mirror of
https://github.com/danny-avila/LibreChat.git
synced 2025-12-17 00:40:14 +01:00
🎚️ feat: Anthropic Parameter Set Support via Custom Endpoints (#9415)
* 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>
This commit is contained in:
Dustin Healy
GitHub
parent
7de6f6e44c
commit
c6ecf0095b
40 changed files with 1736 additions and 432 deletions
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@ -1,7 +1,7 @@
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const axios = require('axios');
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const deriveBaseURL = require('./deriveBaseURL');
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jest.mock('~/utils', () => {
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const originalUtils = jest.requireActual('~/utils');
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jest.mock('@librechat/api', () => {
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const originalUtils = jest.requireActual('@librechat/api');
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return {
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...originalUtils,
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processModelData: jest.fn((...args) => {
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@ -1,4 +1,3 @@
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const tokenHelpers = require('./tokens');
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const deriveBaseURL = require('./deriveBaseURL');
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const extractBaseURL = require('./extractBaseURL');
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const findMessageContent = require('./findMessageContent');
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@ -6,6 +5,5 @@ const findMessageContent = require('./findMessageContent');
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module.exports = {
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deriveBaseURL,
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extractBaseURL,
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...tokenHelpers,
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findMessageContent,
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};
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@ -1,493 +0,0 @@
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const z = require('zod');
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const { EModelEndpoint } = require('librechat-data-provider');
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const openAIModels = {
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'o4-mini': 200000,
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'o3-mini': 195000, // -5000 from max
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o3: 200000,
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o1: 195000, // -5000 from max
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'o1-mini': 127500, // -500 from max
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'o1-preview': 127500, // -500 from max
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'gpt-4': 8187, // -5 from max
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'gpt-4-0613': 8187, // -5 from max
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'gpt-4-32k': 32758, // -10 from max
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'gpt-4-32k-0314': 32758, // -10 from max
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'gpt-4-32k-0613': 32758, // -10 from max
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'gpt-4-1106': 127500, // -500 from max
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'gpt-4-0125': 127500, // -500 from max
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'gpt-4.5': 127500, // -500 from max
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'gpt-4.1': 1047576,
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'gpt-4.1-mini': 1047576,
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'gpt-4.1-nano': 1047576,
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'gpt-5': 400000,
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'gpt-5-mini': 400000,
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'gpt-5-nano': 400000,
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'gpt-4o': 127500, // -500 from max
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'gpt-4o-mini': 127500, // -500 from max
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'gpt-4o-2024-05-13': 127500, // -500 from max
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'gpt-4o-2024-08-06': 127500, // -500 from max
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'gpt-4-turbo': 127500, // -500 from max
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'gpt-4-vision': 127500, // -500 from max
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'gpt-3.5-turbo': 16375, // -10 from max
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'gpt-3.5-turbo-0613': 4092, // -5 from max
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'gpt-3.5-turbo-0301': 4092, // -5 from max
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'gpt-3.5-turbo-16k': 16375, // -10 from max
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'gpt-3.5-turbo-16k-0613': 16375, // -10 from max
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'gpt-3.5-turbo-1106': 16375, // -10 from max
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'gpt-3.5-turbo-0125': 16375, // -10 from max
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};
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const mistralModels = {
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'mistral-': 31990, // -10 from max
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'mistral-7b': 31990, // -10 from max
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'mistral-small': 31990, // -10 from max
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'mixtral-8x7b': 31990, // -10 from max
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'mistral-large': 131000,
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'mistral-large-2402': 127500,
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'mistral-large-2407': 127500,
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'pixtral-large': 131000,
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'mistral-saba': 32000,
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codestral: 256000,
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'ministral-8b': 131000,
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'ministral-3b': 131000,
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};
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const cohereModels = {
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'command-light': 4086, // -10 from max
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'command-light-nightly': 8182, // -10 from max
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command: 4086, // -10 from max
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'command-nightly': 8182, // -10 from max
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'command-r': 127500, // -500 from max
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'command-r-plus': 127500, // -500 from max
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};
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const googleModels = {
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/* Max I/O is combined so we subtract the amount from max response tokens for actual total */
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gemma: 8196,
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'gemma-2': 32768,
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'gemma-3': 32768,
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'gemma-3-27b': 131072,
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gemini: 30720, // -2048 from max
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'gemini-pro-vision': 12288,
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'gemini-exp': 2000000,
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'gemini-2.5': 1000000, // 1M input tokens, 64k output tokens
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'gemini-2.5-pro': 1000000,
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'gemini-2.5-flash': 1000000,
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'gemini-2.0': 2000000,
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'gemini-2.0-flash': 1000000,
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'gemini-2.0-flash-lite': 1000000,
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'gemini-1.5': 1000000,
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'gemini-1.5-flash': 1000000,
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'gemini-1.5-flash-8b': 1000000,
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'text-bison-32k': 32758, // -10 from max
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'chat-bison-32k': 32758, // -10 from max
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'code-bison-32k': 32758, // -10 from max
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'codechat-bison-32k': 32758,
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/* Codey, -5 from max: 6144 */
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'code-': 6139,
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'codechat-': 6139,
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/* PaLM2, -5 from max: 8192 */
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'text-': 8187,
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'chat-': 8187,
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};
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const anthropicModels = {
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'claude-': 100000,
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'claude-instant': 100000,
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'claude-2': 100000,
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'claude-2.1': 200000,
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'claude-3': 200000,
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'claude-3-haiku': 200000,
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'claude-3-sonnet': 200000,
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'claude-3-opus': 200000,
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'claude-3.5-haiku': 200000,
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'claude-3-5-haiku': 200000,
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'claude-3-5-sonnet': 200000,
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'claude-3.5-sonnet': 200000,
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'claude-3-7-sonnet': 200000,
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'claude-3.7-sonnet': 200000,
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'claude-3-5-sonnet-latest': 200000,
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'claude-3.5-sonnet-latest': 200000,
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'claude-sonnet-4': 1000000,
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'claude-opus-4': 200000,
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'claude-4': 200000,
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};
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const deepseekModels = {
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'deepseek-reasoner': 63000, // -1000 from max (API)
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deepseek: 63000, // -1000 from max (API)
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'deepseek.r1': 127500,
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};
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const metaModels = {
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// Basic patterns
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llama3: 8000,
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llama2: 4000,
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'llama-3': 8000,
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'llama-2': 4000,
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// llama3.x pattern
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'llama3.1': 127500,
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'llama3.2': 127500,
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'llama3.3': 127500,
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// llama3-x pattern
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'llama3-1': 127500,
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'llama3-2': 127500,
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'llama3-3': 127500,
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// llama-3.x pattern
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'llama-3.1': 127500,
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'llama-3.2': 127500,
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'llama-3.3': 127500,
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// llama3.x:Nb pattern
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'llama3.1:405b': 127500,
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'llama3.1:70b': 127500,
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'llama3.1:8b': 127500,
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'llama3.2:1b': 127500,
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'llama3.2:3b': 127500,
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'llama3.2:11b': 127500,
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'llama3.2:90b': 127500,
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'llama3.3:70b': 127500,
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// llama3-x-Nb pattern
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'llama3-1-405b': 127500,
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'llama3-1-70b': 127500,
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'llama3-1-8b': 127500,
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'llama3-2-1b': 127500,
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'llama3-2-3b': 127500,
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'llama3-2-11b': 127500,
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'llama3-2-90b': 127500,
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'llama3-3-70b': 127500,
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// llama-3.x-Nb pattern
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'llama-3.1-405b': 127500,
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'llama-3.1-70b': 127500,
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'llama-3.1-8b': 127500,
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'llama-3.2-1b': 127500,
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'llama-3.2-3b': 127500,
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'llama-3.2-11b': 127500,
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'llama-3.2-90b': 127500,
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'llama-3.3-70b': 127500,
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// Original llama2/3 patterns
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'llama3-70b': 8000,
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'llama3-8b': 8000,
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'llama2-70b': 4000,
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'llama2-13b': 4000,
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'llama3:70b': 8000,
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'llama3:8b': 8000,
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'llama2:70b': 4000,
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};
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const ollamaModels = {
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'qwen2.5': 32000,
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};
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const ai21Models = {
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'ai21.j2-mid-v1': 8182, // -10 from max
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'ai21.j2-ultra-v1': 8182, // -10 from max
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'ai21.jamba-instruct-v1:0': 255500, // -500 from max
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};
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const amazonModels = {
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'amazon.titan-text-lite-v1': 4000,
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'amazon.titan-text-express-v1': 8000,
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'amazon.titan-text-premier-v1:0': 31500, // -500 from max
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// https://aws.amazon.com/ai/generative-ai/nova/
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'amazon.nova-micro-v1:0': 127000, // -1000 from max,
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'amazon.nova-lite-v1:0': 295000, // -5000 from max,
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'amazon.nova-pro-v1:0': 295000, // -5000 from max,
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'amazon.nova-premier-v1:0': 995000, // -5000 from max,
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};
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const bedrockModels = {
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...anthropicModels,
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...mistralModels,
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...cohereModels,
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...ollamaModels,
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...deepseekModels,
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...metaModels,
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...ai21Models,
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...amazonModels,
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};
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const xAIModels = {
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grok: 131072,
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'grok-beta': 131072,
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'grok-vision-beta': 8192,
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'grok-2': 131072,
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'grok-2-latest': 131072,
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'grok-2-1212': 131072,
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'grok-2-vision': 32768,
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'grok-2-vision-latest': 32768,
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'grok-2-vision-1212': 32768,
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'grok-3': 131072,
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'grok-3-fast': 131072,
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'grok-3-mini': 131072,
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'grok-3-mini-fast': 131072,
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'grok-4': 256000, // 256K context
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};
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const aggregateModels = {
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...openAIModels,
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...googleModels,
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...bedrockModels,
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...xAIModels,
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// misc.
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kimi: 131000,
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// GPT-OSS
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'gpt-oss-20b': 131000,
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'gpt-oss-120b': 131000,
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};
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const maxTokensMap = {
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[EModelEndpoint.azureOpenAI]: openAIModels,
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[EModelEndpoint.openAI]: aggregateModels,
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[EModelEndpoint.agents]: aggregateModels,
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[EModelEndpoint.custom]: aggregateModels,
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[EModelEndpoint.google]: googleModels,
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[EModelEndpoint.anthropic]: anthropicModels,
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[EModelEndpoint.bedrock]: bedrockModels,
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};
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const modelMaxOutputs = {
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o1: 32268, // -500 from max: 32,768
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'o1-mini': 65136, // -500 from max: 65,536
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'o1-preview': 32268, // -500 from max: 32,768
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'gpt-5': 128000,
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'gpt-5-mini': 128000,
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'gpt-5-nano': 128000,
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'gpt-oss-20b': 131000,
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'gpt-oss-120b': 131000,
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system_default: 1024,
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};
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/** Outputs from https://docs.anthropic.com/en/docs/about-claude/models/all-models#model-names */
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const anthropicMaxOutputs = {
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'claude-3-haiku': 4096,
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'claude-3-sonnet': 4096,
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'claude-3-opus': 4096,
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'claude-opus-4': 32000,
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'claude-sonnet-4': 64000,
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'claude-3.5-sonnet': 8192,
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'claude-3-5-sonnet': 8192,
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'claude-3.7-sonnet': 128000,
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'claude-3-7-sonnet': 128000,
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};
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const maxOutputTokensMap = {
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[EModelEndpoint.anthropic]: anthropicMaxOutputs,
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[EModelEndpoint.azureOpenAI]: modelMaxOutputs,
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[EModelEndpoint.openAI]: modelMaxOutputs,
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[EModelEndpoint.custom]: modelMaxOutputs,
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};
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/**
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* Finds the first matching pattern in the tokens map.
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* @param {string} modelName
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* @param {Record<string, number>} tokensMap
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* @returns {string|null}
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*/
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function findMatchingPattern(modelName, tokensMap) {
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const keys = Object.keys(tokensMap);
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for (let i = keys.length - 1; i >= 0; i--) {
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const modelKey = keys[i];
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if (modelName.includes(modelKey)) {
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return modelKey;
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}
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}
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return null;
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}
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/**
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* Retrieves a token value for a given model name from a tokens map.
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*
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* @param {string} modelName - The name of the model to look up.
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* @param {EndpointTokenConfig | Record<string, number>} tokensMap - The map of model names to token values.
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* @param {string} [key='context'] - The key to look up in the tokens map.
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* @returns {number|undefined} The token value for the given model or undefined if no match is found.
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*/
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function getModelTokenValue(modelName, tokensMap, key = 'context') {
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if (typeof modelName !== 'string' || !tokensMap) {
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return undefined;
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}
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if (tokensMap[modelName]?.context) {
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return tokensMap[modelName].context;
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}
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if (tokensMap[modelName]) {
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return tokensMap[modelName];
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}
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const matchedPattern = findMatchingPattern(modelName, tokensMap);
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if (matchedPattern) {
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const result = tokensMap[matchedPattern];
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return result?.[key] ?? result ?? tokensMap.system_default;
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}
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return tokensMap.system_default;
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}
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/**
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* Retrieves the maximum tokens for a given model name.
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*
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* @param {string} modelName - The name of the model to look up.
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* @param {string} endpoint - The endpoint (default is 'openAI').
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* @param {EndpointTokenConfig} [endpointTokenConfig] - Token Config for current endpoint to use for max tokens lookup
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* @returns {number|undefined} The maximum tokens for the given model or undefined if no match is found.
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*/
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function getModelMaxTokens(modelName, endpoint = EModelEndpoint.openAI, endpointTokenConfig) {
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const tokensMap = endpointTokenConfig ?? maxTokensMap[endpoint];
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return getModelTokenValue(modelName, tokensMap);
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}
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/**
|
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* Retrieves the maximum output tokens for a given model name.
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*
|
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* @param {string} modelName - The name of the model to look up.
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* @param {string} endpoint - The endpoint (default is 'openAI').
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* @param {EndpointTokenConfig} [endpointTokenConfig] - Token Config for current endpoint to use for max tokens lookup
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* @returns {number|undefined} The maximum output tokens for the given model or undefined if no match is found.
|
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*/
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function getModelMaxOutputTokens(modelName, endpoint = EModelEndpoint.openAI, endpointTokenConfig) {
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const tokensMap = endpointTokenConfig ?? maxOutputTokensMap[endpoint];
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return getModelTokenValue(modelName, tokensMap, 'output');
|
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}
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|
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/**
|
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* Retrieves the model name key for a given model name input. If the exact model name isn't found,
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* it searches for partial matches within the model name, checking keys in reverse order.
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*
|
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* @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 = {
|
||||
inputSchema,
|
||||
modelSchema,
|
||||
maxTokensMap,
|
||||
tiktokenModels,
|
||||
maxOutputTokensMap,
|
||||
matchModelName,
|
||||
processModelData,
|
||||
getModelMaxTokens,
|
||||
getModelTokenValue,
|
||||
findMatchingPattern,
|
||||
getModelMaxOutputTokens,
|
||||
};
|
||||
|
|
@ -1,12 +1,12 @@
|
|||
const { EModelEndpoint } = require('librechat-data-provider');
|
||||
const {
|
||||
maxTokensMap,
|
||||
matchModelName,
|
||||
processModelData,
|
||||
getModelMaxTokens,
|
||||
maxOutputTokensMap,
|
||||
findMatchingPattern,
|
||||
getModelMaxTokens,
|
||||
processModelData,
|
||||
matchModelName,
|
||||
maxTokensMap,
|
||||
} = require('./tokens');
|
||||
} = require('@librechat/api');
|
||||
|
||||
describe('getModelMaxTokens', () => {
|
||||
test('should return correct tokens for exact match', () => {
|
||||
|
|
@ -394,7 +394,7 @@ describe('getModelMaxTokens', () => {
|
|||
});
|
||||
|
||||
test('should return correct max output tokens for GPT-5 models', () => {
|
||||
const { getModelMaxOutputTokens } = require('./tokens');
|
||||
const { getModelMaxOutputTokens } = require('@librechat/api');
|
||||
['gpt-5', 'gpt-5-mini', 'gpt-5-nano'].forEach((model) => {
|
||||
expect(getModelMaxOutputTokens(model)).toBe(maxOutputTokensMap[EModelEndpoint.openAI][model]);
|
||||
expect(getModelMaxOutputTokens(model, EModelEndpoint.openAI)).toBe(
|
||||
|
|
@ -407,7 +407,7 @@ describe('getModelMaxTokens', () => {
|
|||
});
|
||||
|
||||
test('should return correct max output tokens for GPT-OSS models', () => {
|
||||
const { getModelMaxOutputTokens } = require('./tokens');
|
||||
const { getModelMaxOutputTokens } = require('@librechat/api');
|
||||
['gpt-oss-20b', 'gpt-oss-120b'].forEach((model) => {
|
||||
expect(getModelMaxOutputTokens(model)).toBe(maxOutputTokensMap[EModelEndpoint.openAI][model]);
|
||||
expect(getModelMaxOutputTokens(model, EModelEndpoint.openAI)).toBe(
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue