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ChatPerplexity

This page will help you get started with Perplexity chat models. For detailed documentation of all ChatPerplexity features and configurations head to the API reference.

Overview​

Integration details​

ClassPackageLocalSerializableJS supportPackage downloadsPackage latest
ChatPerplexitylangchain-perplexity❌beta❌PyPI - DownloadsPyPI - Version

Model features​

Tool callingStructured outputJSON modeImage inputAudio inputVideo inputToken-level streamingNative asyncToken usageLogprobs
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Setup​

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

Credentials​

Head to this page to sign up for Perplexity and generate an API key. Once you've done this set the PPLX_API_KEY environment variable:

import getpass
import os

if "PPLX_API_KEY" not in os.environ:
    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_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"
from langchain_core.prompts import ChatPromptTemplate
from langchain_perplexity import ChatPerplexity

The code provided assumes that your PPLX_API_KEY is set in your environment variables. If you would like to manually specify your API key and also choose a different model, you can use the following code:

chat = ChatPerplexity(
    temperature=0, pplx_api_key="YOUR_API_KEY", model="llama-3-sonar-small-32k-online"
)

You can check a list of available models here. For reproducibility, we can set the API key dynamically by taking it as an input in this notebook.

chat = ChatPerplexity(temperature=0, model="llama-3.1-sonar-small-128k-online")
system = "You are a helpful assistant."
human = "{input}"
prompt = ChatPromptTemplate.from_messages([("system", system), ("human", human)])

chain = prompt | chat
response = chain.invoke({"input": "Why is the Higgs Boson important?"})
response.content
'The Higgs Boson is an elementary subatomic particle that plays a crucial role in the Standard Model of particle physics, which accounts for three of the four fundamental forces governing the behavior of our universe: the strong and weak nuclear forces, electromagnetism, and gravity. The Higgs Boson is important for several reasons:\n\n1. **Final Elementary Particle**: The Higgs Boson is the last elementary particle waiting to be discovered under the Standard Model. Its detection helps complete the Standard Model and further our understanding of the fundamental forces in the universe.\n\n2. **Mass Generation**: The Higgs Boson is responsible for giving mass to other particles, a process that occurs through its interaction with the Higgs field. This mass generation is essential for the formation of atoms, molecules, and the visible matter we observe in the universe.\n\n3. **Implications for New Physics**: While the detection of the Higgs Boson has confirmed many aspects of the Standard Model, it also opens up new possibilities for discoveries beyond the Standard Model. Further research on the Higgs Boson could reveal insights into the nature of dark matter, supersymmetry, and other exotic phenomena.\n\n4. **Advancements in Technology**: The search for the Higgs Boson has led to significant advancements in technology, such as the development of artificial intelligence and machine learning algorithms used in particle accelerators like the Large Hadron Collider (LHC). These advancements have not only contributed to the discovery of the Higgs Boson but also have potential applications in various other fields.\n\nIn summary, the Higgs Boson is important because it completes the Standard Model, plays a crucial role in mass generation, hints at new physics phenomena beyond the Standard Model, and drives advancements in technology.\n'

You can format and structure the prompts like you would typically. In the following example, we ask the model to tell us a joke about cats.

chat = ChatPerplexity(temperature=0, model="llama-3.1-sonar-small-128k-online")
prompt = ChatPromptTemplate.from_messages([("human", "Tell me a joke about {topic}")])
chain = prompt | chat
response = chain.invoke({"topic": "cats"})
response.content
'Here\'s a joke about cats:\n\nWhy did the cat want math lessons from a mermaid?\n\nBecause it couldn\'t find its "core purpose" in life!\n\nRemember, cats are unique and fascinating creatures, and each one has its own special traits and abilities. While some may see them as mysterious or even a bit aloof, they are still beloved pets that bring joy and companionship to their owners. So, if your cat ever seeks guidance from a mermaid, just remember that they are on their own journey to self-discovery!\n'

Using Perplexity-specific parameters through ChatPerplexity​

You can also use Perplexity-specific parameters through the ChatPerplexity class. For example, parameters like search_domain_filter, return_images, return_related_questions or search_recency_filter using the extra_body parameter as shown below:

chat = ChatPerplexity(temperature=0.7, model="llama-3.1-sonar-small-128k-online")
response = chat.invoke(
    "Tell me a joke about cats", extra_body={"search_recency_filter": "week"}
)
response.content
"Sure, here's a cat joke for you:\n\nWhy are cats bad storytellers?\n\nBecause they only have one tale. (Pun alert!)\n\nSource: OneLineFun.com [4]"

ChatPerplexity also supports streaming functionality:​

chat = ChatPerplexity(temperature=0.7, model="llama-3.1-sonar-small-128k-online")

for chunk in chat.stream("Give me a list of famous tourist attractions in Pakistan"):
    print(chunk.content, end="", flush=True)
Here is a list of some famous tourist attractions in Pakistan:

1. **Minar-e-Pakistan**: A 62-meter high minaret in Lahore that represents the history of Pakistan.
2. **Badshahi Mosque**: A historic mosque in Lahore with a capacity of 10,000 worshippers.
3. **Shalimar Gardens**: A beautiful garden in Lahore with landscaped grounds and a series of cascading pools.
4. **Pakistan Monument**: A national monument in Islamabad representing the four provinces and three districts of Pakistan.
5. **National Museum of Pakistan**: A museum in Karachi showcasing the country's cultural history.
6. **Faisal Mosque**: A large mosque in Islamabad that can accommodate up to 300,000 worshippers.
7. **Clifton Beach**: A popular beach in Karachi offering water activities and recreational facilities.
8. **Kartarpur Corridor**: A visa-free border crossing and religious corridor connecting Gurdwara Darbar Sahib in Pakistan to Gurudwara Sri Kartarpur Sahib in India.
9. **Mohenjo-daro**: An ancient Indus Valley civilization site in Sindh, Pakistan, dating back to around 2500 BCE.
10. **Hunza Valley**: A picturesque valley in Gilgit-Baltistan known for its stunning mountain scenery and unique culture.

These attractions showcase the rich history, diverse culture, and natural beauty of Pakistan, making them popular destinations for both local and international tourists.

ChatPerplexity Supports Structured Outputs for Tier 3+ Users​

from pydantic import BaseModel


class AnswerFormat(BaseModel):
    first_name: str
    last_name: str
    year_of_birth: int
    num_seasons_in_nba: int


chat = ChatPerplexity(temperature=0.7, model="sonar-pro")
structured_chat = chat.with_structured_output(AnswerFormat)
response = structured_chat.invoke(
    "Tell me about Michael Jordan. Return your answer "
    "as JSON with keys first_name (str), last_name (str), "
    "year_of_birth (int), and num_seasons_in_nba (int)."
)
response
AnswerFormat(first_name='Michael', last_name='Jordan', year_of_birth=1963, num_seasons_in_nba=15)

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