BGE base English v1.5, producing 768-dimensional vectors, running on Cloudflare Workers AI.

Can I embed many texts at once?

Yes. POST a batch of strings and get one vector per string in the same order, which is the efficient way to index a corpus.

How do I compare vectors?

Use cosine similarity. Store vectors in any vector database or in your own tables and compare at query time.

Which languages work?

The model is trained on English and performs best there. Other languages produce usable but weaker vectors.

What does a call cost?

10 tokens per request, whether it carries one string or a batch, so batching is much cheaper per text.

AIMAINTAINED10 tokens / call

Text Embeddings API

768-dimensional text embeddings for semantic search, clustering and RAG, from an on-platform model

Turn text into 768-dimensional vectors for semantic search, clustering, deduplication and retrieval-augmented generation, with a BGE model that runs on the platform. Embed one string or a batch per call. No third-party keys and nothing leaves the platform.

Every call needs an X-Api-Key header. A free key includes 500 tokens a month, no card required. 10 tokens per call, so the free plan covers about 50 calls a month.

Start free — 500 tokens / mo →See all plans

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response.json

{
  "model": "@cf/baai/bge-base-en-v1.5",
  "dimensions": 768,
  "count": 1,
  "embeddings": [
    [
      0.0123,
      -0.0456,
      0.0789,
      "...768 floats..."
    ]
  ]
}

Production Ready: Battle-tested infrastructure with a 99.9% uptime SLA. Rate limits are generous, responses are consistent.
Simple Integration: Standard REST + JSON. Works with any HTTP client. Copy-ready code snippets in 4 languages.
One Key, 125+ APIs: Your DataAndAPIs subscription unlocks every API on the platform — anime, finance, sports, and more.

Use cases for Text Embeddings API

Build semantic search over documents, tickets or products that matches meaning rather than keywords.
Deduplicate near-identical records such as job titles or support questions by vector similarity.
Feed a retrieval-augmented generation pipeline without running your own embedding server.
Cluster feedback or content into themes for analysis.

Text Embeddings API endpoints

Every endpoint with its parameters and a real example response.

OpenAPI definition Postman workspace

GETEmbed Text
POSTEmbed Text (POST)

GETEmbed Text

https://api-embeddings.dataandapis.com/v1/embeddings

Query parameters

Parameter	Example
text	hello world	Required

Example request

curl "https://api-embeddings.dataandapis.com/v1/embeddings?text=hello%20world" \
  -H "X-Api-Key: YOUR_API_KEY"

Example response

{
  "model": "@cf/baai/bge-base-en-v1.5",
  "dimensions": 768,
  "count": 1,
  "embeddings": [
    [
      0.0123,
      -0.0456,
      0.0789,
      "...768 floats..."
    ]
  ]
}

POSTEmbed Text (POST)

https://api-embeddings.dataandapis.com/v1/embeddings

Request body

{
  "text": [
    "the quick brown fox",
    "a lazy dog"
  ]
}

Example request

curl -X POST "https://api-embeddings.dataandapis.com/v1/embeddings" \
  -H "X-Api-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{ "text": [ "the quick brown fox", "a lazy dog" ] }'

Example response

{
  "model": "@cf/baai/bge-base-en-v1.5",
  "dimensions": 768,
  "count": 2,
  "embeddings": [
    [
      -0.037689208984375,
      -0.073974609375,
      0.01934814453125,
      0.04351806640625,
      "...768 floats..."
    ],
    [
      0.00478363037109375,
      -0.0276947021484375,
      -0.003910064697265625,
      0.031524658203125,
      "...768 floats..."
    ]
  ]
}

Where the data comes from

Source: Cloudflare Workers AI embedding model (BGE base en v1.5, 768 dimensions). No stored dataset.
Freshness: Inference runs live on every call.

One install. Every endpoint.

No proprietary SDKs needed. Any HTTP client works. Pick your language and paste.

3languages

2endpoints

RESTarchitecture

1key for all APIs

Fully typed responses — auto-completion in your IDE
Tracks the API in lockstep — new endpoints land same day
One key works across 125+ APIs on the platform

import axios from 'axios';

const { data } = await axios.get(
  'https://api-embeddings.dataandapis.com/v1/embeddings',
  { headers: { 'X-Api-Key': process.env.API_KEY } }
);

Text Embeddings API · X-Api-Key

Explore the endpoints

Browse request parameters and inspect live example responses.

Available Endpoints

Request Parameters

API URL

Headers

X-Api-Key

Query Parameters

text(Required)

Target:

Client:

import axios from 'axios';

const url = 'https://api-embeddings.dataandapis.com/v1/embeddings';
const { data } = await axios.get(url, {
  headers: { 'X-Api-Key': 'YOUR_API_KEY' }
});