AI Data Stack

Purpose: Quarto + LanceDB for AI-accessible, multimodal data analysis and document generation.

---

Stack Overview

Tool	Purpose	Why
Quarto (.qmd)	Computational documents	Code + narrative + outputs in one file
LanceDB	Vector database	Multimodal (text, image, audio), AI-native, serverless
Python	Glue language	Both tools have great Python support
OpenClaw	Agent interface	I can read/write qmd, query LanceDB

---

Quarto (qmd)

What It Is

• Computational markdown — mix Python/R/Julia code with narrative
• Export to HTML, PDF, Word, presentations
• Jupyter alternative but text-first (git-friendly)

Use Cases

• Analysis reports with live code
• Reproducible research
• Automated documentation
• Parameterized reports (same template, different data)

Example Workflow

---
title: "Convergence Protocol Analysis"
format: html
---
 
## Load Data
 
```{python}
import lancedb
import pandas as pd
 
# Connect to LanceDB
db = lancedb.connect("~/data/convergence.lance")
table = db.open_table("concepts")
 
# Query
df = table.to_pandas()
print(f"Total concepts: {len(df)}")
```

Visualization

import plotly.express as px
 
fig = px.scatter(df, x="complexity", y="interconnections",
                 color="category", hover_data=["name"])
fig.show()

---

## LanceDB

### What It Is
- Serverless vector database
- Built on Apache Arrow / Lance format
- Native multimodal (text, image, audio, video embeddings)
- Embeddings + metadata + search

### Why LanceDB for AI Projects
| Feature | Benefit |
|---------|---------|
| **Multimodal** | Store text, image, audio embeddings together |
| **Vector search** | Semantic similarity, not just keyword |
| **Serverless** | No separate server to manage |
| **Arrow-native** | Fast, zero-copy with Python/Pandas |
| **Hybrid search** | Combine vector + SQL filters |

### Use Cases
- Semantic search across all your content
- Image similarity (find similar visuals)
- Audio pattern matching
- Multimodal RAG (Retrieval Augmented Generation)
- Recommendation systems

---

## Integration: Quarto + LanceDB

### Pattern 1: Query → Analyze → Document

LanceDB (data) → Python analysis → Quarto report

### Pattern 2: Live Dashboard

Quarto dashboard → queries LanceDB → live visualizations

### Pattern 3: Agent-Generated Reports

You: “Analyze my convergence data” Me: Query LanceDB → Generate qmd → Render HTML → Show results

---

## Project Structure

~/data/ # LanceDB databases ├── lancedb/ │ └── cognitive_biases.lance # Bias notes + embeddings ├── convergence.lance/ ├── media_embeddings.lance/ └── knowledge_base.lance/

~/quarto/ # Quarto documents ├── reports/ │ ├── convergence_analysis.qmd │ └── bias_explorer.qmd ├── dashboards/ │ └── system_monitor.qmd └── _templates/ # Reusable templates └── project_report.qmd

~/.openclaw/workspace/ # Agent scripts ├── lancedb_tools.py # Helper functions ├── setup_biases_lancedb.py # Bias database setup ├── quarto_render.py # Render automation └── data_sync.py # Sync pipelines

---

## Agent Accessibility

### I Can Read/Write
- **.qmd files** — Full access, can generate reports
- **LanceDB** — Query, insert, update via Python
- **Rendered outputs** — HTML, images, tables

### You Can Say
| Request | I Do |
|---------|------|
| "Query my convergence data" | LanceDB query → show results |
| "Generate a report on X" | Create .qmd → render → show |
| "Find similar images to Y" | Vector search in LanceDB |
| "Update the dashboard" | Re-render quarto dashboard |
| "What patterns in my data?" | Analysis → qmd report |

---

## Setup Tasks

- [x] #ask-agent Install Quarto CLI
- [ ] #ask-agent Install LanceDB Python package
- [x] #ask-agent Create ~/data/ directory structure
- [ ] #ask-agent Create ~/quarto/ directory structure
- [x] #ask-agent Set up first LanceDB (cognitive biases)
- [ ] #ask-agent Create sample .qmd report
- [ ] #ask-agent Create agent helper scripts
- [ ] #ask-agent Add vector embeddings to bias database
- [ ] #ask-agent Index podcast transcripts

---

## Quick Commands

```bash
# Render a quarto document
quarto render report.qmd

# Preview with live reload
quarto preview report.qmd

# Create new project
quarto create-project my-analysis

# Python: Connect to LanceDB
python -c "import lancedb; db = lancedb.connect('~/data/test.lance')"

---

Resources

• Quarto: https://quarto.org
• LanceDB: https://lancedb.github.io/lancedb/
• Lance format: https://lancedb.github.io/lance/

---

Created: 2026-02-22