> For the complete documentation index, see [llms.txt](https://openai.gitbook.io/code-cheatsheets/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://openai.gitbook.io/code-cheatsheets/ml/embedding.md).

# Embedding

Embeddings Can Be Used For:

* Similarity Search/Retrieval: query for similar items/lines/docs (distance) for RAG, rec systems
* Classification/Clustering/Anomaly Detection

Embeddings can be at the word, sentence, paragraph, document level and be across mediums into images/audio([CLIP](https://huggingface.co/docs/transformers/en/model_doc/clip)) too. Can be based on context too(BERT & GPT), so bank different embedding depending on context

## Options

Comparing:

* [MTEB(Massive Text Embedding Benchmark)](https://huggingface.co/spaces/mteb/leaderboard)
  * Consider embeddings for your use case/lang, as no model is SOTA for all tasks including
* Also see
* Recommendation 9/21/24:
  * [VoyageAI](/code-cheatsheets/ml/embedding.md), `voyage-3` and `voyage-3-lite`
  * First 200 million tokens [free](https://docs.voyageai.com/docs/pricing)
  * `voyage-3-lite` 3.82% better retrieval than OpenAI v3 large with 6x less cost and embedding size
  * [`voyage-code-2`](https://blog.voyageai.com/2024/01/23/voyage-code-2-elevate-your-code-retrieval/), [`voyage-law-2`](https://blog.voyageai.com/2024/04/15/domain-specific-embeddings-and-retrieval-legal-edition-voyage-law-2/), [`voyage-finance-2`](https://blog.voyageai.com/2024/06/03/domain-specific-embeddings-finance-edition-voyage-finance-2/), and [`voyage-multilingual-2`](https://blog.voyageai.com/2024/06/10/voyage-multilingual-2-multilingual-embedding-model/),
* Other Options
  * [OpenAI](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings)
  * <https://replicate.com/collections/embedding-models>

## Voyage AI

js

```ts
import { VoyageAIClient } from "voyageai";

const client = new VoyageAIClient({ apiKey: "YOUR_API_KEY" });
await client.embed({
    input: ["input1", "input2", "input3", "input4"],
    model: "voyage-3-lite",
});
```

py

```python
import voyageai

vo = voyageai.Client()
# This will automatically use the environment variable VOYAGE_API_KEY.
# Alternatively, you can use vo = voyageai.Client(api_key="<your secret key>")

# Embed the documents
documents_embeddings = vo.embed(
    documents, model="voyage-3", input_type="document"
).embeddings
```

## OpenAI

Js

```js
import OpenAI from "openai";

const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
});

export async function generateEmbedding(text: string): Promise<number[]> {
  const response = await openai.embeddings.create({
    model: "text-embedding-3-large",
    input: text,
  });

  return response.data[0].embedding;
}
```


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://openai.gitbook.io/code-cheatsheets/ml/embedding.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.