Image Retrieval
Image retrieval is a technique within retrieval-augmented generation (RAG) systems that enables AI agents to locate and return relevant images based on user queries or context. Rather than treating images as static files, modern image retrieval systems use multimodal embedding models that convert both text queries and image content into comparable numerical representations within a shared semantic space. This allows systems to match text-based user requests with visually similar or semantically relevant images.
How It Works
The process relies on multimodal embedding models—such as Jina Embeddings v4—that process both text and images through the same embedding pipeline. When a user submits a text query, the model converts it into an embedding vector. Simultaneously, a corpus of images is pre-processed and converted into embeddings using the same model. The system then performs a similarity search to identify images whose embeddings are closest to the query embedding, returning the most relevant results ranked by relevance score.
Application in RAG Systems
In RAG architectures, image retrieval functions alongside traditional text retrieval to provide comprehensive answers to user queries. When a question might be better answered with visual content—such as product images, diagrams, photographs, or charts—the image retrieval component identifies and ranks suitable candidates. This allows AI agents to augment text-based responses with contextually appropriate images, improving response quality and user understanding for queries that benefit from visual information.
Source Notes
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