multimodal-rag
CLIP, SigLIP 2, Voyage multimodal-3 patterns for image+text retrieval, cross-modal search, and multimodal document chunking. Use when building RAG with images, implementing visual search, or hybrid retrieval.
npx skills add majiayu000/claude-skill-registry --skill multimodal-rag --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Multimodal RAG (2026) Build retrieval-augmented generation systems that handle images, text, and mixed content. ## Overview - Image + text retrieval (product search, documentation) - Cross-modal search (text query -> image results) - Multimodal document processing (PDFs with charts) - Visual question answering with context - Image similarity and deduplication - Hybrid search pipelines ## Architecture Approaches | Approach | Pros | Cons | Best For | |----------|------|------|----------| | **Joint Embedding** (CLIP) | Direct comparison | Limited context | Pure image search | | **Caption-based** | Works with text LLMs | Lossy conversion | Existing text RAG | | **Hybrid** | Best accuracy | More complex | Production systems | ## Embedding Models (2026) | Model | Context | Modalities | Best For | |-------|---------|------------|----------| | **Voyage multimodal-3** | 32K tokens | Text + Image | Long documents | | **SigLIP 2** | Standard | Text + Image | Large-scale retrieval | | **CLIP ViT-L/14** | 77 tokens | Text + Image | General purpose | | **ImageBind** | Standard | 6 modalities | Audio/video included | | **ColPali** | Document | Text + Image | PDF/document RAG | ## CLIP-Based Ima
- Overview
- Architecture Approaches
- Embedding Models (2026)
- CLIP-Based Image Embeddings
- Voyage Multimodal-3 (Long Context)
- Hybrid RAG Pipeline
- Multimodal Document Chunking
- Vector Database Setup (Milvus)
- Multimodal Generation
- Key Decisions
- Common Mistakes
- Related Skills
- Capability Details
- image-embeddings
What does the multimodal-rag skill do?
CLIP, SigLIP 2, Voyage multimodal-3 patterns for image+text retrieval, cross-modal search, and multimodal document chunking. Use when building RAG with images, implementing visual search, or hybrid retrieval.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill multimodal-rag --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.