image retrieval

**Image retrieval** is the **retrieval process that finds relevant images from a corpus using visual similarity, text queries, or both** - it is important when key evidence is encoded in figures, schematics, and photos. **What Is Image retrieval?** - **Definition**: Search and ranking over image assets using embeddings, tags, and metadata. - **Query Modes**: Supports text-to-image retrieval, image-to-image similarity, and hybrid search. - **Index Signals**: Uses visual embeddings, OCR text, captions, and source metadata. - **RAG Role**: Provides visual evidence that can be summarized or cited in final answers. **Why Image retrieval Matters** - **Visual Evidence**: Many troubleshooting clues appear only in photos or interface screenshots. - **Context Enrichment**: Images can clarify procedural steps better than text alone. - **Recall Gains**: Image channel recovers facts missed by sparse textual descriptions. - **Domain Utility**: Engineering and manufacturing workflows rely heavily on diagram interpretation. - **Trust Improvement**: Showing matched visuals increases answer verifiability. **How It Is Used in Practice** - **Embedding Pipeline**: Generate image vectors and store links to original assets and captions. - **OCR and Captioning**: Extract text overlays and semantic descriptions for hybrid indexing. - **Result Grounding**: Attach top visual matches to generated responses with provenance metadata. Image retrieval is **a critical retrieval capability for visually grounded AI systems** - effective image indexing and ranking expands evidence coverage and response quality.