# Embeddings

### Create embeddings

`POST /v1/embeddings`

#### Request body

**Required**

* `model` (string). The embedding model to use.

**Common**

* `input` (string or array of strings). The text to embed.
  * If you pass an array, you will get one embedding per item.

{% hint style="info" %}
The proxy supports OpenAI-style payloads. It also exposes a few proxy-only fields (below) for routing and reliability. openapi
{% endhint %}

#### Basic example (single input)

```bash
curl https://proxy.alfnrl.io/v1/embeddings \
  -H "Authorization: Bearer $ALPHANEURAL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "text-embedding-3-small",
    "input": "The quick brown fox jumps over the lazy dog"
  }'
```

The proxy documentation includes the same request pattern for embeddings. openapi

#### Batch example (multiple inputs)

```bash
curl https://proxy.alfnrl.io/v1/embeddings \
  -H "Authorization: Bearer $ALPHANEURAL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "text-embedding-3-small",
    "input": [
      "Paris is the capital of France.",
      "Berlin is the capital of Germany."
    ]
  }'
```

#### Python (OpenAI SDK)

```python
from openai import OpenAI
import os
client = OpenAI(api_key=os.environ["ALPHANEURAL_API_KEY"], base_url="https://proxy.alfnrl.io/v1")
resp = client.embeddings.create(model="text-embedding-3-small", input=["hello", "world"])
print(len(resp.data), len(resp.data[0].embedding))
```

#### JavaScript/TypeScript (OpenAI SDK)

```js
import OpenAI from "openai";
const client = new OpenAI({ apiKey: process.env.ALPHANEURAL_API_KEY, baseURL: "https://proxy.alfnrl.io/v1" });
const resp = await client.embeddings.create({ model: "text-embedding-3-small", input: ["hello", "world"] });
console.log(resp.data.length, resp.data[0].embedding.length);
```

### Response

The response matches the OpenAI embeddings format. You receive a `data` array with one embedding per input, plus `usage` metadata.

Example (truncated):

```json
{
  "object": "list",
  "data": [
    { "object": "embedding", "index": 0, "embedding": [0.0123, -0.0456, 0.0789] }
  ],
  "model": "text-embedding-3-small",
  "usage": { "prompt_tokens": 8, "total_tokens": 8 }
}
```

### Proxy-only options

Most teams do not need these. They exist to control proxy behaviour across multiple upstream providers.

* `timeout` (integer, default `600`). Request timeout in seconds.
* `caching` (boolean, default `false`). Enable proxy caching when configured.
* `user` (string). End-user identifier for tracing and abuse monitoring.


---

# Agent Instructions: 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:

```
GET https://docs.alphaneural.io/embeddings.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
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.