Integrating hybrid vector and graph technologies for effective data processing.<\/p><\/li>\n\n\n\n
Maintaining robust data modeling to ensure consistent ai results.<\/p><\/li>\n\n\n\n
Investing in observability tools to guarantee data quality for ai-native database solutions.<\/p><\/li>\n<\/ul>\n\n\n\n
<\/span>AI-native Database Differentiators<\/span><\/h2>\n\n\n\n![\"AI-native](\"https:\/\/static.pingcap.com\/files\/2025\/08\/25011235\/814c7e115abc4607bb06ea3894de9585.webp\")
<\/figure>\n\n\n\nUnified Retrieval and Reasoning<\/h3>\n\n\n\n
You need a database that can handle the demands of modern AI-native applications<\/a>. TiDB stands out by offering unified retrieval and reasoning within a single distributed SQL engine. This approach lets you combine vector search, full-text search, and structured queries without switching between multiple systems. You gain the ability to process data for retrieval-augmented generation, agentic workflows, and real-time analytics\u2014all in one place.<\/p>\n\n\n\n\nTiDB\u2019s distributed SQL engine<\/a> supports MySQL compatibility, horizontal scalability, and strong consistency. You can rely on built-in features like distributed transactions, global secondary indexing, and Change Data Capture (CDC) to manage your data efficiently.<\/p><\/li>\n\n\n\nBy integrating with frameworks such as Structured Datastore (SDS), TiDB enables seamless access to multiple data models. This flexibility supports a wide range of AI-native database workloads.<\/p><\/li>\n\n\n\n
Real-world deployments show that TiDB improves p99 latency by up to 10 times compared to legacy systems. You can also achieve up to 80% cost reduction<\/a> through better compute efficiency and query pushdown.<\/p><\/li>\n\n\n\nDevelopers like you benefit from increased velocity and reduced complexity. MySQL compatibility and strong consistency make it easier to build and reason about ai-native applications.<\/p><\/li>\n<\/ul>\n\n\n\n
You can see these advantages in action at companies like Dify, where TiDB replaced several in-house systems. This change simplified the architecture and improved performance for large-scale, cloud-native distributed database workloads.<\/p>\n\n\n\n
Hybrid Search Capabilities<\/h3>\n\n\n\n
Hybrid search<\/a> is essential for next-generation database architecture. TiDB lets you combine vector, full-text, and structured SQL queries in a single platform. This unified approach helps you deliver the most relevant and grounded information to large language models and other ai-powered tools.<\/p>\n\n\n\n\nYou can run hybrid queries that blend semantic understanding (vector search) with keyword matching (full-text search) and structured filtering. This combination ensures your ai-native database retrieves both semantically relevant and lexically precise data.<\/p><\/li>\n\n\n\n
Graph-based reasoning supports multi-hop traversal and relationship-aware retrieval. Your AI applications can connect ideas and answer complex questions more accurately.<\/p><\/li>\n\n\n\n
TiDB\u2019s real-time, distributed architecture supports high-throughput and low-latency operations. You can build responsive chat agents, search assistants, and real-time decision engines that rely on up-to-date data.<\/p><\/li>\n\n\n\n
Hybrid search reduces AI hallucinations and improves the quality of generated content. By combining different search methods, you provide more accurate and contextual results.<\/p><\/li>\n\n\n\n
Businesses using TiDB improved user experience, faster search results, and better data discovery. You gain a competitive edge by leveraging a unified platform for advanced ai-native applications.<\/p><\/li>\n<\/ul>\n\n\n\n
Hybrid search in TiDB also supports multi-lingual content<\/a> and unifies structured and unstructured data. This flexibility is vital for GenAI SaaS services and other cloud-based solutions that serve diverse customer needs.<\/p>\n\n\n\nGraph RAG and Knowledge Graphs<\/h3>\n\n\n\n
Advanced AI-native database workloads require more than simple retrieval. TiDB empowers you to build and query knowledge graphs and use Graph Retrieval-Augmented Generation (Graph RAG) for deeper reasoning.<\/p>\n\n\n\n
\nGraph RAG enables your ai models to retrieve context based on relationships, not just keywords or vectors. You can perform multi-hop traversal and semantic path finding, connecting ideas across different data sources.<\/p><\/li>\n\n\n\n
Knowledge graphs in TiDB allow you to represent complex data relationships natively. You can visualize and query these graphs to support advanced reasoning and discovery.<\/p><\/li>\n\n\n\n
Agent memory and state management features let you create dynamic, multi-turn ai interactions. Your applications can personalize responses and maintain context over time.<\/p><\/li>\n\n\n\n
Real-world use cases include chat agents, search assistants, and real-time analytics engines. These applications benefit from graph-based reasoning and knowledge graph traversal to deliver more accurate and relevant results.<\/p><\/li>\n<\/ul>\n\n\n\n
\nTip:<\/strong> TiDB\u2019s graph capabilities help you reduce manual intervention and improve resource utilization. You can automate data enrichment, validation, and personalization at scale.<\/p>\n<\/blockquote>\n\n\n\nThe following table highlights some of the most significant improvements in TiDB\u2019s architecture that support unified workloads for AI in 2025:<\/p>\n\n\n\n
<\/figure>\n\n\n\nUnified Retrieval and Reasoning<\/h3>\n\n\n\n
You need a database that can handle the demands of modern AI-native applications<\/a>. TiDB stands out by offering unified retrieval and reasoning within a single distributed SQL engine. This approach lets you combine vector search, full-text search, and structured queries without switching between multiple systems. You gain the ability to process data for retrieval-augmented generation, agentic workflows, and real-time analytics\u2014all in one place.<\/p>\n\n\n\n TiDB\u2019s distributed SQL engine<\/a> supports MySQL compatibility, horizontal scalability, and strong consistency. You can rely on built-in features like distributed transactions, global secondary indexing, and Change Data Capture (CDC) to manage your data efficiently.<\/p><\/li>\n\n\n\n By integrating with frameworks such as Structured Datastore (SDS), TiDB enables seamless access to multiple data models. This flexibility supports a wide range of AI-native database workloads.<\/p><\/li>\n\n\n\n Real-world deployments show that TiDB improves p99 latency by up to 10 times compared to legacy systems. You can also achieve up to 80% cost reduction<\/a> through better compute efficiency and query pushdown.<\/p><\/li>\n\n\n\n Developers like you benefit from increased velocity and reduced complexity. MySQL compatibility and strong consistency make it easier to build and reason about ai-native applications.<\/p><\/li>\n<\/ul>\n\n\n\n You can see these advantages in action at companies like Dify, where TiDB replaced several in-house systems. This change simplified the architecture and improved performance for large-scale, cloud-native distributed database workloads.<\/p>\n\n\n\n Hybrid search<\/a> is essential for next-generation database architecture. TiDB lets you combine vector, full-text, and structured SQL queries in a single platform. This unified approach helps you deliver the most relevant and grounded information to large language models and other ai-powered tools.<\/p>\n\n\n\n You can run hybrid queries that blend semantic understanding (vector search) with keyword matching (full-text search) and structured filtering. This combination ensures your ai-native database retrieves both semantically relevant and lexically precise data.<\/p><\/li>\n\n\n\n Graph-based reasoning supports multi-hop traversal and relationship-aware retrieval. Your AI applications can connect ideas and answer complex questions more accurately.<\/p><\/li>\n\n\n\n TiDB\u2019s real-time, distributed architecture supports high-throughput and low-latency operations. You can build responsive chat agents, search assistants, and real-time decision engines that rely on up-to-date data.<\/p><\/li>\n\n\n\n Hybrid search reduces AI hallucinations and improves the quality of generated content. By combining different search methods, you provide more accurate and contextual results.<\/p><\/li>\n\n\n\n Businesses using TiDB improved user experience, faster search results, and better data discovery. You gain a competitive edge by leveraging a unified platform for advanced ai-native applications.<\/p><\/li>\n<\/ul>\n\n\n\n Hybrid search in TiDB also supports multi-lingual content<\/a> and unifies structured and unstructured data. This flexibility is vital for GenAI SaaS services and other cloud-based solutions that serve diverse customer needs.<\/p>\n\n\n\n Advanced AI-native database workloads require more than simple retrieval. TiDB empowers you to build and query knowledge graphs and use Graph Retrieval-Augmented Generation (Graph RAG) for deeper reasoning.<\/p>\n\n\n\n Graph RAG enables your ai models to retrieve context based on relationships, not just keywords or vectors. You can perform multi-hop traversal and semantic path finding, connecting ideas across different data sources.<\/p><\/li>\n\n\n\n Knowledge graphs in TiDB allow you to represent complex data relationships natively. You can visualize and query these graphs to support advanced reasoning and discovery.<\/p><\/li>\n\n\n\n Agent memory and state management features let you create dynamic, multi-turn ai interactions. Your applications can personalize responses and maintain context over time.<\/p><\/li>\n\n\n\n Real-world use cases include chat agents, search assistants, and real-time analytics engines. These applications benefit from graph-based reasoning and knowledge graph traversal to deliver more accurate and relevant results.<\/p><\/li>\n<\/ul>\n\n\n\n Tip:<\/strong> TiDB\u2019s graph capabilities help you reduce manual intervention and improve resource utilization. You can automate data enrichment, validation, and personalization at scale.<\/p>\n<\/blockquote>\n\n\n\n The following table highlights some of the most significant improvements in TiDB\u2019s architecture that support unified workloads for AI in 2025:<\/p>\n\n\n\n\n
Hybrid Search Capabilities<\/h3>\n\n\n\n
\n
Graph RAG and Knowledge Graphs<\/h3>\n\n\n\n
\n
\n