Detailed Docs & Onboarding improvements

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+Architecture Overview
+====================
+
+This document provides a comprehensive overview of the AI-Writer architecture, explaining the system's components, their interactions, and the design principles behind the implementation.
+
+High-Level Architecture
+----------------------
+
+.. image:: diagrams/high_level_architecture.png
+   :alt: High-level architecture diagram of AI-Writer
+   :width: 100%
+
+The AI-Writer platform consists of several key components:
+
+1. **User Interface Layer**
+   
+   * Streamlit-based web interface
+   * Command-line interface for automation
+   * API endpoints for programmatic access
+
+2. **Core Services Layer**
+   
+   * AI Writers: Various specialized content generation modules
+   * Web Research: Tools for gathering factual information from the internet
+   * SEO Tools: Utilities for optimizing content for search engines
+   * Analytics: Content performance tracking and analysis
+
+3. **Data Storage Layer**
+   
+   * Vector Database (ChromaDB): Stores embeddings for semantic search
+   * Relational Database (SQLite): Stores structured data like user preferences and content metadata
+
+4. **External Integrations Layer**
+   
+   * LLM Providers: OpenAI, Google Gemini, Anthropic, etc.
+   * Search Providers: Tavily, SerperDev, Exa, etc.
+   * Image Generation: Stability AI
+   * Publishing Platforms: WordPress, Jekyll, etc.
+
+Database Architecture
+--------------------
+
+.. image:: diagrams/database_architecture.png
+   :alt: Database architecture diagram of AI-Writer
+   :width: 100%
+
+The database architecture consists of two main components:
+
+1. **Vector Storage**
+   
+   * Uses ChromaDB for storing and retrieving text embeddings
+   * Enables semantic search capabilities
+   * Stores content in collections for efficient retrieval
+
+2. **Relational Storage**
+   
+   * Uses SQLite for structured data storage
+   * Key models include:
+     - User: Stores user preferences and settings
+     - ContentItem: Represents content created by users
+     - ContentVersion: Tracks version history of content
+     - Analytics: Stores performance metrics for content
+
+Content Generation Workflow
+--------------------------
+
+.. image:: diagrams/content_generation_workflow.png
+   :alt: Content generation workflow diagram of AI-Writer
+   :width: 100%
+
+The content generation process follows these steps:
+
+1. **Input Phase**
+   
+   * User provides keywords, topics, or other input parameters
+   * System configures the generation process based on user preferences
+
+2. **Research Phase**
+   
+   * Web research is conducted using various search providers
+   * Relevant information is gathered and processed
+   * Facts are extracted and organized for use in content generation
+
+3. **Content Creation Phase**
+   
+   * Content outline is generated based on research
+   * Initial draft is created using AI models
+   * Final content is refined and polished
+
+4. **Enhancement Phase**
+   
+   * SEO optimization is applied to improve search visibility
+   * Images are generated or selected to complement the content
+   * Metadata is generated for better categorization and discovery
+
+5. **Storage Phase**
+   
+   * Content is stored in both vector and relational databases
+   * Embeddings are created for semantic search capabilities
+   * Metadata is indexed for efficient retrieval
+
+6. **Publishing Phase**
+   
+   * Content is formatted for the target platform
+   * Publishing options include WordPress, Markdown, and others
+   * Content is delivered to the user or published directly
+
+Design Principles
+----------------
+
+The AI-Writer architecture is built on the following design principles:
+
+1. **Modularity**
+   
+   * Components are designed to be independent and interchangeable
+   * New AI models and services can be added with minimal changes
+   * Functionality is organized into logical modules
+
+2. **Extensibility**
+   
+   * The system is designed to be easily extended with new features
+   * Plugin architecture allows for custom integrations
+   * Configuration options enable customization without code changes
+
+3. **Reliability**
+   
+   * Error handling is implemented throughout the system
+   * Fallback mechanisms ensure continued operation
+   * Logging provides visibility into system behavior
+
+4. **Performance**
+   
+   * Caching is used to improve response times
+   * Asynchronous processing for long-running tasks
+   * Efficient data storage and retrieval mechanisms
+
+5. **Security**
+   
+   * API keys are securely stored and managed
+   * User data is protected with appropriate measures
+   * Input validation prevents common security issues
+
+Future Architecture Enhancements
+-------------------------------
+
+Planned improvements to the architecture include:
+
+1. **Distributed Processing**
+   
+   * Support for distributed content generation
+   * Load balancing for improved scalability
+   * Parallel processing of research and generation tasks
+
+2. **Advanced Caching**
+   
+   * Intelligent caching of common queries and results
+   * Cache invalidation strategies for fresh content
+   * Distributed cache for multi-user environments
+
+3. **Enhanced Security**
+   
+   * Role-based access control
+   * End-to-end encryption for sensitive data
+   * Advanced authentication mechanisms
+
+4. **Containerization**
+   
+   * Docker containers for easier deployment
+   * Kubernetes support for orchestration
+   * Microservices architecture for better scalability