> ## Documentation Index
> Fetch the complete documentation index at: https://docs.memvid.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Vercel AI SDK
> Build AI-powered applications with Memvid and Vercel AI SDK
Integrate Memvid with the Vercel AI SDK to build AI-powered web applications. The `vercel-ai` adapter provides tools formatted for use with `generateText`, `streamText`, and other AI SDK functions.
## Installation
```bash theme={null}
npm install @memvid/sdk ai @ai-sdk/openai
```
## Quick Start
```typescript theme={null}
import { use } from '@memvid/sdk';
// Open with Vercel AI adapter
const mem = await use('vercel-ai', 'knowledge.mv2');
// Access Vercel AI tools
const tools = mem.tools; // Object with tool definitions
```
## Available Tools
The Vercel AI adapter provides three tools:
| Tool | Description |
| ------------- | ----------------------------------------------------- |
| `memvid_put` | Store documents in memory with title, label, and text |
| `memvid_find` | Search for relevant documents by query |
| `memvid_ask` | Ask questions with RAG-style answer synthesis |
## Using with generateText
```typescript theme={null}
import { use } from '@memvid/sdk';
import { generateText } from 'ai';
import { openai } from '@ai-sdk/openai';
// Get Memvid tools
const mem = await use('vercel-ai', 'knowledge.mv2');
// Use with generateText
const result = await generateText({
model: openai('gpt-4o-mini'),
tools: mem.tools,
maxSteps: 5, // Allow multiple tool calls
system: 'You are a helpful assistant with access to a knowledge base.',
prompt: 'Search for information about authentication and summarize it.',
});
// Access the result
console.log(result.text);
// View tool calls made
for (const step of result.steps) {
if (step.toolCalls) {
for (const call of step.toolCalls) {
console.log(`Tool: ${call.toolName}, Args: ${JSON.stringify(call.args)}`);
}
}
}
```
## Using with streamText
```typescript theme={null}
import { use } from '@memvid/sdk';
import { streamText } from 'ai';
import { openai } from '@ai-sdk/openai';
// Get Memvid tools
const mem = await use('vercel-ai', 'knowledge.mv2');
// Stream response with tool use
const result = await streamText({
model: openai('gpt-4o-mini'),
tools: mem.tools,
maxSteps: 3,
system: 'You are a helpful assistant with access to a knowledge base.',
prompt: 'What features does the product have?',
});
// Stream the text output
for await (const chunk of result.textStream) {
process.stdout.write(chunk);
}
```
## Direct Tool Usage
You can also call tools directly without using an LLM:
```typescript theme={null}
import { use } from '@memvid/sdk';
const mem = await use('vercel-ai', 'knowledge.mv2');
const tools = mem.tools;
// Store a document
const putResult = await tools.memvid_put.execute({
title: 'API Documentation',
label: 'docs',
text: 'The API supports REST and GraphQL endpoints...',
});
console.log(putResult); // "Document stored with frame_id: 2"
// Search for documents
const findResult = await tools.memvid_find.execute({
query: 'API endpoints',
top_k: 5,
});
console.log(findResult);
// Ask a question
const askResult = await tools.memvid_ask.execute({
question: 'How do I authenticate with the API?',
mode: 'auto',
});
console.log(askResult);
```
## Next.js API Route
```typescript theme={null}
// app/api/chat/route.ts
import { use } from '@memvid/sdk';
import { streamText } from 'ai';
import { openai } from '@ai-sdk/openai';
export async function POST(req: Request) {
const { messages } = await req.json();
// Get Memvid tools
const mem = await use('vercel-ai', 'knowledge.mv2');
const result = await streamText({
model: openai('gpt-4o-mini'),
tools: mem.tools,
messages,
maxSteps: 5,
});
return result.toDataStreamResponse();
}
```
## Next.js with useChat
```tsx theme={null}
// app/page.tsx
'use client';
import { useChat } from 'ai/react';
export default function Chat() {
const { messages, input, handleInputChange, handleSubmit } = useChat({
api: '/api/chat',
});
return (
{messages.map((m) => (
{m.role}: {m.content}
))}