SuperAPI AI Directory
Partner with us中文AI newsFavorites
DirectoryNews and tutorials

Seekdb - OceanBase's first open-source AI-native hybrid search database

Author: AI小集 · Original source: AI工具集

What is Seekdb

Seekdb is an AI-native database open-sourced by OceanBase, addressing the pain points of multimodal data fusion and real-time processing in AI application development. It supports fusion queries of vector retrieval, full-text search, and structured data filtering, enabling cross-modal data processing with a single SQL statement, such as complex queries like "transactions over 50,000 yuan in the past 7 days, abnormal location, and behavior similar to historical fraud samples," without the need for cross-system calls. It requires a minimum of only 1 CPU core and 2GB of memory, supports one-click startup via pip installation, and can be embedded in intelligent Agents, local applications, or run in service mode. Developers can quickly build AI applications with just three lines of code. Based on OceanBase's transaction engine, it ensures real-time index updates after data writes, guaranteeing data consistency and real-time performance in highly sensitive scenarios such as finance and government affairs. It is open-sourced under the Apache 2.0 license and is compatible with more than 30 mainstream AI frameworks including LangChain and HuggingFace, providing SQL and Python SDKs to lower the development threshold.

Seekdb

Main Features of Seekdb

  • Hybrid Search Capabilities: Supports unified retrieval of vector, full-text, scalar, and geospatial data, enabling efficient querying of multimodal data.
  • Integration of AI Inference and Data Processing: Directly performs operations such as embedding, reranking, and large language model inference within the database, improving data processing efficiency.
  • Lightweight Deployment and Quick Startup: Runs with a minimum of only 1 CPU core + 2 GB memory, supports one-click installation via pip install, and starts in just a few seconds.
  • Open Source and Broad Compatibility: Open-sourced under the Apache 2.0 license, seamlessly compatible with the MySQL ecosystem, and supports more than 30 mainstream AI frameworks such as Hugging Face and LangChain.
  • Low Latency and High Performance: Responds in milliseconds for multimodal data retrieval at the scale of tens of billions, meeting the needs of scenarios with high real-time requirements.
  • Support for Multiple Running Modes: Provides embedded and client/server running modes, flexibly adapting to different development needs.
  • Simplified Development Process: Developers can build AI applications such as knowledge bases and Agents with just three lines of code, lowering the development threshold.

Technical Principles of Seekdb

  • AI-Native Architecture: Seekdb adopts an AI-native design, deeply integrating AI inference with data processing. It supports directly performing operations such as embedding, reranking, and large language model inference within the database, improving overall performance through an optimized execution engine.
  • Hybrid Search Engine: Integrates unified retrieval capabilities for vector, full-text, scalar, and geospatial data. Through a multi-stage retrieval mechanism, it achieves high-precision search at low latency, meeting the complex query needs of multimodal data.
  • Lightweight Deployment and Quick Startup: Seekd is designed to be lightweight, runs with a minimum of only 1 CPU core + 2 GB memory, supports one-click installation via pip install, and starts in just a few seconds.
  • Open Source and Compatibility: Open-sourced under the Apache 2.0 license, seamlessly compatible with the MySQL ecosystem, and also supports more than 30 mainstream AI frameworks such as Hugging Face and LangChain.
  • Low Latency and High Performance: Through optimized index structures and retrieval algorithms, SeekDB can respond in milliseconds for multimodal data retrieval at the scale of tens of billions, suitable for scenarios with high real-time requirements.
  • Flexible Running Modes: Supports both embedded and client/server operation modes, meeting different development needs and providing greater flexibility.
  • Deep integration with AI frameworks: Seekdb deeply integrates multiple mainstream AI frameworks, supporting vector embedding and model inference directly within the database, reducing data transmission and processing latency.

Seekdb's project address

  • Project official website：https://www.oceanbase.ai/
  • Github repository：https://github.com/oceanbase/seekdb

Seekdb's application scenarios

  • Semantic search: Through hybrid search capabilities, it quickly retrieves text content, achieves precise semantic matching, and improves search efficiency and user experience.
  • Knowledge base question answering: Supports building intelligent knowledge bases, quickly responding to user queries, and providing accurate answers and information, suitable for enterprise internal knowledge management and service support.
  • Recommendation system: Leveraging its high performance and multimodal data processing capabilities, it provides users with personalized recommendations, improving recommendation accuracy and real-time performance.
  • In-database model inference: Supports directly executing AI model inference within the database, reducing data transmission and processing latency, and improving overall performance.
  • Multimodal data retrieval: Suitable for scenarios that need to handle multiple data types (such as text, vectors, geospatial data), enabling unified retrieval and analysis.
  • Intelligent customer service and virtual assistants: Through fast retrieval and semantic understanding, it supports rapid response and precise answers for intelligent customer service and virtual assistants, improving service efficiency.