---
title: "OpenAITextEmbedder"
id: openaitextembedder
slug: "/openaitextembedder"
description: "OpenAITextEmbedder transforms a string into a vector that captures its semantics using an OpenAI embedding model."
---

# OpenAITextEmbedder

OpenAITextEmbedder transforms a string into a vector that captures its semantics using an OpenAI embedding model.

When you perform embedding retrieval, you use this component to transform your query into a vector. Then, the embedding Retriever looks for similar or relevant documents.

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | Before an embedding [Retriever](../retrievers.mdx)  in a query/RAG pipeline |
| **Mandatory init variables** | `api_key`: An OpenAI API key. Can be set with `OPENAI_API_KEY` env var. |
| **Mandatory run variables** | `text`: A string |
| **Output variables** | `embedding`: A list of float numbers  <br /> <br />`meta`: A dictionary of metadata |
| **API reference** | [Embedders](/reference/embedders-api) |
| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/embedders/openai_text_embedder.py |

</div>

## Overview

To see the list of compatible OpenAI embedding models, head over to OpenAI [documentation](https://platform.openai.com/docs/guides/embeddings/embedding-models). The default model for `OpenAITextEmbedder` is `text-embedding-ada-002`. You can specify another model with the `model` parameter when initializing this component.

Use `OpenAITextEmbedder` to embed a simple string (such as a query) into a vector. For embedding lists of documents, use the [OpenAIDocumentEmbedder](openaidocumentembedder.mdx), which enriches the document with the computed embedding, also known as vector.

The component uses an `OPENAI_API_KEY` environment variable by default. Otherwise, you can pass an API key at initialization with `api_key`:

```python
embedder = OpenAITextEmbedder(api_key=Secret.from_token("<your-api-key>"))
```

## Usage

### On its own

Here is how you can use the component on its own:

```python
from haystack.components.embedders import OpenAITextEmbedder

text_to_embed = "I love pizza!"

text_embedder = OpenAITextEmbedder(api_key=Secret.from_token("<your-api-key>"))

print(text_embedder.run(text_to_embed))

## {'embedding': [0.017020374536514282, -0.023255806416273117, ...],
## 'meta': {'model': 'text-embedding-ada-002-v2',
##              'usage': {'prompt_tokens': 4, 'total_tokens': 4}}}
```

:::note
We recommend setting OPENAI_API_KEY as an environment variable instead of setting it as a parameter.

:::

### In a pipeline

```python
from haystack import Document
from haystack import Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.embedders import OpenAITextEmbedder, OpenAIDocumentEmbedder
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever

document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")

documents = [
    Document(content="My name is Wolfgang and I live in Berlin"),
    Document(content="I saw a black horse running"),
    Document(content="Germany has many big cities"),
]

document_embedder = OpenAIDocumentEmbedder()
documents_with_embeddings = document_embedder.run(documents)["documents"]
document_store.write_documents(documents_with_embeddings)

query_pipeline = Pipeline()
query_pipeline.add_component("text_embedder", OpenAITextEmbedder())
query_pipeline.add_component(
    "retriever",
    InMemoryEmbeddingRetriever(document_store=document_store),
)
query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")

query = "Who lives in Berlin?"

result = query_pipeline.run({"text_embedder": {"text": query}})

print(result["retriever"]["documents"][0])

## Document(id=..., mimetype: 'text/plain',
##  text: 'My name is Wolfgang and I live in Berlin')
```
