171 lines
6.6 KiB
ReStructuredText
171 lines
6.6 KiB
ReStructuredText
Component Diagram
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This document provides detailed information about the components of the AI-Writer system and their interactions.
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Core Components
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AI Writers
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~~~~~~~~~~
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The AI Writers component is responsible for generating various types of content using AI models. It includes several specialized writers:
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- **Blog Writer**: Generates blog posts based on keywords and web research
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- **News Article Writer**: Creates news articles with citations from current events
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- **Social Media Writer**: Produces content for various social platforms
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- **Email Writer**: Generates professional and business emails
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- **Story Writer**: Creates narrative content based on user input
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- **YouTube Script Writer**: Develops scripts for video content
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Each writer implements a common interface but has specialized logic for its specific content type. The writers interact with LLM providers through a unified API layer that handles authentication, rate limiting, and error handling.
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Web Research
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~~~~~~~~~~~
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The Web Research component gathers information from the internet to provide factual context for content generation. It includes:
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- **SERP Integration**: Retrieves search engine results
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- **Tavily Integration**: Uses AI-powered search for relevant information
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- **Exa Integration**: Performs semantic search for related content
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- **Web Crawler**: Extracts content from specified URLs
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- **Content Analyzer**: Processes and summarizes gathered information
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This component ensures that generated content is factually accurate and up-to-date by providing relevant research data to the AI Writers.
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SEO Tools
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~~~~~~~~~
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The SEO Tools component provides utilities for optimizing content for search engines:
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- **Keyword Analyzer**: Identifies and analyzes target keywords
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- **Meta Description Generator**: Creates SEO-friendly meta descriptions
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- **Title Generator**: Produces optimized titles for content
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- **Structured Data Generator**: Creates schema markup for rich snippets
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- **Image Optimizer**: Optimizes images for web performance
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- **On-Page SEO Analyzer**: Evaluates content for SEO best practices
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These tools work together to ensure that generated content has the best chance of ranking well in search engines.
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Analytics
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~~~~~~~~
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The Analytics component tracks and analyzes content performance:
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- **Content Metrics**: Measures readability, engagement potential, and other metrics
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- **Performance Tracker**: Monitors content performance over time
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- **Recommendation Engine**: Suggests improvements based on analytics
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- **Report Generator**: Creates reports on content effectiveness
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This component helps users understand how their content is performing and how it can be improved.
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Data Storage
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-----------
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Vector Database
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~~~~~~~~~~~~~~
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The Vector Database component uses ChromaDB to store and retrieve text embeddings:
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- **Embedding Generator**: Creates vector representations of text
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- **Collection Manager**: Organizes embeddings into collections
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- **Semantic Search**: Performs similarity searches on embeddings
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- **Metadata Manager**: Associates metadata with embeddings
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This component enables semantic search capabilities, allowing users to find content based on meaning rather than just keywords.
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Relational Database
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The Relational Database component uses SQLite to store structured data:
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- **User Manager**: Handles user data and preferences
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- **Content Repository**: Stores content items and metadata
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- **Version Control**: Tracks content versions and changes
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- **Analytics Storage**: Stores performance metrics and analytics data
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This component provides persistent storage for all structured data in the system.
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External Integrations
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LLM Providers
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The LLM Providers component integrates with various AI models:
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- **OpenAI Integration**: Connects to GPT models
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- **Google Gemini Integration**: Interfaces with Gemini models
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- **Anthropic Integration**: Works with Claude models
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- **Ollama Integration**: Supports local LLM deployment
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This component provides a unified interface to different AI models, allowing the system to use the best model for each task.
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Search Providers
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The Search Providers component connects to external search services:
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- **Tavily Client**: Interfaces with Tavily AI search
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- **SerperDev Client**: Connects to SerperDev API
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- **Exa Client**: Integrates with Exa search API
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- **Google Search Client**: Provides access to Google search results
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These integrations enable the system to gather relevant information from the internet for content generation.
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Image Generation
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The Image Generation component creates images to complement content:
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- **Stability AI Integration**: Connects to Stable Diffusion models
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- **DALL-E Integration**: Interfaces with OpenAI's DALL-E
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- **Image Processor**: Optimizes and formats generated images
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- **Image Repository**: Stores and manages generated images
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This component enhances content with relevant visuals, improving engagement and comprehension.
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Publishing Platforms
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The Publishing Platforms component enables content distribution:
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- **WordPress Integration**: Publishes content to WordPress sites
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- **Markdown Exporter**: Creates Markdown files for static sites
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- **HTML Exporter**: Generates HTML for web publishing
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- **API Connectors**: Interfaces with various content platforms
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This component streamlines the process of publishing generated content to various platforms.
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Component Interactions
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Content Generation Flow
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1. User provides input parameters through the UI
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2. Web Research gathers relevant information
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3. AI Writers generate content using research data and LLM providers
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4. SEO Tools optimize the content for search engines
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5. Content is stored in both Vector and Relational databases
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6. Analytics evaluates the content quality and potential performance
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7. Content is prepared for publishing through the Publishing Platforms
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Data Flow
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1. User preferences and settings flow from UI to Relational Database
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2. Research data flows from Web Research to AI Writers
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3. Generated content flows from AI Writers to SEO Tools
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4. Optimized content flows to Data Storage components
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5. Content metrics flow from Analytics to Relational Database
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6. Published content flows from Publishing Platforms to external systems
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Error Handling
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1. LLM provider errors are handled by fallback mechanisms
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2. Web Research failures trigger alternative search methods
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3. Database errors are logged and retried with exponential backoff
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4. Publishing failures are queued for retry
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5. All errors are logged for monitoring and debugging |