mirror of
https://github.com/danny-avila/LibreChat.git
synced 2025-09-22 08:12:00 +02:00

* 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 commitf8067e91d4
. * Revert "refactor: dynamic form elements using react-hook-form Controllers" This reverts commit2150c4815d
. * 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
319 lines
12 KiB
JavaScript
319 lines
12 KiB
JavaScript
const { EModelEndpoint } = require('librechat-data-provider');
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const { getModelMaxTokens, matchModelName, maxTokensMap } = require('./tokens');
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describe('getModelMaxTokens', () => {
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test('should return correct tokens for exact match', () => {
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expect(getModelMaxTokens('gpt-4-32k-0613')).toBe(
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maxTokensMap[EModelEndpoint.openAI]['gpt-4-32k-0613'],
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);
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});
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test('should return correct tokens for partial match', () => {
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expect(getModelMaxTokens('gpt-4-32k-unknown')).toBe(
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maxTokensMap[EModelEndpoint.openAI]['gpt-4-32k'],
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);
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});
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test('should return correct tokens for partial match (OpenRouter)', () => {
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expect(getModelMaxTokens('openai/gpt-4-32k')).toBe(
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maxTokensMap[EModelEndpoint.openAI]['gpt-4-32k'],
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);
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});
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test('should return undefined for no match', () => {
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expect(getModelMaxTokens('unknown-model')).toBeUndefined();
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});
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test('should return correct tokens for another exact match', () => {
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expect(getModelMaxTokens('gpt-3.5-turbo-16k-0613')).toBe(
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maxTokensMap[EModelEndpoint.openAI]['gpt-3.5-turbo-16k-0613'],
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);
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});
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test('should return correct tokens for another partial match', () => {
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expect(getModelMaxTokens('gpt-3.5-turbo-unknown')).toBe(
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maxTokensMap[EModelEndpoint.openAI]['gpt-3.5-turbo'],
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);
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});
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test('should return undefined for undefined input', () => {
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expect(getModelMaxTokens(undefined)).toBeUndefined();
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});
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test('should return undefined for null input', () => {
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expect(getModelMaxTokens(null)).toBeUndefined();
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});
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test('should return undefined for number input', () => {
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expect(getModelMaxTokens(123)).toBeUndefined();
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});
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// 11/06 Update
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test('should return correct tokens for gpt-3.5-turbo-1106 exact match', () => {
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expect(getModelMaxTokens('gpt-3.5-turbo-1106')).toBe(
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maxTokensMap[EModelEndpoint.openAI]['gpt-3.5-turbo-1106'],
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);
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});
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test('should return correct tokens for gpt-4-1106 exact match', () => {
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expect(getModelMaxTokens('gpt-4-1106')).toBe(maxTokensMap[EModelEndpoint.openAI]['gpt-4-1106']);
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});
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test('should return correct tokens for gpt-4-vision exact match', () => {
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expect(getModelMaxTokens('gpt-4-vision')).toBe(
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maxTokensMap[EModelEndpoint.openAI]['gpt-4-vision'],
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);
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});
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test('should return correct tokens for gpt-3.5-turbo-1106 partial match', () => {
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expect(getModelMaxTokens('something-/gpt-3.5-turbo-1106')).toBe(
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maxTokensMap[EModelEndpoint.openAI]['gpt-3.5-turbo-1106'],
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);
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expect(getModelMaxTokens('gpt-3.5-turbo-1106/something-/')).toBe(
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maxTokensMap[EModelEndpoint.openAI]['gpt-3.5-turbo-1106'],
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);
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});
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test('should return correct tokens for gpt-4-1106 partial match', () => {
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expect(getModelMaxTokens('gpt-4-1106/something')).toBe(
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maxTokensMap[EModelEndpoint.openAI]['gpt-4-1106'],
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);
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expect(getModelMaxTokens('gpt-4-1106-preview')).toBe(
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maxTokensMap[EModelEndpoint.openAI]['gpt-4-1106'],
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);
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expect(getModelMaxTokens('gpt-4-1106-vision-preview')).toBe(
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maxTokensMap[EModelEndpoint.openAI]['gpt-4-1106'],
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);
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});
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// 01/25 Update
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test('should return correct tokens for gpt-4-turbo/0125 matches', () => {
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expect(getModelMaxTokens('gpt-4-turbo')).toBe(
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maxTokensMap[EModelEndpoint.openAI]['gpt-4-turbo'],
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);
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expect(getModelMaxTokens('gpt-4-turbo-preview')).toBe(
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maxTokensMap[EModelEndpoint.openAI]['gpt-4-turbo'],
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);
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expect(getModelMaxTokens('gpt-4-0125')).toBe(maxTokensMap[EModelEndpoint.openAI]['gpt-4-0125']);
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expect(getModelMaxTokens('gpt-4-0125-preview')).toBe(
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maxTokensMap[EModelEndpoint.openAI]['gpt-4-0125'],
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);
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expect(getModelMaxTokens('gpt-3.5-turbo-0125')).toBe(
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maxTokensMap[EModelEndpoint.openAI]['gpt-3.5-turbo-0125'],
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);
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});
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test('should return correct tokens for Anthropic models', () => {
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const models = [
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'claude-2.1',
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'claude-2',
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'claude-1.2',
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'claude-1',
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'claude-1-100k',
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'claude-instant-1',
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'claude-instant-1-100k',
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'claude-3-haiku',
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'claude-3-sonnet',
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'claude-3-opus',
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'claude-3-5-sonnet',
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];
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const maxTokens = {
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'claude-': maxTokensMap[EModelEndpoint.anthropic]['claude-'],
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'claude-2.1': maxTokensMap[EModelEndpoint.anthropic]['claude-2.1'],
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'claude-3': maxTokensMap[EModelEndpoint.anthropic]['claude-3-sonnet'],
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};
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models.forEach((model) => {
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let expectedTokens;
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if (model === 'claude-2.1') {
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expectedTokens = maxTokens['claude-2.1'];
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} else if (model.startsWith('claude-3')) {
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expectedTokens = maxTokens['claude-3'];
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} else {
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expectedTokens = maxTokens['claude-'];
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}
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expect(getModelMaxTokens(model, EModelEndpoint.anthropic)).toEqual(expectedTokens);
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});
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});
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// Tests for Google models
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test('should return correct tokens for exact match - Google models', () => {
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expect(getModelMaxTokens('text-bison-32k', EModelEndpoint.google)).toBe(
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maxTokensMap[EModelEndpoint.google]['text-bison-32k'],
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);
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expect(getModelMaxTokens('codechat-bison-32k', EModelEndpoint.google)).toBe(
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maxTokensMap[EModelEndpoint.google]['codechat-bison-32k'],
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);
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});
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test('should return undefined for no match - Google models', () => {
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expect(getModelMaxTokens('unknown-google-model', EModelEndpoint.google)).toBeUndefined();
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});
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test('should return correct tokens for partial match - Google models', () => {
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expect(getModelMaxTokens('gemini-1.5-pro-latest', EModelEndpoint.google)).toBe(
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maxTokensMap[EModelEndpoint.google]['gemini-1.5'],
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);
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expect(getModelMaxTokens('gemini-1.5-pro-preview-0409', EModelEndpoint.google)).toBe(
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maxTokensMap[EModelEndpoint.google]['gemini-1.5'],
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);
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expect(getModelMaxTokens('gemini-pro-vision', EModelEndpoint.google)).toBe(
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maxTokensMap[EModelEndpoint.google]['gemini-pro-vision'],
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);
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expect(getModelMaxTokens('gemini-1.0', EModelEndpoint.google)).toBe(
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maxTokensMap[EModelEndpoint.google]['gemini'],
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);
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expect(getModelMaxTokens('gemini-pro', EModelEndpoint.google)).toBe(
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maxTokensMap[EModelEndpoint.google]['gemini'],
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);
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expect(getModelMaxTokens('code-', EModelEndpoint.google)).toBe(
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maxTokensMap[EModelEndpoint.google]['code-'],
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);
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expect(getModelMaxTokens('chat-', EModelEndpoint.google)).toBe(
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maxTokensMap[EModelEndpoint.google]['chat-'],
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);
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});
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test('should return correct tokens for partial match - Cohere models', () => {
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expect(getModelMaxTokens('command', EModelEndpoint.custom)).toBe(
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maxTokensMap[EModelEndpoint.custom]['command'],
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);
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expect(getModelMaxTokens('command-r-plus', EModelEndpoint.custom)).toBe(
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maxTokensMap[EModelEndpoint.custom]['command-r-plus'],
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);
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});
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test('should return correct tokens when using a custom endpointTokenConfig', () => {
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const customTokenConfig = {
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'custom-model': 12345,
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};
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expect(getModelMaxTokens('custom-model', EModelEndpoint.openAI, customTokenConfig)).toBe(12345);
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});
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test('should prioritize endpointTokenConfig over the default configuration', () => {
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const customTokenConfig = {
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'gpt-4-32k': 9999,
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};
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expect(getModelMaxTokens('gpt-4-32k', EModelEndpoint.openAI, customTokenConfig)).toBe(9999);
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});
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test('should return undefined if the model is not found in custom endpointTokenConfig', () => {
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const customTokenConfig = {
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'custom-model': 12345,
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};
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expect(
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getModelMaxTokens('nonexistent-model', EModelEndpoint.openAI, customTokenConfig),
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).toBeUndefined();
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});
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test('should return correct tokens for exact match in azureOpenAI models', () => {
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expect(getModelMaxTokens('gpt-4-turbo', EModelEndpoint.azureOpenAI)).toBe(
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maxTokensMap[EModelEndpoint.azureOpenAI]['gpt-4-turbo'],
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);
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});
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test('should return undefined for no match in azureOpenAI models', () => {
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expect(
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getModelMaxTokens('nonexistent-azure-model', EModelEndpoint.azureOpenAI),
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).toBeUndefined();
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});
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test('should return undefined for undefined, null, or number model argument with azureOpenAI endpoint', () => {
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expect(getModelMaxTokens(undefined, EModelEndpoint.azureOpenAI)).toBeUndefined();
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expect(getModelMaxTokens(null, EModelEndpoint.azureOpenAI)).toBeUndefined();
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expect(getModelMaxTokens(1234, EModelEndpoint.azureOpenAI)).toBeUndefined();
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});
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test('should respect custom endpointTokenConfig over azureOpenAI defaults', () => {
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const customTokenConfig = {
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'custom-azure-model': 4096,
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};
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expect(
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getModelMaxTokens('custom-azure-model', EModelEndpoint.azureOpenAI, customTokenConfig),
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).toBe(4096);
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});
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test('should return correct tokens for partial match with custom endpointTokenConfig in azureOpenAI', () => {
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const customTokenConfig = {
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'azure-custom-': 1024,
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};
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expect(
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getModelMaxTokens('azure-custom-gpt-3', EModelEndpoint.azureOpenAI, customTokenConfig),
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).toBe(1024);
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});
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test('should return undefined for a model when using an unsupported endpoint', () => {
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expect(getModelMaxTokens('azure-gpt-3', 'unsupportedEndpoint')).toBeUndefined();
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});
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});
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describe('matchModelName', () => {
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it('should return the exact model name if it exists in maxTokensMap', () => {
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expect(matchModelName('gpt-4-32k-0613')).toBe('gpt-4-32k-0613');
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});
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it('should return the closest matching key for partial matches', () => {
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expect(matchModelName('gpt-4-32k-unknown')).toBe('gpt-4-32k');
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});
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it('should return the input model name if no match is found', () => {
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expect(matchModelName('unknown-model')).toBe('unknown-model');
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});
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it('should return undefined for non-string inputs', () => {
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expect(matchModelName(undefined)).toBeUndefined();
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expect(matchModelName(null)).toBeUndefined();
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expect(matchModelName(123)).toBeUndefined();
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expect(matchModelName({})).toBeUndefined();
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});
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// 11/06 Update
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it('should return the exact model name for gpt-3.5-turbo-1106 if it exists in maxTokensMap', () => {
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expect(matchModelName('gpt-3.5-turbo-1106')).toBe('gpt-3.5-turbo-1106');
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});
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it('should return the exact model name for gpt-4-1106 if it exists in maxTokensMap', () => {
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expect(matchModelName('gpt-4-1106')).toBe('gpt-4-1106');
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});
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it('should return the closest matching key for gpt-3.5-turbo-1106 partial matches', () => {
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expect(matchModelName('gpt-3.5-turbo-1106/something')).toBe('gpt-3.5-turbo-1106');
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expect(matchModelName('something/gpt-3.5-turbo-1106')).toBe('gpt-3.5-turbo-1106');
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});
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it('should return the closest matching key for gpt-4-1106 partial matches', () => {
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expect(matchModelName('something/gpt-4-1106')).toBe('gpt-4-1106');
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expect(matchModelName('gpt-4-1106-preview')).toBe('gpt-4-1106');
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expect(matchModelName('gpt-4-1106-vision-preview')).toBe('gpt-4-1106');
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});
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// 01/25 Update
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it('should return the closest matching key for gpt-4-turbo/0125 matches', () => {
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expect(matchModelName('openai/gpt-4-0125')).toBe('gpt-4-0125');
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expect(matchModelName('gpt-4-turbo-preview')).toBe('gpt-4-turbo');
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expect(matchModelName('gpt-4-turbo-vision-preview')).toBe('gpt-4-turbo');
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expect(matchModelName('gpt-4-0125')).toBe('gpt-4-0125');
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expect(matchModelName('gpt-4-0125-preview')).toBe('gpt-4-0125');
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expect(matchModelName('gpt-4-0125-vision-preview')).toBe('gpt-4-0125');
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});
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// Tests for Google models
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it('should return the exact model name if it exists in maxTokensMap - Google models', () => {
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expect(matchModelName('text-bison-32k', EModelEndpoint.google)).toBe('text-bison-32k');
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expect(matchModelName('codechat-bison-32k', EModelEndpoint.google)).toBe('codechat-bison-32k');
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});
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it('should return the input model name if no match is found - Google models', () => {
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expect(matchModelName('unknown-google-model', EModelEndpoint.google)).toBe(
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'unknown-google-model',
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);
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});
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it('should return the closest matching key for partial matches - Google models', () => {
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expect(matchModelName('code-', EModelEndpoint.google)).toBe('code-');
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expect(matchModelName('chat-', EModelEndpoint.google)).toBe('chat-');
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});
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});
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