Create embeddings

Create vector embeddings from text using OpenAI-compatible models. Perfect for semantic search, document similarity, and building RAG systems for legal documents.

POST /llm/v1/embeddings

Create embeddings

cURL

curl --request POST \
  --url https://api.case.dev/llm/v1/embeddings \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "input": "<string>",
  "model": "<string>",
  "encoding_format": "float",
  "dimensions": 123,
  "user": "<string>"
}
'

200

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [
        0.0023,
        -0.0087,
        0.0156
      ]
    }
  ],
  "model": "text-embedding-ada-002",
  "usage": {
    "prompt_tokens": 12,
    "total_tokens": 12
  }
}

Authorizations

Authorization · string · header · required

API key starting with sk_case_

Body

application/json

Embedding request configuration

input · string string[] · required

Text or array of texts to create embeddings for

model · string · required

Embedding model to use (e.g., text-embedding-ada-002, text-embedding-3-small)

encoding_format · enum<string> · default: float

Format for returned embeddings

Available options: float, base64

dimensions · integer

Number of dimensions for the embeddings (model-specific)

user · string

Unique identifier for the end-user

Response

200

application/json

Embeddings created successfully

object · string

Example:

"list"

data · object[]

Show child attributes

model · string

usage · object

Show child attributes