SFR Embedding 2 R

import { SIEClient } from '@superlinked/sie-sdk';

const client = new SIEClient('http://localhost:8080');

const query = "How do I keep my vector index fresh as documents change?";
const documents = [
  "Incremental indexing re-embeds only the changed documents.",
  "A reranker reorders the shortlist for higher precision.",
  "Dense embeddings put similar meanings close together.",
  "Cron the encode job nightly to re-vectorise edited rows.",
  "GPU price per hour depends on your cloud provider.",
];

const model = 'Salesforce/SFR-Embedding-2_R';
const [queryVector] = await client.encode(model, [{ text: query }], { outputTypes: ['dense'], isQuery: true });
const documentVectors = await client.encode(model, documents.map((text) => ({ text })), { outputTypes: ['dense'] });
console.log(queryVector?.dense);
console.log(documentVectors.map((vector) => vector.dense));

# Encode the query with is_query, then the candidate documents.
curl http://localhost:8080/v1/encode/Salesforce%2FSFR-Embedding-2_R \
  -H "Content-Type: application/json" \
  -d "{\"items\":[{\"text\":\"How do I keep my vector index fresh as documents change?\"}],\"params\":{\"output_types\":[\"dense\"],\"options\":{\"is_query\":true}}}"

curl http://localhost:8080/v1/encode/Salesforce%2FSFR-Embedding-2_R \
  -H "Content-Type: application/json" \
  -d "{\"items\":[{\"text\":\"Incremental indexing re-embeds only the changed documents.\"},{\"text\":\"A reranker reorders the shortlist for higher precision.\"},{\"text\":\"Dense embeddings put similar meanings close together.\"},{\"text\":\"Cron the encode job nightly to re-vectorise edited rows.\"},{\"text\":\"GPU price per hour depends on your cloud provider.\"}],\"params\":{\"output_types\":[\"dense\"]}}"