OpenNotebookLM

English | 中文

Open-source NotebookLM alternative — Upload documents, chat with sources, generate PPTs / mind maps / podcasts / DrawIO diagrams / flashcards / quizzes / deep research reports in one click

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📅 Changelog

• 2026.03.11 — Code refactoring: strict layered architecture; integrated local TTS model (Qwen3-TTS); added source-based note QA editing (Notion AI style); UI improvements; simplified configuration structure
• 2026.03.08 — Added user management: Supabase email + OTP authentication, multi-user data isolation, email-based user directories; cleaned up deprecated scripts
• 2026.02.27 — Integrated Qwen-DeepResearch deep research module; PPT generation now supports Nano Banana 2 image model
• 2026.02.13 — Initial release

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📸 Screenshots

Dashboard — Notebook management

Notebook workspace — Knowledge base + Smart QA + One-click generation

Generation panel — Multiple output formats

Chat and knowledge base details

PPT generation

Mind map

DrawIO diagram — Inline editor

Knowledge podcast

Flashcard study

Quiz

Web search to import sources

Deep research report generation

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✨ Core Features

Feature	Description
📚 Knowledge Base	Upload PDFs, paste URLs/text, import from web search — aggregate multiple sources into a notebook
🔐 User Management	Supabase email + OTP authentication, multi-user data isolation; works without login when unconfigured
💬 Smart QA	RAG-based Q&A grounded in selected documents, with persistent chat history
🎨 PPT Generation	One-click editable slide decks from knowledge base content
🧠 Mind Maps	Generate Mermaid mind maps with preview and export
🎙️ Knowledge Podcast	Turn knowledge base content into podcast scripts and narration assets
🧩 DrawIO Diagrams	Generate editable DrawIO diagrams from text or images, with inline editor
🃏 Flashcards	Auto-generate study flashcards from knowledge base content
📝 Quizzes	Auto-generate multiple-choice questions with scoring
🔍 Web Search	Supports Serper / SerpAPI / Google CSE / Brave / Bocha search providers
📊 Deep Research Reports	Web search + LLM synthesis to produce structured research reports
🔗 Semantic Search	Local embedding-based vector retrieval with configurable Top-K and models

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🚀 Quick Start

1. Clone & Install

git clone https://github.com/OpenDCAI/opennotebookLM.git
cd opennotebookLM

# Create virtual environment (Conda recommended)
conda create -n opennotebook python=3.11 -y
conda activate opennotebook

# Install Python dependencies
pip install -r requirements-base.txt

2. Configure API Keys

cp fastapi_app/.env.example fastapi_app/.env

Edit fastapi_app/.env with at least the following:

LLM API (Required)

The project calls LLMs via an OpenAI-compatible API. By default it uses APIyi as a relay service (supports GPT / Claude / Gemini and more).

# LLM API endpoint (OpenAI-compatible format)
DEFAULT_LLM_API_URL=https://api.apiyi.com/v1

# Your API key (obtain from APIyi or another LLM provider)
# Can also be configured dynamically in the frontend settings panel

You can use any OpenAI-compatible API service (OpenAI official, Azure OpenAI, local Ollama, etc.) — just change DEFAULT_LLM_API_URL.

Search API (Required for web search features)

Web search and deep research report features require a search engine API. Any one of the following providers will work:

Provider	Configuration	Sign up
Serper (recommended)	Env variable SERPER_API_KEY	serper.dev
SerpAPI	Pass search_api_key from frontend	serpapi.com
Google CSE	Pass search_api_key + google_cse_id from frontend	programmablesearchengine.google.com
Brave Search	Pass search_api_key from frontend	brave.com/search/api
Bocha	Pass search_api_key from frontend	open.bochaai.com

Serper is configured via a backend environment variable. Other providers can be set in the frontend settings panel.

# Serper (Google search), recommended
SERPER_API_KEY=your_serper_api_key

Supabase (Optional — User Management)

For multi-user authentication and data isolation. If not configured or left empty, the system automatically enters trial mode (no login required, single local user, all core features work normally).

When configured: email + password sign-up/login, OTP email verification, per-user data isolation (separate directories per user).

# If you don't need multi-user features, you can delete or leave empty
SUPABASE_URL=https://your-project-id.supabase.co
SUPABASE_ANON_KEY=your_supabase_anon_key
SUPABASE_SERVICE_ROLE_KEY=your_supabase_service_role_key

TTS Voice Synthesis (Optional — Podcast Feature)

Podcast generation supports local TTS models. When enabled, it will automatically download the Qwen3-TTS model (~3.4GB).

# Enable local TTS (0=disabled, 1=enabled)
USE_LOCAL_TTS=1

# TTS engine: qwen (recommended) or firered
TTS_ENGINE=qwen

# Model idle auto-unload timeout (seconds, default 300 = 5 minutes)
TTS_IDLE_TIMEOUT=300

Tip: If you don't need podcast features, set USE_LOCAL_TTS=0 or delete this config to save disk space.

3. Start Backend

uvicorn fastapi_app.main:app --host 0.0.0.0 --port 8213 --reload

On startup, the backend automatically launches a local embedding service (Octen-Embedding-0.6B on port 26210 by default). The model is downloaded on first run. To disable local embedding, set USE_LOCAL_EMBEDDING=0.

• Health check: http://localhost:8213/health
• API docs: http://localhost:8213/docs

4. Start Frontend

Both English and Chinese frontends are provided — pick either:

# English frontend
cd frontend_en && npm install && npm run dev

# Chinese frontend
cd frontend_zh && npm install && npm run dev

Open http://localhost:3000 (or the port shown in the terminal).

npm run dev uses each frontend's vite.config.ts, and the current default frontend port is 3000. If you use the repository's scripts/start.sh, it starts the Chinese frontend on port 3001, the backend on 8213, and the cpolar tunnel together.

The LLM API URL and API key can be changed dynamically in the settings panel (top-right corner) without restarting.

Frontend Configuration (Optional)

For local deployment (frontend and backend on the same machine): No configuration needed. The default setup works out of the box.

For public deployment (via cpolar/ngrok tunneling):

The frontend has built-in smart detection:

• When .env is set to localhost but accessed from a public URL, it automatically uses relative paths (current domain)
• In dev mode, Vite proxies /api and /outputs to the local backend at http://localhost:8213
• Recommended: Use nginx reverse proxy to unify frontend and backend under the same domain, no extra configuration needed

Note: The ports shown here, such as 3000, 3001, 8080, and 8213, are example ports only. In a real deployment, replace them with the actual ports used by your frontend, backend, and proxy services. For personal testing or lightweight usage, scripts/start.sh + Vite proxy + cpolar is sufficient; for more stable public access or larger-scale deployments, nginx reverse proxy is still the recommended approach. In the current repository, scripts/start.sh uses CPOLAR_TUNNEL_NAME=opennotebook and prints the configured CPOLAR_PUBLIC_URL. If you change your reserved cpolar tunnel, update both variables in the script as well.

Create frontend_zh/.env (or frontend_en/.env):

# Backend API base URL (for local development)
VITE_API_BASE_URL=http://localhost:8213

Deployment comparison:

Deployment Type	Configuration	Description
Local development	VITE_API_BASE_URL=http://localhost:8213	Frontend and backend both run locally
Using scripts/start.sh	VITE_API_BASE_URL=http://localhost:8213	The current script starts the Chinese frontend on 3001, backend on 8213, and exposes the frontend through a named cpolar tunnel
Public deployment (recommended)	VITE_API_BASE_URL=http://localhost:8213	Use nginx reverse proxy for unified domain, smart detection auto-switches to relative paths
Public deployment (separated)	VITE_API_BASE_URL=https://backend-xxx.cpolar.io	Frontend and backend use different domains, requires manual backend URL configuration

Recommended: Use nginx reverse proxy for unified domain

Create nginx.conf:

server {
    listen 8080;

    # Frontend
    location / {
        proxy_pass http://localhost:3000;
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
    }

    # Backend API
    location /api/ {
        proxy_pass http://localhost:8213/api/;
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
    }

    # Backend output files
    location /outputs/ {
        proxy_pass http://localhost:8213/outputs/;
    }
}

If you are not running npm run dev directly and instead use the current repository's scripts/start.sh, change the frontend upstream above from http://localhost:3000 to http://localhost:3001.

Then expose nginx port via cpolar:

cpolar http 8080

This way frontend and backend share the same domain, smart detection will automatically use relative paths without configuration changes. Replace the example ports with the real ports used in your environment.

Note: After changing .env, rebuild the frontend (npm run build) or restart dev server (npm run dev).

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📂 Project Structure

opennotebookLM/
├── fastapi_app/             # Backend API (FastAPI)
│   ├── routers/             #   Routes: KB, auth, Paper2PPT, Paper2Drawio, etc.
│   ├── services/            #   Business logic: search, flashcards, quizzes, etc.
│   ├── config/              #   Configuration & environment variables
│   ├── dependencies/        #   Dependency injection (auth, Supabase client)
│   ├── middleware/           #   Middleware (API key validation)
│   └── workflow_adapters/   #   Workflow adapter layer
├── workflow_engine/         # Workflow engine (DataFlow-Agent)
│   ├── agentroles/          #   Agent role definitions
│   ├── workflow/            #   Workflows (Paper2PPT, PDF2PPT, Image2Drawio, etc.)
│   ├── promptstemplates/    #   Prompt templates
│   └── toolkits/            #   Toolkits (search, parsing, etc.)
├── frontend_en/             # English frontend (React + Vite + Tailwind)
├── frontend_zh/             # Chinese frontend
├── database/                # Database migration scripts
├── docs/                    # Documentation
├── script/                  # Utility scripts (DB init, etc.)
├── static/                  # Static assets
└── outputs/                 # Generated file output directory (isolated by user email)

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⚙️ Model Configuration

The project uses a three-layer model configuration system, from coarse to fine-grained:

1. Base model layer — Define available model names (MODEL_GPT_4O, MODEL_CLAUDE_HAIKU, etc.)
2. Workflow layer — Set default models per workflow (PAPER2PPT_DEFAULT_MODEL, etc.)
3. Role layer — Fine-grained control over each role within a workflow (PAPER2PPT_OUTLINE_MODEL, etc.)

See fastapi_app/.env.example for the full configuration reference.

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🗺️ Roadmap

• Knowledge base management (upload files / paste URLs / text)
• RAG smart Q&A
• PPT generation
• Mind map generation
• DrawIO diagram generation
• Knowledge podcast generation
• Flashcards & quizzes
• Web search source import
• Deep research reports
• Local embedding vector retrieval
• User management (Supabase email auth + multi-user isolation)
• Video generation (in progress)
• Video source import (in progress)
• Audio source import (in progress)

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🤝 Contributing

Issues and pull requests are welcome. See Contributing Guide.

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📄 License

Apache License 2.0

Generation features are built on OpenDCAI/Paper2Any.

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💬 Community

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