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interface/Enum.ts\nvar ChatModelProvider = /* @__PURE__ */ ((ChatModelProvider2) => {\n ChatModelProvider2[\"OpenAI\"] = \"openai\";\n ChatModelProvider2[\"DeepSeek\"] = \"deepseek\";\n ChatModelProvider2[\"IFlyTek\"] = \"iflytek\";\n ChatModelProvider2[\"Baidu\"] = \"baidu\";\n ChatModelProvider2[\"Google\"] = \"google\";\n ChatModelProvider2[\"GLM\"] = \"glm\";\n ChatModelProvider2[\"MoonShot\"] = \"moonshot\";\n ChatModelProvider2[\"AliYun\"] = \"aliyun\";\n ChatModelProvider2[\"XAI\"] = \"xai\";\n ChatModelProvider2[\"Other\"] = \"other\";\n return ChatModelProvider2;\n})(ChatModelProvider || {});\nvar EmbedModelProvider = /* @__PURE__ */ ((EmbedModelProvider2) => {\n EmbedModelProvider2[\"OpenAI\"] = \"openai\";\n EmbedModelProvider2[\"Google\"] = \"google\";\n EmbedModelProvider2[\"GLM\"] = \"glm\";\n EmbedModelProvider2[\"AliYun\"] = \"aliyun\";\n EmbedModelProvider2[\"Other\"] = \"other\";\n return EmbedModelProvider2;\n})(EmbedModelProvider || {});\nvar ImagineModelProvider = 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OtherEmbedModel2[\"BASE_CHN_PARAPH\"] = \"text2vec-base-chinese-paraphrase\";\n OtherEmbedModel2[\"BASE_CHN_SENTENCE\"] = \"text2vec-base-chinese-sentence\";\n OtherEmbedModel2[\"BASE_MUL\"] = \"text2vec-base-multilingual\";\n OtherEmbedModel2[\"PARAPH_MUL_MINI\"] = \"paraphrase-multilingual-MiniLM-L12-v2\";\n return OtherEmbedModel2;\n})(OtherEmbedModel || {});\nvar GLMEmbedModel = /* @__PURE__ */ ((GLMEmbedModel2) => {\n GLMEmbedModel2[\"EMBED_2\"] = \"embedding-2\";\n GLMEmbedModel2[\"EMBED_3\"] = \"embedding-3\";\n return GLMEmbedModel2;\n})(GLMEmbedModel || {});\nvar GoogleEmbedModel = /* @__PURE__ */ ((GoogleEmbedModel2) => {\n GoogleEmbedModel2[\"EMBED_4\"] = \"text-embedding-004\";\n return GoogleEmbedModel2;\n})(GoogleEmbedModel || {});\nvar AliEmbedModel = /* @__PURE__ */ ((AliEmbedModel2) => {\n AliEmbedModel2[\"ALI_V3\"] = \"text-embedding-v3\";\n AliEmbedModel2[\"ALI_V2\"] = \"text-embedding-v2\";\n AliEmbedModel2[\"ALI_V1\"] = \"text-embedding-v1\";\n AliEmbedModel2[\"ALI_ASYNC_V2\"] = \"text-embedding-async-v2\";\n AliEmbedModel2[\"ALI_ASYNC_V1\"] = \"text-embedding-async-v1\";\n return AliEmbedModel2;\n})(AliEmbedModel || {});\nvar EmbedModel = {\n ...OpenAIEmbedModel,\n ...OtherEmbedModel,\n ...GLMEmbedModel,\n ...GoogleEmbedModel,\n ...AliEmbedModel\n};\nvar OpenAIChatModel = /* @__PURE__ */ ((OpenAIChatModel2) => {\n OpenAIChatModel2[\"GPT_4O\"] = \"gpt-4o\";\n OpenAIChatModel2[\"GPT_4O_MINI\"] = \"gpt-4o-mini\";\n OpenAIChatModel2[\"CHAT_GPT_4O\"] = \"chatgpt-4o-latest\";\n OpenAIChatModel2[\"GPT4_TURBO\"] = \"gpt-4-turbo\";\n OpenAIChatModel2[\"GPT4\"] = \"gpt-4\";\n OpenAIChatModel2[\"O1\"] = \"o1\";\n OpenAIChatModel2[\"O1_PRE\"] = \"o1-preview\";\n OpenAIChatModel2[\"O1_MINI\"] = \"o1-mini\";\n OpenAIChatModel2[\"O3_MINI\"] = \"o3-mini\";\n OpenAIChatModel2[\"GPT3\"] = \"gpt-3.5-turbo\";\n return OpenAIChatModel2;\n})(OpenAIChatModel || {});\nvar DeepSeekChatModel = /* @__PURE__ */ ((DeepSeekChatModel2) => {\n 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GLMChatModel2[\"GLM_4_AIR\"] = \"glm-4-air\";\n GLMChatModel2[\"GLM_4_AIRX\"] = \"glm-4-airx\";\n GLMChatModel2[\"GLM_4_FLASH\"] = \"glm-4-flash\";\n GLMChatModel2[\"GLM_4_FLASHX\"] = \"glm-4-flashx\";\n GLMChatModel2[\"GLM_4V\"] = \"glm-4v\";\n GLMChatModel2[\"GLM_4V_PLUS\"] = \"glm-4v-plus\";\n GLMChatModel2[\"GLM_4_LONG\"] = \"glm-4-long\";\n GLMChatModel2[\"GLM_4_PLUS\"] = \"glm-4-plus\";\n return GLMChatModel2;\n})(GLMChatModel || {});\nvar BaiduChatModel = /* @__PURE__ */ ((BaiduChatModel2) => {\n BaiduChatModel2[\"ERNIE_3_5\"] = \"completions\";\n BaiduChatModel2[\"ERNIE_3_5_PRE\"] = \"ernie-3.5-8k-preview\";\n BaiduChatModel2[\"ERNIE_3_5_128K\"] = \"ernie-3.5-128k\";\n BaiduChatModel2[\"ERNIE_4_0_LATEST\"] = \"ernie-4.0-8k-latest\";\n BaiduChatModel2[\"ERNIE_4_0_PREVIEW\"] = \"ernie-4.0-8k-preview\";\n BaiduChatModel2[\"ERNIE_4_0_8K\"] = \"completions_pro\";\n BaiduChatModel2[\"ERNIE_4_0_TURBO_LATEST\"] = \"ernie-4.0-turbo-8k-latest\";\n BaiduChatModel2[\"ERNIE_4_0_TURBO_PREVIEW\"] = \"ernie-4.0-turbo-8k-preview\";\n BaiduChatModel2[\"ERNIE_4_0_TURBO_8K\"] = \"ernie-4.0-turbo-8k\";\n BaiduChatModel2[\"ERNIE_4_0_TURBO_128K\"] = \"ernie-4.0-turbo-128k\";\n BaiduChatModel2[\"ERNIE_SPEED_8K\"] = \"ernie_speed\";\n BaiduChatModel2[\"ERNIE_SPEED_128K\"] = \"ernie-speed-128k\";\n BaiduChatModel2[\"ERNIE_SPEED_PRO_128K\"] = \"ernie-speed-pro-128k\";\n BaiduChatModel2[\"ERNIE_LITE_8K\"] = \"ernie-lite-8k\";\n BaiduChatModel2[\"ERNIE_LITE_PRO_128K\"] = \"ernie-lite-pro-128k\";\n BaiduChatModel2[\"ERNIE_TINY_8K\"] = \"ernie-tiny-8k\";\n BaiduChatModel2[\"ERNIE_CHAR_8K\"] = \"ernie-char-8k\";\n BaiduChatModel2[\"ERNIE_CHAR_FICTION_8K\"] = \"ernie-char-fiction-8k\";\n BaiduChatModel2[\"ERNIE_NOVEL_8K\"] = \"ernie-novel-8k\";\n return BaiduChatModel2;\n})(BaiduChatModel || {});\nvar IFlyTekChatModel = /* @__PURE__ */ ((IFlyTekChatModel2) => {\n IFlyTekChatModel2[\"SPARK_LITE\"] = \"lite\";\n IFlyTekChatModel2[\"SPARK_PRO\"] = \"generalv3\";\n IFlyTekChatModel2[\"SPARK_PRO_128K\"] = \"pro-128k\";\n IFlyTekChatModel2[\"SPARK_MAX\"] = \"generalv3.5\";\n IFlyTekChatModel2[\"SPARK_MAX_32K\"] = \"max-32k\";\n IFlyTekChatModel2[\"SPARK_ULTRA\"] = \"4.0Ultra\";\n return IFlyTekChatModel2;\n})(IFlyTekChatModel || {});\nvar MoonShotChatModel = /* @__PURE__ */ ((MoonShotChatModel2) => {\n MoonShotChatModel2[\"MOON_V1_8K\"] = \"moonshot-v1-8k\";\n MoonShotChatModel2[\"MOON_V1_32K\"] = \"moonshot-v1-32k\";\n MoonShotChatModel2[\"MOON_V1_128K\"] = \"moonshot-v1-128k\";\n return MoonShotChatModel2;\n})(MoonShotChatModel || {});\nvar AliChatModel = /* @__PURE__ */ ((AliChatModel2) => {\n AliChatModel2[\"QWEN_MAX\"] = \"qwen-max\";\n AliChatModel2[\"QWEN_PLUS\"] = \"qwen-plus\";\n AliChatModel2[\"QWEN_TURBO\"] = \"qwen-turbo\";\n AliChatModel2[\"QWEN_LONG\"] = \"qwen-long\";\n AliChatModel2[\"QWEN_CODE\"] = \"qwen-coder-turbo\";\n AliChatModel2[\"QWEN_MATH\"] = \"qwen-math-plus\";\n AliChatModel2[\"QWEN_VL_MAX\"] = \"qwen-vl-max\";\n AliChatModel2[\"QWEN_VL_PLUS\"] = \"qwen-vl-plus\";\n return AliChatModel2;\n})(AliChatModel || {});\nvar XAIChatModel = /* @__PURE__ */ ((XAIChatModel2) => {\n XAIChatModel2[\"GROK2\"] = \"grok-2\";\n XAIChatModel2[\"GROK2_VISION\"] = \"grok-2-vision\";\n XAIChatModel2[\"GROK3\"] = \"grok-3\";\n XAIChatModel2[\"GROK3_VISION\"] = \"grok-3-vision\";\n return XAIChatModel2;\n})(XAIChatModel || {});\nvar ChatModel = {\n ...OpenAIChatModel,\n ...DeepSeekChatModel,\n ...BaiduChatModel,\n ...GLMChatModel,\n ...IFlyTekChatModel,\n ...GoogleChatModel,\n ...OpenAIChatModel,\n ...MoonShotChatModel,\n ...AliChatModel,\n ...XAIChatModel\n};\nvar MidJourneyImagineModel = /* @__PURE__ */ ((MidJourneyImagineModel2) => {\n MidJourneyImagineModel2[\"MJ\"] = \"midjourney\";\n return MidJourneyImagineModel2;\n})(MidJourneyImagineModel || {});\nvar OpenAIImagineModel = /* @__PURE__ */ ((OpenAIImagineModel2) => {\n OpenAIImagineModel2[\"DALL_E_2\"] = \"dall-e-2\";\n OpenAIImagineModel2[\"DALL_E_3\"] = \"dall-e-3\";\n return OpenAIImagineModel2;\n})(OpenAIImagineModel || {});\nvar StabilityAIImagineModel = /* @__PURE__ */ ((StabilityAIImagineModel2) => {\n StabilityAIImagineModel2[\"SD_1_6\"] = \"stable-diffusion-v1-6\";\n StabilityAIImagineModel2[\"SD_XL_1024\"] = \"stable-diffusion-xl-1024-v1-0\";\n return StabilityAIImagineModel2;\n})(StabilityAIImagineModel || {});\nvar IFlyTekImagineModel = /* @__PURE__ */ ((IFlyTekImagineModel2) => {\n IFlyTekImagineModel2[\"V2\"] = \"v2.1\";\n return IFlyTekImagineModel2;\n})(IFlyTekImagineModel || {});\nvar ImagineModel = {\n ...OpenAIImagineModel,\n ...MidJourneyImagineModel,\n ...StabilityAIImagineModel,\n ...IFlyTekImagineModel\n};\nvar ModelModel = { ...ChatModel, ...ImagineModel, ...EmbedModel };\nvar MJTaskType = /* @__PURE__ */ ((MJTaskType4) => {\n MJTaskType4[\"IMAGINE\"] = \"IMAGINE\";\n MJTaskType4[\"UPSCALE\"] = \"UPSCALE\";\n MJTaskType4[\"VARIATION\"] = \"VARIATION\";\n MJTaskType4[\"REROLL\"] = \"REROLL\";\n MJTaskType4[\"DESCRIBE\"] = \"DESCRIBE\";\n MJTaskType4[\"BLEND\"] = \"BLEND\";\n return MJTaskType4;\n})(MJTaskType || {});\nvar DETaskType = /* @__PURE__ */ ((DETaskType2) => {\n DETaskType2[\"GENERATION\"] = \"generations\";\n DETaskType2[\"EDIT\"] = \"edits\";\n DETaskType2[\"VARIATION\"] = \"variation\";\n return DETaskType2;\n})(DETaskType || {});\nvar SDTaskType = /* @__PURE__ */ ((SDTaskType2) => {\n SDTaskType2[\"GENERATION\"] = \"generation\";\n return SDTaskType2;\n})(SDTaskType || {});\nvar SPKTaskType = /* @__PURE__ */ ((SPKTaskType2) => {\n SPKTaskType2[\"GENERATION\"] = \"generation\";\n return SPKTaskType2;\n})(SPKTaskType || {});\nvar ImgTaskType = { ...MJTaskType, ...DETaskType, ...SDTaskType, ...SPKTaskType };\nvar ChatRoleEnum = /* @__PURE__ */ ((ChatRoleEnum2) => {\n ChatRoleEnum2[\"SYSTEM\"] = \"system\";\n ChatRoleEnum2[\"USER\"] = \"user\";\n ChatRoleEnum2[\"ASSISTANT\"] = \"assistant\";\n ChatRoleEnum2[\"TOOL\"] = \"tool\";\n ChatRoleEnum2[\"DEV\"] = \"developer\";\n return ChatRoleEnum2;\n})(ChatRoleEnum || {});\nvar GPTChatRoleEnum = /* @__PURE__ */ ((GPTChatRoleEnum2) => {\n GPTChatRoleEnum2[\"SYSTEM\"] = \"system\";\n GPTChatRoleEnum2[\"USER\"] = \"user\";\n GPTChatRoleEnum2[\"ASSISTANT\"] = \"assistant\";\n GPTChatRoleEnum2[\"DEV\"] = \"developer\";\n GPTChatRoleEnum2[\"TOOL\"] = \"tool\";\n return GPTChatRoleEnum2;\n})(GPTChatRoleEnum || {});\nvar DSChatRoleEnum = /* @__PURE__ */ ((DSChatRoleEnum2) => {\n DSChatRoleEnum2[\"SYSTEM\"] = \"system\";\n DSChatRoleEnum2[\"USER\"] = \"user\";\n DSChatRoleEnum2[\"ASSISTANT\"] = \"assistant\";\n DSChatRoleEnum2[\"TOOL\"] = \"tool\";\n return DSChatRoleEnum2;\n})(DSChatRoleEnum || {});\nvar SPKChatRoleEnum = /* @__PURE__ */ ((SPKChatRoleEnum2) => {\n SPKChatRoleEnum2[\"USER\"] = \"user\";\n SPKChatRoleEnum2[\"ASSISTANT\"] = \"assistant\";\n SPKChatRoleEnum2[\"SYSTEM\"] = \"system\";\n SPKChatRoleEnum2[\"TOOL\"] = \"tool\";\n return SPKChatRoleEnum2;\n})(SPKChatRoleEnum || {});\nvar GLMChatRoleEnum = /* @__PURE__ */ ((GLMChatRoleEnum2) => {\n GLMChatRoleEnum2[\"SYSTEM\"] = \"system\";\n GLMChatRoleEnum2[\"USER\"] = \"user\";\n GLMChatRoleEnum2[\"ASSISTANT\"] = \"assistant\";\n GLMChatRoleEnum2[\"TOOL\"] = \"tool\";\n return GLMChatRoleEnum2;\n})(GLMChatRoleEnum || {});\nvar GEMChatRoleEnum = /* @__PURE__ */ ((GEMChatRoleEnum2) => {\n GEMChatRoleEnum2[\"USER\"] = \"user\";\n GEMChatRoleEnum2[\"MODEL\"] = \"model\";\n return GEMChatRoleEnum2;\n})(GEMChatRoleEnum || {});\nvar BDUChatRoleEnum = /* @__PURE__ */ ((BDUChatRoleEnum2) => {\n BDUChatRoleEnum2[\"USER\"] = \"user\";\n BDUChatRoleEnum2[\"ASSISTANT\"] = \"assistant\";\n return BDUChatRoleEnum2;\n})(BDUChatRoleEnum || {});\n\n// src/providers/OpenAI.ts\nimport { PassThrough, Readable } from \"stream\";\nimport EventSourceStream from \"@server-sent-stream/node\";\nimport { decodeStream } from \"iconv-lite\";\n\n// src/util.ts\nimport { writeFileSync } from \"fs\";\nimport axios from \"axios\";\nimport { LocalStorage } from \"node-localstorage\";\nimport path from \"path\";\nimport isBase64 from \"is-base64\";\nvar localStorage = new LocalStorage(\"./cache\", Infinity);\nvar util_default = {\n /**\n * Performs an HTTP GET request.\n *\n * @param url - The URL to make the request to.\n * @param params - Optional request parameters.\n * @param config - Optional Axios request configuration.\n * @returns A Promise that resolves with the response data.\n */\n async get(url, params, config) {\n return (await axios.get(url, { params, ...config })).data;\n },\n /**\n * Performs an HTTP POST request.\n *\n * @param url - The URL to make the request to.\n * @param body - The request body.\n * @param config - Optional Axios request configuration.\n * @returns A Promise that resolves with the response data.\n */\n async post(url, body, config) {\n return (await axios.post(url, body, config)).data;\n },\n /**\n * Parses JSON from a string and returns it as a generic type T.\n *\n * @param str - The JSON string to parse.\n * @returns The parsed JSON as a generic type T.\n */\n json(str) {\n try {\n if (!str) return null;\n return JSON.parse(str);\n } catch (e) {\n return null;\n }\n },\n getRandomKey(arr) {\n return arr[Math.floor(Math.random() * arr.length)];\n },\n getRandomId(length = 16) {\n let result = \"\";\n while (result.length < length) {\n let rand = Math.floor(Math.random() * 10);\n if (result.length === 0 && rand === 0) continue;\n result += rand.toString();\n }\n return result;\n },\n /**\n * Computes the greatest common divisor (GCD) of two numbers using Euclidean algorithm.\n *\n * @param a - The first number.\n * @param b - The second number.\n * @returns The GCD of the two numbers.\n */\n getGCD(a, b) {\n if (b === 0) return a;\n return this.getGCD(b, a % b);\n },\n /**\n * Calculates and returns the aspect ratio of a width and height.\n *\n * @param width - The width dimension.\n * @param height - The height dimension.\n * @returns The aspect ratio in the format \"width:height\".\n */\n getAspect(width, height) {\n if (!width || !height) return \"1:1\";\n const gcd = this.getGCD(width, height);\n const aspectRatioWidth = width / gcd;\n const aspectRatioHeight = height / gcd;\n return `${aspectRatioWidth}:${aspectRatioHeight}`;\n },\n /**\n * Stores an item in local storage with the specified key.\n *\n * @param key - The key under which to store the item.\n * @param value - The value to be stored.\n */\n setItem(key, value) {\n localStorage.setItem(key, JSON.stringify(value));\n },\n /**\n * Retrieves an item from local storage by its key and parses it as a generic type T.\n *\n * @param key - The key of the item to retrieve.\n * @returns The parsed item as a generic type T.\n */\n getItem(key) {\n return this.json(localStorage.getItem(key));\n },\n /**\n * This method is used to write given data to a file. The data can either be a base64 string or an HTTP/HTTPS URL.\n * If the data is a URL, the method fetches the data as a readable stream and writes it to the file.\n * If it's a base64 string, it writes it directly to the file.\n * @param data - The data to be written to the file. Can be a base64 string or a HTTP/HTTPS img URL.\n * @param filename - The name of the file where the data will be written. If not provided, a unique filename will be generated.\n * @returns - The file path where the data was written.\n */\n async writeFile(data, filename = \"\") {\n const filepath = path.join(\"./cache\", filename);\n if (data.startsWith(\"http://\") || data.startsWith(\"https://\")) {\n const res = await this.get(data, {}, { responseType: \"arraybuffer\" });\n if (!(res instanceof Buffer)) throw new Error(\"Img is not a buffer\");\n writeFileSync(filepath, res);\n } else writeFileSync(filepath, Buffer.from(data, \"base64\"));\n return filepath;\n },\n isBase64(data, allowMime = true) {\n return isBase64(data, { allowMime });\n }\n};\n\n// src/providers/OpenAI.ts\nvar STORAGE_KEY = \"task_open_ai\";\nvar API = \"https://api.openai.com\";\nvar VER = \"v1\";\nvar OpenAI = class {\n /**\n * Constructor for the OpenAI class.\n *\n * @param key - The API key for OpenAI.\n * @param api - The API endpoint for proxy (optional).\n */\n constructor(key, api = API) {\n this.key = key;\n this.api = api;\n }\n /**\n * Fetches embeddings for input text.\n *\n * @param input - An array of input strings.\n * @param model - The model to use for embeddings (default: text-embedding-ada-002).\n * @returns A promise resolving to the embedding response.\n */\n async embedding(input, model = \"text-embedding-ada-002\" /* ADA */) {\n const key = Array.isArray(this.key) ? util_default.getRandomKey(this.key) : this.key;\n if (!key) throw new Error(\"OpenAI API key is not set in config\");\n const res = await util_default.post(\n `${this.api}/${VER}/embeddings`,\n { model, input },\n { headers: { Authorization: `Bearer ${key}` }, responseType: \"json\" }\n );\n const data = {\n embedding: res.data.map((v) => v.embedding),\n object: \"embedding\",\n model,\n promptTokens: res.usage.prompt_tokens || 0,\n totalTokens: res.usage.total_tokens || 0\n };\n return data;\n }\n /**\n * Sends messages to the GPT chat model.\n *\n * @param messages - An array of chat messages.\n * @param model - The model to use for chat (default: gpt-3.5-turbo).\n * @param stream - Whether to use stream response (default: false).\n * @param top - Top probability to sample (optional).\n * @param temperature - Temperature for sampling (optional).\n * @param maxLength - Maximum token length for response (optional).\n * @param tools - Tools for model to use (optional).\n * @param toolChoice - Controls which (if any) tool is called by the model: none, required, auto (optional).\n * @returns A promise resolving to the chat response or a stream.\n */\n async chat(messages, model = \"gpt-4o\" /* GPT_4O */, stream = false, top, temperature, maxLength, tools, toolChoice) {\n const key = Array.isArray(this.key) ? util_default.getRandomKey(this.key) : this.key;\n if (!key) throw new Error(\"OpenAI API key is not set in config\");\n if (![\n \"gpt-4-turbo\" /* GPT4_TURBO */,\n \"gpt-4o\" /* GPT_4O */,\n \"gpt-4o-mini\" /* GPT_4O_MINI */,\n \"chatgpt-4o-latest\" /* CHAT_GPT_4O */,\n \"o1\" /* O1 */,\n \"o1-mini\" /* O1_MINI */,\n \"o1-preview\" /* O1_PRE */\n ].includes(model))\n messages = messages.map(({ role, content }) => ({ role, content }));\n if (typeof temperature === \"number\") {\n if (temperature < 0) temperature = 0;\n if (temperature > 1) temperature = 1;\n }\n if (typeof top === \"number\") {\n if (top < 0) top = 0;\n if (top > 1) top = 1;\n }\n const res = await util_default.post(\n `${this.api}/${VER}/chat/completions`,\n {\n model,\n messages: this.formatMessage(messages),\n stream,\n temperature,\n top_p: top,\n max_completion_tokens: maxLength,\n tools,\n tool_choice: toolChoice\n },\n { headers: { Authorization: `Bearer ${key}` }, responseType: stream ? \"stream\" : \"json\" }\n );\n const data = {\n content: \"\",\n model,\n object: \"\",\n promptTokens: 0,\n completionTokens: 0,\n totalTokens: 0\n };\n if (res instanceof Readable) {\n const output = new PassThrough();\n const parser = new EventSourceStream();\n parser.on(\"data\", (e) => {\n const obj = util_default.json(e.data);\n if (obj) {\n data.content = obj.choices[0]?.delta?.content || \"\";\n if (obj.choices[0]?.delta?.tool_calls) data.tools = obj.choices[0]?.delta?.tool_calls;\n data.model = obj.model;\n data.object = obj.object;\n data.promptTokens = obj.usage?.prompt_tokens || 0;\n data.completionTokens = obj.usage?.completion_tokens || 0;\n data.totalTokens = obj.usage?.total_tokens || 0;\n output.write(JSON.stringify(data));\n }\n });\n parser.on(\"error\", (e) => output.destroy(e));\n parser.on(\"end\", () => output.end());\n res.pipe(decodeStream(\"utf-8\")).pipe(parser);\n return output;\n } else {\n data.content = res.choices[0]?.message?.content || \"\";\n if (res.choices[0]?.message?.tool_calls) data.tools = res.choices[0]?.message?.tool_calls;\n data.model = res.model;\n data.object = res.object;\n data.promptTokens = res.usage?.prompt_tokens || 0;\n data.completionTokens = res.usage?.completion_tokens || 0;\n data.totalTokens = res.usage?.total_tokens || 0;\n return data;\n }\n }\n /**\n * Generates images based on a prompt.\n *\n * @param prompt - The prompt for image generation.\n * @param width - Image width (default: 1024).\n * @param height - Image height (default: 1024).\n * @param n - Number of images to generate (default: 1).\n * @param model - Model choice (default: dall-e-3).\n * @returns A promise resolving to the image generation response.\n */\n async imagine(prompt, width = 1024, height = 1024, n = 1, model = \"dall-e-3\" /* DALL_E_3 */) {\n const key = Array.isArray(this.key) ? util_default.getRandomKey(this.key) : this.key;\n if (!key) throw new Error(\"OpenAI API key is not set in config\");\n const res = await util_default.post(\n `${this.api}/${VER}/images/${\"generations\" /* GENERATION */}`,\n { model, prompt, n, size: `${width}x${height}`, response_format: \"b64_json\" },\n { headers: { Authorization: `Bearer ${key}` }, responseType: \"json\" }\n );\n const id = util_default.getRandomId();\n const imgs = [];\n for (const i in res.data) imgs.push(await util_default.writeFile(res.data[i].b64_json, `${id}-${i}.png`));\n const time = Date.now();\n const task = {\n id,\n type: \"generations\" /* GENERATION */,\n info: \"success\",\n progress: 100,\n imgs,\n fail: \"\",\n created: time,\n model\n };\n const tasks = util_default.getItem(STORAGE_KEY) || [];\n tasks.push(task);\n util_default.setItem(STORAGE_KEY, tasks);\n return { taskId: task.id, time };\n }\n /**\n * Simulate tasks.\n *\n * @param id - The task ID to retrieve (optional).\n * @returns An array of task responses or a specific task by ID.\n */\n task(id) {\n const tasks = util_default.getItem(STORAGE_KEY) || [];\n if (id) return tasks.filter((v) => v.id === id);\n else return tasks;\n }\n /**\n * Formats chat messages according to the GPT model's message format.\n *\n * @param messages - An array of chat messages.\n * @returns Formatted messages compatible with the GPT model.\n */\n formatMessage(messages) {\n const prompt = [];\n for (const { role, content, img, tool } of messages) {\n switch (role) {\n case \"user\" /* USER */:\n if (img)\n prompt.push({\n role,\n content: [\n { type: \"text\", text: content },\n { type: \"image_url\", image_url: { url: img } }\n ]\n });\n else prompt.push({ role, content });\n break;\n case \"tool\" /* TOOL */:\n prompt.push({ role, content, tool_call_id: tool });\n break;\n default:\n prompt.push({ role, content });\n break;\n }\n }\n return prompt;\n }\n};\n\n// src/providers/GLM.ts\nimport { PassThrough as PassThrough2, Readable as Readable2 } from \"stream\";\nimport EventSourceStream2 from \"@server-sent-stream/node\";\nimport { decodeStream as decodeStream2 } from \"iconv-lite\";\nvar API2 = \"https://open.bigmodel.cn\";\nvar GLM = class {\n constructor(key, proxyAPI = API2) {\n this.key = key;\n this.proxyAPI = proxyAPI;\n }\n /**\n * Fetches embeddings for input text.\n *\n * @param input - An array of input strings.\n * @param model - The model to use for embeddings (default: text-embedding-ada-002).\n * @returns A promise resolving to the embedding response.\n */\n async embedding(input, model = \"embedding-2\" /* EMBED_2 */, dimensions = 1024) {\n const key = Array.isArray(this.key) ? util_default.getRandomKey(this.key) : this.key;\n if (!key) throw new Error(\"ZhiPu GLM API key is not set in config\");\n const res = await util_default.post(\n `${this.proxyAPI}/api/paas/v4/embeddings`,\n { model, input, dimensions },\n { headers: { Authorization: `Bearer ${key}` }, responseType: \"json\" }\n );\n return {\n embedding: res.data.map((v) => v.embedding),\n object: \"embedding\",\n model,\n promptTokens: res.usage.prompt_tokens,\n totalTokens: res.usage.total_tokens\n };\n }\n /**\n * Sends messages to the GLM chat model.\n *\n * @param messages - An array of chat messages.\n * @param model - The model to use for chat (default: GLM_6B a local deployed chatglm3-6b-32k).\n * @param stream - Whether to use stream response (default: false).\n * @param top - Top probability to sample (optional).\n * @param temperature - Temperature for sampling (optional).\n * @param maxLength - Maximum token length for response (optional).\n * @returns A promise resolving to the chat response or a stream.\n */\n async chat(messages, model = \"glm-3-turbo\" /* GLM_3_TURBO */, stream = false, top, temperature, maxLength, tools, toolChoice) {\n if (![\"glm-4v\" /* GLM_4V */, \"glm-4v-plus\" /* GLM_4V_PLUS */].includes(model))\n messages = messages.map(({ role, content }) => ({ role, content }));\n if (typeof temperature === \"number\") {\n if (temperature <= 0) temperature = 0.1;\n if (temperature > 1) temperature = 1;\n }\n if (typeof top === \"number\") {\n if (top <= 0) top = 0.1;\n if (top >= 1) top = 0.9;\n }\n const data = {\n content: \"\",\n model,\n object: \"\",\n promptTokens: 0,\n completionTokens: 0,\n totalTokens: 0\n };\n const key = Array.isArray(this.key) ? util_default.getRandomKey(this.key) : this.key;\n if (!key) throw new Error(\"ZhiPu GLM API key is not set in config\");\n const res = await util_default.post(\n `${this.proxyAPI}/api/paas/v4/chat/completions`,\n {\n model,\n messages: this.formatMessage(messages),\n stream,\n temperature,\n top_p: top,\n max_tokens: maxLength,\n tools,\n tool_choice: toolChoice\n },\n { headers: { Authorization: `Bearer ${key}` }, responseType: stream ? \"stream\" : \"json\" }\n );\n if (res instanceof Readable2) {\n const output = new PassThrough2();\n const parser = new EventSourceStream2();\n parser.on(\"data\", (e) => {\n const obj = util_default.json(e.data);\n if (obj) {\n data.content = obj.choices[0]?.delta?.content || \"\";\n if (obj.choices[0]?.delta?.tool_calls) data.tools = obj.choices[0]?.delta?.tool_calls;\n data.model = obj.model;\n data.object = obj.object || \"chat.completion.chunk\";\n data.promptTokens = obj.usage?.prompt_tokens || 0;\n data.completionTokens = obj.usage?.completion_tokens || 0;\n data.totalTokens = obj.usage?.total_tokens || 0;\n if (data.content) output.write(JSON.stringify(data));\n }\n });\n parser.on(\"error\", (e) => output.destroy(e));\n parser.on(\"end\", () => output.end());\n res.pipe(decodeStream2(\"utf-8\")).pipe(parser);\n return output;\n } else {\n data.content = res.choices[0]?.message?.content || \"\";\n if (res.choices[0]?.message?.tool_calls) data.tools = res.choices[0]?.message?.tool_calls;\n data.model = res.model;\n data.object = res.object || \"chat.completion\";\n data.promptTokens = res.usage?.prompt_tokens || 0;\n data.completionTokens = res.usage?.completion_tokens || 0;\n data.totalTokens = res.usage?.total_tokens || 0;\n return data;\n }\n }\n /**\n * Formats chat messages according to the GPT model's message format.\n *\n * @param messages - An array of chat messages.\n * @returns Formatted messages compatible with the GPT model.\n */\n formatMessage(messages) {\n const prompt = [];\n for (const { role, content, img, tool } of messages) {\n if (role === \"developer\" /* DEV */) continue;\n switch (role) {\n case \"user\" /* USER */:\n if (img)\n prompt.push({\n role,\n content: [\n { type: \"text\", text: content },\n { type: \"image_url\", image_url: { url: img } }\n ]\n });\n else prompt.push({ role, content });\n break;\n case \"tool\" /* TOOL */:\n prompt.push({\n role,\n content,\n tool_call_id: tool || \"\"\n });\n break;\n default:\n prompt.push({ role, content });\n break;\n }\n }\n return prompt;\n }\n};\n\n// src/providers/Other.ts\nimport { decodeStream as decodeStream3 } from \"iconv-lite\";\nimport { PassThrough as PassThrough3, Readable as Readable3 } from \"stream\";\nimport EventSourceStream3 from \"@server-sent-stream/node\";\nvar Other = class {\n /**\n * Constructor for the OpenAI class.\n *\n * @param api - The API endpoint for the model (optional).\n * @param key - The API key for the model (Optional).\n */\n constructor(api, key) {\n this.api = api;\n this.key = key;\n }\n /**\n * Fetches embeddings for input text.\n *\n * @param input - An array of input strings.\n * @param model - The model to use for embeddings (default: text-embedding-ada-002).\n * @returns A promise resolving to the embedding response.\n */\n async embedding(input, model = EmbedModel.BGE_M3) {\n if (!this.api) throw new Error(\"Other embed model API is not set in config\");\n const key = Array.isArray(this.key) ? util_default.getRandomKey(this.key) : this.key;\n const res = await util_default.post(\n `${this.api}/v1/embeddings`,\n { model, input },\n { headers: { Authorization: `Bearer ${key}` }, responseType: \"json\" }\n );\n const data = {\n embedding: res.data.map((v) => v.embedding),\n object: \"embedding\",\n model,\n promptTokens: res.usage.prompt_tokens || 0,\n totalTokens: res.usage.total_tokens || 0\n };\n return data;\n }\n /**\n * Sends messages to the GPT compatible chat model.\n *\n * @param messages - An array of chat messages.\n * @param model - The model to use for chat (default: gpt-3.5-turbo).\n * @param stream - Whether to use stream response (default: false).\n * @param top - Top probability to sample (optional).\n * @param temperature - Temperature for sampling (optional).\n * @param maxLength - Maximum token length for response (optional).\n * @returns A promise resolving to the chat response or a stream.\n */\n async chat(messages, model = ChatModel.GPT3, stream = false, top, temperature, maxLength, tools, toolChoice) {\n if (!this.api) throw new Error(\"Other chat model API is not set in config\");\n const key = Array.isArray(this.key) ? util_default.getRandomKey(this.key) : this.key;\n const res = await util_default.post(\n `${this.api}/v1/chat/completions`,\n {\n model,\n messages: this.formatMessage(messages),\n stream,\n temperature,\n top_p: top,\n max_tokens: maxLength,\n tools,\n tool_choice: toolChoice\n },\n { headers: { Authorization: `Bearer ${key}` }, responseType: stream ? \"stream\" : \"json\" }\n );\n const data = {\n content: \"\",\n model,\n object: \"\",\n promptTokens: 0,\n completionTokens: 0,\n totalTokens: 0\n };\n if (res instanceof Readable3) {\n const output = new PassThrough3();\n const parser = new EventSourceStream3();\n parser.on(\"data\", (e) => {\n const obj = util_default.json(e.data);\n if (obj) {\n data.content = obj.choices[0]?.delta?.content || \"\";\n if (obj.choices[0]?.delta?.tool_calls) data.tools = obj.choices[0]?.delta?.tool_calls;\n data.model = obj.model || model;\n data.object = obj.object || \"chat.completion.chunk\";\n data.promptTokens = obj.usage?.prompt_tokens || 0;\n data.completionTokens = obj.usage?.completion_tokens || 0;\n data.totalTokens = obj.usage?.total_tokens || 0;\n output.write(JSON.stringify(data));\n }\n });\n parser.on(\"error\", (e) => output.destroy(e));\n parser.on(\"end\", () => output.end());\n res.pipe(decodeStream3(\"utf-8\")).pipe(parser);\n return output;\n } else {\n data.content = res.choices[0]?.message?.content || \"\";\n if (res.choices[0]?.message?.tool_calls) data.tools = res.choices[0]?.message?.tool_calls;\n data.model = res.model || model;\n data.object = res.object || \"chat.completion\";\n data.promptTokens = res.usage?.prompt_tokens || 0;\n data.completionTokens = res.usage?.completion_tokens || 0;\n data.totalTokens = res.usage?.total_tokens || 0;\n return data;\n }\n }\n /**\n * Formats chat messages according to the GPT model's message format.\n *\n * @param messages - An array of chat messages.\n * @returns Formatted messages compatible with the GPT model.\n */\n formatMessage(messages) {\n const prompt = [];\n for (const { role, content, img, tool } of messages) {\n switch (role) {\n case \"user\" /* USER */:\n if (img)\n prompt.push({\n role,\n content: [\n { type: \"text\", text: content },\n { type: \"image_url\", image_url: { url: img } }\n ]\n });\n else prompt.push({ role, content });\n break;\n case \"tool\" /* TOOL */:\n prompt.push({\n role,\n content,\n tool_call_id: tool || \"\"\n });\n break;\n default:\n prompt.push({ role, content });\n break;\n }\n }\n return prompt;\n }\n};\n\n// src/providers/Google.ts\nimport { PassThrough as PassThrough4, Readable as Readable4 } from \"stream\";\nimport { JSONParser } from \"@streamparser/json-node\";\nimport { decodeStream as decodeStream4 } from \"iconv-lite\";\nimport { extname } from \"path\";\nimport { readFileSync } from \"fs\";\nvar API3 = \"https://generativelanguage.googleapis.com\";\nvar SAFE_SET = [\n { category: \"HARM_CATEGORY_HARASSMENT\", threshold: \"BLOCK_NONE\" },\n { category: \"HARM_CATEGORY_HATE_SPEECH\", threshold: \"BLOCK_NONE\" },\n { category: \"HARM_CATEGORY_SEXUALLY_EXPLICIT\", threshold: \"BLOCK_NONE\" },\n { category: \"HARM_CATEGORY_DANGEROUS_CONTENT\", threshold: \"BLOCK_NONE\" }\n];\nvar Google = class {\n /**\n * Google API client class for chat functionalities.\n * @param key - The Google API key or an array of keys.\n * @param api - The API endpoint (default: 'https://generativelanguage.googleapis.com').\n */\n constructor(key, api = API3) {\n this.key = key;\n this.api = api;\n }\n /**\n * Fetches embeddings for input text.\n *\n * @param input - An array of input strings.\n * @param model - The model to use for embeddings (default: text-embedding-ada-002).\n * @returns A promise resolving to the embedding response.\n */\n async embedding(input, model = \"text-embedding-004\" /* EMBED_4 */) {\n const key = Array.isArray(this.key) ? util_default.getRandomKey(this.key) : this.key;\n if (!key) throw new Error(\"Google API key is not set in config\");\n const request = [];\n for (const text of input) {\n request.push(\n util_default.post(\n `${this.api}/v1beta/models/${model}:embedContent?key=${key}`,\n { model: `models/${model}`, content: { parts: [{ text }] } }\n )\n );\n }\n const res = await Promise.all(request);\n const data = {\n embedding: res.map((v) => v.embedding.values),\n object: \"embedding\",\n model,\n promptTokens: 0,\n totalTokens: 0\n };\n return data;\n }\n /**\n * Sends messages to the Google Gemini chat model.\n * @param messages - An array of chat messages.\n * @param model - The model to use for chat (default: gemini-pro).\n * @param stream - Whether to use stream response (default: false).\n * @param top - Top probability to sample (optional).\n * @param temperature - Temperature for sampling (optional).\n * @param maxLength - Maximum token length for response (optional).\n * @returns A promise resolving to the chat response or a stream.\n */\n async chat(messages, model = \"gemini-1.5-pro\" /* GEM_PRO_1_5 */, stream = false, top, temperature, maxLength) {\n const key = Array.isArray(this.key) ? util_default.getRandomKey(this.key) : this.key;\n if (!key) throw new Error(\"Google AI API key is not set in config\");\n if (typeof temperature === \"number\") {\n if (temperature < 0) temperature = 0;\n if (temperature > 1) temperature = 1;\n }\n if (typeof top === \"number\") {\n if (top < 0) top = 0;\n if (top > 1) top = 1;\n }\n const res = await util_default.post(\n `${this.api}/v1beta/models/${model}:${stream ? \"streamGenerateContent\" : \"generateContent\"}?key=${key}`,\n {\n contents: await this.formatMessage(messages),\n system_instruction: {\n parts: {\n text: messages.filter((v) => v.role === \"system\" /* SYSTEM */).map((v) => v.content).join(\"\\n\")\n }\n },\n generationConfig: { topP: top, temperature, maxOutputTokens: maxLength },\n safetySettings: SAFE_SET\n },\n { responseType: stream ? \"stream\" : \"json\" }\n );\n const data = {\n content: \"\",\n model,\n object: \"\",\n promptTokens: 0,\n completionTokens: 0,\n totalTokens: 0\n };\n if (res instanceof Readable4) {\n const output = new PassThrough4();\n const parser = new JSONParser();\n parser.on(\"data\", ({ value }) => {\n if (value.candidates || value.promptFeedback) {\n const obj = value;\n const block = obj.promptFeedback?.blockReason;\n if (block) return output.destroy(new Error(`Content blocked, reason: ${block}`));\n if (!obj.candidates) return output.destroy(new Error(\"Google API error, no candidates\"));\n const candidate = obj.candidates[0];\n if (!candidate.content) return output.destroy(new Error(candidate.finishReason));\n data.content = candidate.content.parts[0].text || \"\";\n data.object = `chat.completion.chunk`;\n return output.write(JSON.stringify(data));\n } else return output;\n });\n parser.on(\"error\", (e) => output.destroy(e));\n parser.on(\"end\", () => output.end());\n res.pipe(decodeStream4(\"utf-8\")).pipe(parser);\n return output;\n } else {\n const block = res.promptFeedback?.blockReason;\n if (block) throw new Error(`Content blocked, reason: ${block}`);\n if (!res.candidates) throw new Error(\"Google API error, no candidates\");\n const candidate = res.candidates[0];\n if (!candidate.content) throw new Error(candidate.finishReason);\n data.content = candidate.content.parts[0].text || candidate.finishReason;\n data.object = `chat.completion`;\n return data;\n }\n }\n /**\n * Formats chat messages into GEMChatMessage format.\n * @param messages - An array of chat messages.\n * @returns A formatted array of GEMChatMessage.\n */\n async formatMessage(messages) {\n const prompt = [];\n let input = \"\";\n let base64 = null;\n for (const { role, content, img } of messages) {\n if (!content) continue;\n if (role === \"system\" /* SYSTEM */) continue;\n if (img) base64 = await this.toBase64(img);\n if (role !== \"assistant\" /* ASSISTANT */) input += `\n${content}`;\n else {\n input = input.trim();\n prompt.push({\n role: \"user\" /* USER */,\n parts: base64 ? [{ text: input || \" \" }, { inline_data: { mime_type: base64.mime, data: base64.data } }] : [{ text: input || \" \" }]\n });\n prompt.push({ role: \"model\" /* MODEL */, parts: [{ text: content }] });\n input = \"\";\n }\n }\n input = input.trim();\n if (!input) throw new Error(\"User input nothing\");\n prompt.push({\n role: \"user\" /* USER */,\n parts: base64 ? [{ text: input || \" \" }, { inline_data: { mime_type: base64.mime, data: base64.data } }] : [{ text: input || \" \" }]\n });\n return base64 ? [prompt[prompt.length - 1]] : prompt;\n }\n /**\n * Convert an image to base64 format object.\n * @param img The image string to convert.\n * @returns An object containing the MIME type and base64 data.\n */\n async toBase64(img) {\n let mime = \"\";\n let data = \"\";\n if (util_default.isBase64(img)) {\n if (util_default.isBase64(img, false)) {\n data = img;\n mime = \"image/png\";\n } else {\n const match = img.match(/^data:image\\/([a-zA-Z]*);base64,([^\\\"']*)$/);\n if (match) {\n mime = `image/${match[1]}`;\n data = match[2];\n }\n }\n } else if (img.startsWith(\"http\")) {\n const res = await util_default.get(img, {}, { responseType: \"arraybuffer\" });\n mime = `image/${[\"png\", \"jpg\", \"jpeg\", \"webp\", \"heic\", \"heif\"].find((f) => img.toLowerCase().includes(f))?.replace(\"jpg\", \"jpeg\") || \"png\"}`;\n data = res.toString(\"base64\");\n } else {\n mime = `image/${extname(img).replace(\".\", \"\").toLowerCase()}`;\n data = readFileSync(img).toString(\"base64\");\n }\n if (!mime || !data) throw new Error(\"Can not transfer base64\");\n return { mime, data };\n }\n};\n\n// src/providers/IFlyTek.ts\nimport { createHmac } from \"crypto\";\nimport { hostname } from \"os\";\nimport { PassThrough as PassThrough5, Readable as Readable5 } from \"stream\";\nimport EventSourceStream4 from \"@server-sent-stream/node\";\nimport { decodeStream as decodeStream5 } from \"iconv-lite\";\nvar CHAT_API = \"https://spark-api-open.xf-yun.com\";\nvar IMAGINE_API = \"https://spark-api.cn-huabei-1.xf-yun.com\";\nvar STORAGE_KEY2 = \"task_iflytek\";\nvar IFlyTek = class {\n /**\n * Constructor for IFlyTek class.\n * @param key - The API key for MoonShot.\n * @param api - The API endpoint for proxy (optional).\n */\n constructor(pass, key, secret, appid, api = CHAT_API) {\n this.pass = pass;\n this.key = key;\n this.secret = secret;\n this.appid = appid;\n this.api = api;\n }\n /**\n * Initiates a chat conversation with IFLYTEK Spark API.\n *\n * @param messages - An array of chat messages.\n * @param model - The Spark model version to use (default: SPARK_DEFAULT_MODEL_VERSION).\n * @param stream - Whether to use stream response (default: false).\n * @param top - Top probability to sample (optional).\n * @param temperature - Temperature for sampling (optional).\n * @param maxLength - Maximum token length for response (optional).\n * @returns A promise resolving to the chat response or a stream.\n */\n async chat(messages, model = \"lite\" /* SPARK_LITE */, stream = false, top, temperature, maxLength, tools, toolChoice) {\n const key = Array.isArray(this.pass) ? util_default.getRandomKey(this.pass) : this.pass;\n if (!key) throw new Error(\"IFlyTek Spark API Password is not set in config\");\n if (typeof top === \"number\") {\n if (top <= 0) top = 0.1;\n if (top > 1) top = 1;\n }\n if (typeof temperature === \"number\") {\n if (temperature < 0) temperature = 0;\n if (temperature > 2) temperature = 2;\n }\n const res = await util_default.post(\n `${this.api}/v1/chat/completions`,\n {\n model,\n messages: this.formatMessage(messages),\n stream,\n temperature,\n top_p: top,\n max_tokens: maxLength,\n tools,\n tool_choice: toolChoice\n },\n { headers: { Authorization: `Bearer ${key}` }, responseType: stream ? \"stream\" : \"json\" }\n );\n const data = {\n content: \"\",\n model,\n object: \"\",\n promptTokens: 0,\n completionTokens: 0,\n totalTokens: 0\n };\n if (res instanceof Readable5) {\n const output = new PassThrough5();\n const parser = new EventSourceStream4();\n parser.on(\"data\", (e) => {\n const obj = util_default.json(e.data);\n if (obj) {\n data.content = obj.choices[0]?.delta?.content || \"\";\n if (obj.choices[0]?.delta?.tool_calls) data.tools = obj.choices[0]?.delta?.tool_calls;\n data.model = model;\n data.object = \"chat.completion.chunk\";\n data.promptTokens = obj.usage?.prompt_tokens || 0;\n data.completionTokens = obj.usage?.completion_tokens || 0;\n data.totalTokens = obj.usage?.total_tokens || 0;\n output.write(JSON.stringify(data));\n }\n });\n parser.on(\"error\", (e) => output.destroy(e));\n parser.on(\"end\", () => output.end());\n res.pipe(decodeStream5(\"utf-8\")).pipe(parser);\n return output;\n } else {\n data.content = res.choices[0]?.message?.content || \"\";\n if (res.choices[0]?.message?.tool_calls) data.tools = res.choices[0]?.message?.tool_calls;\n data.model = model;\n data.object = \"chat.completion\";\n data.promptTokens = res.usage?.prompt_tokens || 0;\n data.completionTokens = res.usage?.completion_tokens || 0;\n data.totalTokens = res.usage?.total_tokens || 0;\n return data;\n }\n }\n /**\n * Generate an image based on the given prompt.\n * @param prompt The prompt for image generation.\n * @param width The width of the image, default is 512.\n * @param height The height of the image, default is 512.\n * @param model The model to use for generating the image, default is IFlyTekImagineModel.V2.\n */\n async imagine(prompt, width = 512, height = 512, model = \"v2.1\" /* V2 */) {\n if (!this.appid) throw new Error(\"IFlyTek APP ID is not set in config\");\n const url = this.getImagineURL(model);\n const res = await util_default.post(url, {\n header: { app_id: this.appid },\n payload: { message: { text: [{ role: \"user\", content: prompt }] } },\n parameter: { chat: { domain: \"general\", width, height } }\n });\n if (res.header.code !== 0) throw new Error(res.header.message);\n if (!res.payload) throw new Error(\"Fail to generate image, empty payload\");\n const id = util_default.getRandomId();\n const imgs = [];\n for (const i in res.payload.choices.text)\n imgs.push(await util_default.writeFile(res.payload.choices.text[i].content, `${id}-${i}.png`));\n const time = Date.now();\n const task = {\n id,\n type: \"generation\" /* GENERATION */,\n info: \"success\",\n progress: 100,\n imgs,\n fail: \"\",\n created: time,\n model\n };\n const tasks = util_default.getItem(STORAGE_KEY2) || [];\n tasks.push(task);\n util_default.setItem(STORAGE_KEY2, tasks);\n return { taskId: task.id, time };\n }\n /**\n * Simulate tasks.\n *\n * @param id - The task ID to retrieve (optional).\n * @returns An array of task responses or a specific task by ID.\n */\n task(id) {\n const tasks = util_default.getItem(STORAGE_KEY2) || [];\n if (id) return tasks.filter((v) => v.id === id);\n else return tasks;\n }\n /**\n * Generates the Imagine API URL.\n *\n * @param model - The Spark imagine model.\n */\n getImagineURL(model) {\n if (!this.secret) throw new Error(\"IFlyTek API secret is not set in config\");\n if (!this.key) throw new Error(\"IFlyTek API key is not set in config\");\n const host = hostname();\n const date = (/* @__PURE__ */ new Date()).toUTCString();\n const algorithm = \"hmac-sha256\";\n const headers = \"host date request-line\";\n const signatureOrigin = `host: ${host}\ndate: ${date}\nPOST /${model}/tti HTTP/1.1`;\n const signatureSha = createHmac(\"sha256\", this.secret).update(signatureOrigin).digest(\"hex\");\n const signature = Buffer.from(signatureSha, \"hex\").toString(\"base64\");\n const authorizationOrigin = `api_key=\"${this.key}\", algorithm=\"${algorithm}\", headers=\"${headers}\", signature=\"${signature}\"`;\n const authorization = Buffer.from(authorizationOrigin).toString(\"base64\");\n return `${IMAGINE_API}/${model}/tti?authorization=${authorization}&date=${date}&host=${host}`;\n }\n /**\n * Format the chat message for IFlyTek Spark model.\n *\n * @param messages - Original chat messages\n */\n formatMessage(messages) {\n const prompt = [];\n for (const { role, content } of messages) {\n if (role === \"developer\" /* DEV */) continue;\n prompt.push({ role, content });\n }\n return prompt;\n }\n};\n\n// src/providers/Baidu.ts\nimport { PassThrough as PassThrough6, Readable as Readable6 } from \"stream\";\nimport EventSourceStream5 from \"@server-sent-stream/node\";\nimport { decodeStream as decodeStream6 } from \"iconv-lite\";\nvar API4 = \"https://aip.baidubce.com\";\nvar STORAGE_KEY3 = \"baidu\";\nvar Baidu = class {\n constructor(key, secret, api = API4) {\n this.key = key;\n this.secret = secret;\n this.api = api;\n }\n /**\n * Sends messages to the GLM chat model.\n *\n * @param messages - An array of chat messages.\n * @param model - The submodel to use for chat (default: ERNIE 8K).\n * @param stream - Whether to use stream response (default: false).\n * @param top - Top probability to sample (optional).\n * @param temperature - Temperature for sampling (optional).\n * @param maxLength - Maximum token length for response (optional).\n * @returns A promise resolving to the chat response or a stream.\n */\n async chat(messages, model = \"completions\" /* ERNIE_3_5 */, stream = false, top, temperature, maxLength) {\n if (typeof temperature === \"number\") {\n if (temperature <= 0) temperature = 0.1;\n if (temperature > 1) temperature = 1;\n }\n if (typeof top === \"number\") {\n if (top < 0) top = 0;\n if (top > 1) top = 1;\n }\n const token = await this.getAccessToken();\n const res = await util_default.post(\n `${this.api}/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/${model}?access_token=${token}`,\n {\n messages: this.formatMessage(messages),\n system: messages[0]?.role === \"system\" /* SYSTEM */ ? messages[0].content : \"\",\n stream,\n temperature,\n top_p: top,\n max_output_tokens: maxLength\n },\n { responseType: stream ? \"stream\" : \"json\" }\n );\n const data = {\n content: \"\",\n model,\n object: \"\",\n promptTokens: 0,\n completionTokens: 0,\n totalTokens: 0\n };\n if (res instanceof Readable6) {\n const output = new PassThrough6();\n const parser = new EventSourceStream5();\n parser.on(\"data\", (e) => {\n const obj = util_default.json(e.data);\n if (obj) {\n data.content = obj.result || \"\";\n data.model = model;\n data.object = obj.object;\n data.promptTokens = obj.usage?.prompt_tokens || 0;\n data.completionTokens = obj.usage?.completion_tokens || 0;\n data.totalTokens = obj.usage?.total_tokens || 0;\n output.write(JSON.stringify(data));\n }\n });\n parser.on(\"error\", (e) => output.destroy(e));\n parser.on(\"end\", () => output.end());\n res.pipe(decodeStream6(\"utf-8\")).pipe(parser);\n return output;\n } else {\n if (res.error_code) throw new Error(res.error_msg);\n data.content = res.result || \"\";\n data.model = model;\n data.object = res.object;\n data.promptTokens = res.usage?.prompt_tokens || 0;\n data.completionTokens = res.usage?.completion_tokens || 0;\n data.totalTokens = res.usage?.total_tokens || 0;\n return data;\n }\n }\n // get baidu access token\n async getAccessToken() {\n if (!this.key) throw new Error(\"Baidu API key is not set in config\");\n if (!this.secret) throw new Error(\"Baidu API secret is not set in config\");\n const now = Date.now();\n const cache = util_default.getItem(STORAGE_KEY3);\n if (cache && cache.expires_in > now) return cache.access_token;\n const res = await util_default.get(`${this.api}/oauth/2.0/token`, {\n grant_type: \"client_credentials\",\n client_id: this.key,\n client_secret: this.secret\n });\n if (res.error) throw new Error(res.error_description);\n res.expires_in = now + res.expires_in * 1e3;\n util_default.setItem(STORAGE_KEY3, res);\n return res.access_token;\n }\n // format to baidu message\n formatMessage(messages) {\n const prompt = [];\n let input = \"\";\n const { USER, ASSISTANT, SYSTEM } = ChatRoleEnum;\n for (const { role, content } of messages) {\n if (!content.trim() || role === SYSTEM) continue;\n if (role !== ASSISTANT) input += `\n${content}`;\n else {\n prompt.push({ role: USER, content: input.trim() || \" \" });\n prompt.push({ role: ASSISTANT, content });\n input = \"\";\n }\n }\n if (!input.trim()) throw new Error(\"User input nothing\");\n prompt.push({ role: USER, content: input.trim() });\n return prompt;\n }\n};\n\n// src/providers/MoonShot.ts\nimport { PassThrough as PassThrough7, Readable as Readable7 } from \"stream\";\nimport EventSourceStream6 from \"@server-sent-stream/node\";\nimport { decodeStream as decodeStream7 } from \"iconv-lite\";\nvar API5 = \"https://api.moonshot.cn\";\nvar VER2 = \"v1\";\nvar MoonShot = class {\n /**\n * Constructor for MoonShot class.\n * @param key - The API key for MoonShot.\n * @param api - The API endpoint for proxy (optional).\n */\n constructor(key, api = API5) {\n this.key = key;\n this.api = api;\n }\n /**\n * Sends messages to the MoonShot chat model.\n *\n * @param messages - An array of chat messages.\n * @param model - The model to use for chat (default: moonshot-v1-8k).\n * @param stream - Whether to use stream response (default: false).\n * @param top - Top probability to sample (optional).\n * @param temperature - Temperature for sampling (optional).\n * @param maxLength - Maximum token length for response (optional).\n * @returns A promise resolving to the chat response or a stream.\n */\n async chat(messages, model = \"moonshot-v1-8k\" /* MOON_V1_8K */, stream = false, top, temperature, maxLength) {\n const key = Array.isArray(this.key) ? util_default.getRandomKey(this.key) : this.key;\n if (!key) throw new Error(\"MoonShot API key is not set in config\");\n if (typeof temperature === \"number\") {\n if (temperature < 0) temperature = 0;\n if (temperature > 1) temperature = 1;\n }\n if (typeof top === \"number\") {\n if (top < 0) top = 0;\n if (top > 1) top = 1;\n }\n const res = await util_default.post(\n `${this.api}/${VER2}/chat/completions`,\n { model, messages: this.formatMessage(messages), stream, temperature, top_p: top, max_tokens: maxLength },\n { headers: { Authorization: `Bearer ${key}` }, responseType: stream ? \"stream\" : \"json\" }\n );\n const data = {\n content: \"\",\n model,\n object: \"\",\n promptTokens: 0,\n completionTokens: 0,\n totalTokens: 0\n };\n if (res instanceof Readable7) {\n const output = new PassThrough7();\n const parser = new EventSourceStream6();\n parser.on(\"data\", (e) => {\n const obj = util_default.json(e.data);\n if (obj) {\n data.content = obj.choices[0]?.delta?.content || \"\";\n data.model = obj.model;\n data.object = obj.object;\n data.promptTokens = obj.usage?.prompt_tokens || 0;\n data.completionTokens = obj.usage?.completion_tokens || 0;\n data.totalTokens = obj.usage?.total_tokens || 0;\n output.write(JSON.stringify(data));\n }\n });\n parser.on(\"error\", (e) => output.destroy(e));\n parser.on(\"end\", () => output.end());\n res.pipe(decodeStream7(\"utf-8\")).pipe(parser);\n return output;\n } else {\n data.content = res.choices[0]?.message?.content || \"\";\n data.model = res.model;\n data.object = res.object;\n data.promptTokens = res.usage?.prompt_tokens || 0;\n data.completionTokens = res.usage?.completion_tokens || 0;\n data.totalTokens = res.usage?.total_tokens || 0;\n return data;\n }\n }\n /**\n * Formats chat messages according to the GPT model's message format.\n *\n * @param messages - An array of chat messages.\n * @returns Formatted messages compatible with the GPT model.\n */\n formatMessage(messages) {\n const prompt = [];\n for (const { role, content } of messages) {\n if (role === \"tool\" /* TOOL */ || role === \"developer\" /* DEV */) continue;\n prompt.push({ role, content });\n }\n return prompt;\n }\n};\n\n// src/providers/MidJourney.ts\nvar MidJourney = class {\n // User token of MidJourney\n constructor(proxy, token, imgProxy) {\n this.proxy = proxy;\n this.token = token;\n this.imgProxy = imgProxy;\n }\n /**\n * Call the imagine API of MidJourney to generate text.\n * @param prompt - The input prompt text.\n * @param nPrompt - The excluded text.\n * @param width - The image width. Default value is 1.\n * @param height - The image height. Default value is 1.\n * @returns The generated text.\n */\n async imagine(prompt, nPrompt = \"\", width = 1024, height = 1024) {\n if (!this.proxy) throw new Error(\"MidJourney image model proxy is not set in config\");\n const aspect = util_default.getAspect(width, height);\n const res = await util_default.post(\n `${this.proxy}/mj/submit/imagine`,\n { prompt: `${prompt} --ar ${aspect} ${nPrompt ? \"--no \" + nPrompt : \"\"}` },\n { headers: { \"mj-api-secret\": this.token } }\n );\n if (res.code !== 1) throw new Error(res.description);\n return { taskId: res.result, time: Date.now() };\n }\n /**\n * Get task information by id or get a list of imagining tasks.\n * @param id - The task id. If provided, it will return the task information with the specified id. Otherwise, it will return the task list.\n * @returns The task list or the task information with the specified id.\n */\n async task(id) {\n if (!this.proxy) throw new Error(\"MidJourney image model proxy is not set in config\");\n if (id) {\n const res = await util_default.get(`${this.proxy}/mj/task/${id}/fetch`, null, {\n headers: { \"mj-api-secret\": this.token }\n });\n if (res.imageUrl && this.imgProxy) {\n const url = new URL(res.imageUrl);\n const pro = new URL(this.imgProxy);\n url.hostname = pro.hostname;\n url.protocol = pro.protocol;\n url.port = pro.port;\n res.imageUrl = url.toString();\n }\n return [\n {\n id: res.id,\n type: res.action,\n imgs: res.imageUrl ? [await util_default.writeFile(res.imageUrl, `${res.id}.png`)] : [],\n info: res.description,\n fail: res.failReason || \"\",\n progress: parseInt(res.progress) || 0,\n created: res.startTime,\n model: \"midjourney\" /* MJ */\n }\n ];\n } else {\n const res = await util_default.get(`${this.proxy}/mj/task/list`, null, {\n headers: { \"mj-api-secret\": this.token }\n });\n return res.map((v) => ({\n id: v.id,\n type: v.action,\n imgs: v.imageUrl ? [v.imageUrl] : [],\n info: v.description,\n fail: v.failReason || \"\",\n progress: parseInt(v.progress),\n created: v.startTime,\n model: \"midjourney\" /* MJ */\n }));\n }\n }\n /**\n * Submit a change request for a imagined task.\n * @param taskId - The task id.\n * @param action - The action type of the task.\n * @param index - The index for modification (optional).\n * @returns The modified task response.\n */\n async change(taskId, action, index) {\n if (!this.proxy) throw new Error(\"MidJourney image model proxy is not set in config\");\n const res = await util_default.post(\n `${this.proxy}/mj/submit/change`,\n { taskId, action, index },\n { headers: { \"mj-api-secret\": this.token } }\n );\n return { taskId: res.result, time: Date.now() };\n }\n};\n\n// src/providers/Stability.ts\nvar API6 = \"https://api.stability.ai\";\nvar STORAGE_KEY4 = \"task_stability_ai\";\nvar Stability = class {\n constructor(key, api = API6) {\n this.key = key;\n this.api = api;\n }\n /**\n * Generate images using Stability AI.\n *\n * @param prompt - The main text prompt for image generation.\n * @param negativePrompt - The negative text prompt (optional).\n * @param width - Image width (default: 1024).\n * @param height - Image height (default: 1024).\n * @param samples - Number of images to generate (default: 1).\n * @param model - The Stability AI model to use (default: StabilityAIImagineModel.SD_1_6).\n * @returns A promise resolving to the image generation response.\n */\n async imagine(prompt, negativePrompt = \"\", width = 1024, height = 1024, samples = 1, model = \"stable-diffusion-v1-6\" /* SD_1_6 */) {\n const key = Array.isArray(this.key) ? util_default.getRandomKey(this.key) : this.key;\n if (!key) throw new Error(\"Stability key is not set in config\");\n const prompts = [{ text: prompt, weight: 1 }];\n if (negativePrompt) prompts.push({ text: negativePrompt, weight: -1 });\n const res = await util_default.post(\n `${this.api}/v1/${\"generation\" /* GENERATION */}/${model}/text-to-image`,\n { width, height, samples, text_prompts: prompts },\n { headers: { Accept: \"application/json\", Authorization: `Bearer ${key}` } }\n );\n const id = util_default.getRandomId();\n const imgs = [];\n for (const i in res.artifacts) imgs.push(await util_default.writeFile(res.artifacts[i].base64, `${id}-${i}.png`));\n const time = Date.now();\n const task = {\n id,\n type: \"generation\" /* GENERATION */,\n info: res.artifacts[0].finishReason,\n progress: 100,\n imgs,\n fail: \"\",\n created: time,\n model\n };\n const tasks = util_default.getItem(STORAGE_KEY4) || [];\n tasks.push(task);\n util_default.setItem(STORAGE_KEY4, tasks);\n return { taskId: task.id, time };\n }\n /**\n * Simulate tasks or retrieve a specific task by ID.\n *\n * @param id - The task ID to retrieve (optional).\n * @returns An array of task responses or a specific task by ID.\n */\n task(id) {\n const tasks = util_default.getItem(STORAGE_KEY4) || [];\n if (id) return tasks.filter((v) => v.id === id);\n else return tasks;\n }\n};\n\n// src/providers/AliYun.ts\nimport { PassThrough as PassThrough8, Readable as Readable8 } from \"stream\";\nimport EventSourceStream7 from \"@server-sent-stream/node\";\nimport { decodeStream as decodeStream8 } from \"iconv-lite\";\nvar API7 = \"https://dashscope.aliyuncs.com\";\nvar VER3 = \"v1\";\nvar AliYun = class {\n /**\n * Constructor for AliYun class.\n * @param key - The API key for MoonShot.\n * @param api - The API endpoint for proxy (optional).\n */\n constructor(key, api = API7) {\n this.key = key;\n this.api = api;\n }\n /**\n * Fetches embeddings for input text.\n *\n * @param input - An array of input strings.\n * @param model - The model to use for embeddings (default: text-embedding-v3).\n * @param dimensions - The dimensions of output embedding vector (default: 1024)\n * @returns A promise resolving to the embedding response.\n */\n async embedding(input, model = \"text-embedding-v3\" /* ALI_V3 */, dimensions = 1024) {\n const key = Array.isArray(this.key) ? util_default.getRandomKey(this.key) : this.key;\n if (!key) throw new Error(\"AliYun API key is not set in config\");\n const res = await util_default.post(\n `${this.api}/compatible-mode/${VER3}/embeddings`,\n { model, input, dimensions },\n { headers: { Authorization: `Bearer ${key}` }, responseType: \"json\" }\n );\n const data = {\n embedding: res.data.map((v) => v.embedding),\n object: \"embedding\",\n model,\n promptTokens: res.usage.prompt_tokens || 0,\n totalTokens: res.usage.total_tokens || 0\n };\n return data;\n }\n /**\n * Sends messages to the AliYun LLMs.\n *\n * @param messages - An array of chat messages.\n * @param model - The model to use for chat (default: moonshot-v1-8k).\n * @param stream - Whether to use stream response (default: false).\n * @param top - Top probability to sample (optional).\n * @param temperature - Temperature for sampling (optional).\n * @param maxLength - Maximum token length for response (optional).\n * @returns A promise resolving to the chat response or a stream.\n */\n async chat(messages, model = \"qwen-turbo\" /* QWEN_TURBO */, stream = false, top, temperature, maxLength) {\n const key = Array.isArray(this.key) ? util_default.getRandomKey(this.key) : this.key;\n if (!key) throw new Error(\"Qian Wen API key is not set in config\");\n if (typeof temperature === \"number\") {\n if (temperature < 0) temperature = 0;\n if (temperature >= 2) temperature = 1.9;\n }\n if (typeof top === \"number\") {\n if (top <= 0) top = 0.1;\n if (top > 1) top = 1;\n }\n if (![\"qwen-vl-max\" /* QWEN_VL_MAX */, \"qwen-vl-plus\" /* QWEN_VL_PLUS */].includes(model))\n messages = messages.map(({ role, content }) => ({ role, content }));\n const res = await util_default.post(\n `${this.api}/compatible-mode/${VER3}/chat/completions`,\n {\n model,\n messages: this.formatMessage(messages),\n stream,\n temperature,\n top_p: top,\n max_tokens: maxLength\n },\n { headers: { Authorization: `Bearer ${key}` }, responseType: stream ? \"stream\" : \"json\" }\n );\n const data = {\n content: \"\",\n model,\n object: \"\",\n promptTokens: 0,\n completionTokens: 0,\n totalTokens: 0\n };\n if (res instanceof Readable8) {\n const output = new PassThrough8();\n const parser = new EventSourceStream7();\n parser.on(\"data\", (e) => {\n const obj = util_default.json(e.data);\n if (obj) {\n data.content = obj?.choices[0]?.delta?.content || \"\";\n data.model = obj.model;\n data.object = obj.object;\n data.promptTokens = obj.usage?.prompt_tokens || 0;\n data.completionTokens = obj.usage?.completion_tokens || 0;\n data.totalTokens = obj.usage?.total_tokens || 0;\n output.write(JSON.stringify(data));\n }\n });\n parser.on(\"error\", (e) => output.destroy(e));\n parser.on(\"end\", () => output.end());\n res.pipe(decodeStream8(\"utf-8\")).pipe(parser);\n return output;\n } else {\n data.content = res.choices[0]?.message?.content || \"\";\n if (res.choices[0]?.message?.tool_calls) data.tools = res.choices[0]?.message?.tool_calls;\n data.model = res.model;\n data.object = res.object || \"chat.completion\";\n data.promptTokens = res.usage?.prompt_tokens || 0;\n data.completionTokens = res.usage?.completion_tokens || 0;\n data.totalTokens = res.usage?.total_tokens || 0;\n return data;\n }\n }\n /**\n * Formats chat messages according to the GPT model's message format.\n *\n * @param messages - An array of chat messages.\n * @returns Formatted messages compatible with the GPT model.\n */\n formatMessage(messages) {\n const prompt = [];\n for (const { role, content, img } of messages) {\n if (role === \"tool\" /* TOOL */ || role === \"developer\" /* DEV */) continue;\n if (img) {\n if (!img.startsWith(\"http\")) throw new Error(\"Invalid img HTTP URL\");\n prompt.push({\n role: \"user\",\n content: [\n { type: \"text\", text: content },\n { type: \"image_url\", image_url: { url: img } }\n ]\n });\n } else prompt.push({ role, content });\n }\n return prompt;\n }\n};\n\n// src/providers/DeepSeek.ts\nimport { PassThrough as PassThrough9, Readable as Readable9 } from \"stream\";\nimport EventSourceStream8 from \"@server-sent-stream/node\";\nimport { decodeStream as decodeStream9 } from \"iconv-lite\";\nvar API8 = \"https://api.deepseek.com\";\nvar DeepSeek = class {\n /**\n * Constructor for MoonShot class.\n * @param key - The API key for MoonShot.\n * @param api - The API endpoint for proxy (optional).\n */\n constructor(key, api = API8) {\n this.key = key;\n this.api = api;\n }\n /**\n * Sends messages to the Ali QianWen chat model.\n *\n * @param messages - An array of chat messages.\n * @param model - The model to use for chat (default: moonshot-v1-8k).\n * @param stream - Whether to use stream response (default: false).\n * @param top - Top probability to sample (optional).\n * @param temperature - Temperature for sampling (optional).\n * @param maxLength - Maximum token length for response (optional).\n * @returns A promise resolving to the chat response or a stream.\n */\n async chat(messages, model = \"deepseek-chat\" /* DEEPSEEK_V3 */, stream = false, top, temperature, maxLength) {\n const key = Array.isArray(this.key) ? util_default.getRandomKey(this.key) : this.key;\n if (!key) throw new Error(\"DeepSeek API key is not set in config\");\n if (typeof maxLength === \"number\") {\n if (maxLength < 1) maxLength = 1;\n if (maxLength > 8192) maxLength = 8192;\n }\n if (typeof temperature === \"number\") {\n if (temperature < 0) temperature = 0;\n if (temperature > 2) temperature = 2;\n }\n if (typeof top === \"number\") {\n if (top <= 0) top = 0.1;\n if (top > 1) top = 1;\n }\n const res = await util_default.post(\n `${this.api}/chat/completions`,\n {\n model,\n messages: this.formatMessage(messages),\n stream,\n temperature,\n top_p: top,\n max_tokens: maxLength\n },\n { headers: { Authorization: `Bearer ${key}` }, responseType: stream ? \"stream\" : \"json\" }\n );\n const data = {\n content: \"\",\n model,\n object: \"\",\n promptTokens: 0,\n completionTokens: 0,\n totalTokens: 0\n };\n if (res instanceof Readable9) {\n const output = new PassThrough9();\n const parser = new EventSourceStream8();\n parser.on(\"data\", (e) => {\n const obj = util_default.json(e.data);\n if (obj) {\n data.content = obj.choices[0]?.delta?.content || \"\";\n data.model = obj.model;\n data.object = obj.object;\n data.promptTokens = obj.usage?.prompt_tokens || 0;\n data.completionTokens = obj.usage?.completion_tokens || 0;\n data.totalTokens = obj.usage?.total_tokens || 0;\n output.write(JSON.stringify(data));\n }\n });\n parser.on(\"error\", (e) => output.destroy(e));\n parser.on(\"end\", () => output.end());\n res.pipe(decodeStream9(\"utf-8\")).pipe(parser);\n return output;\n } else {\n data.content = res.choices[0]?.message?.content || \"\";\n if (res.choices[0]?.message?.tool_calls) data.tools = res.choices[0]?.message?.tool_calls;\n data.model = res.model;\n data.object = res.object;\n data.promptTokens = res.usage?.prompt_tokens || 0;\n data.completionTokens = res.usage?.completion_tokens || 0;\n data.totalTokens = res.usage?.total_tokens || 0;\n return data;\n }\n }\n /**\n * Formats chat messages according to the GPT model's message format.\n *\n * @param messages - An array of chat messages.\n * @returns Formatted messages compatible with the GPT model.\n */\n formatMessage(messages) {\n const prompt = [];\n for (const { role, content, tool } of messages) {\n if (role === \"developer\" /* DEV */) continue;\n else if (role === \"tool\" /* TOOL */) prompt.push({ role, content, tool_call_id: tool });\n else prompt.push({ role, content });\n }\n return prompt;\n }\n};\n\n// src/providers/IX.ts\nimport { PassThrough as PassThrough10, Readable as Readable10 } from \"stream\";\nimport EventSourceStream9 from \"@server-sent-stream/node\";\nimport { decodeStream as decodeStream10 } from \"iconv-lite\";\nvar API9 = \"https://api.x.ai\";\nvar VER4 = \"v1\";\nvar XAI = class {\n /**\n * Constructor for the OpenAI class.\n *\n * @param key - The API key for OpenAI.\n * @param api - The API endpoint for proxy (optional).\n */\n constructor(key, api = API9) {\n this.key = key;\n this.api = api;\n }\n /**\n * Sends messages to the GPT chat model.\n *\n * @param messages - An array of chat messages.\n * @param model - The model to use for chat (default: gpt-3.5-turbo).\n * @param stream - Whether to use stream response (default: false).\n * @param top - Top probability to sample (optional).\n * @param temperature - Temperature for sampling (optional).\n * @param maxLength - Maximum token length for response (optional).\n * @param tools - Tools for model to use (optional).\n * @param toolChoice - Controls which (if any) tool is called by the model: none, required, auto (optional).\n * @returns A promise resolving to the chat response or a stream.\n */\n async chat(messages, model = \"grok-2\" /* GROK2 */, stream = false, top, temperature, maxLength, tools, toolChoice) {\n const key = Array.isArray(this.key) ? util_default.getRandomKey(this.key) : this.key;\n if (!key) throw new Error(\"X AI API key is not set in config\");\n if (![\"grok-2-vision\" /* GROK2_VISION */].includes(model))\n messages = messages.map(({ role, content }) => ({ role, content }));\n if (typeof temperature === \"number\") {\n if (temperature < 0) temperature = 0;\n if (temperature > 2) temperature = 2;\n }\n if (typeof top === \"number\") {\n if (top < 0) top = 0;\n if (top > 1) top = 1;\n }\n const res = await util_default.post(\n `${this.api}/${VER4}/chat/completions`,\n {\n model,\n messages: this.formatMessage(messages),\n stream,\n temperature,\n top_p: top,\n max_tokens: maxLength,\n tools,\n tool_choice: toolChoice\n },\n { headers: { Authorization: `Bearer ${key}` }, responseType: stream ? \"stream\" : \"json\" }\n );\n const data = {\n content: \"\",\n model,\n object: \"\",\n promptTokens: 0,\n completionTokens: 0,\n totalTokens: 0\n };\n if (res instanceof Readable10) {\n const output = new PassThrough10();\n const parser = new EventSourceStream9();\n parser.on(\"data\", (e) => {\n const obj = util_default.json(e.data);\n if (obj) {\n data.content = obj.choices[0]?.delta?.content || \"\";\n data.model = obj.model;\n data.object = obj.object;\n data.promptTokens = obj.usage?.prompt_tokens || 0;\n data.completionTokens = obj.usage?.completion_tokens || 0;\n data.totalTokens = obj.usage?.total_tokens || 0;\n output.write(JSON.stringify(data));\n }\n });\n parser.on(\"error\", (e) => output.destroy(e));\n parser.on(\"end\", () => output.end());\n res.pipe(decodeStream10(\"utf-8\")).pipe(parser);\n return output;\n } else {\n data.content = res.choices[0]?.message?.content || \"\";\n if (res.choices[0]?.message?.tool_calls) data.tools = res.choices[0]?.message?.tool_calls;\n data.model = res.model;\n data.object = res.object;\n data.promptTokens = res.usage?.prompt_tokens || 0;\n data.completionTokens = res.usage?.completion_tokens || 0;\n data.totalTokens = res.usage?.total_tokens || 0;\n return data;\n }\n }\n /**\n * Formats chat messages according to the GPT model's message format.\n *\n * @param messages - An array of chat messages.\n * @returns Formatted messages compatible with the GPT model.\n */\n formatMessage(messages) {\n const prompt = [];\n for (const { role, content, img } of messages) {\n switch (role) {\n case \"user\" /* USER */:\n if (img)\n prompt.push({\n role,\n content: [\n { type: \"text\", text: content },\n { type: \"image_url\", image_url: { url: img } }\n ]\n });\n else prompt.push({ role, content });\n break;\n case (\"assistant\" /* ASSISTANT */ || \"system\" /* SYSTEM */):\n prompt.push({ role, content });\n break;\n default:\n break;\n }\n }\n return prompt;\n }\n};\n\n// src/UniAI.ts\nvar DEFAULT_MESSAGE = \"Hi, who are you? Answer in 10 words!\";\nvar UniAI = class {\n constructor(config = {}) {\n this.config = null;\n this.config = config;\n this.openai = new OpenAI(config.OpenAI?.key, config.OpenAI?.proxy);\n this.deepseek = new DeepSeek(config.DeepSeek?.key, config.DeepSeek?.proxy);\n this.glm = new GLM(config.GLM?.key, config.GLM?.proxy);\n this.google = new Google(config.Google?.key, config.Google?.proxy);\n this.fly = new IFlyTek(\n config.IFlyTek?.apiPassword,\n config.IFlyTek?.appId,\n config.IFlyTek?.apiKey,\n config.IFlyTek?.apiSecret,\n config.IFlyTek?.proxy\n );\n this.baidu = new Baidu(config.Baidu?.apiKey, config.Baidu?.secretKey, config.Baidu?.proxy);\n this.moon = new MoonShot(config.MoonShot?.key, config.MoonShot?.proxy);\n this.ali = new AliYun(config.AliYun?.key, config.AliYun?.proxy);\n this.xai = new XAI(config.XAI?.key, config.XAI?.proxy);\n this.other = new Other(config.Other?.api, config.Other?.key);\n this.mj = new MidJourney(config.MidJourney?.proxy, config.MidJourney?.token, config.MidJourney?.imgProxy);\n this.stability = new Stability(config.StabilityAI?.key, config.StabilityAI?.proxy);\n this.chatModels = Object.entries(ChatModelProvider).map(([k, v]) => ({\n provider: k,\n value: v,\n models: Object.values(\n {\n [\"openai\" /* OpenAI */]: OpenAIChatModel,\n [\"deepseek\" /* DeepSeek */]: DeepSeekChatModel,\n [\"baidu\" /* Baidu */]: BaiduChatModel,\n [\"iflytek\" /* IFlyTek */]: IFlyTekChatModel,\n [\"glm\" /* GLM */]: GLMChatModel,\n [\"google\" /* Google */]: GoogleChatModel,\n [\"moonshot\" /* MoonShot */]: MoonShotChatModel,\n [\"aliyun\" /* AliYun */]: AliChatModel,\n [\"xai\" /* XAI */]: XAIChatModel,\n [\"other\" /* Other */]: []\n }[v]\n )\n }));\n this.imgModels = Object.entries(ImagineModelProvider).map(([k, v]) => ({\n provider: k,\n value: v,\n models: Object.values(\n {\n [\"openai\" /* OpenAI */]: OpenAIImagineModel,\n [\"midjourney\" /* MidJourney */]: MidJourneyImagineModel,\n [\"stability.ai\" /* StabilityAI */]: StabilityAIImagineModel,\n [\"iflytek\" /* IFlyTek */]: IFlyTekImagineModel\n }[v]\n )\n }));\n this.embedModels = Object.entries(EmbedModelProvider).map(([k, v]) => ({\n provider: k,\n value: v,\n models: Object.values(\n {\n [\"openai\" /* OpenAI */]: OpenAIEmbedModel,\n [\"glm\" /* GLM */]: GLMEmbedModel,\n [\"google\" /* Google */]: GoogleEmbedModel,\n [\"aliyun\" /* AliYun */]: AliEmbedModel,\n [\"other\" /* Other */]: OtherEmbedModel\n }[v]\n )\n }));\n this.models = [...this.chatModels, ...this.embedModels, ...this.imgModels];\n }\n async chat(messages = DEFAULT_MESSAGE, option = {}) {\n if (typeof messages === \"string\") messages = [{ role: \"user\" /* USER */, content: messages }];\n const provider = option.provider || \"openai\" /* OpenAI */;\n const { model, stream, top, temperature, maxLength, tools, toolChoice } = option;\n if (provider === \"openai\" /* OpenAI */)\n return await this.openai.chat(\n messages,\n model,\n stream,\n top,\n temperature,\n maxLength,\n tools,\n toolChoice\n );\n else if (provider === \"deepseek\" /* DeepSeek */)\n return await this.deepseek.chat(messages, model, stream, top, temperature, maxLength);\n else if (provider === \"google\" /* Google */)\n return await this.google.chat(messages, model, stream, top, temperature, maxLength);\n else if (provider === \"glm\" /* GLM */)\n return await this.glm.chat(\n messages,\n model,\n stream,\n top,\n temperature,\n maxLength,\n tools,\n toolChoice\n );\n else if (provider === \"iflytek\" /* IFlyTek */)\n return await this.fly.chat(\n messages,\n model,\n stream,\n top,\n temperature,\n maxLength,\n tools,\n toolChoice\n );\n else if (provider === \"baidu\" /* Baidu */)\n return await this.baidu.chat(messages, model, stream, top, temperature, maxLength);\n else if (provider === \"moonshot\" /* MoonShot */)\n return await this.moon.chat(messages, model, stream, top, temperature, maxLength);\n else if (provider === \"aliyun\" /* AliYun */)\n return await this.ali.chat(messages, model, stream, top, temperature, maxLength);\n else if (provider === \"xai\" /* XAI */)\n return await this.xai.chat(\n messages,\n model,\n stream,\n top,\n temperature,\n maxLength,\n tools,\n toolChoice\n );\n else if (provider === \"other\" /* Other */)\n return await this.other.chat(\n messages,\n model,\n stream,\n top,\n temperature,\n maxLength,\n tools,\n toolChoice\n );\n else throw new Error(\"Chat model Provider not found\");\n }\n async embedding(content, option = {}) {\n const provider = option.provider || ModelProvider.OpenAI;\n const { model } = option;\n if (typeof content === \"string\") content = [content];\n if (provider === \"openai\" /* OpenAI */)\n return await this.openai.embedding(content, model);\n else if (provider === \"glm\" /* GLM */)\n return await this.glm.embedding(content, model, option.dimensions);\n else if (provider === \"google\" /* Google */)\n return await this.google.embedding(content, model);\n else if (provider === \"aliyun\" /* AliYun */)\n return await this.ali.embedding(content, model, option.dimensions);\n else if (provider === \"other\" /* Other */)\n return await this.other.embedding(content, model);\n else throw new Error(\"Embedding model provider not found\");\n }\n async imagine(prompt, option = {}) {\n const provider = option.provider || \"openai\" /* OpenAI */;\n const { negativePrompt, width, height, num, model } = option;\n if (provider === \"openai\" /* OpenAI */)\n return await this.openai.imagine(prompt, width, height, num, model);\n else if (provider === \"midjourney\" /* MidJourney */)\n return await this.mj.imagine(prompt, negativePrompt, width, height);\n else if (provider === \"stability.ai\" /* StabilityAI */)\n return await this.stability.imagine(\n prompt,\n negativePrompt,\n width,\n height,\n num,\n model\n );\n else if (provider === \"iflytek\" /* IFlyTek */)\n return await this.fly.imagine(prompt, width, height, model);\n else throw new Error(\"Imagine model provider not found\");\n }\n async task(provider, id) {\n if (provider === \"openai\" /* OpenAI */) return this.openai.task(id);\n else if (provider === \"midjourney\" /* MidJourney */) return await this.mj.task(id);\n else if (provider === \"stability.ai\" /* StabilityAI */) return this.stability.task(id);\n else if (provider === \"iflytek\" /* IFlyTek */) return this.fly.task(id);\n else throw new Error(\"Imagine model provider not found\");\n }\n async change(provider, taskId, action, index) {\n if (provider === \"midjourney\" /* MidJourney */) return this.mj.change(taskId, action, index);\n else throw new Error(\"Imagine model provider not found\");\n }\n};\n\n// src/index.ts\nvar index_default = UniAI;\nexport {\n AliChatModel,\n AliEmbedModel,\n BDUChatRoleEnum,\n BaiduChatModel,\n ChatModel,\n ChatModelProvider,\n ChatRoleEnum,\n DETaskType,\n DSChatRoleEnum,\n DeepSeekChatModel,\n EmbedModel,\n EmbedModelProvider,\n GEMChatRoleEnum,\n GLMChatModel,\n GLMChatRoleEnum,\n GLMEmbedModel,\n GPTChatRoleEnum,\n GoogleChatModel,\n GoogleEmbedModel,\n IFlyTekChatModel,\n IFlyTekImagineModel,\n ImagineModel,\n ImagineModelProvider,\n ImgTaskType,\n MJTaskType,\n MidJourneyImagineModel,\n ModelModel,\n ModelProvider,\n MoonShotChatModel,\n OpenAIChatModel,\n OpenAIEmbedModel,\n OpenAIImagineModel,\n OtherEmbedModel,\n SDTaskType,\n SPKChatRoleEnum,\n SPKTaskType,\n StabilityAIImagineModel,\n XAIChatModel,\n index_default as default\n};\n","/** @format */\n\n// model providers\n\nexport enum ChatModelProvider {\n OpenAI = 'openai',\n DeepSeek = 'deepseek',\n IFlyTek = 'iflytek',\n Baidu = 'baidu',\n Google = 'google',\n GLM = 'glm',\n MoonShot = 'moonshot',\n AliYun = 'aliyun',\n XAI = 'xai',\n Other = 'other'\n}\n\nexport enum EmbedModelProvider {\n OpenAI = 'openai',\n Google = 'google',\n GLM = 'glm',\n AliYun = 'aliyun',\n Other = 'other'\n}\n\nexport enum ImagineModelProvider {\n OpenAI = 'openai',\n MidJourney = 'midjourney',\n StabilityAI = 'stability.ai',\n IFlyTek = 'iflytek'\n}\n\nexport type ModelProvider = ChatModelProvider | EmbedModelProvider | ImagineModelProvider\nexport const ModelProvider = { ...ChatModelProvider, ...EmbedModelProvider, ...ImagineModelProvider }\n\n// openai embed models\nexport enum OpenAIEmbedModel {\n ADA = 'text-embedding-ada-002',\n LARGE = 'text-embedding-3-large',\n SMALL = 'text-embedding-3-small'\n}\n\n// local embedding models\nexport enum OtherEmbedModel {\n BGE_M3 = 'bge-m3',\n BASE_CHN = 'text2vec-base-chinese',\n LARGE_CHN = 'text2vec-large-chinese',\n BASE_CHN_PARAPH = 'text2vec-base-chinese-paraphrase',\n BASE_CHN_SENTENCE = 'text2vec-base-chinese-sentence',\n BASE_MUL = 'text2vec-base-multilingual',\n PARAPH_MUL_MINI = 'paraphrase-multilingual-MiniLM-L12-v2'\n}\n\nexport enum GLMEmbedModel {\n EMBED_2 = 'embedding-2',\n EMBED_3 = 'embedding-3'\n}\n\nexport enum GoogleEmbedModel {\n EMBED_4 = 'text-embedding-004'\n}\n\nexport enum AliEmbedModel {\n ALI_V3 = 'text-embedding-v3',\n ALI_V2 = 'text-embedding-v2',\n ALI_V1 = 'text-embedding-v1',\n ALI_ASYNC_V2 = 'text-embedding-async-v2',\n ALI_ASYNC_V1 = 'text-embedding-async-v1'\n}\n\nexport type EmbedModel = OpenAIEmbedModel | OtherEmbedModel | GLMEmbedModel | GoogleEmbedModel | AliEmbedModel\nexport const EmbedModel = {\n ...OpenAIEmbedModel,\n ...OtherEmbedModel,\n ...GLMEmbedModel,\n ...GoogleEmbedModel,\n ...AliEmbedModel\n}\n\n// openai chat models\nexport enum OpenAIChatModel {\n GPT_4O = 'gpt-4o',\n GPT_4O_MINI = 'gpt-4o-mini',\n CHAT_GPT_4O = 'chatgpt-4o-latest',\n GPT4_TURBO = 'gpt-4-turbo',\n GPT4 = 'gpt-4',\n O1 = 'o1',\n O1_PRE = 'o1-preview',\n O1_MINI = 'o1-mini',\n O3_MINI = 'o3-mini',\n GPT3 = 'gpt-3.5-turbo'\n}\n\nexport enum DeepSeekChatModel {\n DEEPSEEK_V3 = 'deepseek-chat',\n DEEPSEEK_R1 = 'deepseek-reasoner'\n}\n\n// google chat models\nexport enum GoogleChatModel {\n GEM_PRO_1_5 = 'gemini-1.5-pro',\n GEM_FLASH_1_5 = 'gemini-1.5-flash',\n GEM_FLASH_1_5_8B = 'gemini-1.5-flash-8b',\n GEM_FLASH_2 = 'gemini-2.0-flash',\n GEM_FLASH_2_LITE = 'gemini-2.0-flash-lite-preview',\n // experimental\n GEM_PRO_2 = 'gemini-2.0-pro-exp',\n GEM_FLASH_2_THINK_EXP = 'gemini-2.0-flash-thinking-exp',\n LEARNLM_PRO_1_5 = 'learnlm-1.5-pro-experimental'\n}\n\n// glm chat models\nexport enum GLMChatModel {\n GLM_3_TURBO = 'glm-3-turbo',\n GLM_4 = 'glm-4',\n GLM_4_AIR = 'glm-4-air',\n GLM_4_AIRX = 'glm-4-airx',\n GLM_4_FLASH = 'glm-4-flash',\n GLM_4_FLASHX = 'glm-4-flashx',\n GLM_4V = 'glm-4v',\n GLM_4V_PLUS = 'glm-4v-plus',\n GLM_4_LONG = 'glm-4-long',\n GLM_4_PLUS = 'glm-4-plus'\n}\n\n// https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Fm2vrveyu\nexport enum BaiduChatModel {\n ERNIE_3_5 = 'completions', // 'ernie-3.5-8k', // ERNIE 3.5 8K\n ERNIE_3_5_PRE = 'ernie-3.5-8k-preview', // ERNIE 3.5 8K Preview\n ERNIE_3_5_128K = 'ernie-3.5-128k', // ERNIE 3.5 128K\n ERNIE_4_0_LATEST = 'ernie-4.0-8k-latest', // ERNIE 4.0 8K Latest\n ERNIE_4_0_PREVIEW = 'ernie-4.0-8k-preview', // ERNIE 4.0 8K Preview\n ERNIE_4_0_8K = 'completions_pro', // 'ernie-4.0-8k', // ERNIE 4.0 8K\n ERNIE_4_0_TURBO_LATEST = 'ernie-4.0-turbo-8k-latest', // ERNIE 4.0 Turbo 8K Latest\n ERNIE_4_0_TURBO_PREVIEW = 'ernie-4.0-turbo-8k-preview', // ERNIE 4.0 Turbo 8K Preview\n ERNIE_4_0_TURBO_8K = 'ernie-4.0-turbo-8k', // ERNIE 4.0 Turbo 8K\n ERNIE_4_0_TURBO_128K = 'ernie-4.0-turbo-128k', // ERNIE 4.0 Turbo 128K\n ERNIE_SPEED_8K = 'ernie_speed', // ERNIE Speed 8K\n ERNIE_SPEED_128K = 'ernie-speed-128k', // ERNIE Speed 128K\n ERNIE_SPEED_PRO_128K = 'ernie-speed-pro-128k', // ERNIE Speed Pro 128K\n ERNIE_LITE_8K = 'ernie-lite-8k', // ERNIE Lite 8K\n ERNIE_LITE_PRO_128K = 'ernie-lite-pro-128k', // ERNIE Lite Pro 128K\n ERNIE_TINY_8K = 'ernie-tiny-8k', // ERNIE Tiny 8K\n ERNIE_CHAR_8K = 'ernie-char-8k', // ERNIE Character 8K\n ERNIE_CHAR_FICTION_8K = 'ernie-char-fiction-8k', // ERNIE Character Fiction 8K\n ERNIE_NOVEL_8K = 'ernie-novel-8k' // ERNIE Novel 8K\n}\n\n// iFlyTek spark model\nexport enum IFlyTekChatModel {\n SPARK_LITE = 'lite',\n SPARK_PRO = 'generalv3',\n SPARK_PRO_128K = 'pro-128k',\n SPARK_MAX = 'generalv3.5',\n SPARK_MAX_32K = 'max-32k',\n SPARK_ULTRA = '4.0Ultra'\n}\n\nexport enum MoonShotChatModel {\n MOON_V1_8K = 'moonshot-v1-8k',\n MOON_V1_32K = 'moonshot-v1-32k',\n MOON_V1_128K = 'moonshot-v1-128k'\n}\n\nexport enum AliChatModel {\n QWEN_MAX = 'qwen-max',\n QWEN_PLUS = 'qwen-plus',\n QWEN_TURBO = 'qwen-turbo',\n QWEN_LONG = 'qwen-long',\n QWEN_CODE = 'qwen-coder-turbo',\n QWEN_MATH = 'qwen-math-plus',\n QWEN_VL_MAX = 'qwen-vl-max',\n QWEN_VL_PLUS = 'qwen-vl-plus'\n}\n\nexport enum XAIChatModel {\n GROK2 = 'grok-2',\n GROK2_VISION = 'grok-2-vision',\n GROK3 = 'grok-3',\n GROK3_VISION = 'grok-3-vision'\n}\n\n// All chat models\nexport type ChatModel =\n | OpenAIChatModel\n | DeepSeekChatModel\n | BaiduChatModel\n | GLMChatModel\n | IFlyTekChatModel\n | GoogleChatModel\n | MoonShotChatModel\n | AliChatModel\n | XAIChatModel\n | string\n\nexport const ChatModel = {\n ...OpenAIChatModel,\n ...DeepSeekChatModel,\n ...BaiduChatModel,\n ...GLMChatModel,\n ...IFlyTekChatModel,\n ...GoogleChatModel,\n ...OpenAIChatModel,\n ...MoonShotChatModel,\n ...AliChatModel,\n ...XAIChatModel\n}\n\n// image models\nexport enum MidJourneyImagineModel {\n MJ = 'midjourney'\n}\nexport enum OpenAIImagineModel {\n DALL_E_2 = 'dall-e-2',\n DALL_E_3 = 'dall-e-3'\n}\nexport enum StabilityAIImagineModel {\n SD_1_6 = 'stable-diffusion-v1-6',\n SD_XL_1024 = 'stable-diffusion-xl-1024-v1-0'\n}\nexport enum IFlyTekImagineModel {\n V2 = 'v2.1'\n}\n\nexport const ImagineModel = {\n ...OpenAIImagineModel,\n ...MidJourneyImagineModel,\n ...StabilityAIImagineModel,\n ...IFlyTekImagineModel\n}\nexport type ImagineModel = OpenAIImagineModel | MidJourneyImagineModel | StabilityAIImagineModel | IFlyTekImagineModel\n\nexport type ModelModel = ChatModel | ImagineModel | EmbedModel\nexport const ModelModel = { ...ChatModel, ...ImagineModel, ...EmbedModel }\n\nexport enum MJTaskType {\n IMAGINE = 'IMAGINE',\n UPSCALE = 'UPSCALE',\n VARIATION = 'VARIATION',\n REROLL = 'REROLL',\n DESCRIBE = 'DESCRIBE',\n BLEND = 'BLEND'\n}\n\nexport enum DETaskType {\n GENERATION = 'generations',\n EDIT = 'edits',\n VARIATION = 'variation'\n}\n\nexport enum SDTaskType {\n GENERATION = 'generation'\n}\n\nexport enum SPKTaskType {\n GENERATION = 'generation'\n}\n\nexport const ImgTaskType = { ...MJTaskType, ...DETaskType, ...SDTaskType, ...SPKTaskType }\nexport type ImgTaskType = MJTaskType | DETaskType | SDTaskType | SPKTaskType\n\n// UniAI specified model roles\nexport enum ChatRoleEnum {\n SYSTEM = 'system',\n USER = 'user',\n ASSISTANT = 'assistant',\n TOOL = 'tool',\n DEV = 'developer'\n}\n\n// OpenAI GPT model roles\nexport enum GPTChatRoleEnum {\n SYSTEM = 'system',\n USER = 'user',\n ASSISTANT = 'assistant',\n DEV = 'developer',\n TOOL = 'tool'\n}\n\n// DeepSeek model roles\nexport enum DSChatRoleEnum {\n SYSTEM = 'system',\n USER = 'user',\n ASSISTANT = 'assistant',\n TOOL = 'tool'\n}\n\n// IflyTek Spark model roles\nexport enum SPKChatRoleEnum {\n USER = 'user',\n ASSISTANT = 'assistant',\n SYSTEM = 'system',\n TOOL = 'tool'\n}\n\n// ChatGLM model roles\nexport enum GLMChatRoleEnum {\n SYSTEM = 'system',\n USER = 'user',\n ASSISTANT = 'assistant',\n TOOL = 'tool'\n}\n\n// Google Gemini\nexport enum GEMChatRoleEnum {\n USER = 'user',\n MODEL = 'model'\n}\n\n// Baidu WenXin Workshop\nexport enum BDUChatRoleEnum {\n USER = 'user',\n ASSISTANT = 'assistant'\n}\n","/**\n * Utility for connecting to the OpenAI model API.\n *\n * @format prettier\n * @author devilyouwei\n */\nimport { PassThrough, Readable } from 'stream'\nimport EventSourceStream from '@server-sent-stream/node'\nimport { decodeStream } from 'iconv-lite'\n\nimport {\n GPTChatRequest,\n GPTChatResponse,\n GPTChatStreamRequest,\n GPTImagineSize,\n GPTChatStreamResponse,\n GPTChatMessage,\n OpenAIEmbedRequest,\n OpenAIEmbedResponse,\n OpenAIImagineRequest,\n OpenAIImagineResponse\n} from '../../interface/IOpenAI'\n\nimport { ChatRoleEnum, DETaskType, OpenAIChatModel, OpenAIEmbedModel, OpenAIImagineModel } from '../../interface/Enum'\n\nimport { ChatResponse, ChatMessage, TaskResponse, ImagineResponse } from '../../interface/IModel'\nimport { EmbeddingResponse } from '../../interface/IModel'\nimport $ from '../util'\nimport { ChatCompletionTool, ChatCompletionToolChoiceOption } from 'openai/resources'\n\nconst STORAGE_KEY = 'task_open_ai'\nconst API = 'https://api.openai.com'\nconst VER = 'v1'\n\nexport default class OpenAI {\n private api: string\n private key?: string | string[]\n\n /**\n * Constructor for the OpenAI class.\n *\n * @param key - The API key for OpenAI.\n * @param api - The API endpoint for proxy (optional).\n */\n constructor(key?: string | string[], api: string = API) {\n this.key = key\n this.api = api\n }\n\n /**\n * Fetches embeddings for input text.\n *\n * @param input - An array of input strings.\n * @param model - The model to use for embeddings (default: text-embedding-ada-002).\n * @returns A promise resolving to the embedding response.\n */\n async embedding(input: string[], model: OpenAIEmbedModel = OpenAIEmbedModel.ADA) {\n const key = Array.isArray(this.key) ? $.getRandomKey(this.key) : this.key\n if (!key) throw new Error('OpenAI API key is not set in config')\n\n const res = await $.post(\n `${this.api}/${VER}/embeddings`,\n { model, input },\n { headers: { Authorization: `Bearer ${key}` }, responseType: 'json' }\n )\n\n const data: EmbeddingResponse = {\n embedding: res.data.map(v => v.embedding),\n object: 'embedding',\n model,\n promptTokens: res.usage.prompt_tokens || 0,\n totalTokens: res.usage.total_tokens || 0\n }\n return data\n }\n\n /**\n * Sends messages to the GPT chat model.\n *\n * @param messages - An array of chat messages.\n * @param model - The model to use for chat (default: gpt-3.5-turbo).\n * @param stream - Whether to use stream response (default: false).\n * @param top - Top probability to sample (optional).\n * @param temperature - Temperature for sampling (optional).\n * @param maxLength - Maximum token length for response (optional).\n * @param tools - Tools for model to use (optional).\n * @param toolChoice - Controls which (if any) tool is called by the model: none, required, auto (optional).\n * @returns A promise resolving to the chat response or a stream.\n */\n async chat(\n messages: ChatMessage[],\n model: OpenAIChatModel = OpenAIChatModel.GPT_4O,\n stream: boolean = false,\n top?: number,\n temperature?: number,\n maxLength?: number,\n tools?: ChatCompletionTool[],\n toolChoice?: ChatCompletionToolChoiceOption\n ) {\n // if (!Object.values(OpenAIChatModel).includes(model)) throw new Error('OpenAI chat model not found')\n\n const key = Array.isArray(this.key) ? $.getRandomKey(this.key) : this.key\n if (!key) throw new Error('OpenAI API key is not set in config')\n\n // remove imgs for not vision model\n if (\n ![\n OpenAIChatModel.GPT4_TURBO,\n OpenAIChatModel.GPT_4O,\n OpenAIChatModel.GPT_4O_MINI,\n OpenAIChatModel.CHAT_GPT_4O,\n OpenAIChatModel.O1,\n OpenAIChatModel.O1_MINI,\n OpenAIChatModel.O1_PRE\n ].includes(model)\n )\n messages = messages.map(({ role, content }) => ({ role, content }))\n\n // temperature is float in [0,1]\n if (typeof temperature === 'number') {\n if (temperature < 0) temperature = 0\n if (temperature > 1) temperature = 1\n }\n // top is float in [0,1]\n if (typeof top === 'number') {\n if (top < 0) top = 0\n if (top > 1) top = 1\n }\n\n const res = await $.post(\n `${this.api}/${VER}/chat/completions`,\n {\n model,\n messages: this.formatMessage(messages),\n stream,\n temperature,\n top_p: top,\n max_completion_tokens: maxLength,\n tools,\n tool_choice: toolChoice\n },\n { headers: { Authorization: `Bearer ${key}` }, responseType: stream ? 'stream' : 'json' }\n )\n\n const data: ChatResponse = {\n content: '',\n model,\n object: '',\n promptTokens: 0,\n completionTokens: 0,\n totalTokens: 0\n }\n\n if (res instanceof Readable) {\n const output = new PassThrough()\n const parser = new EventSourceStream()\n\n parser.on('data', (e: MessageEvent) => {\n const obj = $.json(e.data)\n if (obj) {\n data.content = obj.choices[0]?.delta?.content || ''\n if (obj.choices[0]?.delta?.tool_calls) data.tools = obj.choices[0]?.delta?.tool_calls\n data.model = obj.model\n data.object = obj.object\n data.promptTokens = obj.usage?.prompt_tokens || 0\n data.completionTokens = obj.usage?.completion_tokens || 0\n data.totalTokens = obj.usage?.total_tokens || 0\n output.write(JSON.stringify(data))\n }\n })\n parser.on('error', e => output.destroy(e))\n parser.on('end', () => output.end())\n\n res.pipe(decodeStream('utf-8')).pipe(parser)\n return output as Readable\n } else {\n data.content = res.choices[0]?.message?.content || ''\n if (res.choices[0]?.message?.tool_calls) data.tools = res.choices[0]?.message?.tool_calls\n data.model = res.model\n data.object = res.object\n data.promptTokens = res.usage?.prompt_tokens || 0\n data.completionTokens = res.usage?.completion_tokens || 0\n data.totalTokens = res.usage?.total_tokens || 0\n return data\n }\n }\n\n /**\n * Generates images based on a prompt.\n *\n * @param prompt - The prompt for image generation.\n * @param width - Image width (default: 1024).\n * @param height - Image height (default: 1024).\n * @param n - Number of images to generate (default: 1).\n * @param model - Model choice (default: dall-e-3).\n * @returns A promise resolving to the image generation response.\n */\n async imagine(\n prompt: string,\n width: number = 1024,\n height: number = 1024,\n n: number = 1,\n model: OpenAIImagineModel = OpenAIImagineModel.DALL_E_3\n ): Promise {\n const key = Array.isArray(this.key) ? $.getRandomKey(this.key) : this.key\n if (!key) throw new Error('OpenAI API key is not set in config')\n\n const res = await $.post(\n `${this.api}/${VER}/images/${DETaskType.GENERATION}`,\n { model, prompt, n, size: `${width}x${height}` as GPTImagineSize, response_format: 'b64_json' },\n { headers: { Authorization: `Bearer ${key}` }, responseType: 'json' }\n )\n\n const id = $.getRandomId()\n const imgs: string[] = []\n for (const i in res.data) imgs.push(await $.writeFile(res.data[i].b64_json!, `${id}-${i}.png`))\n\n const time = Date.now()\n const task: TaskResponse = {\n id,\n type: DETaskType.GENERATION,\n info: 'success',\n progress: 100,\n imgs,\n fail: '',\n created: time,\n model\n }\n\n const tasks: TaskResponse[] = $.getItem(STORAGE_KEY) || []\n tasks.push(task)\n $.setItem(STORAGE_KEY, tasks)\n return { taskId: task.id, time }\n }\n\n /**\n * Simulate tasks.\n *\n * @param id - The task ID to retrieve (optional).\n * @returns An array of task responses or a specific task by ID.\n */\n task(id?: string) {\n const tasks: TaskResponse[] = $.getItem(STORAGE_KEY) || []\n\n if (id) return tasks.filter(v => v.id === id)\n else return tasks\n }\n\n /**\n * Formats chat messages according to the GPT model's message format.\n *\n * @param messages - An array of chat messages.\n * @returns Formatted messages compatible with the GPT model.\n */\n private formatMessage(messages: ChatMessage[]) {\n const prompt: GPTChatMessage[] = []\n\n for (const { role, content, img, tool } of messages) {\n // with image\n switch (role) {\n case ChatRoleEnum.USER:\n if (img)\n prompt.push({\n role,\n content: [\n { type: 'text', text: content },\n { type: 'image_url', image_url: { url: img } }\n ]\n })\n else prompt.push({ role, content })\n break\n case ChatRoleEnum.TOOL:\n prompt.push({ role, content, tool_call_id: tool! })\n break\n default:\n prompt.push({ role, content })\n break\n }\n }\n\n return prompt\n }\n}\n","/**\n * @author devilyouwei\n * @format prettier\n * Utils for UniAI\n **/\n\nimport { writeFileSync } from 'fs'\nimport axios, { AxiosRequestConfig } from 'axios'\nimport { LocalStorage } from 'node-localstorage'\nimport path from 'path'\nimport isBase64 from 'is-base64'\n\n// Initialize local storage\nconst localStorage = new LocalStorage('./cache', Infinity)\n\nexport default {\n /**\n * Performs an HTTP GET request.\n *\n * @param url - The URL to make the request to.\n * @param params - Optional request parameters.\n * @param config - Optional Axios request configuration.\n * @returns A Promise that resolves with the response data.\n */\n async get(url: string, params?: RequestT, config?: AxiosRequestConfig) {\n return (await axios.get(url, { params, ...config })).data\n },\n\n /**\n * Performs an HTTP POST request.\n *\n * @param url - The URL to make the request to.\n * @param body - The request body.\n * @param config - Optional Axios request configuration.\n * @returns A Promise that resolves with the response data.\n */\n async post(url: string, body?: RequestT, config?: AxiosRequestConfig) {\n return (await axios.post(url, body, config)).data\n },\n /**\n * Parses JSON from a string and returns it as a generic type T.\n *\n * @param str - The JSON string to parse.\n * @returns The parsed JSON as a generic type T.\n */\n json(str?: string | null) {\n try {\n if (!str) return null\n return JSON.parse(str) as T\n } catch (e) {\n return null\n }\n },\n getRandomKey(arr: T[]): T {\n return arr[Math.floor(Math.random() * arr.length)]\n },\n getRandomId(length = 16): string {\n let result = ''\n while (result.length < length) {\n let rand = Math.floor(Math.random() * 10)\n // Ensure the number doesn't start with zero\n if (result.length === 0 && rand === 0) continue\n // Append the number to the result string\n result += rand.toString()\n }\n return result\n },\n /**\n * Computes the greatest common divisor (GCD) of two numbers using Euclidean algorithm.\n *\n * @param a - The first number.\n * @param b - The second number.\n * @returns The GCD of the two numbers.\n */\n getGCD(a: number, b: number): number {\n if (b === 0) return a\n return this.getGCD(b, a % b)\n },\n /**\n * Calculates and returns the aspect ratio of a width and height.\n *\n * @param width - The width dimension.\n * @param height - The height dimension.\n * @returns The aspect ratio in the format \"width:height\".\n */\n getAspect(width: number, height: number) {\n if (!width || !height) return '1:1'\n\n const gcd = this.getGCD(width, height)\n const aspectRatioWidth = width / gcd\n const aspectRatioHeight = height / gcd\n\n return `${aspectRatioWidth}:${aspectRatioHeight}`\n },\n /**\n * Stores an item in local storage with the specified key.\n *\n * @param key - The key under which to store the item.\n * @param value - The value to be stored.\n */\n setItem(key: string, value: T) {\n localStorage.setItem(key, JSON.stringify(value))\n },\n /**\n * Retrieves an item from local storage by its key and parses it as a generic type T.\n *\n * @param key - The key of the item to retrieve.\n * @returns The parsed item as a generic type T.\n */\n getItem(key: string) {\n return this.json(localStorage.getItem(key))\n },\n /**\n * This method is used to write given data to a file. The data can either be a base64 string or an HTTP/HTTPS URL.\n * If the data is a URL, the method fetches the data as a readable stream and writes it to the file.\n * If it's a base64 string, it writes it directly to the file.\n * @param data - The data to be written to the file. Can be a base64 string or a HTTP/HTTPS img URL.\n * @param filename - The name of the file where the data will be written. If not provided, a unique filename will be generated.\n * @returns - The file path where the data was written.\n */\n async writeFile(data: string, filename = '') {\n // Generate a unique filename for the output\n const filepath = path.join('./cache', filename)\n\n if (data.startsWith('http://') || data.startsWith('https://')) {\n // Use the get method to fetch the image\n const res: Buffer = await this.get(data, {}, { responseType: 'arraybuffer' })\n if (!(res instanceof Buffer)) throw new Error('Img is not a buffer')\n // write to file\n writeFileSync(filepath, res)\n } else writeFileSync(filepath, Buffer.from(data, 'base64'))\n\n return filepath\n },\n\n isBase64(data: string, allowMime: boolean = true) {\n return isBase64(data, { allowMime })\n }\n}\n","/** @format */\n\nimport { PassThrough, Readable } from 'stream'\nimport EventSourceStream from '@server-sent-stream/node'\nimport { decodeStream } from 'iconv-lite'\nimport {\n GLMChatMessage,\n GLMChatRequest,\n GLMChatResponse,\n GLMEmbedRequest,\n GLMEmbedResponse,\n GLMTool,\n GLMToolChoice\n} from '../../interface/IGLM'\nimport { ChatRoleEnum, GLMChatModel, GLMEmbedModel } from '../../interface/Enum'\nimport { ChatMessage, ChatResponse, EmbeddingResponse } from '../../interface/IModel'\nimport $ from '../util'\n\nconst API = 'https://open.bigmodel.cn'\n\nexport default class GLM {\n private key?: string | string[]\n private proxyAPI: string\n\n constructor(key?: string | string[], proxyAPI: string = API) {\n this.key = key\n this.proxyAPI = proxyAPI\n }\n\n /**\n * Fetches embeddings for input text.\n *\n * @param input - An array of input strings.\n * @param model - The model to use for embeddings (default: text-embedding-ada-002).\n * @returns A promise resolving to the embedding response.\n */\n async embedding(input: string[], model: GLMEmbedModel = GLMEmbedModel.EMBED_2, dimensions = 1024) {\n const key = Array.isArray(this.key) ? $.getRandomKey(this.key) : this.key\n if (!key) throw new Error('ZhiPu GLM API key is not set in config')\n\n const res = await $.post(\n `${this.proxyAPI}/api/paas/v4/embeddings`,\n { model, input, dimensions },\n { headers: { Authorization: `Bearer ${key}` }, responseType: 'json' }\n )\n\n return {\n embedding: res.data.map(v => v.embedding),\n object: 'embedding',\n model,\n promptTokens: res.usage.prompt_tokens,\n totalTokens: res.usage.total_tokens\n } as EmbeddingResponse\n }\n\n /**\n * Sends messages to the GLM chat model.\n *\n * @param messages - An array of chat messages.\n * @param model - The model to use for chat (default: GLM_6B a local deployed chatglm3-6b-32k).\n * @param stream - Whether to use stream response (default: false).\n * @param top - Top probability to sample (optional).\n * @param temperature - Temperature for sampling (optional).\n * @param maxLength - Maximum token length for response (optional).\n * @returns A promise resolving to the chat response or a stream.\n */\n async chat(\n messages: ChatMessage[],\n model: GLMChatModel = GLMChatModel.GLM_3_TURBO,\n stream: boolean = false,\n top?: number,\n temperature?: number,\n maxLength?: number,\n tools?: GLMTool[],\n toolChoice?: GLMToolChoice\n ) {\n // filter images\n if (![GLMChatModel.GLM_4V, GLMChatModel.GLM_4V_PLUS].includes(model))\n messages = messages.map(({ role, content }) => ({ role, content }))\n\n // temperature is float in (0,1]\n if (typeof temperature === 'number') {\n if (temperature <= 0) temperature = 0.1\n if (temperature > 1) temperature = 1\n }\n // top is float in (0,1)\n if (typeof top === 'number') {\n if (top <= 0) top = 0.1\n if (top >= 1) top = 0.9\n }\n\n const data: ChatResponse = {\n content: '',\n model,\n object: '',\n promptTokens: 0,\n completionTokens: 0,\n totalTokens: 0\n }\n\n // ZhiPu GLM official API\n const key = Array.isArray(this.key) ? $.getRandomKey(this.key) : this.key\n if (!key) throw new Error('ZhiPu GLM API key is not set in config')\n\n const res = await $.post(\n `${this.proxyAPI}/api/paas/v4/chat/completions`,\n {\n model,\n messages: this.formatMessage(messages),\n stream,\n temperature,\n top_p: top,\n max_tokens: maxLength,\n tools,\n tool_choice: toolChoice\n },\n { headers: { Authorization: `Bearer ${key}` }, responseType: stream ? 'stream' : 'json' }\n )\n\n if (res instanceof Readable) {\n const output = new PassThrough()\n const parser = new EventSourceStream()\n\n parser.on('data', (e: MessageEvent) => {\n const obj = $.json(e.data)\n if (obj) {\n data.content = obj.choices[0]?.delta?.content || ''\n if (obj.choices[0]?.delta?.tool_calls) data.tools = obj.choices[0]?.delta?.tool_calls\n data.model = obj.model\n data.object = obj.object || 'chat.completion.chunk'\n data.promptTokens = obj.usage?.prompt_tokens || 0\n data.completionTokens = obj.usage?.completion_tokens || 0\n data.totalTokens = obj.usage?.total_tokens || 0\n if (data.content) output.write(JSON.stringify(data))\n }\n })\n\n parser.on('error', e => output.destroy(e))\n parser.on('end', () => output.end())\n\n res.pipe(decodeStream('utf-8')).pipe(parser)\n return output as Readable\n } else {\n data.content = res.choices[0]?.message?.content || ''\n if (res.choices[0]?.message?.tool_calls) data.tools = res.choices[0]?.message?.tool_calls\n data.model = res.model\n data.object = res.object || 'chat.completion'\n data.promptTokens = res.usage?.prompt_tokens || 0\n data.completionTokens = res.usage?.completion_tokens || 0\n data.totalTokens = res.usage?.total_tokens || 0\n return data\n }\n }\n\n /**\n * Formats chat messages according to the GPT model's message format.\n *\n * @param messages - An array of chat messages.\n * @returns Formatted messages compatible with the GPT model.\n */\n private formatMessage(messages: ChatMessage[]) {\n const prompt: GLMChatMessage[] = []\n\n for (const { role, content, img, tool } of messages) {\n if (role === ChatRoleEnum.DEV) continue\n\n // with image\n switch (role) {\n case ChatRoleEnum.USER:\n if (img)\n prompt.push({\n role,\n content: [\n { type: 'text', text: content },\n { type: 'image_url', image_url: { url: img } }\n ]\n })\n else prompt.push({ role, content })\n break\n case ChatRoleEnum.TOOL:\n prompt.push({\n role,\n content,\n tool_call_id: tool || ''\n })\n break\n default:\n prompt.push({ role, content })\n break\n }\n }\n\n return prompt\n }\n}\n","/**\n * Other.ts - A class for accessing the Other model's chat and embedding functionality.\n * Such as models from hugging face, text2vec\n *\n * @format by prettier\n * @author devilyouwei\n */\n\nimport { decodeStream } from 'iconv-lite'\nimport { PassThrough, Readable } from 'stream'\nimport EventSourceStream from '@server-sent-stream/node'\nimport { ChatModel, ChatRoleEnum, EmbedModel } from '../../interface/Enum'\nimport { ChatMessage, ChatResponse, EmbeddingResponse } from '../../interface/IModel'\nimport $ from '../util'\nimport {\n GPTChatMessage,\n GPTChatRequest,\n GPTChatResponse,\n GPTChatStreamRequest,\n GPTChatStreamResponse,\n OpenAIEmbedRequest,\n OpenAIEmbedResponse\n} from '../../interface/IOpenAI'\nimport { ChatCompletionTool, ChatCompletionToolChoiceOption } from 'openai/resources'\n\nexport default class Other {\n private api?: string\n private key?: string | string[]\n\n /**\n * Constructor for the OpenAI class.\n *\n * @param api - The API endpoint for the model (optional).\n * @param key - The API key for the model (Optional).\n */\n constructor(api?: string, key?: string | string[]) {\n this.api = api\n this.key = key\n }\n\n /**\n * Fetches embeddings for input text.\n *\n * @param input - An array of input strings.\n * @param model - The model to use for embeddings (default: text-embedding-ada-002).\n * @returns A promise resolving to the embedding response.\n */\n async embedding(input: string[], model: EmbedModel = EmbedModel.BGE_M3) {\n if (!this.api) throw new Error('Other embed model API is not set in config')\n const key = Array.isArray(this.key) ? $.getRandomKey(this.key) : this.key\n\n const res = await $.post(\n `${this.api}/v1/embeddings`,\n { model, input },\n { headers: { Authorization: `Bearer ${key}` }, responseType: 'json' }\n )\n\n const data: EmbeddingResponse = {\n embedding: res.data.map(v => v.embedding),\n object: 'embedding',\n model,\n promptTokens: res.usage.prompt_tokens || 0,\n totalTokens: res.usage.total_tokens || 0\n }\n return data\n }\n\n /**\n * Sends messages to the GPT compatible chat model.\n *\n * @param messages - An array of chat messages.\n * @param model - The model to use for chat (default: gpt-3.5-turbo).\n * @param stream - Whether to use stream response (default: false).\n * @param top - Top probability to sample (optional).\n * @param temperature - Temperature for sampling (optional).\n * @param maxLength - Maximum token length for response (optional).\n * @returns A promise resolving to the chat response or a stream.\n */\n async chat(\n messages: ChatMessage[],\n model: ChatModel = ChatModel.GPT3,\n stream: boolean = false,\n top?: number,\n temperature?: number,\n maxLength?: number,\n tools?: ChatCompletionTool[],\n toolChoice?: ChatCompletionToolChoiceOption\n ) {\n if (!this.api) throw new Error('Other chat model API is not set in config')\n const key = Array.isArray(this.key) ? $.getRandomKey(this.key) : this.key\n\n const res = await $.post(\n `${this.api}/v1/chat/completions`,\n {\n model,\n messages: this.formatMessage(messages),\n stream,\n temperature,\n top_p: top,\n max_tokens: maxLength,\n tools,\n tool_choice: toolChoice\n },\n { headers: { Authorization: `Bearer ${key}` }, responseType: stream ? 'stream' : 'json' }\n )\n\n const data: ChatResponse = {\n content: '',\n model,\n object: '',\n promptTokens: 0,\n completionTokens: 0,\n totalTokens: 0\n }\n\n if (res instanceof Readable) {\n const output = new PassThrough()\n const parser = new EventSourceStream()\n\n parser.on('data', (e: MessageEvent) => {\n const obj = $.json(e.data)\n if (obj) {\n data.content = obj.choices[0]?.delta?.content || ''\n if (obj.choices[0]?.delta?.tool_calls) data.tools = obj.choices[0]?.delta?.tool_calls\n data.model = obj.model || model\n data.object = obj.object || 'chat.completion.chunk'\n data.promptTokens = obj.usage?.prompt_tokens || 0\n data.completionTokens = obj.usage?.completion_tokens || 0\n data.totalTokens = obj.usage?.total_tokens || 0\n output.write(JSON.stringify(data))\n }\n })\n parser.on('error', e => output.destroy(e))\n parser.on('end', () => output.end())\n\n res.pipe(decodeStream('utf-8')).pipe(parser)\n return output as Readable\n } else {\n data.content = res.choices[0]?.message?.content || ''\n if (res.choices[0]?.message?.tool_calls) data.tools = res.choices[0]?.message?.tool_calls\n data.model = res.model || model\n data.object = res.object || 'chat.completion'\n data.promptTokens = res.usage?.prompt_tokens || 0\n data.completionTokens = res.usage?.completion_tokens || 0\n data.totalTokens = res.usage?.total_tokens || 0\n return data\n }\n }\n\n /**\n * Formats chat messages according to the GPT model's message format.\n *\n * @param messages - An array of chat messages.\n * @returns Formatted messages compatible with the GPT model.\n */\n private formatMessage(messages: ChatMessage[]) {\n const prompt: GPTChatMessage[] = []\n\n for (const { role, content, img, tool } of messages) {\n // with image\n switch (role) {\n case ChatRoleEnum.USER:\n if (img)\n prompt.push({\n role,\n content: [\n { type: 'text', text: content },\n { type: 'image_url', image_url: { url: img } }\n ]\n })\n else prompt.push({ role, content })\n break\n case ChatRoleEnum.TOOL:\n prompt.push({\n role,\n content,\n tool_call_id: tool || ''\n })\n break\n default:\n prompt.push({ role, content })\n break\n }\n }\n\n return prompt\n }\n}\n","/** @format */\n\nimport { PassThrough, Readable } from 'stream'\nimport { JSONParser } from '@streamparser/json-node'\nimport { decodeStream } from 'iconv-lite'\nimport {\n GEMChatRequest,\n GEMChatResponse,\n GEMChatMessage,\n GoogleEmbedRequest,\n GoogleEmbedResponse\n} from '../../interface/IGoogle'\nimport { ChatRoleEnum, GEMChatRoleEnum, GoogleChatModel, GoogleEmbedModel } from '../../interface/Enum'\nimport { ChatMessage, ChatResponse, EmbeddingResponse } from '../../interface/IModel'\nimport { extname } from 'path'\nimport { readFileSync } from 'fs'\nimport $ from '../util'\n\nconst API = 'https://generativelanguage.googleapis.com'\nconst SAFE_SET = [\n { category: 'HARM_CATEGORY_HARASSMENT', threshold: 'BLOCK_NONE' },\n { category: 'HARM_CATEGORY_HATE_SPEECH', threshold: 'BLOCK_NONE' },\n { category: 'HARM_CATEGORY_SEXUALLY_EXPLICIT', threshold: 'BLOCK_NONE' },\n { category: 'HARM_CATEGORY_DANGEROUS_CONTENT', threshold: 'BLOCK_NONE' }\n]\n\nexport default class Google {\n private key?: string | string[]\n private api: string\n\n /**\n * Google API client class for chat functionalities.\n * @param key - The Google API key or an array of keys.\n * @param api - The API endpoint (default: 'https://generativelanguage.googleapis.com').\n */\n constructor(key?: string | string[], api: string = API) {\n this.key = key\n this.api = api\n }\n\n /**\n * Fetches embeddings for input text.\n *\n * @param input - An array of input strings.\n * @param model - The model to use for embeddings (default: text-embedding-ada-002).\n * @returns A promise resolving to the embedding response.\n */\n async embedding(input: string[], model: GoogleEmbedModel = GoogleEmbedModel.EMBED_4) {\n const key = Array.isArray(this.key) ? $.getRandomKey(this.key) : this.key\n if (!key) throw new Error('Google API key is not set in config')\n\n const request: Promise[] = []\n for (const text of input) {\n request.push(\n $.post(\n `${this.api}/v1beta/models/${model}:embedContent?key=${key}`,\n { model: `models/${model}`, content: { parts: [{ text }] } }\n )\n )\n }\n const res = await Promise.all(request)\n\n const data: EmbeddingResponse = {\n embedding: res.map(v => v.embedding.values),\n object: 'embedding',\n model,\n promptTokens: 0,\n totalTokens: 0\n }\n return data\n }\n\n /**\n * Sends messages to the Google Gemini chat model.\n * @param messages - An array of chat messages.\n * @param model - The model to use for chat (default: gemini-pro).\n * @param stream - Whether to use stream response (default: false).\n * @param top - Top probability to sample (optional).\n * @param temperature - Temperature for sampling (optional).\n * @param maxLength - Maximum token length for response (optional).\n * @returns A promise resolving to the chat response or a stream.\n */\n async chat(\n messages: ChatMessage[],\n model: GoogleChatModel = GoogleChatModel.GEM_PRO_1_5,\n stream: boolean = false,\n top?: number,\n temperature?: number,\n maxLength?: number\n ) {\n const key = Array.isArray(this.key) ? $.getRandomKey(this.key) : this.key\n if (!key) throw new Error('Google AI API key is not set in config')\n\n // temperature is float in [0,1]\n if (typeof temperature === 'number') {\n if (temperature < 0) temperature = 0\n if (temperature > 1) temperature = 1\n }\n // top is float in [0,1]\n if (typeof top === 'number') {\n if (top < 0) top = 0\n if (top > 1) top = 1\n }\n\n const res = await $.post(\n `${this.api}/v1beta/models/${model}:${stream ? 'streamGenerateContent' : 'generateContent'}?key=${key}`,\n {\n contents: await this.formatMessage(messages),\n system_instruction: {\n parts: {\n text: messages\n .filter(v => v.role === ChatRoleEnum.SYSTEM)\n .map(v => v.content)\n .join('\\n')\n }\n },\n generationConfig: { topP: top, temperature, maxOutputTokens: maxLength },\n safetySettings: SAFE_SET\n },\n { responseType: stream ? 'stream' : 'json' }\n )\n const data: ChatResponse = {\n content: '',\n model,\n object: '',\n promptTokens: 0,\n completionTokens: 0,\n totalTokens: 0\n }\n if (res instanceof Readable) {\n const output = new PassThrough()\n const parser = new JSONParser()\n\n parser.on('data', ({ value }) => {\n if (value.candidates || value.promptFeedback) {\n const obj: GEMChatResponse = value\n const block = obj.promptFeedback?.blockReason\n if (block) return output.destroy(new Error(`Content blocked, reason: ${block}`))\n if (!obj.candidates) return output.destroy(new Error('Google API error, no candidates'))\n const candidate = obj.candidates[0]\n if (!candidate.content) return output.destroy(new Error(candidate.finishReason))\n data.content = candidate.content.parts[0].text || ''\n data.object = `chat.completion.chunk`\n return output.write(JSON.stringify(data))\n } else return output\n })\n\n parser.on('error', e => output.destroy(e))\n parser.on('end', () => output.end())\n\n res.pipe(decodeStream('utf-8')).pipe(parser)\n return output as Readable\n } else {\n const block = res.promptFeedback?.blockReason\n if (block) throw new Error(`Content blocked, reason: ${block}`)\n if (!res.candidates) throw new Error('Google API error, no candidates')\n\n const candidate = res.candidates[0]\n if (!candidate.content) throw new Error(candidate.finishReason)\n data.content = candidate.content.parts[0].text || candidate.finishReason\n data.object = `chat.completion`\n return data\n }\n }\n\n /**\n * Formats chat messages into GEMChatMessage format.\n * @param messages - An array of chat messages.\n * @returns A formatted array of GEMChatMessage.\n */\n private async formatMessage(messages: ChatMessage[]) {\n const prompt: GEMChatMessage[] = []\n let input = ''\n let base64: { mime: string; data: string } | null = null\n\n for (const { role, content, img } of messages) {\n if (!content) continue\n if (role === ChatRoleEnum.SYSTEM) continue\n if (img) base64 = await this.toBase64(img)\n\n if (role !== ChatRoleEnum.ASSISTANT) input += `\\n${content}`\n else {\n input = input.trim()\n prompt.push({\n role: GEMChatRoleEnum.USER,\n parts: base64\n ? [{ text: input || ' ' }, { inline_data: { mime_type: base64.mime, data: base64.data } }]\n : [{ text: input || ' ' }]\n })\n prompt.push({ role: GEMChatRoleEnum.MODEL, parts: [{ text: content }] })\n input = ''\n }\n }\n input = input.trim()\n\n if (!input) throw new Error('User input nothing')\n prompt.push({\n role: GEMChatRoleEnum.USER,\n parts: base64\n ? [{ text: input || ' ' }, { inline_data: { mime_type: base64.mime, data: base64.data } }]\n : [{ text: input || ' ' }]\n })\n // Gemini Vision currently only support 1 prompt\n return base64 ? [prompt[prompt.length - 1]] : prompt\n }\n\n /**\n * Convert an image to base64 format object.\n * @param img The image string to convert.\n * @returns An object containing the MIME type and base64 data.\n */\n private async toBase64(img: string): Promise<{ mime: string; data: string }> {\n let mime: string = ''\n let data: string = ''\n\n if ($.isBase64(img)) {\n // Handle pure base64 data\n if ($.isBase64(img, false)) {\n data = img\n mime = 'image/png'\n } else {\n const match = img.match(/^data:image\\/([a-zA-Z]*);base64,([^\\\"']*)$/)\n if (match) {\n mime = `image/${match[1]}`\n data = match[2]\n }\n }\n } else if (img.startsWith('http')) {\n const res: Buffer = await $.get(img, {}, { responseType: 'arraybuffer' })\n mime = `image/${['png', 'jpg', 'jpeg', 'webp', 'heic', 'heif'].find(f => img.toLowerCase().includes(f))?.replace('jpg', 'jpeg') || 'png'}`\n data = res.toString('base64')\n } else {\n mime = `image/${extname(img).replace('.', '').toLowerCase()}`\n data = readFileSync(img).toString('base64')\n }\n\n if (!mime || !data) throw new Error('Can not transfer base64')\n\n return { mime, data }\n }\n}\n","/** @format */\n\nimport { createHmac } from 'crypto'\nimport { hostname } from 'os'\nimport { PassThrough, Readable } from 'stream'\nimport EventSourceStream from '@server-sent-stream/node'\nimport { decodeStream } from 'iconv-lite'\nimport { ChatMessage, ChatResponse, TaskResponse } from '../../interface/IModel'\nimport {\n SparkChatRequest,\n SparkChatResponse,\n SPKChatMessage,\n SPKImagineRequest,\n SPKImagineResponse,\n SPKTool,\n SPKToolChoice\n} from '../../interface/IFlyTek'\nimport { ChatRoleEnum, IFlyTekChatModel, IFlyTekImagineModel, SPKTaskType } from '../../interface/Enum'\nimport $ from '../util'\n\nconst CHAT_API = 'https://spark-api-open.xf-yun.com'\nconst IMAGINE_API = 'https://spark-api.cn-huabei-1.xf-yun.com'\nconst STORAGE_KEY = 'task_iflytek'\n\nexport default class IFlyTek {\n private pass?: string | string[]\n private key?: string\n private secret?: string\n private appid?: string\n private api?: string\n\n /**\n * Constructor for IFlyTek class.\n * @param key - The API key for MoonShot.\n * @param api - The API endpoint for proxy (optional).\n */\n constructor(pass?: string | string[], key?: string, secret?: string, appid?: string, api: string = CHAT_API) {\n this.pass = pass\n this.key = key\n this.secret = secret\n this.appid = appid\n this.api = api\n }\n\n /**\n * Initiates a chat conversation with IFLYTEK Spark API.\n *\n * @param messages - An array of chat messages.\n * @param model - The Spark model version to use (default: SPARK_DEFAULT_MODEL_VERSION).\n * @param stream - Whether to use stream response (default: false).\n * @param top - Top probability to sample (optional).\n * @param temperature - Temperature for sampling (optional).\n * @param maxLength - Maximum token length for response (optional).\n * @returns A promise resolving to the chat response or a stream.\n */\n async chat(\n messages: ChatMessage[],\n model: IFlyTekChatModel = IFlyTekChatModel.SPARK_LITE,\n stream: boolean = false,\n top?: number,\n temperature?: number,\n maxLength?: number,\n tools?: SPKTool[],\n toolChoice?: SPKToolChoice\n ) {\n const key = Array.isArray(this.pass) ? $.getRandomKey(this.pass) : this.pass\n if (!key) throw new Error('IFlyTek Spark API Password is not set in config')\n\n // top is integer in [1,6]\n if (typeof top === 'number') {\n if (top <= 0) top = 0.1\n if (top > 1) top = 1\n }\n // temperature is float in (0,1]\n if (typeof temperature === 'number') {\n if (temperature < 0) temperature = 0\n if (temperature > 2) temperature = 2\n }\n\n const res = await $.post(\n `${this.api}/v1/chat/completions`,\n {\n model,\n messages: this.formatMessage(messages),\n stream,\n temperature,\n top_p: top,\n max_tokens: maxLength,\n tools,\n tool_choice: toolChoice\n },\n { headers: { Authorization: `Bearer ${key}` }, responseType: stream ? 'stream' : 'json' }\n )\n const data: ChatResponse = {\n content: '',\n model,\n object: '',\n promptTokens: 0,\n completionTokens: 0,\n totalTokens: 0\n }\n if (res instanceof Readable) {\n const output = new PassThrough()\n const parser = new EventSourceStream()\n\n parser.on('data', (e: MessageEvent) => {\n const obj = $.json(e.data)\n if (obj) {\n data.content = obj.choices[0]?.delta?.content || ''\n if (obj.choices[0]?.delta?.tool_calls) data.tools = obj.choices[0]?.delta?.tool_calls\n data.model = model\n data.object = 'chat.completion.chunk'\n data.promptTokens = obj.usage?.prompt_tokens || 0\n data.completionTokens = obj.usage?.completion_tokens || 0\n data.totalTokens = obj.usage?.total_tokens || 0\n output.write(JSON.stringify(data))\n }\n })\n\n parser.on('error', e => output.destroy(e))\n parser.on('end', () => output.end())\n\n res.pipe(decodeStream('utf-8')).pipe(parser)\n return output as Readable\n } else {\n data.content = res.choices[0]?.message?.content || ''\n if (res.choices[0]?.message?.tool_calls) data.tools = res.choices[0]?.message?.tool_calls\n data.model = model\n data.object = 'chat.completion'\n data.promptTokens = res.usage?.prompt_tokens || 0\n data.completionTokens = res.usage?.completion_tokens || 0\n data.totalTokens = res.usage?.total_tokens || 0\n return data\n }\n }\n\n /**\n * Generate an image based on the given prompt.\n * @param prompt The prompt for image generation.\n * @param width The width of the image, default is 512.\n * @param height The height of the image, default is 512.\n * @param model The model to use for generating the image, default is IFlyTekImagineModel.V2.\n */\n async imagine(\n prompt: string,\n width: number = 512,\n height: number = 512,\n model: IFlyTekImagineModel = IFlyTekImagineModel.V2\n ) {\n if (!this.appid) throw new Error('IFlyTek APP ID is not set in config')\n // get specific generated URL\n const url = this.getImagineURL(model)\n const res = await $.post(url, {\n header: { app_id: this.appid },\n payload: { message: { text: [{ role: 'user', content: prompt }] } },\n parameter: { chat: { domain: 'general', width, height } }\n })\n if (res.header.code !== 0) throw new Error(res.header.message)\n if (!res.payload) throw new Error('Fail to generate image, empty payload')\n\n const id = $.getRandomId()\n const imgs: string[] = []\n for (const i in res.payload.choices.text)\n imgs.push(await $.writeFile(res.payload.choices.text[i].content, `${id}-${i}.png`))\n\n const time = Date.now()\n const task: TaskResponse = {\n id,\n type: SPKTaskType.GENERATION,\n info: 'success',\n progress: 100,\n imgs,\n fail: '',\n created: time,\n model\n }\n\n const tasks: TaskResponse[] = $.getItem(STORAGE_KEY) || []\n tasks.push(task)\n $.setItem(STORAGE_KEY, tasks)\n return { taskId: task.id, time }\n }\n\n /**\n * Simulate tasks.\n *\n * @param id - The task ID to retrieve (optional).\n * @returns An array of task responses or a specific task by ID.\n */\n task(id?: string) {\n const tasks: TaskResponse[] = $.getItem(STORAGE_KEY) || []\n\n if (id) return tasks.filter(v => v.id === id)\n else return tasks\n }\n\n /**\n * Generates the Imagine API URL.\n *\n * @param model - The Spark imagine model.\n */\n private getImagineURL(model: IFlyTekImagineModel) {\n if (!this.secret) throw new Error('IFlyTek API secret is not set in config')\n if (!this.key) throw new Error('IFlyTek API key is not set in config')\n\n const host = hostname()\n const date = new Date().toUTCString()\n const algorithm = 'hmac-sha256'\n const headers = 'host date request-line'\n const signatureOrigin = `host: ${host}\\ndate: ${date}\\nPOST /${model}/tti HTTP/1.1`\n const signatureSha = createHmac('sha256', this.secret).update(signatureOrigin).digest('hex')\n const signature = Buffer.from(signatureSha, 'hex').toString('base64')\n const authorizationOrigin = `api_key=\"${this.key}\", algorithm=\"${algorithm}\", headers=\"${headers}\", signature=\"${signature}\"`\n const authorization = Buffer.from(authorizationOrigin).toString('base64')\n return `${IMAGINE_API}/${model}/tti?authorization=${authorization}&date=${date}&host=${host}`\n }\n\n /**\n * Format the chat message for IFlyTek Spark model.\n *\n * @param messages - Original chat messages\n */\n private formatMessage(messages: ChatMessage[]) {\n const prompt: SPKChatMessage[] = []\n\n for (const { role, content } of messages) {\n if (role === ChatRoleEnum.DEV) continue\n prompt.push({ role, content })\n }\n\n return prompt\n }\n}\n","/** @format */\n\nimport { PassThrough, Readable } from 'stream'\nimport EventSourceStream from '@server-sent-stream/node'\nimport { decodeStream } from 'iconv-lite'\nimport {\n BaiduAccessTokenRequest,\n BaiduAccessTokenResponse,\n BaiduChatMessage,\n BaiduChatRequest,\n BaiduChatResponse\n} from '../../interface/IBaidu'\nimport { BaiduChatModel, ChatRoleEnum } from '../../interface/Enum'\nimport { ChatMessage, ChatResponse } from '../../interface/IModel'\nimport $ from '../util'\n\nconst API = 'https://aip.baidubce.com'\nconst STORAGE_KEY = 'baidu'\n\nexport default class Baidu {\n private key?: string\n private secret?: string\n private api?: string\n\n constructor(key?: string, secret?: string, api: string = API) {\n this.key = key\n this.secret = secret\n this.api = api\n }\n /**\n * Sends messages to the GLM chat model.\n *\n * @param messages - An array of chat messages.\n * @param model - The submodel to use for chat (default: ERNIE 8K).\n * @param stream - Whether to use stream response (default: false).\n * @param top - Top probability to sample (optional).\n * @param temperature - Temperature for sampling (optional).\n * @param maxLength - Maximum token length for response (optional).\n * @returns A promise resolving to the chat response or a stream.\n */\n async chat(\n messages: ChatMessage[],\n model: BaiduChatModel = BaiduChatModel.ERNIE_3_5,\n stream: boolean = false,\n top?: number,\n temperature?: number,\n maxLength?: number\n ) {\n // temperature is float in (0,1]\n if (typeof temperature === 'number') {\n if (temperature <= 0) temperature = 0.1\n if (temperature > 1) temperature = 1\n }\n // top is float in [0,1]\n if (typeof top === 'number') {\n if (top < 0) top = 0\n if (top > 1) top = 1\n }\n\n const token = await this.getAccessToken()\n\n const res = await $.post(\n `${this.api}/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/${model}?access_token=${token}`,\n {\n messages: this.formatMessage(messages),\n system: messages[0]?.role === ChatRoleEnum.SYSTEM ? messages[0].content : '',\n stream,\n temperature,\n top_p: top,\n max_output_tokens: maxLength\n },\n { responseType: stream ? 'stream' : 'json' }\n )\n\n const data: ChatResponse = {\n content: '',\n model,\n object: '',\n promptTokens: 0,\n completionTokens: 0,\n totalTokens: 0\n }\n if (res instanceof Readable) {\n const output = new PassThrough()\n const parser = new EventSourceStream()\n parser.on('data', (e: MessageEvent) => {\n const obj = $.json(e.data)\n if (obj) {\n data.content = obj.result || ''\n data.model = model\n data.object = obj.object\n data.promptTokens = obj.usage?.prompt_tokens || 0\n data.completionTokens = obj.usage?.completion_tokens || 0\n data.totalTokens = obj.usage?.total_tokens || 0\n output.write(JSON.stringify(data))\n }\n })\n\n parser.on('error', e => output.destroy(e))\n parser.on('end', () => output.end())\n\n res.pipe(decodeStream('utf-8')).pipe(parser)\n return output as Readable\n } else {\n if (res.error_code) throw new Error(res.error_msg)\n data.content = res.result || ''\n data.model = model\n data.object = res.object\n data.promptTokens = res.usage?.prompt_tokens || 0\n data.completionTokens = res.usage?.completion_tokens || 0\n data.totalTokens = res.usage?.total_tokens || 0\n return data\n }\n }\n\n // get baidu access token\n private async getAccessToken() {\n if (!this.key) throw new Error('Baidu API key is not set in config')\n if (!this.secret) throw new Error('Baidu API secret is not set in config')\n\n const now = Date.now()\n\n // load access token\n const cache = $.getItem(STORAGE_KEY)\n if (cache && cache.expires_in > now) return cache.access_token\n\n // get new access token\n const res = await $.get(`${this.api}/oauth/2.0/token`, {\n grant_type: 'client_credentials',\n client_id: this.key,\n client_secret: this.secret\n })\n if (res.error) throw new Error(res.error_description)\n\n res.expires_in = now + res.expires_in * 1000\n\n $.setItem(STORAGE_KEY, res)\n\n return res.access_token\n }\n\n // format to baidu message\n private formatMessage(messages: ChatMessage[]) {\n const prompt: BaiduChatMessage[] = []\n let input = ''\n const { USER, ASSISTANT, SYSTEM } = ChatRoleEnum\n for (const { role, content } of messages) {\n if (!content.trim() || role === SYSTEM) continue\n if (role !== ASSISTANT) input += `\\n${content}`\n else {\n prompt.push({ role: USER, content: input.trim() || ' ' })\n prompt.push({ role: ASSISTANT, content })\n input = ''\n }\n }\n if (!input.trim()) throw new Error('User input nothing')\n prompt.push({ role: USER, content: input.trim() })\n return prompt\n }\n}\n","/** @format */\n\nimport { PassThrough, Readable } from 'stream'\nimport EventSourceStream from '@server-sent-stream/node'\nimport { decodeStream } from 'iconv-lite'\nimport { ChatRoleEnum, MoonShotChatModel } from '../../interface/Enum'\nimport { ChatMessage, ChatResponse } from '../../interface/IModel'\nimport {\n GPTChatMessage,\n GPTChatRequest,\n GPTChatResponse,\n GPTChatStreamRequest,\n GPTChatStreamResponse\n} from '../../interface/IOpenAI'\nimport $ from '../util'\n\nconst API = 'https://api.moonshot.cn'\nconst VER = 'v1'\n\nexport default class MoonShot {\n private key?: string | string[]\n private api?: string\n\n /**\n * Constructor for MoonShot class.\n * @param key - The API key for MoonShot.\n * @param api - The API endpoint for proxy (optional).\n */\n constructor(key?: string | string[], api: string = API) {\n this.key = key\n this.api = api\n }\n\n /**\n * Sends messages to the MoonShot chat model.\n *\n * @param messages - An array of chat messages.\n * @param model - The model to use for chat (default: moonshot-v1-8k).\n * @param stream - Whether to use stream response (default: false).\n * @param top - Top probability to sample (optional).\n * @param temperature - Temperature for sampling (optional).\n * @param maxLength - Maximum token length for response (optional).\n * @returns A promise resolving to the chat response or a stream.\n */\n async chat(\n messages: ChatMessage[],\n model: MoonShotChatModel = MoonShotChatModel.MOON_V1_8K,\n stream: boolean = false,\n top?: number,\n temperature?: number,\n maxLength?: number\n ) {\n // if (!Object.values(MoonShotChatModel).includes(model)) throw new Error('MoonShot chat model not found')\n\n const key = Array.isArray(this.key) ? $.getRandomKey(this.key) : this.key\n if (!key) throw new Error('MoonShot API key is not set in config')\n\n // temperature is float in [0,1]\n if (typeof temperature === 'number') {\n if (temperature < 0) temperature = 0\n if (temperature > 1) temperature = 1\n }\n // top is float in [0,1]\n if (typeof top === 'number') {\n if (top < 0) top = 0\n if (top > 1) top = 1\n }\n\n const res = await $.post(\n `${this.api}/${VER}/chat/completions`,\n { model, messages: this.formatMessage(messages), stream, temperature, top_p: top, max_tokens: maxLength },\n { headers: { Authorization: `Bearer ${key}` }, responseType: stream ? 'stream' : 'json' }\n )\n const data: ChatResponse = {\n content: '',\n model,\n object: '',\n promptTokens: 0,\n completionTokens: 0,\n totalTokens: 0\n }\n if (res instanceof Readable) {\n const output = new PassThrough()\n const parser = new EventSourceStream()\n parser.on('data', (e: MessageEvent) => {\n const obj = $.json(e.data)\n if (obj) {\n data.content = obj.choices[0]?.delta?.content || ''\n data.model = obj.model\n data.object = obj.object\n data.promptTokens = obj.usage?.prompt_tokens || 0\n data.completionTokens = obj.usage?.completion_tokens || 0\n data.totalTokens = obj.usage?.total_tokens || 0\n output.write(JSON.stringify(data))\n }\n })\n\n parser.on('error', e => output.destroy(e))\n parser.on('end', () => output.end())\n\n res.pipe(decodeStream('utf-8')).pipe(parser)\n return output as Readable\n } else {\n data.content = res.choices[0]?.message?.content || ''\n data.model = res.model\n data.object = res.object\n data.promptTokens = res.usage?.prompt_tokens || 0\n data.completionTokens = res.usage?.completion_tokens || 0\n data.totalTokens = res.usage?.total_tokens || 0\n return data\n }\n }\n\n /**\n * Formats chat messages according to the GPT model's message format.\n *\n * @param messages - An array of chat messages.\n * @returns Formatted messages compatible with the GPT model.\n */\n private formatMessage(messages: ChatMessage[]) {\n const prompt: GPTChatMessage[] = []\n\n for (const { role, content } of messages) {\n if (role === ChatRoleEnum.TOOL || role === ChatRoleEnum.DEV) continue\n\n prompt.push({ role, content })\n }\n\n return prompt\n }\n}\n","/**\n * Class for interacting with MidJourney using the midjourney-proxy API.\n *\n * Make sure to have the midjourney-proxy deployed before using this class.\n *\n * The midjourney-proxy project can be found at: https://github.com/novicezk/midjourney-proxy.git\n * If you already have a deployed proxy, provide the proxy URL when initializing the Midjourney class.\n *\n * @format by prettier\n * @author devilyouwei\n */\n\nimport { MJTaskType, MidJourneyImagineModel } from '../../interface/Enum'\nimport { MJChangeRequest, MJImagineRequest, MJImagineResponse, MJTaskResponse } from '../../interface/IMidJourney'\nimport { ImagineResponse, TaskResponse } from '../../interface/IModel'\nimport $ from '../util'\n\nexport default class MidJourney {\n private proxy?: string // Proxy address of MidJourney\n private token?: string // User token of MidJourney\n private imgProxy?: string // User token of MidJourney\n\n constructor(proxy?: string, token?: string, imgProxy?: string) {\n this.proxy = proxy\n this.token = token\n this.imgProxy = imgProxy\n }\n\n /**\n * Call the imagine API of MidJourney to generate text.\n * @param prompt - The input prompt text.\n * @param nPrompt - The excluded text.\n * @param width - The image width. Default value is 1.\n * @param height - The image height. Default value is 1.\n * @returns The generated text.\n */\n async imagine(\n prompt: string,\n nPrompt: string = '',\n width: number = 1024,\n height: number = 1024\n ): Promise {\n if (!this.proxy) throw new Error('MidJourney image model proxy is not set in config')\n\n const aspect = $.getAspect(width, height)\n const res = await $.post(\n `${this.proxy}/mj/submit/imagine`,\n { prompt: `${prompt} --ar ${aspect} ${nPrompt ? '--no ' + nPrompt : ''}` },\n { headers: { 'mj-api-secret': this.token } }\n )\n if (res.code !== 1) throw new Error(res.description)\n return { taskId: res.result, time: Date.now() }\n }\n\n /**\n * Get task information by id or get a list of imagining tasks.\n * @param id - The task id. If provided, it will return the task information with the specified id. Otherwise, it will return the task list.\n * @returns The task list or the task information with the specified id.\n */\n async task(id?: string): Promise {\n if (!this.proxy) throw new Error('MidJourney image model proxy is not set in config')\n\n if (id) {\n const res = await $.get(`${this.proxy}/mj/task/${id}/fetch`, null, {\n headers: { 'mj-api-secret': this.token }\n })\n // image proxy, replace hostname\n if (res.imageUrl && this.imgProxy) {\n const url = new URL(res.imageUrl)\n const pro = new URL(this.imgProxy)\n url.hostname = pro.hostname\n url.protocol = pro.protocol\n url.port = pro.port\n res.imageUrl = url.toString()\n }\n return [\n {\n id: res.id,\n type: res.action,\n imgs: res.imageUrl ? [await $.writeFile(res.imageUrl, `${res.id}.png`)] : [],\n info: res.description,\n fail: res.failReason || '',\n progress: parseInt(res.progress) || 0,\n created: res.startTime,\n model: MidJourneyImagineModel.MJ\n }\n ]\n } else {\n const res = await $.get(`${this.proxy}/mj/task/list`, null, {\n headers: { 'mj-api-secret': this.token }\n })\n return res.map(v => ({\n id: v.id,\n type: v.action,\n imgs: v.imageUrl ? [v.imageUrl] : [],\n info: v.description,\n fail: v.failReason || '',\n progress: parseInt(v.progress),\n created: v.startTime,\n model: MidJourneyImagineModel.MJ\n }))\n }\n }\n\n /**\n * Submit a change request for a imagined task.\n * @param taskId - The task id.\n * @param action - The action type of the task.\n * @param index - The index for modification (optional).\n * @returns The modified task response.\n */\n async change(taskId: string, action: MJTaskType, index?: number): Promise {\n if (!this.proxy) throw new Error('MidJourney image model proxy is not set in config')\n\n const res = await $.post(\n `${this.proxy}/mj/submit/change`,\n { taskId, action, index },\n { headers: { 'mj-api-secret': this.token } }\n )\n return { taskId: res.result, time: Date.now() }\n }\n}\n","/**\n * Class for interacting with Stability AI.\n *\n * To interact with Stability AI, refer to the documentation at: https://platform.stability.ai/docs/getting-started\n * You need to create an account and generate an API key to use this class.\n *\n * @format by prettier\n * @author devilyouwei\n */\nimport { SDTaskType, StabilityAIImagineModel } from '../../interface/Enum'\nimport { ImagineResponse, TaskResponse } from '../../interface/IModel'\nimport { StabilityImagineRequest, StabilityImagineResponse } from '../../interface/IStability'\nimport $ from '../util'\n\nconst API = 'https://api.stability.ai'\nconst STORAGE_KEY = 'task_stability_ai'\n\nexport default class Stability {\n private api?: string\n private key?: string | string[]\n\n constructor(key?: string | string[], api: string = API) {\n this.key = key\n this.api = api\n }\n\n /**\n * Generate images using Stability AI.\n *\n * @param prompt - The main text prompt for image generation.\n * @param negativePrompt - The negative text prompt (optional).\n * @param width - Image width (default: 1024).\n * @param height - Image height (default: 1024).\n * @param samples - Number of images to generate (default: 1).\n * @param model - The Stability AI model to use (default: StabilityAIImagineModel.SD_1_6).\n * @returns A promise resolving to the image generation response.\n */\n async imagine(\n prompt: string,\n negativePrompt: string = '',\n width: number = 1024,\n height: number = 1024,\n samples: number = 1,\n model: StabilityAIImagineModel = StabilityAIImagineModel.SD_1_6\n ): Promise {\n const key = Array.isArray(this.key) ? $.getRandomKey(this.key) : this.key\n if (!key) throw new Error('Stability key is not set in config')\n const prompts = [{ text: prompt, weight: 1 }]\n if (negativePrompt) prompts.push({ text: negativePrompt, weight: -1 })\n\n const res = await $.post(\n `${this.api}/v1/${SDTaskType.GENERATION}/${model}/text-to-image`,\n { width, height, samples, text_prompts: prompts },\n { headers: { Accept: 'application/json', Authorization: `Bearer ${key}` } }\n )\n\n const id = $.getRandomId()\n const imgs: string[] = []\n for (const i in res.artifacts) imgs.push(await $.writeFile(res.artifacts[i].base64, `${id}-${i}.png`))\n\n const time = Date.now()\n const task: TaskResponse = {\n id,\n type: SDTaskType.GENERATION,\n info: res.artifacts[0].finishReason,\n progress: 100,\n imgs,\n fail: '',\n created: time,\n model\n }\n\n const tasks: TaskResponse[] = $.getItem(STORAGE_KEY) || []\n tasks.push(task)\n $.setItem(STORAGE_KEY, tasks)\n return { taskId: task.id, time }\n }\n\n /**\n * Simulate tasks or retrieve a specific task by ID.\n *\n * @param id - The task ID to retrieve (optional).\n * @returns An array of task responses or a specific task by ID.\n */\n task(id?: string) {\n const tasks: TaskResponse[] = $.getItem(STORAGE_KEY) || []\n\n if (id) return tasks.filter(v => v.id === id)\n else return tasks\n }\n}\n","/** @format */\n\nimport { PassThrough, Readable } from 'stream'\nimport EventSourceStream from '@server-sent-stream/node'\nimport { decodeStream } from 'iconv-lite'\nimport { ChatRoleEnum, AliChatModel, AliEmbedModel } from '../../interface/Enum'\nimport { ChatMessage, ChatResponse, EmbeddingResponse } from '../../interface/IModel'\nimport {\n GPTChatMessage,\n GPTChatRequest,\n GPTChatResponse,\n GPTChatStreamRequest,\n GPTChatStreamResponse,\n OpenAIEmbedRequest,\n OpenAIEmbedResponse\n} from '../../interface/IOpenAI'\nimport $ from '../util'\n\nconst API = 'https://dashscope.aliyuncs.com'\nconst VER = 'v1'\n\nexport default class AliYun {\n private key?: string | string[]\n private api?: string\n\n /**\n * Constructor for AliYun class.\n * @param key - The API key for MoonShot.\n * @param api - The API endpoint for proxy (optional).\n */\n constructor(key?: string | string[], api: string = API) {\n this.key = key\n this.api = api\n }\n\n /**\n * Fetches embeddings for input text.\n *\n * @param input - An array of input strings.\n * @param model - The model to use for embeddings (default: text-embedding-v3).\n * @param dimensions - The dimensions of output embedding vector (default: 1024)\n * @returns A promise resolving to the embedding response.\n */\n async embedding(input: string[], model: AliEmbedModel = AliEmbedModel.ALI_V3, dimensions = 1024) {\n const key = Array.isArray(this.key) ? $.getRandomKey(this.key) : this.key\n if (!key) throw new Error('AliYun API key is not set in config')\n\n const res = await $.post(\n `${this.api}/compatible-mode/${VER}/embeddings`,\n { model, input, dimensions },\n { headers: { Authorization: `Bearer ${key}` }, responseType: 'json' }\n )\n\n const data: EmbeddingResponse = {\n embedding: res.data.map(v => v.embedding),\n object: 'embedding',\n model,\n promptTokens: res.usage.prompt_tokens || 0,\n totalTokens: res.usage.total_tokens || 0\n }\n return data\n }\n\n /**\n * Sends messages to the AliYun LLMs.\n *\n * @param messages - An array of chat messages.\n * @param model - The model to use for chat (default: moonshot-v1-8k).\n * @param stream - Whether to use stream response (default: false).\n * @param top - Top probability to sample (optional).\n * @param temperature - Temperature for sampling (optional).\n * @param maxLength - Maximum token length for response (optional).\n * @returns A promise resolving to the chat response or a stream.\n */\n async chat(\n messages: ChatMessage[],\n model: AliChatModel = AliChatModel.QWEN_TURBO,\n stream: boolean = false,\n top?: number,\n temperature?: number,\n maxLength?: number\n ) {\n // if (!Object.values(AliChatModel).includes(model)) throw new Error('Qian Wen chat model not found')\n\n const key = Array.isArray(this.key) ? $.getRandomKey(this.key) : this.key\n if (!key) throw new Error('Qian Wen API key is not set in config')\n\n // temperature is float in [0,1]\n if (typeof temperature === 'number') {\n if (temperature < 0) temperature = 0\n if (temperature >= 2) temperature = 1.9\n }\n // top is float in [0,1]\n if (typeof top === 'number') {\n if (top <= 0) top = 0.1\n if (top > 1) top = 1.0\n }\n\n // remove imgs for not vision model\n if (![AliChatModel.QWEN_VL_MAX, AliChatModel.QWEN_VL_PLUS].includes(model))\n messages = messages.map(({ role, content }) => ({ role, content }))\n\n const res = await $.post(\n `${this.api}/compatible-mode/${VER}/chat/completions`,\n {\n model,\n messages: this.formatMessage(messages),\n stream,\n temperature,\n top_p: top,\n max_tokens: maxLength\n },\n { headers: { Authorization: `Bearer ${key}` }, responseType: stream ? 'stream' : 'json' }\n )\n const data: ChatResponse = {\n content: '',\n model,\n object: '',\n promptTokens: 0,\n completionTokens: 0,\n totalTokens: 0\n }\n if (res instanceof Readable) {\n const output = new PassThrough()\n const parser = new EventSourceStream()\n parser.on('data', (e: MessageEvent) => {\n const obj = $.json(e.data)\n if (obj) {\n data.content = obj?.choices[0]?.delta?.content || ''\n data.model = obj.model\n data.object = obj.object\n data.promptTokens = obj.usage?.prompt_tokens || 0\n data.completionTokens = obj.usage?.completion_tokens || 0\n data.totalTokens = obj.usage?.total_tokens || 0\n output.write(JSON.stringify(data))\n }\n })\n\n parser.on('error', e => output.destroy(e))\n parser.on('end', () => output.end())\n\n res.pipe(decodeStream('utf-8')).pipe(parser)\n return output as Readable\n } else {\n data.content = res.choices[0]?.message?.content || ''\n if (res.choices[0]?.message?.tool_calls) data.tools = res.choices[0]?.message?.tool_calls\n data.model = res.model\n data.object = res.object || 'chat.completion'\n data.promptTokens = res.usage?.prompt_tokens || 0\n data.completionTokens = res.usage?.completion_tokens || 0\n data.totalTokens = res.usage?.total_tokens || 0\n return data\n }\n }\n\n /**\n * Formats chat messages according to the GPT model's message format.\n *\n * @param messages - An array of chat messages.\n * @returns Formatted messages compatible with the GPT model.\n */\n private formatMessage(messages: ChatMessage[]) {\n const prompt: GPTChatMessage[] = []\n\n for (const { role, content, img } of messages) {\n // GPT not support function role\n if (role === ChatRoleEnum.TOOL || role === ChatRoleEnum.DEV) continue\n\n // with image\n if (img) {\n if (!img.startsWith('http')) throw new Error('Invalid img HTTP URL')\n prompt.push({\n role: 'user',\n content: [\n { type: 'text', text: content },\n { type: 'image_url', image_url: { url: img } }\n ]\n })\n }\n // only text\n else prompt.push({ role, content })\n }\n\n return prompt\n }\n}\n","/** @format */\n\nimport { PassThrough, Readable } from 'stream'\nimport EventSourceStream from '@server-sent-stream/node'\nimport { decodeStream } from 'iconv-lite'\nimport { ChatRoleEnum, DeepSeekChatModel } from '../../interface/Enum'\nimport { ChatMessage, ChatResponse } from '../../interface/IModel'\nimport $ from '../util'\nimport { DSChatMessage, DSChatRequest, DSChatResponse } from '../../interface/IDeepSeek'\n\nconst API = 'https://api.deepseek.com'\n\nexport default class DeepSeek {\n private key?: string | string[]\n private api?: string\n\n /**\n * Constructor for MoonShot class.\n * @param key - The API key for MoonShot.\n * @param api - The API endpoint for proxy (optional).\n */\n constructor(key?: string | string[], api: string = API) {\n this.key = key\n this.api = api\n }\n\n /**\n * Sends messages to the Ali QianWen chat model.\n *\n * @param messages - An array of chat messages.\n * @param model - The model to use for chat (default: moonshot-v1-8k).\n * @param stream - Whether to use stream response (default: false).\n * @param top - Top probability to sample (optional).\n * @param temperature - Temperature for sampling (optional).\n * @param maxLength - Maximum token length for response (optional).\n * @returns A promise resolving to the chat response or a stream.\n */\n async chat(\n messages: ChatMessage[],\n model: DeepSeekChatModel = DeepSeekChatModel.DEEPSEEK_V3,\n stream: boolean = false,\n top?: number,\n temperature?: number,\n maxLength?: number\n ) {\n // if (!Object.values(AliChatModel).includes(model)) throw new Error('Qian Wen chat model not found')\n\n const key = Array.isArray(this.key) ? $.getRandomKey(this.key) : this.key\n if (!key) throw new Error('DeepSeek API key is not set in config')\n\n if (typeof maxLength === 'number') {\n if (maxLength < 1) maxLength = 1\n if (maxLength > 8192) maxLength = 8192\n }\n\n // temperature is float in [0,1]\n if (typeof temperature === 'number') {\n if (temperature < 0) temperature = 0\n if (temperature > 2) temperature = 2\n }\n // top is float in [0,1]\n if (typeof top === 'number') {\n if (top <= 0) top = 0.1\n if (top > 1) top = 1.0\n }\n\n const res = await $.post(\n `${this.api}/chat/completions`,\n {\n model,\n messages: this.formatMessage(messages),\n stream,\n temperature,\n top_p: top,\n max_tokens: maxLength\n },\n { headers: { Authorization: `Bearer ${key}` }, responseType: stream ? 'stream' : 'json' }\n )\n\n const data: ChatResponse = {\n content: '',\n model,\n object: '',\n promptTokens: 0,\n completionTokens: 0,\n totalTokens: 0\n }\n\n if (res instanceof Readable) {\n const output = new PassThrough()\n const parser = new EventSourceStream()\n\n parser.on('data', (e: MessageEvent) => {\n const obj = $.json(e.data)\n if (obj) {\n data.content = obj.choices[0]?.delta?.content || ''\n data.model = obj.model\n data.object = obj.object\n data.promptTokens = obj.usage?.prompt_tokens || 0\n data.completionTokens = obj.usage?.completion_tokens || 0\n data.totalTokens = obj.usage?.total_tokens || 0\n output.write(JSON.stringify(data))\n }\n })\n parser.on('error', e => output.destroy(e))\n parser.on('end', () => output.end())\n\n res.pipe(decodeStream('utf-8')).pipe(parser)\n return output as Readable\n } else {\n data.content = res.choices[0]?.message?.content || ''\n if (res.choices[0]?.message?.tool_calls) data.tools = res.choices[0]?.message?.tool_calls\n data.model = res.model\n data.object = res.object\n data.promptTokens = res.usage?.prompt_tokens || 0\n data.completionTokens = res.usage?.completion_tokens || 0\n data.totalTokens = res.usage?.total_tokens || 0\n return data\n }\n }\n\n /**\n * Formats chat messages according to the GPT model's message format.\n *\n * @param messages - An array of chat messages.\n * @returns Formatted messages compatible with the GPT model.\n */\n private formatMessage(messages: ChatMessage[]) {\n const prompt: DSChatMessage[] = []\n\n for (const { role, content, tool } of messages) {\n // DeepSeek not support develop role\n if (role === ChatRoleEnum.DEV) continue\n else if (role === ChatRoleEnum.TOOL) prompt.push({ role, content, tool_call_id: tool! })\n // DeepSeek not support image, only text\n else prompt.push({ role, content })\n }\n\n return prompt\n }\n}\n","/**\n * Utility for connecting to the OpenAI model API.\n *\n * @format prettier\n * @author devilyouwei\n */\n\nimport { PassThrough, Readable } from 'stream'\nimport EventSourceStream from '@server-sent-stream/node'\nimport { decodeStream } from 'iconv-lite'\nimport { ChatRoleEnum, XAIChatModel } from '../../interface/Enum'\nimport { ChatMessage, ChatResponse } from '../../interface/IModel'\nimport {\n GrokChatMessage,\n GrokChatRequest,\n GrokChatResponse,\n GrokChatResponseChunk,\n GrokTool,\n GrokToolChoice\n} from '../../interface/IX'\nimport $ from '../util'\n\nconst API = 'https://api.x.ai'\nconst VER = 'v1'\n\nexport default class XAI {\n private api: string\n private key?: string | string[]\n\n /**\n * Constructor for the OpenAI class.\n *\n * @param key - The API key for OpenAI.\n * @param api - The API endpoint for proxy (optional).\n */\n constructor(key?: string | string[], api: string = API) {\n this.key = key\n this.api = api\n }\n\n /**\n * Sends messages to the GPT chat model.\n *\n * @param messages - An array of chat messages.\n * @param model - The model to use for chat (default: gpt-3.5-turbo).\n * @param stream - Whether to use stream response (default: false).\n * @param top - Top probability to sample (optional).\n * @param temperature - Temperature for sampling (optional).\n * @param maxLength - Maximum token length for response (optional).\n * @param tools - Tools for model to use (optional).\n * @param toolChoice - Controls which (if any) tool is called by the model: none, required, auto (optional).\n * @returns A promise resolving to the chat response or a stream.\n */\n async chat(\n messages: ChatMessage[],\n model: XAIChatModel = XAIChatModel.GROK2,\n stream: boolean = false,\n top?: number,\n temperature?: number,\n maxLength?: number,\n tools?: GrokTool[],\n toolChoice?: GrokToolChoice\n ) {\n const key = Array.isArray(this.key) ? $.getRandomKey(this.key) : this.key\n if (!key) throw new Error('X AI API key is not set in config')\n\n // remove imgs for not vision model\n if (![XAIChatModel.GROK2_VISION].includes(model))\n messages = messages.map(({ role, content }) => ({ role, content }))\n\n // temperature is float in [0,1]\n if (typeof temperature === 'number') {\n if (temperature < 0) temperature = 0\n if (temperature > 2) temperature = 2\n }\n // top is float in [0,1]\n if (typeof top === 'number') {\n if (top < 0) top = 0\n if (top > 1) top = 1\n }\n\n const res = await $.post(\n `${this.api}/${VER}/chat/completions`,\n {\n model,\n messages: this.formatMessage(messages),\n stream,\n temperature,\n top_p: top,\n max_tokens: maxLength,\n tools,\n tool_choice: toolChoice\n },\n { headers: { Authorization: `Bearer ${key}` }, responseType: stream ? 'stream' : 'json' }\n )\n\n const data: ChatResponse = {\n content: '',\n model,\n object: '',\n promptTokens: 0,\n completionTokens: 0,\n totalTokens: 0\n }\n\n if (res instanceof Readable) {\n const output = new PassThrough()\n const parser = new EventSourceStream()\n\n parser.on('data', (e: MessageEvent) => {\n const obj = $.json(e.data)\n if (obj) {\n data.content = obj.choices[0]?.delta?.content || ''\n data.model = obj.model\n data.object = obj.object\n data.promptTokens = obj.usage?.prompt_tokens || 0\n data.completionTokens = obj.usage?.completion_tokens || 0\n data.totalTokens = obj.usage?.total_tokens || 0\n output.write(JSON.stringify(data))\n }\n })\n parser.on('error', e => output.destroy(e))\n parser.on('end', () => output.end())\n\n res.pipe(decodeStream('utf-8')).pipe(parser)\n return output as Readable\n } else {\n data.content = res.choices[0]?.message?.content || ''\n if (res.choices[0]?.message?.tool_calls) data.tools = res.choices[0]?.message?.tool_calls\n data.model = res.model\n data.object = res.object\n data.promptTokens = res.usage?.prompt_tokens || 0\n data.completionTokens = res.usage?.completion_tokens || 0\n data.totalTokens = res.usage?.total_tokens || 0\n return data\n }\n }\n\n /**\n * Formats chat messages according to the GPT model's message format.\n *\n * @param messages - An array of chat messages.\n * @returns Formatted messages compatible with the GPT model.\n */\n private formatMessage(messages: ChatMessage[]) {\n const prompt: GrokChatMessage[] = []\n\n for (const { role, content, img } of messages) {\n // with image\n switch (role) {\n case ChatRoleEnum.USER:\n if (img)\n prompt.push({\n role,\n content: [\n { type: 'text', text: content },\n { type: 'image_url', image_url: { url: img } }\n ]\n })\n else prompt.push({ role, content })\n break\n case ChatRoleEnum.ASSISTANT || ChatRoleEnum.SYSTEM:\n prompt.push({ role, content })\n break\n default:\n break\n }\n }\n\n return prompt\n }\n}\n","/** @format */\nimport {\n AliChatModel,\n AliEmbedModel,\n BaiduChatModel,\n ChatModel,\n ChatModelProvider,\n ChatRoleEnum,\n DeepSeekChatModel,\n EmbedModel,\n EmbedModelProvider,\n GLMChatModel,\n GLMEmbedModel,\n GoogleChatModel,\n GoogleEmbedModel,\n IFlyTekChatModel,\n IFlyTekImagineModel,\n ImagineModel,\n ImagineModelProvider,\n MJTaskType,\n MidJourneyImagineModel,\n ModelProvider,\n MoonShotChatModel,\n OpenAIChatModel,\n OpenAIEmbedModel,\n OpenAIImagineModel,\n OtherEmbedModel,\n StabilityAIImagineModel,\n XAIChatModel\n} from '../interface/Enum'\nimport { UniAIConfig } from '../interface/IConfig'\nimport { ChatMessage, ChatOption, EmbedOption, ImagineOption, ModelList, Provider } from '../interface/IModel'\nimport OpenAI from './providers/OpenAI'\nimport GLM from './providers/GLM'\nimport Other from './providers/Other'\nimport Google from './providers/Google'\nimport IFlyTek from './providers/IFlyTek'\nimport Baidu from './providers/Baidu'\nimport MoonShot from './providers/MoonShot'\nimport MidJourney from './providers/MidJourney'\nimport Stability from './providers/Stability'\nimport AliYun from './providers/AliYun'\nimport { ChatCompletionTool, ChatCompletionToolChoiceOption } from 'openai/resources'\nimport { GLMTool, GLMToolChoice } from '../interface/IGLM'\nimport { SPKTool, SPKToolChoice } from '../interface/IFlyTek'\nimport DeepSeek from './providers/DeepSeek'\nimport XAI from './providers/IX'\nimport { GrokTool, GrokToolChoice } from '../interface/IX'\n\nconst DEFAULT_MESSAGE = 'Hi, who are you? Answer in 10 words!'\n\nexport default class UniAI {\n public config: UniAIConfig | null = null\n public models: ModelList\n public embedModels: ModelList\n public chatModels: ModelList\n public imgModels: ModelList\n\n private openai: OpenAI\n private deepseek: DeepSeek\n private google: Google\n private glm: GLM\n private fly: IFlyTek\n private baidu: Baidu\n private other: Other\n private moon: MoonShot\n private ali: AliYun\n private mj: MidJourney\n private xai: XAI\n private stability: Stability\n\n constructor(config: UniAIConfig = {}) {\n this.config = config\n // OpenAI key, your OpenAI proxy API (optional)\n this.openai = new OpenAI(config.OpenAI?.key, config.OpenAI?.proxy)\n // DeepSeek Key\n this.deepseek = new DeepSeek(config.DeepSeek?.key, config.DeepSeek?.proxy)\n // ZhiPu AI with ChatGLM6B(local)\n this.glm = new GLM(config.GLM?.key, config.GLM?.proxy)\n // Google AI key, your Google AI API proxy (optional)\n this.google = new Google(config.Google?.key, config.Google?.proxy)\n // IFlyTek appid, API key, API secret\n this.fly = new IFlyTek(\n config.IFlyTek?.apiPassword,\n config.IFlyTek?.appId,\n config.IFlyTek?.apiKey,\n config.IFlyTek?.apiSecret,\n config.IFlyTek?.proxy\n )\n // Baidu WenXin Workshop, baidu api key, baidu secret key\n this.baidu = new Baidu(config.Baidu?.apiKey, config.Baidu?.secretKey, config.Baidu?.proxy)\n // MoonShot, moonshot API key\n this.moon = new MoonShot(config.MoonShot?.key, config.MoonShot?.proxy)\n // AliYun, QWen API key\n this.ali = new AliYun(config.AliYun?.key, config.AliYun?.proxy)\n // XAI Grok, XAI API key\n this.xai = new XAI(config.XAI?.key, config.XAI?.proxy)\n // Other model text2vec\n this.other = new Other(config.Other?.api, config.Other?.key)\n // Midjourney, proxy\n this.mj = new MidJourney(config.MidJourney?.proxy, config.MidJourney?.token, config.MidJourney?.imgProxy)\n // Stability AI, key, proxy\n this.stability = new Stability(config.StabilityAI?.key, config.StabilityAI?.proxy)\n\n // expand chat models to list\n this.chatModels = Object.entries(ChatModelProvider).map(([k, v]) => ({\n provider: k as keyof typeof ChatModelProvider,\n value: v,\n models: Object.values(\n {\n [ChatModelProvider.OpenAI]: OpenAIChatModel,\n [ChatModelProvider.DeepSeek]: DeepSeekChatModel,\n [ChatModelProvider.Baidu]: BaiduChatModel,\n [ChatModelProvider.IFlyTek]: IFlyTekChatModel,\n [ChatModelProvider.GLM]: GLMChatModel,\n [ChatModelProvider.Google]: GoogleChatModel,\n [ChatModelProvider.MoonShot]: MoonShotChatModel,\n [ChatModelProvider.AliYun]: AliChatModel,\n [ChatModelProvider.XAI]: XAIChatModel,\n [ChatModelProvider.Other]: []\n }[v]\n )\n }))\n\n // expand chat models to list\n this.imgModels = Object.entries(ImagineModelProvider).map(([k, v]) => ({\n provider: k as keyof typeof ImagineModelProvider,\n value: v,\n models: Object.values(\n {\n [ImagineModelProvider.OpenAI]: OpenAIImagineModel,\n [ImagineModelProvider.MidJourney]: MidJourneyImagineModel,\n [ImagineModelProvider.StabilityAI]: StabilityAIImagineModel,\n [ImagineModelProvider.IFlyTek]: IFlyTekImagineModel\n }[v]\n )\n }))\n\n // expand chat models to list\n this.embedModels = Object.entries(EmbedModelProvider).map(([k, v]) => ({\n provider: k as keyof typeof EmbedModelProvider,\n value: v,\n models: Object.values(\n {\n [EmbedModelProvider.OpenAI]: OpenAIEmbedModel,\n [EmbedModelProvider.GLM]: GLMEmbedModel,\n [EmbedModelProvider.Google]: GoogleEmbedModel,\n [EmbedModelProvider.AliYun]: AliEmbedModel,\n [EmbedModelProvider.Other]: OtherEmbedModel\n }[v]\n )\n }))\n\n this.models = [...this.chatModels, ...this.embedModels, ...this.imgModels]\n }\n\n async chat(messages: ChatMessage[] | string = DEFAULT_MESSAGE, option: ChatOption = {}) {\n if (typeof messages === 'string') messages = [{ role: ChatRoleEnum.USER, content: messages }]\n const provider = option.provider || ChatModelProvider.OpenAI // default is OpenAI gpt-3.5-turbo\n const { model, stream, top, temperature, maxLength, tools, toolChoice } = option\n\n if (provider === ChatModelProvider.OpenAI)\n return await this.openai.chat(\n messages,\n model as OpenAIChatModel,\n stream,\n top,\n temperature,\n maxLength,\n tools as ChatCompletionTool[],\n toolChoice as ChatCompletionToolChoiceOption\n )\n else if (provider === ChatModelProvider.DeepSeek)\n return await this.deepseek.chat(messages, model as DeepSeekChatModel, stream, top, temperature, maxLength)\n else if (provider === ChatModelProvider.Google)\n return await this.google.chat(messages, model as GoogleChatModel, stream, top, temperature, maxLength)\n else if (provider === ChatModelProvider.GLM)\n return await this.glm.chat(\n messages,\n model as GLMChatModel,\n stream,\n top,\n temperature,\n maxLength,\n tools as GLMTool[],\n toolChoice as GLMToolChoice\n )\n else if (provider === ChatModelProvider.IFlyTek)\n return await this.fly.chat(\n messages,\n model as IFlyTekChatModel,\n stream,\n top,\n temperature,\n maxLength,\n tools as SPKTool[],\n toolChoice as SPKToolChoice\n )\n else if (provider === ChatModelProvider.Baidu)\n return await this.baidu.chat(messages, model as BaiduChatModel, stream, top, temperature, maxLength)\n else if (provider === ChatModelProvider.MoonShot)\n return await this.moon.chat(messages, model as MoonShotChatModel, stream, top, temperature, maxLength)\n else if (provider === ChatModelProvider.AliYun)\n return await this.ali.chat(messages, model as AliChatModel, stream, top, temperature, maxLength)\n else if (provider === ChatModelProvider.XAI)\n return await this.xai.chat(\n messages,\n model as XAIChatModel,\n stream,\n top,\n temperature,\n maxLength,\n tools as GrokTool[],\n toolChoice as GrokToolChoice\n )\n else if (provider === ChatModelProvider.Other)\n return await this.other.chat(\n messages,\n model as ChatModel,\n stream,\n top,\n temperature,\n maxLength,\n tools as ChatCompletionTool[],\n toolChoice as ChatCompletionToolChoiceOption\n )\n else throw new Error('Chat model Provider not found')\n }\n\n async embedding(content: string | string[], option: EmbedOption = {}) {\n const provider = option.provider || ModelProvider.OpenAI\n const { model } = option\n if (typeof content === 'string') content = [content]\n\n if (provider === EmbedModelProvider.OpenAI)\n return await this.openai.embedding(content, model as OpenAIEmbedModel)\n else if (provider === EmbedModelProvider.GLM)\n return await this.glm.embedding(content, model as GLMEmbedModel, option.dimensions)\n else if (provider === EmbedModelProvider.Google)\n return await this.google.embedding(content, model as GoogleEmbedModel)\n else if (provider === EmbedModelProvider.AliYun)\n return await this.ali.embedding(content, model as AliEmbedModel, option.dimensions)\n else if (provider === EmbedModelProvider.Other)\n return await this.other.embedding(content, model as OtherEmbedModel)\n else throw new Error('Embedding model provider not found')\n }\n\n async imagine(prompt: string, option: ImagineOption = {}) {\n const provider = option.provider || ImagineModelProvider.OpenAI\n const { negativePrompt, width, height, num, model } = option\n if (provider === ImagineModelProvider.OpenAI)\n return await this.openai.imagine(prompt, width, height, num, model as OpenAIImagineModel)\n else if (provider === ImagineModelProvider.MidJourney)\n return await this.mj.imagine(prompt, negativePrompt, width, height)\n else if (provider === ImagineModelProvider.StabilityAI)\n return await this.stability.imagine(\n prompt,\n negativePrompt,\n width,\n height,\n num,\n model as StabilityAIImagineModel\n )\n else if (provider === ImagineModelProvider.IFlyTek)\n return await this.fly.imagine(prompt, width, height, model as IFlyTekImagineModel)\n else throw new Error('Imagine model provider not found')\n }\n\n async task(provider: ImagineModelProvider, id?: string) {\n if (provider === ImagineModelProvider.OpenAI) return this.openai.task(id)\n else if (provider === ImagineModelProvider.MidJourney) return await this.mj.task(id)\n else if (provider === ImagineModelProvider.StabilityAI) return this.stability.task(id)\n else if (provider === ImagineModelProvider.IFlyTek) return this.fly.task(id)\n else throw new Error('Imagine model provider not found')\n }\n\n async change(provider: ImagineModelProvider, taskId: string, action: string, index?: number) {\n if (provider === ImagineModelProvider.MidJourney) return this.mj.change(taskId, action as MJTaskType, index)\n else throw new Error('Imagine model provider not found')\n }\n}\n","/** @format */\nexport * from '../interface/Enum'\nexport * from '../interface/IBaidu'\nexport * from '../interface/IConfig'\nexport * from '../interface/IFlyTek'\nexport * from '../interface/IGLM'\nexport * from '../interface/IGoogle'\nexport * from '../interface/IModel'\nexport * from '../interface/IOpenAI'\nexport * from '../interface/IX'\nexport * from '../interface/IDeepSeek'\nexport * from '../interface/IMidJourney'\nexport * from '../interface/IStability'\n\nimport UniAI from './UniAI'\nexport default UniAI\n"]}