---
title: "OpenAIDocumentEmbedder"
id: openaidocumentembedder
slug: "/openaidocumentembedder"
description: "OpenAIDocumentEmbedder computes the embeddings of a list of documents and stores the obtained vectors in the embedding field of each document. It uses OpenAI embedding models."
---

# OpenAIDocumentEmbedder

OpenAIDocumentEmbedder computes the embeddings of a list of documents and stores the obtained vectors in the embedding field of each document. It uses OpenAI embedding models.

The vectors computed by this component are necessary to perform embedding retrieval on a collection of documents. At retrieval time, the vector representing the query is compared with those of the documents to find the most similar or relevant documents.

|  |  |
| --- | --- |
| **Most common position in a pipeline** | Before a [`DocumentWriter`](/docs/pipeline-components/writers/documentwriter.mdx)  in an indexing pipeline |
| **Mandatory init variables** | "api_key": An OpenAI API key. Can be set with `OPENAI_API_KEY` env var. |
| **Mandatory run variables** | "documents": A list of documents |
| **Output variables** | "documents": A list of documents (enriched with embeddings)  <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_document_embedder.py |

## 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 `OpenAIDocumentEmbedder` is `text-embedding-ada-002`. You can specify another model with the `model` parameter when initializing this component.

This component should be used to embed a list of documents. To embed a string, use the [OpenAITextEmbedder](openaitextembedder.mdx).

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

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

### Embedding Metadata

Text documents often come with a set of metadata. If they are distinctive and semantically meaningful, you can embed them along with the text of the document to improve retrieval.

You can do this easily by using the Document Embedder:

```python
from haystack import Document
from haystack.components.embedders import OpenAIDocumentEmbedder

doc = Document(content="some text",meta={"title": "relevant title", "page number": 18})

embedder = OpenAIDocumentEmbedder(meta_fields_to_embed=["title"])

docs_w_embeddings = embedder.run(documents=[doc])["documents"]
```

## Usage

### On its own

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

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

doc = Document(content="I love pizza!")

document_embedder = OpenAIDocumentEmbedder(api_key=Secret.from_token("<your-api-key>"))

result = document_embedder.run([doc])
print(result['documents'][0].embedding)

## [0.017020374536514282, -0.023255806416273117, ...]
```

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

### In a pipeline

```python
from haystack import Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.embedders import OpenAITextEmbedder, OpenAIDocumentEmbedder
from haystack.components.writers import DocumentWriter
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")]

indexing_pipeline = Pipeline()
indexing_pipeline.add_component("embedder", OpenAIDocumentEmbedder())
indexing_pipeline.add_component("writer", DocumentWriter(document_store=document_store))
indexing_pipeline.connect("embedder", "writer")

indexing_pipeline.run({"embedder": {"documents": documents}})

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')
```