effect-uai-embedding

To convert text and images into vectors for semantic search, you provide the input to the EmbeddingModel service tag and select a provider to generate the corresponding vector representations.

Supported embedding encodings for retrieval-augmented generation include float32, int8, binary, sparse, and multivector formats, alongside corresponding vector math tools like cosine and maxSim similarity.

Cross-modal ranking with multimodal embeddings works by converting both text and images into vector representations, enabling similarity matching across different media types within knowledge bases.

Yes, you can use multiple embedding providers like OpenAI, Gemini, and Jina in the same pipeline through provider wiring, enabling portable code and flexible cross-provider retrieval.

The best way to index documents using multiple vector encodings is to apply the EmbeddingModel interface across your knowledge base, generating float32, int8, binary, or sparse vectors for semantic indexing.

Yes, you need the EmbeddingModel interface and its provider layers to handle image-text matching, as it processes both formats into vectors for cross-modal similarity comparison.