Snippet / Abstract

KnowNote is a local-first AI workspace built on Electron and React 19 designed to transform static documents (PDF, Word, PPT) into an interactive, queryable personal knowledge base. By leveraging SQLite with sqlite-vec for semantic vector retrieval and RAG (Retrieval-Augmented Generation) technology, KnowNote enables secure, offline-capable AI Q&A using custom LLMs like OpenAI and DeepSeek. It offers a privacy-centric alternative to cloud-based tools, ensuring total data sovereignty while streamlining research and writing workflows.

Deep Dive into KnowNote: Building Your Local-First AI Knowledge Base with RAG and React 19

In the current era of digital information overload, the primary challenge for professionals is no longer acquiring information, but utilizing it effectively. We hoard gigabytes of PDF reports, Word manuscripts, and PowerPoint slides, yet these files often remain “dead data”—stored in folders, difficult to retrieve, and disconnected from our active thinking processes.

Enter KnowNote, an open-source tool redefining personal knowledge management. Inspired by Google NotebookLM but architected for strict privacy, KnowNote is a local-first AI workspace that helps you think, research, and write. It places the power of document parsing, vector retrieval, and AI dialogue directly on your local machine.

This article provides a comprehensive technical review of KnowNote, based strictly on its official documentation and release specifications. We will explore its core architecture, RAG capabilities, modern tech stack, and deployment guide.

The Core Philosophy: Why “Local-First” Matters

KnowNote’s design philosophy is distinct and purposeful: it focuses on user-owned knowledge.

1. The Return of Data Sovereignty

With the proliferation of cloud-based AI services, data privacy is a significant concern for researchers, developers, and legal professionals. KnowNote adopts a “Lock-in Local” strategy:

• Local Storage: All data resides physically on your local device via SQLite.
• Privacy Assurance: There is no risk of sensitive documents being uploaded to third-party servers for training public models.
• Total Control: Users maintain complete ownership of their knowledge assets, eliminating risks associated with cloud service outages or account bans.

2. From Static Storage to Dynamic Dialogue

Traditional file management is passive. KnowNote uses technology to make documents “alive.” By employing RAG (Retrieval-Augmented Generation), it allows users to interview their documents. Instead of manually scrolling through hundreds of pages, you can engage in a natural language dialogue with your knowledge base to extract insights, connect ideas, and reuse information.

Feature Breakdown: How Tech Empowers Knowledge

KnowNote’s workflow is designed around “Input – Processing – Output.” Here is how the technology facilitates this flow.

📚 Multi-Format Document Management & Parsing

A robust knowledge base depends on its ability to ingest data. KnowNote supports a wide array of formats, utilizing specific libraries for high-fidelity parsing:

• PDF Documents: Uses pdfjs-dist to extract content while maintaining the structural integrity of academic papers or manuals.
• Word Documents (.docx): Leverages mammoth to convert complex Word formatting into clean, usable text data.
• PowerPoint (.pptx): Supports the extraction of presentation content, ideal for reviewing meeting minutes or lecture slides.
• Web Content: Capable of scraping and importing web pages directly into the local database.
• Intelligent Parsing: Beyond simple text reading, the system automatically extracts document structure and key content to optimize downstream retrieval.

🤖 RAG-Driven AI Intelligence

This is the “brain” of KnowNote. It is not just a chatbot; it is a domain expert grounded in your specific data.

• Retrieval-Augmented Generation (RAG): When a user asks a question, the system first queries the local database for relevant segments. These segments are fed to the AI as context, significantly reducing AI “hallucinations” and ensuring answers are factual.
• Multi-LLM Support: Users are not locked into a single provider. You can configure OpenAI, DeepSeek, or other compatible AI services.
• Source Traceability: To satisfy academic and professional rigor, every answer generated by KnowNote includes citations tracing back to the specific location in the original document.

🔍 SQLite-Based Vector Retrieval

To find answers based on meaning rather than just keywords, KnowNote integrates advanced vector database technology locally:

• Semantic Search: Powered by the sqlite-vec extension, the system understands the intent behind a query. For example, a search for “nutritional facts” can retrieve paragraphs discussing “vitamins” even if the exact keyword matches are absent.
• Intelligent Matching: Rapidly locates the specific document slices most relevant to the user’s prompt.
• Structured Storage: The foundation is SQLite combined with Drizzle ORM, ensuring type-safe interactions and high-performance data handling without the overhead of a server.

⚡ Native Desktop Experience

• Framework: Built on Electron, providing a consistent, native experience across Windows and macOS.
• Performance: Optimized for fast startup and fluid interaction, respecting local resource usage.
• Offline Capability: Core functions—document management, search, and reading—work entirely offline. (Note: AI generation requires an internet connection unless a local LLM API is configured).

Tech Stack Analysis: Modern Electron Architecture

KnowNote utilizes a cutting-edge stack that ensures maintainability and performance. For developers, this serves as a reference architecture for high-performance local apps.

Frontend & UI Layer

Data & Backend Layer (Local)

User Interface & Workflow

KnowNote follows a “Three-Column Layout” designed to minimize cognitive load:

1. Left Column: Knowledge Base Management
   Your central command. Import PDFs, Word docs, and URLs here. The system displays file lists and real-time parsing status.
2. Center Column: AI Q&A Interface
   The interaction hub. Users input questions, and the AI responds using knowledge retrieved from the left column, displaying clickable citations.
3. Right Column: Note Output
   The creation zone. An integrated Tiptap editor allows you to refine AI answers, synthesize thoughts, or draft structured notes based on your research.

Installation & Deployment Guide

KnowNote offers straightforward access for both end-users and developers.

1. For Users: Fast Installation

The quickest way to start is via the pre-compiled binaries available on GitHub Releases.

• Windows: Download KnowNote-Setup-{version}.exe.
• macOS:

  • Intel Macs: KnowNote-{version}.dmg.
  • Apple Silicon (M1/M2/M3): KnowNote-{version}-arm64.dmg.

2. For Developers: Source Deployment

If you wish to inspect the code or contribute, follow this setup for the development environment.

Prerequisites:

• Node.js: Version 18+.
• Package Manager: pnpm (recommended) or npm.

Step 1: Clone the Repository

git clone https://github.com/MrSibe/KnowNote.git
cd KnowNote

Step 2: Install Dependencies
This installs Electron, React, and parsing libraries like mammoth and pdfjs-dist.

pnpm install

Step 3: Launch Development Server
This boots up both the renderer and the main Electron process.

pnpm dev

3. Database Management Commands

KnowNote uses Drizzle ORM for database operations. Developers can use the following commands:

• Generate Migrations: pnpm db:generate
• Run Migrations: pnpm db:migrate
• Visual DB Management: pnpm db:studio (Opens a web interface to inspect the local SQLite file).
• Build for Production:

  • Win: pnpm db:build:win
  • Mac: pnpm db:build:mac
  • Linux: pnpm db:build:linux

Product Roadmap

KnowNote is actively evolving. The official roadmap highlights a shift from “Answering” to “Generating.”

✅ Completed Features

• Integration with major LLM services.
• RAG-based semantic document retrieval.
• Multi-format import (PDF, Word, PPT, Web).
• Basic note generation.

🚧 In Development

• Quiz Generation: Automatically generate test questions from documentation—a vital feature for educators and students.
• One-Click PPT Generation: Converting notes directly into presentation slides.
• Mind Map Generation: Visualizing conversation structures and knowledge connections automatically.

FAQ

Q: Where is my data stored when using KnowNote?
A: KnowNote follows a strictly local-first policy. All imported documents and generated vector indexes (SQLite) are stored directly on your local device. Your files are never uploaded to the cloud, ensuring complete data privacy.

Q: Can I use KnowNote offline?
A: Yes, core functionalities like document management, reading, and searching work offline. However, the AI Q&A feature requires an internet connection to communicate with LLM APIs (like OpenAI), unless you configure a local LLM provider that mimics compatible API endpoints.

Q: Which file formats does KnowNote support?
A: KnowNote currently supports parsing for PDF, Word (.docx), PowerPoint (.pptx), and web links. It uses specialized libraries like pdfjs-dist and mammoth for accurate data extraction.

Q: Does KnowNote support Linux?
A: While pre-built installers are primarily for Windows and macOS, the codebase supports Linux. Developers can clone the repository and run pnpm build:linux to create a Linux-compatible build.

Conclusion

KnowNote represents the future of personal knowledge management: Decentralized, Intelligent, and Deep. It avoids the trap of building a massive cloud ecosystem, focusing instead on leveraging modern technologies like React 19 and sqlite-vec to build a responsive, private “Second Brain” on your local machine.

Whether you are a graduate student synthesizing literature or a professional managing complex project data, KnowNote offers a new interaction paradigm. By democratizing RAG technology, it makes “conversing with your knowledge” a reality.