---
title: Vercel | Developer Documentation
description: Integrate LlamaIndex with Vercel's AI SDK
---

LlamaIndex provides integration with Vercel’s AI SDK, allowing you to create powerful search and retrieval applications. You can:

- Use any of Vercel AI’s [model providers](https://sdk.vercel.ai/docs/foundations/providers-and-models) as LLMs in LlamaIndex
- Use indexes (e.g. VectorStoreIndex, LlamaCloudIndex) from LlamaIndexTS in your Vercel AI applications

## Setup

First, install the required dependencies:

```
npm i @llamaindex/vercel ai
```

## Using Vercel AI’s Model Providers

Using the `VercelLLM` adapter, it’s easy to use any of Vercel AI’s [model providers](https://sdk.vercel.ai/docs/foundations/providers-and-models) as LLMs in LlamaIndex. Here’s an example of how to use OpenAI’s GPT-4o model:

```
const llm = new VercelLLM({ model: openai("gpt-4o") });
const result = await llm.complete({
  prompt: "What is the capital of France?",
  stream: false, // Set to true if you want streaming responses
});
console.log(result.text);
```

## Use Indexes

### Using VectorStoreIndex

Here’s how to create a simple vector store index and query it using Vercel’s AI SDK:

```
import { openai } from "@ai-sdk/openai";
import { llamaindex } from "@llamaindex/vercel";
import { streamText } from "ai";
import { Document } from "llamaindex";
import { LlamaCloudIndex } from "llama-cloud-services";


// Create an index from your documents
const document = new Document({ text: yourText, id_: "unique-id" });
const index = await VectorStoreIndex.fromDocuments([document]);


// Create a query tool
const queryTool = llamaindex({
  model: openai("gpt-4"),
  index,
  description: "Search through the documents", // optional
});


// Use the tool with Vercel's AI SDK
streamText({
  model: openai("gpt-4"),
  prompt: "Your question here",
  tools: { queryTool },
  onFinish({ response }) {
    console.log("Response:", response.messages); // log the response
  },
}).toDataStream();
```

> Note: the Vercel AI model referenced in the `llamaindex` function is used by the response synthesizer to generate a response for the tool call.

### Using LlamaCloud

For production deployments, you can use LlamaCloud to store and manage your documents:

```
import { LlamaCloudIndex } from "llama-cloud-services";


// Create a LlamaCloud index
const index = await LlamaCloudIndex.fromDocuments({
  documents: [document],
  name: "your-index-name",
  projectName: "your-project",
  apiKey: process.env.LLAMA_CLOUD_API_KEY,
});


// Use it the same way as VectorStoreIndex
const queryTool = llamaindex({
  model: openai("gpt-4"),
  index,
  description: "Search through the documents",
  options: { fields: ["sourceNodes", "messages"]}
});


// Use the tool with Vercel's AI SDK
streamText({
  model: openai("gpt-4"),
  prompt: "Your question here",
  tools: { queryTool },
}).toDataStream();
```

## Next Steps

1. Explore [LlamaCloud](https://cloud.llamaindex.ai/) for managed document storage and retrieval
2. Join our [Discord community](https://discord.gg/dGcwcsnxhU) for support and discussions