AI w bazie danych Oracle: Vector Search, Select AI i Machine Learning (23ai/26ai)

说明与用途

Oracle 23ai/26ai 允许将向量数据作为原生列存储，使用 VECTOR_DISTANCE 进行语义搜索，在数据库内生成嵌入，并通过 Select AI 将自然语言查询转换为 SQL。

问题

Integrating semantic search and machine learning directly inside Oracle Database without exporting data to external tools.

结果

Oracle 23ai/26ai enables end‑to‑end AI workflows inside the database, reducing data movement, improving latency, and simplifying architecture.

工作流

• Define VECTOR column with dimensions and format, e.g., VECTOR(384,FLOAT32).
• Insert or generate embeddings using VECTOR_EMBEDDING on an imported ONNX model.
• Create vector index (HNSW or IVF) on the VECTOR column.
• Query semantic similarity with VECTOR_DISTANCE and optional FETCH APPROX for performance.
• Use Select AI (DBMS_CLOUD_AI) to convert natural language to SQL queries.
• Leverage Oracle Machine Learning for additional ML tasks within SQL/PL-SQL.
• Monitor performance and adjust index parameters as needed.

证据

Vector type is a first‑class citizen in Oracle 23ai/26ai, allowing embeddings to live alongside business data.

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