langchain/docs/snippets/modules/data_connection/text_embedding/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.embeddings import OpenAIEmbeddings

embeddings_model = OpenAIEmbeddings(openai_api_key="...")
```

otherwise you can initialize without any params:
```python
from langchain.embeddings import OpenAIEmbeddings

embeddings_model = OpenAIEmbeddings()
```

### `embed_documents`
#### Embed list of texts

```python
embeddings = embeddings_model.embed_documents(
    [
        "Hi there!",
        "Oh, hello!",
        "What's your name?",
        "My friends call me World",
        "Hello World!"
    ]
)
len(embeddings), len(embeddings[0])
```

<CodeOutputBlock language="python">

```
(5, 1536)
```

</CodeOutputBlock>

### `embed_query`
#### Embed single query
Embed a single piece of text for the purpose of comparing to other embedded pieces of texts.

```python
embedded_query = embeddings_model.embed_query("What was the name mentioned in the conversation?")
embedded_query[:5]
```

<CodeOutputBlock language="python">

```
[0.0053587136790156364,
 -0.0004999046213924885,
 0.038883671164512634,
 -0.003001077566295862,
 -0.00900818221271038]
```

</CodeOutputBlock>