mirror of
https://github.com/danny-avila/LibreChat.git
synced 2025-12-17 08:50:15 +01:00
* 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
316 lines
13 KiB
JavaScript
316 lines
13 KiB
JavaScript
const { EModelEndpoint } = require('librechat-data-provider');
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const {
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defaultRate,
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tokenValues,
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getValueKey,
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getMultiplier,
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getCacheMultiplier,
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} = require('./tx');
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describe('getValueKey', () => {
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it('should return "16k" for model name containing "gpt-3.5-turbo-16k"', () => {
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expect(getValueKey('gpt-3.5-turbo-16k-some-other-info')).toBe('16k');
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});
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it('should return "4k" for model name containing "gpt-3.5"', () => {
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expect(getValueKey('gpt-3.5-some-other-info')).toBe('4k');
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});
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it('should return "32k" for model name containing "gpt-4-32k"', () => {
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expect(getValueKey('gpt-4-32k-some-other-info')).toBe('32k');
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});
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it('should return "8k" for model name containing "gpt-4"', () => {
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expect(getValueKey('gpt-4-some-other-info')).toBe('8k');
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});
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it('should return undefined for model names that do not match any known patterns', () => {
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expect(getValueKey('gpt-5-some-other-info')).toBeUndefined();
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});
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it('should return "gpt-3.5-turbo-1106" for model name containing "gpt-3.5-turbo-1106"', () => {
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expect(getValueKey('gpt-3.5-turbo-1106-some-other-info')).toBe('gpt-3.5-turbo-1106');
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expect(getValueKey('openai/gpt-3.5-turbo-1106')).toBe('gpt-3.5-turbo-1106');
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expect(getValueKey('gpt-3.5-turbo-1106/openai')).toBe('gpt-3.5-turbo-1106');
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});
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it('should return "gpt-4-1106" for model name containing "gpt-4-1106"', () => {
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expect(getValueKey('gpt-4-1106-some-other-info')).toBe('gpt-4-1106');
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expect(getValueKey('gpt-4-1106-vision-preview')).toBe('gpt-4-1106');
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expect(getValueKey('gpt-4-1106-preview')).toBe('gpt-4-1106');
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expect(getValueKey('openai/gpt-4-1106')).toBe('gpt-4-1106');
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expect(getValueKey('gpt-4-1106/openai/')).toBe('gpt-4-1106');
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});
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it('should return "gpt-4-1106" for model type of "gpt-4-1106"', () => {
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expect(getValueKey('gpt-4-vision-preview')).toBe('gpt-4-1106');
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expect(getValueKey('openai/gpt-4-1106')).toBe('gpt-4-1106');
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expect(getValueKey('gpt-4-turbo')).toBe('gpt-4-1106');
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expect(getValueKey('gpt-4-0125')).toBe('gpt-4-1106');
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});
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it('should return "gpt-4o" for model type of "gpt-4o"', () => {
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expect(getValueKey('gpt-4o-2024-05-13')).toBe('gpt-4o');
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expect(getValueKey('openai/gpt-4o')).toBe('gpt-4o');
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expect(getValueKey('gpt-4o-turbo')).toBe('gpt-4o');
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expect(getValueKey('gpt-4o-0125')).toBe('gpt-4o');
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});
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it('should return "gpt-4o-mini" for model type of "gpt-4o-mini"', () => {
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expect(getValueKey('gpt-4o-mini-2024-07-18')).toBe('gpt-4o-mini');
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expect(getValueKey('openai/gpt-4o-mini')).toBe('gpt-4o-mini');
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expect(getValueKey('gpt-4o-mini-0718')).toBe('gpt-4o-mini');
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expect(getValueKey('gpt-4o-2024-08-06-0718')).not.toBe('gpt-4o');
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});
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it('should return "gpt-4o-2024-08-06" for model type of "gpt-4o-2024-08-06"', () => {
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expect(getValueKey('gpt-4o-2024-08-06-2024-07-18')).toBe('gpt-4o-2024-08-06');
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expect(getValueKey('openai/gpt-4o-2024-08-06')).toBe('gpt-4o-2024-08-06');
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expect(getValueKey('gpt-4o-2024-08-06-0718')).toBe('gpt-4o-2024-08-06');
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expect(getValueKey('gpt-4o-2024-08-06-0718')).not.toBe('gpt-4o');
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});
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it('should return "gpt-4o" for model type of "chatgpt-4o"', () => {
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expect(getValueKey('chatgpt-4o-latest')).toBe('gpt-4o');
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expect(getValueKey('openai/chatgpt-4o-latest')).toBe('gpt-4o');
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expect(getValueKey('chatgpt-4o-latest-0916')).toBe('gpt-4o');
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expect(getValueKey('chatgpt-4o-latest-0718')).toBe('gpt-4o');
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});
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it('should return "claude-3-5-sonnet" for model type of "claude-3-5-sonnet-"', () => {
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expect(getValueKey('claude-3-5-sonnet-20240620')).toBe('claude-3-5-sonnet');
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expect(getValueKey('anthropic/claude-3-5-sonnet')).toBe('claude-3-5-sonnet');
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expect(getValueKey('claude-3-5-sonnet-turbo')).toBe('claude-3-5-sonnet');
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expect(getValueKey('claude-3-5-sonnet-0125')).toBe('claude-3-5-sonnet');
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});
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it('should return "claude-3.5-sonnet" for model type of "claude-3.5-sonnet-"', () => {
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expect(getValueKey('claude-3.5-sonnet-20240620')).toBe('claude-3.5-sonnet');
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expect(getValueKey('anthropic/claude-3.5-sonnet')).toBe('claude-3.5-sonnet');
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expect(getValueKey('claude-3.5-sonnet-turbo')).toBe('claude-3.5-sonnet');
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expect(getValueKey('claude-3.5-sonnet-0125')).toBe('claude-3.5-sonnet');
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});
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});
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describe('getMultiplier', () => {
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it('should return the correct multiplier for a given valueKey and tokenType', () => {
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expect(getMultiplier({ valueKey: '8k', tokenType: 'prompt' })).toBe(tokenValues['8k'].prompt);
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expect(getMultiplier({ valueKey: '8k', tokenType: 'completion' })).toBe(
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tokenValues['8k'].completion,
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);
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});
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it('should return defaultRate if tokenType is provided but not found in tokenValues', () => {
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expect(getMultiplier({ valueKey: '8k', tokenType: 'unknownType' })).toBe(defaultRate);
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});
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it('should derive the valueKey from the model if not provided', () => {
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expect(getMultiplier({ tokenType: 'prompt', model: 'gpt-4-some-other-info' })).toBe(
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tokenValues['8k'].prompt,
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);
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});
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it('should return 1 if only model or tokenType is missing', () => {
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expect(getMultiplier({ tokenType: 'prompt' })).toBe(1);
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expect(getMultiplier({ model: 'gpt-4-some-other-info' })).toBe(1);
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});
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it('should return the correct multiplier for gpt-3.5-turbo-1106', () => {
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expect(getMultiplier({ valueKey: 'gpt-3.5-turbo-1106', tokenType: 'prompt' })).toBe(
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tokenValues['gpt-3.5-turbo-1106'].prompt,
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);
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expect(getMultiplier({ valueKey: 'gpt-3.5-turbo-1106', tokenType: 'completion' })).toBe(
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tokenValues['gpt-3.5-turbo-1106'].completion,
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);
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});
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it('should return the correct multiplier for gpt-4-1106', () => {
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expect(getMultiplier({ valueKey: 'gpt-4-1106', tokenType: 'prompt' })).toBe(
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tokenValues['gpt-4-1106'].prompt,
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);
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expect(getMultiplier({ valueKey: 'gpt-4-1106', tokenType: 'completion' })).toBe(
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tokenValues['gpt-4-1106'].completion,
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);
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});
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it('should return the correct multiplier for gpt-4o', () => {
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const valueKey = getValueKey('gpt-4o-2024-05-13');
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expect(getMultiplier({ valueKey, tokenType: 'prompt' })).toBe(tokenValues['gpt-4o'].prompt);
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expect(getMultiplier({ valueKey, tokenType: 'completion' })).toBe(
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tokenValues['gpt-4o'].completion,
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);
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expect(getMultiplier({ valueKey, tokenType: 'completion' })).not.toBe(
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tokenValues['gpt-4-1106'].completion,
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);
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});
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it('should return the correct multiplier for gpt-4o-mini', () => {
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const valueKey = getValueKey('gpt-4o-mini-2024-07-18');
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expect(getMultiplier({ valueKey, tokenType: 'prompt' })).toBe(
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tokenValues['gpt-4o-mini'].prompt,
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);
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expect(getMultiplier({ valueKey, tokenType: 'completion' })).toBe(
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tokenValues['gpt-4o-mini'].completion,
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);
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expect(getMultiplier({ valueKey, tokenType: 'completion' })).not.toBe(
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tokenValues['gpt-4-1106'].completion,
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);
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});
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it('should return the correct multiplier for chatgpt-4o-latest', () => {
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const valueKey = getValueKey('chatgpt-4o-latest');
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expect(getMultiplier({ valueKey, tokenType: 'prompt' })).toBe(tokenValues['gpt-4o'].prompt);
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expect(getMultiplier({ valueKey, tokenType: 'completion' })).toBe(
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tokenValues['gpt-4o'].completion,
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);
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expect(getMultiplier({ valueKey, tokenType: 'completion' })).not.toBe(
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tokenValues['gpt-4o-mini'].completion,
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);
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});
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it('should derive the valueKey from the model if not provided for new models', () => {
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expect(
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getMultiplier({ tokenType: 'prompt', model: 'gpt-3.5-turbo-1106-some-other-info' }),
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).toBe(tokenValues['gpt-3.5-turbo-1106'].prompt);
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expect(getMultiplier({ tokenType: 'completion', model: 'gpt-4-1106-vision-preview' })).toBe(
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tokenValues['gpt-4-1106'].completion,
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);
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expect(getMultiplier({ tokenType: 'completion', model: 'gpt-4-0125-preview' })).toBe(
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tokenValues['gpt-4-1106'].completion,
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);
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expect(getMultiplier({ tokenType: 'completion', model: 'gpt-4-turbo-vision-preview' })).toBe(
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tokenValues['gpt-4-1106'].completion,
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);
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expect(getMultiplier({ tokenType: 'completion', model: 'gpt-3.5-turbo-0125' })).toBe(
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tokenValues['gpt-3.5-turbo-0125'].completion,
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);
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});
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it('should return defaultRate if derived valueKey does not match any known patterns', () => {
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expect(getMultiplier({ tokenType: 'prompt', model: 'gpt-5-some-other-info' })).toBe(
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defaultRate,
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);
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});
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});
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describe('AWS Bedrock Model Tests', () => {
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const awsModels = [
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'anthropic.claude-3-haiku-20240307-v1:0',
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'anthropic.claude-3-sonnet-20240229-v1:0',
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'anthropic.claude-3-opus-20240229-v1:0',
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'anthropic.claude-3-5-sonnet-20240620-v1:0',
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'anthropic.claude-v2:1',
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'anthropic.claude-instant-v1',
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'meta.llama2-13b-chat-v1',
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'meta.llama2-70b-chat-v1',
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'meta.llama3-8b-instruct-v1:0',
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'meta.llama3-70b-instruct-v1:0',
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'meta.llama3-1-8b-instruct-v1:0',
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'meta.llama3-1-70b-instruct-v1:0',
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'meta.llama3-1-405b-instruct-v1:0',
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'mistral.mistral-7b-instruct-v0:2',
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'mistral.mistral-small-2402-v1:0',
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'mistral.mixtral-8x7b-instruct-v0:1',
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'mistral.mistral-large-2402-v1:0',
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'mistral.mistral-large-2407-v1:0',
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'cohere.command-text-v14',
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'cohere.command-light-text-v14',
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'cohere.command-r-v1:0',
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'cohere.command-r-plus-v1:0',
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'ai21.j2-mid-v1',
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'ai21.j2-ultra-v1',
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'amazon.titan-text-lite-v1',
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'amazon.titan-text-express-v1',
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];
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it('should return the correct prompt multipliers for all models', () => {
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const results = awsModels.map((model) => {
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const valueKey = getValueKey(model, EModelEndpoint.bedrock);
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const multiplier = getMultiplier({ valueKey, tokenType: 'prompt' });
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return tokenValues[valueKey].prompt && multiplier === tokenValues[valueKey].prompt;
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});
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expect(results.every(Boolean)).toBe(true);
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});
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it('should return the correct completion multipliers for all models', () => {
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const results = awsModels.map((model) => {
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const valueKey = getValueKey(model, EModelEndpoint.bedrock);
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const multiplier = getMultiplier({ valueKey, tokenType: 'completion' });
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return tokenValues[valueKey].completion && multiplier === tokenValues[valueKey].completion;
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});
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expect(results.every(Boolean)).toBe(true);
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});
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});
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describe('getCacheMultiplier', () => {
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it('should return the correct cache multiplier for a given valueKey and cacheType', () => {
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expect(getCacheMultiplier({ valueKey: 'claude-3-5-sonnet', cacheType: 'write' })).toBe(3.75);
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expect(getCacheMultiplier({ valueKey: 'claude-3-5-sonnet', cacheType: 'read' })).toBe(0.3);
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expect(getCacheMultiplier({ valueKey: 'claude-3-haiku', cacheType: 'write' })).toBe(0.3);
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expect(getCacheMultiplier({ valueKey: 'claude-3-haiku', cacheType: 'read' })).toBe(0.03);
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});
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it('should return null if cacheType is provided but not found in cacheTokenValues', () => {
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expect(
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getCacheMultiplier({ valueKey: 'claude-3-5-sonnet', cacheType: 'unknownType' }),
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).toBeNull();
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});
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it('should derive the valueKey from the model if not provided', () => {
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expect(getCacheMultiplier({ cacheType: 'write', model: 'claude-3-5-sonnet-20240620' })).toBe(
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3.75,
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);
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expect(getCacheMultiplier({ cacheType: 'read', model: 'claude-3-haiku-20240307' })).toBe(0.03);
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});
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it('should return null if only model or cacheType is missing', () => {
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expect(getCacheMultiplier({ cacheType: 'write' })).toBeNull();
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expect(getCacheMultiplier({ model: 'claude-3-5-sonnet' })).toBeNull();
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});
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it('should return null if derived valueKey does not match any known patterns', () => {
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expect(getCacheMultiplier({ cacheType: 'write', model: 'gpt-4-some-other-info' })).toBeNull();
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});
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it('should handle endpointTokenConfig if provided', () => {
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const endpointTokenConfig = {
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'custom-model': {
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write: 5,
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read: 1,
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},
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};
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expect(
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getCacheMultiplier({ model: 'custom-model', cacheType: 'write', endpointTokenConfig }),
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).toBe(5);
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expect(
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getCacheMultiplier({ model: 'custom-model', cacheType: 'read', endpointTokenConfig }),
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).toBe(1);
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});
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it('should return null if model is not found in endpointTokenConfig', () => {
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const endpointTokenConfig = {
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'custom-model': {
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write: 5,
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read: 1,
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},
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};
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expect(
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getCacheMultiplier({ model: 'unknown-model', cacheType: 'write', endpointTokenConfig }),
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).toBeNull();
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});
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it('should handle models with "bedrock/" prefix', () => {
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expect(
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getCacheMultiplier({
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model: 'bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0',
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cacheType: 'write',
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}),
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).toBe(3.75);
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expect(
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getCacheMultiplier({
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model: 'bedrock/anthropic.claude-3-haiku-20240307-v1:0',
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cacheType: 'read',
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}),
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).toBe(0.03);
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});
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});
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