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13
docs/snippets/get_started/quickstart/import_llms.mdx
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```python
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
llm = OpenAI()
chat_model = ChatOpenAI()
llm.predict("hi!")
>>> "Hi"
chat_model.predict("hi!")
>>> "Hi"
```

12
docs/snippets/get_started/quickstart/input_messages.mdx
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```python
from langchain.schema import HumanMessage
text = "What would be a good company name for a company that makes colorful socks?"
messages = [HumanMessage(content=text)]
llm.predict_messages(messages)
# >> Feetful of Fun
chat_model.predict_messages(messages)
# >> Socks O'Color
```

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docs/snippets/get_started/quickstart/input_string.mdx
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```python
text = "What would be a good company name for a company that makes colorful socks?"
llm.predict(text)
# >> Feetful of Fun
chat_model.predict(text)
# >> Socks O'Color
```

12
docs/snippets/get_started/quickstart/installation.mdx
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import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
import CodeBlock from "@theme/CodeBlock";
<Tabs>
<TabItem value="pip" label="Pip" default>
<CodeBlock language="bash">pip install langchain</CodeBlock>
</TabItem>
<TabItem value="conda" label="Conda">
<CodeBlock language="bash">conda install langchain -c conda-forge</CodeBlock>
</TabItem>
</Tabs>

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docs/snippets/get_started/quickstart/llm_chain.mdx
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```python
from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
ChatPromptTemplate,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate,
)
from langchain.chains import LLMChain
from langchain.schema import BaseOutputParser
class CommaSeparatedListOutputParser(BaseOutputParser):
"""Parse the output of an LLM call to a comma-separated list."""
def parse(self, text: str):
"""Parse the output of an LLM call."""
return text.strip().split(", ")
template = """You are a helpful assistant who generates comma separated lists.
A user will pass in a category, and you should generated 5 objects in that category in a comma separated list.
ONLY return a comma separated list, and nothing more."""
system_message_prompt = SystemMessagePromptTemplate.from_template(template)
human_template = "{text}"
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
chain = LLMChain(
llm=ChatOpenAI(),
prompt=chat_prompt,
output_parser=CommaSeparatedListOutputParser()
)
chain.run("colors")
# >> ['red', 'blue', 'green', 'yellow', 'orange']
```

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docs/snippets/get_started/quickstart/openai_setup.mdx
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First we'll need to install their 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.llms import OpenAI
llm = OpenAI(openai_api_key="...")
```

14
docs/snippets/get_started/quickstart/output_parser.mdx
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```python
from langchain.schema import BaseOutputParser
class CommaSeparatedListOutputParser(BaseOutputParser):
"""Parse the output of an LLM call to a comma-separated list."""
def parse(self, text: str):
"""Parse the output of an LLM call."""
return text.strip().split(", ")
CommaSeparatedListOutputParser().parse("hi, bye")
# >> ['hi', 'bye']
```

23
docs/snippets/get_started/quickstart/prompt_templates_chat_models.mdx
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```python
from langchain.prompts.chat import (
ChatPromptTemplate,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate,
)
template = "You are a helpful assistant that translates {input_language} to {output_language}."
system_message_prompt = SystemMessagePromptTemplate.from_template(template)
human_template = "{text}"
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
chat_prompt.format_messages(input_language="English", output_language="French", text="I love programming.")
```
```pycon
[
SystemMessage(content="You are a helpful assistant that translates English to French.", additional_kwargs={}),
HumanMessage(content="I love programming.")
]
```

10
docs/snippets/get_started/quickstart/prompt_templates_llms.mdx
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```python
from langchain.prompts import PromptTemplate
prompt = PromptTemplate.from_template("What is a good name for a company that makes {product}?")
prompt.format(product="colorful socks")
```
```pycon
What is a good name for a company that makes colorful socks?
```