---
title: "PerplexityDocumentEmbedder"
id: perplexitydocumentembedder
slug: "/perplexitydocumentembedder"
description: "`PerplexityDocumentEmbedder` computes embeddings for a list of documents using Perplexity embedding models and stores the vectors in each document's `embedding` field."
---

# PerplexityDocumentEmbedder

`PerplexityDocumentEmbedder` computes the embeddings of a list of documents and stores the obtained vectors in the `embedding` field of each document. It uses Perplexity embedding models.

The vectors computed by this component are necessary to perform embedding retrieval on a collection of documents. At retrieval time, the vector representing the query is compared with those of the documents to find the most similar or relevant documents.

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

|  |  |
| --- | --- |
| **Most common position in a pipeline** | Before a [`DocumentWriter`](../writers/documentwriter.mdx) in an indexing pipeline |
| **Mandatory init variables** | `api_key`: A Perplexity API key. Can be set with `PERPLEXITY_API_KEY` env var. |
| **Mandatory run variables** | `documents`: A list of documents |
| **Output variables** | `documents`: A list of documents (enriched with embeddings) <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/document_embedder.py |
| **Package name** | `perplexity-haystack` |

</div>

## Overview

`PerplexityDocumentEmbedder` supports the following embedding models:

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

Use this component to embed a list of documents. To embed a single string (such as a query), use [PerplexityTextEmbedder](perplexitytextembedder.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 (
    PerplexityDocumentEmbedder,
)
from haystack.utils import Secret

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

### Embedding Metadata

If your documents have semantically meaningful metadata fields, you can embed them alongside the document text to improve retrieval quality:

```python
from haystack import Document
from haystack_integrations.components.embedders.perplexity import (
    PerplexityDocumentEmbedder,
)

doc = Document(content="some text", meta={"title": "relevant title", "page_number": 18})

embedder = PerplexityDocumentEmbedder(meta_fields_to_embed=["title"])
docs_with_embeddings = embedder.run(documents=[doc])["documents"]
```

## Usage

### On its own

```python
from haystack import Document
from haystack_integrations.components.embedders.perplexity import (
    PerplexityDocumentEmbedder,
)

doc = Document(content="I love pizza!")

document_embedder = PerplexityDocumentEmbedder()
result = document_embedder.run([doc])
print(result["documents"][0].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.components.writers import DocumentWriter
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"),
]

indexing_pipeline = Pipeline()
indexing_pipeline.add_component("embedder", PerplexityDocumentEmbedder())
indexing_pipeline.add_component("writer", DocumentWriter(document_store=document_store))
indexing_pipeline.connect("embedder", "writer")
indexing_pipeline.run({"embedder": {"documents": documents}})

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])
```