Support Gitee AI(LLM Provider) (#3361)

* Support Gitee AI(LLM Provider)

* refactor(server): 重构 GiteeAI 模型窗口限制功能,暂时将窗口限制硬编码,计划使用外部 API 数据和缓存

* updates for Gitee AI

* use legacy lookup since gitee does not enable getting token context windows

* add more missing records

* reorder imports

---------

Co-authored-by: 方程 <fangcheng@oschina.cn>
Co-authored-by: timothycarambat <rambat1010@gmail.com>

README.md

@@ -102,6 +102,7 @@ AnythingLLM divides your documents into objects called `workspaces`. A Workspace
 - [Z.AI (chat models)](https://z.ai/model-api)
 - [Novita AI (chat models)](https://novita.ai/model-api/product/llm-api?utm_source=github_anything-llm&utm_medium=github_readme&utm_campaign=link)
 - [PPIO](https://ppinfra.com?utm_source=github_anything-llm)
+- [Gitee AI](https://ai.gitee.com/)
 - [Moonshot AI](https://www.moonshot.ai/)
 - [Microsoft Foundry Local](https://github.com/microsoft/Foundry-Local)
 - [CometAPI (chat models)](https://api.cometapi.com/)

docker/.env.example

@@ -157,6 +157,11 @@ GID='1000'
 # FOUNDRY_MODEL_PREF='phi-3.5-mini'
 # FOUNDRY_MODEL_TOKEN_LIMIT=4096
 
+# LLM_PROVIDER='giteeai'
+# GITEE_AI_API_KEY=
+# GITEE_AI_MODEL_PREF=
+# GITEE_AI_MODEL_TOKEN_LIMIT=
+
 ###########################################
 ######## Embedding API SElECTION ##########
 ###########################################

frontend/src/components/LLMSelection/GiteeAIOptions/index.jsx

@@ -0,0 +1,116 @@
+import { useState, useEffect } from "react";
+import System from "@/models/system";
+
+export default function GiteeAIOptions({ settings }) {
+  return (
+    <div className="flex gap-[36px] mt-1.5">
+      <div className="flex flex-col w-60">
+        <label className="text-white text-sm font-semibold block mb-3">
+          API Key
+        </label>
+        <input
+          type="password"
+          name="GiteeAIApiKey"
+          className="border-none bg-theme-settings-input-bg text-white placeholder:text-theme-settings-input-placeholder text-sm rounded-lg focus:outline-primary-button active:outline-primary-button outline-none block w-full p-2.5"
+          placeholder="GiteeAI API Key"
+          defaultValue={settings?.GiteeAIApiKey ? "*".repeat(20) : ""}
+          required={true}
+          autoComplete="off"
+          spellCheck={false}
+        />
+      </div>
+      {!settings?.credentialsOnly && (
+        <>
+          <GiteeAIModelSelection settings={settings} />
+          <div className="flex flex-col w-60">
+            <label className="text-white text-sm font-semibold block mb-2">
+              Token context window
+            </label>
+            <input
+              type="number"
+              name="GiteeAITokenLimit"
+              className="border-none bg-theme-settings-input-bg text-white placeholder:text-theme-settings-input-placeholder text-sm rounded-lg focus:outline-primary-button active:outline-primary-button outline-none block w-full p-2.5"
+              placeholder="Content window limit (eg: 8192)"
+              min={1}
+              onScroll={(e) => e.target.blur()}
+              defaultValue={settings?.GiteeAITokenLimit}
+              required={true}
+              autoComplete="off"
+            />
+          </div>
+        </>
+      )}
+    </div>
+  );
+}
+
+function GiteeAIModelSelection({ settings }) {
+  const [groupedModels, setGroupedModels] = useState({});
+  const [loading, setLoading] = useState(true);
+
+  useEffect(() => {
+    async function findCustomModels() {
+      setLoading(true);
+      const { models = [] } = await System.customModels("giteeai");
+      if (models?.length > 0) {
+        const modelsByOrganization = models.reduce((acc, model) => {
+          acc[model.organization] = acc[model.organization] || [];
+          acc[model.organization].push(model);
+          return acc;
+        }, {});
+        setGroupedModels(modelsByOrganization);
+      }
+
+      setLoading(false);
+    }
+    findCustomModels();
+  }, []);
+
+  if (loading) {
+    return (
+      <div className="flex flex-col w-60">
+        <label className="text-white text-sm font-semibold block mb-3">
+          Chat Model Selection
+        </label>
+        <select
+          name="GiteeAIModelPref"
+          disabled={true}
+          className="border-none bg-theme-settings-input-bg border-gray-500 text-white text-sm rounded-lg block w-full p-2.5"
+        >
+          <option disabled={true} selected={true}>
+            -- loading available models --
+          </option>
+        </select>
+      </div>
+    );
+  }
+
+  return (
+    <div className="flex flex-col w-60">
+      <label className="text-white text-sm font-semibold block mb-3">
+        Chat Model Selection
+      </label>
+      <select
+        name="GiteeAIModelPref"
+        required={true}
+        className="border-none bg-theme-settings-input-bg border-gray-500 text-white text-sm rounded-lg block w-full p-2.5"
+      >
+        {Object.keys(groupedModels)
+          .sort()
+          .map((organization) => (
+            <optgroup key={organization} label={organization}>
+              {groupedModels[organization].map((model) => (
+                <option
+                  key={model.id}
+                  value={model.id}
+                  selected={settings?.GiteeAIModelPref === model.id}
+                >
+                  {model.name}
+                </option>
+              ))}
+            </optgroup>
+          ))}
+      </select>
+    </div>
+  );
+}

frontend/src/pages/GeneralSettings/LLMPreference/index.jsx

@@ -36,6 +36,7 @@ import DellProAiStudioLogo from "@/media/llmprovider/dpais.png";
 import MoonshotAiLogo from "@/media/llmprovider/moonshotai.png";
 import CometApiLogo from "@/media/llmprovider/cometapi.png";
 import FoundryLogo from "@/media/llmprovider/foundry-local.png";
+import GiteeAILogo from "@/media/llmprovider/giteeai.png";
 
 import PreLoader from "@/components/Preloader";
 import OpenAiOptions from "@/components/LLMSelection/OpenAiOptions";
@@ -69,6 +70,7 @@ import PPIOLLMOptions from "@/components/LLMSelection/PPIOLLMOptions";
 import DellProAiStudioOptions from "@/components/LLMSelection/DPAISOptions";
 import MoonshotAiOptions from "@/components/LLMSelection/MoonshotAiOptions";
 import FoundryOptions from "@/components/LLMSelection/FoundryOptions";
+import GiteeAIOptions from "@/components/LLMSelection/GiteeAIOptions/index.jsx";
 
 import LLMItem from "@/components/LLMSelection/LLMItem";
 import { CaretUpDown, MagnifyingGlass, X } from "@phosphor-icons/react";
@@ -345,6 +347,14 @@ export const AVAILABLE_LLM_PROVIDERS = [
     description: "Run Z.AI's powerful GLM models.",
     requiredConfig: ["ZAiApiKey"],
   },
+  {
+    name: "GiteeAI",
+    value: "giteeai",
+    logo: GiteeAILogo,
+    options: (settings) => <GiteeAIOptions settings={settings} />,
+    description: "Run GiteeAI's powerful LLMs.",
+    requiredConfig: ["GiteeAIApiKey"],
+  },
   {
     name: "Generic OpenAI",
     value: "generic-openai",

frontend/src/pages/OnboardingFlow/Steps/DataHandling/index.jsx

@@ -42,6 +42,7 @@ import DPAISLogo from "@/media/llmprovider/dpais.png";
 import MoonshotAiLogo from "@/media/llmprovider/moonshotai.png";
 import CometApiLogo from "@/media/llmprovider/cometapi.png";
 import FoundryLogo from "@/media/llmprovider/foundry-local.png";
+import GiteeAILogo from "@/media/llmprovider/giteeai.png";
 
 import React, { useState, useEffect } from "react";
 import paths from "@/utils/paths";
@@ -279,6 +280,13 @@ export const LLM_SELECTION_PRIVACY = {
     ],
     logo: FoundryLogo,
   },
+  giteeai: {
+    name: "GiteeAI",
+    description: [
+      "Your model and chat contents are visible to GiteeAI in accordance with their terms of service.",
+    ],
+    logo: GiteeAILogo,
+  },
 };
 
 export const VECTOR_DB_PRIVACY = {

frontend/src/pages/OnboardingFlow/Steps/LLMPreference/index.jsx

@@ -30,6 +30,7 @@ import PPIOLogo from "@/media/llmprovider/ppio.png";
 import DellProAiStudioLogo from "@/media/llmprovider/dpais.png";
 import MoonshotAiLogo from "@/media/llmprovider/moonshotai.png";
 import CometApiLogo from "@/media/llmprovider/cometapi.png";
+import GiteeAILogo from "@/media/llmprovider/giteeai.png";
 
 import OpenAiOptions from "@/components/LLMSelection/OpenAiOptions";
 import GenericOpenAiOptions from "@/components/LLMSelection/GenericOpenAiOptions";
@@ -61,6 +62,7 @@ import PPIOLLMOptions from "@/components/LLMSelection/PPIOLLMOptions";
 import DellProAiStudioOptions from "@/components/LLMSelection/DPAISOptions";
 import MoonshotAiOptions from "@/components/LLMSelection/MoonshotAiOptions";
 import CometApiLLMOptions from "@/components/LLMSelection/CometApiLLMOptions";
+import GiteeAiOptions from "@/components/LLMSelection/GiteeAIOptions";
 
 import LLMItem from "@/components/LLMSelection/LLMItem";
 import System from "@/models/system";
@@ -290,6 +292,13 @@ const LLMS = [
     options: (settings) => <CometApiLLMOptions settings={settings} />,
     description: "500+ AI Models all in one API.",
   },
+  {
+    name: "GiteeAI",
+    value: "giteeai",
+    logo: GiteeAILogo,
+    options: (settings) => <GiteeAiOptions settings={settings} />,
+    description: "Run GiteeAI's powerful LLMs.",
+  },
 ];
 
 export default function LLMPreference({

frontend/src/pages/WorkspaceSettings/AgentConfig/AgentLLMSelection/index.jsx

@@ -35,6 +35,7 @@ const ENABLED_PROVIDERS = [
   "cometapi",
   "foundry",
   "zai",
+  "giteeai",
   // TODO: More agent support.
   // "cohere",         // Has tool calling and will need to build explicit support
   // "huggingface"     // Can be done but already has issues with no-chat templated. Needs to be tested.

server/.env.example

@@ -156,6 +156,11 @@ SIG_SALT='salt' # Please generate random string at least 32 chars long.
 # FOUNDRY_MODEL_PREF='phi-3.5-mini'
 # FOUNDRY_MODEL_TOKEN_LIMIT=4096
 
+# LLM_PROVIDER='giteeai'
+# GITEE_AI_API_KEY=
+# GITEE_AI_MODEL_PREF=
+# GITEE_AI_MODEL_TOKEN_LIMIT=
+
 ###########################################
 ######## Embedding API SElECTION ##########
 ###########################################

server/endpoints/utils.js

@@ -148,6 +148,9 @@ function getModelTag() {
     case "zai":
       model = process.env.ZAI_MODEL_PREF;
       break;
+    case "giteeai":
+      model = process.env.GITEE_AI_MODEL_PREF;
+      break;
     default:
       model = "--";
       break;

server/models/systemSettings.js

@@ -641,6 +641,11 @@ const SystemSettings = {
       // Z.AI Keys
       ZAiApiKey: !!process.env.ZAI_API_KEY,
       ZAiModelPref: process.env.ZAI_MODEL_PREF,
+
+      // GiteeAI API Keys
+      GiteeAIApiKey: !!process.env.GITEE_AI_API_KEY,
+      GiteeAIModelPref: process.env.GITEE_AI_MODEL_PREF,
+      GiteeAITokenLimit: process.env.GITEE_AI_MODEL_TOKEN_LIMIT || 8192,
     };
   },
 

server/storage/models/.gitignore

@@ -13,3 +13,4 @@ context-windows/*
 MintplexLabs
 cometapi
 fireworks
+giteeai

server/utils/AiProviders/giteeai/index.js

@@ -0,0 +1,397 @@
+const fs = require("fs");
+const path = require("path");
+const { v4: uuidv4 } = require("uuid");
+const { safeJsonParse, toValidNumber } = require("../../http");
+const LEGACY_MODEL_MAP = require("../modelMap/legacy");
+const { NativeEmbedder } = require("../../EmbeddingEngines/native");
+const {
+  LLMPerformanceMonitor,
+} = require("../../helpers/chat/LLMPerformanceMonitor");
+const {
+  writeResponseChunk,
+  clientAbortedHandler,
+} = require("../../helpers/chat/responses");
+const cacheFolder = path.resolve(
+  process.env.STORAGE_DIR
+    ? path.resolve(process.env.STORAGE_DIR, "models", "giteeai")
+    : path.resolve(__dirname, `../../../storage/models/giteeai`)
+);
+
+class GiteeAILLM {
+  constructor(embedder = null, modelPreference = null) {
+    if (!process.env.GITEE_AI_API_KEY)
+      throw new Error("No Gitee AI API key was set.");
+    const { OpenAI: OpenAIApi } = require("openai");
+
+    this.openai = new OpenAIApi({
+      apiKey: process.env.GITEE_AI_API_KEY,
+      baseURL: "https://ai.gitee.com/v1",
+    });
+    this.model = modelPreference || process.env.GITEE_AI_MODEL_PREF || "";
+    this.limits = {
+      history: this.promptWindowLimit() * 0.15,
+      system: this.promptWindowLimit() * 0.15,
+      user: this.promptWindowLimit() * 0.7,
+    };
+
+    this.embedder = embedder ?? new NativeEmbedder();
+    this.defaultTemp = 0.7;
+
+    if (!fs.existsSync(cacheFolder))
+      fs.mkdirSync(cacheFolder, { recursive: true });
+    this.cacheModelPath = path.resolve(cacheFolder, "models.json");
+    this.cacheAtPath = path.resolve(cacheFolder, ".cached_at");
+    this.log("Initialized with model:", this.model);
+  }
+
+  log(text, ...args) {
+    console.log(`\x1b[36m[${this.constructor.name}]\x1b[0m ${text}`, ...args);
+  }
+
+  // This checks if the .cached_at file has a timestamp that is more than 1Week (in millis)
+  // from the current date. If it is, then we will refetch the API so that all the models are up
+  // to date.
+  #cacheIsStale() {
+    const MAX_STALE = 6.048e8; // 1 Week in MS
+    if (!fs.existsSync(this.cacheAtPath)) return true;
+    const now = Number(new Date());
+    const timestampMs = Number(fs.readFileSync(this.cacheAtPath));
+    return now - timestampMs > MAX_STALE;
+  }
+
+  // This function fetches the models from the GiteeAI API and caches them locally.
+  async #syncModels() {
+    if (fs.existsSync(this.cacheModelPath) && !this.#cacheIsStale())
+      return false;
+
+    this.log("Model cache is not present or stale. Fetching from GiteeAI API.");
+    await giteeAiModels();
+    return;
+  }
+
+  models() {
+    if (!fs.existsSync(this.cacheModelPath)) return {};
+    return safeJsonParse(
+      fs.readFileSync(this.cacheModelPath, { encoding: "utf-8" }),
+      {}
+    );
+  }
+
+  #appendContext(contextTexts = []) {
+    if (!contextTexts || !contextTexts.length) return "";
+    return (
+      "\nContext:\n" +
+      contextTexts
+        .map((text, i) => {
+          return `[CONTEXT ${i}]:\n${text}\n[END CONTEXT ${i}]\n\n`;
+        })
+        .join("")
+    );
+  }
+
+  streamingEnabled() {
+    return "streamGetChatCompletion" in this;
+  }
+
+  static promptWindowLimit(model) {
+    return (
+      toValidNumber(process.env.GITEE_AI_MODEL_TOKEN_LIMIT) ||
+      LEGACY_MODEL_MAP.giteeai[model] ||
+      8192
+    );
+  }
+
+  promptWindowLimit() {
+    return (
+      toValidNumber(process.env.GITEE_AI_MODEL_TOKEN_LIMIT) ||
+      LEGACY_MODEL_MAP.giteeai[this.model] ||
+      8192
+    );
+  }
+
+  async isValidChatCompletionModel(modelName = "") {
+    return true;
+  }
+
+  constructPrompt({
+    systemPrompt = "",
+    contextTexts = [],
+    chatHistory = [],
+    userPrompt = "",
+  }) {
+    const prompt = {
+      role: "system",
+      content: `${systemPrompt}${this.#appendContext(contextTexts)}`,
+    };
+    return [prompt, ...chatHistory, { role: "user", content: userPrompt }];
+  }
+
+  /**
+   * Parses and prepends reasoning from the response and returns the full text response.
+   * @param {Object} response
+   * @returns {string}
+   */
+  #parseReasoningFromResponse({ message }) {
+    let textResponse = message?.content;
+    if (
+      !!message?.reasoning_content &&
+      message.reasoning_content.trim().length > 0
+    )
+      textResponse = `<think>${message.reasoning_content}</think>${textResponse}`;
+    return textResponse;
+  }
+
+  async getChatCompletion(messages = null, { temperature = 0.7 }) {
+    const result = await LLMPerformanceMonitor.measureAsyncFunction(
+      this.openai.chat.completions
+        .create({
+          model: this.model,
+          messages,
+          temperature,
+        })
+        .catch((e) => {
+          throw new Error(e.message);
+        })
+    );
+
+    if (
+      !result?.output?.hasOwnProperty("choices") ||
+      result?.output?.choices?.length === 0
+    )
+      throw new Error(
+        `Invalid response body returned from GiteeAI: ${JSON.stringify(result.output)}`
+      );
+
+    return {
+      textResponse: this.#parseReasoningFromResponse(result.output.choices[0]),
+      metrics: {
+        prompt_tokens: result.output.usage.prompt_tokens || 0,
+        completion_tokens: result.output.usage.completion_tokens || 0,
+        total_tokens: result.output.usage.total_tokens || 0,
+        outputTps: result.output.usage.completion_tokens / result.duration,
+        duration: result.duration,
+      },
+    };
+  }
+
+  async streamGetChatCompletion(messages = null, { temperature = 0.7 }) {
+    const measuredStreamRequest = await LLMPerformanceMonitor.measureStream(
+      this.openai.chat.completions.create({
+        model: this.model,
+        stream: true,
+        messages,
+        temperature,
+      }),
+      messages,
+      false
+    );
+
+    return measuredStreamRequest;
+  }
+
+  // TODO: This is a copy of the generic handleStream function in responses.js
+  // to specifically handle the GiteeAI reasoning model `reasoning_content` field.
+  // When or if ever possible, we should refactor this to be in the generic function.
+  handleStream(response, stream, responseProps) {
+    const { uuid = uuidv4(), sources = [] } = responseProps;
+    let hasUsageMetrics = false;
+    let usage = {
+      completion_tokens: 0,
+    };
+
+    return new Promise(async (resolve) => {
+      let fullText = "";
+      let reasoningText = "";
+
+      // Establish listener to early-abort a streaming response
+      // in case things go sideways or the user does not like the response.
+      // We preserve the generated text but continue as if chat was completed
+      // to preserve previously generated content.
+      const handleAbort = () => {
+        stream?.endMeasurement(usage);
+        clientAbortedHandler(resolve, fullText);
+      };
+      response.on("close", handleAbort);
+
+      try {
+        for await (const chunk of stream) {
+          const message = chunk?.choices?.[0];
+          const token = message?.delta?.content;
+          const reasoningToken = message?.delta?.reasoning_content;
+
+          if (
+            chunk.hasOwnProperty("usage") && // exists
+            !!chunk.usage && // is not null
+            Object.values(chunk.usage).length > 0 // has values
+          ) {
+            if (chunk.usage.hasOwnProperty("prompt_tokens")) {
+              usage.prompt_tokens = Number(chunk.usage.prompt_tokens);
+            }
+
+            if (chunk.usage.hasOwnProperty("completion_tokens")) {
+              hasUsageMetrics = true; // to stop estimating counter
+              usage.completion_tokens = Number(chunk.usage.completion_tokens);
+            }
+          }
+
+          // Reasoning models will always return the reasoning text before the token text.
+          if (reasoningToken) {
+            // If the reasoning text is empty (''), we need to initialize it
+            // and send the first chunk of reasoning text.
+            if (reasoningText.length === 0) {
+              writeResponseChunk(response, {
+                uuid,
+                sources: [],
+                type: "textResponseChunk",
+                textResponse: `<think>${reasoningToken}`,
+                close: false,
+                error: false,
+              });
+              reasoningText += `<think>${reasoningToken}`;
+              continue;
+            } else {
+              writeResponseChunk(response, {
+                uuid,
+                sources: [],
+                type: "textResponseChunk",
+                textResponse: reasoningToken,
+                close: false,
+                error: false,
+              });
+              reasoningText += reasoningToken;
+            }
+          }
+
+          // If the reasoning text is not empty, but the reasoning token is empty
+          // and the token text is not empty we need to close the reasoning text and begin sending the token text.
+          if (!!reasoningText && !reasoningToken && token) {
+            writeResponseChunk(response, {
+              uuid,
+              sources: [],
+              type: "textResponseChunk",
+              textResponse: `</think>`,
+              close: false,
+              error: false,
+            });
+            fullText += `${reasoningText}</think>`;
+            reasoningText = "";
+          }
+
+          if (token) {
+            fullText += token;
+            // If we never saw a usage metric, we can estimate them by number of completion chunks
+            if (!hasUsageMetrics) usage.completion_tokens++;
+            writeResponseChunk(response, {
+              uuid,
+              sources: [],
+              type: "textResponseChunk",
+              textResponse: token,
+              close: false,
+              error: false,
+            });
+          }
+
+          // LocalAi returns '' and others return null on chunks - the last chunk is not "" or null.
+          // Either way, the key `finish_reason` must be present to determine ending chunk.
+          if (
+            message?.hasOwnProperty("finish_reason") && // Got valid message and it is an object with finish_reason
+            message.finish_reason !== "" &&
+            message.finish_reason !== null
+          ) {
+            writeResponseChunk(response, {
+              uuid,
+              sources,
+              type: "textResponseChunk",
+              textResponse: "",
+              close: true,
+              error: false,
+            });
+            response.removeListener("close", handleAbort);
+            stream?.endMeasurement(usage);
+            resolve(fullText);
+            break; // Break streaming when a valid finish_reason is first encountered
+          }
+        }
+      } catch (e) {
+        console.log(`\x1b[43m\x1b[34m[STREAMING ERROR]\x1b[0m ${e.message}`);
+        writeResponseChunk(response, {
+          uuid,
+          type: "abort",
+          textResponse: null,
+          sources: [],
+          close: true,
+          error: e.message,
+        });
+        stream?.endMeasurement(usage);
+        resolve(fullText); // Return what we currently have - if anything.
+      }
+    });
+  }
+
+  async embedTextInput(textInput) {
+    return await this.embedder.embedTextInput(textInput);
+  }
+
+  async embedChunks(textChunks = []) {
+    return await this.embedder.embedChunks(textChunks);
+  }
+
+  async compressMessages(promptArgs = {}, rawHistory = []) {
+    const { messageArrayCompressor } = require("../../helpers/chat");
+    const messageArray = this.constructPrompt(promptArgs);
+    return await messageArrayCompressor(this, messageArray, rawHistory);
+  }
+}
+
+async function giteeAiModels() {
+  const url = new URL("https://ai.gitee.com/v1/models");
+  url.searchParams.set("type", "text2text");
+  return await fetch(url.toString(), {
+    method: "GET",
+    headers: {
+      "Content-Type": "application/json",
+      Authorization: `Bearer ${process.env.GITEE_AI_API_KEY}`,
+    },
+  })
+    .then((res) => res.json())
+    .then(({ data = [] }) => data)
+    .then((models = []) => {
+      const validModels = {};
+      models.forEach(
+        (model) =>
+          (validModels[model.id] = {
+            id: model.id,
+            name: model.id,
+            organization: model.owned_by,
+          })
+      );
+      // Cache all response information
+      if (!fs.existsSync(cacheFolder))
+        fs.mkdirSync(cacheFolder, { recursive: true });
+      fs.writeFileSync(
+        path.resolve(cacheFolder, "models.json"),
+        JSON.stringify(validModels),
+        {
+          encoding: "utf-8",
+        }
+      );
+      fs.writeFileSync(
+        path.resolve(cacheFolder, ".cached_at"),
+        String(Number(new Date())),
+        {
+          encoding: "utf-8",
+        }
+      );
+
+      return validModels;
+    })
+    .catch((e) => {
+      console.error(e);
+      return {};
+    });
+}
+
+module.exports = {
+  GiteeAILLM,
+  giteeAiModels,
+};

server/utils/AiProviders/modelMap/legacy.js

@@ -120,5 +120,27 @@ const LEGACY_MODEL_MAP = {
   xai: {
     "grok-beta": 131072,
   },
+  giteeai: {
+    "Qwen2.5-72B-Instruct": 16_384,
+    "Qwen2.5-14B-Instruct": 24_576,
+    "Qwen2-7B-Instruct": 24_576,
+    "Qwen2.5-32B-Instruct": 32_768,
+    "Qwen2-72B-Instruct": 32_768,
+    "Qwen2-VL-72B": 32_768,
+    "QwQ-32B-Preview": 32_768,
+    "Yi-34B-Chat": 4_096,
+    "glm-4-9b-chat": 32_768,
+    "deepseek-coder-33B-instruct": 8_192,
+    "codegeex4-all-9b": 32_768,
+    "InternVL2-8B": 32_768,
+    "InternVL2.5-26B": 32_768,
+    "InternVL2.5-78B": 32_768,
+    "DeepSeek-R1-Distill-Qwen-32B": 32_768,
+    "DeepSeek-R1-Distill-Qwen-1.5B": 32_768,
+    "DeepSeek-R1-Distill-Qwen-14B": 32_768,
+    "DeepSeek-R1-Distill-Qwen-7B": 32_768,
+    "DeepSeek-V3": 32_768,
+    "DeepSeek-R1": 32_768,
+  },
 };
 module.exports = LEGACY_MODEL_MAP;

server/utils/agents/aibitat/index.js

@@ -988,6 +988,8 @@ ${this.getHistory({ to: route.to })
         return new Providers.CometApiProvider({ model: config.model });
       case "foundry":
         return new Providers.FoundryProvider({ model: config.model });
+      case "giteeai":
+        return new Providers.GiteeAIProvider({ model: config.model });
       default:
         throw new Error(
           `Unknown provider: ${config.provider}. Please use a valid provider.`

server/utils/agents/aibitat/providers/ai-provider.js

@@ -231,6 +231,14 @@ class Provider {
           apiKey: process.env.COMETAPI_LLM_API_KEY ?? null,
           ...config,
         });
+      case "giteeai":
+        return new ChatOpenAI({
+          configuration: {
+            baseURL: "https://ai.gitee.com/v1",
+          },
+          apiKey: process.env.GITEE_AI_API_KEY ?? null,
+          ...config,
+        });
       // OSS Model Runners
       // case "anythingllm_ollama":
       //   return new ChatOllama({

server/utils/agents/aibitat/providers/giteeai.js

@@ -0,0 +1,85 @@
+const OpenAI = require("openai");
+const Provider = require("./ai-provider.js");
+const InheritMultiple = require("./helpers/classes.js");
+const UnTooled = require("./helpers/untooled.js");
+
+class GiteeAIProvider extends InheritMultiple([Provider, UnTooled]) {
+  model;
+
+  constructor(config = {}) {
+    super();
+    const { model = "DeepSeek-R1" } = config;
+    this._client = new OpenAI({
+      baseURL: "https://ai.gitee.com/v1",
+      apiKey: process.env.GITEE_AI_API_KEY ?? null,
+      maxRetries: 3,
+    });
+    this.model = model;
+    this.verbose = true;
+  }
+
+  get client() {
+    return this._client;
+  }
+
+  get supportsAgentStreaming() {
+    return true;
+  }
+
+  async #handleFunctionCallChat({ messages = [] }) {
+    return await this.client.chat.completions
+      .create({
+        model: this.model,
+        messages,
+      })
+      .then((result) => {
+        if (!result.hasOwnProperty("choices"))
+          throw new Error("GiteeAI chat: No results!");
+        if (result.choices.length === 0)
+          throw new Error("GiteeAI chat: No results length!");
+        return result.choices[0].message.content;
+      })
+      .catch((_) => {
+        return null;
+      });
+  }
+
+  async #handleFunctionCallStream({ messages = [] }) {
+    return await this.client.chat.completions.create({
+      model: this.model,
+      stream: true,
+      messages,
+    });
+  }
+
+  async stream(messages, functions = [], eventHandler = null) {
+    return await UnTooled.prototype.stream.call(
+      this,
+      messages,
+      functions,
+      this.#handleFunctionCallStream.bind(this),
+      eventHandler
+    );
+  }
+
+  async complete(messages, functions = []) {
+    return await UnTooled.prototype.complete.call(
+      this,
+      messages,
+      functions,
+      this.#handleFunctionCallChat.bind(this)
+    );
+  }
+
+  /**
+   * Get the cost of the completion.
+   *
+   * @param _usage The completion to get the cost for.
+   * @returns The cost of the completion.
+   */
+  getCost(_usage) {
+    return 0;
+  }
+}
+
+module.exports = GiteeAIProvider;

server/utils/agents/aibitat/providers/index.js

@@ -27,6 +27,7 @@ const DellProAiStudioProvider = require("./dellProAiStudio.js");
 const MoonshotAiProvider = require("./moonshotAi.js");
 const CometApiProvider = require("./cometapi.js");
 const FoundryProvider = require("./foundry.js");
+const GiteeAIProvider = require("./giteeai.js");
 
 module.exports = {
   OpenAIProvider,
@@ -58,4 +59,5 @@ module.exports = {
   DellProAiStudioProvider,
   MoonshotAiProvider,
   FoundryProvider,
+  GiteeAIProvider,
 };

server/utils/agents/index.js

@@ -208,17 +208,17 @@ class AgentHandler {
         if (!process.env.MOONSHOT_AI_MODEL_PREF)
           throw new Error("Moonshot AI model must be set to use agents.");
         break;
-
       case "cometapi":
         if (!process.env.COMETAPI_LLM_API_KEY)
           throw new Error("CometAPI API Key must be provided to use agents.");
         break;
-
       case "foundry":
         if (!process.env.FOUNDRY_BASE_PATH)
           throw new Error("Foundry base path must be provided to use agents.");
         break;
-
+      case "giteeai":
+        if (!process.env.GITEE_AI_API_KEY)
+          throw new Error("GiteeAI API Key must be provided to use agents.");
       default:
         throw new Error(
           "No workspace agent provider set. Please set your agent provider in the workspace's settings"
@@ -295,6 +295,8 @@ class AgentHandler {
         return process.env.COMETAPI_LLM_MODEL_PREF ?? "gpt-5-mini";
       case "foundry":
         return process.env.FOUNDRY_MODEL_PREF ?? null;
+      case "giteeai":
+        return process.env.GITEE_AI_MODEL_PREF ?? null;
       default:
         return null;
     }

server/utils/helpers/customModels.js

@@ -42,6 +42,7 @@ const SUPPORT_CUSTOM_MODELS = [
   "foundry",
   "cohere",
   "zai",
+  "giteeai",
   // Embedding Engines
   "native-embedder",
   "cohere-embedder",
@@ -113,6 +114,8 @@ async function getCustomModels(provider = "", apiKey = null, basePath = null) {
       return await getCohereModels(apiKey, "embed");
     case "openrouter-embedder":
       return await getOpenRouterEmbeddingModels();
+    case "giteeai":
+      return await getGiteeAIModels(apiKey);
     default:
       return { models: [], error: "Invalid provider for custom models" };
   }
@@ -596,6 +599,20 @@ async function getDeepSeekModels(apiKey = null) {
   return { models, error: null };
 }
 
+async function getGiteeAIModels() {
+  const { giteeAiModels } = require("../AiProviders/giteeai");
+  const modelMap = await giteeAiModels();
+  if (!Object.keys(modelMap).length === 0) return { models: [], error: null };
+  const models = Object.values(modelMap).map((model) => {
+    return {
+      id: model.id,
+      organization: model.organization ?? "GiteeAI",
+      name: model.id,
+    };
+  });
+  return { models, error: null };
+}
+
 async function getXAIModels(_apiKey = null) {
   const { OpenAI: OpenAIApi } = require("openai");
   const apiKey =

server/utils/helpers/index.js

@@ -225,6 +225,9 @@ function getLLMProvider({ provider = null, model = null } = {}) {
     case "zai":
       const { ZAiLLM } = require("../AiProviders/zai");
       return new ZAiLLM(embedder, model);
+    case "giteeai":
+      const { GiteeAILLM } = require("../AiProviders/giteeai");
+      return new GiteeAILLM(embedder, model);
     default:
       throw new Error(
         `ENV: No valid LLM_PROVIDER value found in environment! Using ${process.env.LLM_PROVIDER}`
@@ -387,6 +390,9 @@ function getLLMProviderClass({ provider = null } = {}) {
     case "zai":
       const { ZAiLLM } = require("../AiProviders/zai");
       return ZAiLLM;
+    case "giteeai":
+      const { GiteeAILLM } = require("../AiProviders/giteeai");
+      return GiteeAILLM;
     default:
       return null;
   }
@@ -461,6 +467,8 @@ function getBaseLLMProviderModel({ provider = null } = {}) {
       return process.env.FOUNDRY_MODEL_PREF;
     case "zai":
       return process.env.ZAI_MODEL_PREF;
+    case "giteeai":
+      return process.env.GITEE_AI_MODEL_PREF;
     default:
       return null;
   }

server/utils/helpers/updateENV.js

@@ -775,6 +775,20 @@ const KEY_MAPPING = {
     envKey: "ZAI_MODEL_PREF",
     checks: [isNotEmpty],
   },
+
+  // GiteeAI Options
+  GiteeAIApiKey: {
+    envKey: "GITEE_AI_API_KEY",
+    checks: [isNotEmpty],
+  },
+  GiteeAIModelPref: {
+    envKey: "GITEE_AI_MODEL_PREF",
+    checks: [isNotEmpty],
+  },
+  GiteeAITokenLimit: {
+    envKey: "GITEE_AI_MODEL_TOKEN_LIMIT",
+    checks: [nonZero],
+  },
 };
 
 function isNotEmpty(input = "") {
@@ -887,6 +901,7 @@ function supportedLLM(input = "") {
     "cometapi",
     "foundry",
     "zai",
+    "giteeai",
   ].includes(input);
   return validSelection ? null : `${input} is not a valid LLM provider.`;
 }