TiDB for AI
TiDB is a distributed SQL database designed for modern AI applications, offering integrated vector search, full-text search, and hybrid search capabilities. This document provides an overview of the AI features and tools available for building AI-powered applications with TiDB.
Quick Start
Get up and running quickly with TiDB's AI capabilities.
| Document | Description |
|---|---|
| Get Started with Python | Build your first AI application with TiDB in minutes using Python. |
| Get Started with SQL | Quick start guide for vector search using SQL. |
Concepts
Understand the foundational concepts behind AI-powered search in TiDB.
| Document | Description |
|---|---|
| Vector Search | Comprehensive overview of vector search, including concepts, how it works, and use cases. |
Guides
Step-by-step guides for building AI applications with TiDB using the pytidb SDK or SQL.
| Document | Description |
|---|---|
| Connect to TiDB | Connect to TiDB Cloud or self-managed clusters using pytidb. |
| Working with Tables | Create, query, and manage tables with vector fields. |
| Vector Search | Perform semantic similarity searches using pytidb. |
| Full-Text Search | Keyword-based text search with BM25 ranking. |
| Hybrid Search | Combine vector and full-text search for better results. |
| Image Search | Search images using multimodal embeddings. |
| Auto Embedding | Automatically generate embeddings on data insertion. |
| Filtering | Filter search results with metadata conditions. |
Examples
Complete code examples and demos showcasing TiDB's AI capabilities.
| Document | Description |
|---|---|
| Basic CRUD Operations | Fundamental table operations with pytidb. |
| Vector Search | Semantic similarity search example. |
| RAG Application | Build a Retrieval-Augmented Generation application. |
| Image Search | Multimodal image search with Jina AI embeddings. |
| Conversational Memory | Persistent memory for AI agents and chatbots. |
| Text-to-SQL | Convert natural language to SQL queries. |
Integrations
Integrate TiDB with popular AI frameworks, embedding providers, and development tools.
| Document | Description |
|---|---|
| Integration Overview | Overview of all available integrations. |
| Embedding Providers | Unified interface for OpenAI, Cohere, Jina AI, and more. |
| LangChain | Use TiDB as a vector store with LangChain. |
| LlamaIndex | Use TiDB as a vector store with LlamaIndex. |
| MCP Server | Connect TiDB to Claude Code, Cursor, and other AI-powered IDEs. |
Reference
Technical reference documentation for TiDB's AI and vector search features.
| Document | Description |
|---|---|
| Vector Data Types | Vector column types and usage. |
| Functions and Operators | Distance functions and vector operations. |
| Vector Search Index | Create and manage vector indexes for performance. |
| Performance Tuning | Optimize vector search performance. |
| Limitations | Current limitations and constraints. |