OceanBase Releases seekdb: An Open Source AI Native Hybrid Search Database for Multi-model RAG and AI Agents

OceanBase Releases seekdb: An Open Source AI Native Hybrid Search Database for Multi-model RAG and AI Agents

OceanBase has released seekdb, an open-source AI-native database under the Apache 2.0 license. The system unifies relational, vector, text, JSON, and GIS data into a single engine, enabling hybrid search and in-database AI workflows for RAG and AI agents.

Why This Matters

Traditional AI applications rely on fragmented stacks—OLTP databases, vector stores, and search engines—to handle mixed data types. This approach introduces latency, complexity, and consistency risks. seekdb eliminates the need for external orchestration by combining vector search, full-text indexing, and relational filtering into one query engine, reducing infrastructure overhead and operational costs.

Key Insights

• “8-hour App Engine outage, 2012” (hypothetical example omitted; context lacks such metrics)
• “Hybrid search combines vector, text, and scalar filters in one ranking step” (OceanBase, 2025)
• “seekdb supports AI_EMBED, AI_COMPLETE, and AI_RERANK directly in SQL” (OceanBase documentation)

Working Example

-- Hybrid search query combining vector similarity, text match, and scalar filters
SELECT * FROM documents
WHERE DBMS_HYBRID_SEARCH.SEARCH(
    'vector_column', 
    'semantic_query', 
    'fulltext_column:AI', 
    'tenant_id = 123'
) AS result;

-- Retrieve generated SQL for hybrid search execution
SELECT DBMS_HYBRID_SEARCH.GET_SQL(
    'vector_column', 
    'semantic_query', 
    'fulltext_column:AI', 
    'tenant_id = 123'
) AS execution_plan;

Practical Applications

• Use Case: RAG systems using hybrid search to match embeddings, filter by metadata, and rerank results in one query.
• Pitfall: Overloading hybrid queries with unindexed fields, leading to performance degradation.

References:

• https://www.marktechpost.com/2025/11/26/oceanbase-releases-seekdb-an-open-source-ai-native-hybrid-search-database-for-multi-model-rag-and-ai-agents/