sillytavern / src /vectors /vllm-vectors.js
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const fetch = require('node-fetch').default;
const { setAdditionalHeadersByType } = require('../additional-headers');
const { TEXTGEN_TYPES } = require('../constants');
/**
* Gets the vector for the given text from VLLM
* @param {string[]} texts - The array of texts to get the vectors for
* @param {string} apiUrl - The API URL
* @param {string} model - The model to use
* @param {import('../users').UserDirectoryList} directories - The directories object for the user
* @returns {Promise<number[][]>} - The array of vectors for the texts
*/
async function getVllmBatchVector(texts, apiUrl, model, directories) {
const url = new URL(apiUrl);
url.pathname = '/v1/embeddings';
const headers = {};
setAdditionalHeadersByType(headers, TEXTGEN_TYPES.VLLM, apiUrl, directories);
const response = await fetch(url, {
method: 'POST',
headers: {
'Content-Type': 'application/json',
...headers,
},
body: JSON.stringify({ input: texts, model }),
});
if (!response.ok) {
const responseText = await response.text();
throw new Error(`VLLM: Failed to get vector for text: ${response.statusText} ${responseText}`);
}
const data = await response.json();
if (!Array.isArray(data?.data)) {
throw new Error('API response was not an array');
}
// Sort data by x.index to ensure the order is correct
data.data.sort((a, b) => a.index - b.index);
const vectors = data.data.map(x => x.embedding);
return vectors;
}
/**
* Gets the vector for the given text from VLLM
* @param {string} text - The text to get the vector for
* @param {string} apiUrl - The API URL
* @param {string} model - The model to use
* @param {import('../users').UserDirectoryList} directories - The directories object for the user
* @returns {Promise<number[]>} - The vector for the text
*/
async function getVllmVector(text, apiUrl, model, directories) {
const vectors = await getVllmBatchVector([text], apiUrl, model, directories);
return vectors[0];
}
module.exports = {
getVllmBatchVector,
getVllmVector,
};