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* refactor: modularize openai llm config logic into new getOpenAILLMConfig function (#9412) * ✈️ refactor: Migrate Anthropic's getLLMConfig to TypeScript (#9413) * refactor: move tokens.js over to packages/api and update imports * refactor: port tokens.js to typescript * refactor: move helpers.js over to packages/api and update imports * refactor: port helpers.js to typescript * refactor: move anthropic/llm.js over to packages/api and update imports * refactor: port anthropic/llm.js to typescript with supporting types in types/anthropic.ts and updated tests in llm.spec.js * refactor: move llm.spec.js over to packages/api and update import * refactor: port llm.spec.js over to typescript * 📝 Add Prompt Parameter Support for Anthropic Custom Endpoints (#9414) feat: add anthropic llm config support for openai-like (custom) endpoints * fix: missed compiler / type issues from addition of getAnthropicLLMConfig * refactor: update tokens.ts to export constants and functions, enhance type definitions, and adjust default values * WIP: first pass, decouple `llmConfig` from `configOptions` * chore: update import path for OpenAI configuration from 'llm' to 'config' * refactor: enhance type definitions for ThinkingConfig and update modelOptions in AnthropicConfigOptions * refactor: cleanup type, introduce openai transform from alt provider * chore: integrate removeNullishValues in Google llmConfig and update OpenAI exports * chore: bump version of @librechat/api to 1.3.5 in package.json and package-lock.json * refactor: update customParams type in OpenAIConfigOptions to use TConfig['customParams'] * refactor: enhance transformToOpenAIConfig to include fromEndpoint and improve config extraction * refactor: conform userId field for anthropic/openai, cleanup anthropic typing * ci: add backward compatibility tests for getOpenAIConfig with various endpoints and configurations * ci: replace userId with user in clientOptions for getLLMConfig * test: add Azure OpenAI endpoint tests for various configurations in getOpenAIConfig * refactor: defaultHeaders retrieval for prompt caching for anthropic-based custom endpoint (litellm) * test: add unit tests for getOpenAIConfig with various Anthropic model configurations * test: enhance Anthropic compatibility tests with addParams and dropParams handling * chore: update @librechat/agents dependency to version 2.4.78 in package.json and package-lock.json * chore: update @librechat/agents dependency to version 2.4.79 in package.json and package-lock.json --------- Co-authored-by: Danny Avila <danny@librechat.ai>
79 lines
2.4 KiB
JavaScript
79 lines
2.4 KiB
JavaScript
const { getModelMaxTokens } = require('@librechat/api');
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const { createContentAggregator } = require('@librechat/agents');
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const {
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EModelEndpoint,
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providerEndpointMap,
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getResponseSender,
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} = require('librechat-data-provider');
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const { getDefaultHandlers } = require('~/server/controllers/agents/callbacks');
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const getOptions = require('~/server/services/Endpoints/bedrock/options');
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const AgentClient = require('~/server/controllers/agents/client');
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const initializeClient = async ({ req, res, endpointOption }) => {
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if (!endpointOption) {
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throw new Error('Endpoint option not provided');
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}
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/** @type {Array<UsageMetadata>} */
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const collectedUsage = [];
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const { contentParts, aggregateContent } = createContentAggregator();
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const eventHandlers = getDefaultHandlers({ res, aggregateContent, collectedUsage });
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/** @type {Agent} */
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const agent = {
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id: EModelEndpoint.bedrock,
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name: endpointOption.name,
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provider: EModelEndpoint.bedrock,
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endpoint: EModelEndpoint.bedrock,
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instructions: endpointOption.promptPrefix,
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model: endpointOption.model_parameters.model,
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model_parameters: endpointOption.model_parameters,
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};
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if (typeof endpointOption.artifactsPrompt === 'string' && endpointOption.artifactsPrompt) {
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agent.instructions = `${agent.instructions ?? ''}\n${endpointOption.artifactsPrompt}`.trim();
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}
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// TODO: pass-in override settings that are specific to current run
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const options = await getOptions({
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req,
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res,
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endpointOption,
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});
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agent.model_parameters = Object.assign(agent.model_parameters, options.llmConfig);
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if (options.configOptions) {
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agent.model_parameters.configuration = options.configOptions;
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}
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const sender =
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agent.name ??
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getResponseSender({
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...endpointOption,
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model: endpointOption.model_parameters.model,
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});
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const client = new AgentClient({
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req,
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res,
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agent,
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sender,
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// tools,
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contentParts,
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eventHandlers,
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collectedUsage,
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spec: endpointOption.spec,
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iconURL: endpointOption.iconURL,
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endpoint: EModelEndpoint.bedrock,
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resendFiles: endpointOption.resendFiles,
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maxContextTokens:
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endpointOption.maxContextTokens ??
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agent.max_context_tokens ??
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getModelMaxTokens(agent.model_parameters.model, providerEndpointMap[agent.provider]) ??
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4000,
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attachments: endpointOption.attachments,
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});
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return { client };
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};
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module.exports = { initializeClient };
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