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

# Perplexity

Chroma provides a convenient wrapper around Perplexity's embedding API. This embedding function runs remotely on Perplexity's servers, and requires an API key. You can get an API key by signing up for an account at [Perplexity](https://www.perplexity.ai/).

<Tabs>
  <Tab title="Python" icon="python">
    This embedding function relies on the `perplexityai` python package, which you can install with `pip install perplexityai`.

    ```python theme={null}
    import chromadb.utils.embedding_functions as embedding_functions

    perplexity_ef = embedding_functions.PerplexityEmbeddingFunction(
        api_key="YOUR_API_KEY",
        model_name="pplx-embed-v1-4b"
    )

    perplexity_ef(input=["document1", "document2"])
    ```
  </Tab>

  <Tab title="TypeScript" icon="js">
    ```typescript theme={null}
    // npm install @chroma-core/perplexity

    import { PerplexityEmbeddingFunction } from "@chroma-core/perplexity";

    const embedder = new PerplexityEmbeddingFunction({
        apiKey: "YOUR_API_KEY",
        modelName: "pplx-embed-v1-4b",
    });

    // use directly
    const embeddings = await embedder.generate(["document1", "document2"]);

    // pass documents to query for .add and .query
    const collection = await client.createCollection({
        name: "name",
        embeddingFunction: embedder,
    });
    const collectionGet = await client.getCollection({
        name: "name",
        embeddingFunction: embedder,
    });
    ```
  </Tab>
</Tabs>

## Available Models

Perplexity offers two embedding models:

| Model                | Dimensions | Context Window | Price             |
| -------------------- | ---------- | -------------- | ----------------- |
| `pplx-embed-v1-0.6b` | 1024       | 32K tokens     | \$0.004/1M tokens |
| `pplx-embed-v1-4b`   | 2560       | 32K tokens     | \$0.03/1M tokens  |

## Matryoshka Dimensions

Both models support [Matryoshka Representation Learning](https://arxiv.org/abs/2205.13147), allowing you to reduce embedding dimensions while maintaining quality. This is useful for reducing storage costs and improving search speed.

<Tabs>
  <Tab title="Python" icon="python">
    ```python theme={null}
    # Reduce dimensions from 2560 to 512 for the 4b model
    perplexity_ef = embedding_functions.PerplexityEmbeddingFunction(
        api_key="YOUR_API_KEY",
        model_name="pplx-embed-v1-4b",
        dimensions=512
    )

    embeddings = perplexity_ef(input=["document1", "document2"])
    print(len(embeddings[0]))  # 512
    ```
  </Tab>

  <Tab title="TypeScript" icon="js">
    ```typescript theme={null}
    // Reduce dimensions from 2560 to 512 for the 4b model
    const embedder = new PerplexityEmbeddingFunction({
        apiKey: "YOUR_API_KEY",
        modelName: "pplx-embed-v1-4b",
        dimensions: 512,
    });

    const embeddings = await embedder.generate(["document1", "document2"]);
    console.log(embeddings[0].length);  // 512
    ```
  </Tab>
</Tabs>

Supported dimension ranges:

* `pplx-embed-v1-0.6b`: 128 to 1024
* `pplx-embed-v1-4b`: 128 to 2560

For more details on Perplexity's embedding models, check the [documentation](https://docs.perplexity.ai/docs/embeddings/standard-embeddings).