Open Notebook organizes research in three connected layers. Understanding this hierarchy is key to using the system effectively.
┌─────────────────────────────────────┐
│ NOTEBOOK (The Container) │
│ "My AI Safety Research 2026" │
├─────────────────────────────────────┤
│ │
│ SOURCES (The Raw Materials) │
│ ├─ safety_paper.pdf │
│ ├─ alignment_video.mp4 │
│ └─ prompt_injection_article.html │
│ │
│ NOTES (The Processed Insights) │
│ ├─ AI Summary (auto-generated) │
│ ├─ Key Concepts (transformation) │
│ ├─ My Research Notes (manual) │
│ └─ Chat Insights (from conversation)
│ │
└─────────────────────────────────────┘
A notebook is a scoped container for a research project or topic. It's your research workspace.
Think of it like a physical notebook: everything inside is about the same topic, shares the same context, and builds toward the same goals.
- A description — "This notebook collects research on X topic"
- Sources — The raw materials you add
- Notes — Your insights and outputs
- Conversation history — Your chats and questions
Isolation: Each notebook is completely separate. Sources in Notebook A never appear in Notebook B. This lets you:
- Keep different research topics completely isolated
- Reuse source names across notebooks without conflicts
- Control which AI context applies to which research
Shared Context: All sources and notes in a notebook inherit the notebook's context. If your notebook is titled "AI Safety 2026" with description "Focusing on alignment and interpretability," that context applies to all AI interactions within that notebook.
Parallel Projects: You can have 10 notebooks running simultaneously. Each one is its own isolated research environment.
Notebook: "Customer Research - Product Launch"
Description: "User interviews and feedback for Q1 2026 launch"
→ All sources added to this notebook are about customer feedback
→ All notes generated are in that context
→ When you chat, the AI knows you're analyzing product launch feedback
→ Different from your "Market Analysis - Competitors" notebook
A source is a single piece of input material — the raw content you bring in. Sources never change; they're just processed and indexed.
- PDFs — Research papers, reports, documents
- Web links — Articles, blog posts, web pages
- Audio files — Podcasts, interviews, lectures
- Video files — Tutorials, presentations, recordings
- Plain text — Notes, transcripts, passages
- Uploaded text — Paste content directly
1. EXTRACTION
File/URL → Extract text and metadata
(OCR for PDFs, web scraping for URLs, speech-to-text for audio)
2. CHUNKING
Long text → Break into searchable chunks
(Prevents "too much context" in single query)
3. EMBEDDING
Each chunk → Generate semantic vector
(Allows AI to find conceptually similar content)
4. STORAGE
Chunks + vectors → Store in database
(Ready for search and retrieval)
Immutable: Once added, the source doesn't change. If you need a new version, add it as a new source.
Indexed: Sources are automatically indexed for search (both text and semantic).
Scoped: A source belongs to exactly one notebook.
Referenceable: Other sources and notes can reference this source by citation.
Source: "openai_charter.pdf"
Type: PDF document
What happens:
→ PDF is uploaded
→ Text is extracted (including images)
→ Text is split into 50 chunks (paragraphs, sections)
→ Each chunk gets an embedding vector
→ Now searchable by: "OpenAI's approach to safety"
A note is a processed output — something you created or AI created based on your sources. Notes are the "results" of your research work.
You write them yourself. They're your original thinking, capturing:
- What you learned from sources
- Your analysis and interpretations
- Your next steps and questions
Created by applying AI processing to sources:
- Transformations — Structured extraction (main points, key concepts, methodology)
- Chat Responses — Answers you saved from conversations
- Ask Results — Comprehensive answers saved to your notebook
Notes you explicitly saved from interactions:
- "Save this response as a note"
- "Save this transformation result"
- Convert any AI output into a permanent note
- Text — Your writing or AI-generated content
- Citations — References to specific sources
- Metadata — When created, how created (manual/AI), which sources influenced it
- Tags — Your categorization (optional but useful)
Knowledge Accumulation: Notes become your actual knowledge base. They're what you take away from the research.
Searchable: Notes are searchable along with sources. "Find everything about X" includes your notes, not just sources.
Citable: Notes can cite sources, creating an audit trail of where insights came from.
Shareable: Notes are your outputs. You can share them, publish them, or build on them in other projects.
YOU
│
├─→ Create Notebook ("AI Research")
│
├─→ Add Sources (papers, articles, videos)
│ └─→ System: Extract, embed, index
│
├─→ Search Sources (text or semantic)
│ └─→ System: Find relevant chunks
│
├─→ Apply Transformations (extract insights)
│ └─→ Creates Notes
│
├─→ Chat with Sources (explore with context control)
│ ├─→ Can save responses as Notes
│ └─→ Notes include citations
│
├─→ Ask Questions (automated comprehensive search)
│ ├─→ Can save results as Notes
│ └─→ Notes include citations
│
└─→ Generate Podcast (transform notebook into audio)
└─→ Uses all sources + notes for content
Each source belongs to exactly one notebook. This creates clear boundaries:
- No ambiguity about which research project a source is in
- Easy to isolate or export a complete project
- Clean permissions model (if someone gets access to notebook, they get access to all its sources)
Sources never change (once added, always the same). But notes can be edited or deleted. Why?
- Sources are evidence → evidence shouldn't be altered
- Notes are your thinking → thinking evolves as you learn
Sources don't automatically go to AI. You decide which sources are "in context" for each interaction:
- Chat: You manually select which sources to include
- Ask: System automatically figures out which sources to search
- Transformations: You choose which sources to transform
This is different from systems that always send everything to AI.
Think of a notebook like a Git repository:
- Everything in it is about the same topic
- You can clone/fork it (copy to new project)
- It has clear entry/exit points
- You know exactly what's included
Think of sources like exhibits in a legal case:
- Once filed, they don't change
- They can be cited and referenced
- They're the ground truth for what you're basing claims on
- Multiple sources can be cross-referenced
Think of notes like your case brief:
- You write them based on evidence
- They're your interpretation
- You can cite which evidence supports each claim
- They're what you actually share or act on
Not directly. Each source is tied to one notebook. If you want it in multiple notebooks, add it again (uploads are fast if it's already processed).
No. Notes stay within their notebook and reference sources within that notebook. This keeps boundaries clean.
Use tags. You can tag sources ("primary research," "background," "methodology") and filter by tags.
Not built-in, but you can manually copy sources from one notebook to another by re-uploading them.
| Concept | Purpose | Lifecycle | Scope |
|---|---|---|---|
| Notebook | Container + context | Create once, configure | All its sources + notes |
| Source | Raw material | Add → Process → Store | One notebook |
| Note | Processed output | Create/capture → Edit → Share | One notebook |
This three-layer model gives you:
- Clear organization (everything scoped to projects)
- Privacy control (isolated notebooks)
- Audit trails (notes cite sources)
- Flexibility (notes can be manual or AI-generated)