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Add complete MiniMax provider support
1 parent c83ecbd commit aa5f3be

8 files changed

Lines changed: 861 additions & 3 deletions

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‎src/LLMProviders/index.ts‎

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@@ -14,6 +14,9 @@ import LangchainChatGoogleGenerativeAIProvider from "./langchain/googleGenerativ
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import LangchainOpenAIAgentProvider from "./langchain/openaiAgent";
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import LangchainPerplexityChatProvider from "./langchain/perplexityChat";
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import LangchainTogetherChatProvider from "./langchain/togetherChat";
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import LangchainMiniMaxOpenAIProvider, {
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LangchainMiniMaxAnthropicProvider,
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} from "./langchain/minimaxChat";
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// import LangchainReplicaProvider from "./langchain/replica"
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// import { LOCClone1, LOCClone2 } from "./langchain/clones";
@@ -46,6 +49,10 @@ export const defaultProviders = [
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// together ai
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LangchainTogetherChatProvider,
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// minimax
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LangchainMiniMaxOpenAIProvider,
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LangchainMiniMaxAnthropicProvider,
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// azure
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LangchainAzureOpenAIChatProvider,
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LangchainAzureOpenAIInstructProvider,

‎src/LLMProviders/interface.ts‎

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@@ -87,4 +87,10 @@ export interface LLMConfig {
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reasoningEffort?: "low" | "medium" | "high";
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/** Manual override: treat this model as a thinking/reasoning model regardless of auto-detection */
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isThinkingModel?: boolean;
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/** Thinking mode for models that support adaptive and disabled modes */
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thinkingMode?: "adaptive" | "disabled";
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/** Request admission tier for providers that support tier selection */
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serviceTier?: "standard" | "priority";
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/** Official API region selected in provider settings */
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apiRegion?: "Global" | "China";
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}

‎src/LLMProviders/langchain/minimaxChat.tsx‎

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@@ -0,0 +1,357 @@
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import React from "react";
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import type { BaseLanguageModelParams } from "@langchain/core/language_models/base";
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import type { AnthropicInput } from "@langchain/anthropic";
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import type { OpenAIChatInput } from "@langchain/openai";
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import { IconExternalLink } from "#/ui/icons";
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import LangchainBase from "./base";
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import LLMProviderInterface, { LLMConfig } from "../interface";
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import { HeaderEditor, ModelsHandler } from "../utils";
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import { Dropdown, Input, Message, SettingItem, useGlobal } from "../refs";
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import {
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calculateMiniMaxPrice,
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getMiniMaxEndpoint,
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getMiniMaxModelKwargs,
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MINIMAX_DOCS,
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MINIMAX_MODELS,
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MiniMaxProtocol,
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MiniMaxRegion,
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MiniMaxServiceTier,
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MiniMaxThinkingMode,
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normalizeMiniMaxMessages,
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wrapMiniMaxAnthropicFetch,
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} from "./minimaxConfig";
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const commonDefaultValues = {
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apiRegion: "Global" as MiniMaxRegion,
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model: MINIMAX_MODELS.m3,
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thinkingMode: "adaptive" as const,
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serviceTier: "standard" as const,
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};
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const openAIDefaultValues = {
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...commonDefaultValues,
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basePath: getMiniMaxEndpoint("Global", "openai"),
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};
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const anthropicDefaultValues = {
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...commonDefaultValues,
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basePath: getMiniMaxEndpoint("Global", "anthropic"),
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};
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type MiniMaxDefaultValues = {
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apiRegion: MiniMaxRegion;
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model: string;
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thinkingMode: MiniMaxThinkingMode;
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serviceTier: MiniMaxServiceTier;
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basePath: string;
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};
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function MiniMaxSettings({
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props,
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protocol,
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defaultValues,
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}: {
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props: Parameters<LLMProviderInterface["RenderSettings"]>[0];
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protocol: MiniMaxProtocol;
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defaultValues: MiniMaxDefaultValues;
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}) {
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const global = useGlobal();
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const id = props.self.id;
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const config = (global.plugin.settings.LLMProviderOptions[id] ??= {
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...defaultValues,
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});
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const isM3 = (config.model || defaultValues.model) === MINIMAX_MODELS.m3;
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return (
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<>
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<SettingItem
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name="API Key"
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register={props.register}
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sectionId={props.sectionId}
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>
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<Input
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type="password"
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value={config.api_key || ""}
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setValue={async (value) => {
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config.api_key = value;
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global.triggerReload();
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global.plugin.encryptAllKeys();
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await global.plugin.saveSettings();
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}}
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/>
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</SettingItem>
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<SettingItem
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name="API Region"
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register={props.register}
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sectionId={props.sectionId}
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>
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<Dropdown
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value={config.apiRegion || defaultValues.apiRegion}
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values={["Global", "China"]}
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setValue={async (value) => {
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const region = value as MiniMaxRegion;
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config.apiRegion = region;
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config.basePath = getMiniMaxEndpoint(region, protocol);
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global.triggerReload();
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await global.plugin.saveSettings();
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}}
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/>
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</SettingItem>
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<SettingItem
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name="Base Path"
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description={`${
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protocol === "openai" ? "OpenAI" : "Anthropic"
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}-compatible API endpoint`}
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register={props.register}
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sectionId={props.sectionId}
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>
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<Input
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value={config.basePath || defaultValues.basePath}
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placeholder="Enter your API Base Path"
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setValue={async (value) => {
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config.basePath = value || defaultValues.basePath;
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global.triggerReload();
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await global.plugin.saveSettings();
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}}
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/>
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</SettingItem>
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<ModelsHandler
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register={props.register}
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sectionId={props.sectionId}
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llmProviderId={props.self.originalId || id}
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default_values={defaultValues}
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config={config}
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/>
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{isM3 && (
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<>
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<SettingItem
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name="Thinking Mode"
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register={props.register}
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sectionId={props.sectionId}
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>
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<Dropdown
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value={config.thinkingMode || defaultValues.thinkingMode}
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values={["adaptive", "disabled"]}
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setValue={async (value) => {
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config.thinkingMode = value;
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global.triggerReload();
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await global.plugin.saveSettings();
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}}
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/>
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</SettingItem>
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<SettingItem
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name="Service Tier"
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register={props.register}
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sectionId={props.sectionId}
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>
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<Dropdown
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value={config.serviceTier || defaultValues.serviceTier}
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values={["standard", "priority"]}
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setValue={async (value) => {
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config.serviceTier = value;
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global.triggerReload();
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await global.plugin.saveSettings();
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}}
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/>
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</SettingItem>
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</>
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)}
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<HeaderEditor
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enabled={!!config.headers}
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setEnabled={async (value) => {
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config.headers = value ? "{}" : undefined;
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global.triggerReload();
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await global.plugin.saveSettings();
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}}
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headers={config.headers}
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setHeaders={async (value) => {
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config.headers = value;
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global.triggerReload();
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await global.plugin.saveSettings();
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}}
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/>
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<div className="plug-tg-flex plug-tg-flex-col plug-tg-gap-2">
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<div className="plug-tg-text-lg plug-tg-opacity-70">Useful links</div>
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{(["Global", "China"] as MiniMaxRegion[]).map((region) => (
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<a key={region} href={MINIMAX_DOCS[region][protocol]}>
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<SettingItem
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name={`${region} API documentation`}
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className="plug-tg-text-xs plug-tg-opacity-50 hover:plug-tg-opacity-100"
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register={props.register}
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sectionId={props.sectionId}
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>
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<IconExternalLink />
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</SettingItem>
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</a>
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))}
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</div>
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</>
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);
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}
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abstract class LangchainMiniMaxBaseProvider extends LangchainBase {
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abstract protocol: MiniMaxProtocol;
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abstract default_values: MiniMaxDefaultValues;
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abstract getConfig(options: LLMConfig): any;
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RenderSettings(props: Parameters<LLMProviderInterface["RenderSettings"]>[0]) {
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return (
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<MiniMaxSettings
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props={props}
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protocol={this.protocol}
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defaultValues={this.default_values}
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/>
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);
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}
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generate(
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messages: Message[],
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reqParams: Partial<Omit<LLMConfig, "n">>,
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onToken?: (
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token: string,
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first: boolean
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) => Promise<string | void | null | undefined>,
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customConfig?: any
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) {
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return super.generate(
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normalizeMiniMaxMessages(messages, this.protocol),
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reqParams,
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onToken,
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customConfig
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);
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}
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generateMultiple(messages: Message[], reqParams: Partial<LLMConfig>) {
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return super.generateMultiple(
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normalizeMiniMaxMessages(messages, this.protocol),
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reqParams
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);
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}
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async calcPrice(tokens: number, reqParams: Partial<LLMConfig>) {
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return calculateMiniMaxPrice(
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tokens,
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reqParams.max_tokens || 100,
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reqParams.model || MINIMAX_MODELS.m3,
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reqParams.serviceTier || "standard"
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);
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}
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}
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export default class LangchainMiniMaxOpenAIProvider
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extends LangchainMiniMaxBaseProvider
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implements LLMProviderInterface
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{
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static provider = "Langchain" as const;
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static id = "MiniMax OpenAI (Langchain)" as const;
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static slug = "minimaxOpenAI" as const;
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static displayName = "MiniMax OpenAI";
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protocol = "openai" as const;
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streamable = true;
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id = LangchainMiniMaxOpenAIProvider.id;
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provider = LangchainMiniMaxOpenAIProvider.provider;
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originalId = LangchainMiniMaxOpenAIProvider.id;
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default_values = openAIDefaultValues;
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async load() {
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const { ChatOpenAI } = await import("@langchain/openai");
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this.llmClass = ChatOpenAI;
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}
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getConfig(options: LLMConfig): Partial<OpenAIChatInput> {
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return this.cleanConfig({
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apiKey: options.api_key,
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openAIApiKey: options.api_key,
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modelKwargs: getMiniMaxModelKwargs(
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options.model,
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options.thinkingMode,
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options.serviceTier,
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options.modelKwargs
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),
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modelName: options.model,
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maxTokens: +options.max_tokens,
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temperature: +options.temperature,
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frequencyPenalty: +options.frequency_penalty || 0,
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presencePenalty: +options.presence_penalty || 0,
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n: options.n || 1,
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stop: options.stop || undefined,
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streaming: options.stream || false,
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maxRetries: 3,
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headers: options.headers || undefined,
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} as Partial<OpenAIChatInput>);
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}
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}
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export class LangchainMiniMaxAnthropicProvider
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extends LangchainMiniMaxBaseProvider
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implements LLMProviderInterface
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{
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static provider = "Langchain" as const;
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static id = "MiniMax Anthropic (Langchain)" as const;
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static slug = "minimaxAnthropic" as const;
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static displayName = "MiniMax Anthropic";
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protocol = "anthropic" as const;
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streamable = true;
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corsBypass = true;
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id = LangchainMiniMaxAnthropicProvider.id;
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provider = LangchainMiniMaxAnthropicProvider.provider;
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originalId = LangchainMiniMaxAnthropicProvider.id;
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default_values = anthropicDefaultValues;
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async load() {
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const { ChatAnthropic } = await import("@langchain/anthropic");
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this.llmClass = class MiniMaxChatAnthropic extends ChatAnthropic {
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constructor(fields: any) {
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const { configuration = {}, ...rest } = fields;
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const fetchImpl = configuration.fetch || globalThis.fetch;
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super({
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...rest,
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clientOptions: {
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...rest.clientOptions,
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...configuration,
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fetch: wrapMiniMaxAnthropicFetch(fetchImpl),
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},
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});
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}
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};
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}
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getConfig(
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options: LLMConfig
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): Partial<AnthropicInput & BaseLanguageModelParams> {
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const modelKwargs = getMiniMaxModelKwargs(
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options.model,
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options.thinkingMode,
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options.serviceTier,
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options.modelKwargs
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);
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const { thinking, service_tier, ...invocationKwargs } = modelKwargs as any;
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return this.cleanConfig({
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anthropicApiKey: options.api_key,
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anthropicApiUrl: options.basePath,
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modelName: options.model,
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maxTokens: options.max_tokens,
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temperature:
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thinking?.type === "adaptive" ? undefined : options.temperature,
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stopSequences: options.stop,
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streaming: options.stream,
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maxRetries: 3,
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thinking,
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invocationKwargs: {
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...invocationKwargs,
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...(service_tier ? { service_tier } : {}),
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},
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} as any);
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}
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}

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