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name: data-pods description: Create and manage modular portable database pods (SQLite + metadata + embeddings). Includes document ingestion with embeddings for semantic search. Full automation - just ask.

Data Pods

Overview

Create and manage portable, consent-scoped database pods. Handles document ingestion with embeddings and semantic search.

Architecture

┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│  Ingestion  │ ──► │   DB Pods   │ ──► │  Generation │
│  (ingest)   │     │  (storage)  │     │   (query)   │
└─────────────┘     └─────────────┘     └─────────────┘

Triggers

• "create a pod" / "new pod"
• "list my pods" / "what pods do I have"
• "add to pod" / "add note" / "add content"
• "query pod" / "search pod"
• "ingest documents" / "add files"
• "semantic search" / "find相关内容"
• "export pod" / "pack pod"

Core Features

1. Create Pod

When user asks to create a pod:

1. Ask for pod name and type (scholar/health/shared/projects)
2. Run: python3 .../scripts/pod.py create <name> --type <type>
3. Confirm creation

2. Add Content (Manual)

When user asks to add content:

1. Ask for pod name, title, content, tags
2. Run: python3 .../scripts/pod.py add <pod> --title "<title>" --content "<content>" --tags "<tags>"
3. Confirm

3. Ingest Documents (Automated)

When user wants to ingest files:

1. Ask for pod name and folder path
2. Run: python3 .../scripts/ingest.py ingest <pod> <folder>
3. Supports: PDF, TXT, MD, DOCX, PNG, JPG
4. Auto-embeds text (if sentence-transformers installed)

4. Semantic Search

When user wants to search:

1. Ask for pod name and query
2. Run: python3 .../scripts/ingest.py search <pod> "<query>"
3. Returns ranked results with citations

5. Query (Basic)

When user asks to search notes:

1. Run: python3 .../scripts/pod.py query <pod> --text "<query>"

6. Export

When user asks to export:

1. Run: python3 .../scripts/podsync.py pack <pod>

Dependencies

pip install PyPDF2 python-docx pillow pytesseract sentence-transformers

Storage Location

~/.openclaw/data-pods/

Key Commands

# Create pod
python3 .../scripts/pod.py create research --type scholar

# Add note
python3 .../scripts/pod.py add research --title "..." --content "..." --tags "..."

# Ingest folder
python3 .../scripts/ingest.py ingest research ./documents/

# Semantic search
python3 .../scripts/ingest.py search research "transformers"

# List documents
python3 .../scripts/ingest.py list research

# Query notes
python3 .../scripts/pod.py query research --text "..."

Notes

• Ingestion auto-chunks large documents
• Embeddings enable semantic search
• File hash prevents duplicate ingestion
• All data stored locally in SQLite