LibreChat/api/utils/tokens.js
Danny Avila 561ce8e86a
feat: Google Gemini ❇️ (#1355)
* refactor: add gemini-pro to google Models list; use defaultModels for central model listing

* refactor(SetKeyDialog): create useMultipleKeys hook to use for Azure, export `isJson` from utils, use EModelEndpoint

* refactor(useUserKey): change variable names to make keyName setting more clear

* refactor(FileUpload): allow passing container className string

* feat(GoogleClient): Gemini support

* refactor(GoogleClient): alternate stream speed for Gemini models

* feat(Gemini): styling/settings configuration for Gemini

* refactor(GoogleClient): substract max response tokens from max context tokens if context is above 32k (I/O max is combined between the two)

* refactor(tokens): correct google max token counts and subtract max response tokens when input/output count are combined towards max context count

* feat(google/initializeClient): handle both local and user_provided credentials and write tests

* fix(GoogleClient): catch if credentials are undefined, handle if serviceKey is string or object correctly, handle no examples passed, throw error if not a Generative Language model and no service account JSON key is provided, throw error if it is a Generative m
odel, but not google API key was provided

* refactor(loadAsyncEndpoints/google): activate Google endpoint if either the service key JSON file is provided in /api/data, or a GOOGLE_KEY is defined.

* docs: updated Google configuration

* fix(ci): Mock import of Service Account Key JSON file (auth.json)

* Update apis_and_tokens.md

* feat: increase max output tokens slider for gemini pro

* refactor(GoogleSettings): handle max and default maxOutputTokens on model change

* chore: add sensitive redact regex

* docs: add warning about data privacy

* Update apis_and_tokens.md
2023-12-15 02:18:07 -05:00

157 lines
4.2 KiB
JavaScript

const { EModelEndpoint } = require('librechat-data-provider');
const models = [
'text-davinci-003',
'text-davinci-002',
'text-davinci-001',
'text-curie-001',
'text-babbage-001',
'text-ada-001',
'davinci',
'curie',
'babbage',
'ada',
'code-davinci-002',
'code-davinci-001',
'code-cushman-002',
'code-cushman-001',
'davinci-codex',
'cushman-codex',
'text-davinci-edit-001',
'code-davinci-edit-001',
'text-embedding-ada-002',
'text-similarity-davinci-001',
'text-similarity-curie-001',
'text-similarity-babbage-001',
'text-similarity-ada-001',
'text-search-davinci-doc-001',
'text-search-curie-doc-001',
'text-search-babbage-doc-001',
'text-search-ada-doc-001',
'code-search-babbage-code-001',
'code-search-ada-code-001',
'gpt2',
'gpt-4',
'gpt-4-0314',
'gpt-4-32k',
'gpt-4-32k-0314',
'gpt-3.5-turbo',
'gpt-3.5-turbo-0301',
];
// Order is important here: by model series and context size (gpt-4 then gpt-3, ascending)
const maxTokensMap = {
[EModelEndpoint.openAI]: {
'gpt-4': 8191,
'gpt-4-0613': 8191,
'gpt-4-32k': 32767,
'gpt-4-32k-0314': 32767,
'gpt-4-32k-0613': 32767,
'gpt-3.5-turbo': 4095,
'gpt-3.5-turbo-0613': 4095,
'gpt-3.5-turbo-0301': 4095,
'gpt-3.5-turbo-16k': 15999,
'gpt-3.5-turbo-16k-0613': 15999,
'gpt-3.5-turbo-1106': 16380, // -5 from max
'gpt-4-1106': 127995, // -5 from max
},
[EModelEndpoint.google]: {
/* Max I/O is combined so we subtract the amount from max response tokens for actual total */
gemini: 32750, // -10 from max
'text-bison-32k': 32758, // -10 from max
'chat-bison-32k': 32758, // -10 from max
'code-bison-32k': 32758, // -10 from max
'codechat-bison-32k': 32758,
/* Codey, -5 from max: 6144 */
'code-': 6139,
'codechat-': 6139,
/* PaLM2, -5 from max: 8192 */
'text-': 8187,
'chat-': 8187,
},
[EModelEndpoint.anthropic]: {
'claude-2.1': 200000,
'claude-': 100000,
},
};
/**
* Retrieves the maximum tokens for a given model name. If the exact model name isn't found,
* it searches for partial matches within the model name, checking keys in reverse order.
*
* @param {string} modelName - The name of the model to look up.
* @param {string} endpoint - The endpoint (default is 'openAI').
* @returns {number|undefined} The maximum tokens for the given model or undefined if no match is found.
*
* @example
* getModelMaxTokens('gpt-4-32k-0613'); // Returns 32767
* getModelMaxTokens('gpt-4-32k-unknown'); // Returns 32767
* getModelMaxTokens('unknown-model'); // Returns undefined
*/
function getModelMaxTokens(modelName, endpoint = EModelEndpoint.openAI) {
if (typeof modelName !== 'string') {
return undefined;
}
const tokensMap = maxTokensMap[endpoint];
if (!tokensMap) {
return undefined;
}
if (tokensMap[modelName]) {
return tokensMap[modelName];
}
const keys = Object.keys(tokensMap);
for (let i = keys.length - 1; i >= 0; i--) {
if (modelName.includes(keys[i])) {
return tokensMap[keys[i]];
}
}
return undefined;
}
/**
* Retrieves the model name key for a given model name input. If the exact model name isn't found,
* it searches for partial matches within the model name, checking keys in reverse order.
*
* @param {string} modelName - The name of the model to look up.
* @param {string} endpoint - The endpoint (default is 'openAI').
* @returns {string|undefined} The model name key for the given model; returns input if no match is found and is string.
*
* @example
* matchModelName('gpt-4-32k-0613'); // Returns 'gpt-4-32k-0613'
* matchModelName('gpt-4-32k-unknown'); // Returns 'gpt-4-32k'
* matchModelName('unknown-model'); // Returns undefined
*/
function matchModelName(modelName, endpoint = EModelEndpoint.openAI) {
if (typeof modelName !== 'string') {
return undefined;
}
const tokensMap = maxTokensMap[endpoint];
if (!tokensMap) {
return modelName;
}
if (tokensMap[modelName]) {
return modelName;
}
const keys = Object.keys(tokensMap);
for (let i = keys.length - 1; i >= 0; i--) {
if (modelName.includes(keys[i])) {
return keys[i];
}
}
return modelName;
}
module.exports = {
tiktokenModels: new Set(models),
maxTokensMap,
getModelMaxTokens,
matchModelName,
};