AI Image and Audio Generation Improvements.
AI Video Generation Pre-Flight Checklist. Cost Estimate Improvements.
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ajaysi
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"""
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Enhanced GitHub Content Generator
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This module provides various content generation capabilities from GitHub repository data,
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including getting started guides, technical documentation, tutorials, and more.
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"""
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import sys
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from typing import Dict, List, Optional
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from loguru import logger
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from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
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logger.remove()
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logger.add(sys.stdout,
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colorize=True,
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format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}")
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def generate_technical_documentation(repo_data: Dict, gpt_provider: str = "gemini") -> str:
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"""Generate comprehensive technical documentation from repository data."""
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prompt = f"""As an expert technical writer, create detailed technical documentation for the following GitHub repository:
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Repository Data:
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{repo_data}
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Please create a comprehensive technical documentation that includes:
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1. Architecture Overview
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2. Core Components
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3. Technical Specifications
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4. Integration Points
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5. Performance Considerations
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6. Security Features
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7. API Documentation (if applicable)
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8. Configuration Options
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9. Deployment Guidelines
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10. Troubleshooting Guide
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Format the documentation in markdown with appropriate headers, code blocks, and diagrams.
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Include real-world examples and best practices.
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"""
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return _get_llm_response(prompt, gpt_provider)
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def generate_getting_started_guide(repo_data: Dict, gpt_provider: str = "gemini") -> str:
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"""Generate a beginner-friendly getting started guide."""
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prompt = f"""As an expert programmer and teacher, create a comprehensive getting started guide for the following GitHub repository:
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Repository Data:
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{repo_data}
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Create a step-by-step guide that includes:
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1. Introduction and Overview
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2. Prerequisites and Setup
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3. Installation Instructions
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4. Basic Usage Examples
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5. Common Use Cases
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6. Best Practices
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7. Next Steps and Resources
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Make the guide:
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- Beginner-friendly with clear explanations
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- Include practical examples with code snippets
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- Add emojis for better readability
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- Include troubleshooting tips
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- Provide links to additional resources
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"""
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return _get_llm_response(prompt, gpt_provider)
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def generate_tutorial_series(repo_data: Dict, gpt_provider: str = "gemini") -> str:
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"""Generate a series of tutorials for different skill levels."""
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prompt = f"""As an expert educator, create a series of tutorials for the following GitHub repository:
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Repository Data:
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{repo_data}
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Create a structured tutorial series that includes:
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1. Beginner Tutorial
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- Basic concepts
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- Simple examples
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- Step-by-step instructions
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2. Intermediate Tutorial
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- Advanced features
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- Real-world examples
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- Best practices
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3. Advanced Tutorial
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- Complex use cases
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- Performance optimization
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- Integration patterns
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Each tutorial should:
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- Be self-contained
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- Include practical examples
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- Have clear learning objectives
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- Include exercises and challenges
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"""
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return _get_llm_response(prompt, gpt_provider)
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def generate_comparison_analysis(repo_data: Dict, gpt_provider: str = "gemini") -> str:
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"""Generate a comparison analysis with similar tools/frameworks."""
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prompt = f"""As a technical analyst, create a comprehensive comparison analysis for the following GitHub repository:
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Repository Data:
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{repo_data}
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Create a detailed comparison that includes:
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1. Feature Comparison
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2. Performance Analysis
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3. Use Case Suitability
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4. Community and Support
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5. Learning Curve
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6. Integration Capabilities
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7. Future Prospects
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Include:
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- Pros and Cons
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- Real-world use cases
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- Industry adoption
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- Community feedback
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- Future roadmap
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"""
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return _get_llm_response(prompt, gpt_provider)
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def generate_case_studies(repo_data: Dict, gpt_provider: str = "gemini") -> str:
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"""Generate real-world case studies and success stories."""
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prompt = f"""As a technical writer, create compelling case studies for the following GitHub repository:
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Repository Data:
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{repo_data}
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Create detailed case studies that include:
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1. Problem Statement
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2. Solution Implementation
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3. Technical Challenges
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4. Results and Benefits
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5. Lessons Learned
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6. Future Improvements
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Make the case studies:
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- Based on real-world scenarios
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- Include technical details
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- Show measurable results
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- Provide actionable insights
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"""
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return _get_llm_response(prompt, gpt_provider)
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def generate_contribution_guide(repo_data: Dict, gpt_provider: str = "gemini") -> str:
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"""Generate a comprehensive contribution guide."""
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prompt = f"""As an open-source maintainer, create a detailed contribution guide for the following GitHub repository:
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Repository Data:
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{repo_data}
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Create a contribution guide that includes:
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1. Development Setup
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2. Code Style Guidelines
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3. Testing Requirements
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4. Documentation Standards
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5. Pull Request Process
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6. Review Guidelines
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7. Community Guidelines
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Make the guide:
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- Clear and concise
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- Include examples
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- Cover all contribution types
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- Provide templates
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"""
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return _get_llm_response(prompt, gpt_provider)
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def generate_security_guide(repo_data: Dict, gpt_provider: str = "gemini") -> str:
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"""Generate a security best practices guide."""
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prompt = f"""As a security expert, create a comprehensive security guide for the following GitHub repository:
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Repository Data:
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{repo_data}
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Create a security guide that includes:
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1. Security Architecture
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2. Authentication & Authorization
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3. Data Protection
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4. Secure Configuration
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5. Vulnerability Management
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6. Incident Response
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7. Compliance Requirements
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Make the guide:
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- Practical and actionable
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- Include security checklists
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- Provide code examples
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- Cover common vulnerabilities
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"""
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return _get_llm_response(prompt, gpt_provider)
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def generate_performance_guide(repo_data: Dict, gpt_provider: str = "gemini") -> str:
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"""Generate a performance optimization guide."""
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prompt = f"""As a performance optimization expert, create a detailed performance guide for the following GitHub repository:
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Repository Data:
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{repo_data}
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Create a performance guide that includes:
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1. Performance Metrics
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2. Optimization Techniques
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3. Benchmarking Guidelines
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4. Resource Management
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5. Scaling Strategies
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6. Monitoring Setup
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7. Troubleshooting
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Make the guide:
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- Data-driven
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- Include benchmarks
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- Provide optimization tips
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- Cover different scales
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"""
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return _get_llm_response(prompt, gpt_provider)
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def _get_llm_response(prompt: str, gpt_provider: str) -> str:
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"""Get response from the specified LLM provider."""
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system_prompt = """You are an expert technical writer and GitHub repository analyst with deep expertise in software development, documentation, and technical communication.
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Your role is to create high-quality, accurate, and engaging content based on GitHub repository data. You should:
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1. **Technical Accuracy**
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- Ensure all technical information is precise and up-to-date
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- Verify code examples and configurations
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- Cross-reference documentation and source code
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- Maintain consistency with repository standards
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2. **Content Structure**
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- Use clear hierarchical organization
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- Include appropriate code blocks and examples
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- Add relevant diagrams and visual aids
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- Break complex topics into digestible sections
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3. **Writing Style**
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- Maintain a professional yet approachable tone
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- Use active voice and clear language
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- Include practical examples and use cases
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- Add relevant emojis for better readability
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4. **Best Practices**
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- Follow industry-standard documentation practices
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- Include troubleshooting sections
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- Add performance considerations
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- Address security implications
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"""
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try:
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llm_response = llm_text_gen(prompt, system_prompt=system_prompt)
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except Exception as err:
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logger.error(f"Failed to get response from {gpt_provider}: {err}")
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raise
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