# Introduction

{% hint style="info" %}
AlphaNeural is using an LLM proxy, so one endpoint can route to many underlying LLM providers while keeping OpenAI-style request and response formats.
{% endhint %}

All OpenAI-style endpoints are served under:

```
https://proxy.alfnrl.io/v1
```

Example. Chat completions:

```
POST https://proxy.alfnrl.io/v1/chat/completions
```

### Authentication

Authenticate with a bearer token:

* Header: `Authorization: Bearer <YOUR_API_KEY>`

### Compatibility

AlphaNeural follows the OpenAI API surface for the endpoints we expose. For example:

* Chat Completions: `POST /v1/chat/completions`
* Embeddings: `POST /v1/embeddings`
* Image generation: `POST /v1/images/generations`
* List models: `GET /v1/models`

That means you can typically keep the same payloads, streaming behaviour, and error handling you already use with OpenAI.

***

### Quickstart

#### cURL

```bash
curl https://proxy.alfnrl.io/v1/chat/completions \
  -H "Authorization: Bearer $ALPHANEURAL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3",
    "messages": [
      { "role": "user", "content": "Hello AlphaNeural" }
    ]
  }'
```

#### Python (OpenAI SDK)

```python
from openai import OpenAI
client = OpenAI(api_key=os.environ["ALPHANEURAL_API_KEY"], base_url="https://proxy.alfnrl.io/v1")
resp = client.chat.completions.create(model="qwen3", messages=[{"role":"user","content":"Hello AlphaNeural"}])
print(resp.choices[0].message.content)
```

#### 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.chat.completions.create({ model: "qwen3", messages: [{ role: "user", content: "Hello AlphaNeural" }] });
console.log(resp.choices[0].message.content);
```

### Models

Use the models endpoint to see what is available to your API key:

```bash
curl https://proxy.alfnrl.io/v1/models \
  -H "Authorization: Bearer $ALPHANEURAL_API_KEY"
```

Your `model` string in requests should match one of the returned model IDs.

### What you can build

* **Chat and agents** with tool calling and streaming (Chat Completions)
* **Embeddings** for search and RAG (Embeddings)
* **Image generation** (Images)&#x20;

### Next steps

* Chat Completions
* Embeddings
* Images
* Models


---

# 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/introduction.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.