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Endpoint
POST /llm/v1/embeddings

import Casedev from 'casedev';

const client = new Casedev({ apiKey: 'sk_case_YOUR_API_KEY' });

const result = await client.llm.v1.createEmbedding({
  model: 'openai/text-embedding-3-small',
  input: 'Plaintiff alleges negligence in post-operative care'
});

console.log(result.data[0].embedding);  // [0.016, -0.022, ...]
Response
{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [-0.016, 0.022, -0.011, ...]
    }
  ],
  "model": "openai/text-embedding-3-small",
  "usage": {
    "prompt_tokens": 12,
    "total_tokens": 12
  }
}

​
Parameters

ParameterTypeRequiredDescription
modelstringYesEmbedding model ID
inputstring or arrayYesText(s) to embed

​
Embedding models

ModelDimensionsBest for$/1K tokens
openai/text-embedding-3-small1536General purpose$0.00002
openai/text-embedding-3-large3072Higher quality$0.00013
voyage/voyage-law-21024Legal documents$0.00012
voyage/voyage-3.51536General purpose$0.00006
For legal documents, use voyage/voyage-law-2. It’s specifically trained on legal text.

​
Batch embeddings

Embed multiple texts in one request: 
const result = await client.llm.v1.createEmbedding({
  model: 'openai/text-embedding-3-small',
  input: [
    'Medical record from January 2024',
    'Deposition transcript page 45',
    'Expert witness report summary'
  ]
});

// result.data[0].embedding, result.data[1].embedding, ...

​
Use cases

TypeScript
// 1. Embed your query
const queryResult = await client.llm.v1.createEmbedding({
  model: 'voyage/voyage-law-2',
  input: 'negligence in surgical procedure'
});

// 2. Compare to document embeddings (cosine similarity)
// 3. Return most similar documents

​
Document similarity

TypeScript
// Compare two documents
const result = await client.llm.v1.createEmbedding({
  model: 'voyage/voyage-law-2',
  input: [
    'Plaintiff expert testimony on standard of care',
    'Defense expert rebuttal on treatment protocols'
  ]
});

// Calculate cosine similarity:
// ~1.0 = very similar, ~0.5 = somewhat related, ~0.0 = unrelated
Tip: Use the same model for indexing and querying. Mixing models produces poor results.