---
title: "PerplexityTextEmbedder"
id: perplexitytextembedder
slug: "/perplexitytextembedder"
description: "`PerplexityTextEmbedder` transforms a string into a vector using a Perplexity embedding model."
---

# PerplexityTextEmbedder

`PerplexityTextEmbedder` transforms a string into a vector that captures its semantics using a Perplexity embedding model.

When you perform embedding retrieval, use this component to transform your query into a vector. Then, the embedding Retriever looks for similar or relevant documents.

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | Before an embedding [Retriever](../retrievers.mdx) in a query/RAG pipeline |
| **Mandatory init variables** | `api_key`: A Perplexity API key. Can be set with `PERPLEXITY_API_KEY` env var. |
| **Mandatory run variables** | `text`: A string |
| **Output variables** | `embedding`: A list of float numbers <br /> <br />`meta`: A dictionary of metadata |
| **API reference** | [Integrations](/reference/integrations-perplexity) |
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/blob/main/integrations/perplexity/src/haystack_integrations/components/embedders/perplexity/text_embedder.py |
| **Package name** | `perplexity-haystack` |

</div>

## Overview

`PerplexityTextEmbedder` supports the following embedding models:

- `pplx-embed-v1-0.6b` (default)
- `pplx-embed-v1-4b`

Use `PerplexityTextEmbedder` to embed a single string, such as a query. For embedding lists of documents, use [PerplexityDocumentEmbedder](perplexitydocumentembedder.mdx).

The component uses a `PERPLEXITY_API_KEY` environment variable by default. You can also pass an API key directly at initialization:

```python
from haystack_integrations.components.embedders.perplexity import PerplexityTextEmbedder
from haystack.utils import Secret

embedder = PerplexityTextEmbedder(api_key=Secret.from_token("<your-api-key>"))
```

## Usage

### On its own

```python
from haystack_integrations.components.embedders.perplexity import PerplexityTextEmbedder

text_embedder = PerplexityTextEmbedder()
result = text_embedder.run("I love pizza!")
print(result["embedding"])

# [0.017020374536514282, -0.023255806416273117, ...]
```

:::info
We recommend setting `PERPLEXITY_API_KEY` as an environment variable instead of passing it as a parameter.
:::

### In a pipeline

```python
from haystack import Document, Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
from haystack_integrations.components.embedders.perplexity import (
    PerplexityTextEmbedder,
    PerplexityDocumentEmbedder,
)

document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")

documents = [
    Document(content="My name is Wolfgang and I live in Berlin"),
    Document(content="I saw a black horse running"),
    Document(content="Germany has many big cities"),
]

document_embedder = PerplexityDocumentEmbedder()
documents_with_embeddings = document_embedder.run(documents)["documents"]
document_store.write_documents(documents_with_embeddings)

query_pipeline = Pipeline()
query_pipeline.add_component("text_embedder", PerplexityTextEmbedder())
query_pipeline.add_component(
    "retriever",
    InMemoryEmbeddingRetriever(document_store=document_store),
)
query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")

result = query_pipeline.run({"text_embedder": {"text": "Who lives in Berlin?"}})
print(result["retriever"]["documents"][0])
```