> ## 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.

# AI21LLM integration

> Integrate with the AI21LLM using LangChain Python.

:::caution This service is deprecated.
See [this page](https://python.langchain.com/docs/integrations/chat/ai21/) for the updated ChatAI21 object. :::

This example goes over how to use LangChain to interact with `AI21` Jurassic models. To use the Jamba model, use the [ChatAI21 object](https://python.langchain.com/docs/integrations/chat/ai21/) instead.

[See a full list of AI21 models and tools on LangChain.](https://pypi.org/project/langchain-ai21/)

## Installation

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
!pip install -qU langchain-ai21
```

## Environment setup

We'll need to get a [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()
```

## Usage

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_ai21 import AI21LLM
from langchain_core.prompts import PromptTemplate

template = """Question: {question}

Answer: Let's think step by step."""

prompt = PromptTemplate.from_template(template)

model = AI21LLM(model="j2-ultra")

chain = prompt | model

chain.invoke({"question": "What is LangChain?"})
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
'\nLangChain is a (database)\nLangChain is a database for storing and processing documents'
```

# AI21 contextual answer

You can use AI21's contextual answers model to receives text or document, serving as a context,
and a question and returns an answer based entirely on this context.

This means that if the answer to your question is not in the document,
the model will indicate it (instead of providing a false answer)

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_ai21 import AI21ContextualAnswers

tsm = AI21ContextualAnswers()

response = tsm.invoke(input={"context": "Your context", "question": "Your question"})
```

You can also use it with chains and output parsers and vector DBs

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_ai21 import AI21ContextualAnswers
from langchain_core.output_parsers import StrOutputParser

tsm = AI21ContextualAnswers()
chain = tsm | StrOutputParser()

response = chain.invoke(
    {"context": "Your context", "question": "Your question"},
)
```

***

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