LibreChat/api/utils/tokens.js
Danny Avila 29473a72db
💫 feat: Config File & Custom Endpoints (#1474)
* WIP(backend/api): custom endpoint

* WIP(frontend/client): custom endpoint

* chore: adjust typedefs for configs

* refactor: use data-provider for cache keys and rename enums and custom endpoint for better clarity and compatibility

* feat: loadYaml utility

* refactor: rename back to  from  and proof-of-concept for creating schemas from user-defined defaults

* refactor: remove custom endpoint from default endpointsConfig as it will be exclusively managed by yaml config

* refactor(EndpointController): rename variables for clarity

* feat: initial load custom config

* feat(server/utils): add simple `isUserProvided` helper

* chore(types): update TConfig type

* refactor: remove custom endpoint handling from model services as will be handled by config, modularize fetching of models

* feat: loadCustomConfig, loadConfigEndpoints, loadConfigModels

* chore: reorganize server init imports, invoke loadCustomConfig

* refactor(loadConfigEndpoints/Models): return each custom endpoint as standalone endpoint

* refactor(Endpoint/ModelController): spread config values after default (temporary)

* chore(client): fix type issues

* WIP: first pass for multiple custom endpoints
- add endpointType to Conversation schema
- add update zod schemas for both convo/presets to allow non-EModelEndpoint value as endpoint (also using type assertion)
- use `endpointType` value as `endpoint` where mapping to type is necessary using this field
- use custom defined `endpoint` value and not type for mapping to modelsConfig
- misc: add return type to `getDefaultEndpoint`
- in `useNewConvo`, add the endpointType if it wasn't already added to conversation
- EndpointsMenu: use user-defined endpoint name as Title in menu
- TODO: custom icon via custom config, change unknown to robot icon

* refactor(parseConvo): pass args as an object and change where used accordingly; chore: comment out 'create schema' code

* chore: remove unused availableModels field in TConfig type

* refactor(parseCompactConvo): pass args as an object and change where used accordingly

* feat: chat through custom endpoint

* chore(message/convoSchemas): avoid saving empty arrays

* fix(BaseClient/saveMessageToDatabase): save endpointType

* refactor(ChatRoute): show Spinner if endpointsQuery or modelsQuery are still loading, which is apparent with slow fetching of models/remote config on first serve

* fix(useConversation): assign endpointType if it's missing

* fix(SaveAsPreset): pass real endpoint and endpointType when saving Preset)

* chore: recorganize types order for TConfig, add `iconURL`

* feat: custom endpoint icon support:
- use UnknownIcon in all icon contexts
- add mistral and openrouter as known endpoints, and add their icons
- iconURL support

* fix(presetSchema): move endpointType to default schema definitions shared between convoSchema and defaults

* refactor(Settings/OpenAI): remove legacy `isOpenAI` flag

* fix(OpenAIClient): do not invoke abortCompletion on completion error

* feat: add responseSender/label support for custom endpoints:
- use defaultModelLabel field in endpointOption
- add model defaults for custom endpoints in `getResponseSender`
- add `useGetSender` hook which uses EndpointsQuery to determine `defaultModelLabel`
- include defaultModelLabel from endpointConfig in custom endpoint client options
- pass `endpointType` to `getResponseSender`

* feat(OpenAIClient): use custom options from config file

* refactor: rename `defaultModelLabel` to `modelDisplayLabel`

* refactor(data-provider): separate concerns from `schemas` into `parsers`, `config`, and fix imports elsewhere

* feat: `iconURL` and extract environment variables from custom endpoint config values

* feat: custom config validation via zod schema, rename and move to `./projectRoot/librechat.yaml`

* docs: custom config docs and examples

* fix(OpenAIClient/mistral): mistral does not allow singular system message, also add `useChatCompletion` flag to use openai-node for title completions

* fix(custom/initializeClient): extract env var and use `isUserProvided` function

* Update librechat.example.yaml

* feat(InputWithLabel): add className props, and forwardRef

* fix(streamResponse): handle error edge case where either messages or convos query throws an error

* fix(useSSE): handle errorHandler edge cases where error response is and is not properly formatted from API, especially when a conversationId is not yet provided, which ensures stream is properly closed on error

* feat: user_provided keys for custom endpoints

* fix(config/endpointSchema): do not allow default endpoint values in custom endpoint `name`

* feat(loadConfigModels): extract env variables and optimize fetching models

* feat: support custom endpoint iconURL for messages and Nav

* feat(OpenAIClient): add/dropParams support

* docs: update docs with default params, add/dropParams, and notes to use config file instead of `OPENAI_REVERSE_PROXY`

* docs: update docs with additional notes

* feat(maxTokensMap): add mistral models (32k context)

* docs: update openrouter notes

* Update ai_setup.md

* docs(custom_config): add table of contents and fix note about custom name

* docs(custom_config): reorder ToC

* Update custom_config.md

* Add note about `max_tokens` field in custom_config.md
2024-01-03 09:22:48 -05:00

161 lines
4.3 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',
];
const openAIModels = {
'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
'mistral-': 31995, // -5 from max
};
// Order is important here: by model series and context size (gpt-4 then gpt-3, ascending)
const maxTokensMap = {
[EModelEndpoint.openAI]: openAIModels,
[EModelEndpoint.custom]: openAIModels,
[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,
};