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Embeddings API

Generate vector embeddings for semantic search and RAG.

Generate vector embeddings from text for semantic search, similarity matching, and RAG applications.

Basic Request

 
const response = await cencori.ai.embeddings({
  model: 'text-embedding-3-small',
  input: 'Hello world'
});
 
console.log(response.embeddings[0]); // [0.1, 0.2, ...]

Batch Request

 
const response = await cencori.ai.embeddings({
  model: 'text-embedding-3-small',
  input: [
    'First text to embed',
    'Second text to embed',
    'Third text to embed'
  ]
});
 
// response.embeddings is an array of vectors
console.log(response.embeddings.length); // 3

Request Parameters
ParameterTypeRequiredDescription
modelstringYesModel identifier
inputstring | string[]YesText to embed
dimensionsnumberNoOutput dimensions
encodingFormatstringNo'float' or 'base64'

Response

 
{
  embeddings: [
    [0.1, 0.2, -0.3, ...],
    [0.4, -0.1, 0.2, ...]
  ],
  model: 'text-embedding-3-small',
  usage: {
    promptTokens: 15,
    totalTokens: 15
  }
}

Model Comparison
ModelProviderDimensionsBest For
text-embedding-3-smallOpenAI1536General purpose
text-embedding-3-largeOpenAI3072High accuracy
text-embedding-004Google768Multilingual
embed-english-v3.0Cohere1024English text

HTTP API

 
curl -X POST https://cencori.com/api/ai/embeddings \
  -H "CENCORI_API_KEY: csk_..." \
  -H "Content-Type: application/json" \
  -d '{
    "model": "text-embedding-3-small",
    "input": "Hello world"
  }'

Use with Memory

Embeddings are automatically generated when storing memories:

await cencori.memory.store({
  namespace: 'docs',
  content: 'Refund policy allows returns within 30 days'
  // Embedding generated automatically
});
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