LibreChat/api/utils/tokens.js
Danny Avila d59b62174f
🪨 feat: AWS Bedrock support (#3935)
* feat: Add BedrockIcon component to SVG library

* feat: EModelEndpoint.bedrock

* feat: first pass, bedrock chat. note: AgentClient is returning `agents` as conversation.endpoint

* fix: declare endpoint in initialization step

* chore: Update @librechat/agents dependency to version 1.4.5

* feat: backend content aggregation for agents/bedrock

* feat: abort agent requests

* feat: AWS Bedrock icons

* WIP: agent provider schema parsing

* chore: Update EditIcon props type

* refactor(useGenerationsByLatest): make agents and bedrock editable

* refactor: non-assistant message content, parts

* fix: Bedrock response `sender`

* fix: use endpointOption.model_parameters not endpointOption.modelOptions

* fix: types for step handler

* refactor: Update Agents.ToolCallDelta type

* refactor: Remove unnecessary assignment of parentMessageId in AskController

* refactor: remove unnecessary assignment of parentMessageId (agent request handler)

* fix(bedrock/agents): message regeneration

* refactor: dynamic form elements using react-hook-form Controllers

* fix: agent icons/labels for messages

* fix: agent actions

* fix: use of new dynamic tags causing application crash

* refactor: dynamic settings touch-ups

* refactor: update Slider component to allow custom track class name

* refactor: update DynamicSlider component styles

* refactor: use Constants value for GLOBAL_PROJECT_NAME (enum)

* feat: agent share global methods/controllers

* fix: agents query

* fix: `getResponseModel`

* fix: share prompt a11y issue

* refactor: update SharePrompt dialog theme styles

* refactor: explicit typing for SharePrompt

* feat: add agent roles/permissions

* chore: update @librechat/agents dependency to version 1.4.7 for tool_call_ids edge case

* fix(Anthropic): messages.X.content.Y.tool_use.input: Input should be a valid dictionary

* fix: handle text parts with tool_call_ids and empty text

* fix: role initialization

* refactor: don't make instructions required

* refactor: improve typing of Text part

* fix: setShowStopButton for agents route

* chore: remove params for now

* fix: add streamBuffer and streamRate to help prevent 'Overloaded' errors from Anthropic API

* refactor: remove console.log statement in ContentRender component

* chore: typing, rename Context to Delete Button

* chore(DeleteButton): logging

* refactor(Action): make accessible

* style(Action): improve a11y again

* refactor: remove use/mention of mongoose sessions

* feat: first pass, sharing agents

* feat: visual indicator for global agent, remove author when serving to non-author

* wip: params

* chore: fix typing issues

* fix(schemas): typing

* refactor: improve accessibility of ListCard component and fix console React warning

* wip: reset templates for non-legacy new convos

* Revert "wip: params"

This reverts commit f8067e91d4.

* Revert "refactor: dynamic form elements using react-hook-form Controllers"

This reverts commit 2150c4815d.

* fix(Parameters): types and parameter effect update to only update local state to parameters

* refactor: optimize useDebouncedInput hook for better performance

* feat: first pass, anthropic bedrock params

* chore: paramEndpoints check for endpointType too

* fix: maxTokens to use coerceNumber.optional(),

* feat: extra chat model params

* chore: reduce code repetition

* refactor: improve preset title handling in SaveAsPresetDialog component

* refactor: improve preset handling in HeaderOptions component

* chore: improve typing, replace legacy dialog for SaveAsPresetDialog

* feat: save as preset from parameters panel

* fix: multi-search in select dropdown when using Option type

* refactor: update default showDefault value to false in Dynamic components

* feat: Bedrock presets settings

* chore: config, fix agents schema, update config version

* refactor: update AWS region variable name in bedrock options endpoint to BEDROCK_AWS_DEFAULT_REGION

* refactor: update baseEndpointSchema in config.ts to include baseURL property

* refactor: update createRun function to include req parameter and set streamRate based on provider

* feat: availableRegions via config

* refactor: remove unused demo agent controller file

* WIP: title

* Update @librechat/agents to version 1.5.0

* chore: addTitle.js to handle empty responseText

* feat: support images and titles

* feat: context token updates

* Refactor BaseClient test to use expect.objectContaining

* refactor: add model select, remove header options params, move side panel params below prompts

* chore: update models list, catch title error

* feat: model service for bedrock models (env)

* chore: Remove verbose debug log in AgentClient class following stream

* feat(bedrock): track token spend; fix: token rates, value key mapping for AWS models

* refactor: handle streamRate in `handleLLMNewToken` callback

* chore: AWS Bedrock example config in `.env.example`

* refactor: Rename bedrockMeta to bedrockGeneral in settings.ts and use for AI21 and Amazon Bedrock providers

* refactor: Update `.env.example` with AWS Bedrock model IDs URL and additional notes

* feat: titleModel support for bedrock

* refactor: Update `.env.example` with additional notes for AWS Bedrock model IDs
2024-09-09 12:06:59 -04:00

304 lines
8.4 KiB
JavaScript

const z = require('zod');
const { EModelEndpoint } = require('librechat-data-provider');
const openAIModels = {
'gpt-4': 8187, // -5 from max
'gpt-4-0613': 8187, // -5 from max
'gpt-4-32k': 32758, // -10 from max
'gpt-4-32k-0314': 32758, // -10 from max
'gpt-4-32k-0613': 32758, // -10 from max
'gpt-4-1106': 127500, // -500 from max
'gpt-4-0125': 127500, // -500 from max
'gpt-4o': 127500, // -500 from max
'gpt-4o-mini': 127500, // -500 from max
'gpt-4o-2024-08-06': 127500, // -500 from max
'gpt-4-turbo': 127500, // -500 from max
'gpt-4-vision': 127500, // -500 from max
'gpt-3.5-turbo': 16375, // -10 from max
'gpt-3.5-turbo-0613': 4092, // -5 from max
'gpt-3.5-turbo-0301': 4092, // -5 from max
'gpt-3.5-turbo-16k': 16375, // -10 from max
'gpt-3.5-turbo-16k-0613': 16375, // -10 from max
'gpt-3.5-turbo-1106': 16375, // -10 from max
'gpt-3.5-turbo-0125': 16375, // -10 from max
};
const mistralModels = {
'mistral-': 31990, // -10 from max
'mistral-7b': 31990, // -10 from max
'mistral-small': 31990, // -10 from max
'mixtral-8x7b': 31990, // -10 from max
'mistral-large-2402': 127500,
'mistral-large-2407': 127500,
};
const cohereModels = {
'command-light': 4086, // -10 from max
'command-light-nightly': 8182, // -10 from max
command: 4086, // -10 from max
'command-nightly': 8182, // -10 from max
'command-r': 127500, // -500 from max
'command-r-plus': 127500, // -500 from max
};
const googleModels = {
/* Max I/O is combined so we subtract the amount from max response tokens for actual total */
gemini: 30720, // -2048 from max
'gemini-pro-vision': 12288, // -4096 from max
'gemini-1.5': 1048576, // -8192 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,
};
const anthropicModels = {
'claude-': 100000,
'claude-instant': 100000,
'claude-2': 100000,
'claude-2.1': 200000,
'claude-3-haiku': 200000,
'claude-3-sonnet': 200000,
'claude-3-opus': 200000,
'claude-3-5-sonnet': 200000,
'claude-3.5-sonnet': 200000,
};
const metaModels = {
'llama2-13b': 4000,
'llama2-70b': 4000,
'llama3-8b': 8000,
'llama3-70b': 8000,
'llama3-1-8b': 127500,
'llama3-1-70b': 127500,
'llama3-1-405b': 127500,
};
const ai21Models = {
'ai21.j2-mid-v1': 8182, // -10 from max
'ai21.j2-ultra-v1': 8182, // -10 from max
'ai21.jamba-instruct-v1:0': 255500, // -500 from max
};
const amazonModels = {
'amazon.titan-text-lite-v1': 4000,
'amazon.titan-text-express-v1': 8000,
'amazon.titan-text-premier-v1:0': 31500, // -500 from max
};
const bedrockModels = {
...anthropicModels,
...mistralModels,
...cohereModels,
...metaModels,
...ai21Models,
...amazonModels,
};
const aggregateModels = { ...openAIModels, ...googleModels, ...bedrockModels };
const maxTokensMap = {
[EModelEndpoint.azureOpenAI]: openAIModels,
[EModelEndpoint.openAI]: aggregateModels,
[EModelEndpoint.agents]: aggregateModels,
[EModelEndpoint.custom]: aggregateModels,
[EModelEndpoint.google]: googleModels,
[EModelEndpoint.anthropic]: anthropicModels,
[EModelEndpoint.bedrock]: bedrockModels,
};
/**
* Finds the first matching pattern in the tokens map.
* @param {string} modelName
* @param {Record<string, number>} tokensMap
* @returns {string|null}
*/
function findMatchingPattern(modelName, tokensMap) {
const keys = Object.keys(tokensMap);
for (let i = keys.length - 1; i >= 0; i--) {
const modelKey = keys[i];
if (modelName.includes(modelKey)) {
return modelKey;
}
}
return null;
}
/**
* 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').
* @param {EndpointTokenConfig} [endpointTokenConfig] - Token Config for current endpoint to use for max tokens lookup
* @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, endpointTokenConfig) {
if (typeof modelName !== 'string') {
return undefined;
}
/** @type {EndpointTokenConfig | Record<string, number>} */
const tokensMap = endpointTokenConfig ?? maxTokensMap[endpoint];
if (!tokensMap) {
return undefined;
}
if (tokensMap[modelName]?.context) {
return tokensMap[modelName].context;
}
if (tokensMap[modelName]) {
return tokensMap[modelName];
}
const matchedPattern = findMatchingPattern(modelName, tokensMap);
if (matchedPattern) {
const result = tokensMap[matchedPattern];
return result?.context ?? result;
}
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 matchedPattern = findMatchingPattern(modelName, tokensMap);
return matchedPattern || modelName;
}
const modelSchema = z.object({
id: z.string(),
pricing: z.object({
prompt: z.string(),
completion: z.string(),
}),
context_length: z.number(),
});
const inputSchema = z.object({
data: z.array(modelSchema),
});
/**
* Processes a list of model data from an API and organizes it into structured data based on URL and specifics of rates and context.
* @param {{ data: Array<z.infer<typeof modelSchema>> }} input The input object containing base URL and data fetched from the API.
* @returns {EndpointTokenConfig} The processed model data.
*/
function processModelData(input) {
const validationResult = inputSchema.safeParse(input);
if (!validationResult.success) {
throw new Error('Invalid input data');
}
const { data } = validationResult.data;
/** @type {EndpointTokenConfig} */
const tokenConfig = {};
for (const model of data) {
const modelKey = model.id;
if (modelKey === 'openrouter/auto') {
model.pricing = {
prompt: '0.00001',
completion: '0.00003',
};
}
const prompt = parseFloat(model.pricing.prompt) * 1000000;
const completion = parseFloat(model.pricing.completion) * 1000000;
tokenConfig[modelKey] = {
prompt,
completion,
context: model.context_length,
};
}
return tokenConfig;
}
const tiktokenModels = new Set([
'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',
]);
module.exports = {
tiktokenModels,
maxTokensMap,
inputSchema,
modelSchema,
getModelMaxTokens,
matchModelName,
processModelData,
};