1. Go to your notebook
2. Type in search box
3. See results (both sources and notes)
4. Click result to view source/note
5. Done!

That works for basic searches.
But you can do much better...

Search: "attention mechanism"
Results:
  1. "The attention mechanism allows..." (perfect match)
  2. "Attention and other mechanisms..." (partial match)
  3. "How mechanisms work in attention..." (includes words separately)

All contain "attention" AND "mechanism"
Ranked by how close together they are

Search: "What's the mechanism for understanding in models?"
(Notice: No chunk likely says exactly that)

Results:
  1. "Mechanistic interpretability allows understanding..." (semantic match)
  2. "Feature attribution reveals how models work..." (conceptually similar)
  3. "Attention visualization shows model decisions..." (same topic)

None contain your exact words
But all are semantically related

Question: "Do I remember the exact words?"

→ YES: Use TEXT SEARCH
   Example: "I remember the paper said 'attention is all you need'"

→ NO: Use VECTOR SEARCH
   Example: "I'm looking for content about how models process information"

→ UNSURE: Try TEXT SEARCH first (faster)
         If no results, try VECTOR SEARCH

Text search: "I know what I'm looking for"
Vector search: "I'm exploring an idea"

1. Go to search box
2. Type your keywords: "transformer", "attention", "2017"
3. Press Enter
4. Results appear (usually instant)
5. Click result to see context

Results show:
  - Which source contains it
  - How many times it appears
  - Relevance score
  - Preview of surrounding text

1. Go to search box
2. Type your concept: "How do models understand language?"
3. Choose "Vector Search" from dropdown
4. Press Enter
5. Results appear (1-2 seconds)
6. Click result to see context

Results show:
  - Semantically related chunks
  - Similarity score (higher = more related)
  - Preview of surrounding text
  - Different sources mixed together

Stage 1: QUESTION UNDERSTANDING
  "Compare the approaches in my papers"
  → System: "This asks for comparison"

Stage 2: SEARCH STRATEGY
  → System: "I should search for each approach separately"

Stage 3: PARALLEL SEARCHES
  → Search 1: "Approach in paper A"
  → Search 2: "Approach in paper B"
  (Multiple searches happen at once)

Stage 4: ANALYSIS & SYNTHESIS
  → Per-result analysis: "Based on paper A, the approach is..."
  → Per-result analysis: "Based on paper B, the approach is..."
  → Final synthesis: "Comparing A and B: A differs from B in..."

Result: Comprehensive answer, not just search results

1. Text search: "attention mechanism"
   Results: 50 matches

2. Too many. Follow up with vector search:
   "Why is attention useful?" (concept search)
   Results: Most relevant papers/notes

3. Better results with less noise

1. Ask: "What are the main approaches to X?"
   Result: Comprehensive answer about A, B, C

2. Use that to identify specific sources

3. Text search in those specific sources:
   "Why did they choose method X?"
   Result: Detailed information

1. Vector search: "How do transformers generalize?"
   Results: Related conceptual papers

2. Skim to understand landscape

3. Text search in promising sources:
   "generalization", "extrapolation", "transfer"
   Results: Specific passages to read carefully

1. Vector search: "What's new in AI 2026?"
   Results: Latest papers

2. Go to Chat
3. Add those papers to context
4. Ask detailed follow-up questions
5. Get deep analysis of results

Step 1: Text search
  "transformer 2017" (or year you remember)

If that works: Done!

If no results: Try
  "attention is all you need" (famous paper title)

Check result for exact date

Step 1: Vector search
  "How do we make AI interpretable?"

Results: Papers on interpretability, transparency, alignment

Step 2: Review results
  See which papers are most relevant

Step 3: Deep dive
  Go to Chat, add top 2-3 papers
  Ask detailed questions about alignment

Step 1: Ask
  "Compare the main approaches to AI safety in my sources"

Result: Comprehensive analysis comparing approaches

Step 2: Identify sources
  From answer, see which papers were most relevant

Step 3: Deep dive
  Text search in those papers:
  "limitations", "critiques", "open problems"

Step 4: Save as notes
  Create comparison note from Ask result

Step 1: Text search
  "transformer"

Results: All papers mentioning "transformer"

Step 2: Vector search
  "neural network architecture for sequence processing"

Results: Papers that don't say "transformer" but discuss similar concept

Step 3: Combine
  Union of text + vector results shows full landscape

Step 4: Analyze
  Go to Chat with all results
  Ask: "What's common across all these?"

SOURCES
  ↓
SEARCH (find what matters)
  ├─ Text search (precise)
  ├─ Vector search (exploration)
  └─ Ask (comprehensive)
  ↓
CHAT (explore with follow-ups)
  ↓
TRANSFORMATIONS (batch extract)
  ↓
NOTES (save insights)

1. Add 10 papers to notebook

2. Search: "What's the state of the art?"
   (Vector search explores landscape)

3. Ask: "Compare these 3 approaches"
   (Comprehensive synthesis)

4. Chat: Deep questions about winner
   (Follow-up exploration)

5. Save best insights as notes
   (Knowledge capture)

6. Transform remaining papers
   (Batch extraction for later)

7. Create podcast from notes + sources
   (Share findings)