# AI & Vector Search
> LLM chat completions, streaming responses, vector embeddings, semantic search, and RAG pipelines with the Aerostack SDK.
The AI module gives you access to 30+ LLM models through a unified API with streaming support. Combined with the built-in Vector Search module, you can build RAG pipelines, semantic search, and intelligent content systems without any external services.
**Beta** -- AI and Vector Search APIs are stable but may receive non-breaking additions.
## What you can build
- **AI chat interfaces** -- Streaming chat completions with token-by-token rendering
- **Content generation** -- Blog posts, product descriptions, email drafts
- **Document summarization** -- Condense long documents into key points
- **Knowledge bases** -- Ingest documents and answer questions using RAG
- **Semantic search** -- Find content by meaning, not just keywords
- **FAQ auto-matching** -- Match user questions to existing answers
- **Product recommendations** -- Find similar products based on description embeddings
- **Code search** -- Semantic search across codebases and documentation
- **Classification** -- Categorize text, detect sentiment, extract entities
## AI completions
### Basic completion
```tsx
function AISummary({ text }: { text: string }) {
const { chat, loading } = useAI()
const [summary, setSummary] = useState('')
const handleSummarize = async () => {
const result = await chat({
prompt: `Summarize this text in 3 bullet points:\n\n${text}`,
model: 'gpt-4o-mini',
maxTokens: 256,
})
setSummary(result.text)
}
return (
{summary &&
{summary}
}
)
}
```
```ts
const result = await sdk.ai.chat({
prompt: 'Explain WebSocket pub/sub in two sentences.',
model: 'gpt-4o-mini',
maxTokens: 256,
temperature: 0.7,
})
console.log(result.text) // The completion text
console.log(result.usage) // { promptTokens, completionTokens, totalTokens }
console.log(result.finishReason) // 'stop' | 'length'
```
```go
result, err := client.AI.Complete(ctx, aerostack.CompletionInput{
Prompt: "Explain WebSocket pub/sub in two sentences.",
Model: "gpt-4o-mini",
MaxTokens: 256,
})
fmt.Println(result.Text)
```
```python
result = await client.ai.complete(
prompt="Explain WebSocket pub/sub in two sentences.",
model="gpt-4o-mini",
max_tokens=256
)
print(result.text)
```
### Chat with message history
```ts
const result = await sdk.ai.chat({
messages: [
{ role: 'system', content: 'You are a helpful coding assistant.' },
{ role: 'user', content: 'How do I sort an array in JavaScript?' },
{ role: 'assistant', content: 'Use Array.prototype.sort()...' },
{ role: 'user', content: 'What about sorting objects by a property?' },
],
model: 'gpt-4o',
maxTokens: 512,
})
```
### System prompts
```ts
const result = await sdk.ai.chat({
system: 'You are a customer support agent for Acme Corp. Be concise and helpful.',
prompt: userMessage,
model: 'claude-3-5-sonnet-20241022',
maxTokens: 1024,
})
```
---
## Streaming responses
Stream completions token-by-token for chat interfaces. This provides a much better user experience than waiting for the full response.
### React: useGatewayChat
The `useGatewayChat` hook provides a complete streaming chat experience with token wallet integration:
```tsx
function ChatInterface() {
const {
messages,
input,
setInput,
sendMessage,
isStreaming,
error,
} = useGatewayChat({
model: 'gpt-4o-mini',
systemPrompt: 'You are a helpful assistant.',
})
return (