Product Requirements Document (PRD)

# Product Requirements Document (PRD)

## 📌 Project Name

NovaDocs: The Intelligent Workspace (placeholder - can be renamed later)

## 🎯 Objective

To build a cutting-edge, Notion-style collaborative workspace that empowers individuals and teams to create, organize, and manage knowledge more efficiently. The core differentiator will be its deep integration with advanced AI capabilities (primarily Google Gemini and OpenAI's ChatGPT) for smarter information discovery, content generation, and knowledge utilization.

## 🧭 Product Goals

- **MVP (Minimum Viable Product)**: Deliver a robust and intuitive document editor with a flexible, nested workspace structure, robust real-time collaboration, and foundational user management. This forms the essential platform for future AI integration.
- **AI-First Integration**: Seamlessly integrate AI features (Gemini and ChatGPT APIs) as a core part of the user experience, making AI-powered functionalities feel native and deeply embedded, not as separate add-ons.
- **User Experience (UX) Consistency**: Maintain a UI/UX consistent with the existing Notion application, ensuring familiarity and ease of adoption for users.
- **Ease of Use**: Ensure a minimal learning curve, allowing users to quickly create structured content and collaborate effectively without extensive onboarding. The UI should be clean, responsive, and familiar to users of modern productivity tools.
- **Collaboration-Ready**: Provide seamless, real-time multi-user editing, enabling teams to work together on documents synchronously and asynchronously.
- **Extensible & Scalable Foundation**: Architect the system to readily integrate advanced AI features and support future growth in user base and feature set.
- **No Monetization (V1)**: The initial version (V1) will not include monetization features. Focus is on core functionality and AI integration.

## 👤 Target Users

- **Knowledge Workers**: Individuals who extensively create, organize, and consume information (e.g., consultants, researchers, writers).
- **Small to Medium Teams**: Organizations (e.g., startups, product teams, marketing agencies) needing a central hub for shared knowledge, project documentation, and team collaboration.
- **Students & Educators**: For note-taking, project collaboration, and research organization.
- **Product & Design Teams**: For specs, roadmaps, research notes, and design documentation.

## 🧱 Core Features (MVP Scope)

1. **Document Editor**
   - **Rich Text Editor**:
     - Basic Formatting: Headers (H1-H6), bold, italic, underline, strikethrough, code inline.
     - Color Palette: Primarily black and white for text and background.
     - Lists: Ordered, unordered, and toggle lists (collapsible sections, similar to Notion).
     - Checkboxes: Interactive to-do list items.
     - Blocks: Support for code blocks (with syntax highlighting for common languages), quotes, callouts.
     - Tables: Basic table creation (rows, columns, text input), resizing columns, and basic cell merging capabilities. No complex formulas in MVP.
   - **Content Embedding**:
     - Image Embedding: Drag-and-drop or upload for image embedding. Support for common image formats (JPG, PNG, GIF, SVG). Images should be resizable within the editor. No current file size limit.
     - File Uploads: Ability to upload any file type (e.g., PDFs, documents, spreadsheets). No current file size limit. Files will be stored securely and be downloadable.
     - Social Media/External Embeds: Automatic identification and embedding of content from popular social media platforms (e.g., YouTube videos, Twitter posts, Figma prototypes, Spotify tracks) when a URL is pasted. This will render the embedded content directly within the page.
     - Hyperlinking: Internal page links (via @page_name or similar inline search) and external URL links.
   - **Markdown Support**: Two-way live Markdown rendering (type Markdown syntax, see rich text, and vice-versa). Support for CommonMark.
   - **Content Blocks**: Ability to insert various content types as distinct blocks (e.g., text block, image block, code block, table block, embedded content block).
   - **Block Handles**: Hover-to-reveal handles for rearranging, duplicating, and deleting blocks.
2. **Workspace & Navigation**
   - **Workspace**: Each user has a primary workspace. Ability to create multiple workspaces for different contexts (e.g., personal, team A, team B).
   - **Sidebar Navigation**: Collapsible, hierarchical page tree in a left sidebar, mirroring the nested structure.
   - **Nested Pages**: Support for infinitely nested pages (pages within pages), no practical depth limit for now.
   - **Page Management**:
     - Create new pages (as sub-pages or top-level pages).
     - Rename, delete, duplicate pages.
     - Drag-and-drop to reorder pages within the hierarchy.
   - **Breadcrumbs**: Clear navigation path displayed at the top of each page.
   - **Favorites/Starred Pages**: Users can mark important pages for quick access.
3. **Real-Time Collaboration**
   - **Multi-User Editing**: Real-time presence indicators (avatars/cursors) for active collaborators on the same page.
   - **Concurrent Editing**: Synchronized text editing, allowing multiple users to type on the same page simultaneously with minimal latency. Conflict resolution should be seamless and transparent (handled by CRDT library).
   - **Concurrent Users**: System should support up to 10 concurrent users per document/workspace.
   - **Basic Version History**: A simple log of major page changes (e.g., timestamped snapshots) with the ability to view previous versions and potentially revert to them (per page, not individual user edits).
4. **User Accounts & Permissions**
   - **Authentication**: Email + password authentication. Integration with OAuth providers (Google, GitHub) for easier sign-up/login.
   - **Workspace Sharing**:
     - Invite users to a workspace via email.
     - Define roles for invited users:
       - Owner: Full control over workspace, user management, and invitations.
       - Editor: Can create, edit, and delete any content within the workspace.
       - Viewer: Can only view content within the workspace.
   - **Page-level Access Control** (Post-MVP consideration): Initially, permissions are workspace-wide.
5. **Search**
   - **Full-Text Search**: Efficient keyword-based search across all document content within the user's accessible workspaces.
   - **Basic Filtering**: Ability to filter search results by page title, creation date, and AI-generated tags (once implemented).

## 🧪 AI-Powered Killer Add-ons (Deep Integration - Built with Gemini/ChatGPT)

These features are core to NovaDocs' vision and will be integrated using Google Gemini and OpenAI API keys.

1. **AI Auto-Linker**
   - **Description**: As a user types, the AI will analyze the content and suggest linking to other relevant pages within the same workspace.
   - **Suggestion Mechanism**: Suggestions will appear as:
     - Inline Highlights: Underline or subtle highlight of text that could be linked.
     - Tooltips: On hover over suggested text, a tooltip appears with suggested pages.
   - **Triggering**: Suggestions are triggered by:
     - Semantic Similarity: Identifying text segments semantically similar to existing page content.
     - Keyword Matching: Direct matching of page titles or existing tags.
     - Contextual Understanding: AI will leverage its understanding to suggest links based on the broader context of the text being written.
   - **Interaction**: Users can:
     - Click-to-Link: Clicking on a suggested link in the tooltip or highlight automatically inserts the link.
     - Drag-and-Drop: Dragging the suggested page from a sidebar panel (if implemented) onto the highlighted text to create a link.
   - **Link Scope (V1)**: Only internal links to pages within the current workspace are supported. No linking to content in other workspaces or external web URLs through this feature in V1. Embedding of other social media content (from the core editor) is a separate feature.
2. **Knowledge Graph Builder**
   - **Description**: Provides a dynamic, visual representation of the relationships between documents and key entities across the entire workspace.
   - **Visualization**: An interactive graph view (e.g., D3.js based) showing nodes (documents, key entities) and edges (relationships).
   - **Automatic Generation**: The graph is automatically built by the AI analyzing all content within the workspace.
   - **Entity & Relationship Focus**: AI will primarily focus on extracting and linking:
     - People: Names of individuals mentioned.
     - Concepts: Key ideas, terms, or topics discussed.
     - Projects: Specific project names or initiatives.
     - Implicit Relationships: Connections inferred from co-occurrence, semantic similarity, or direct mentions between documents.
   - **Interactivity**:
     - Zooming & Panning: Standard graph navigation.
     - Filtering: Ability to filter nodes/edges by type (e.g., show only "People" nodes, or "Concept" relationships).
     - Node Expansion: Clicking a node (document or entity) reveals more details or related nodes.
     - Jump to Document: Clicking a document node in the graph navigates directly to that document.
   - **Primary Use Case**: Exploring relationships between knowledge, identifying undocumented connections, and understanding the overall structure of information within the workspace.
3. **Auto Tag Generator**
   - **Description**: AI automatically generates relevant, semantic tags for every page based on its content, significantly enhancing searchability and organization.
   - **Semantic Tags**: AI will analyze the full text of a page to identify and generate:
     - Keywords: Important single or multi-word terms.
     - Categories: Broader subject classifications.
     - Topics: Specific themes discussed.
   - **Generation Process**: Data from the page will be sent to Google Gemini (or ChatGPT as a fallback/secondary model) to generate these tags.
   - **Display & Usage**:
     - Tags are displayed prominently on each page (e.g., at the top or in a dedicated section).
     - Tags are integrated into the search functionality, allowing users to filter search results by specific tags.
   - **User Control**: Users can edit (add, remove, modify) any AI-generated tags. Changes made by users will be prioritized and potentially used to refine the AI's tagging behavior over time.
4. **Question Answering on Workspace**
   - **Description**: Users can ask natural language questions about the content across their entire workspace, and the AI will provide a concise, summarized answer with source citations.
   - **Input**: A dedicated search bar or chat interface for natural language queries.
   - **AI Models**: Google Gemini will be the primary LLM for generating answers. ChatGPT will be used as a fallback or for specific types of queries if Gemini does not provide a satisfactory response.
   - **Output Format**:
     - Summarized Answer: A direct, concise summary of the answer.
     - Source Links: Accompanying links to the specific documents or even relevant sections/paragraphs from which the answer was derived.
   - **Ambiguity/Conflicting Information Handling**: If the AI identifies ambiguity or conflicting information across documents, it will:
     - Attempt to synthesize the information, noting the different perspectives if possible.
     - If a clear answer cannot be synthesized, it will list all relevant documents/answers for the user to review.
   - **Scope of Content**: The AI answers questions using all content within the user's accessible workspaces, including private user documents. If a user explicitly makes content public for a "community" feature (Post-MVP), that content would also be included.
   - **User Feedback**: A "Was this helpful?" (e.g., thumbs up/down) mechanism will be implemented to collect user feedback, which will be used to improve the AI's answering capabilities over time.
   - **Out-of-Scope Questions**:
     - If the initial query to Gemini (or ChatGPT) does not yield a relevant response, the query will be retried up to 3 times with slightly refined prompts or different models.
     - If after 3 retries a satisfactory answer cannot be found within the workspace content, the AI will inform the user that it couldn't find relevant information in the workspace.

## 📐 Technical Requirements

### Frontend

- **Framework**: React (with Next.js for server-side rendering benefits like faster initial load and better SEO, or Vite for faster development cycles).
- **Styling**: TailwindCSS for utility-first CSS.
- **State Management**: Zustand or Redux for complex state management, especially for real-time collaboration synchronization and sidebar navigation state.

### Backend

- **Language/Framework**: Node.js + Express (or NestJS for a more structured, scalable approach).
- **Database**: MongoDB (for flexible document storage and hierarchical data).
- **Authentication**: JWT for API authentication, integrated with OAuth providers (Google, GitHub).

### Editor Stack

- **Rich Text Editor Library**: Tiptap (based on ProseMirror) or Slate.js – both offer extensibility crucial for AI integrations and custom block types.
- **Real-Time Sync Algorithm**: CRDT (Conflict-free Replicated Data Type) libraries like Yjs or Automerge for robust real-time collaboration and offline capabilities.
- **Markdown Parsing**: Dedicated Markdown-to-HTML parser for robust Markdown support.

### AI/ML Infrastructure

- **LLM Integration**:
  - Primary: Google Gemini API (for various AI tasks, especially Q&A and semantic understanding).
  - Secondary/Fallback: OpenAI API (e.g., GPT-4o, GPT-4) for specific tasks or as a robust fallback.
- **API Key Management**: Secure handling and rotation of API keys.
- **Vector Database**: Dedicated vector database (e.g., Pinecone, Weaviate, Milvus, Qdrant, or integrated into PostgreSQL with pgvector) for storing document embeddings. This is critical for efficient semantic search, auto-linking, and Q&A.
- **Graph Database**: (For Knowledge Graph) Neo4j or ArangoDB for storing and querying graph data and relationships extracted by AI.
- **Embedding Generation**: Utilize text embedding models (from Google, OpenAI, or open-source) to generate embeddings for all document content upon creation/update.
- **Scalability**: Consider serverless functions (AWS Lambda, Google Cloud Functions) for AI inference endpoints to handle fluctuating computational load efficiently and cost-effectively.

## 📋 Success Metrics (MVP)

### User Onboarding:

- Time to create first page < 30 seconds.
- Time to understand basic workspace navigation < 2 minutes.

### Engagement:

- Daily Active Users (DAU) / Weekly Active Users (WAU) ratio.
- Average number of pages created per active user per week.
- Average session duration.

### Collaboration:

- Percentage of documents with multiple collaborators.
- Latency of real-time updates (target: < 100ms for text changes).
- Concurrent user stability: No significant performance degradation with 10 concurrent users per document.

### AI Feature Adoption (Post-MVP):

- Percentage of pages with AI-generated tags accepted by users.
- Frequency of AI Auto-Linker suggestions accepted/used.
- Number of queries made to the Question Answering system per day.
- User satisfaction with AI answers (based on feedback mechanism).

### Performance:

- System uptime > 99.5%.
- Page load times < 2 seconds.

### User Retention:

- 7-day retention rate.
- 30-day retention rate.

## 🛠 Tools & Integrations

- **Design**: Figma (for UI/UX consistent with Notion)
- **Issue Tracking**: Linear, Jira, or Trello
- **Version Control**: GitHub
- **CI/CD**: Vercel/Netlify (frontend), GitHub Actions/CircleCI (backend)
- **Communication**: Slack/Discord
- **Monitoring**: Sentry (error tracking), Prometheus/Grafana (performance monitoring), specialized AI monitoring tools (e.g., Weights & Biases for LLM metrics)
- **Analytics**: Mixpanel, Google Analytics
- **AI APIs**: Google Gemini API, OpenAI API