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

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+### 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>
+