Building Scalable Multimodal Search Applications with Python — Zain Hasan

Multimodal search enables combining different types of data (text, images, audio, video) into unified vector spaces for more comprehensive search capabilities

Vector databases preserve semantic meaning while enabling fast retrieval across billions of documents with sub-50ms latency

Key applications include e-commerce product search/recommendations by combining product descriptions, images, and sensory data

Multi-vector approach allows searching across different modalities (text, image, nutritional, brand vectors) independently and combining results

Vector similarity search works by converting queries and documents into vectors and finding nearest neighbors in vector space

Retrieval Augmented Generation (RAG) can be enhanced with multimodal context by adding images/video alongside text for better AI responses

Different products may be purchased based on different sensory inputs (looks, descriptions, smell) - multimodal search helps capture this

Current AI systems struggle with basic sensory/motor tasks (Moravec’s paradox) but excel at language/reasoning tasks

Emerging research enables digitizing additional senses like smell to expand multimodal capabilities

Companies like Google, OpenAI and Anthropic are moving from pure language models toward multimodal understanding

Open source tools like Weaviate make it possible to build scalable multimodal search applications