> ## Documentation Index
> Fetch the complete documentation index at: https://docs.tensormesh.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Chat and Streaming

> Generate and stream text with Tensormesh chat and completion models.

## Generate A Single Response

`tensormesh(modelId)`, `tensormesh.chatModel(modelId)`, and `tensormesh.languageModel(modelId)` create AI SDK chat models backed by `/v1/chat/completions`.

```ts theme={null}
import { generateText } from "ai";
import { tensormesh } from "@tensormesh/ai-sdk-provider";

const result = await generateText({
  model: tensormesh("Qwen/Qwen3-Coder-30B-A3B-Instruct"),
  prompt: "Write a concise implementation plan for adding streaming chat.",
});

console.log(result.text);
```

## Stream A Response

```ts theme={null}
import { streamText } from "ai";
import { tensormesh } from "@tensormesh/ai-sdk-provider";

const result = streamText({
  model: tensormesh("Qwen/Qwen3-Coder-30B-A3B-Instruct"),
  prompt: "Explain how serverless inference helps developer tooling.",
});

for await (const text of result.textStream) {
  process.stdout.write(text);
}
```

## Text Completions

Use `completionModel` when you specifically need the `/v1/completions` route.

```ts theme={null}
import { generateText } from "ai";
import { tensormesh } from "@tensormesh/ai-sdk-provider";

const result = await generateText({
  model: tensormesh.completionModel("openai/gpt-oss-20b"),
  prompt: "Complete this sentence: Serverless inference is useful because",
});

console.log(result.text);
```

For most applications, prefer `tensormesh(modelId)` because chat models support richer AI SDK features such as tool calling and structured output.