= openai_embeddings
:type: processor
:status: experimental
:categories: ["AI"]



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component_type_dropdown::[]


Generates vector embeddings to represent input text, using the OpenAI API.

Introduced in version 4.32.0.

```yml
# Config fields, showing default values
label: ""
openai_embeddings:
  server_address: https://api.openai.com/v1
  api_key: "" # No default (required)
  model: text-embedding-3-large # No default (required)
  text_mapping: "" # No default (optional)
  dimensions: 0 # No default (optional)
```

This processor sends text strings to the OpenAI API, which generates vector embeddings. By default, the processor submits the entire payload of each message as a string, unless you use the `text_mapping` configuration field to customize it.

To learn more about vector embeddings, see the https://platform.openai.com/docs/guides/embeddings[OpenAI API documentation^].

== Examples

[tabs]
======
Store embedding vectors in Pinecone::
+
--

Compute embeddings for some generated data and store it within xrefs:component:outputs/pinecone.adoc[Pinecone]

```yamlinput:
  generate:
    interval: 1s
    mapping: |
      root = {"text": fake("paragraph")}
pipeline:
  processors:
  - openai_embeddings:
      model: text-embedding-3-large
      api_key: "${OPENAI_API_KEY}"
      text_mapping: "root = this.text"
output:
  pinecone:
    host: "${PINECONE_HOST}"
    api_key: "${PINECONE_API_KEY}"
    id: "root = uuid_v4()"
    vector_mapping: "root = this"```

--
======

== Fields

=== `server_address`

The Open API endpoint that the processor sends requests to. Update the default value to use another OpenAI compatible service.


*Type*: `string`

*Default*: `"https://api.openai.com/v1"`

=== `api_key`

The API key for OpenAI API.
[CAUTION]
====
This field contains sensitive information that usually shouldn't be added to a config directly, read our xref:configuration:secrets.adoc[secrets page for more info].
====



*Type*: `string`


=== `model`

The name of the OpenAI model to use.


*Type*: `string`


```yml
# Examples

model: text-embedding-3-large

model: text-embedding-3-small

model: text-embedding-ada-002
```

=== `text_mapping`

The text you want to generate a vector embedding for. By default, the processor submits the entire payload as a string.


*Type*: `string`


=== `dimensions`

The number of dimensions the resulting output embeddings should have. Only supported in `text-embedding-3` and later models.


*Type*: `int`