YugabyteDB offers the familiarity and extensibility of PostgreSQL, while also delivering scale and resilience. Its distributed nature combines enterprise-grade vector search with ACID transactions. YugabyteDB enables you to store embeddings alongside transactional data, perform vector similarity searches with full SQL capabilities, and scale to billions of vectors across multiple regions, all with PostgreSQL compatibility and zero-downtime operations.

Using the [pgvector](../../additional-features/pg-extensions/extension-pgvector/) PostgreSQL extension, YugabyteDB functions as a highly performant vector database, with enterprise scale and resilience. This means you can use YugabyteDB to support Retrieval-augmented generation (RAG) workloads, providing AI agents with knowledge of your unstructured data, while its scalability allows it to store and search billions of vectors.

Learn more about developing GenAI and RAG applications with YugabyteDB:

- [Introducing New YugabyteDB Functionality for Ultra-Resilient AI Apps](https://www.yugabyte.com/blog/new-yugabytedb-functionality-for-ultra-resilient-ai-apps/)
- [Introducing the YugabyteDB MCP Server](https://www.yugabyte.com/blog/yugabytedb-mcp-server/)
- [How to Build a RAG Workflow for Agentic AI without Code](https://www.yugabyte.com/blog/build-a-rag-workflow-for-agentic-ai-without-codev/)
- [From RAG to Riches: AI That Knows Your Support Stack](https://www.yugabyte.com/blog/rag-ai-that-knows-your-support-stack/)

## Get started

{{<tip>}}
Use YugabyteDB {{<release "2025.2">}} or later to get the latest vector indexing capabilities and MCP features.

No cluster? No problem. Run the [latest YugabyteDB version locally](https://download.yugabyte.com) on macOS (using Docker or the yugabyted binary) or any Linux VM to try these tutorials.
{{</tip>}}

Get started with AI and YugabyteDB using the "Hello RAG" example to build your first AI application. Hello RAG walks you through building a complete Retrieval-Augmented Generation pipeline, which powers many enterprise AI applications, from customer support chatbots to semantic search systems.

In this tutorial, you will:

1. Vectorize data: Split local documents into chunks and generate embeddings using OpenAI.
1. Store and index: Insert those embeddings into YugabyteDB using the pgvector extension.
1. Retrieve and generate: Perform a vector similarity search to find relevant context and use an LLM (like GPT-4) to generate a grounded, accurate response.

{{<lead link="hello-rag/">}}
Build your first AI app with [Hello RAG](hello-rag/).
{{</lead>}}

## AI tutorials

Explore the following tutorials to see how YugabyteDB integrates with different LLMs and frameworks.

| Tutorial | Use case | LLM / framework | LLM location |
| :--- | :--- | :--- | :--- |
| [Hello RAG](hello-rag/) | Build a basic RAG pipeline for document-based question answering. | OpenAI | External |
| [Azure AI](azure-openai/) | Use Azure OpenAI to build a scalable RAG application with vector search. | Azure OpenAI | External |
| [Google Vertex AI](google-vertex-ai/) | Use Google Vertex AI for similarity search and generative AI workflows. | Vertex AI | External |
| [LocalAI](ai-localai/) | Build and run an LLM application entirely on-premises for privacy and security. | LocalAI | Local / on-premises |
| [Ollama](ai-ollama/) | Host and run embedding models locally for vector-based similarity search. | Ollama | Local / on-premises |
| [YugabyteDB MCP server](mcp-server/) | Enable LLMs to interact directly with YugabyteDB using natural language. | Claude / Cursor | External |
| [LlamaIndex](ai-llamaindex-openai/) | Connect LLMs to structured and unstructured data using LlamaIndex. | OpenAI / LlamaIndex | External |
| [LangChain](ai-langchain-openai/) | Build a natural language interface to query your database without writing SQL. | OpenAI / LangChain | External |

<!--
## Retrieval-augmented generation

Build a Retrieval-Augmented Generation pipeline with YugabyteDB.

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## Vector basics

Use YugabyteDB as the database backend for LLM applications.

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{{<index/item
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{{</index/block>}}

## Agentic, multiple data sources, and multi-step reasoning

Learn how you can use YugabyteDB as the foundation for your next AI agent application.

{{<index/block>}}

{{<index/item
    title="YugabyteDB MCP Server"
    body="Get LLMs to interact directly with YugabyteDB."
    href="mcp-server/"
    icon="fa-thin fa-comment">}}

{{<index/item
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    href="ai-llamaindex-openai/"
    icon="/images/tutorials/ai/icons/llamaindex-icon.svg">}}

{{<index/item
    title="Query without SQL using LangChain"
    body="Build scalable applications with LLM integrations using LangChain and OpenAI."
    href="ai-langchain-openai/"
    icon="/images/tutorials/ai/icons/langchain-icon.svg">}}
{{</index/block>}}
-->