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.

Most common position in a pipeline	Before an embedding Retriever 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 meta: A dictionary of metadata
API reference	Integrations
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

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.

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])