

"""

    if error_message:
        prompt += f"\nError message: \\\\{error_message\\\\}"

    prompt += """

Provide:

Corrected code
Explanation of what was wrong

Prevention strategies for similar issues """

 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 fixing code: \\\\{e\\\\}"

def generate_tests(self, code: str, language: str = “python”) -> str: """Generate test cases for code"""

 test_frameworks = \\\\{
     "python": "pytest",
     "javascript": "jest",
     "java": "junit",
     "csharp": "nunit",
     "go": "testing package"
 \\\\}

 framework = test_frameworks.get(language, "appropriate testing framework")

 prompt = f"""

Generate comprehensive test cases for this \\{language\\} code using \\{framework\\}:

{code}

Include:

Unit tests for all functions/methods
Edge cases and boundary conditions
Error handling tests
Mock objects where needed

Test data setup and teardown """

 try:
     response = self.client.chat.completions.create(
         model="gpt-3.5-turbo",
         messages=[
             \\\\{"role": "user", "content": prompt\\\\}
         ],
         max_tokens=2000,
         temperature=0.1
     )

     return response.choices[0].message.content

 except Exception as e:
     return f"Error generating tests: \\\\{e\\\\}"

Example usage

def main(): generator = ModernCodeGenerator()

# Generate a REST API client
prompt = """

Create a Python class for a REST API client that handles:

GET, POST, PUT, DELETE requests
Authentication with API keys
Retry logic with exponential backoff
Rate limiting
Response caching
Comprehensive error handling """

code = generator.generate_code(prompt, “python”, “gpt-4”) print(“Generated Code:”) print(”=” * 50) print(code)

if name == “main”: main()


## 言語別コード生成

### Python開発
```python
# Python-specific code generation examples

# Data science and machine learning
prompt = """
Create a Python script for machine learning that:
- Loads data from CSV files
- Performs data preprocessing and feature engineering
- Implements multiple ML models (Random Forest, SVM, Neural Network)
- Evaluates models with cross-validation
- Visualizes results and feature importance
- Saves the best model for deployment
"""

# Web development with FastAPI
prompt = """
Build a FastAPI application that:
- Implements user authentication with JWT
- Uses SQLAlchemy for database operations
- Includes CRUD operations for a blog system
- Implements rate limiting and CORS
- Includes comprehensive error handling
- Provides OpenAPI documentation
"""

# DevOps automation
prompt = """
Create a Python automation script that:
- Manages Docker containers
- Deploys applications to Kubernetes
- Monitors application health
- Sends alerts via Slack/email
- Implements rollback functionality
- Logs all operations
"""

JavaScript/TypeScript開発

// JavaScript/TypeScript code generation

// React component with hooks
const prompt = `
Create a React TypeScript component that:
- Implements a data table with sorting, filtering, and pagination
- Uses React Query for data fetching
- Includes proper TypeScript interfaces
- Implements accessibility features (ARIA labels, keyboard navigation)
- Uses CSS modules for styling
- Includes comprehensive error boundaries
`;

// Node.js microservice
const prompt = `
Build a Node.js microservice with TypeScript that:
- Implements GraphQL API with Apollo Server
- Uses Prisma for database operations
- Includes authentication middleware
- Implements caching with Redis
- Uses Winston for logging
- Includes health check endpoints
`;

// Frontend build optimization
const prompt = `
Create a Webpack configuration that:
- Optimizes bundle size with code splitting
- Implements tree shaking
- Uses service workers for caching
- Includes source maps for debugging
- Supports hot module replacement
- Generates performance reports
`;

Go開発

// Go-specific code generation

// Microservice with gRPC
prompt := `
Create a Go microservice that:
- Implements gRPC server with protocol buffers
- Uses GORM for database operations
- Includes middleware for logging and authentication
- Implements graceful shutdown
- Uses Prometheus for metrics
- Includes comprehensive error handling
`

// Concurrent data processing
prompt := `
Implement a Go program that:
- Processes large datasets concurrently
- Uses worker pools with goroutines
- Implements backpressure handling
- Includes progress monitoring
- Uses channels for communication
- Handles graceful cancellation
`

高度なコード生成テクニック

コンテキストを意識した生成

#!/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}

Consider:

Code patterns used in the project
Framework-specific best practices
Performance optimizations
Maintainability improvements

Security enhancements """

 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 suggesting refactoring: \\\\{e\\\\}"

def main(): generator = ContextAwareGenerator()

# Analyze current project
context = generator.analyze_codebase(".")
print("Project Context:")
for key, value in context.items():
    print(f"  \\\\{key\\\\}: \\\\{value\\\\}")

# Generate code with context
prompt = "Create a new API endpoint for user management"
code = generator.generate_with_context(prompt)
print("\nGenerated Code:")
print("=" * 50)
print(code)

if name == “main”: main()


### 複数ステップのコード生成
```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}
{previous_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:

{code}

Optimize for:

Code organization and structure
Performance improvements
Error handling consistency
Code reuse and DRY principles
Documentation and comments
Security considerations

Provide the optimized version with explanations of changes. """

    try:
        response = self.client.chat.completions.create(
            model="gpt-4",
            messages=[
                \\\\{"role": "user", "content": prompt\\\\}
            ],
            max_tokens=3000,
            temperature=0.1
        )

        return response.choices[0].message.content

    except Exception as e:
        return f"Error optimizing solution: \\\\{e\\\\}"

def main(): generator = MultiStepGenerator()

requirement = """

Create a web scraping system that:

Scrapes product data from multiple e-commerce sites
Handles rate limiting and anti-bot measures
Stores data in a database
Provides a REST API to query the data
Includes monitoring and alerting
Supports distributed scraping across multiple workers """

solution = generator.generate_complete_solution(requirement, “python”)

print(“\nImplementation Plan:”) print(”=” * 50) for i, step in enumerate(solution[“plan”], 1): print(f”\\{i\\}. \\{step\\}”)

print(f”\nComplete solution generated with \\{len(solution[‘step_implementations’])\\} steps”) print(“Check the generated files for detailed implementation.”)

Save results

with open(“multi_step_solution.py”, “w”) as f: f.write(solution[“optimized_code”])

print(“Optimized solution saved to: multi_step_solution.py”)

if name == “main”: main()


## IDEとエディターの統合

### VS Codeの統合
```json
// VS Code settings for modern code generation
\\\\{
  "openai.apiKey": "$\\\\{env:OPENAI_API_KEY\\\\}",
  "openai.model": "gpt-3.5-turbo",
  "openai.maxTokens": 1000,
  "openai.temperature": 0.1,
  "openai.codeCompletion": true,
  "openai.codeExplanation": true,
  "openai.codeGeneration": true,
  "openai.languages": [
    "python",
    "javascript",
    "typescript",
    "go",
    "java",
    "cpp",
    "csharp"
  ]
\\\\}

Vim/Neovimプラグイン

-- Neovim Lua configuration for code generation
local function generate_code()
  local prompt = vim.fn.input("Code generation prompt: ")
  if prompt == "" then
    return
  end

  local language = vim.bo.filetype
  local api_key = os.getenv("OPENAI_API_KEY")

  if not api_key then
    print("Error: OPENAI_API_KEY not set")
    return
  end

  -- Call Python script for code generation
  local cmd = string.format(
    "python3 -c \"import openai; openai.api_key='%s'; response=openai.ChatCompletion.create(model='gpt-3.5-turbo', messages=[\\\\{'role': 'user', 'content': 'Generate %s code: %s'\\\\}], max_tokens=1000, temperature=0.1); print(response.choices[0].message.content)\"",
    api_key, language, prompt
  )

  local output = vim.fn.system(cmd)

  -- Insert generated code at cursor
  local lines = vim.split(output, "\n")
  vim.api.nvim_put(lines, "l", true, true)
end

-- Key mapping
vim.keymap.set("n", "<leader>cg", generate_code, \\\\{ desc = "Generate code" \\\\})

Emacsの統合

;; Emacs Lisp configuration for code generation
(defun openai-generate-code (prompt)
  "Generate code using OpenAI API"
  (interactive "sCode generation prompt: ")
  (let* ((api-key (getenv "OPENAI_API_KEY"))
         (language (file-name-extension (buffer-file-name)))
         (python-script (format
           "import openai; openai.api_key='%s'; response=openai.ChatCompletion.create(model='gpt-3.5-turbo', messages=[\\\\{'role': 'user', 'content': 'Generate %s code: %s'\\\\}], max_tokens=1000, temperature=0.1); print(response.choices[0].message.content)"
           api-key language prompt)))
    (if api-key
        (let ((output (shell-command-to-string (format "python3 -c \"%s\"" python-script))))
          (insert output))
      (message "Error: OPENAI_API_KEY not set"))))

;; Key binding
(global-set-key (kbd "C-c g") 'openai-generate-code)

コマンドラインツール

CLIコードジェネレーター

Would you like me to continue with the remaining sections or placeholders?```bash #!/bin/bash

codegen-cli.sh - Command line code generator

OPENAI_API_KEY=”$\\{OPENAI_API_KEY\\}” MODEL=“gpt-3.5-turbo” MAX_TOKENS=1500 TEMPERATURE=0.1

if [ -z “$OPENAI_API_KEY” ]; then echo “Error: OPENAI_API_KEY environment variable not set” exit 1 fi

show_help() \\{ echo “Code Generation CLI Tool” echo “Usage:” echo ” codegen-cli.sh generate - Generate code” echo ” codegen-cli.sh complete - Complete code in file” echo ” codegen-cli.sh explain - Explain code in file” echo ” codegen-cli.sh fix [error_message] - Fix buggy code” echo ” codegen-cli.sh test - Generate tests” echo "" echo “Examples:” echo ” codegen-cli.sh generate python ‘Create a REST API client’” echo ” codegen-cli.sh complete main.py” echo ” codegen-cli.sh explain algorithm.py” echo "" echo “Environment variables:” echo ” OPENAI_API_KEY - Your OpenAI API key” \\}

call_openai_api() \\{ local system_prompt=“$1” local user_prompt=“$2”

python3 << EOF

import openai import json import sys

openai.api_key = “$OPENAI_API_KEY”

try: response = openai.ChatCompletion.create( model=“$MODEL”, messages=[ \\{“role”: “system”, “content”: “$system_prompt”\\}, \\{“role”: “user”, “content”: “$user_prompt”\\} ], max_tokens=$MAX_TOKENS, temperature=$TEMPERATURE )

print(response.choices[0].message.content)

except Exception as e: print(f”Error: \\{e\\}”, file=sys.stderr) sys.exit(1) EOF \\}

case “$1” in “generate”) if [ $# -lt 3 ]; then echo “Usage: codegen-cli.sh generate ” exit 1 fi

    language="$2"
    prompt="$3"

    system_prompt="You are an expert $language developer. Generate clean, efficient, and well-documented code."
    user_prompt="Generate $language code: $prompt"

    echo "Generating $language code..."
    echo "=========================="
    call_openai_api "$system_prompt" "$user_prompt"
    ;;

"complete")
    if [ $# -lt 2 ]; then
        echo "Usage: codegen-cli.sh complete <file>"
        exit 1
    fi

    file="$2"

    if [ ! -f "$file" ]; then
        echo "Error: File $file not found"
        exit 1
    fi

    # Detect language from file extension
    extension="$\\\\{file##*.\\\\}"
    case "$extension" in
        "py") language="python" ;;
        "js") language="javascript" ;;
        "ts") language="typescript" ;;
        "go") language="go" ;;
        "java") language="java" ;;
        *) language="unknown" ;;
    esac

    code_content=$(cat "$file")
    user_prompt="Complete this $language code:\n\n$code_content"

    echo "Completing code in $file..."
    echo "=========================="
    call_openai_api "You are an expert programmer. Complete the provided code." "$user_prompt"
    ;;

"explain")
    if [ $# -lt 2 ]; then
        echo "Usage: codegen-cli.sh explain <file>"
        exit 1
    fi

    file="$2"

    if [ ! -f "$file" ]; then
        echo "Error: File $file not found"
        exit 1
    fi

    code_content=$(cat "$file")
    user_prompt="Explain this code in detail:\n\n$code_content"

    echo "Explaining code in $file..."
    echo "=========================="
    call_openai_api "You are an expert programmer. Explain code clearly and comprehensively." "$user_prompt"
    ;;

"fix")
    if [ $# -lt 2 ]; then
        echo "Usage: codegen-cli.sh fix <file> [error_message]"
        exit 1
    fi

    file="$2"
    error_message="$3"

    if [ ! -f "$file" ]; then
        echo "Error: File $file not found"
        exit 1
    fi

    code_content=$(cat "$file")
    user_prompt="Fix this buggy code:\n\n$code_content"

    if [ -n "$error_message" ]; then
        user_prompt="$user_prompt\n\nError message: $error_message"
    fi

    echo "Fixing code in $file..."
    echo "======================"
    call_openai_api "You are an expert debugger. Fix code issues and explain the problems." "$user_prompt"
    ;;

"test")
    if [ $# -lt 2 ]; then
        echo "Usage: codegen-cli.sh test <file>"
        exit 1
    fi

    file="$2"

    if [ ! -f "$file" ]; then
        echo "Error: File $file not found"
        exit 1
    fi

    code_content=$(cat "$file")
    user_prompt="Generate comprehensive test cases for this code:\n\n$code_content"

    echo "Generating tests for $file..."
    echo "============================="
    call_openai_api "You are an expert test engineer. Generate comprehensive test cases." "$user_prompt"
    ;;

*)
    show_help
    ;;

esac

```python
# Best practices for code generation prompts

# Specific and detailed prompts
good_prompt = """
Create a Python class for a database connection pool that:
- Supports PostgreSQL and MySQL
- Implements connection pooling with configurable min/max connections
- Handles connection timeouts and retries
- Includes health checks for connections
- Provides async/await support
- Implements proper resource cleanup
- Includes comprehensive logging
- Follows the context manager protocol
"""

# Include constraints and requirements
constrained_prompt = """
Implement a rate limiter in Go that:
- Uses the token bucket algorithm
- Supports distributed rate limiting with Redis
- Handles burst traffic up to 1000 requests/second
- Includes metrics collection
- Must be thread-safe
- Should have minimal memory footprint
- Include benchmark tests
- Follow Go best practices and idioms
"""

# Request specific output format
formatted_prompt = """
Generate a React TypeScript component with the following structure:

1. Interface definitions for props and state
2. Component implementation with hooks
3. CSS module styles
4. Unit tests with React Testing Library
5. Storybook stories for documentation

Component requirements:
- Data table with sorting and filtering
- Pagination support
- Accessibility compliance (ARIA labels)
- Responsive design
- Error boundary handling
"""
```### コード生成のためのプロンプトエンジニアリング
```python
#!/usr/bin/env python3
# code-quality-validator.py

import ast
import subprocess
import tempfile
import os
from typing import List, Dict

class CodeQualityValidator:
    def __init__(self):
        self.quality_checks = \\\\{
            "python": self.validate_python_code,
            "javascript": self.validate_javascript_code,
            "typescript": self.validate_typescript_code
        \\\\}

    def validate_generated_code(self, code: str, language: str) -> Dict:
        """Validate generated code quality"""

        validator = self.quality_checks.get(language)
        if not validator:
            return \\\\{"error": f"No validator for language: \\\\{language\\\\}"\\\\}

        return validator(code)

    def validate_python_code(self, code: str) -> Dict:
        """Validate Python code quality"""

        issues = []

        # Syntax check
        try:
            ast.parse(code)
        except SyntaxError as e:
            issues.append(f"Syntax error: \\\\{e\\\\}")
            return \\\\{"valid": False, "issues": issues\\\\}

        # Create temporary file for linting
        with tempfile.NamedTemporaryFile(mode='w', suffix='.py', delete=False) as f:
            f.write(code)
            temp_file = f.name

        try:
            # Run flake8 for style checking
            result = subprocess.run(
                ['flake8', '--max-line-length=88', temp_file],
                capture_output=True,
                text=True
            )

            if result.returncode != 0:
                issues.extend(result.stdout.split('\n'))

            # Run bandit for security checking
            result = subprocess.run(
                ['bandit', '-f', 'txt', temp_file],
                capture_output=True,
                text=True
            )

            if result.returncode != 0 and "No issues identified" not in result.stdout:
                issues.append(f"Security issues found: \\\\{result.stdout\\\\}")

            # Check for best practices
            if 'import *' in code:
                issues.append("Avoid wildcard imports")

            if 'except:' in code and 'except Exception:' not in code:
                issues.append("Use specific exception handling")

            # Check for documentation
            if 'def ' in code and '"""' not in code:
                issues.append("Missing docstrings for functions")

        finally:
            os.unlink(temp_file)

        return \\\\{
            "valid": len(issues) == 0,
            "issues": [issue for issue in issues if issue.strip()],
            "language": "python"
        \\\\}

    def validate_javascript_code(self, code: str) -> Dict:
        """Validate JavaScript code quality"""

        issues = []

        # Create temporary file
        with tempfile.NamedTemporaryFile(mode='w', suffix='.js', delete=False) as f:
            f.write(code)
            temp_file = f.name

        try:
            # Run ESLint if available
            result = subprocess.run(
                ['eslint', '--format', 'compact', temp_file],
                capture_output=True,
                text=True
            )

            if result.returncode != 0:
                issues.extend(result.stdout.split('\n'))

            # Basic checks
            if 'eval(' in code:
                issues.append("Avoid using eval() - security risk")

            if 'var ' in code:
                issues.append("Use 'let' or 'const' instead of 'var'")

        except FileNotFoundError:
            issues.append("ESLint not available - install for better validation")

        finally:
            os.unlink(temp_file)

        return \\\\{
            "valid": len(issues) == 0,
            "issues": [issue for issue in issues if issue.strip()],
            "language": "javascript"
        \\\\}

    def validate_typescript_code(self, code: str) -> Dict:
        """Validate TypeScript code quality"""

        issues = []

        # Create temporary file
        with tempfile.NamedTemporaryFile(mode='w', suffix='.ts', delete=False) as f:
            f.write(code)
            temp_file = f.name

        try:
            # Run TypeScript compiler
            result = subprocess.run(
                ['tsc', '--noEmit', '--strict', temp_file],
                capture_output=True,
                text=True
            )

            if result.returncode != 0:
                issues.extend(result.stderr.split('\n'))

            # Check for TypeScript best practices
            if ': any' in code:
                issues.append("Avoid using 'any' type - use specific types")

            if '// @ts-ignore' in code:
                issues.append("Avoid @ts-ignore - fix type issues instead")

        except FileNotFoundError:
            issues.append("TypeScript compiler not available")

        finally:
            os.unlink(temp_file)

        return \\\\{
            "valid": len(issues) == 0,
            "issues": [issue for issue in issues if issue.strip()],
            "language": "typescript"
        \\\\}

def main():
    validator = CodeQualityValidator()

    # Example Python code validation
    python_code = '''
def calculate_factorial(n):
    """Calculate factorial of a number."""
    if n < 0:
        raise ValueError("Factorial not defined for negative numbers")
    if n == 0 or n == 1:
        return 1
    return n * calculate_factorial(n - 1)
'''

    result = validator.validate_generated_code(python_code, "python")
    print("Python Code Validation:")
    print(f"Valid: \\\\{result['valid']\\\\}")
    if result['issues']:
        print("Issues:")
        for issue in result['issues']:
            print(f"  - \\\\{issue\\\\}")

if __name__ == "__main__":
    main()
```### コード品質の検証
```python
# Common migration issues from Codex to GPT models

# Issue 1: Different response format
# Old Codex format
# response.choices[0].text

# New GPT format
# response.choices[0].message.content

# Issue 2: Different prompt structure
# Old Codex (completion)
prompt = "# Function to calculate factorial\ndef factorial(n):"

# New GPT (chat)
messages = [
    \\\\{"role": "system", "content": "You are an expert Python developer."\\\\},
    \\\\{"role": "user", "content": "Write a function to calculate factorial"\\\\}
]

# Issue 3: Stop sequences
# Old Codex
# stop=["#", "\n\n"]

# New GPT (handled differently)
# Use system prompts to control output format
```## トラブルシューティングと一般的な問題
```python
# Optimize API usage for better performance

class OptimizedCodeGenerator:
    def __init__(self):
        self.cache = \\\\{\\\\}
        self.batch_requests = []

    def cached_generation(self, prompt: str, language: str) -> str:
        """Use caching to avoid duplicate requests"""

        cache_key = f"\\\\{language\\\\}:\\\\{hash(prompt)\\\\}"

        if cache_key in self.cache:
            return self.cache[cache_key]

        # Generate code
        result = self.generate_code(prompt, language)

        # Cache result
        self.cache[cache_key] = result

        return result

    def batch_generation(self, prompts: List[str], language: str) -> List[str]:
        """Batch multiple requests for efficiency"""

        # Combine prompts
        combined_prompt = "Generate code for the following requests:\n\n"

        for i, prompt in enumerate(prompts, 1):
            combined_prompt += f"\\\\{i\\\\}. \\\\{prompt\\\\}\n\n"

        combined_prompt += f"Provide \\\\{language\\\\} code for each request, numbered accordingly."

        # Single API call
        response = self.generate_code(combined_prompt, language)

        # Parse responses (simplified)
        responses = response.split(f"\\\\{i+1\\\\}.")

        return responses[:len(prompts)]
```### APIマイグレーションの問題
https://platform.openai.com/docs##

# パフォーマンス最適化
https://github.com/openai/openai-python#

# リソースとドキュメント
https://platform.openai.com/docs/models/gpt-4##

# 公式リソース
- [OpenAI APIドキュメント](https://github.com/openai/openai-cookbook)
- [OpenAI Pythonライブラリ](https://platform.openai.com/docs/guides/code)
- [GPT-4モデルドキュメント](https://platform.openai.com/docs/api-reference)
- [OpenAI Cookbook](https://platform.openai.com/docs/guides/code/best-practices)
https://community.openai.com/##

# マイグレーションガイド
- [CodexからGPTへの移行ガイド](https://docs.github.com/en/copilot)
- [APIマイグレーションドキュメント](https://github.com/openai/openai-cookbook/tree/main/examples)
- [コード生成のベストプラクティス](https://github.com/dair-ai/Prompt-Engineering-Guide)
https://github.com/features/copilot##

# コミュニティリソース
- [OpenAI開発者コミュニティ](https://www.tabnine.com/)
- [GitHub Copilotドキュメント](https://codeium.com/)
- [コード生成の例](https://aws.amazon.com/codewhisperer/)
- [プロンプトエンジニアリングガイド](

OpenAI Codex コード生成チートシート

概要

現行モデルへの移行

コード生成のためのGPT-3.5/GPT-4

レガシーCodex API の使用（歴史的参照）

モダンなコード生成セットアップ

Pythonの SDK 設定

Example usage

JavaScript/TypeScript開発

Go開発

高度なコード生成テクニック

コンテキストを意識した生成

Save results

Vim/Neovimプラグイン

Emacsの統合

コマンドラインツール

CLIコードジェネレーター

codegen-cli.sh - Command line code generator