Embeddings.

Endpoint

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

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"]
  }'

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

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

Response (shape)

{
  "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 }
}

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

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.

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