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

AI applications rarely deal with a single clean table. They mix user profiles, chat logs, JSON metadata, embeds, and sometimes spatial data. Most teams answer this with a combination of an OLTP database, a vector store, and a search engine. Ocean base Released seekdban open source database focused on artificial intelligence (under the Apache 2.0 license). seekdb is described as an AI-native search database that unifies relational, vector, text, JSON, and GIS data into a single engine and exposes hybrid search and database AI workflows.

What is sikdb?

seekdb It is positioned as a compact and lightweight version of the OceanBase engine, targeting AI applications rather than general-purpose distributed deployments. It runs as a single node database, supports embedded mode and client or server mode, and remains compatible with MySQL drivers and SQL syntax.

In the power matrix requestb is marked as follows:

• Built-in database support
• Independent database supported
• Distributed database is not supported

While the full OceanBase product covers the distributed case.

From a data model perspective, SeekDB supports:

• Relational data with standard SQL
• Vector search
• Search for full text
• Jason data
• Geospatial information systems data

All within a single storage and indexing layer.

Hybrid search as a basic feature

The main feature that OceanBase pushes is hybrid search. This is research that combines vector-based semantic retrieval, full-text keyword retrieval, and numerical filters into a single query and a single classification step.

seekdb implements hybrid search through a system package called DBMS_HYBRID_SEARCH with two entry points:

• DBMS_HYBRID_SEARCH.SEARCH which returns results in JSON format, sorted by relevance
• DBMS_HYBRID_SEARCH.GET_SQL which returns the specific SQL string used for execution

The mixed search path can be run:

• Pure vector search
• Pure full text search
• Built-in hybrid search

It can push relational filters and bindings to the storage space. It also supports query reranking strategies such as weighted scores and cross-rank merging and can plug reranking operations based on large language models.

For Retrieval Augmented Generation (RAG) and proxy memory, this means you can write a single SQL query that performs semantic matching on includes, exact matching on product codes or proper names, and relational filtering on user or tenant scopes.

Vector engine details and full text

In essence, requestb Modern vectors and Full text stack.

For vectors, requestb:

• Supports dense vectors and sparse vectors
• Supports Manhattan, Euclidean, inner product, and cosine distance metrics
• Provides memory index types such as HNSW, HNSW SQ, and HNSW BQ
• Provides disc-based index types including IVF and IVF PQ

Mixed Vector Index shows how you can store raw text, let eekdb automatically call the embedding model, and have the system maintain the corresponding vector index without a separate preprocessing path.

For text, requestb offers full-text searching using:

• Keyword, phrase and logical queries
• BM25 ranking in order of importance
• Multiple avatar modes

The key point is that full-text and vector indexes are first-class and are combined in the same query schema as scalar and GIS indexes, so hybrid searching does not need external formatting.

Artificial intelligence functions within the database

seekdb It includes built-in AI function expressions that allow you to call models directly from SQL, without a separate application service mediating each call. The main functions are:

• AI_EMBED to convert text into embeddings
• AI_COMPLETE to generate text using chat or completion form
• AI_RERANK to reorder the list of candidates
  AI_PROMPT to compile claim templates and dynamic values ​​into a JSON object for AI_COMPLETE

Model metadata and endpoints are managed by the DBMS_AI_SERVICE package, which allows you to register external service providers, set URLs, and configure keys, all from the database side.

Multimedia data and workloads

seekdb It is designed to handle multiple data modalities on a single node. It contains a multimedia data and indexing layer covering vector, text, JSON, and GIS, and a multi-model calculation layer for mixed workloads across vector, full-text, and scalar terms.

It also provides JSON indexes for metadata queries and GIS indexes for spatial conditions. This allows queries such as:

• Find morally similar documents
• Filter by JSON metadata such as tenant, region, or category
• Restrict by spatial extent or polygon

Without leaving the same engine.

Because requestdb is derived from the OceanBase engine, it inherits ACID transactions, hybrid row and column storage, and vector implementation, although large-scale distributed deployments remain important for a full OceanBase database.

Comparison table

Key takeaways

1. Native hybrid AI research: seekdb unifies vector search, full-text search, and relational filtering into a single SQL and DBMS_HYBRID_SEARCH interface, so that RAG and proxy workloads can run multiple reference retrievals in a single query instead of caging multiple engines together.
2. Multimedia data in one drive: eekdb stores and indexes relational data, vectors, text, JSON, and GIS in the same engine, allowing AI applications to keep documents, embeds, and metadata consistent without maintaining separate databases.
3. In RAG’s database AI functions: Using AI_EMBED, AI_COMPLETE, AI_RERANK, and AI_PROMPT, eekdb can call inclusion models, LLMs, and reordering directly from SQL, simplifying RAG pipelines and moving more formatting logic to the database layer.
4. Single knot, user-friendly design: Seekdb is a single-node MySQL-compatible engine and supports embedded and standalone modes, while large-scale distributed deployments remain the full OceanBase role, making Seedb suitable for on-premises, edge, and embedded service AI workloads.
5. Open source ecosystem and tools: Seekdb is open source within Apache 2.0 and integrates with the growing ecosystem of AI tools and frameworks, with Python support via pyseekdb and MCP-based integration for code helpers and agents, so it can serve as a unified data plane for AI applications.

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Asif Razzaq is CEO of Marktechpost Media Inc. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of AI for social good. His most recent endeavor is the launch of the AI ​​media platform, Marktechpost, which features in-depth coverage of machine learning and deep learning news that is technically sound and easy to understand by a broad audience. The platform has more than 2 million views per month, which shows its popularity among the masses.