> ## Documentation Index
> Fetch the complete documentation index at: https://langchain.idochub.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# ChatPipeshift

This will help you get started with Pipeshift [chat models](/oss/python/langchain/models/). For detailed documentation of all ChatPipeshift features and configurations head to the [API reference](https://dashboard.pipeshift.com/docs).

## Overview

### Integration details

| Class                                                 | Package                                                              | Local | Serializable | JS support |                                               Downloads                                              |                                              Version                                              |
| :---------------------------------------------------- | :------------------------------------------------------------------- | :---: | :----------: | :--------: | :--------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------: |
| [ChatPipeshift](https://dashboard.pipeshift.com/docs) | [langchain-pipeshift](https://pypi.org/project/langchain-pipeshift/) |   ❌   |       -      |      ❌     | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-pipeshift?style=flat-square\&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-pipeshift?style=flat-square\&label=%20) |

### Model features

| [Tool calling](/oss/python/langchain/tools) | [Structured output](/oss/python/langchain/structured-output) | JSON mode | [Image input](/oss/python/how-to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/oss/python/langchain/streaming/) | Native async | [Token usage](/oss/python/how-to/chat_token_usage_tracking/) | [Logprobs](/oss/python/how-to/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](https://dashboard.pipeshift.com) to sign up to Pipeshift and generate an API key. Once you've done this set the PIPESHIFT\_API\_KEY environment variable:

```python theme={null}
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](https://docs.smith.langchain.com/) API key by uncommenting below:

```python theme={null}
# os.environ["LANGSMITH_TRACING"] = "true"
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
```

### Installation

The LangChain Pipeshift integration lives in the `langchain-pipeshift` package:

```python theme={null}
%pip install -qU langchain-pipeshift
```

```output theme={null}
Note: you may need to restart the kernel to use updated packages.
```

## Instantiation

Now we can instantiate our model object and generate chat completions:

```python theme={null}
from langchain_pipeshift import ChatPipeshift

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

## Invocation

```python theme={null}
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
```

```output theme={null}
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')
```

```python theme={null}
print(ai_msg.content)
```

```output theme={null}
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](/oss/python/how-to/sequence/) our model with a prompt template like so:

```python theme={null}
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.",
    }
)
```

```output theme={null}
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: [dashboard.pipeshift.com/docs](https://dashboard.pipeshift.com/docs)