CAcasemark
LLMs

Create embeddings

Changed on

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

Request

  • Base URL
    https://api.case.dev
  • URL
    https://api.case.dev/llm/v1/embeddings
  • Auth
    HTTP bearer

Request body

modelstring required

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

encoding_format'float' | 'base64'

Format for returned embeddings

dimensionsinteger

Number of dimensions for the embeddings (model-specific)

userstring

Unique identifier for the end-user

Response

Embeddings created successfully

objectstring
modelstring

Example response

{
  "object": "list",
  "data": [
    {
      "object": "embedding"
    }
  ]
}

Changes