I'm building a multimodal search tool for text and images, what embedding approach should I use

For a multimodal search tool over text and images, the best default choice is usually:

Use a shared embedding space from a model like CLIP

CLIP-style models embed both text and images into the same vector space, which makes cross-modal search straightforward:

• text → image search
• image → text search
• image → image search
• text → text search if you store text embeddings too

Why this is the usual best choice

• One index for both modalities
• Simple retrieval pipeline
• Strong zero-shot cross-modal performance
• Easy to scale with vector databases

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Recommended setup

1) Encode text and images with a dual-encoder model

Good options:

• OpenAI CLIP / similar open-source CLIP models
• SigLIP
• OpenCLIP
• For stronger modern open models, look at E5-style text embeddings paired with a CLIP-like multimodal model if you need better text-only retrieval too

2) Normalize embeddings

Use L2 normalization and cosine similarity / dot product:

• Common for CLIP-like embeddings
• Makes ranking more stable

3) Store in a vector database

Examples:

• FAISS for local / DIY
• Milvus
• Weaviate
• Pinecone
• Qdrant

4) Retrieval strategy

• For a text query, embed the query text and search the unified index
• For an image query, embed the image and search the same index
• Keep metadata separate for filtering

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When CLIP is enough vs when it isn’t

CLIP is good if you need:

• General-purpose multimodal search
• Natural-language queries over images
• Fast retrieval at scale

You may need a different or additional approach if:

• You need very strong text-only semantic search
  → Use a stronger text embedding model for text search, and a multimodal model for cross-modal search
• You need fine-grained domain search
  → Fine-tune the model on your domain
• You need best ranking quality → Use a two-stage system:
  1. vector retrieval with CLIP
  2. re-ranking with a cross-encoder or multimodal reranker

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Best-practice architecture

A strong production pattern is:

1. Embed all assets
   • images: CLIP image encoder
   • text: CLIP text encoder
2. Retrieve top-K
3. Re-rank results
   • with a more expensive model if needed
4. Filter by metadata
   • date, category, source, permissions, etc.

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If your use case is more specific

If images are mostly product/catalog photos

Use:

• CLIP/OpenCLIP
• plus OCR extraction for text in images
• plus metadata indexing

If your queries are mostly text and precision matters a lot

Use:

• a strong text embedding model for text corpus
• CLIP for cross-modal search
• possibly separate indices merged at query time

If you need document search with screenshots/PDF pages

Use:

• OCR
• page-image embeddings
• text embeddings
• hybrid search

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My practical recommendation

If you want one good starting point:

• Model: OpenCLIP or SigLIP
• Similarity: cosine / dot product with normalized vectors
• Index: vector DB like Qdrant, Milvus, or FAISS
• Architecture: dual-encoder retrieval + optional reranking

If you want, I can also suggest:

1. the best model choice in 2026 for your exact use case, or
2. a reference architecture for text+image search with chunking, OCR, and reranking.