oceanbase seekdb

Build Fast

From prototype to production in minutes: create AI apps using Python, run VectorDBBench on 1C2G.

Hybrid Search

Combine vector search, full-text search and relational query in a single statement.

Multi-Model

Support relational, vector, text, JSON and GIS in a single engine.

AI Inside

Run embedding, reranking, LLM inference and prompt management inside the database, supporting a complete document-in/data-out RAG workflow.

SQL Inside

Powered by the proven OceanBase engine, delivering real-time writes and queries with full ACID compliance, and seamless MySQL ecosystem compatibility.

 QUICK START 

A minimalist API design that keeps you focused on building your AI

seekdb
import pyseekdb

client = pyseekdb.Client()

# create a knowledge base
collection = client.get_or_create_collection("product_database")
 AI CAPABILITIES 

Full-stack AI capabilities for application development—from search to inference

  • Supports multi-path retrieval in a single SQL query, combining vector-based semantic search with keyword-based search for optimized recall.
  • Query reranking supports weighted scores, Reciprocal Rank Fusion (RRF), and LLM-based reranking for enhanced results.
  • Relational filters are pushed down to storage for optimized performance, and multi-table joins allow relational data retrieval.
Learn more
CREATE TABLE doc_table (
    c1 INT,
    vector VECTOR(3),
    query VARCHAR(255),
    content VARCHAR(255),
    VECTOR INDEX idx1(vector) WITH (distance=l2, type=hnsw, lib=vsag),
    FULLTEXT INDEX idx2(query),
    FULLTEXT INDEX idx3(content)
);

INSERT INTO doc_table VALUES
(1, '[1,2,3]', 'hello seekdb', 'seekdb supports hybrid vector and full-text search.'),
(2, '[1,2,1]', 'try seekdb today', 'fast sql database with vector search.'),
(3, '[1,1,1]', 'vector search in seekdb', 'unified semantic and keyword search.'),
(4, '[2,1,1]', 'goodbye old db', 'migrate to seekdb for modern capabilities.');

SET @parm = '{
    "query": {
        "query_string": {
            "fields": ["query", "content"],
            "query": "hello seekdb"
        }
    },
    "knn": {
        "field": "vector",
        "k": 3,
        "query_vector": [1,2,3]
    }
}';

SELECT json_pretty(DBMS_HYBRID_SEARCH.SEARCH('doc_table', @parm));
  • Supports dense vectors and sparse vectors, with multiple distance metrics including Manhattan, Euclidean, inner product, and cosine similarity.
  • Vector indexes support in-memory types such as HNSW, HNSW_SQ, HNSW_BQ, and disk-based types including IVF and IVF_PQ, optimizing storage costs.
  • Full-text search supports keyword, phrase, and Boolean queries, with BM25 ranking for relevance.
Learn more
CREATE TABLE doc_table (
    c1 INT,
    vector VECTOR(3),
    query VARCHAR(255),
    content VARCHAR(255),
    VECTOR INDEX idx1(vector) WITH (distance=l2, type=hnsw, lib=vsag),
    FULLTEXT INDEX idx2(query),
    FULLTEXT INDEX idx3(content)
);

INSERT INTO doc_table VALUES
(1, '[1,2,3]', 'hello seekdb', 'seekdb supports hybrid vector and full-text search.'),
(2, '[1,2,1]', 'try seekdb today', 'fast sql database with vector search.'),
(3, '[1,1,1]', 'vector search in seekdb', 'unified semantic and keyword search.'),
(4, '[2,1,1]', 'goodbye old db', 'migrate to seekdb for modern capabilities.');

SET @parm = '{
    "query": {
        "query_string": {
            "fields": ["query", "content"],
            "query": "hello seekdb"
        }
    },
    "knn": {
        "field": "vector",
        "k": 3,
        "query_vector": [1,2,3]
    }
}';

SELECT json_pretty(DBMS_HYBRID_SEARCH.SEARCH('doc_table', @parm));
  • Manage built-in AI services via the DBMS_AI_SERVICE package in SQL, and register external LLM services.
  • Convert text to vector embeddings directly in SQL using the AI_EMBED function.
  • Generate text in SQL with AI_COMPLETE, supporting reusable prompt templates.
  • Rerank text using LLM-based models in SQL via AI_RERANK.
Learn more
CREATE TABLE doc_table (
    c1 INT,
    vector VECTOR(3),
    query VARCHAR(255),
    content VARCHAR(255),
    VECTOR INDEX idx1(vector) WITH (distance=l2, type=hnsw, lib=vsag),
    FULLTEXT INDEX idx2(query),
    FULLTEXT INDEX idx3(content)
);

INSERT INTO doc_table VALUES
(1, '[1,2,3]', 'hello seekdb', 'seekdb supports hybrid vector and full-text search.'),
(2, '[1,2,1]', 'try seekdb today', 'fast sql database with vector search.'),
(3, '[1,1,1]', 'vector search in seekdb', 'unified semantic and keyword search.'),
(4, '[2,1,1]', 'goodbye old db', 'migrate to seekdb for modern capabilities.');

SET @parm = '{
    "query": {
        "query_string": {
            "fields": ["query", "content"],
            "query": "hello seekdb"
        }
    },
    "knn": {
        "field": "vector",
        "k": 3,
        "query_vector": [1,2,3]
    }
}';

SELECT json_pretty(DBMS_HYBRID_SEARCH.SEARCH('doc_table', @parm));
    
CREATE TABLE doc_table (
    c1 INT,
    vector VECTOR(3),
    query VARCHAR(255),
    content VARCHAR(255),
    VECTOR INDEX idx1(vector) WITH (distance=l2, type=hnsw, lib=vsag),
    FULLTEXT INDEX idx2(query),
    FULLTEXT INDEX idx3(content)
);

INSERT INTO doc_table VALUES
(1, '[1,2,3]', 'hello seekdb', 'seekdb supports hybrid vector and full-text search.'),
(2, '[1,2,1]', 'try seekdb today', 'fast sql database with vector search.'),
(3, '[1,1,1]', 'vector search in seekdb', 'unified semantic and keyword search.'),
(4, '[2,1,1]', 'goodbye old db', 'migrate to seekdb for modern capabilities.');

SET @parm = '{
    "query": {
        "query_string": {
            "fields": ["query", "content"],
            "query": "hello seekdb"
        }
    },
    "knn": {
        "field": "vector",
        "k": 3,
        "query_vector": [1,2,3]
    }
}';

SELECT json_pretty(DBMS_HYBRID_SEARCH.SEARCH('doc_table', @parm));

AI application development

RAG & Knowledge Retrieval

Large language models are limited by their training data. RAG introduces timely and trusted external knowledge to improve answer quality and reduce hallucination. seekdb enhances search accuracy through vector search, full-text search, hybrid search, built-in AI functions, and efficient indexing, while multi-level access control safeguards data privacy across heterogeneous knowledge sources.

Enterprise QACustomer supportIndustry insightsPersonal knowledge

Semantic Search Engine

Traditional keyword search struggles to capture intent. Semantic search leverages embeddings and vector search to understand meaning and connect text, images, and other modalities. seekdb's hybrid search and multi-model querying deliver more precise, context-aware results across complex search scenarios.

Product searchText-to-imageImage-to-product

AI-Assisted Coding & Development

AI-powered coding combines natural-language understanding and code semantic analysis to enable generation, completion, debugging, testing, and refactoring. seekdb enhances code intelligence with semantic search, multi-model storage for code and documents, isolated multi-project management, and time-travel queries—supporting both local and cloud IDE environments.

IDE pluginsDesign-to-webLocal IDEsWeb IDEs

On-Device & Edge AI Applications

Edge devices—from mobile to vehicle and industrial terminals—operate with constrained compute and storage. seekdb's lightweight architecture supports embedded and micro-server modes, delivering full SQL, JSON, and hybrid search under low resource usage. It integrates seamlessly with OceanBase cloud services to enable unified edge-to-cloud intelligent systems.

Personal assistantsIn-vehicle systemsAI educationCompanion robotsHealthcare devices

Agentic AI Applications

Agentic AI requires memory, planning, perception, and reasoning. seekdb provides a unified foundation for agents through metadata management, vector/text/mixed queries, multimodal data processing, RAG, built-in AI functions and inference, and robust privacy controls—enabling scalable, production-grade agent systems.

Personal assistantsEnterprise automationVertical agentsAgent platforms

Enterprise Application Intelligence

AI transforms enterprise systems from passive tools into proactive collaborators. seekdb provides a unified AI-ready storage layer, fully compatible with MySQL syntax and views, and accelerates mixed workloads with parallel execution and hybrid row-column storage. Legacy applications gain intelligent capabilities with minimal migration across office, workflow, and business analytics scenarios.

Document intelligenceBusiness insightsFinance systems
RAG & Retrieval
Semantic Search
AI Coding
Edge & device
AI agents
Enterprise AI

RAG & Knowledge Retrieval

Large language models are limited by their training data. RAG introduces timely and trusted external knowledge to improve answer quality and reduce hallucination. seekdb enhances search accuracy through vector search, full-text search, hybrid search, built-in AI functions, and efficient indexing, while multi-level access control safeguards data privacy across heterogeneous knowledge sources.

Enterprise QACustomer supportIndustry insightsPersonal knowledge
 Deployment Options 

Flexible deployment modes that support everything from rapid prototyping to large-scale user workloads, meeting the full range of your application needs

Embedded Mode

seekdb embeds as a lightweight library installable with a single pip command, ideal for personal learning or prototyping, and can easily run on various end devices.

Client/Server Mode

A lightweight and easy-to-use deployment mode recommended for both testing and production, delivering stable and efficient service.

Seamless Migration to OceanBase Distributed Database

An enterprise-grade, scalable solution that enables smooth application migration to the OceanBase distributed database and handles massive user traffic with ease.

Go to OceanBase

The AI-Native Search Database

Quick Start
GitHub