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Chapter 12: Multimodal and Semantic Pipelines

Build retrieval systems that span text, images, audio, location, and structured content.

Chapter Overview​

Modern AI applications work with more than text. This chapter covers multimodal retrieval: indexing and searching across images, audio, documents, and spatial data using unified embedding pipelines.

Building multimodal retrieval capabilities enables applications that can find relevant content regardless of its original format.

12.1 Multimodal Indexing​

12.1.1 Images and audio

12.1.2 Documents

12.1.3 Sensor and spatial data

12.2 Embedding Pipelines​

12.2.1 Model selection

12.2.2 Batch vs. real-time processing

12.2.3 Content hashing

12.3 Retrieval Patterns​

12.3.1 Chunk stores

12.3.2 Metadata joins

12.3.3 Projection strategies

Examples​

Examples coming soon.

Code examples for this chapter will demonstrate multimodal embedding pipelines, cross-modal search, and metadata-enriched retrieval with Lucenia.