278 lines
7.4 KiB
TypeScript
278 lines
7.4 KiB
TypeScript
"use client";
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import { ApiPath, Alibaba, ALIBABA_BASE_URL } from "@/app/constant";
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import {
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useAccessStore,
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useAppConfig,
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useChatStore,
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ChatMessageTool,
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usePluginStore,
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} from "@/app/store";
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import {
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preProcessImageContentForAlibabaDashScope,
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streamWithThink,
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} from "@/app/utils/chat";
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import {
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ChatOptions,
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getHeaders,
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LLMApi,
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LLMModel,
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SpeechOptions,
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MultimodalContent,
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MultimodalContentForAlibaba,
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} from "../api";
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import { getClientConfig } from "@/app/config/client";
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import {
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getMessageTextContent,
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getMessageTextContentWithoutThinking,
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getTimeoutMSByModel,
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isVisionModel,
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} from "@/app/utils";
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import { fetch } from "@/app/utils/stream";
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export interface OpenAIListModelResponse {
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object: string;
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data: Array<{
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id: string;
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object: string;
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root: string;
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}>;
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}
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interface RequestInput {
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messages: {
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role: "system" | "user" | "assistant";
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content: string | MultimodalContent[];
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}[];
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}
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interface RequestParam {
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result_format: string;
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incremental_output?: boolean;
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temperature: number;
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repetition_penalty?: number;
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top_p: number;
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max_tokens?: number;
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}
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interface RequestPayload {
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model: string;
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input: RequestInput;
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parameters: RequestParam;
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}
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export class QwenApi implements LLMApi {
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path(path: string): string {
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const accessStore = useAccessStore.getState();
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let baseUrl = "";
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if (accessStore.useCustomConfig) {
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baseUrl = accessStore.alibabaUrl;
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}
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if (baseUrl.length === 0) {
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const isApp = !!getClientConfig()?.isApp;
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baseUrl = isApp ? ALIBABA_BASE_URL : ApiPath.Alibaba;
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}
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if (baseUrl.endsWith("/")) {
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baseUrl = baseUrl.slice(0, baseUrl.length - 1);
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}
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if (!baseUrl.startsWith("http") && !baseUrl.startsWith(ApiPath.Alibaba)) {
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baseUrl = "https://" + baseUrl;
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}
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console.log("[Proxy Endpoint] ", baseUrl, path);
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return [baseUrl, path].join("/");
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}
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extractMessage(res: any) {
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return res?.output?.choices?.at(0)?.message?.content ?? "";
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}
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speech(options: SpeechOptions): Promise<ArrayBuffer> {
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throw new Error("Method not implemented.");
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}
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async chat(options: ChatOptions) {
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const modelConfig = {
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...useAppConfig.getState().modelConfig,
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...useChatStore.getState().currentSession().mask.modelConfig,
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...{
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model: options.config.model,
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},
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};
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const visionModel = isVisionModel(options.config.model);
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const messages: ChatOptions["messages"] = [];
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for (const v of options.messages) {
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const content = (
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visionModel
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? await preProcessImageContentForAlibabaDashScope(v.content)
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: v.role === "assistant"
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? getMessageTextContentWithoutThinking(v)
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: getMessageTextContent(v)
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) as any;
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messages.push({ role: v.role, content });
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}
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const shouldStream = !!options.config.stream;
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const requestPayload: RequestPayload = {
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model: modelConfig.model,
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input: {
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messages,
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},
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parameters: {
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result_format: "message",
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incremental_output: shouldStream,
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temperature: modelConfig.temperature,
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// max_tokens: modelConfig.max_tokens,
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top_p: modelConfig.top_p === 1 ? 0.99 : modelConfig.top_p, // qwen top_p is should be < 1
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},
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};
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const controller = new AbortController();
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options.onController?.(controller);
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try {
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const headers = {
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...getHeaders(),
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"X-DashScope-SSE": shouldStream ? "enable" : "disable",
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};
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const chatPath = this.path(Alibaba.ChatPath(modelConfig.model));
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const chatPayload = {
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method: "POST",
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body: JSON.stringify(requestPayload),
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signal: controller.signal,
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headers: headers,
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};
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// make a fetch request
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const requestTimeoutId = setTimeout(
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() => controller.abort(),
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getTimeoutMSByModel(options.config.model),
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);
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if (shouldStream) {
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const [tools, funcs] = usePluginStore
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.getState()
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.getAsTools(
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useChatStore.getState().currentSession().mask?.plugin || [],
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);
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return streamWithThink(
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chatPath,
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requestPayload,
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headers,
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tools as any,
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funcs,
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controller,
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// parseSSE
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(text: string, runTools: ChatMessageTool[]) => {
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// console.log("parseSSE", text, runTools);
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const json = JSON.parse(text);
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const choices = json.output.choices as Array<{
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message: {
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content: string | null | MultimodalContentForAlibaba[];
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tool_calls: ChatMessageTool[];
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reasoning_content: string | null;
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};
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}>;
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if (!choices?.length) return { isThinking: false, content: "" };
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const tool_calls = choices[0]?.message?.tool_calls;
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if (tool_calls?.length > 0) {
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const index = tool_calls[0]?.index;
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const id = tool_calls[0]?.id;
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const args = tool_calls[0]?.function?.arguments;
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if (id) {
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runTools.push({
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id,
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type: tool_calls[0]?.type,
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function: {
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name: tool_calls[0]?.function?.name as string,
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arguments: args,
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},
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});
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} else {
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// @ts-ignore
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runTools[index]["function"]["arguments"] += args;
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}
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}
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const reasoning = choices[0]?.message?.reasoning_content;
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const content = choices[0]?.message?.content;
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// Skip if both content and reasoning_content are empty or null
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if (
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(!reasoning || reasoning.length === 0) &&
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(!content || content.length === 0)
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) {
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return {
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isThinking: false,
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content: "",
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};
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}
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if (reasoning && reasoning.length > 0) {
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return {
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isThinking: true,
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content: reasoning,
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};
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} else if (content && content.length > 0) {
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return {
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isThinking: false,
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content: Array.isArray(content)
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? content.map((item) => item.text).join(",")
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: content,
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};
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}
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return {
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isThinking: false,
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content: "",
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};
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},
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// processToolMessage, include tool_calls message and tool call results
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(
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requestPayload: RequestPayload,
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toolCallMessage: any,
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toolCallResult: any[],
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) => {
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requestPayload?.input?.messages?.splice(
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requestPayload?.input?.messages?.length,
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0,
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toolCallMessage,
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...toolCallResult,
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);
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},
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options,
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);
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} else {
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const res = await fetch(chatPath, chatPayload);
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clearTimeout(requestTimeoutId);
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const resJson = await res.json();
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const message = this.extractMessage(resJson);
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options.onFinish(message, res);
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}
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} catch (e) {
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console.log("[Request] failed to make a chat request", e);
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options.onError?.(e as Error);
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}
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}
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async usage() {
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return {
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used: 0,
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total: 0,
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};
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}
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async models(): Promise<LLMModel[]> {
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return [];
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}
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}
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export { Alibaba };
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