vector search for text and images

Vector search for text and images usually means:

1. Convert content into embeddings

   • Text → text embedding model
   • Image → image embedding model
   • Sometimes both use a shared multimodal embedding model so text can search images and vice versa

2. Store embeddings in a vector database

   • Examples: FAISS, Milvus, Pinecone, Weaviate, Qdrant, pgvector

3. Search by similarity

   • User query is embedded into a vector
   • Find nearest vectors using cosine similarity, dot product, or Euclidean distance

Common setup

Text search

• Document text → embedding
• Query text → embedding
• Return most similar documents

Image search

• Image → embedding
• Query image → embedding
• Return visually similar images

Cross-modal search

• Text query → retrieve images
• Image query → retrieve text/images
• Usually done with models like CLIP or similar multimodal encoders

Typical pipeline

• Ingest

  • Extract text from documents or metadata
  • Generate embeddings
  • Save vectors + IDs + metadata

• Query

  • Embed the query
  • Run ANN search in vector index
  • Optionally rerank results with a second model

Good model options

Text embeddings

• OpenAI text embedding models
• Sentence Transformers
• bge, e5

Image embeddings

• CLIP
• SigLIP
• DINOv2 for vision features

Multimodal

• CLIP is the classic choice for text-image retrieval

Use cases

• Semantic document search
• Similar image search
• Product search with photos and descriptions
• Media asset management
• Visual question retrieval

Key considerations

• Chunking text documents before embedding
• Metadata filtering alongside vector search
• Index type for scale and latency
• Normalization if using cosine similarity
• Hybrid search often works best: keyword + vector

If you want, I can also give:

• a system design
• a Python example
• or a comparison of vector DBs for text+image search