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

# ChatAI21 integration

> Integrate with the ChatAI21 chat model using LangChain Python.

This notebook covers how to get started with AI21 chat models.
Note that different chat models support different parameters. See the [AI21 documentation](https://docs.ai21.com/reference) to learn more about the parameters in your chosen model.
[See all AI21's LangChain components.](https://pypi.org/project/langchain-ai21/)

### Integration details

| Class      | Package          | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/__package_name_short_snake__) |                                            Downloads                                            |                                            Version                                           |
| :--------- | :--------------- | :----------: | :----------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------: |
| `ChatAI21` | `langchain-ai21` |     beta     |                                              ✅                                             | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-ai21?style=flat-square\&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-ai21?style=flat-square\&label=%20) |

### Model features

| [Tool calling](/oss/python/langchain/tools) | [Structured output](/oss/python/langchain/structured-output) | [Image input](/oss/python/langchain/messages#multimodal) | Audio input | Video input | [Token-level streaming](/oss/python/langchain/streaming/) | Native async | [Token usage](/oss/python/langchain/models#token-usage) | [Logprobs](/oss/python/langchain/models#log-probabilities) |
| :-----------------------------------------: | :----------------------------------------------------------: | :------------------------------------------------------: | :---------: | :---------: | :-------------------------------------------------------: | :----------: | :-----------------------------------------------------: | :--------------------------------------------------------: |
|                      ✅                      |                               ✅                              |                             ❌                            |      ❌      |      ❌      |                             ✅                             |       ✅      |                            ✅                            |                              ❌                             |

## Setup

### Credentials

We'll need to get an [AI21 API key](https://docs.ai21.com/) and set the `AI21_API_KEY` environment variable:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import os
from getpass import getpass

if "AI21_API_KEY" not in os.environ:
    os.environ["AI21_API_KEY"] = getpass()
```

To enable automated tracing of your model calls, set your [LangSmith](/langsmith/observability) API key:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
os.environ["LANGSMITH_TRACING"] = "true"
os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
```

### Installation

!pip install -qU langchain-ai21

## Instantiation

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

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_ai21 import ChatAI21

llm = ChatAI21(model="jamba-instruct", temperature=0)
```

## Invocation

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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
```

# Tool calls / function calling

This example shows how to use tool calling with AI21 models:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import os
from getpass import getpass

from langchain_ai21.chat_models import ChatAI21
from langchain.messages import HumanMessage, SystemMessage, ToolMessage
from langchain.tools import tool
from langchain_core.utils.function_calling import convert_to_openai_tool

if "AI21_API_KEY" not in os.environ:
    os.environ["AI21_API_KEY"] = getpass()


@tool
def get_weather(location: str, date: str) -> str:
    """“Provide the weather for the specified location on the given date.”"""
    if location == "New York" and date == "2024-12-05":
        return "25 celsius"
    elif location == "New York" and date == "2024-12-06":
        return "27 celsius"
    elif location == "London" and date == "2024-12-05":
        return "22 celsius"
    return "32 celsius"


llm = ChatAI21(model="jamba-1.5-mini")

llm_with_tools = llm.bind_tools([convert_to_openai_tool(get_weather)])

chat_messages = [
    SystemMessage(
        content="You are a helpful assistant. You can use the provided tools "
        "to assist with various tasks and provide accurate information"
    )
]

human_messages = [
    HumanMessage(
        content="What is the forecast for the weather in New York on December 5, 2024?"
    ),
    HumanMessage(content="And what about the 2024-12-06?"),
    HumanMessage(content="OK, thank you."),
    HumanMessage(content="What is the expected weather in London on December 5, 2024?"),
]


for human_message in human_messages:
    print(f"User: {human_message.content}")
    chat_messages.append(human_message)
    response = llm_with_tools.invoke(chat_messages)
    chat_messages.append(response)
    if response.tool_calls:
        tool_call = response.tool_calls[0]
        if tool_call["name"] == "get_weather":
            weather = get_weather.invoke(
                {
                    "location": tool_call["args"]["location"],
                    "date": tool_call["args"]["date"],
                }
            )
            chat_messages.append(
                ToolMessage(content=weather, tool_call_id=tool_call["id"])
            )
            llm_answer = llm_with_tools.invoke(chat_messages)
            print(f"Assistant: {llm_answer.content}")
    else:
        print(f"Assistant: {response.content}")
```

***

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