Product Requirements Document (PRD)

# Product Requirements Document (PRD)
## Ollama Chat - Local AI Conversation Interface

---

## 1. Overview

Ollama Chat is a production-ready, ChatGPT-style web application that enables users to have natural language conversations with locally-hosted AI models through Ollama. The application addresses the growing need for privacy-conscious AI interactions by keeping all data and processing on the user's local machine, eliminating dependence on external cloud services and paid API subscriptions. Built with modern web technologies (Next.js 15, React 18, TypeScript 5.6, and Tailwind CSS 3.4), it provides a polished user experience with real-time streaming responses, persistent chat history, and comprehensive markdown support with syntax highlighting.

The target users include developers, researchers, privacy-conscious individuals, students, and organizations requiring local AI deployment due to data sensitivity concerns or offline operation requirements.

---

## 2. Goals & Non-Goals

### Goals

- **Enable seamless local AI conversations** with a ChatGPT-quality interface that feels familiar and intuitive to users
- **Maintain complete data privacy** by storing all conversations locally without any external API calls or telemetry
- **Provide real-time streaming responses** to create an engaging, responsive user experience during AI generation
- **Support persistent chat history** with automatic saving, browsing, and resumption of previous conversations
- **Deliver a production-quality application** with comprehensive testing (unit, component, and E2E tests), proper error handling, and deployment readiness
- **Enable multi-model support** allowing users to switch between different AI models based on their specific needs
- **Ensure responsive design** that works seamlessly across desktop, tablet, and mobile devices
- **Support rich markdown rendering** including code syntax highlighting, tables, lists, and GitHub Flavored Markdown

### Non-Goals

- Cloud-based or hosted SaaS offering (this is a self-hosted solution only)
- Integration with commercial AI APIs (OpenAI, Anthropic, etc.)
- User authentication or multi-user support (single-user local application)
- Model training or fine-tuning capabilities
- Real-time collaboration or chat sharing features
- Mobile native applications (web-only, though mobile-responsive)
- Voice input/output or multimodal capabilities beyond text
- Built-in model management beyond listing and selection (users manage models via Ollama CLI)

---

## 3. Background / Context

The rapid advancement of large language models has created demand for AI-powered conversational interfaces. However, existing solutions typically require:
- Paid subscriptions to cloud services
- Sending sensitive data to external servers
- Internet connectivity for all operations
- Trust in third-party data handling practices

**Ollama** emerged as an open-source solution to run AI models locally, but it provides only a command-line interface and REST API. This creates a significant usability gap for non-technical users and those who prefer graphical interfaces.

**Market Need:**
- Enterprises with data governance requirements need on-premises AI solutions
- Developers want to experiment with AI models without API costs
- Privacy-conscious users seek alternatives to cloud-based AI services
- Educational institutions require offline-capable tools for teaching and research

**Related Projects:**
- [Ollama](https://ollama.ai) - The underlying local AI inference engine
- ChatGPT - Inspiration for UI/UX patterns
- Open WebUI - Alternative interface for Ollama (more complex, feature-heavy)
- LM Studio - Desktop application for local AI (macOS-focused)

**Why This Project Exists:**
To bridge the gap between Ollama's powerful but command-line-only interface and the polished experience users expect from modern conversational AI tools, while maintaining complete local control and privacy.

---

## 4. User Stories

> As a **privacy-conscious professional**, I want to have AI conversations without sending my data to external servers so that I can maintain confidentiality of sensitive information.

> As a **developer**, I want to quickly test different AI models without switching between terminal windows so that I can efficiently compare model outputs and select the best one for my use case.

> As a **student**, I want to access AI assistance for learning without requiring internet connectivity or paid subscriptions so that I can study anywhere and avoid ongoing costs.

> As a **researcher**, I want to save and revisit my previous AI conversations so that I can track my thought process and reference past discussions.

> As a **casual user**, I want a familiar ChatGPT-like interface with dark mode and mobile support so that I can use AI comfortably on any device at any time of day.

> As a **technical writer**, I want AI responses to render markdown with proper code highlighting so that I can easily read and copy formatted code examples.

> As an **enterprise IT administrator**, I want a self-contained application that runs entirely on-premises so that I can deploy AI capabilities without violating data residency policies.

> As a **power user**, I want my conversations to maintain context across multiple messages so that I can have natural, flowing discussions without repeating information.

---

## 5. Functional Requirements

### 5.1 Model Management
- **FR-1.1**: System shall fetch and display all locally available Ollama models on application startup
- **FR-1.2**: Users shall be able to view model details including name and size in GB
- **FR-1.3**: Users shall be able to switch between models at any time during a session
- **FR-1.4**: System shall automatically select the first available model if none is selected
- **FR-1.5**: System shall display appropriate error messages when Ollama is not running or no models are installed

### 5.2 Chat Interface
- **FR-2.1**: Users shall be able to send text messages through an input field at the bottom of the screen
- **FR-2.2**: System shall support Enter key to send messages and Shift+Enter for newlines
- **FR-2.3**: Chat input shall be disabled during message processing and when no model is selected
- **FR-2.4**: System shall display user messages with a distinct visual style (blue avatar, "U" indicator)
- **FR-2.5**: System shall display AI responses with a distinct visual style (green avatar, "AI" indicator)
- **FR-2.6**: System shall show a typing indicator (animated dots) while waiting for AI response
- **FR-2.7**: Messages shall auto-scroll to the bottom as new content arrives

### 5.3 Real-Time Streaming
- **FR-3.1**: AI responses shall stream token-by-token in real-time as they are generated
- **FR-3.2**: System shall use Server-Sent Events (SSE) for efficient streaming
- **FR-3.3**: Partial responses shall be visible and update continuously during generation
- **FR-3.4**: System shall maintain conversation context across multiple message exchanges
- **FR-3.5**: Context tokens shall be preserved and sent with each subsequent request in a conversation

### 5.4 Markdown & Code Rendering
- **FR-4.1**: AI responses shall render full GitHub Flavored Markdown (GFM) including:
  - Headers (H1-H6)
  - Bold, italic, and inline code
  - Ordered and unordered lists
  - Blockquotes
  - Tables
  - Links and images
- **FR-4.2**: Code blocks shall include syntax highlighting for common languages (JavaScript, Python, TypeScript, Java, Go, etc.)
- **FR-4.3**: User messages shall preserve whitespace and newlines but not render markdown
- **FR-4.4**: Long code blocks or content shall not break the layout or cause horizontal scrolling

### 5.5 Chat History Persistence
- **FR-5.1**: System shall automatically save conversations to local disk after each message exchange (debounced by 500ms)
- **FR-5.2**: Chat history shall be stored as JSON files in a `chat-history/` directory (git-ignored)
- **FR-5.3**: Each chat shall have a unique ID, title (derived from first user message), timestamp, model, messages, and context
- **FR-5.4**: Users shall be able to browse all saved conversations in the sidebar History tab, sorted by most recent
- **FR-5.5**: Users shall be able to load any previous conversation, restoring all messages and context
- **FR-5.6**: Users shall be able to delete individual conversations with confirmation
- **FR-5.7**: System shall display chat metadata including model name, date, and message count for each history entry
- **FR-5.8**: Continuing a loaded conversation shall maintain its original chat ID and update timestamp

### 5.6 User Interface & Navigation
- **FR-6.1**: Application shall feature a collapsible sidebar with History and Models tabs
- **FR-6.2**: Sidebar shall be persistent on desktop (≥1024px) and overlay/collapsible on mobile/tablet
- **FR-6.3**: Users shall be able to create a new chat via the "+ New Chat" button, clearing current conversation
- **FR-6.4**: System shall display the currently selected model name in the header
- **FR-6.5**: When no messages exist, system shall display a welcome screen with instructions
- **FR-6.6**: Currently active chat shall be visually highlighted in the history list

### 5.7 Theme & Appearance
- **FR-7.1**: Application shall support both light and dark modes
- **FR-7.2**: Theme preference shall persist in browser localStorage
- **FR-7.3**: System shall detect and respect user's OS theme preference on first visit
- **FR-7.4**: Theme toggle button shall be accessible in the header at all times
- **FR-7.5**: All UI elements shall be styled consistently across both themes

### 5.8 Error Handling
- **FR-8.1**: System shall display user-friendly error messages when Ollama is unreachable
- **FR-8.2**: System shall gracefully handle network failures and provide retry guidance
- **FR-8.3**: System shall validate that model and prompt are present before sending API requests
- **FR-8.4**: File system errors during history save/load shall be logged and reported to user
- **FR-8.5**: Streaming errors shall not crash the application; partial responses shall remain visible

---

## 6. Technical Overview

### Architecture Summary

Ollama Chat follows a modern **full-stack Next.js architecture** using the App Router pattern with clear separation of concerns:

**Frontend (Client-Side):**
- React 18 with TypeScript for type-safe component development
- Client-side state management using React hooks (useState, useEffect, useRef)
- Tailwind CSS for utility-first styling and responsive design
- ReactMarkdown with remark-gfm and rehype-highlight for rich content rendering

**Backend (API Routes):**
- Next.js API Routes serve as a REST API layer
- Server-side streaming using Web Streams API and ReadableStream
- File system operations for JSON-based chat persistence
- Proxy layer to Ollama's local API (http://localhost:11434)

**Data Storage:**
- Chat histories stored as individual JSON files in `chat-history/` directory
- LocalStorage for theme preference persistence
- No database required (file-based for simplicity and portability)

**External Integration:**
- Ollama API for model listing and generation (local HTTP API)

### Key Technologies & Frameworks

| Layer | Technology | Version | Purpose |
|-------|-----------|---------|---------|
| Framework | Next.js | 15.0 | Full-stack React framework with App Router |
| UI Library | React | 18.3 | Component-based UI development |
| Language | TypeScript | 5.6 | Type safety and developer experience |
| Styling | Tailwind CSS | 3.4 | Utility-first CSS framework |
| Markdown | react-markdown | 9.0 | Markdown rendering |
| Markdown Extensions | remark-gfm | 4.0 | GitHub Flavored Markdown support |
| Syntax Highlighting | rehype-highlight | 7.0 | Code block syntax highlighting |
| Unit Testing | Vitest | 4.0 | Fast unit test runner |
| Testing Library | React Testing Library | 16.3 | Component testing utilities |
| E2E Testing | Playwright | 1.56 | End-to-end browser testing |
| Runtime | Node.js | 18+ | JavaScript runtime |

### Data Flow

```mermaid
sequenceDiagram
    participant User
    participant Frontend
    participant API Routes
    participant Ollama
    participant FileSystem

    User->>Frontend: Type message & send
    Frontend->>Frontend: Add user message to state
    Frontend->>API Routes: POST /api/chat (model, prompt, context)
    API Routes->>Ollama: POST /api/generate (streaming)

    loop Stream tokens
        Ollama-->>API Routes: Token chunk
        API Routes-->>Frontend: SSE stream chunk
        Frontend->>Frontend: Update assistant message
    end

    Ollama-->>API Routes: Final chunk with context
    API Routes-->>Frontend: Complete response
    Frontend->>Frontend: Update context state

    Note over Frontend: Debounce 500ms

    Frontend->>API Routes: POST /api/history (save chat)
    API Routes->>FileSystem: Write JSON file
    FileSystem-->>API Routes: Success
    API Routes-->>Frontend: Saved confirmation
```

### Component Architecture

```
┌─────────────────────────────────────────────┐
│           app/page.tsx (Main App)           │
│  - State management                         │
│  - API orchestration                        │
│  - Layout composition                       │
└────────────┬────────────────────────────────┘
             │
     ┌───────┴───────┬──────────────┬─────────────┐
     │               │              │             │
┌────▼────┐   ┌──────▼──────┐  ┌───▼────┐  ┌─────▼──────┐
│ Sidebar │   │ ChatMessage │  │ Input  │  │ThemeToggle │
│         │   │  Component  │  │Compont │  │            │
└─────────┘   └─────────────┘  └────────┘  └────────────┘


┌─────────────────────────────────────────────┐
│              API Routes Layer               │
├─────────────────────────────────────────────┤
│  /api/chat       → Streaming responses      │
│  /api/models     → Model listing            │
│  /api/history    → CRUD operations          │
│  /api/history/[id] → Get specific chat      │
└───────────────────┬─────────────────────────┘
                    │
          ┌─────────┴─────────┐
          │                   │
     ┌────▼─────┐      ┌──────▼─────┐
     │  Ollama  │      │ FileSystem │
     │   API    │      │   (JSON)   │
     └──────────┘      └────────────┘
```

### API Endpoints

**POST /api/chat**
- **Purpose**: Send message to AI and receive streaming response
- **Request Body**: `{ model: string, prompt: string, context?: number[] }`
- **Response**: Server-Sent Events stream with JSON objects
- **Response Format**: `{ model: string, response: string, done: boolean, context?: number[] }`

**GET /api/models**
- **Purpose**: Retrieve list of available Ollama models
- **Response**: `{ models: Array<{ name: string, size: number, modified_at: string, digest: string }> }`

**GET /api/history**
- **Purpose**: List all saved chat histories
- **Response**: `{ histories: ChatHistory[] }` (sorted by updatedAt, descending)

**POST /api/history**
- **Purpose**: Save or update a chat history
- **Request Body**: `ChatHistory` object
- **Response**: `{ success: boolean, history: ChatHistory }`

**DELETE /api/history?id={id}**
- **Purpose**: Delete specific chat history
- **Response**: `{ success: boolean }`

**GET /api/history/[id]**
- **Purpose**: Retrieve specific chat by ID
- **Response**: `ChatHistory` object

### Type Definitions

```typescript
interface Message {
  id: string;
  role: 'user' | 'assistant';
  content: string;
  timestamp: Date;
}

interface Model {
  name: string;
  modified_at: string;
  size: number;
  digest: string;
}

interface ChatHistory {
  id: string;
  title: string;
  model: string;
  messages: Message[];
  context: number[];
  createdAt: string;
  updatedAt: string;
}

interface OllamaResponse {
  model: string;
  created_at: string;
  response: string;
  done: boolean;
  context?: number[];
}
```

---

## 7. UX / UI Considerations

### User Experience Principles

**1. Familiarity Over Novelty**
- Interface closely mirrors ChatGPT to minimize learning curve
- Standard patterns: sidebar navigation, centered chat area, bottom input
- Users should feel immediately comfortable without tutorial

**2. Responsive Feedback**
- Real-time streaming shows AI "thinking" process
- Typing indicators during wait times
- Smooth animations and transitions (sidebar, scrolling)
- Immediate visual feedback for all user actions

**3. Progressive Disclosure**
- Sidebar collapses on mobile to maximize chat space
- History and Models in separate tabs to reduce cognitive load
- Empty states provide clear next steps
- Errors include actionable guidance

**4. Accessibility & Readability**
- High contrast text in both light and dark modes
- Readable font sizes (base 16px)
- Proper semantic HTML structure
- Keyboard navigation support (Enter to send, Shift+Enter for newline)

### Visual Design

**Color Palette:**
- Light Mode: White backgrounds, gray accents, blue highlights
- Dark Mode: Dark gray backgrounds (#1a1a1a), lighter gray for cards, blue highlights
- User messages: Blue accent (#2563eb)
- AI messages: Green accent (#16a34a)

**Layout:**
- Fixed sidebar (256px) on desktop
- Overlay sidebar on mobile/tablet (triggered by hamburger menu)
- Centered chat content (max-width: 1024px)
- Full-bleed message backgrounds for visual separation

**Typography:**
- System font stack for performance and native feel
- Monospace for code blocks
- Clear hierarchy: larger headers, smaller metadata

**Animations:**
- Sidebar slide transitions (300ms ease-in-out)
- Smooth scroll to new messages
- Bounce animation for typing indicator
- Hover states on interactive elements

### User Flows

**First-Time User Flow:**
1. Land on empty welcome screen
2. Notice "Select a model from sidebar" instruction
3. Open sidebar (visible by default on desktop)
4. See "Models" tab, select a model
5. Return to chat, type first message
6. Watch AI response stream in
7. Continue conversation naturally
8. Notice chat automatically appears in History tab

**Returning User Flow:**
1. Land on last conversation or empty state
2. Open sidebar to History tab
3. Browse previous chats by title/date
4. Click to load desired conversation
5. Context fully restored, continue where left off

**Model Switching Flow:**
1. In active conversation, decide to try different model
2. Open sidebar → Models tab
3. Select new model (highlighted immediately)
4. Current chat preserved; can continue or start new
5. New messages use newly selected model

---

## 8. Performance & Scalability Requirements

### Performance Targets

| Metric | Target | Notes |
|--------|--------|-------|
| Initial Page Load | < 2 seconds | First contentful paint (FCP) |
| Time to Interactive | < 3 seconds | Fully interactive UI |
| First Token Latency | 1-5 seconds | Depends on model and hardware |
| Streaming Token Rate | 10-50 tokens/sec | Depends on model and hardware |
| History Load Time | < 500ms | For 100 saved chats |
| Auto-save Debounce | 500ms | Balance between safety and performance |
| Bundle Size | < 500KB (gzipped) | Initial JavaScript bundle |

### Scalability Considerations

**Chat History Scale:**
- System designed for 100-1000 chat histories per user
- File-based storage is suitable for single-user local deployment
- No database overhead for small-to-medium usage
- For >1000 chats, consider implementing pagination or database migration

**Message Length:**
- No hard limit on message length (client or server)
- Very long messages (>10,000 tokens) may impact model performance
- UI handles long messages gracefully with proper scrolling

**Concurrent Usage:**
- Designed for single user (localhost only)
- Ollama itself handles one request at a time per model
- Multiple browser tabs will compete for Ollama resources

**Resource Constraints:**
- Frontend memory usage: ~50-100MB typical
- Backend memory usage: ~100-200MB typical
- Disk usage: ~1MB per 50 conversations (average)
- Ollama resource usage: 4-16GB RAM depending on model

### Reliability & Uptime

**Availability:**
- Application uptime depends on local Node.js process
- No external dependencies beyond Ollama (also local)
- Target: 99.9% uptime during active use sessions

**Error Recovery:**
- Graceful degradation when Ollama is offline
- Streaming errors don't lose partial responses
- File system errors don't crash the app
- User can retry failed operations

**Data Durability:**
- Chat history saved to disk with 500ms debounce
- Immediate save on page unload (best effort via effect cleanup)
- No RAID or backup built-in (user responsibility)
- JSON format is human-readable for manual recovery

---

## 9. Security, Privacy, and Compliance

### Authentication & Authorization

**Current State:**
- No authentication required (localhost-only application)
- No user accounts or multi-tenancy
- Access control relies on localhost network restriction
- Suitable for personal/single-user deployment only

**Future Consideration:**
- If deployed on LAN, consider adding basic auth
- Not recommended for public internet exposure without significant security hardening

### Data Protection

**Privacy-First Architecture:**
- **Zero external data transmission**: All processing happens locally
- **No telemetry or analytics**: No tracking of any kind
- **No cloud dependencies**: Works completely offline (except initial npm install)
- **User data never leaves machine**: Conversations stored only in local filesystem

**Data Storage Security:**
- Chat history stored as plain JSON files (not encrypted)
- Files are git-ignored to prevent accidental repository commits
- File permissions inherited from OS user (no additional hardening)
- Context arrays contain model state, not raw conversation data

**Sensitive Data Handling:**
- Application does not distinguish between sensitive and non-sensitive data
- Users must manage their own data if handling confidential information
- Recommendation: Regular manual backups of `chat-history/` directory
- Recommendation: Use OS-level encryption (FileVault, BitLocker) for disk protection

### Input Sanitization

**Frontend:**
- User input is not sanitized before sending to Ollama (Ollama handles injection risks)
- ReactMarkdown library sanitizes HTML in AI responses by default
- Code blocks are safely rendered without execution

**Backend:**
- API routes validate presence of required fields (model, prompt)
- No SQL injection risk (no database)
- File paths are constructed safely to prevent directory traversal
- JSON parsing is wrapped in try-catch for error handling

### Compliance Considerations

**GDPR (EU General Data Protection Regulation):**
- Data processing is entirely local; GDPR doesn't typically apply to personal use
- If deployed in organizational setting, GDPR compliance is simplified by local storage
- No data processors or controllers beyond the user themselves
- Users have complete control to export (JSON files) or delete data

**SOC 2 / Enterprise Compliance:**
- No built-in audit logging (would need to be added for enterprise use)
- No access controls beyond localhost restriction
- Chat history has timestamps for basic audit trail
- Suitable for organizations with on-premises AI requirements

**Data Residency:**
- All data remains on user's machine by design
- Ideal for organizations with strict data residency requirements
- No cross-border data transfer concerns

**Recommendations for Enterprise Deployment:**
- Implement authentication middleware (e.g., HTTP Basic Auth, OAuth proxy)
- Add audit logging for all API calls and chat operations
- Enable HTTPS with valid certificates
- Implement role-based access control if multi-user support added
- Regular security audits and penetration testing

---

## 10. Success Metrics / KPIs

### User Adoption & Engagement

| Metric | Target | Measurement Method |
|--------|--------|-------------------|
| Time to First Message | < 5 minutes | From npm install to first AI response |
| Daily Active Usage | > 10 interactions/day | Count of message exchanges per user |
| Chat History Retention | > 80% of chats saved | Percentage of chats with >1 message that get saved |
| Return Usage Rate | > 60% within 7 days | Users who return after first session |
| Session Duration | 15-30 minutes average | Time spent in active conversation |

### Technical Performance

| Metric | Target | Measurement Method |
|--------|--------|-------------------|
| API Response Time | < 200ms | Time from API request to first byte (excluding model inference) |
| First Token Latency (P50) | < 3 seconds | Median time to first AI token |
| First Token Latency (P95) | < 8 seconds | 95th percentile first token time |
| Streaming Throughput | > 20 tokens/sec | Average token generation rate |
| UI Responsiveness | < 100ms | Time from user action to UI feedback |
| Error Rate | < 1% | Percentage of failed message sends |
| Auto-save Success Rate | > 99% | Successful history saves / total attempts |

### User Experience

| Metric | Target | Measurement Method |
|--------|--------|-------------------|
| Zero-State Clarity | > 90% understand next steps | User testing / survey |
| Model Switching Success | > 95% complete successfully | Track successful model changes |
| History Load Time | < 500ms | Time to load and render previous chat |
| Mobile Usability Score | > 85/100 | Lighthouse mobile score |
| Accessibility Score | > 90/100 | Lighthouse accessibility score |
| Theme Toggle Persistence | 100% | Theme correctly restored on reload |

### Quality & Reliability

| Metric | Target | Measurement Method |
|--------|--------|-------------------|
| Test Coverage | > 80% | Vitest code coverage report |
| E2E Test Pass Rate | > 95% | Playwright test results |
| Zero Critical Bugs | 0 P0 bugs in production | Issue tracker |
| Error Recovery Rate | > 90% | Successful retries after errors |
| Data Loss Rate | 0% | Chat history preservation |

### Comparison Benchmarks

**vs. Ollama CLI:**
- 10x reduction in time to start conversation (GUI vs. terminal commands)
- 100% improvement in conversation continuity (automatic context vs. manual)
- Visual appeal and markdown rendering (immeasurable improvement)

**vs. ChatGPT:**
- 100% privacy (no data sent to external servers)
- 100% cost savings (no subscription fees)
- Comparable UX quality (similar interface patterns)
- Slightly higher friction (must install and manage Ollama)

**vs. Open WebUI:**
- Simpler setup (fewer dependencies and configuration)
- Faster initial load time (smaller bundle)
- More focused feature set (less overwhelming)
- Better suited for individual users vs. teams

---

## 11. Future Considerations

### Near-Term Enhancements (Next 3-6 months)

**Chat Management Features:**
- Search functionality across chat history (full-text search)
- Tagging and categorization of conversations
- Export individual chats to markdown/PDF
- Bulk operations (delete multiple, export all)
- Chat renaming (custom titles beyond first message)

**UX Improvements:**
- Copy-to-clipboard button for code blocks and AI responses
- Message regeneration (re-run with same prompt)
- Edit sent messages and branch conversations
- Keyboard shortcuts for navigation (Ctrl+K command palette)
- Message timestamps (show/hide toggle)

**Model Management:**
- Display more model metadata (capabilities, context window size)
- Model download/pull directly from UI (wrapper around Ollama CLI)
- Model performance hints (speed/quality tradeoffs)
- Recent models quick-access list

### Mid-Term Features (6-12 months)

**Advanced Conversation Features:**
- System prompts / custom instructions per chat
- Temperature and token limit controls
- Stop sequences configuration
- Context window management (truncate old messages)
- Conversation templates for common use cases

**Collaboration & Sharing:**
- Export conversations as shareable HTML
- Import/export all settings and history
- Sync history across devices (self-hosted sync server)
- Read-only chat viewer URLs

**Developer Experience:**
- API key for programmatic access to chat history
- Webhook support for chat events
- Plugin/extension system for custom functionality
- Docker container for easier deployment

**Enhanced Privacy:**
- Optional encryption for chat history files
- Conversation auto-expiry (TTL for sensitive chats)
- Secure deletion (overwrite before delete)
- Privacy mode (no history saving)

### Long-Term Vision (12+ months)

**Multi-Modal Support:**
- Image input support (for vision-capable models)
- Document upload and analysis (PDF, DOCX)
- Voice input/output (TTS/STT integration)
- Code execution sandbox for runnable examples

**Advanced AI Features:**
- Multi-agent conversations (multiple models in one chat)
- Retrieval-Augmented Generation (RAG) with document indexing
- Function calling / tool use integration
- Fine-tuning workflow integration

**Enterprise Features:**
- Multi-user support with authentication
- Role-based access control (RBAC)
- Audit logging and compliance reporting
- Centralized model and policy management
- SSO integration (SAML, OIDC)

**Platform Expansion:**
- Desktop application (Electron or Tauri)
- Mobile apps (React Native or PWA)
- Browser extension for inline AI assistance
- VS Code extension for code-related queries

### Technical Debt & Refactoring

**Code Quality:**
- Migrate to server actions for better type safety
- Implement proper state management (Zustand, Jotai)
- Extract business logic into custom hooks
- Add more granular component tests

**Performance:**
- Implement virtual scrolling for long conversations
- Lazy load chat history list
- Optimize bundle size (tree shaking, code splitting)
- Add service worker for offline capability

**Infrastructure:**
- Database migration for better chat history scalability (SQLite, PostgreSQL)
- Redis caching for model metadata
- Rate limiting for API routes
- Monitoring and observability (Prometheus, Grafana)

---

## 12. Appendix

### References & Links

**Project Repository:**
- GitHub: (Not specified in current codebase)
- License: MIT License
- Documentation: README.md (comprehensive)

**External Dependencies:**
- [Ollama Official Site](https://ollama.ai)
- [Next.js Documentation](https://nextjs.org/docs)
- [React Documentation](https://react.dev)
- [Tailwind CSS Documentation](https://tailwindcss.com/docs)
- [ReactMarkdown Documentation](https://github.com/remarkjs/react-markdown)
- [Playwright Documentation](https://playwright.dev)
- [Vitest Documentation](https://vitest.dev)

**Related Standards:**
- [GitHub Flavored Markdown Spec](https://github.github.com/gfm/)
- [Server-Sent Events (SSE) Specification](https://html.spec.whatwg.org/multipage/server-sent-events.html)
- [Ollama API Documentation](https://github.com/ollama/ollama/blob/main/docs/api.md)

### Code Insights

**Test Coverage:**
- Unit tests: 4 component test files (ChatMessage, ChatInput, Sidebar, ThemeToggle)
- API tests: 1 route test file (models endpoint)
- E2E tests: 1 comprehensive test file (chat.spec.ts)
- Test frameworks: Vitest (unit/component), Playwright (E2E)
- Coverage target: >80% (configured in vitest.config.ts)

**Code Structure Quality:**
- TypeScript strict mode: Enabled
- Component organization: Clear separation (components/, app/, lib/)
- Type definitions: Centralized in lib/types.ts
- API routes: RESTful design with proper HTTP methods
- Error handling: Try-catch blocks in all async operations

**Technical Highlights:**
- **Streaming Implementation**: Custom ReadableStream with proper cleanup
- **Debounced Auto-save**: useEffect with 500ms timeout and cleanup
- **Context Preservation**: Ollama context array maintained in state
- **Responsive Design**: Mobile-first with lg: breakpoint for desktop
- **Dark Mode**: System preference detection + localStorage persistence

### Open Questions & Assumptions

**Assumptions:**
1. Users have technical capability to install Node.js and Ollama
2. Localhost deployment is acceptable for target users
3. File-based storage is sufficient (no need for database)
4. Single-user usage pattern (no concurrent users)
5. Users manage their own backups of chat history
6. English language is sufficient (no i18n requirements)

**Open Questions:**
1. Should we add authentication for LAN deployment scenarios?
2. Is encryption of chat history files necessary for v1.0?
3. Should we implement conversation context limits (to prevent token overflow)?
4. Do users need more granular control over model parameters (temperature, top-p)?
5. Should we add conversation import/export features in v1.0 or defer?
6. Is there demand for a hosted/cloud version (conflicts with privacy goals)?

**Technical Decisions:**
- **Why Next.js 15?** App Router for cleaner API design, server components for future optimization
- **Why File Storage?** Simplicity, no database maintenance, portable JSON format
- **Why No Database?** Overkill for single-user local application, adds setup complexity
- **Why Tailwind CSS?** Rapid development, small bundle size, excellent dark mode support
- **Why Vitest over Jest?** Faster, better ESM support, Vite integration
- **Why Playwright over Cypress?** Better performance, multi-browser support, modern API

### Version History & Roadmap

**Current Version: 1.0.0**
- ✅ Core chat functionality with streaming
- ✅ Model selection and switching
- ✅ Chat history persistence (CRUD)
- ✅ Markdown rendering with code highlighting
- ✅ Dark/light theme toggle
- ✅ Responsive design (mobile, tablet, desktop)
- ✅ Comprehensive test coverage (unit + E2E)
- ✅ Production-ready error handling

**Planned Version: 1.1.0** (Future)
- Search in chat history
- Copy code block button
- Message regeneration
- Custom chat titles
- Export to markdown

**Planned Version: 2.0.0** (Future)
- System prompts per chat
- Model parameter controls
- Document upload support
- Desktop application packaging

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## Document Metadata

**Version:** 1.0
**Last Updated:** 2024-01-04
**Author:** Ollama Chat Product Team
**Status:** Approved for Development
**Review Date:** (To be scheduled)

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**This PRD is a living document. It should be reviewed and updated quarterly or when significant product changes are proposed.**