---
name: "marqo-multimodal-vector-tensor-search"
description: "Search across text and images simultaneously using Marqo multimodal embedding pipelines."
metadata:
  author: "Marqo AI"
  version: "1.0.0"
  runtime_compatibility:
    - "codex"
    - "claude-code"
    - "cursor"
    - "windsurf"
triggers:
  - "setup marqo multimodal index"
  - "search images with text marqo"
  - "index multimodal documents"
permissions:
  fs_read: true
  fs_write: "workspace"
  shell_exec: false
  net_access: false
---

# Marqo Multimodal Vector & Tensor Search Engine

Deploys Marqo: configuring multimodal combinations, text-to-image and image-to-image search, custom CLIP/OpenCLIP models, filtering, and running sub-second vector queries via Python SDK.

## When to Invoke
Invoke this skill whenever you need to:
- setup marqo multimodal index
- search images with text marqo
- index multimodal documents

## Execution SOP & Rules
1. Start Marqo container: docker run -p 8882:8882 marqoai/marqo:latest.
2. Create index: mq.create_index('products', model='open_clip/ViT-B-32/laion2b_s34b_b79k').
3. Add multimodal documents with weighted fields: {'multimodal_field': {'title': 0.3, 'img': 0.7}}.
4. Query index: mq.index('products').search(q='vintage leather jacket', filter_string='price < 150').

## Verification Checklist
- Run local linting, syntax validation, and type-checks before reporting completion.
- Ensure all created or modified files adhere to workspace boundaries and avoid uncommitted secrets.
- Verify tests or build commands exit cleanly with return code 0.