mirror of
https://github.com/Yidadaa/ChatGPT-Next-Web.git
synced 2025-08-09 05:34:01 +08:00
feat: add multi-model support
This commit is contained in:
Yidadaa
@@ -1,151 +0,0 @@
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import { getClientConfig } from "../config/client";
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import { ACCESS_CODE_PREFIX } from "../constant";
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import { ChatMessage, ModelType, useAccessStore } from "../store";
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import { ChatGPTApi } from "./platforms/openai";
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export const ROLES = ["system", "user", "assistant"] as const;
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export type MessageRole = (typeof ROLES)[number];
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export const Models = ["gpt-3.5-turbo", "gpt-4"] as const;
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export type ChatModel = ModelType;
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export interface RequestMessage {
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role: MessageRole;
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content: string;
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}
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export interface LLMConfig {
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model: string;
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temperature?: number;
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top_p?: number;
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stream?: boolean;
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presence_penalty?: number;
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frequency_penalty?: number;
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}
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export interface ChatOptions {
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messages: RequestMessage[];
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config: LLMConfig;
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onUpdate?: (message: string, chunk: string) => void;
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onFinish: (message: string) => void;
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onError?: (err: Error) => void;
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onController?: (controller: AbortController) => void;
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}
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export interface LLMUsage {
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used: number;
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total: number;
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}
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export interface LLMModel {
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name: string;
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available: boolean;
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}
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export abstract class LLMApi {
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abstract chat(options: ChatOptions): Promise<void>;
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abstract usage(): Promise<LLMUsage>;
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abstract models(): Promise<LLMModel[]>;
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}
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type ProviderName = "openai" | "azure" | "claude" | "palm";
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interface Model {
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name: string;
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provider: ProviderName;
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ctxlen: number;
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}
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interface ChatProvider {
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name: ProviderName;
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apiConfig: {
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baseUrl: string;
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apiKey: string;
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summaryModel: Model;
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};
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models: Model[];
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chat: () => void;
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usage: () => void;
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}
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export class ClientApi {
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public llm: LLMApi;
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constructor() {
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this.llm = new ChatGPTApi();
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}
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config() {}
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prompts() {}
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masks() {}
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async share(messages: ChatMessage[], avatarUrl: string | null = null) {
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const msgs = messages
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.map((m) => ({
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from: m.role === "user" ? "human" : "gpt",
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value: m.content,
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}))
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.concat([
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{
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from: "human",
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value:
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"Share from [ChatGPT Next Web]: https://github.com/Yidadaa/ChatGPT-Next-Web",
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},
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]);
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// 敬告二开开发者们，为了开源大模型的发展，请不要修改上述消息，此消息用于后续数据清洗使用
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// Please do not modify this message
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console.log("[Share]", messages, msgs);
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const clientConfig = getClientConfig();
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const proxyUrl = "/sharegpt";
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const rawUrl = "https://sharegpt.com/api/conversations";
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const shareUrl = clientConfig?.isApp ? rawUrl : proxyUrl;
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const res = await fetch(shareUrl, {
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body: JSON.stringify({
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avatarUrl,
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items: msgs,
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}),
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headers: {
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"Content-Type": "application/json",
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},
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method: "POST",
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});
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const resJson = await res.json();
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console.log("[Share]", resJson);
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if (resJson.id) {
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return `https://shareg.pt/${resJson.id}`;
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}
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}
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}
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export const api = new ClientApi();
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export function getHeaders() {
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const accessStore = useAccessStore.getState();
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let headers: Record<string, string> = {
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"Content-Type": "application/json",
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"x-requested-with": "XMLHttpRequest",
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};
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const makeBearer = (token: string) => `Bearer ${token.trim()}`;
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const validString = (x: string) => x && x.length > 0;
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// use user's api key first
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if (validString(accessStore.token)) {
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headers.Authorization = makeBearer(accessStore.token);
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} else if (
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accessStore.enabledAccessControl() &&
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validString(accessStore.accessCode)
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) {
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headers.Authorization = makeBearer(
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ACCESS_CODE_PREFIX + accessStore.accessCode,
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);
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}
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return headers;
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}
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28
app/client/common/auth.ts
Normal file
28
app/client/common/auth.ts
Normal file
@@ -0,0 +1,28 @@
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import { getClientConfig } from "@/app/config/client";
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import { ACCESS_CODE_PREFIX } from "@/app/constant";
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import { useAccessStore } from "@/app/store";
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export function bearer(value: string) {
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return `Bearer ${value.trim()}`;
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}
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export function getAuthHeaders(apiKey = "") {
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const accessStore = useAccessStore.getState();
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const isApp = !!getClientConfig()?.isApp;
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let headers: Record<string, string> = {};
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if (apiKey) {
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// use user's api key first
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headers.Authorization = bearer(apiKey);
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} else if (
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accessStore.enabledAccessControl() &&
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!isApp &&
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!!accessStore.accessCode
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) {
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// or use access code
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headers.Authorization = bearer(ACCESS_CODE_PREFIX + accessStore.accessCode);
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}
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return headers;
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}
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5
app/client/common/config.ts
Normal file
5
app/client/common/config.ts
Normal file
@@ -0,0 +1,5 @@
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export const COMMON_PROVIDER_CONFIG = {
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customModels: "",
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models: [] as string[],
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autoFetchModels: false, // fetch available models from server or not
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};
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44
app/client/common/share.ts
Normal file
44
app/client/common/share.ts
Normal file
@@ -0,0 +1,44 @@
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import { getClientConfig } from "@/app/config/client";
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import { ChatMessage } from "@/app/store";
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export async function shareToShareGPT(
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messages: ChatMessage[],
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avatarUrl: string | null = null,
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) {
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const msgs = messages
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.map((m) => ({
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from: m.role === "user" ? "human" : "gpt",
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value: m.content,
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}))
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.concat([
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{
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from: "human",
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// 敬告二开开发者们，为了开源大模型的发展，请不要修改上述消息，此消息用于后续数据清洗使用
|
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// Please do not modify this message
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value:
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"Share from [ChatGPT Next Web]: https://github.com/Yidadaa/ChatGPT-Next-Web",
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},
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]);
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console.log("[Share]", messages, msgs);
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const clientConfig = getClientConfig();
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const proxyUrl = "/sharegpt";
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const rawUrl = "https://sharegpt.com/api/conversations";
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const shareUrl = clientConfig?.isApp ? rawUrl : proxyUrl;
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const res = await fetch(shareUrl, {
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body: JSON.stringify({
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avatarUrl,
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items: msgs,
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}),
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headers: {
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"Content-Type": "application/json",
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},
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method: "POST",
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});
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const resJson = await res.json();
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console.log("[Share]", resJson);
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if (resJson.id) {
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return `https://shareg.pt/${resJson.id}`;
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}
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}
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28
app/client/core.ts
Normal file
28
app/client/core.ts
Normal file
@@ -0,0 +1,28 @@
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import { MaskConfig, ProviderConfig } from "../store";
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import { shareToShareGPT } from "./common/share";
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import { createOpenAiClient } from "./openai";
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import { ChatControllerPool } from "./common/controller";
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export const LLMClients = {
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openai: createOpenAiClient,
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};
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export function createLLMClient(
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config: ProviderConfig,
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maskConfig: MaskConfig,
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) {
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return LLMClients[maskConfig.provider as any as keyof typeof LLMClients](
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config,
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maskConfig.modelConfig,
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);
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}
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export function createApi() {
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return {
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createLLMClient,
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shareToShareGPT,
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controllerManager: ChatControllerPool,
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};
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}
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export const api = createApi();
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2
app/client/index.ts
Normal file
2
app/client/index.ts
Normal file
@@ -0,0 +1,2 @@
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export * from "./types";
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export * from "./core";
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20
app/client/openai/config.ts
Normal file
20
app/client/openai/config.ts
Normal file
@@ -0,0 +1,20 @@
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import { COMMON_PROVIDER_CONFIG } from "../common/config";
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export const OpenAIConfig = {
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model: {
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model: "gpt-3.5-turbo" as string,
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summarizeModel: "gpt-3.5-turbo",
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temperature: 0.5,
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top_p: 1,
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max_tokens: 2000,
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presence_penalty: 0,
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frequency_penalty: 0,
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},
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provider: {
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name: "OpenAI",
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endpoint: "https://api.openai.com",
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apiKey: "",
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...COMMON_PROVIDER_CONFIG,
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},
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};
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295
app/client/openai/index.ts
Normal file
295
app/client/openai/index.ts
Normal file
@@ -0,0 +1,295 @@
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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 {
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API_PREFIX,
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ApiPath,
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DEFAULT_MODELS,
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OpenaiPath,
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} from "@/app/constant";
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import { ModelConfig, ProviderConfig } from "@/app/store";
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import { OpenAI } from "./types";
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import { ChatOptions, LLMModel, LLMUsage } from "../types";
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import Locale from "@/app/locales";
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import { prettyObject } from "@/app/utils/format";
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import { getApiPath } from "@/app/utils/path";
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import { trimEnd } from "@/app/utils/string";
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import { omit } from "@/app/utils/object";
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import { createLogger } from "@/app/utils/log";
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import { getAuthHeaders } from "../common/auth";
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export function createOpenAiClient(
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providerConfigs: ProviderConfig,
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modelConfig: ModelConfig,
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) {
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const openaiConfig = { ...providerConfigs.openai };
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const logger = createLogger("[OpenAI Client]");
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const openaiModelConfig = { ...modelConfig.openai };
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return {
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headers() {
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return {
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"Content-Type": "application/json",
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...getAuthHeaders(openaiConfig.apiKey),
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};
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},
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path(path: OpenaiPath): string {
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let baseUrl = openaiConfig.endpoint;
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// if endpoint is empty, use default endpoint
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if (baseUrl.trim().length === 0) {
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baseUrl = getApiPath(ApiPath.OpenAI);
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}
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if (!baseUrl.startsWith("http") && !baseUrl.startsWith(API_PREFIX)) {
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baseUrl = "https://" + baseUrl;
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}
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|
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baseUrl = trimEnd(baseUrl, "/");
|
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|
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return `${baseUrl}/${path}`;
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},
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extractMessage(res: OpenAI.ChatCompletionResponse) {
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return res.choices[0]?.message?.content ?? "";
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},
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beforeRequest(options: ChatOptions, stream = false) {
|
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const messages = options.messages.map((v) => ({
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role: v.role,
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||||
content: v.content,
|
||||
}));
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||||
|
||||
if (options.shouldSummarize) {
|
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openaiModelConfig.model = openaiModelConfig.summarizeModel;
|
||||
}
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const requestBody: OpenAI.ChatCompletionRequest = {
|
||||
messages,
|
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stream,
|
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...omit(openaiModelConfig, "summarizeModel"),
|
||||
};
|
||||
|
||||
const path = this.path(OpenaiPath.Chat);
|
||||
|
||||
logger.log("path = ", path, requestBody);
|
||||
|
||||
const controller = new AbortController();
|
||||
options.onController?.(controller);
|
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|
||||
const payload = {
|
||||
method: "POST",
|
||||
body: JSON.stringify(requestBody),
|
||||
signal: controller.signal,
|
||||
headers: this.headers(),
|
||||
};
|
||||
|
||||
return {
|
||||
path,
|
||||
payload,
|
||||
controller,
|
||||
};
|
||||
},
|
||||
|
||||
async chat(options: ChatOptions) {
|
||||
try {
|
||||
const { path, payload, controller } = this.beforeRequest(
|
||||
options,
|
||||
false,
|
||||
);
|
||||
|
||||
controller.signal.onabort = () => options.onFinish("");
|
||||
|
||||
const res = await fetch(path, payload);
|
||||
const resJson = await res.json();
|
||||
|
||||
const message = this.extractMessage(resJson);
|
||||
options.onFinish(message);
|
||||
} catch (e) {
|
||||
logger.error("failed to chat", e);
|
||||
options.onError?.(e as Error);
|
||||
}
|
||||
},
|
||||
|
||||
async chatStream(options: ChatOptions) {
|
||||
try {
|
||||
const { path, payload, controller } = this.beforeRequest(options, true);
|
||||
|
||||
const context = {
|
||||
text: "",
|
||||
finished: false,
|
||||
};
|
||||
|
||||
const finish = () => {
|
||||
if (!context.finished) {
|
||||
options.onFinish(context.text);
|
||||
context.finished = true;
|
||||
}
|
||||
};
|
||||
|
||||
controller.signal.onabort = finish;
|
||||
|
||||
fetchEventSource(path, {
|
||||
...payload,
|
||||
async onopen(res) {
|
||||
const contentType = res.headers.get("content-type");
|
||||
logger.log("response content type: ", contentType);
|
||||
|
||||
if (contentType?.startsWith("text/plain")) {
|
||||
context.text = await res.clone().text();
|
||||
return finish();
|
||||
}
|
||||
|
||||
if (
|
||||
!res.ok ||
|
||||
!res.headers
|
||||
.get("content-type")
|
||||
?.startsWith(EventStreamContentType) ||
|
||||
res.status !== 200
|
||||
) {
|
||||
const responseTexts = [context.text];
|
||||
let extraInfo = await res.clone().text();
|
||||
try {
|
||||
const resJson = await res.clone().json();
|
||||
extraInfo = prettyObject(resJson);
|
||||
} catch {}
|
||||
|
||||
if (res.status === 401) {
|
||||
responseTexts.push(Locale.Error.Unauthorized);
|
||||
}
|
||||
|
||||
if (extraInfo) {
|
||||
responseTexts.push(extraInfo);
|
||||
}
|
||||
|
||||
context.text = responseTexts.join("\n\n");
|
||||
|
||||
return finish();
|
||||
}
|
||||
},
|
||||
onmessage(msg) {
|
||||
if (msg.data === "[DONE]" || context.finished) {
|
||||
return finish();
|
||||
}
|
||||
const chunk = msg.data;
|
||||
try {
|
||||
const chunkJson = JSON.parse(
|
||||
chunk,
|
||||
) as OpenAI.ChatCompletionStreamResponse;
|
||||
const delta = chunkJson.choices[0].delta.content;
|
||||
if (delta) {
|
||||
context.text += delta;
|
||||
options.onUpdate?.(context.text, delta);
|
||||
}
|
||||
} catch (e) {
|
||||
logger.error("[Request] parse error", chunk, msg);
|
||||
}
|
||||
},
|
||||
onclose() {
|
||||
finish();
|
||||
},
|
||||
onerror(e) {
|
||||
options.onError?.(e);
|
||||
},
|
||||
openWhenHidden: true,
|
||||
});
|
||||
} catch (e) {
|
||||
logger.error("failed to chat", e);
|
||||
options.onError?.(e as Error);
|
||||
}
|
||||
},
|
||||
|
||||
async usage() {
|
||||
const formatDate = (d: Date) =>
|
||||
`${d.getFullYear()}-${(d.getMonth() + 1)
|
||||
.toString()
|
||||
.padStart(2, "0")}-${d.getDate().toString().padStart(2, "0")}`;
|
||||
const ONE_DAY = 1 * 24 * 60 * 60 * 1000;
|
||||
const now = new Date();
|
||||
const startOfMonth = new Date(now.getFullYear(), now.getMonth(), 1);
|
||||
const startDate = formatDate(startOfMonth);
|
||||
const endDate = formatDate(new Date(Date.now() + ONE_DAY));
|
||||
|
||||
const [used, subs] = await Promise.all([
|
||||
fetch(
|
||||
`${this.path(
|
||||
OpenaiPath.Usage,
|
||||
)}?start_date=${startDate}&end_date=${endDate}`,
|
||||
{
|
||||
method: "GET",
|
||||
headers: this.headers(),
|
||||
},
|
||||
),
|
||||
fetch(this.path(OpenaiPath.Subs), {
|
||||
method: "GET",
|
||||
headers: this.headers(),
|
||||
}),
|
||||
]);
|
||||
|
||||
if (!used.ok || !subs.ok) {
|
||||
throw new Error("Failed to query usage from openai");
|
||||
}
|
||||
|
||||
const response = (await used.json()) as {
|
||||
total_usage?: number;
|
||||
error?: {
|
||||
type: string;
|
||||
message: string;
|
||||
};
|
||||
};
|
||||
|
||||
const total = (await subs.json()) as {
|
||||
hard_limit_usd?: number;
|
||||
};
|
||||
|
||||
if (response.error?.type) {
|
||||
throw Error(response.error?.message);
|
||||
}
|
||||
|
||||
response.total_usage = Math.round(response.total_usage ?? 0) / 100;
|
||||
total.hard_limit_usd =
|
||||
Math.round((total.hard_limit_usd ?? 0) * 100) / 100;
|
||||
|
||||
return {
|
||||
used: response.total_usage,
|
||||
total: total.hard_limit_usd,
|
||||
} as LLMUsage;
|
||||
},
|
||||
|
||||
async models(): Promise<LLMModel[]> {
|
||||
const customModels = openaiConfig.customModels
|
||||
.split(",")
|
||||
.map((v) => v.trim())
|
||||
.map((v) => ({
|
||||
name: v,
|
||||
available: true,
|
||||
}));
|
||||
|
||||
if (!openaiConfig.autoFetchModels) {
|
||||
return [...DEFAULT_MODELS.slice(), ...customModels];
|
||||
}
|
||||
|
||||
const res = await fetch(this.path(OpenaiPath.ListModel), {
|
||||
method: "GET",
|
||||
headers: this.headers(),
|
||||
});
|
||||
|
||||
const resJson = (await res.json()) as OpenAI.ListModelResponse;
|
||||
const chatModels =
|
||||
resJson.data?.filter((m) => m.id.startsWith("gpt-")) ?? [];
|
||||
|
||||
return chatModels
|
||||
.map((m) => ({
|
||||
name: m.id,
|
||||
available: true,
|
||||
}))
|
||||
.concat(customModels);
|
||||
},
|
||||
};
|
||||
}
|
||||
79
app/client/openai/types.ts
Normal file
79
app/client/openai/types.ts
Normal file
@@ -0,0 +1,79 @@
|
||||
export namespace OpenAI {
|
||||
export type Role = "system" | "user" | "assistant" | "function";
|
||||
export type FinishReason = "stop" | "length" | "function_call";
|
||||
|
||||
export interface Message {
|
||||
role: Role;
|
||||
content?: string;
|
||||
function_call?: {
|
||||
name: string;
|
||||
arguments: string;
|
||||
};
|
||||
}
|
||||
|
||||
export interface Function {
|
||||
name: string;
|
||||
description?: string;
|
||||
parameters: object;
|
||||
}
|
||||
|
||||
export interface ListModelResponse {
|
||||
object: string;
|
||||
data: Array<{
|
||||
id: string;
|
||||
object: string;
|
||||
root: string;
|
||||
}>;
|
||||
}
|
||||
|
||||
export interface ChatCompletionChoice {
|
||||
index: number;
|
||||
message: Message;
|
||||
finish_reason: FinishReason;
|
||||
}
|
||||
|
||||
export interface ChatCompletionUsage {
|
||||
prompt_tokens: number;
|
||||
completion_tokens: number;
|
||||
total_tokens: number;
|
||||
}
|
||||
|
||||
export interface ChatCompletionResponse {
|
||||
id: string;
|
||||
object: string;
|
||||
created: number;
|
||||
model: string;
|
||||
choices: ChatCompletionChoice[];
|
||||
usage: ChatCompletionUsage;
|
||||
}
|
||||
|
||||
export interface ChatCompletionChunkChoice {
|
||||
index: number;
|
||||
delta: Message;
|
||||
finish_reason?: FinishReason;
|
||||
}
|
||||
|
||||
export interface ChatCompletionStreamResponse {
|
||||
object: string;
|
||||
created: number;
|
||||
model: string;
|
||||
choices: ChatCompletionChunkChoice[];
|
||||
}
|
||||
|
||||
export interface ChatCompletionRequest {
|
||||
model: string;
|
||||
messages: Message[];
|
||||
|
||||
functions?: Function[];
|
||||
function_call?: "none" | "auto";
|
||||
|
||||
temperature?: number;
|
||||
top_p?: number;
|
||||
n?: number;
|
||||
stream?: boolean;
|
||||
stop?: string | string[];
|
||||
max_tokens?: number;
|
||||
presence_penalty?: number;
|
||||
frequency_penalty?: number;
|
||||
}
|
||||
}
|
||||
@@ -1,281 +0,0 @@
|
||||
import {
|
||||
DEFAULT_API_HOST,
|
||||
DEFAULT_MODELS,
|
||||
OpenaiPath,
|
||||
REQUEST_TIMEOUT_MS,
|
||||
} from "@/app/constant";
|
||||
import { useAccessStore, useAppConfig, useChatStore } from "@/app/store";
|
||||
|
||||
import { ChatOptions, getHeaders, LLMApi, LLMModel, LLMUsage } from "../api";
|
||||
import Locale from "../../locales";
|
||||
import {
|
||||
EventStreamContentType,
|
||||
fetchEventSource,
|
||||
} from "@fortaine/fetch-event-source";
|
||||
import { prettyObject } from "@/app/utils/format";
|
||||
import { getClientConfig } from "@/app/config/client";
|
||||
|
||||
export interface OpenAIListModelResponse {
|
||||
object: string;
|
||||
data: Array<{
|
||||
id: string;
|
||||
object: string;
|
||||
root: string;
|
||||
}>;
|
||||
}
|
||||
|
||||
export class ChatGPTApi implements LLMApi {
|
||||
private disableListModels = true;
|
||||
|
||||
path(path: string): string {
|
||||
let openaiUrl = useAccessStore.getState().openaiUrl;
|
||||
const apiPath = "/api/openai";
|
||||
|
||||
if (openaiUrl.length === 0) {
|
||||
const isApp = !!getClientConfig()?.isApp;
|
||||
openaiUrl = isApp ? DEFAULT_API_HOST : apiPath;
|
||||
}
|
||||
if (openaiUrl.endsWith("/")) {
|
||||
openaiUrl = openaiUrl.slice(0, openaiUrl.length - 1);
|
||||
}
|
||||
if (!openaiUrl.startsWith("http") && !openaiUrl.startsWith(apiPath)) {
|
||||
openaiUrl = "https://" + openaiUrl;
|
||||
}
|
||||
return [openaiUrl, path].join("/");
|
||||
}
|
||||
|
||||
extractMessage(res: any) {
|
||||
return res.choices?.at(0)?.message?.content ?? "";
|
||||
}
|
||||
|
||||
async chat(options: ChatOptions) {
|
||||
const messages = options.messages.map((v) => ({
|
||||
role: v.role,
|
||||
content: v.content,
|
||||
}));
|
||||
|
||||
const modelConfig = {
|
||||
...useAppConfig.getState().modelConfig,
|
||||
...useChatStore.getState().currentSession().mask.modelConfig,
|
||||
...{
|
||||
model: options.config.model,
|
||||
},
|
||||
};
|
||||
|
||||
const requestPayload = {
|
||||
messages,
|
||||
stream: options.config.stream,
|
||||
model: modelConfig.model,
|
||||
temperature: modelConfig.temperature,
|
||||
presence_penalty: modelConfig.presence_penalty,
|
||||
frequency_penalty: modelConfig.frequency_penalty,
|
||||
top_p: modelConfig.top_p,
|
||||
};
|
||||
|
||||
console.log("[Request] openai payload: ", requestPayload);
|
||||
|
||||
const shouldStream = !!options.config.stream;
|
||||
const controller = new AbortController();
|
||||
options.onController?.(controller);
|
||||
|
||||
try {
|
||||
const chatPath = this.path(OpenaiPath.ChatPath);
|
||||
const chatPayload = {
|
||||
method: "POST",
|
||||
body: JSON.stringify(requestPayload),
|
||||
signal: controller.signal,
|
||||
headers: getHeaders(),
|
||||
};
|
||||
|
||||
// make a fetch request
|
||||
const requestTimeoutId = setTimeout(
|
||||
() => controller.abort(),
|
||||
REQUEST_TIMEOUT_MS,
|
||||
);
|
||||
|
||||
if (shouldStream) {
|
||||
let responseText = "";
|
||||
let finished = false;
|
||||
|
||||
const finish = () => {
|
||||
if (!finished) {
|
||||
options.onFinish(responseText);
|
||||
finished = true;
|
||||
}
|
||||
};
|
||||
|
||||
controller.signal.onabort = finish;
|
||||
|
||||
fetchEventSource(chatPath, {
|
||||
...chatPayload,
|
||||
async onopen(res) {
|
||||
clearTimeout(requestTimeoutId);
|
||||
const contentType = res.headers.get("content-type");
|
||||
console.log(
|
||||
"[OpenAI] request response content type: ",
|
||||
contentType,
|
||||
);
|
||||
|
||||
if (contentType?.startsWith("text/plain")) {
|
||||
responseText = await res.clone().text();
|
||||
return finish();
|
||||
}
|
||||
|
||||
if (
|
||||
!res.ok ||
|
||||
!res.headers
|
||||
.get("content-type")
|
||||
?.startsWith(EventStreamContentType) ||
|
||||
res.status !== 200
|
||||
) {
|
||||
const responseTexts = [responseText];
|
||||
let extraInfo = await res.clone().text();
|
||||
try {
|
||||
const resJson = await res.clone().json();
|
||||
extraInfo = prettyObject(resJson);
|
||||
} catch {}
|
||||
|
||||
if (res.status === 401) {
|
||||
responseTexts.push(Locale.Error.Unauthorized);
|
||||
}
|
||||
|
||||
if (extraInfo) {
|
||||
responseTexts.push(extraInfo);
|
||||
}
|
||||
|
||||
responseText = responseTexts.join("\n\n");
|
||||
|
||||
return finish();
|
||||
}
|
||||
},
|
||||
onmessage(msg) {
|
||||
if (msg.data === "[DONE]" || finished) {
|
||||
return finish();
|
||||
}
|
||||
const text = msg.data;
|
||||
try {
|
||||
const json = JSON.parse(text);
|
||||
const delta = json.choices[0].delta.content;
|
||||
if (delta) {
|
||||
responseText += delta;
|
||||
options.onUpdate?.(responseText, delta);
|
||||
}
|
||||
} catch (e) {
|
||||
console.error("[Request] parse error", text, msg);
|
||||
}
|
||||
},
|
||||
onclose() {
|
||||
finish();
|
||||
},
|
||||
onerror(e) {
|
||||
options.onError?.(e);
|
||||
throw e;
|
||||
},
|
||||
openWhenHidden: true,
|
||||
});
|
||||
} else {
|
||||
const res = await fetch(chatPath, chatPayload);
|
||||
clearTimeout(requestTimeoutId);
|
||||
|
||||
const resJson = await res.json();
|
||||
const message = this.extractMessage(resJson);
|
||||
options.onFinish(message);
|
||||
}
|
||||
} catch (e) {
|
||||
console.log("[Request] failed to make a chat request", e);
|
||||
options.onError?.(e as Error);
|
||||
}
|
||||
}
|
||||
async usage() {
|
||||
const formatDate = (d: Date) =>
|
||||
`${d.getFullYear()}-${(d.getMonth() + 1).toString().padStart(2, "0")}-${d
|
||||
.getDate()
|
||||
.toString()
|
||||
.padStart(2, "0")}`;
|
||||
const ONE_DAY = 1 * 24 * 60 * 60 * 1000;
|
||||
const now = new Date();
|
||||
const startOfMonth = new Date(now.getFullYear(), now.getMonth(), 1);
|
||||
const startDate = formatDate(startOfMonth);
|
||||
const endDate = formatDate(new Date(Date.now() + ONE_DAY));
|
||||
|
||||
const [used, subs] = await Promise.all([
|
||||
fetch(
|
||||
this.path(
|
||||
`${OpenaiPath.UsagePath}?start_date=${startDate}&end_date=${endDate}`,
|
||||
),
|
||||
{
|
||||
method: "GET",
|
||||
headers: getHeaders(),
|
||||
},
|
||||
),
|
||||
fetch(this.path(OpenaiPath.SubsPath), {
|
||||
method: "GET",
|
||||
headers: getHeaders(),
|
||||
}),
|
||||
]);
|
||||
|
||||
if (used.status === 401) {
|
||||
throw new Error(Locale.Error.Unauthorized);
|
||||
}
|
||||
|
||||
if (!used.ok || !subs.ok) {
|
||||
throw new Error("Failed to query usage from openai");
|
||||
}
|
||||
|
||||
const response = (await used.json()) as {
|
||||
total_usage?: number;
|
||||
error?: {
|
||||
type: string;
|
||||
message: string;
|
||||
};
|
||||
};
|
||||
|
||||
const total = (await subs.json()) as {
|
||||
hard_limit_usd?: number;
|
||||
};
|
||||
|
||||
if (response.error && response.error.type) {
|
||||
throw Error(response.error.message);
|
||||
}
|
||||
|
||||
if (response.total_usage) {
|
||||
response.total_usage = Math.round(response.total_usage) / 100;
|
||||
}
|
||||
|
||||
if (total.hard_limit_usd) {
|
||||
total.hard_limit_usd = Math.round(total.hard_limit_usd * 100) / 100;
|
||||
}
|
||||
|
||||
return {
|
||||
used: response.total_usage,
|
||||
total: total.hard_limit_usd,
|
||||
} as LLMUsage;
|
||||
}
|
||||
|
||||
async models(): Promise<LLMModel[]> {
|
||||
if (this.disableListModels) {
|
||||
return DEFAULT_MODELS.slice();
|
||||
}
|
||||
|
||||
const res = await fetch(this.path(OpenaiPath.ListModelPath), {
|
||||
method: "GET",
|
||||
headers: {
|
||||
...getHeaders(),
|
||||
},
|
||||
});
|
||||
|
||||
const resJson = (await res.json()) as OpenAIListModelResponse;
|
||||
const chatModels = resJson.data?.filter((m) => m.id.startsWith("gpt-"));
|
||||
console.log("[Models]", chatModels);
|
||||
|
||||
if (!chatModels) {
|
||||
return [];
|
||||
}
|
||||
|
||||
return chatModels.map((m) => ({
|
||||
name: m.id,
|
||||
available: true,
|
||||
}));
|
||||
}
|
||||
}
|
||||
export { OpenaiPath };
|
||||
39
app/client/types.ts
Normal file
39
app/client/types.ts
Normal file
@@ -0,0 +1,39 @@
|
||||
import { DEFAULT_MODELS } from "../constant";
|
||||
|
||||
export interface LLMUsage {
|
||||
used: number;
|
||||
total: number;
|
||||
available: boolean;
|
||||
}
|
||||
|
||||
export interface LLMModel {
|
||||
name: string;
|
||||
available: boolean;
|
||||
}
|
||||
|
||||
export const ROLES = ["system", "user", "assistant"] as const;
|
||||
export type MessageRole = (typeof ROLES)[number];
|
||||
|
||||
export type ChatModel = (typeof DEFAULT_MODELS)[number]["name"];
|
||||
|
||||
export interface RequestMessage {
|
||||
role: MessageRole;
|
||||
content: string;
|
||||
}
|
||||
|
||||
export interface ChatOptions {
|
||||
messages: RequestMessage[];
|
||||
shouldSummarize?: boolean;
|
||||
|
||||
onUpdate?: (message: string, chunk: string) => void;
|
||||
onFinish: (message: string) => void;
|
||||
onError?: (err: Error) => void;
|
||||
onController?: (controller: AbortController) => void;
|
||||
}
|
||||
|
||||
export type LLMClient = {
|
||||
chat(options: ChatOptions): Promise<void>;
|
||||
chatStream(options: ChatOptions): Promise<void>;
|
||||
usage(): Promise<LLMUsage>;
|
||||
models(): Promise<LLMModel[]>;
|
||||
};
|
||||
Reference in New Issue
Block a user