464 lines
13 KiB
TypeScript
464 lines
13 KiB
TypeScript
"use client";
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// azure and openai, using same models. so using same LLMApi.
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import {
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ApiPath,
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DEFAULT_API_HOST,
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DEFAULT_MODELS,
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OpenaiPath,
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Azure,
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REQUEST_TIMEOUT_MS,
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ServiceProvider,
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} from "@/app/constant";
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import {
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ChatMessageTool,
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useAccessStore,
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useAppConfig,
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useChatStore,
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usePluginStore,
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} from "@/app/store";
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import { collectModelsWithDefaultModel } from "@/app/utils/model";
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import {
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preProcessImageContent,
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uploadImage,
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base64Image2Blob,
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stream,
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} from "@/app/utils/chat";
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import { cloudflareAIGatewayUrl } from "@/app/utils/cloudflare";
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import { DalleSize, DalleQuality, DalleStyle } from "@/app/typing";
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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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LLMUsage,
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MultimodalContent,
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} from "../api";
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import Locale from "../../locales";
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import {
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EventStreamContentType,
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fetchEventSource,
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} from "@fortaine/fetch-event-source";
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import { prettyObject } from "@/app/utils/format";
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import { getClientConfig } from "@/app/config/client";
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import {
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getMessageTextContent,
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getMessageImages,
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isVisionModel,
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isDalle3 as _isDalle3,
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} from "@/app/utils";
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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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export interface RequestPayload {
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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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stream?: boolean;
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model: string;
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temperature: number;
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presence_penalty: number;
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frequency_penalty: number;
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top_p: number;
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max_tokens?: number;
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}
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export interface DalleRequestPayload {
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model: string;
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prompt: string;
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response_format: "url" | "b64_json";
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n: number;
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size: DalleSize;
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quality: DalleQuality;
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style: DalleStyle;
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}
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export class ChatGPTApi implements LLMApi {
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private disableListModels = true;
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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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const isAzure = path.includes("deployments");
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if (accessStore.useCustomConfig) {
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if (isAzure && !accessStore.isValidAzure()) {
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throw Error(
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"incomplete azure config, please check it in your settings page",
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);
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}
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baseUrl = isAzure ? accessStore.azureUrl : accessStore.openaiUrl;
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}
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if (baseUrl.length === 0) {
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const isApp = !!getClientConfig()?.isApp;
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const apiPath = isAzure ? ApiPath.Azure : ApiPath.OpenAI;
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baseUrl = isApp ? DEFAULT_API_HOST + "/proxy" + apiPath : apiPath;
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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 (
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!baseUrl.startsWith("http") &&
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!isAzure &&
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!baseUrl.startsWith(ApiPath.OpenAI)
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) {
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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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// try rebuild url, when using cloudflare ai gateway in client
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return cloudflareAIGatewayUrl([baseUrl, path].join("/"));
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}
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async extractMessage(res: any) {
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if (res.error) {
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return "```\n" + JSON.stringify(res, null, 4) + "\n```";
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}
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// dalle3 model return url, using url create image message
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if (res.data) {
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let url = res.data?.at(0)?.url ?? "";
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const b64_json = res.data?.at(0)?.b64_json ?? "";
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if (!url && b64_json) {
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// uploadImage
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url = await uploadImage(base64Image2Blob(b64_json, "image/png"));
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}
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return [
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{
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type: "image_url",
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image_url: {
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url,
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},
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},
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];
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}
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return res.choices?.at(0)?.message?.content ?? res;
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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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providerName: options.config.providerName,
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},
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};
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let requestPayload: RequestPayload | DalleRequestPayload;
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const isDalle3 = _isDalle3(options.config.model);
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if (isDalle3) {
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const prompt = getMessageTextContent(
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options.messages.slice(-1)?.pop() as any,
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);
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requestPayload = {
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model: options.config.model,
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prompt,
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// URLs are only valid for 60 minutes after the image has been generated.
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response_format: "b64_json", // using b64_json, and save image in CacheStorage
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n: 1,
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size: options.config?.size ?? "1024x1024",
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quality: options.config?.quality ?? "standard",
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style: options.config?.style ?? "vivid",
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};
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} else {
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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 = visionModel
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? await preProcessImageContent(v.content)
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: getMessageTextContent(v);
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messages.push({ role: v.role, content });
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}
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requestPayload = {
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messages,
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stream: options.config.stream,
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model: modelConfig.model,
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temperature: modelConfig.temperature,
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presence_penalty: modelConfig.presence_penalty,
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frequency_penalty: modelConfig.frequency_penalty,
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top_p: modelConfig.top_p,
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// max_tokens: Math.max(modelConfig.max_tokens, 1024),
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// Please do not ask me why not send max_tokens, no reason, this param is just shit, I dont want to explain anymore.
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};
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// add max_tokens to vision model
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if (visionModel && modelConfig.model.includes("preview")) {
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requestPayload["max_tokens"] = Math.max(modelConfig.max_tokens, 4000);
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}
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}
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console.log("[Request] openai payload: ", requestPayload);
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const shouldStream = !isDalle3 && !!options.config.stream;
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const controller = new AbortController();
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options.onController?.(controller);
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try {
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let chatPath = "";
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if (modelConfig.providerName === ServiceProvider.Azure) {
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// find model, and get displayName as deployName
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const { models: configModels, customModels: configCustomModels } =
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useAppConfig.getState();
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const {
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defaultModel,
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customModels: accessCustomModels,
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useCustomConfig,
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} = useAccessStore.getState();
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const models = collectModelsWithDefaultModel(
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configModels,
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[configCustomModels, accessCustomModels].join(","),
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defaultModel,
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);
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const model = models.find(
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(model) =>
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model.name === modelConfig.model &&
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model?.provider?.providerName === ServiceProvider.Azure,
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);
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chatPath = this.path(
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(isDalle3 ? Azure.ImagePath : Azure.ChatPath)(
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(model?.displayName ?? model?.name) as string,
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useCustomConfig ? useAccessStore.getState().azureApiVersion : "",
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),
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);
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} else {
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chatPath = this.path(
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isDalle3 ? OpenaiPath.ImagePath : OpenaiPath.ChatPath,
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);
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}
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if (shouldStream) {
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const [tools1, funcs2] = usePluginStore
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.getState()
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.getAsTools(useChatStore.getState().currentSession().mask?.plugin);
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console.log("getAsTools", tools1, funcs2);
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// return
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// TODO mock tools and funcs
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const tools = [
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{
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type: "function",
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function: {
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name: "get_current_weather",
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description: "Get the current weather",
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parameters: {
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type: "object",
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properties: {
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location: {
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type: "string",
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description: "The city and country, eg. San Francisco, USA",
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},
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format: {
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type: "string",
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enum: ["celsius", "fahrenheit"],
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},
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},
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required: ["location", "format"],
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},
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},
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},
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];
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const funcs = {
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get_current_weather: (args: any) => {
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console.log("call get_current_weather", args);
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return new Promise((resolve) => {
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setTimeout(() => resolve("30"), 3000);
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});
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},
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};
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stream(
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chatPath,
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requestPayload,
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getHeaders(),
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tools1,
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funcs2,
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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.choices as Array<{
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delta: {
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content: string;
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tool_calls: ChatMessageTool[];
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};
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}>;
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const tool_calls = choices[0]?.delta?.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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return choices[0]?.delta?.content;
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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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// @ts-ignore
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requestPayload?.messages?.splice(
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// @ts-ignore
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requestPayload?.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 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: getHeaders(),
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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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isDalle3 ? REQUEST_TIMEOUT_MS * 2 : REQUEST_TIMEOUT_MS, // dalle3 using b64_json is slow.
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);
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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 = await this.extractMessage(resJson);
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options.onFinish(message);
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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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const formatDate = (d: Date) =>
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`${d.getFullYear()}-${(d.getMonth() + 1).toString().padStart(2, "0")}-${d
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.getDate()
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.toString()
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.padStart(2, "0")}`;
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const ONE_DAY = 1 * 24 * 60 * 60 * 1000;
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const now = new Date();
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const startOfMonth = new Date(now.getFullYear(), now.getMonth(), 1);
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const startDate = formatDate(startOfMonth);
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const endDate = formatDate(new Date(Date.now() + ONE_DAY));
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const [used, subs] = await Promise.all([
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fetch(
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this.path(
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`${OpenaiPath.UsagePath}?start_date=${startDate}&end_date=${endDate}`,
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),
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{
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method: "GET",
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headers: getHeaders(),
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},
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),
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fetch(this.path(OpenaiPath.SubsPath), {
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method: "GET",
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headers: getHeaders(),
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}),
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]);
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if (used.status === 401) {
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throw new Error(Locale.Error.Unauthorized);
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}
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if (!used.ok || !subs.ok) {
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throw new Error("Failed to query usage from openai");
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}
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const response = (await used.json()) as {
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total_usage?: number;
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error?: {
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type: string;
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message: string;
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};
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};
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const total = (await subs.json()) as {
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hard_limit_usd?: number;
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};
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if (response.error && response.error.type) {
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throw Error(response.error.message);
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}
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if (response.total_usage) {
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response.total_usage = Math.round(response.total_usage) / 100;
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}
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if (total.hard_limit_usd) {
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total.hard_limit_usd = Math.round(total.hard_limit_usd * 100) / 100;
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}
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return {
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used: response.total_usage,
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total: total.hard_limit_usd,
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} as LLMUsage;
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}
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async models(): Promise<LLMModel[]> {
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if (this.disableListModels) {
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return DEFAULT_MODELS.slice();
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}
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const res = await fetch(this.path(OpenaiPath.ListModelPath), {
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method: "GET",
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headers: {
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...getHeaders(),
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},
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});
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const resJson = (await res.json()) as OpenAIListModelResponse;
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const chatModels = resJson.data?.filter((m) => m.id.startsWith("gpt-"));
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console.log("[Models]", chatModels);
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if (!chatModels) {
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return [];
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}
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//由于目前 OpenAI 的 disableListModels 默认为 true，所以当前实际不会运行到这场
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let seq = 1000; //同 Constant.ts 中的排序保持一致
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return chatModels.map((m) => ({
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name: m.id,
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available: true,
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sorted: seq++,
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provider: {
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id: "openai",
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providerName: "OpenAI",
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providerType: "openai",
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sorted: 1,
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},
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}));
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}
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}
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export { OpenaiPath };
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