The Most Advanced SQL-compatible Vector SolutionPublic Beta

TiDB is introducing a built-in vector search to the SQL database family, enabling support for your AI applications without requiring a new database or additional technical stacks. With vectors as a new data type in MySQL, you can now store and search for vectors directly using SQL.

Eliminate redundancy. Store vector embeddings alongside MySQL data directly. No new DB, no data duplication. Just SQL simplicity.

Leverage familiar SQL to join, index, and query operational and vector data together, enabling advanced semantic searches with ease.

Powering RAG, semantic searches and more, with integrations like OpenAI, Hugging Face, LangChain, and LlamaIndex, etc.

Comprehensive ecosystem of AI Integrations

Simplify your development process by leveraging TiDB's seamless integrations with leading AI vendors and frameworks


Experience Superior Search and AI Performance
with TiDB

Semantic search with high performance and accuracy

Leverage your existing MySQL expertise to seamlessly integrate advanced vector search

  • Knowledge graph integration to improve query performance
  • Auto scaling to support resource-intensive AI workloads
  • Columnar data store

Unified database solution for enhanced developer experience

Simplify your tech stack with a single, scalable database for both traditional and AI workloads

  • Serverless to get started instantly
  • Supports transactional, analytical and vector search workloads
  • Native integrations with OpenAI, LlamaIndex, HuggingFace etc.

MySQL compatible

Leverage your existing MySQL expertise to seamlessly integrate advanced vector search

  • New “Vector” datatype and similarity search indexes
  • Store data and vector embeddings together
  • Join, index, and query both operational and vector data with SQL

Simplest way to create end-to-end
RAG-Enabled Search Apps

TiDB's vector search capabilities make it incredibly easy to integrate Retrieval-Augmented Generation (RAG) into your applications

Connect to a TiDB cloud cluster

engine = create_engine(your_tidb_url)

Create table and vector index

class Entity(Base):
    __tablename__ = "entity"

    id = Column(Integer, primary_key=True)
    content = Column(Text)
    content_vec = Column(


Create embedding vector and save data

content = 'TiDB is an open-source distributed SQL database'

open_ai_client = openai.OpenAI(api_key=open_ai_api_key)
embedding = open_ai_client.embeddings.create(
   input=[content], model=model_name).data[0].embedding

with Session(engine) as session:
   session.add(Entity(content = content, content_vec = embedding))

Retrieve content

query = 'What is TiDB?'

embedding_query = open_ai_client.embeddings.create(
   input=[query], model=model_name).data[0].embedding

with Session(engine) as session:
   entity = session.query(Entity).order_by(


Key Use Cases Where Scalability
and Vector Search Meet

Real-Time Recommendation Systems

Understand user preferences and similarities between products to deliver accurate recommendations

Chatbots and Virtual Assistants

Handle numerous concurrent customer interactions using vector search to provide quick and accurate responses

Image and Video Recognition

Implement robust image and video analysis for content moderation

NLP Applications

Perform sentiment analysis, language translation, and text summarization for more accurate processing

Ad Targeting and Personalization

Analyze user behavior and preferences to serve the most relevant ads