# Embeddings API

## Overview

Perplexity's Embeddings API generates high-quality text embeddings for semantic search and retrieval. Choose between **standard embeddings** for independent texts or **contextualized embeddings** for document chunks that share context.

:::callout{intent="info"}
We recommend using our [official SDKs](/guides/perplexity-sdk-overview) for a more convenient and type-safe way to interact with the Embeddings API.
:::

:::card{title="Pricing" href="/guides/getting-started-pricing" icon="receipt" cta="See pricing" horizontal="true"}
Pay-as-you-go pricing for all APIs. No subscription required.
:::

## Available Models

|             Model            | Dimensions | Context | MRL | Quantization | Price ($/1M tokens) |
| :--------------------------: | :--------: | :-----: | :-: | :----------: | :-----------------: |
|     `pplx-embed-v1-0.6b`     |    1024    |   32K   | Yes |  INT8/BINARY |        $0.004       |
|      `pplx-embed-v1-4b`      |    2560    |   32K   | Yes |  INT8/BINARY |        $0.03        |
| `pplx-embed-context-v1-0.6b` |    1024    |   32K   | Yes |  INT8/BINARY |        $0.008       |
|  `pplx-embed-context-v1-4b`  |    2560    |   32K   | Yes |  INT8/BINARY |        $0.05        |

All models use mean pooling and require no instruction prefix—you can embed text directly without prompt engineering.

:::callout{intent="warning"}
Perplexity embeddings are **unnormalized**. Always compare `base64_int8` embeddings via **cosine similarity** (not inner product or L2 distance). Compare `base64_binary` embeddings via **Hamming distance**. See [Best Practices](/guides/embeddings-api-best-practices) for details and normalization helpers.
:::

:::callout{intent="tip"}
**When to use which:**

- **Standard embeddings** (`pplx-embed-v1-*`) - Independent texts, search queries, single sentences
- **Contextualized embeddings** (`pplx-embed-context-v1-*`) - Document chunks that benefit from shared context (e.g., paragraphs from the same article)
:::

## Installation

:::code-group
```bash Python theme={null}
pip install perplexityai
```

```bash TypeScript/JavaScript theme={null}
npm install @perplexity-ai/perplexity_ai
```
:::

## Authentication

Set your API key as an environment variable:

::::tabs
:::tab{title="macOS/Linux"}
```bash theme={null}
export PERPLEXITY_API_KEY="your_api_key_here"
```
:::

:::tab{title="Windows"}
```powershell theme={null}
setx PERPLEXITY_API_KEY "your_api_key_here"
```
:::
::::

## Next Steps

::::card-grid
:::card{title="Standard Embeddings" href="/guides/embeddings-api-standard-embeddings" icon="cube"}
Embed independent texts, queries, and sentences.
:::

:::card{title="Contextualized Embeddings" href="/guides/embeddings-api-contextualized-embeddings" icon="file-lines"}
Document-aware embeddings for chunks that share context.
:::

:::card{title="Best Practices" href="/guides/embeddings-api-best-practices" icon="star"}
Batch processing, caching, RAG patterns, and performance optimization.
:::

:::card{title="Model Cards" href="https://huggingface.co/perplexity-ai" icon="link"}
See the model cards on HuggingFace.
:::
::::

## Related pages

- [Admin & Management](./admin-management-index.md)
- [Agent API](./agent-api-2-index.md)
- [Agent API](./agent-api-index.md)
- [Analytics API](./analytics-api-index.md)
- [Authentication](./authentication-index.md)
- [Changelog](../changelog.md)
- [Cookbook](./cookbook-2-index.md)
- [Embeddings API](./embeddings-api-2-index.md)
- [Embeddings API](./embeddings-api-index.md)
- [Getting Started](./getting-started-index.md)

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