Edits history of script submission #12087 for ' Embed (cohere)'

• bun

  Ap­pro­ved version

  Edit in Windmill

  Embed

  //native
  type Cohere = {
    apiKey: string;
  };
  /**
   * Embed
   * This endpoint returns text embeddings. An embedding is a list of floating point numbers that captures semantic information about the text that it represents.
  
  Embeddings can be used to create text classifiers as well as empower semantic search. To learn more about embeddings, see the embedding page.
  
  If you want to learn more how to use the embedding model, have a look at the [Semantic Search Guide](https://docs.cohere.com/docs/semantic-search).
   */
  export async function main(
    auth: Cohere,
    body: {
      texts?: string[];
      images?: string[];
      model: string;
      input_type:
        | "search_document"
        | "search_query"
        | "classification"
        | "clustering"
        | "image";
      embedding_types: "float" | "int8" | "uint8" | "binary" | "ubinary"[];
      truncate?: "NONE" | "START" | "END";
    },
  ) {
    const url = new URL(`https://api.cohere.com/v2/embed`);
  
    const response = await fetch(url, {
      method: "POST",
      headers: {
        "Content-Type": "application/json",
        Authorization: "Bearer " + auth.apiKey,
      },
      body: JSON.stringify(body),
    });
    if (!response.ok) {
      const text = await response.text();
      throw new Error(`${response.status} ${text}`);
    }
    return await response.json();
  }
  

  Submitted by hugo697 428 days ago