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

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Overview

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Integration details

ClassPackageLocalSerializableJS supportPackage downloadsPackage latest
ChatPipeshiftlangchain-pipeshift❌-❌PyPI - DownloadsPyPI - Version

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Model features

Tool callingStructured outputJSON modeImage inputAudio inputVideo inputToken-level streamingNative asyncToken usageLogprobs
❌❌✅✅❌❌✅✅✅-

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Setup

To access Pipeshift models you’ll need to create an account on Pipeshift, get an API key, and install the langchain-pipeshift integration package.

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Credentials

Head to Pipeshift to sign up to Pipeshift and generate an API key. Once you’ve done this set the PIPESHIFT_API_KEY environment variable: 
import getpass
import os

if not os.getenv("PIPESHIFT_API_KEY"):
    os.environ["PIPESHIFT_API_KEY"] = getpass.getpass("Enter your Pipeshift API key: ")
If you want to get automated tracing of your model calls you can also set your LangSmith API key by uncommenting below: 
# os.environ["LANGSMITH_TRACING"] = "true"
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")

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Installation

The LangChain Pipeshift integration lives in the langchain-pipeshift package: 
%pip install -qU langchain-pipeshift

Note: you may need to restart the kernel to use updated packages.

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Instantiation

Now we can instantiate our model object and generate chat completions: 
from langchain_pipeshift import ChatPipeshift

llm = ChatPipeshift(
    model="meta-llama/Meta-Llama-3.1-8B-Instruct",
    temperature=0,
    max_tokens=512,
    # other params...
)

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Invocation

messages = [
    (
        "system",
        "You are a helpful assistant that translates English to French. Translate the user sentence.",
    ),
    ("human", "I love programming."),
]
ai_msg = llm.invoke(messages)
ai_msg

AIMessage(content='Here is the translation:\n\nJe suis amoureux du programme. \n\nHowever, a more common translation would be:\n\nJ\'aime programmer.\n\nNote that "Je suis amoureux" typically implies romantic love, whereas "J\'aime" is a more casual way to express affection or enjoyment for an activity, in this case, programming.', additional_kwargs={}, response_metadata={}, id='run-5cad8e5c-d089-44a8-8dcd-22736cde7d7b-0')

print(ai_msg.content)

Here is the translation:

Je suis amoureux du programme. 

However, a more common translation would be:

J'aime programmer.

Note that "Je suis amoureux" typically implies romantic love, whereas "J'aime" is a more casual way to express affection or enjoyment for an activity, in this case, programming.

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Chaining

We can chain our model with a prompt template like so: 
from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate(
    [
        (
            "system",
            "You are a helpful assistant that translates {input_language} to {output_language}.",
        ),
        ("human", "{input}"),
    ]
)

chain = prompt | llm
chain.invoke(
    {
        "input_language": "English",
        "output_language": "German",
        "input": "I love programming.",
    }
)

AIMessage(content="Das ist schön! Du liebst Programmieren! (That's great! You love programming!)\n\nWould you like to know the German translation of a specific programming-related term or phrase, or would you like me to help you with something else?", additional_kwargs={}, response_metadata={}, id='run-8a4b7d56-23d9-43a7-8fb2-e05f556d94bd-0')

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API reference

For detailed documentation of all ChatPipeshift features and configurations head to the API reference: https://dashboard.pipeshift.com/docs