feat: enhance OllaHub with new features and UI improvements
- Introduced a comprehensive README.md detailing the application, its architecture, and key features. - Implemented a local-first architecture with deep research capabilities, including query decomposition and parallel web scraping. - Added a reactive UI built with React and Next.js, featuring Tailwind CSS for styling and Zustand for state management. - Integrated a task scheduler for recurring tasks and system monitoring for real-time resource management. - Enhanced settings page with debug mode options and improved hardware dashboard loading states. - Updated chat interface to include debug data visualization and improved user experience during message streaming. - Removed unused favicon and updated application icons for better branding. - Improved navigation handling with non-blocking transitions and state management for setup completion.
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This is a [Next.js](https://nextjs.org) project bootstrapped with [`create-next-app`](https://nextjs.org/docs/app/api-reference/cli/create-next-app).
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# OllaHub
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Local-first LLM workspace with deep research capabilities and native OS integration.
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[](https://www.rust-lang.org/)
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[](https://tauri.app/)
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[](https://nextjs.org/)
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[](https://www.typescriptlang.org/)
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[](LICENSE)
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[](https://github.com/evandrodevbr/OllaHub)
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## Overview
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OllaHub is a desktop application that orchestrates local Large Language Models (LLMs) through Ollama, providing a private, offline-capable workspace for research, analysis, and conversation. The application executes complex research workflows by decomposing queries, performing parallel web scraping, and synthesizing context from multiple sources before generating responses.
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The architecture prioritizes data privacy by keeping all processing local, using SQLite with full-text search for efficient storage, and integrating ONNX Runtime for semantic relevance ranking without external API dependencies.
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## Key Features
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### Deep Research Pipeline
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The application implements a multi-stage research workflow:
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1. **Query Decomposition**: Uses LLM to break complex queries into 3-5 atomic, search-optimized sub-queries
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2. **Parallel Web Scraping**: Executes multiple searches concurrently using headless Chrome and DuckDuckGo metadata API
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3. **Knowledge Base Aggregation**: Collects and deduplicates content from multiple sources
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4. **Context Condensation**: Applies relevance scoring and summarization to fit context within token limits
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5. **Validation**: Verifies knowledge base sufficiency before response generation
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The pipeline includes safety checks to ensure decomposed queries remain relevant to the original intent, with automatic fallback to the original query if decomposition fails.
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### Local-First Architecture
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- **Ollama Integration**: Direct subprocess management of Ollama for model execution
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- **ONNX Embeddings**: Local semantic similarity using all-MiniLM-L6-v2 (384-dimensional embeddings)
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- **SQLite with FTS5**: Full-text search with virtual tables for session titles and message content
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- **Hybrid Storage**: Dual persistence (SQLite + JSON) with automatic migration and fallback
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### Hybrid Storage System
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The application uses SQLite as the primary storage backend with JSON as a fallback:
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- **SQLite (Primary)**: WAL mode for concurrent reads/writes, optimized cache settings, FTS5 virtual tables for search
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- **JSON (Legacy)**: Maintained for backward compatibility and export functionality
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- **Automatic Migration**: Seamless transition from JSON to SQLite with data preservation
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### Reactive UI
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Built with React 19 and Next.js 16, the interface uses:
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- **Tailwind CSS**: Utility-first styling with design tokens
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- **Radix UI / Shadcn**: Accessible, headless component primitives
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- **Zustand**: Lightweight state management for settings and UI state
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- **Framer Motion**: Smooth animations for state transitions
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- **Resizable Panels**: Customizable layout with persistent preferences
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### MCP (Model Context Protocol) Integration
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Supports external tool integration through MCP servers:
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- **JSON-RPC Communication**: Bidirectional communication with MCP servers via stdin/stdout
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- **Tool Discovery**: Automatic enumeration of available tools from running servers
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- **Process Management**: Lifecycle management (start, stop, restart) with health monitoring
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- **Tool Execution**: Runtime invocation of MCP tools with result injection into LLM context
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### System Monitoring
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Real-time hardware monitoring and resource management:
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- **GPU Detection**: NVIDIA and AMD GPU identification with utilization tracking
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- **CPU/Memory Stats**: System-wide resource usage via `sysinfo` crate
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- **Model Recommendations**: Hardware-aware model suggestions based on available VRAM/RAM
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### Task Scheduler
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Cron-based task execution system:
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- **Recurring Tasks**: Schedule periodic operations (e.g., knowledge base updates)
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- **Task Queue**: Priority-based execution with retry logic
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- **State Persistence**: Task state survives application restarts
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## Architecture
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```mermaid
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graph TB
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subgraph Frontend["Frontend (Next.js)"]
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UI[React Components]
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Hooks[Custom Hooks]
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Store[Zustand Store]
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end
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subgraph Bridge["Tauri Bridge"]
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Commands[Tauri Commands]
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Events[Event System]
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end
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subgraph Backend["Rust Core"]
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Orchestrator[Orchestrator]
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Scraper[Web Scraper]
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DB[(SQLite + FTS5)]
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Embeddings[ONNX Embeddings]
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Scheduler[Task Scheduler]
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MCP[MCP Manager]
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end
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subgraph External["External Services"]
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Ollama[Ollama Subprocess]
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Chrome[Headless Chrome]
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MCP_Servers[MCP Servers]
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end
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UI --> Hooks
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Hooks --> Store
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Store --> Commands
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Commands --> Orchestrator
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Orchestrator --> Scraper
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Orchestrator --> DB
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Orchestrator --> Embeddings
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Orchestrator --> Scheduler
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Orchestrator --> MCP
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Scraper --> Chrome
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Orchestrator --> Ollama
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MCP --> MCP_Servers
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Events --> UI
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style Frontend fill:#2563eb,color:#fff
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style Bridge fill:#7c3aed,color:#fff
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style Backend fill:#dc2626,color:#fff
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style External fill:#059669,color:#fff
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```
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### Data Flow
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1. **User Query** → Frontend captures input and triggers preprocessing
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2. **Query Preprocessing** → Intent classification, entity extraction, query enrichment
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3. **Deep Research** → Query decomposition → Parallel search → Scraping → Knowledge base aggregation
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4. **Context Condensation** → Relevance scoring (ONNX embeddings) → Summarization → Token optimization
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5. **LLM Generation** → Ollama subprocess with streaming response
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6. **Storage** → SQLite persistence with FTS5 indexing for future retrieval
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## Tech Stack
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### Core
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| Layer | Technology | Version | Purpose |
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|-------|-----------|---------|---------|
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| **Desktop Framework** | Tauri | 2.9.2 | Native desktop app with Rust backend |
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| **Backend Language** | Rust | 1.77.2 | System-level operations, performance-critical paths |
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| **Frontend Framework** | Next.js | 16.0.3 | React-based UI with SSR capabilities |
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| **UI Library** | React | 19.2.0 | Component-based interface |
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| **Language** | TypeScript | 5.0 | Type-safe frontend development |
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### Frontend
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| Category | Technology | Purpose |
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|----------|-----------|---------|
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| **State Management** | Zustand | 5.0.8 | Settings and UI state |
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| **Styling** | Tailwind CSS | 4.0 | Utility-first CSS framework |
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| **Components** | Radix UI | Latest | Accessible primitives (dialogs, dropdowns, tooltips) |
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| **Components** | Shadcn/ui | Custom | Pre-built component library |
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| **Animations** | Framer Motion | 12.23.24 | Smooth transitions |
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| **Markdown** | react-markdown | 10.1.0 | Message rendering with GFM support |
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| **Syntax Highlighting** | react-syntax-highlighter | 16.1.0 | Code block rendering |
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### Backend
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| Category | Technology | Purpose |
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|----------|-----------|---------|
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| **Database** | SQLite (rusqlite) | 0.31 | Local data persistence with FTS5 |
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| **Web Scraping** | headless_chrome | 1.0 | Browser automation for content extraction |
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| **HTML Parsing** | scraper | 0.18 | DOM parsing and content extraction |
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| **Text Processing** | readability | 0.3 | Main content extraction from HTML |
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| **HTTP Client** | reqwest | 0.12 | API requests with streaming support |
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| **Async Runtime** | tokio | 1.0 | Async I/O and concurrency |
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| **Task Scheduling** | tokio-cron-scheduler | 0.9 | Cron-based task execution |
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| **System Info** | sysinfo | 0.36.1 | Hardware monitoring |
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| **Embeddings** | ONNX Runtime (ort) | 2.0.0-rc.10 | Local semantic similarity |
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| **Tokenization** | tokenizers | 0.21 | Text tokenization for embeddings |
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| **Array Operations** | ndarray | 0.16 | Numerical computations |
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### AI/ML
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| Component | Technology | Purpose |
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|-----------|-----------|---------|
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| **LLM Runtime** | Ollama | Local model execution |
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| **Embeddings Model** | all-MiniLM-L6-v2 | 384-dim semantic embeddings |
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| **Embeddings Runtime** | ONNX Runtime | Model inference |
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## Getting Started
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First, run the development server:
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### Prerequisites
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- **Node.js**: 20.x or later
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- **Rust**: 1.77.2 or later
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- **pnpm**: Latest version (`npm install -g pnpm`)
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- **Ollama**: [Install Ollama](https://ollama.ai) and ensure it's running on `http://localhost:11434`
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### Installation
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1. Clone the repository:
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```bash
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npm run dev
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# or
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yarn dev
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# or
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pnpm dev
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# or
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bun dev
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git clone https://github.com/evandrodevbr/OllaHub.git
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cd OllaHub
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```
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Open [http://localhost:3000](http://localhost:3000) with your browser to see the result.
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2. Install dependencies:
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```bash
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pnpm install
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```
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You can start editing the page by modifying `app/page.tsx`. The page auto-updates as you edit the file.
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3. Run in development mode:
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```bash
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pnpm tauri dev
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```
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This project uses [`next/font`](https://nextjs.org/docs/app/building-your-application/optimizing/fonts) to automatically optimize and load [Geist](https://vercel.com/font), a new font family for Vercel.
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This command will:
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- Start the Next.js development server on `http://localhost:3000`
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- Build the Rust backend
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- Launch the Tauri application window
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## Learn More
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### Building for Production
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To learn more about Next.js, take a look at the following resources:
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```bash
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pnpm tauri build
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```
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- [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js features and API.
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- [Learn Next.js](https://nextjs.org/learn) - an interactive Next.js tutorial.
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The built application will be in `src-tauri/target/release/bundle/`:
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- **Windows**: `.msi` installer
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- **macOS**: `.dmg` or `.app`
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- **Linux**: `.deb`, `.AppImage`, or `.rpm`
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You can check out [the Next.js GitHub repository](https://github.com/vercel/next.js) - your feedback and contributions are welcome!
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### Environment Configuration
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## Deploy on Vercel
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The application uses Tauri's built-in configuration. No environment variables are required for basic operation. MCP server configurations are stored in the application data directory and managed through the UI.
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The easiest way to deploy your Next.js app is to use the [Vercel Platform](https://vercel.com/new?utm_medium=default-template&filter=next.js&utm_source=create-next-app&utm_campaign=create-next-app-readme) from the creators of Next.js.
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## Project Structure
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Check out our [Next.js deployment documentation](https://nextjs.org/docs/app/building-your-application/deploying) for more details.
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```
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OllaHub/
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├── app/ # Next.js app directory (pages)
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│ ├── chat/ # Chat interface
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│ ├── settings/ # Application settings
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│ ├── setup/ # Initial setup wizard
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│ └── tasks/ # Task management
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├── components/ # React components
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│ ├── chat/ # Chat-specific components
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│ ├── settings/ # Settings UI components
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│ ├── setup/ # Setup wizard components
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│ └── ui/ # Reusable UI primitives (Shadcn)
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├── hooks/ # React custom hooks
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│ ├── use-chat.ts # Chat state management
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│ ├── use-deep-research.ts # Research pipeline orchestration
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│ ├── use-mcp-servers.ts # MCP server management
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│ └── ...
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├── lib/ # Utility functions
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│ ├── knowledge-base-processor.ts # Context condensation
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│ ├── contextual-analyzer.ts # Query analysis
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│ ├── query-enricher.ts # Query expansion
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│ └── ...
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├── services/ # Service layer
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│ └── webSearch.ts # Web search abstraction
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├── store/ # Zustand stores
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│ └── settings-store.ts # Application settings
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├── src-tauri/ # Rust backend
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│ ├── src/
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│ │ ├── lib.rs # Main Tauri commands
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│ │ ├── db.rs # SQLite database layer
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│ │ ├── embeddings.rs # ONNX embeddings module
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│ │ ├── web_scraper.rs # Web scraping logic
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│ │ ├── scheduler.rs # Task scheduling
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│ │ └── ...
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│ └── Cargo.toml # Rust dependencies
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└── data/ # Static data
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├── models/ # Model metadata JSON files
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└── prompts/ # LLM prompt templates
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```
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## Design Patterns
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### Backend (Rust)
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- **Command Pattern**: Tauri commands encapsulate backend operations
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- **State Management**: Arc<Mutex<>> for shared state (browser instances, MCP processes)
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- **Error Handling**: Result<T, E> with custom error types and fallback strategies
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- **Async/Await**: Tokio runtime for concurrent operations (scraping, MCP communication)
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### Frontend (TypeScript)
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- **Custom Hooks**: Encapsulate complex logic (deep research, chat management)
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- **State Machines**: Step-based workflows (idle → planning → searching → aggregating)
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- **Optimistic Updates**: UI updates before backend confirmation
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- **Streaming**: Real-time token rendering via Tauri events
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### Data Layer
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- **Repository Pattern**: Database abstraction in `db.rs`
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- **Migration Strategy**: Dual storage (SQLite + JSON) with automatic fallback
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- **Full-Text Search**: FTS5 virtual tables with trigger-based synchronization
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## Roadmap
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### Planned Features
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- **Vector Store Integration**: Replace SQLite BLOB embeddings with dedicated vector database (LanceDB or Qdrant)
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- **Cloud Sync**: Optional encrypted synchronization of chat sessions across devices
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- **Plugin System**: Extensible architecture for custom research workflows
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- **Advanced RAG**: Multi-modal retrieval with image and document support
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- **Model Fine-tuning UI**: Interface for training custom models on user data
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- **Collaborative Features**: Shared workspaces and team collaboration
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### Technical Improvements
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- **Performance**: Optimize SQLite queries with prepared statements caching
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- **Memory Management**: Implement LRU cache for embeddings model
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- **Error Recovery**: Enhanced retry logic with exponential backoff
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- **Testing**: Comprehensive unit and integration test coverage
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- **Documentation**: API documentation with code examples
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## Contributing
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Contributions are welcome. Please follow these guidelines:
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1. **Code Style**: Follow existing patterns (Rust: `rustfmt`, TypeScript: ESLint)
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2. **Commits**: Use conventional commits (feat:, fix:, docs:, etc.)
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3. **Pull Requests**: Include description of changes and test results
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4. **Issues**: Use GitHub Issues for bug reports and feature requests
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## License
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OllaHub is released under a custom license that combines freeware, creditware, and non-commercial restrictions. See the [LICENSE](LICENSE) file for full terms.
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### Quick Summary
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**You may:**
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- Use OllaHub freely for personal, educational, and internal professional use
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- Modify and distribute the Software (with proper attribution)
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- Fork the project and create derivative works (with attribution)
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**You must:**
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- Give credit to **Evandro Fonseca Junior** and link to [evandro.dev.br](https://evandro.dev.br)
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- Keep copyright notices and attribution intact
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- Not sell the Software or charge for access without written permission
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**You may not:**
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- Sell the Software or bundle it in paid products (without permission)
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- Use it as a monetization mechanism
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- Remove attribution or copyright notices
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- Collect donations on behalf of OllaHub
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For commercial licensing inquiries, visit [evandro.dev.br](https://evandro.dev.br).
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## Acknowledgments
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- [Ollama](https://ollama.ai) for local LLM execution
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- [Tauri](https://tauri.app) for desktop framework
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- [Radix UI](https://www.radix-ui.com) for accessible components
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- [Shadcn](https://ui.shadcn.com) for component library
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- [ONNX Runtime](https://onnxruntime.ai) for embeddings inference
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