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Storing and querying AI embeddings efficiently using vector databases like Pinecone, Weaviate, and FAISS. Covers best practices for indexing, querying, and optimizing search for AI applications.

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How to build a custom embedder in LlamaIndex: AWS Titan Multimodal example

· 14 min read
Norah Sakal
Norah Sakal
AI Consultant & Developer

How to build a custom embedder in LlamaIndex: AWS Titan Multimodal example

LlamaIndex makes it easy to build AI-powered search, but if you're working with multimodal embeddings (text + images), like the AWS Titan multimodal model, you'll notice it's not natively supported.

For e-commerce search, I need embeddings that capture both product descriptions and images to generate more accurate search results.

This guide will show you how to override LlamaIndex's default embedder to use AWS Titan Multimodal.