langchain/docs/snippets/modules/model_io/models/chat/get_started.mdx

### Setup

To start we'll need to install the OpenAI Python package:

```bash
pip install openai
```

Accessing the API requires an API key, which you can get by creating an account and heading [here](https://platform.openai.com/account/api-keys). Once we have a key we'll want to set it as an environment variable by running:

```bash
export OPENAI_API_KEY="..."
```
If you'd prefer not to set an environment variable you can pass the key in directly via the `openai_api_key` named parameter when initiating the OpenAI LLM class:

```python
from langchain.chat_models import ChatOpenAI

chat = ChatOpenAI(openai_api_key="...")
```

otherwise you can initialize without any params:
```python
from langchain.chat_models import ChatOpenAI

chat = ChatOpenAI()
```

### Messages

The chat model interface is based around messages rather than raw text.
The types of messages currently supported in LangChain are `AIMessage`, `HumanMessage`, `SystemMessage`, and `ChatMessage` -- `ChatMessage` takes in an arbitrary role parameter. Most of the time, you'll just be dealing with `HumanMessage`, `AIMessage`, and `SystemMessage`

### `__call__`
#### Messages in -> message out

You can get chat completions by passing one or more messages to the chat model. The response will be a message.

```python
from langchain.schema import (
    AIMessage,
    HumanMessage,
    SystemMessage
)

chat([HumanMessage(content="Translate this sentence from English to French: I love programming.")])
```

<CodeOutputBlock lang="python">

```
    AIMessage(content="J'aime programmer.", additional_kwargs={})
```

</CodeOutputBlock>

OpenAI's chat model supports multiple messages as input. See [here](https://platform.openai.com/docs/guides/chat/chat-vs-completions) for more information. Here is an example of sending a system and user message to the chat model:


```python
messages = [
    SystemMessage(content="You are a helpful assistant that translates English to French."),
    HumanMessage(content="I love programming.")
]
chat(messages)
```

<CodeOutputBlock lang="python">

```
    AIMessage(content="J'aime programmer.", additional_kwargs={})
```

</CodeOutputBlock>

### `generate`
#### Batch calls, richer outputs

You can go one step further and generate completions for multiple sets of messages using `generate`. This returns an `LLMResult` with an additional `message` parameter.

```python
batch_messages = [
    [
        SystemMessage(content="You are a helpful assistant that translates English to French."),
        HumanMessage(content="I love programming.")
    ],
    [
        SystemMessage(content="You are a helpful assistant that translates English to French."),
        HumanMessage(content="I love artificial intelligence.")
    ],
]
result = chat.generate(batch_messages)
result
```

<CodeOutputBlock lang="python">

```
    LLMResult(generations=[[ChatGeneration(text="J'aime programmer.", generation_info=None, message=AIMessage(content="J'aime programmer.", additional_kwargs={}))], [ChatGeneration(text="J'aime l'intelligence artificielle.", generation_info=None, message=AIMessage(content="J'aime l'intelligence artificielle.", additional_kwargs={}))]], llm_output={'token_usage': {'prompt_tokens': 57, 'completion_tokens': 20, 'total_tokens': 77}})
```

</CodeOutputBlock>

You can recover things like token usage from this LLMResult


```python
result.llm_output
```

<CodeOutputBlock lang="python">

```
    {'token_usage': {'prompt_tokens': 57,
      'completion_tokens': 20,
      'total_tokens': 77}}
```

</CodeOutputBlock>