Building ONNX Embedding Workflows in Oracle AI Database with Python

The guide shows how to load an augmented ONNX embedding model into Oracle AI Database with DBMS_VECTOR.LOAD_ONNX_MODEL.

It then uses SQL functions like VECTOR_EMBEDDING to generate embeddings, store them in native VECTOR columns, and validate the model.

Semantic search is performed in-database with VECTOR_DISTANCE, reducing the need to move data into separate vector services.

The same workflow is also demonstrated through LangChain using OracleEmbeddings and OracleVS.