db-vector
Version:
Client adapters for vector databases with utilities
19 lines • 820 B
JavaScript
;
Object.defineProperty(exports, "__esModule", { value: true });
exports.createExtractor = void 0;
const transformers_1 = require("@huggingface/transformers");
/**
* Creates a feature extraction pipeline for generating embeddings from text
* @param modelName - The HuggingFace model name to use for feature extraction
* @returns Promise that resolves to a FeatureExtractionPipeline instance
* @example
* ```typescript
* const extractor = await createExtractor('mixedbread-ai/mxbai-embed-large-v1');
* const embeddings = await extractor(['Hello world'], { pooling: 'cls' });
* ```
*/
const createExtractor = async (modelName) => {
return await (0, transformers_1.pipeline)('feature-extraction', modelName);
};
exports.createExtractor = createExtractor;
//# sourceMappingURL=createExtractor.js.map