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Frequently Asked Questions

Common questions about Open Notebook usage, configuration, and best practices.

General Usage

What is Open Notebook?

Open Notebook is an open-source, privacy-focused alternative to Google's Notebook LM. It allows you to:

  • Create and manage research notebooks
  • Chat with your documents using AI
  • Generate podcasts from your content
  • Search across all your sources with semantic search
  • Transform and analyze your content

How is it different from Google Notebook LM?

Privacy: Your data stays local by default. Only your chosen AI providers receive queries. Flexibility: Support for 15+ AI providers (OpenAI, Anthropic, Google, local models, etc.) Customization: Open source, so you can modify and extend functionality Control: You control your data, models, and processing

Can I use Open Notebook offline?

Partially: The application runs locally, but requires internet for:

  • AI model API calls (unless using local models like Ollama)
  • Web content scraping

Fully offline: Possible with local models (Ollama) for basic functionality.

What file types are supported?

Documents: PDF, DOCX, TXT, Markdown Web Content: URLs, YouTube videos Media: MP3, WAV, M4A (audio), MP4, AVI, MOV (video) Other: Direct text input, CSV, code files

How much does it cost?

Software: Free (open source) AI API costs: Pay-per-use to providers:

  • OpenAI: ~$0.50-5 per 1M tokens
  • Anthropic: ~$3-75 per 1M tokens
  • Google: Often free tier available
  • Local models: Free after initial setup

Typical monthly costs: $5-50 for moderate usage.

AI Models and Providers

Which AI provider should I choose?

For beginners: OpenAI (reliable, well-documented) For privacy: Local models (Ollama) or European providers (Mistral) For cost optimization: Groq, Google (free tier), or OpenRouter For long context: Anthropic (200K tokens) or Google Gemini (1M tokens)

Can I use multiple providers?

Yes: Configure different providers for different tasks:

  • OpenAI for chat
  • Google for embeddings
  • ElevenLabs for text-to-speech
  • Anthropic for complex reasoning

What are the best model combinations?

Budget-friendly:

  • Language: gpt-4o-mini (OpenAI) or deepseek-chat
  • Embedding: text-embedding-3-small (OpenAI)

High-quality:

  • Language: claude-3-5-sonnet (Anthropic) or gpt-4o (OpenAI)
  • Embedding: text-embedding-3-large (OpenAI)

Privacy-focused:

  • Language: Local Ollama models (mistral, llama3)
  • Embedding: Local embedding models

How do I optimize AI costs?

Model selection:

  • Use smaller models for simple tasks (gpt-4o-mini, claude-3-5-haiku)
  • Use larger models only for complex reasoning
  • Leverage free tiers when available

Usage optimization:

  • Use "Summary Only" context for background sources
  • Ask more specific questions
  • Use local models (Ollama) for frequent tasks

Data Management

Where is my data stored?

Local storage: By default, all data is stored locally:

  • Database: SurrealDB files in surreal_data/
  • Uploads: Files in data/uploads/
  • Podcasts: Generated audio in data/podcasts/
  • No external data transmission (except to chosen AI providers)

How do I backup my data?

# Create backup
tar -czf backup-$(date +%Y%m%d).tar.gz data/ surreal_data/

# Restore backup
tar -xzf backup-20240101.tar.gz

Can I sync data between devices?

Currently: No built-in sync functionality. Workarounds:

  • Use shared network storage for data directories
  • Manual backup/restore between devices

What happens if I delete a notebook?

Soft deletion: Notebooks are marked as archived, not permanently deleted. Recovery: Archived notebooks can be restored from the database.

Best Practices

How should I organize my notebooks?

  • By topic: Separate notebooks for different research areas
  • By project: One notebook per project or course
  • By time period: Monthly or quarterly notebooks

Recommended size: 20-100 sources per notebook for best performance.

How do I get the best search results?

  • Use descriptive queries ("data analysis methods" not just "data")
  • Combine multiple related terms
  • Use natural language (ask questions as you would to a human)
  • Try both text search (keywords) and vector search (concepts)

How can I improve chat responses?

  • Provide context: Reference specific sources or topics
  • Be specific: Ask detailed questions rather than general ones
  • Request citations: "Answer with page citations"
  • Use follow-up questions: Build on previous responses

What are the security best practices?

  • Never share API keys publicly
  • Use OPEN_NOTEBOOK_PASSWORD for public deployments
  • Use HTTPS for production (via reverse proxy)
  • Keep Docker images updated
  • Encrypt backups if they contain sensitive data

Technical Questions

Can I use Open Notebook programmatically?

Yes: Open Notebook provides a REST API:

  • Full API documentation at http://localhost:5055/docs
  • Support for all UI functionality
  • Authentication via password header

Can I run Open Notebook in production?

Yes: Designed for production use with:

  • Docker deployment
  • Security features (password protection)
  • Monitoring and logging
  • Reverse proxy support (nginx, Caddy, Traefik)

What are the system requirements?

Minimum:

  • 4GB RAM
  • 2 CPU cores
  • 10GB disk space

Recommended:

  • 8GB+ RAM
  • 4+ CPU cores
  • SSD storage
  • For local models: 16GB+ RAM, GPU recommended

Timeout and Performance

Why do I get timeout errors?

Common causes:

  • Large context (too many sources)
  • Slow AI provider
  • Local models on CPU (slow)
  • First request (model loading)

Solutions:

# In .env:
API_CLIENT_TIMEOUT=600  # 10 minutes for slow setups
ESPERANTO_LLM_TIMEOUT=180  # 3 minutes for model inference

Recommended timeouts by setup:

Setup API_CLIENT_TIMEOUT
Cloud APIs (OpenAI, Anthropic) 300 (default)
Local Ollama with GPU 600
Local Ollama with CPU 1200
Remote LM Studio 900

Getting Help

My question isn't answered here

  1. Check the troubleshooting guides in this section
  2. Search existing GitHub issues
  3. Ask in the Discord community
  4. Create a GitHub issue with detailed information

How do I report a bug?

Include:

  • Steps to reproduce
  • Expected vs actual behavior
  • Error messages and logs
  • System information
  • Configuration details (without API keys)

Submit to: GitHub Issues

Where can I get help?

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