mirror of
https://github.com/Yidadaa/ChatGPT-Next-Web.git
synced 2025-08-08 17:25:52 +08:00
Merge branch 'main' into main
This commit is contained in:
黑云白土
GitHub
@@ -8,6 +8,7 @@ import {
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import { ChatMessage, ModelType, useAccessStore, useChatStore } from "../store";
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import { ChatGPTApi } from "./platforms/openai";
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import { GeminiProApi } from "./platforms/google";
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import { ClaudeApi } from "./platforms/anthropic";
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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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@@ -94,11 +95,16 @@ export class ClientApi {
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public llm: LLMApi;
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constructor(provider: ModelProvider = ModelProvider.GPT) {
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if (provider === ModelProvider.GeminiPro) {
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this.llm = new GeminiProApi();
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return;
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switch (provider) {
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case ModelProvider.GeminiPro:
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this.llm = new GeminiProApi();
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break;
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case ModelProvider.Claude:
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this.llm = new ClaudeApi();
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break;
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default:
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this.llm = new ChatGPTApi();
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}
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this.llm = new ChatGPTApi();
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}
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config() {}
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404
app/client/platforms/anthropic.ts
Normal file
404
app/client/platforms/anthropic.ts
Normal file
@@ -0,0 +1,404 @@
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import { ACCESS_CODE_PREFIX, Anthropic, ApiPath } from "@/app/constant";
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import { ChatOptions, LLMApi, MultimodalContent } from "../api";
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import { useAccessStore, useAppConfig, useChatStore } from "@/app/store";
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import { getClientConfig } from "@/app/config/client";
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import { DEFAULT_API_HOST } from "@/app/constant";
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import { RequestMessage } from "@/app/typing";
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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 Locale from "../../locales";
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import { prettyObject } from "@/app/utils/format";
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import { getMessageTextContent, isVisionModel } from "@/app/utils";
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export type MultiBlockContent = {
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type: "image" | "text";
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source?: {
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type: string;
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media_type: string;
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data: string;
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};
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text?: string;
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};
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export type AnthropicMessage = {
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role: (typeof ClaudeMapper)[keyof typeof ClaudeMapper];
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content: string | MultiBlockContent[];
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};
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export interface AnthropicChatRequest {
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model: string; // The model that will complete your prompt.
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messages: AnthropicMessage[]; // The prompt that you want Claude to complete.
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max_tokens: number; // The maximum number of tokens to generate before stopping.
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stop_sequences?: string[]; // Sequences that will cause the model to stop generating completion text.
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temperature?: number; // Amount of randomness injected into the response.
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top_p?: number; // Use nucleus sampling.
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top_k?: number; // Only sample from the top K options for each subsequent token.
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metadata?: object; // An object describing metadata about the request.
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stream?: boolean; // Whether to incrementally stream the response using server-sent events.
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}
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export interface ChatRequest {
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model: string; // The model that will complete your prompt.
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prompt: string; // The prompt that you want Claude to complete.
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max_tokens_to_sample: number; // The maximum number of tokens to generate before stopping.
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stop_sequences?: string[]; // Sequences that will cause the model to stop generating completion text.
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temperature?: number; // Amount of randomness injected into the response.
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top_p?: number; // Use nucleus sampling.
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top_k?: number; // Only sample from the top K options for each subsequent token.
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metadata?: object; // An object describing metadata about the request.
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stream?: boolean; // Whether to incrementally stream the response using server-sent events.
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}
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export interface ChatResponse {
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completion: string;
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stop_reason: "stop_sequence" | "max_tokens";
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model: string;
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}
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export type ChatStreamResponse = ChatResponse & {
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stop?: string;
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log_id: string;
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};
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const ClaudeMapper = {
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assistant: "assistant",
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user: "user",
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system: "user",
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} as const;
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const keys = ["claude-2, claude-instant-1"];
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export class ClaudeApi implements LLMApi {
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extractMessage(res: any) {
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console.log("[Response] claude response: ", res);
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return res?.content?.[0]?.text;
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}
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async chat(options: ChatOptions): Promise<void> {
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const visionModel = isVisionModel(options.config.model);
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const accessStore = useAccessStore.getState();
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const shouldStream = !!options.config.stream;
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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 messages = [...options.messages];
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const keys = ["system", "user"];
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// roles must alternate between "user" and "assistant" in claude, so add a fake assistant message between two user messages
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for (let i = 0; i < messages.length - 1; i++) {
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const message = messages[i];
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const nextMessage = messages[i + 1];
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if (keys.includes(message.role) && keys.includes(nextMessage.role)) {
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messages[i] = [
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message,
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{
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role: "assistant",
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content: ";",
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},
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] as any;
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}
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}
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const prompt = messages
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.flat()
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.filter((v) => {
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if (!v.content) return false;
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if (typeof v.content === "string" && !v.content.trim()) return false;
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return true;
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})
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.map((v) => {
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const { role, content } = v;
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const insideRole = ClaudeMapper[role] ?? "user";
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if (!visionModel || typeof content === "string") {
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return {
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role: insideRole,
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content: getMessageTextContent(v),
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};
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}
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return {
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role: insideRole,
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content: content
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.filter((v) => v.image_url || v.text)
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.map(({ type, text, image_url }) => {
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if (type === "text") {
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return {
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type,
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text: text!,
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};
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}
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const { url = "" } = image_url || {};
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const colonIndex = url.indexOf(":");
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const semicolonIndex = url.indexOf(";");
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const comma = url.indexOf(",");
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const mimeType = url.slice(colonIndex + 1, semicolonIndex);
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const encodeType = url.slice(semicolonIndex + 1, comma);
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const data = url.slice(comma + 1);
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return {
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type: "image" as const,
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source: {
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type: encodeType,
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media_type: mimeType,
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data,
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},
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};
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}),
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};
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});
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const requestBody: AnthropicChatRequest = {
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messages: prompt,
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stream: shouldStream,
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model: modelConfig.model,
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max_tokens: modelConfig.max_tokens,
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temperature: modelConfig.temperature,
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top_p: modelConfig.top_p,
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// top_k: modelConfig.top_k,
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top_k: 5,
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};
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const path = this.path(Anthropic.ChatPath);
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const controller = new AbortController();
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options.onController?.(controller);
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const payload = {
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method: "POST",
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body: JSON.stringify(requestBody),
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signal: controller.signal,
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headers: {
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"Content-Type": "application/json",
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Accept: "application/json",
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"x-api-key": accessStore.anthropicApiKey,
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"anthropic-version": accessStore.anthropicApiVersion,
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Authorization: getAuthKey(accessStore.anthropicApiKey),
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},
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};
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if (shouldStream) {
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try {
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const context = {
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text: "",
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finished: false,
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};
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const finish = () => {
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if (!context.finished) {
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options.onFinish(context.text);
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context.finished = true;
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}
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};
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controller.signal.onabort = finish;
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fetchEventSource(path, {
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...payload,
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async onopen(res) {
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const contentType = res.headers.get("content-type");
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console.log("response content type: ", contentType);
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if (contentType?.startsWith("text/plain")) {
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context.text = await res.clone().text();
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return finish();
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}
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if (
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!res.ok ||
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!res.headers
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.get("content-type")
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?.startsWith(EventStreamContentType) ||
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res.status !== 200
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) {
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const responseTexts = [context.text];
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let extraInfo = await res.clone().text();
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try {
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const resJson = await res.clone().json();
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extraInfo = prettyObject(resJson);
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} catch {}
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if (res.status === 401) {
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responseTexts.push(Locale.Error.Unauthorized);
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}
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if (extraInfo) {
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responseTexts.push(extraInfo);
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}
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context.text = responseTexts.join("\n\n");
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return finish();
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}
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},
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onmessage(msg) {
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let chunkJson:
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| undefined
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| {
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type: "content_block_delta" | "content_block_stop";
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delta?: {
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type: "text_delta";
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text: string;
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};
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index: number;
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};
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try {
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chunkJson = JSON.parse(msg.data);
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} catch (e) {
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console.error("[Response] parse error", msg.data);
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}
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if (!chunkJson || chunkJson.type === "content_block_stop") {
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return finish();
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}
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const { delta } = chunkJson;
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if (delta?.text) {
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context.text += delta.text;
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options.onUpdate?.(context.text, delta.text);
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}
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},
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onclose() {
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finish();
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},
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onerror(e) {
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options.onError?.(e);
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throw e;
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},
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openWhenHidden: true,
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});
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} catch (e) {
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console.error("failed to chat", e);
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options.onError?.(e as Error);
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}
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} else {
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try {
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controller.signal.onabort = () => options.onFinish("");
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const res = await fetch(path, payload);
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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);
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} catch (e) {
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console.error("failed to chat", e);
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options.onError?.(e as Error);
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}
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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() {
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// const provider = {
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// id: "anthropic",
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// providerName: "Anthropic",
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// providerType: "anthropic",
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// };
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return [
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// {
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// name: "claude-instant-1.2",
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// available: true,
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// provider,
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// },
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// {
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// name: "claude-2.0",
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// available: true,
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// provider,
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// },
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// {
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// name: "claude-2.1",
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// available: true,
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// provider,
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// },
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// {
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// name: "claude-3-opus-20240229",
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// available: true,
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// provider,
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// },
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// {
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// name: "claude-3-sonnet-20240229",
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// available: true,
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// provider,
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// },
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// {
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// name: "claude-3-haiku-20240307",
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// available: true,
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// provider,
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// },
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];
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}
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path(path: string): string {
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const accessStore = useAccessStore.getState();
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let baseUrl: string = accessStore.anthropicUrl;
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// if endpoint is empty, use default endpoint
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if (baseUrl.trim().length === 0) {
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const isApp = !!getClientConfig()?.isApp;
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baseUrl = isApp
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? DEFAULT_API_HOST + "/api/proxy/anthropic"
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: ApiPath.Anthropic;
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}
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if (!baseUrl.startsWith("http") && !baseUrl.startsWith("/api")) {
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baseUrl = "https://" + baseUrl;
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}
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baseUrl = trimEnd(baseUrl, "/");
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return `${baseUrl}/${path}`;
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}
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}
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function trimEnd(s: string, end = " ") {
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if (end.length === 0) return s;
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while (s.endsWith(end)) {
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s = s.slice(0, -end.length);
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}
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return s;
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}
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function bearer(value: string) {
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return `Bearer ${value.trim()}`;
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}
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function getAuthKey(apiKey = "") {
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const accessStore = useAccessStore.getState();
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const isApp = !!getClientConfig()?.isApp;
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let authKey = "";
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if (apiKey) {
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// use user's api key first
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authKey = 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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authKey = bearer(ACCESS_CODE_PREFIX + accessStore.accessCode);
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}
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return authKey;
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}
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@@ -40,6 +40,20 @@ export interface OpenAIListModelResponse {
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}>;
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}
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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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|
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export class ChatGPTApi implements LLMApi {
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private disableListModels = true;
|
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|
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@@ -98,7 +112,7 @@ export class ChatGPTApi implements LLMApi {
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},
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};
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const requestPayload = {
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const requestPayload: 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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@@ -112,12 +126,7 @@ export class ChatGPTApi implements LLMApi {
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|
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// add max_tokens to vision model
|
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if (visionModel) {
|
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Object.defineProperty(requestPayload, "max_tokens", {
|
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enumerable: true,
|
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configurable: true,
|
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writable: true,
|
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value: modelConfig.max_tokens,
|
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});
|
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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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@@ -229,7 +238,9 @@ export class ChatGPTApi implements LLMApi {
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const text = msg.data;
|
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try {
|
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const json = JSON.parse(text);
|
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const choices = json.choices as Array<{ delta: { content: string } }>;
|
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const choices = json.choices as Array<{
|
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delta: { content: string };
|
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}>;
|
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const delta = choices[0]?.delta?.content;
|
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const textmoderation = json?.prompt_filter_results;
|
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|
||||
@@ -237,9 +248,17 @@ export class ChatGPTApi implements LLMApi {
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remainText += delta;
|
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}
|
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|
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if (textmoderation && textmoderation.length > 0 && ServiceProvider.Azure) {
|
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const contentFilterResults = textmoderation[0]?.content_filter_results;
|
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console.log(`[${ServiceProvider.Azure}] [Text Moderation] flagged categories result:`, contentFilterResults);
|
||||
if (
|
||||
textmoderation &&
|
||||
textmoderation.length > 0 &&
|
||||
ServiceProvider.Azure
|
||||
) {
|
||||
const contentFilterResults =
|
||||
textmoderation[0]?.content_filter_results;
|
||||
console.log(
|
||||
`[${ServiceProvider.Azure}] [Text Moderation] flagged categories result:`,
|
||||
contentFilterResults,
|
||||
);
|
||||
}
|
||||
} catch (e) {
|
||||
console.error("[Request] parse error", text, msg);
|
||||
|
||||
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