Embeddings

The Embeddings API turns text into vectors you can use for semantic search, retrieval, clustering, and ranking.

POST https://api.meshapi.ai/v1/embeddings
Authorization: Bearer rsk_<your_key>

Quick start

curl
Node.js SDK
Python SDK

curl https://api.meshapi.ai/v1/embeddings \
  -H "Authorization: Bearer <YOUR_RSK_KEY>" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/text-embedding-3-small",
    "input": "The quick brown fox jumps over the lazy dog."
  }'

import { MeshAPI } from "meshapi-node-sdk";

const client = new MeshAPI({
  baseUrl: "https://api.meshapi.ai",
  token: process.env.MESH_API_KEY!,
});

const response = await client.embeddings.create({
  model: "openai/text-embedding-3-small",
  input: "The quick brown fox jumps over the lazy dog.",
});

console.log(response.data[0]?.embedding);

from meshapi import EmbeddingsParams, MeshAPI

client = MeshAPI(
    base_url="https://api.meshapi.ai",
    token="rsk_...",
)

response = client.embeddings.create(
    EmbeddingsParams(
        model="openai/text-embedding-3-small",
        input="The quick brown fox jumps over the lazy dog.",
    )
)

print(response.data[0].embedding)

Request fields

Field	Type	Notes
`model`	string	Embedding model ID. Required unless your key has a default model.
`input`	string \| string[] \| int[] \| int[][]	Text or tokenized input.
`dimensions`	integer	Optional output size for models that support truncation.
`encoding_format`	`"float"` \| `"base64"`	Output format.
`input_type`	string	Optional hint for asymmetric embedding models.
`provider`	string \| object	Optional provider routing preferences.
`instructions`	string	Optional task instructions for models that support them (e.g. BytePlus).
`sparse_embedding`	boolean	Optional — request a sparse embedding (BytePlus).
`user`	string	Optional end-user identifier for abuse monitoring (max 256 characters).

Batch input in a single request

You can embed multiple strings in one API call:

curl https://api.meshapi.ai/v1/embeddings \
  -H "Authorization: Bearer <YOUR_RSK_KEY>" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/text-embedding-3-small",
    "input": [
      "first sentence",
      "second sentence",
      "third sentence"
    ]
  }'

The returned data array matches the order of your input array.

Response shape

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [-0.0123, 0.0456]
    }
  ],
  "model": "openai/text-embedding-3-small",
  "usage": {
    "prompt_tokens": 12,
    "total_tokens": 12
  }
}

Choosing a model

Use GET /v1/models to inspect available embedding models and their pricing. For embeddings, the most useful fields are:

model_type
context_length
pricing
is_free

SDK coverage

The official SDKs all expose first-class embeddings resources:

Node: client.embeddings.create(...)
Python: client.embeddings.create(...)
Go: client.Embeddings.Create(...)
Java: client.embeddings().create(...)

Common errors

HTTP	Meaning
`401`	Missing or invalid API key
`402`	Balance or spend limit issue
`422`	Invalid request body
`429`	Rate limit exceeded

Available models

Model	Dimensions	Best for
`bedrock/amazon.titan-embed-text-v2:0`	1024	General-purpose English text
`openai/text-embedding-3-small`	1536	Fast, cost-efficient
`openai/text-embedding-3-large`	3072	Highest accuracy
`openai/text-embedding-ada-002`	1536	Legacy OpenAI compatibility

Check GET /v1/models with the type=embedding filter to see the full live list of enabled embedding models.

RAG with file uploads

For retrieval-augmented generation over your own documents, use the Files & RAG workflow — upload files, trigger embedding, and search with POST /v1/files/search. This is more scalable than calling the embeddings endpoint directly for large document corpora.

Introduction

Accounts & Access

Core Inference

Retrieval & Search

Multimodal

Operations

Mesh CLI

Platform

Reference

Quick start

Request fields

Batch input in a single request

Response shape

Choosing a model

SDK coverage

Common errors

Available models

RAG with file uploads

​Quick start

​Request fields

​Batch input in a single request

​Response shape

​Choosing a model

​SDK coverage

​Common errors

​Available models

​RAG with file uploads

Quick start

Request fields

Batch input in a single request

Response shape

Choosing a model

SDK coverage

Common errors

Available models

RAG with file uploads