ChatPipeshift

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

Overview

Integration details

Class	Package	Local	Serializable	JS support	Package downloads	Package latest
ChatPipeshift	langchain-pipeshift	❌	-	❌

Model features

Tool calling	Structured output	JSON mode	Image input	Audio input	Video input	Token-level streaming	Native async	Token usage	Logprobs
❌	❌	✅	✅	❌	❌	✅	✅	✅	-

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.

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["LANGCHAIN_TRACING_V2"] = "true"
# os.environ["LANGCHAIN_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")

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.

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...
)

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.

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.",
    }
)

API Reference:ChatPromptTemplate

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')

API reference

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

Related

• Chat model conceptual guide
• Chat model how-to guides