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- Replace old docs structure with new comprehensive documentation - Organize into 8 major sections (0-START-HERE through 7-DEVELOPMENT) - Convert CONFIGURATION.md, CONTRIBUTING.md, MAINTAINER_GUIDE.md to redirects - Remove outdated MIGRATION.md and DESIGN_PRINCIPLES.md - Fix all internal documentation links and cross-references - Add progressive disclosure paths for different user types - Include 44 focused guides covering all features - Update README.md to remove v1.0 breaking changes notice
258 lines
6.9 KiB
Markdown
258 lines
6.9 KiB
Markdown
# Frequently Asked Questions
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Common questions about Open Notebook usage, configuration, and best practices.
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---
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## General Usage
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### What is Open Notebook?
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Open Notebook is an open-source, privacy-focused alternative to Google's Notebook LM. It allows you to:
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- Create and manage research notebooks
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- Chat with your documents using AI
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- Generate podcasts from your content
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- Search across all your sources with semantic search
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- Transform and analyze your content
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### How is it different from Google Notebook LM?
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**Privacy**: Your data stays local by default. Only your chosen AI providers receive queries.
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**Flexibility**: Support for 15+ AI providers (OpenAI, Anthropic, Google, local models, etc.)
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**Customization**: Open source, so you can modify and extend functionality
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**Control**: You control your data, models, and processing
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### Can I use Open Notebook offline?
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**Partially**: The application runs locally, but requires internet for:
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- AI model API calls (unless using local models like Ollama)
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- Web content scraping
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**Fully offline**: Possible with local models (Ollama) for basic functionality.
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### What file types are supported?
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**Documents**: PDF, DOCX, TXT, Markdown
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**Web Content**: URLs, YouTube videos
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**Media**: MP3, WAV, M4A (audio), MP4, AVI, MOV (video)
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**Other**: Direct text input, CSV, code files
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### How much does it cost?
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**Software**: Free (open source)
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**AI API costs**: Pay-per-use to providers:
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- OpenAI: ~$0.50-5 per 1M tokens
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- Anthropic: ~$3-75 per 1M tokens
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- Google: Often free tier available
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- Local models: Free after initial setup
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**Typical monthly costs**: $5-50 for moderate usage.
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---
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## AI Models and Providers
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### Which AI provider should I choose?
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**For beginners**: OpenAI (reliable, well-documented)
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**For privacy**: Local models (Ollama) or European providers (Mistral)
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**For cost optimization**: Groq, Google (free tier), or OpenRouter
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**For long context**: Anthropic (200K tokens) or Google Gemini (1M tokens)
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### Can I use multiple providers?
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**Yes**: Configure different providers for different tasks:
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- OpenAI for chat
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- Google for embeddings
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- ElevenLabs for text-to-speech
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- Anthropic for complex reasoning
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### What are the best model combinations?
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**Budget-friendly**:
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- Language: `gpt-4o-mini` (OpenAI) or `deepseek-chat`
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- Embedding: `text-embedding-3-small` (OpenAI)
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**High-quality**:
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- Language: `claude-3-5-sonnet` (Anthropic) or `gpt-4o` (OpenAI)
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- Embedding: `text-embedding-3-large` (OpenAI)
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**Privacy-focused**:
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- Language: Local Ollama models (mistral, llama3)
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- Embedding: Local embedding models
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### How do I optimize AI costs?
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**Model selection**:
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- Use smaller models for simple tasks (gpt-4o-mini, claude-3-5-haiku)
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- Use larger models only for complex reasoning
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- Leverage free tiers when available
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**Usage optimization**:
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- Use "Summary Only" context for background sources
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- Ask more specific questions
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- Use local models (Ollama) for frequent tasks
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---
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## Data Management
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### Where is my data stored?
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**Local storage**: By default, all data is stored locally:
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- Database: SurrealDB files in `surreal_data/`
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- Uploads: Files in `data/uploads/`
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- Podcasts: Generated audio in `data/podcasts/`
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- No external data transmission (except to chosen AI providers)
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### How do I backup my data?
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```bash
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# Create backup
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tar -czf backup-$(date +%Y%m%d).tar.gz data/ surreal_data/
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# Restore backup
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tar -xzf backup-20240101.tar.gz
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```
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### Can I sync data between devices?
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**Currently**: No built-in sync functionality.
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**Workarounds**:
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- Use shared network storage for data directories
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- Manual backup/restore between devices
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### What happens if I delete a notebook?
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**Soft deletion**: Notebooks are marked as archived, not permanently deleted.
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**Recovery**: Archived notebooks can be restored from the database.
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---
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## Best Practices
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### How should I organize my notebooks?
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- **By topic**: Separate notebooks for different research areas
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- **By project**: One notebook per project or course
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- **By time period**: Monthly or quarterly notebooks
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**Recommended size**: 20-100 sources per notebook for best performance.
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### How do I get the best search results?
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- Use descriptive queries ("data analysis methods" not just "data")
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- Combine multiple related terms
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- Use natural language (ask questions as you would to a human)
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- Try both text search (keywords) and vector search (concepts)
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### How can I improve chat responses?
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- Provide context: Reference specific sources or topics
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- Be specific: Ask detailed questions rather than general ones
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- Request citations: "Answer with page citations"
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- Use follow-up questions: Build on previous responses
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### What are the security best practices?
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- Never share API keys publicly
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- Use `OPEN_NOTEBOOK_PASSWORD` for public deployments
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- Use HTTPS for production (via reverse proxy)
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- Keep Docker images updated
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- Encrypt backups if they contain sensitive data
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---
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## Technical Questions
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### Can I use Open Notebook programmatically?
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**Yes**: Open Notebook provides a REST API:
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- Full API documentation at `http://localhost:5055/docs`
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- Support for all UI functionality
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- Authentication via password header
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### Can I run Open Notebook in production?
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**Yes**: Designed for production use with:
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- Docker deployment
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- Security features (password protection)
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- Monitoring and logging
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- Reverse proxy support (nginx, Caddy, Traefik)
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### What are the system requirements?
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**Minimum**:
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- 4GB RAM
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- 2 CPU cores
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- 10GB disk space
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**Recommended**:
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- 8GB+ RAM
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- 4+ CPU cores
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- SSD storage
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- For local models: 16GB+ RAM, GPU recommended
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---
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## Timeout and Performance
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### Why do I get timeout errors?
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**Common causes**:
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- Large context (too many sources)
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- Slow AI provider
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- Local models on CPU (slow)
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- First request (model loading)
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**Solutions**:
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```bash
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# In .env:
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API_CLIENT_TIMEOUT=600 # 10 minutes for slow setups
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ESPERANTO_LLM_TIMEOUT=180 # 3 minutes for model inference
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```
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### Recommended timeouts by setup:
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| Setup | API_CLIENT_TIMEOUT |
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| Cloud APIs (OpenAI, Anthropic) | 300 (default) |
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| Local Ollama with GPU | 600 |
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| Local Ollama with CPU | 1200 |
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| Remote LM Studio | 900 |
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---
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## Getting Help
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### My question isn't answered here
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1. Check the troubleshooting guides in this section
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2. Search existing GitHub issues
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3. Ask in the Discord community
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4. Create a GitHub issue with detailed information
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### How do I report a bug?
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Include:
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- Steps to reproduce
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- Expected vs actual behavior
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- Error messages and logs
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- System information
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- Configuration details (without API keys)
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Submit to: [GitHub Issues](https://github.com/lfnovo/open-notebook/issues)
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### Where can I get help?
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- **Discord**: https://discord.gg/37XJPXfz2w (fastest)
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- **GitHub Issues**: Bug reports and feature requests
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- **Documentation**: This docs site
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---
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## Related
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- [Quick Fixes](quick-fixes.md) - Common issues with 1-minute solutions
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- [AI & Chat Issues](ai-chat-issues.md) - Model and chat problems
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- [Connection Issues](connection-issues.md) - Network and API problems
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