PerplexityEmbeddings integration

This will help you get started with Perplexity embedding models using LangChain. For detailed documentation on PerplexityEmbeddings features and configuration options, please refer to the API reference.

Overview

Integration details

Class	Package	Local	Py support	Package downloads	Package latest
PerplexityEmbeddings	langchain-perplexity	❌	✅

Setup

To access Perplexity embedding models you'll need to create a Perplexity account, get an API key, and install the langchain-perplexity integration package.

Credentials

Head to https://www.perplexity.ai/account/api/keys to sign up for the Perplexity API and generate an API key. Once you've done this, set the PPLX_API_KEY (or PERPLEXITY_API_KEY) environment variable:

import getpass
import os

if not os.getenv("PPLX_API_KEY"):
    os.environ["PPLX_API_KEY"] = getpass.getpass("Enter your Perplexity API key: ")

To enable automated tracing of your model calls, set your LangSmith API key:

os.environ["LANGSMITH_TRACING"] = "true"
os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")

Installation

The LangChain Perplexity integration lives in the langchain-perplexity package:

pip install -qU langchain-perplexity

Instantiation

Now we can instantiate our embedding model object and generate embeddings:

from langchain_perplexity import PerplexityEmbeddings

embeddings = PerplexityEmbeddings(
    model="pplx-embed-v1-4b",
    # api_key="...",       # if you prefer to pass the key explicitly
    # request_timeout=60,
    # max_retries=6,
)

Available models include pplx-embed-v1-4b (default) and pplx-embed-v1-0.6b. See the Perplexity Embeddings API reference for the current list and dimensions.

Indexing and retrieval

Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our RAG tutorials.

Below, see how to index and retrieve data using the embeddings object we initialized above. In this example, we will index and retrieve a sample document in the InMemoryVectorStore.

# Create a vector store with a sample text
from langchain_core.vectorstores import InMemoryVectorStore

text = "LangChain is the framework for building context-aware reasoning applications"

vectorstore = InMemoryVectorStore.from_texts(
    [text],
    embedding=embeddings,
)

# Use the vectorstore as a retriever
retriever = vectorstore.as_retriever()

# Retrieve the most similar text
retrieved_documents = retriever.invoke("What is LangChain?")

# show the retrieved document's content
retrieved_documents[0].page_content

'LangChain is the framework for building context-aware reasoning applications'

Direct usage

Under the hood, the vectorstore and retriever implementations are calling embeddings.embed_documents(...) and embeddings.embed_query(...) to create embeddings for the text(s) used in from_texts and retrieval invoke operations, respectively.

You can directly call these methods to get embeddings for your own use cases.

Embed single texts

You can embed single texts or documents with embed_query:

single_vector = embeddings.embed_query(text)
print(str(single_vector)[:100])  # Show the first 100 characters of the vector

Embed multiple texts

You can embed multiple texts with embed_documents:

text2 = (
    "LangGraph is a library for building stateful, multi-actor applications with LLMs"
)
two_vectors = embeddings.embed_documents([text, text2])
for vector in two_vectors:
    print(str(vector)[:100])  # Show the first 100 characters of the vector

Async usage

PerplexityEmbeddings also exposes async methods:

single_vector = await embeddings.aembed_query(text)
two_vectors = await embeddings.aembed_documents([text, text2])

API reference

For detailed documentation on PerplexityEmbeddings features and configuration options, please refer to the API reference and the Perplexity Embeddings API documentation.