> ## Documentation Index
> Fetch the complete documentation index at: https://docs.layerx1.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Embeddings.

> Vectors for retrieval and agent memory, next to your chat calls — same key, same origin. Seven models from 384 to 2048 dimensions, OpenAI-compatible.

## Endpoint

```http theme={null}
POST /v1/embeddings
```

Authenticate with `Authorization: Bearer lx1_...`. Pass any id from the table below as
`model`; it defaults to `lx1-embed`. Embeddings are included on every paid plan.

Embedding requests are never re-routed. The model you name is the model that runs — we
never substitute a different one, because vectors of different widths cannot be compared
against an index you have already built.

| Model                  | Dimensions | Max input    | Notes                                                             |
| ---------------------- | ---------- | ------------ | ----------------------------------------------------------------- |
| `lx1-bge-small-en`     | 384        | 512 tokens   | 384 dimensions — the smallest index and the fastest search.       |
| `lx1-bge-base-en`      | 768        | 512 tokens   | The long-standing default English embedding.                      |
| `lx1-embed-gemma-300m` | 768        | 2,048 tokens | Compact Gemma-family embedding with a 2K input window.            |
| `lx1-bge-large-en`     | 1024       | 512 tokens   | The most accurate English embedding in the catalog.               |
| `lx1-bge-m3`           | 1024       | 8,192 tokens | BAAI's versatile embedding — multilingual, multi-granularity.     |
| `lx1-qwen3-embed-0.6b` | 1024       | 8,192 tokens | Multilingual retrieval with an 8K input window.                   |
| `lx1-plamo-embed-1b`   | 2048       | 4,096 tokens | Japanese-specialist embedding — the widest vector in the catalog. |

## Request parameters

| Parameter         | Type                           | Notes                                                                                                   |
| ----------------- | ------------------------------ | ------------------------------------------------------------------------------------------------------- |
| `model`           | string                         | Any id from the table above. Defaults to `lx1-embed`. An unknown id returns `404` — never a substitute. |
| `input`           | string \| string\[] · required | One text or a batch — up to 2,048 items per request. Token arrays are not supported.                    |
| `encoding_format` | string                         | `float` (default) or `base64`.                                                                          |
| `dimensions`      | integer                        | Optional; if set it must equal the chosen model's own width — each model has one fixed output size.     |

## Example

<CodeGroup>
  ```sh curl theme={null}
  curl https://api.layerx1.com/v1/embeddings \
    -H "authorization: Bearer $LAYERX1_API_KEY" \
    -H "content-type: application/json" \
    -d '{
      "model": "lx1-embed",
      "input": ["agent memory entry one", "agent memory entry two"]
    }'
  ```

  ```python Python theme={null}
  from openai import OpenAI

  client = OpenAI(
      base_url="https://api.layerx1.com/v1",
      api_key="lx1_your_key",
  )

  r = client.embeddings.create(
      model="lx1-embed",
      input=["agent memory entry one", "agent memory entry two"],
  )
  print(len(r.data[0].embedding))  # 768
  ```

  ```ts TypeScript theme={null}
  import OpenAI from "openai";

  const client = new OpenAI({
    baseURL: "https://api.layerx1.com/v1",
    apiKey: process.env.LAYERX1_API_KEY,
  });

  const r = await client.embeddings.create({
    model: "lx1-embed",
    input: ["agent memory entry one", "agent memory entry two"],
  });
  console.log(r.data[0].embedding.length); // 768
  ```
</CodeGroup>

```json title="Response (shape)" theme={null}
{
  "object": "list",
  "data": [
    { "object": "embedding", "index": 0, "embedding": [0.013, -0.021, ...] },
    { "object": "embedding", "index": 1, "embedding": [0.007,  0.049, ...] }
  ],
  "model": "lx1-embed",
  "usage": { "prompt_tokens": 12, "total_tokens": 12 }
}
```

<Note>
  Vectors come back in input order — `index` matches the position of each text in
  `input`. Cosine similarity is the intended distance measure.
</Note>

## Errors

Empty input, more than 2,048 items, or a `dimensions` value that does not match the model
return `400` with an OpenAI-shaped body. An unrecognised `model` returns `404`. On the
free plan, embeddings return `403` `plan_upgrade_required`. Full reference:
[Errors](/errors).