feat: #77

.env.template

@@ -50,4 +50,22 @@ DISABLE_FAST_LINK=
 # (optional)
 # Default: 1
 # If your project is not deployed on Vercel, set this value to 1.
-NEXT_PUBLIC_ENABLE_NODEJS_PLUGIN=1
+NEXT_PUBLIC_ENABLE_NODEJS_PLUGIN=1
+
+# (optional)
+# Default: Empty
+# If you want to enable RAG, set this value to 1.
+NEXT_PUBLIC_ENABLE_RAG=
+
+# (optional)
+# Default: Empty
+# Model used when RAG vectorized data.
+RAG_EMBEDDING_MODEL=text-embedding-ada-002
+
+# Configuration is required when turning on RAG.
+# Default: Empty
+QDRANT_URL=
+
+# Configuration is required when turning on RAG.
+# Default: Empty
+QDRANT_API_KEY=

README.md

@@ -25,7 +25,7 @@
 > [!WARNING]
 > 本项目插件功能基于 [OpenAI API 函数调用](https://platform.openai.com/docs/guides/function-calling) 功能实现，转发 GitHub Copilot 接口或类似实现的模拟接口并不能正常调用插件功能！
 
-![cover](./docs/images/gpt-vision-example.jpg)
+![cover](./docs/images/rag-example.jpg)
 
 ![plugin-example](./docs/images/plugin-example.png)
 
@@ -35,6 +35,9 @@
 
 ## 主要功能
 
+- RAG 功能 （预览）
+  - 配置请参考文档[RAG 功能配置说明](./docs/rag-cn.md)
+
 - 除插件工具外，与原项目保持一致 [ChatGPT-Next-Web 主要功能](https://github.com/Yidadaa/ChatGPT-Next-Web#主要功能)
 
 - 支持 OpenAI TTS（文本转语音）https://github.com/Hk-Gosuto/ChatGPT-Next-Web-LangChain/issues/208
@@ -142,7 +145,7 @@
 
 - [x] 支持语音输入 https://github.com/Hk-Gosuto/ChatGPT-Next-Web-LangChain/issues/208
 
-- [ ] 支持其他类型文件上传 https://github.com/Hk-Gosuto/ChatGPT-Next-Web-LangChain/issues/77
+- [x] 支持其他类型文件上传 https://github.com/Hk-Gosuto/ChatGPT-Next-Web-LangChain/issues/77
 
 - [ ] 支持 Azure Storage https://github.com/Hk-Gosuto/ChatGPT-Next-Web-LangChain/issues/217
 
@@ -295,11 +298,9 @@ docker run -d -p 3000:3000 \
 | [简体中文](./docs/synchronise-chat-logs-cn.md) | [English](./docs/synchronise-chat-logs-en.md) | [Italiano](./docs/synchronise-chat-logs-es.md) | [日本語](./docs/synchronise-chat-logs-ja.md) | [한국어](./docs/synchronise-chat-logs-ko.md)
 
 
-## 贡献者
+## Star History
 
-<a href="https://github.com/Hk-Gosuto/ChatGPT-Next-Web-LangChain/graphs/contributors">
+[![Star History Chart](https://api.star-history.com/svg?repos=Hk-Gosuto/ChatGPT-Next-Web-LangChain&type=Date)](https://star-history.com/#Hk-Gosuto/ChatGPT-Next-Web-LangChain&Date)
-  <img src="https://contrib.rocks/image?repo=Hk-Gosuto/ChatGPT-Next-Web-LangChain" />
-</a>
 
 ## 捐赠
 

app/api/langchain-tools/rag_search.ts

@@ -7,6 +7,8 @@ import { RunnableSequence } from "@langchain/core/runnables";
 import { StringOutputParser } from "@langchain/core/output_parsers";
 import { Pinecone } from "@pinecone-database/pinecone";
 import { PineconeStore } from "@langchain/pinecone";
+import { getServerSideConfig } from "@/app/config/server";
+import { QdrantVectorStore } from "@langchain/community/vectorstores/qdrant";
 
 export class RAGSearch extends Tool {
   static lc_name() {
@@ -34,21 +36,32 @@ export class RAGSearch extends Tool {
 
   /** @ignore */
   async _call(inputs: string, runManager?: CallbackManagerForToolRun) {
-    const pinecone = new Pinecone();
+    const serverConfig = getServerSideConfig();
-    const pineconeIndex = pinecone.Index(process.env.PINECONE_INDEX!);
+    // const pinecone = new Pinecone();
-    const vectorStore = await PineconeStore.fromExistingIndex(this.embeddings, {
+    // const pineconeIndex = pinecone.Index(serverConfig.pineconeIndex!);
-      pineconeIndex,
+    // const vectorStore = await PineconeStore.fromExistingIndex(this.embeddings, {
-    });
+    //   pineconeIndex,
+    // });
+    const vectorStore = await QdrantVectorStore.fromExistingCollection(
+      this.embeddings,
+      {
+        url: process.env.QDRANT_URL,
+        apiKey: process.env.QDRANT_API_KEY,
+        collectionName: this.sessionId,
+      },
+    );
 
     let context;
-    const returnCunt = process.env.RAG_RETURN_COUNT
+    const returnCunt = serverConfig.ragReturnCount
-      ? parseInt(process.env.RAG_RETURN_COUNT, 10)
+      ? parseInt(serverConfig.ragReturnCount, 10)
       : 4;
-    const results = await vectorStore.similaritySearch(inputs, returnCunt, {
+    console.log("[rag-search]", { inputs, returnCunt });
-      sessionId: this.sessionId,
+    // const results = await vectorStore.similaritySearch(inputs, returnCunt, {
-    });
+    //   sessionId: this.sessionId,
+    // });
+    const results = await vectorStore.similaritySearch(inputs, returnCunt);
     context = formatDocumentsAsString(results);
-    console.log("[rag-search]", context);
+    console.log("[rag-search]", { context });
     return context;
     // const input = `Text:${context}\n\nQuestion:${inputs}\n\nI need you to answer the question based on the text.`;
 

app/api/langchain/rag/search/route.ts

@@ -4,6 +4,7 @@ import { ACCESS_CODE_PREFIX, ModelProvider } from "@/app/constant";
 import { OpenAIEmbeddings } from "@langchain/openai";
 import { Pinecone } from "@pinecone-database/pinecone";
 import { PineconeStore } from "@langchain/pinecone";
+import { QdrantVectorStore } from "@langchain/community/vectorstores/qdrant";
 import { getServerSideConfig } from "@/app/config/server";
 
 interface RequestBody {
@@ -27,26 +28,40 @@ async function handle(req: NextRequest) {
     const reqBody: RequestBody = await req.json();
     const authToken = req.headers.get("Authorization") ?? "";
     const token = authToken.trim().replaceAll("Bearer ", "").trim();
-
+    const serverConfig = getServerSideConfig();
-    const pinecone = new Pinecone();
+    // const pinecone = new Pinecone();
-    const pineconeIndex = pinecone.Index(process.env.PINECONE_INDEX!);
+    // const pineconeIndex = pinecone.Index(serverConfig.pineconeIndex!);
     const apiKey = getOpenAIApiKey(token);
     const baseUrl = getOpenAIBaseUrl(reqBody.baseUrl);
     const embeddings = new OpenAIEmbeddings(
       {
-        modelName: process.env.RAG_EMBEDDING_MODEL ?? "text-embedding-3-large",
+        modelName: serverConfig.ragEmbeddingModel ?? "text-embedding-3-large",
         openAIApiKey: apiKey,
       },
       { basePath: baseUrl },
     );
-    const vectorStore = await PineconeStore.fromExistingIndex(embeddings, {
+    // const vectorStore = await PineconeStore.fromExistingIndex(embeddings, {
-      pineconeIndex,
+    //   pineconeIndex,
-    });
+    // });
-    const results = await vectorStore.similaritySearch(reqBody.query, 1, {
+    // const results = await vectorStore.similaritySearch(reqBody.query, 4, {
-      sessionId: reqBody.sessionId,
+    //   sessionId: reqBody.sessionId,
-    });
+    // });
-    console.log(results);
+    const vectorStore = await QdrantVectorStore.fromExistingCollection(
-    return NextResponse.json(results, {
+      embeddings,
+      {
+        url: process.env.QDRANT_URL,
+        apiKey: process.env.QDRANT_API_KEY,
+        collectionName: reqBody.sessionId,
+      },
+    );
+    const returnCunt = serverConfig.ragReturnCount
+      ? parseInt(serverConfig.ragReturnCount, 10)
+      : 4;
+    const response = await vectorStore.similaritySearch(
+      reqBody.query,
+      returnCunt,
+    );
+    return NextResponse.json(response, {
       status: 200,
     });
   } catch (e) {

app/api/langchain/rag/store/route.ts

@@ -20,6 +20,7 @@ import { FileInfo } from "@/app/client/platforms/utils";
 import mime from "mime";
 import LocalFileStorage from "@/app/utils/local_file_storage";
 import S3FileStorage from "@/app/utils/s3_file_storage";
+import { QdrantVectorStore } from "@langchain/community/vectorstores/qdrant";
 
 interface RequestBody {
   sessionId: string;
@@ -80,16 +81,17 @@ async function handle(req: NextRequest) {
     const apiKey = getOpenAIApiKey(token);
     const baseUrl = getOpenAIBaseUrl(reqBody.baseUrl);
     const serverConfig = getServerSideConfig();
-    const pinecone = new Pinecone();
+    // const pinecone = new Pinecone();
-    const pineconeIndex = pinecone.Index(process.env.PINECONE_INDEX!);
+    // const pineconeIndex = pinecone.Index(serverConfig.pineconeIndex!);
     const embeddings = new OpenAIEmbeddings(
       {
-        modelName: process.env.RAG_EMBEDDING_MODEL ?? "text-embedding-3-large",
+        modelName: serverConfig.ragEmbeddingModel,
         openAIApiKey: apiKey,
       },
       { basePath: baseUrl },
     );
-    //https://js.langchain.com/docs/integrations/vectorstores/pinecone
+    // https://js.langchain.com/docs/integrations/vectorstores/pinecone
+    // https://js.langchain.com/docs/integrations/vectorstores/qdrant
     // process files
     for (let i = 0; i < reqBody.fileInfos.length; i++) {
       const fileInfo = reqBody.fileInfos[i];
@@ -121,22 +123,33 @@ async function handle(req: NextRequest) {
         };
       });
       // split
-      const chunkSize = process.env.RAG_CHUNK_SIZE
+      const chunkSize = serverConfig.ragChunkSize
-        ? parseInt(process.env.RAG_CHUNK_SIZE, 10)
+        ? parseInt(serverConfig.ragChunkSize, 10)
         : 2000;
-      const chunkOverlap = process.env.RAG_CHUNK_OVERLAP
+      const chunkOverlap = serverConfig.ragChunkOverlap
-        ? parseInt(process.env.RAG_CHUNK_OVERLAP, 10)
+        ? parseInt(serverConfig.ragChunkOverlap, 10)
         : 200;
       const textSplitter = new RecursiveCharacterTextSplitter({
         chunkSize: chunkSize,
         chunkOverlap: chunkOverlap,
       });
       const splits = await textSplitter.splitDocuments(docs);
-      // remove history
+      const vectorStore = await QdrantVectorStore.fromDocuments(
-      await PineconeStore.fromDocuments(splits, embeddings, {
+        splits,
-        pineconeIndex,
+        embeddings,
-        maxConcurrency: 5,
+        {
-      });
+          url: process.env.QDRANT_URL,
+          apiKey: process.env.QDRANT_API_KEY,
+          collectionName: reqBody.sessionId,
+        },
+      );
+      // await PineconeStore.fromDocuments(splits, embeddings, {
+      //   pineconeIndex,
+      //   maxConcurrency: 5,
+      // });
+      // const vectorStore = await PineconeStore.fromExistingIndex(embeddings, {
+      //   pineconeIndex,
+      // });
     }
     return NextResponse.json(
       {

app/client/api.ts

@@ -115,7 +115,7 @@ export abstract class LLMApi {
   abstract speech(options: SpeechOptions): Promise<ArrayBuffer>;
   abstract transcription(options: TranscriptionOptions): Promise<string>;
   abstract toolAgentChat(options: AgentChatOptions): Promise<void>;
-  abstract createRAGSore(options: CreateRAGStoreOptions): Promise<void>;
+  abstract createRAGStore(options: CreateRAGStoreOptions): Promise<void>;
   abstract usage(): Promise<LLMUsage>;
   abstract models(): Promise<LLMModel[]>;
 }

app/client/platforms/google.ts

@@ -20,7 +20,7 @@ import {
 } from "@/app/utils";
 
 export class GeminiProApi implements LLMApi {
-  createRAGSore(options: CreateRAGStoreOptions): Promise<void> {
+  createRAGStore(options: CreateRAGStoreOptions): Promise<void> {
     throw new Error("Method not implemented.");
   }
   transcription(options: TranscriptionOptions): Promise<string> {

app/client/platforms/openai.ts

@@ -363,7 +363,7 @@ export class ChatGPTApi implements LLMApi {
     }
   }
 
-  async createRAGSore(options: CreateRAGStoreOptions): Promise<void> {
+  async createRAGStore(options: CreateRAGStoreOptions): Promise<void> {
     try {
       const accessStore = useAccessStore.getState();
       const isAzure = accessStore.provider === ServiceProvider.Azure;
@@ -373,7 +373,7 @@ export class ChatGPTApi implements LLMApi {
         fileInfos: options.fileInfos,
         baseUrl: baseUrl,
       };
-      console.log("[Request] openai payload: ", requestPayload);
+      console.log("[Request] rag store payload: ", requestPayload);
       const controller = new AbortController();
       options.onController?.(controller);
       let path = "/api/langchain/rag/store";

app/components/chat.tsx

@@ -509,14 +509,13 @@ export function ChatActions(props: {
   const [showUploadImage, setShowUploadImage] = useState(false);
 
   const [showUploadFile, setShowUploadFile] = useState(false);
+  const accessStore = useAccessStore();
 
   useEffect(() => {
     const show = isVisionModel(currentModel);
     setShowUploadImage(show);
-    const serverConfig = getServerSideConfig();
+    const isEnableRAG = !!process.env.NEXT_PUBLIC_ENABLE_RAG;
-    setShowUploadFile(
+    setShowUploadFile(isEnableRAG && !show && isSupportRAGModel(currentModel));
-      serverConfig.isEnableRAG && !show && isSupportRAGModel(currentModel),
-    );
     if (!show) {
       props.setAttachImages([]);
       props.setUploading(false);
@@ -1039,7 +1038,9 @@ function _Chat() {
     setIsLoading(true);
     const textContent = getMessageTextContent(userMessage);
     const images = getMessageImages(userMessage);
-    chatStore.onUserInput(textContent, images).then(() => setIsLoading(false));
+    chatStore
+      .onUserInput(textContent, images, userMessage.fileInfos)
+      .then(() => setIsLoading(false));
     inputRef.current?.focus();
   };
 

app/config/server.ts

@@ -113,5 +113,10 @@ export const getServerSideConfig = () => {
       !process.env.S3_ENDPOINT,
 
     isEnableRAG: !!process.env.NEXT_PUBLIC_ENABLE_RAG,
+    ragEmbeddingModel:
+      process.env.RAG_EMBEDDING_MODEL ?? "text-embedding-3-large",
+    ragChunkSize: process.env.RAG_CHUNK_SIZE ?? "2000",
+    ragChunkOverlap: process.env.RAG_CHUNK_OVERLAP ?? "200",
+    ragReturnCount: process.env.RAG_RETURN_COUNT ?? "4",
   };
 };

app/store/access.ts

@@ -43,6 +43,7 @@ const DEFAULT_ACCESS_STATE = {
   disableGPT4: false,
   disableFastLink: false,
   customModels: "",
+  isEnableRAG: false,
 };
 
 export const useAccessStore = createPersistStore(
@@ -55,6 +56,10 @@ export const useAccessStore = createPersistStore(
       return get().needCode;
     },
 
+    isEnableRAG() {
+      return ensure(get(), ["isEnableRAG"]);
+    },
+
     isValidOpenAI() {
       return ensure(get(), ["openaiApiKey"]);
     },

app/store/chat.ts

@@ -376,88 +376,96 @@ export const useChatStore = createPersistStore(
         });
         var api: ClientApi;
         api = new ClientApi(ModelProvider.GPT);
+        const isEnableRAG = !!process.env.NEXT_PUBLIC_ENABLE_RAG;
         if (
           config.pluginConfig.enable &&
           session.mask.usePlugins &&
-          (allPlugins.length > 0 || !!process.env.NEXT_PUBLIC_ENABLE_RAG) &&
+          (allPlugins.length > 0 || isEnableRAG) &&
           modelConfig.model.startsWith("gpt") &&
           modelConfig.model != "gpt-4-vision-preview"
         ) {
           console.log("[ToolAgent] start");
           const pluginToolNames = allPlugins.map((m) => m.toolName);
-          if (!!process.env.NEXT_PUBLIC_ENABLE_RAG)
+          if (isEnableRAG) pluginToolNames.push("rag-search");
-            pluginToolNames.push("rag-search");
+          const agentCall = () => {
-          if (attachFiles && attachFiles.length > 0) {
+            api.llm.toolAgentChat({
-            console.log("crete rag store");
-            await api.llm.createRAGSore({
               chatSessionId: session.id,
-              fileInfos: attachFiles,
+              messages: sendMessages,
-            });
+              config: { ...modelConfig, stream: true },
-          }
+              agentConfig: { ...pluginConfig, useTools: pluginToolNames },
-          api.llm.toolAgentChat({
+              onUpdate(message) {
-            chatSessionId: session.id,
+                botMessage.streaming = true;
-            messages: sendMessages,
+                if (message) {
-            config: { ...modelConfig, stream: true },
+                  botMessage.content = message;
-            agentConfig: { ...pluginConfig, useTools: pluginToolNames },
+                }
-            onUpdate(message) {
+                get().updateCurrentSession((session) => {
-              botMessage.streaming = true;
+                  session.messages = session.messages.concat();
-              if (message) {
-                botMessage.content = message;
-              }
-              get().updateCurrentSession((session) => {
-                session.messages = session.messages.concat();
-              });
-            },
-            onToolUpdate(toolName, toolInput) {
-              botMessage.streaming = true;
-              if (toolName && toolInput) {
-                botMessage.toolMessages!.push({
-                  toolName,
-                  toolInput,
                 });
-              }
+              },
-              get().updateCurrentSession((session) => {
+              onToolUpdate(toolName, toolInput) {
-                session.messages = session.messages.concat();
+                botMessage.streaming = true;
-              });
+                if (toolName && toolInput) {
-            },
+                  botMessage.toolMessages!.push({
-            onFinish(message) {
+                    toolName,
-              botMessage.streaming = false;
+                    toolInput,
-              if (message) {
+                  });
-                botMessage.content = message;
+                }
-                get().onNewMessage(botMessage);
+                get().updateCurrentSession((session) => {
-              }
+                  session.messages = session.messages.concat();
-              ChatControllerPool.remove(session.id, botMessage.id);
-            },
-            onError(error) {
-              const isAborted = error.message.includes("aborted");
-              botMessage.content +=
-                "\n\n" +
-                prettyObject({
-                  error: true,
-                  message: error.message,
                 });
-              botMessage.streaming = false;
+              },
-              userMessage.isError = !isAborted;
+              onFinish(message) {
-              botMessage.isError = !isAborted;
+                botMessage.streaming = false;
-              get().updateCurrentSession((session) => {
+                if (message) {
-                session.messages = session.messages.concat();
+                  botMessage.content = message;
-              });
+                  get().onNewMessage(botMessage);
-              ChatControllerPool.remove(
+                }
-                session.id,
+                ChatControllerPool.remove(session.id, botMessage.id);
-                botMessage.id ?? messageIndex,
+              },
-              );
+              onError(error) {
+                const isAborted = error.message.includes("aborted");
+                botMessage.content +=
+                  "\n\n" +
+                  prettyObject({
+                    error: true,
+                    message: error.message,
+                  });
+                botMessage.streaming = false;
+                userMessage.isError = !isAborted;
+                botMessage.isError = !isAborted;
+                get().updateCurrentSession((session) => {
+                  session.messages = session.messages.concat();
+                });
+                ChatControllerPool.remove(
+                  session.id,
+                  botMessage.id ?? messageIndex,
+                );
 
-              console.error("[Chat] failed ", error);
+                console.error("[Chat] failed ", error);
-            },
+              },
-            onController(controller) {
+              onController(controller) {
-              // collect controller for stop/retry
+                // collect controller for stop/retry
-              ChatControllerPool.addController(
+                ChatControllerPool.addController(
-                session.id,
+                  session.id,
-                botMessage.id ?? messageIndex,
+                  botMessage.id ?? messageIndex,
-                controller,
+                  controller,
-              );
+                );
-            },
+              },
-          });
+            });
+          };
+          if (attachFiles && attachFiles.length > 0) {
+            await api.llm
+              .createRAGStore({
+                chatSessionId: session.id,
+                fileInfos: attachFiles,
+              })
+              .then(() => {
+                console.log("[RAG]", "Vector db created");
+                agentCall();
+              });
+          } else {
+            agentCall();
+          }
         } else {
           if (modelConfig.model.startsWith("gemini")) {
             api = new ClientApi(ModelProvider.GeminiPro);

docs/rag-cn.md

@@ -0,0 +1,74 @@
+# RAG 功能配置说明
+
+> [!WARNING]
+> 该功能目前在预览阶段，可能会有较多的问题，请在仔细阅读本文档后再使用。
+
+## 效果图
+
+![example](./images/rag-example.jpg)
+
+## 原理
+
+![example](./images/rag.png)
+
+## 已知问题
+
+- 由于接口中使用 nodejs 运行时，在 vercel 环境下接口可能会超时，建议使用 docker 部署
+- 已开启的插件可能会影响到数据检索，可以关闭部分插件后再使用
+- 已创建的向量数据不会删除
+- 同一聊天窗口内即使“清除聊天”也可以访问已经上传的文件内容
+- RAG 插件需要一定的话术来让模型触发查询
+- 上传文件部分的 UI 交互可能会变更
+- 暂不支持文档总结
+
+## 支持的文件类型
+
+- txt
+- md
+- pdf
+- docx
+- csv
+- json
+- srt
+- mp3 (基于OpenAIWhisper)
+
+## 配置
+
+1. 登录 https://cloud.qdrant.io 并创建一个账户
+2. 在控制面板中创建一个 Cluster
+3. 获取 Cluster 的 Cluster URL 和 API Key
+4. 完善下面的环境变量配置后即可使用
+
+## 环境变量
+
+### `NEXT_PUBLIC_ENABLE_RAG`
+
+如果你想启用 RAG 功能，将此环境变量设置为 1 即可。
+
+### `QDRANT_URL`
+
+qdrant 服务的 Cluster URL。
+
+### `QDRANT_API_KEY`
+
+qdrant 服务的 ApiKey。
+
+### `RAG_CHUNK_SIZE` （可选）
+
+分割后文档的最大大小（按字符数计算），默认：2000。
+
+### `RAG_CHUNK_OVERLAP` （可选）
+
+分割文档时块重叠数量，默认：200。
+
+### `RAG_RETURN_COUNT` （可选）
+
+检索时返回的文档数量，默认：4。
+
+### `RAG_EMBEDDING_MODEL` （可选）
+
+向量化时使用的向量模型，默认：text-embedding-3-large。
+可选项：
+- text-embedding-3-small
+- text-embedding-3-large
+- text-embedding-ada-002

package.json

@@ -27,6 +27,7 @@
     "@langchain/pinecone": "^0.0.4",
     "@next/third-parties": "^14.1.0",
     "@pinecone-database/pinecone": "^2.2.0",
+    "@qdrant/js-client-rest": "^1.8.2",
     "@svgr/webpack": "^6.5.1",
     "@vercel/analytics": "^0.1.11",
     "@vercel/speed-insights": "^1.0.2",

yarn.lock

@@ -1717,6 +1717,11 @@
   resolved "https://registry.yarnpkg.com/@eslint/js/-/js-8.57.0.tgz#a5417ae8427873f1dd08b70b3574b453e67b5f7f"
   integrity sha512-Ys+3g2TaW7gADOJzPt83SJtCDhMjndcDMFVQ/Tj9iA1BfJzFKD9mAUXT3OenpuPHbI6P/myECxRJrofUsDx/5g==
 
+"@fastify/busboy@^2.0.0":
+  version "2.1.1"
+  resolved "https://registry.yarnpkg.com/@fastify/busboy/-/busboy-2.1.1.tgz#b9da6a878a371829a0502c9b6c1c143ef6663f4d"
+  integrity sha512-vBZP4NlzfOlerQTnba4aqZoMhE/a9HY7HRqoOPaETQcSQuWEIyZMHGfVu6w9wGtGK5fED5qRs2DteVCjOH60sA==
+
 "@fortaine/fetch-event-source@^3.0.6":
   version "3.0.6"
   resolved "https://registry.yarnpkg.com/@fortaine/fetch-event-source/-/fetch-event-source-3.0.6.tgz#b8552a2ca2c5202f5699b93a92be0188d422b06e"
@@ -1977,6 +1982,20 @@
   resolved "https://registry.yarnpkg.com/@pkgr/core/-/core-0.1.1.tgz#1ec17e2edbec25c8306d424ecfbf13c7de1aaa31"
   integrity sha512-cq8o4cWH0ibXh9VGi5P20Tu9XF/0fFXl9EUinr9QfTM7a7p0oTA4iJRCQWppXR1Pg8dSM0UCItCkPwsk9qWWYA==
 
+"@qdrant/js-client-rest@^1.8.2":
+  version "1.8.2"
+  resolved "https://registry.yarnpkg.com/@qdrant/js-client-rest/-/js-client-rest-1.8.2.tgz#7ea149c791e6c89da931c5a8fd043f61a97aca56"
+  integrity sha512-BCGC4YRcqjRxXVo500CxjhluPpGO0XpOwojauT8675Duv24YTlkhvDRmc1c9k/df2+yH/typtkecK3VOi3CD7A==
+  dependencies:
+    "@qdrant/openapi-typescript-fetch" "1.2.6"
+    "@sevinf/maybe" "0.5.0"
+    undici "~5.28.4"
+
+"@qdrant/openapi-typescript-fetch@1.2.6":
+  version "1.2.6"
+  resolved "https://registry.yarnpkg.com/@qdrant/openapi-typescript-fetch/-/openapi-typescript-fetch-1.2.6.tgz#c2682a9fa26ded86384f421c991f6c461785af7e"
+  integrity sha512-oQG/FejNpItrxRHoyctYvT3rwGZOnK4jr3JdppO/c78ktDvkWiPXPHNsrDf33K9sZdRb6PR7gi4noIapu5q4HA==
+
 "@remix-run/router@1.15.3":
   version "1.15.3"
   resolved "https://registry.yarnpkg.com/@remix-run/router/-/router-1.15.3.tgz#d2509048d69dbb72d5389a14945339f1430b2d3c"
@@ -1995,6 +2014,11 @@
     domhandler "^5.0.3"
     selderee "^0.11.0"
 
+"@sevinf/maybe@0.5.0":
+  version "0.5.0"
+  resolved "https://registry.yarnpkg.com/@sevinf/maybe/-/maybe-0.5.0.tgz#e59fcea028df615fe87d708bb30e1f338e46bb44"
+  integrity sha512-ARhyoYDnY1LES3vYI0fiG6e9esWfTNcXcO6+MPJJXcnyMV3bim4lnFt45VXouV7y82F4x3YH8nOQ6VztuvUiWg==
+
 "@sinclair/typebox@^0.29.0":
   version "0.29.6"
   resolved "https://registry.yarnpkg.com/@sinclair/typebox/-/typebox-0.29.6.tgz#4cd8372f9247372edd5fc5af44f67e2032c46e2f"
@@ -8263,6 +8287,13 @@ undici-types@~5.26.4:
   resolved "https://registry.yarnpkg.com/undici-types/-/undici-types-5.26.5.tgz#bcd539893d00b56e964fd2657a4866b221a65617"
   integrity sha512-JlCMO+ehdEIKqlFxk6IfVoAUVmgz7cU7zD/h9XZ0qzeosSHmUJVOzSQvvYSYWXkFXC+IfLKSIffhv0sVZup6pA==
 
+undici@~5.28.4:
+  version "5.28.4"
+  resolved "https://registry.yarnpkg.com/undici/-/undici-5.28.4.tgz#6b280408edb6a1a604a9b20340f45b422e373068"
+  integrity sha512-72RFADWFqKmUb2hmmvNODKL3p9hcB6Gt2DOQMis1SEBaV6a4MH8soBvzg+95CYhCKPFedut2JY9bMfrDl9D23g==
+  dependencies:
+    "@fastify/busboy" "^2.0.0"
+
 unicode-canonical-property-names-ecmascript@^2.0.0:
   version "2.0.0"
   resolved "https://registry.yarnpkg.com/unicode-canonical-property-names-ecmascript/-/unicode-canonical-property-names-ecmascript-2.0.0.tgz#301acdc525631670d39f6146e0e77ff6bbdebddc"