Aliyun Qwen Multimodal Embeddingv1.0.0

Enable shoppers to search for products using images or videos instead of text, improving discovery and reducing search friction. Retailers can index product images and videos with multimodal embeddings and retrieve similar items even when textual descriptions are sparse.

Cluster user-uploaded images and videos in social platforms by visual similarity to detect duplicate or inappropriate content. Multimodal embeddings allow grouping multiple media types together for scalable moderation pipelines.

Assist radiologists by retrieving similar medical images based on a reference image or textual findings. Embeddings from both modalities enable cross-modal search between diagnostic images and radiology reports.

Enable journalists and archivists to search large video libraries using text queries or key frames. Multimodal embeddings provide a unified representation for video frames and textual descriptions, facilitating efficient retrieval.

Generate embeddings for documents containing mixed text, images, and diagrams offline, then index them in a vector store for retrieval-augmented generation (RAG). This supports question-answering over multimodal corporate knowledge bases.

Offer the multimodal embedding service as a pay-per-use or subscription API, charging per embedding call or per million tokens/vectors. Customers integrate via REST calls, and revenue scales with usage volume.

Combine the embedding skill with a vector database (DashVector, Milvus) to provide a full managed search solution. Charge for storage, compute, and query operations, plus an upfront setup fee for custom indexing.

Deploy and maintain offline embedding pipelines that process enterprise knowledge bases containing images, videos, and text, with periodic re-indexing. Revenue comes from project-based consulting and recurring maintenance contracts.