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

# Overview

> Generate text embeddings with automatic tracing

The `runEmbedding()` method generates text embeddings using OpenAI, Google, or Amazon Bedrock embedding models. Like `runLocal()`, embedding requests go directly to the provider and traces are sent to Tracia asynchronously in the background.

```typescript theme={null}
import { Tracia } from 'tracia';

const tracia = new Tracia({ apiKey: process.env.TRACIA_API_KEY });

const result = await tracia.runEmbedding({
  model: 'text-embedding-3-small',
  input: 'What is the meaning of life?',
});

console.log(result.embeddings[0].values.length); // 1536
```

## How It Works

When you call `runEmbedding()`, the SDK:

1. **Calls the provider directly** - Your embedding request goes straight to OpenAI, Google, or Amazon Bedrock. Tracia is not in the request path.

2. **Sends the trace asynchronously** - After the provider responds, trace data is sent to Tracia in the background. This is non-blocking and adds zero latency to your application.

Embedding spans appear in the Tracia dashboard with the `EMBEDDING` span kind, so you can track embedding usage, latency, and costs alongside your LLM completions.

## Quick Examples

<CodeGroup>
  ```typescript Single Text theme={null}
  const result = await tracia.runEmbedding({
    model: 'text-embedding-3-small',
    input: 'Hello world',
  });

  console.log(result.embeddings[0].values.length); // 1536
  ```

  ```typescript Batch theme={null}
  const result = await tracia.runEmbedding({
    model: 'text-embedding-3-small',
    input: ['Hello', 'World', 'Goodbye'],
  });

  console.log(result.embeddings.length); // 3
  ```

  ```typescript With Dimensions theme={null}
  const result = await tracia.runEmbedding({
    model: 'text-embedding-3-large',
    input: 'Reduce dimensionality',
    dimensions: 256,
  });

  console.log(result.embeddings[0].values.length); // 256
  ```
</CodeGroup>

## Available Pages

<CardGroup cols={2}>
  <Card title="Basic Usage" icon="play" href="/sdk-node/run-embedding/basic-usage">
    Single and batch embeddings, dimensions, sessions
  </Card>

  <Card title="Supported Models" icon="microchip" href="/sdk-node/run-embedding/models">
    OpenAI, Google, and Amazon Bedrock embedding models
  </Card>
</CardGroup>

## Types

### RunEmbeddingInput

```typescript theme={null}
interface RunEmbeddingInput {
  // Required
  input: string | string[];  // Text(s) to embed
  model: string;             // Embedding model name

  // Optional
  provider?: LLMProvider;    // Provider override (auto-detected from model)
  providerApiKey?: string;   // Provider API key override
  dimensions?: number;       // Dimension override (model-dependent)
  timeoutMs?: number;        // Request timeout in milliseconds

  // Span options
  sendTrace?: boolean;       // Send trace to Tracia (default: true)
  spanId?: string;           // Custom span ID
  tags?: string[];           // Tags for the span
  userId?: string;           // User ID for the span
  sessionId?: string;        // Session ID for the span
  traceId?: string;          // Group related spans together
  parentSpanId?: string;     // Link to parent span
}
```

### RunEmbeddingResult

```typescript theme={null}
interface RunEmbeddingResult {
  embeddings: EmbeddingVector[];  // The generated embeddings
  spanId: string;                 // Unique span ID
  traceId: string;                // Trace ID for grouping
  latencyMs: number;              // Request latency in milliseconds
  usage: EmbeddingUsage;          // Token usage
  cost: number | null;            // Always null (cost calculated server-side)
  provider: LLMProvider;          // The provider used
  model: string;                  // The model used
}
```

### EmbeddingVector

```typescript theme={null}
interface EmbeddingVector {
  values: number[];  // The embedding float values
  index: number;     // Index in the input array
}
```

### EmbeddingUsage

```typescript theme={null}
interface EmbeddingUsage {
  totalTokens: number;  // Total tokens consumed
}
```