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Merge branch 'staging' into release

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anachronos
2023-12-17 10:38:04 +01:00
committed by GitHub
parent 0a9f678399 e74e925428
commit 1e88c8922a
78 changed files with 4592 additions and 2836 deletions
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67
src/openai-vectors.js
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@ -1,6 +1,24 @@
const fetch = require('node-fetch').default;
const { SECRET_KEYS, readSecret } = require('./endpoints/secrets');
const SOURCES = {
'mistral': {
secretKey: SECRET_KEYS.MISTRAL,
url: 'api.mistral.ai',
model: 'mistral-embed',
},
'openai': {
secretKey: SECRET_KEYS.OPENAI,
url: 'api.openai.com',
model: 'text-embedding-ada-002',
},
'togetherai': {
secretKey: SECRET_KEYS.TOGETHERAI,
url: 'api.togetherai.xyz',
model: 'togethercomputer/GPT-NeoXT-Chat-Base-20B',
},
};
/**
* Gets the vector for the given text from an OpenAI compatible endpoint.
* @param {string} text - The text to get the vector for
@ -8,34 +26,23 @@ const { SECRET_KEYS, readSecret } = require('./endpoints/secrets');
* @returns {Promise<number[]>} - The vector for the text
*/
async function getOpenAIVector(text, source) {
const config = SOURCES[source];
// dictionary of sources to endpoints with source as key and endpoint, model and secret key as value
const endpoints = {
'togetherai': {
endpoint: 'https://api.togetherai.xyz/v1/embeddings', // is this correct?
model: 'togethercomputer/GPT-NeoXT-Chat-Base-20B',
secret: SECRET_KEYS.TOGETHERAI,
},
'openai': {
endpoint: 'https://api.openai.com/v1/embeddings',
model: 'text-embedding-ada-002',
secret: SECRET_KEYS.OPENAI,
},
'mistral': {
endpoint: 'https://api.mistral.ai/v1/embeddings',
model: 'mistral-embed',
secret: SECRET_KEYS.MISTRAL,
},
};
const key = readSecret(endpoints[source].secret);
if (!key) {
console.log('No %s key found.', source);
throw new Error('No ${source} key found.');
if (!config) {
console.log('Unknown source', source);
throw new Error('Unknown source');
}
const response = await fetch(endpoints[source].endpoint, {
const key = readSecret(config.secretKey);
if (!key) {
console.log('No API key found');
throw new Error('No API key found');
}
const url = config.url;
const response = await fetch(`https://${url}/v1/embeddings`, {
method: 'POST',
headers: {
'Content-Type': 'application/json',
@ -43,22 +50,22 @@ async function getOpenAIVector(text, source) {
},
body: JSON.stringify({
input: text,
model: endpoints[source].model,
model: config.model,
}),
});
if (!response.ok) {
const text = await response.text();
console.log('${source} request failed', response.statusText, text);
throw new Error('${source} request failed');
console.log('API request failed', response.statusText, text);
throw new Error('API request failed');
}
const data = await response.json();
const vector = data?.data[0]?.embedding;
if (!Array.isArray(vector)) {
console.log('${source} response was not an array');
throw new Error('${source} response was not an array');
console.log('API response was not an array');
throw new Error('API response was not an array');
}
return vector;