// File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
import { APIResource } from "../core/resource.mjs";
import { loggerFor, toFloat32Array } from "../internal/utils.mjs";
/**
 * Get a vector representation of a given input that can be easily consumed by machine learning models and algorithms.
 */
export class Embeddings extends APIResource {
    /**
     * Creates an embedding vector representing the input text.
     *
     * @example
     * ```ts
     * const createEmbeddingResponse =
     *   await client.embeddings.create({
     *     input: 'The quick brown fox jumped over the lazy dog',
     *     model: 'text-embedding-3-small',
     *   });
     * ```
     */
    create(body, options) {
        const hasUserProvidedEncodingFormat = !!body.encoding_format;
        // No encoding_format specified, defaulting to base64 for performance reasons
        // See https://github.com/openai/openai-node/pull/1312
        let encoding_format = hasUserProvidedEncodingFormat ? body.encoding_format : 'base64';
        if (hasUserProvidedEncodingFormat) {
            loggerFor(this._client).debug('embeddings/user defined encoding_format:', body.encoding_format);
        }
        const response = this._client.post('/embeddings', {
            body: {
                ...body,
                encoding_format: encoding_format,
            },
            ...options,
        });
        // if the user specified an encoding_format, return the response as-is
        if (hasUserProvidedEncodingFormat) {
            return response;
        }
        // in this stage, we are sure the user did not specify an encoding_format
        // and we defaulted to base64 for performance reasons
        // we are sure then that the response is base64 encoded, let's decode it
        // the returned result will be a float32 array since this is OpenAI API's default encoding
        loggerFor(this._client).debug('embeddings/decoding base64 embeddings from base64');
        return response._thenUnwrap((response) => {
            if (response && response.data) {
                response.data.forEach((embeddingBase64Obj) => {
                    const embeddingBase64Str = embeddingBase64Obj.embedding;
                    embeddingBase64Obj.embedding = toFloat32Array(embeddingBase64Str);
                });
            }
            return response;
        });
    }
}
//# sourceMappingURL=embeddings.mjs.map