next-chat/app/client/platforms/openai.ts

"use client";
// azure and openai, using same models. so using same LLMApi.
import {
  ApiPath,
  DEFAULT_API_HOST,
  DEFAULT_MODELS,
  OpenaiPath,
  Azure,
  REQUEST_TIMEOUT_MS,
  ServiceProvider,
} from "@/app/constant";
import {
  ChatMessageTool,
  useAccessStore,
  useAppConfig,
  useChatStore,
  usePluginStore,
} from "@/app/store";
import { collectModelsWithDefaultModel } from "@/app/utils/model";
import {
  preProcessImageContent,
  uploadImage,
  base64Image2Blob,
  stream,
} from "@/app/utils/chat";
import { cloudflareAIGatewayUrl } from "@/app/utils/cloudflare";
import { DalleSize, DalleQuality, DalleStyle } from "@/app/typing";

import {
  ChatOptions,
  getHeaders,
  LLMApi,
  LLMModel,
  LLMUsage,
  MultimodalContent,
} 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";
import {
  getMessageTextContent,
  getMessageImages,
  isVisionModel,
  isDalle3 as _isDalle3,
} from "@/app/utils";

export interface OpenAIListModelResponse {
  object: string;
  data: Array<{
    id: string;
    object: string;
    root: string;
  }>;
}

export interface RequestPayload {
  messages: {
    role: "system" | "user" | "assistant";
    content: string | MultimodalContent[];
  }[];
  stream?: boolean;
  model: string;
  temperature: number;
  presence_penalty: number;
  frequency_penalty: number;
  top_p: number;
  max_tokens?: number;
}

export interface DalleRequestPayload {
  model: string;
  prompt: string;
  response_format: "url" | "b64_json";
  n: number;
  size: DalleSize;
  quality: DalleQuality;
  style: DalleStyle;
}

export class ChatGPTApi implements LLMApi {
  private disableListModels = true;

  path(path: string): string {
    const accessStore = useAccessStore.getState();

    let baseUrl = "";

    const isAzure = path.includes("deployments");
    if (accessStore.useCustomConfig) {
      if (isAzure && !accessStore.isValidAzure()) {
        throw Error(
          "incomplete azure config, please check it in your settings page",
        );
      }

      baseUrl = isAzure ? accessStore.azureUrl : accessStore.openaiUrl;
    }

    if (baseUrl.length === 0) {
      const isApp = !!getClientConfig()?.isApp;
      const apiPath = isAzure ? ApiPath.Azure : ApiPath.OpenAI;
      baseUrl = isApp ? DEFAULT_API_HOST + "/proxy" + apiPath : apiPath;
    }

    if (baseUrl.endsWith("/")) {
      baseUrl = baseUrl.slice(0, baseUrl.length - 1);
    }
    if (
      !baseUrl.startsWith("http") &&
      !isAzure &&
      !baseUrl.startsWith(ApiPath.OpenAI)
    ) {
      baseUrl = "https://" + baseUrl;
    }

    console.log("[Proxy Endpoint] ", baseUrl, path);

    // try rebuild url, when using cloudflare ai gateway in client
    return cloudflareAIGatewayUrl([baseUrl, path].join("/"));
  }

  async extractMessage(res: any) {
    if (res.error) {
      return "```\n" + JSON.stringify(res, null, 4) + "\n```";
    }
    // dalle3 model return url, using url create image message
    if (res.data) {
      let url = res.data?.at(0)?.url ?? "";
      const b64_json = res.data?.at(0)?.b64_json ?? "";
      if (!url && b64_json) {
        // uploadImage
        url = await uploadImage(base64Image2Blob(b64_json, "image/png"));
      }
      return [
        {
          type: "image_url",
          image_url: {
            url,
          },
        },
      ];
    }
    return res.choices?.at(0)?.message?.content ?? res;
  }

  async chat(options: ChatOptions) {
    const modelConfig = {
      ...useAppConfig.getState().modelConfig,
      ...useChatStore.getState().currentSession().mask.modelConfig,
      ...{
        model: options.config.model,
        providerName: options.config.providerName,
      },
    };

    let requestPayload: RequestPayload | DalleRequestPayload;

    const isDalle3 = _isDalle3(options.config.model);
    const isO1 = options.config.model.startsWith("o1");
    if (isDalle3) {
      const prompt = getMessageTextContent(
        options.messages.slice(-1)?.pop() as any,
      );
      requestPayload = {
        model: options.config.model,
        prompt,
        // URLs are only valid for 60 minutes after the image has been generated.
        response_format: "b64_json", // using b64_json, and save image in CacheStorage
        n: 1,
        size: options.config?.size ?? "1024x1024",
        quality: options.config?.quality ?? "standard",
        style: options.config?.style ?? "vivid",
      };
    } else {
      const visionModel = isVisionModel(options.config.model);
      const messages: ChatOptions["messages"] = [];
      for (const v of options.messages) {
        const content = visionModel
          ? await preProcessImageContent(v.content)
          : getMessageTextContent(v);
        if(!(isO1 && v.role === "system"))
          messages.push({ role: v.role, content });
      }

      // O1 not support image, tools (plugin in ChatGPTNextWeb) and system, stream, logprobs, temperature, top_p, n, presence_penalty, frequency_penalty yet.
      requestPayload = {
        messages,
        stream: !isO1 ? options.config.stream : false,
        model: modelConfig.model,
        temperature: !isO1 ? modelConfig.temperature : 1,
        presence_penalty: !isO1 ? modelConfig.presence_penalty : 0,
        frequency_penalty: !isO1 ? modelConfig.frequency_penalty : 0,
        top_p: !isO1 ? modelConfig.top_p : 1,
        // max_tokens: Math.max(modelConfig.max_tokens, 1024),
        // Please do not ask me why not send max_tokens, no reason, this param is just shit, I dont want to explain anymore.
      };

      // add max_tokens to vision model
      if (visionModel && modelConfig.model.includes("preview")) {
        requestPayload["max_tokens"] = Math.max(modelConfig.max_tokens, 4000);
      }
    }

    console.log("[Request] openai payload: ", requestPayload);

    const shouldStream = !isDalle3 && !!options.config.stream && !isO1;
    const controller = new AbortController();
    options.onController?.(controller);

    try {
      let chatPath = "";
      if (modelConfig.providerName === ServiceProvider.Azure) {
        // find model, and get displayName as deployName
        const { models: configModels, customModels: configCustomModels } =
          useAppConfig.getState();
        const {
          defaultModel,
          customModels: accessCustomModels,
          useCustomConfig,
        } = useAccessStore.getState();
        const models = collectModelsWithDefaultModel(
          configModels,
          [configCustomModels, accessCustomModels].join(","),
          defaultModel,
        );
        const model = models.find(
          (model) =>
            model.name === modelConfig.model &&
            model?.provider?.providerName === ServiceProvider.Azure,
        );
        chatPath = this.path(
          (isDalle3 ? Azure.ImagePath : Azure.ChatPath)(
            (model?.displayName ?? model?.name) as string,
            useCustomConfig ? useAccessStore.getState().azureApiVersion : "",
          ),
        );
      } else {
        chatPath = this.path(
          isDalle3 ? OpenaiPath.ImagePath : OpenaiPath.ChatPath,
        );
      }
      if (shouldStream) {
        const [tools, funcs] = usePluginStore
          .getState()
          .getAsTools(
            useChatStore.getState().currentSession().mask?.plugin || [],
          );
        // console.log("getAsTools", tools, funcs);
        stream(
          chatPath,
          requestPayload,
          getHeaders(),
          tools as any,
          funcs,
          controller,
          // parseSSE
          (text: string, runTools: ChatMessageTool[]) => {
            // console.log("parseSSE", text, runTools);
            const json = JSON.parse(text);
            const choices = json.choices as Array<{
              delta: {
                content: string;
                tool_calls: ChatMessageTool[];
              };
            }>;
            const tool_calls = choices[0]?.delta?.tool_calls;
            if (tool_calls?.length > 0) {
              const index = tool_calls[0]?.index;
              const id = tool_calls[0]?.id;
              const args = tool_calls[0]?.function?.arguments;
              if (id) {
                runTools.push({
                  id,
                  type: tool_calls[0]?.type,
                  function: {
                    name: tool_calls[0]?.function?.name as string,
                    arguments: args,
                  },
                });
              } else {
                // @ts-ignore
                runTools[index]["function"]["arguments"] += args;
              }
            }
            return choices[0]?.delta?.content;
          },
          // processToolMessage, include tool_calls message and tool call results
          (
            requestPayload: RequestPayload,
            toolCallMessage: any,
            toolCallResult: any[],
          ) => {
            // @ts-ignore
            requestPayload?.messages?.splice(
              // @ts-ignore
              requestPayload?.messages?.length,
              0,
              toolCallMessage,
              ...toolCallResult,
            );
          },
          options,
        );
      } else {
        const chatPayload = {
          method: "POST",
          body: JSON.stringify(requestPayload),
          signal: controller.signal,
          headers: getHeaders(),
        };

        // make a fetch request
        const requestTimeoutId = setTimeout(
          () => controller.abort(),
          isDalle3 ? REQUEST_TIMEOUT_MS * 2 : REQUEST_TIMEOUT_MS, // dalle3 using b64_json is slow.
        );

        const res = await fetch(chatPath, chatPayload);
        clearTimeout(requestTimeoutId);

        const resJson = await res.json();
        const message = await 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-") || m.id.startsWith("chatgpt-"),
    );
    console.log("[Models]", chatModels);

    if (!chatModels) {
      return [];
    }

    //由于目前 OpenAI 的 disableListModels 默认为 true，所以当前实际不会运行到这场
    let seq = 1000; //同 Constant.ts 中的排序保持一致
    return chatModels.map((m) => ({
      name: m.id,
      available: true,
      sorted: seq++,
      provider: {
        id: "openai",
        providerName: "OpenAI",
        providerType: "openai",
        sorted: 1,
      },
    }));
  }
}
export { OpenaiPath };