Yes, SQL can be used with AI databases, especially those designed to integrate traditional database functionalities with AI-specific features like vector search, machine learning model integration, or handling unstructured data. Many modern AI databases either extend SQL with custom functions or provide SQL-like interfaces to interact with AI-driven data structures. This allows developers to leverage familiar SQL syntax while working with specialized capabilities such as similarity searches, embeddings, or model inference, reducing the learning curve for integrating AI into applications.
For example, PostgreSQL, a relational database, has extensions like pgvector that add vector search capabilities. Developers can store vector embeddings (numerical representations of data used in AI models) and perform similarity searches using SQL queries. A query might look like:
SELECT id, content
FROM documents
ORDER BY embedding <=> '[0.1, 0.5, -0.2]'
LIMIT 5;
Here, <=> is a custom operator provided by pgvector to calculate cosine similarity between vectors. Similarly, databases like ClickHouse or Google’s BigQuery ML enable training and deploying machine learning models using SQL syntax. For instance, BigQuery ML lets you create a model with CREATE MODEL and predict results via ML.PREDICT, all within SQL. These examples show how SQL is adapted to handle AI workloads without requiring developers to abandon their existing SQL knowledge.
However, not all AI databases use SQL natively. Vector databases like Milvus or Pinecone prioritize their own APIs for low-level operations but often provide SQL-like wrappers or integrate with SQL-based tools. For instance, Milvus supports SQL-like querying through connectors or via frameworks like LangChain, which can translate SQL-like logic into API calls. This hybrid approach allows teams to use SQL for higher-level workflows (e.g., filtering or joining metadata) while relying on the database’s native API for optimized vector operations. The key takeaway is that while SQL isn’t universally native to all AI databases, its principles or syntax are often supported through extensions, wrappers, or complementary tools, making it a practical choice for developers bridging traditional and AI-driven data workflows.