Uploaded July 2026 | Updated September 2026, 2 weeks ago
Learn how to store embeddings in a database and run Oracle Database vector search without moving your data. Load an ONNX model, generate vectors, and rank results using cosine similarity.
*Chapters*
0:00 Keep embeddings close to your data
0:13 How augmented ONNX embedding models work
0:26 Load and validate the ONNX model
0:39 Store vectors and run semantic search
0:52 Use Oracle embeddings with LangChain
Oracle AI Vector Search keeps embedding generation, storage, and retrieval inside the database. This walkthrough covers augmented ONNX models with tokenization included in the graph, registration with DBMS_VECTOR.LOAD_ONNX_MODEL, validation in USER_MINING_MODELS, and inference with VECTOR_EMBEDDING().
You’ll also see how embeddings are stored in VECTOR(384, FLOAT32) columns, how VECTOR_DISTANCE ranks documents by cosine similarity, and how LangChain’s OracleEmbeddings and OracleVS use the same Oracle-native execution path. This provides a semantic search database workflow without adding a separate vector embeddings database.
*Resources*
📦 langchain-oracledb Python package: pypi.org/project/langchain-oracledb
💻 LangChain–Oracle GitHub repository: github.com/oracle/langchain-oracle
🧠 Explore Oracle AI Agent Memory: oracle.com/database/ai-agent-memory
Subscribe for more Oracle AI Database tutorials and share your questions in the comments.
#OracleAIDatabase #AIVectorSearch #VectorEmbeddings
Learn how to store embeddings in a database and run Oracle Database vector search without moving your data. Load an ONNX model, generate vectors, and rank results using cosine similarity.
*Chapters*
0:00 Keep embeddings close to your data
0:13 How augmented ONNX embedding models work
0:26 Load and validate the ONNX model
0:39 Store vectors and run semantic search
0:52 Use Oracle embeddings with LangChain
Oracle AI Vector Search keeps embedding generation, storage, and retrieval inside the database. This walkthrough covers augmented ONNX models with tokenization included in the graph, registration with DBMS_VECTOR.LOAD_ONNX_MODEL, validation in USER_MINING_MODELS, and inference with VECTOR_EMBEDDING().
You’ll also see how embeddings are stored in VECTOR(384, FLOAT32) columns, how VECTOR_DISTANCE ranks documents by cosine similarity, and how LangChain’s OracleEmbeddings and OracleVS use the same Oracle-native execution path. This provides a semantic search database workflow without adding a separate vector embeddings database.
*Resources*
📦 langchain-oracledb Python package: pypi.org/project/langchain-oracledb
💻 LangChain–Oracle GitHub repository: github.com/oracle/langchain-oracle
🧠 Explore Oracle AI Agent Memory: oracle.com/database/ai-agent-memory
Subscribe for more Oracle AI Database tutorials and share your questions in the comments.
#OracleAIDatabase #AIVectorSearch #VectorEmbeddings
