mirror of
https://github.com/ChatGPTNextWeb/ChatGPT-Next-Web.git
synced 2025-11-13 12:43:42 +08:00
Merge branch 'main' into main
This commit is contained in:
120
app/api/langchain/rag/search/route.ts
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120
app/api/langchain/rag/search/route.ts
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@@ -0,0 +1,120 @@
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import { NextRequest, NextResponse } from "next/server";
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import { auth } from "@/app/api/auth";
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import { ACCESS_CODE_PREFIX, ModelProvider } from "@/app/constant";
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import { OpenAIEmbeddings } from "@langchain/openai";
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import { Pinecone } from "@pinecone-database/pinecone";
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import { PineconeStore } from "@langchain/pinecone";
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import { QdrantVectorStore } from "@langchain/community/vectorstores/qdrant";
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import { getServerSideConfig } from "@/app/config/server";
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interface RequestBody {
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sessionId: string;
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query: string;
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baseUrl?: string;
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}
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async function handle(req: NextRequest) {
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if (req.method === "OPTIONS") {
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return NextResponse.json({ body: "OK" }, { status: 200 });
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}
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try {
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const authResult = auth(req, ModelProvider.GPT);
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if (authResult.error) {
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return NextResponse.json(authResult, {
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status: 401,
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});
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}
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const reqBody: RequestBody = await req.json();
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const authToken = req.headers.get("Authorization") ?? "";
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const token = authToken.trim().replaceAll("Bearer ", "").trim();
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const serverConfig = getServerSideConfig();
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// const pinecone = new Pinecone();
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// const pineconeIndex = pinecone.Index(serverConfig.pineconeIndex!);
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const apiKey = getOpenAIApiKey(token);
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const baseUrl = getOpenAIBaseUrl(reqBody.baseUrl);
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const embeddings = new OpenAIEmbeddings(
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{
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modelName: serverConfig.ragEmbeddingModel ?? "text-embedding-3-large",
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openAIApiKey: apiKey,
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},
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{ basePath: baseUrl },
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);
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// const vectorStore = await PineconeStore.fromExistingIndex(embeddings, {
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// pineconeIndex,
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// });
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// const results = await vectorStore.similaritySearch(reqBody.query, 4, {
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// sessionId: reqBody.sessionId,
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// });
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const vectorStore = await QdrantVectorStore.fromExistingCollection(
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embeddings,
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{
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url: process.env.QDRANT_URL,
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apiKey: process.env.QDRANT_API_KEY,
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collectionName: reqBody.sessionId,
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},
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);
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const returnCunt = serverConfig.ragReturnCount
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? parseInt(serverConfig.ragReturnCount, 10)
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: 4;
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const response = await vectorStore.similaritySearch(
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reqBody.query,
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returnCunt,
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);
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return NextResponse.json(response, {
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status: 200,
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});
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} catch (e) {
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console.error(e);
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return new Response(JSON.stringify({ error: (e as any).message }), {
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status: 500,
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headers: { "Content-Type": "application/json" },
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});
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}
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}
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function getOpenAIApiKey(token: string) {
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const serverConfig = getServerSideConfig();
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const isApiKey = !token.startsWith(ACCESS_CODE_PREFIX);
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let apiKey = serverConfig.apiKey;
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if (isApiKey && token) {
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apiKey = token;
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}
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return apiKey;
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}
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function getOpenAIBaseUrl(reqBaseUrl: string | undefined) {
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const serverConfig = getServerSideConfig();
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let baseUrl = "https://api.openai.com/v1";
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if (serverConfig.baseUrl) baseUrl = serverConfig.baseUrl;
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if (reqBaseUrl?.startsWith("http://") || reqBaseUrl?.startsWith("https://"))
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baseUrl = reqBaseUrl;
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if (!baseUrl.endsWith("/v1"))
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baseUrl = baseUrl.endsWith("/") ? `${baseUrl}v1` : `${baseUrl}/v1`;
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console.log("[baseUrl]", baseUrl);
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return baseUrl;
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}
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export const POST = handle;
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export const runtime = "nodejs";
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export const preferredRegion = [
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"arn1",
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"bom1",
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"cdg1",
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"cle1",
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"cpt1",
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"dub1",
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"fra1",
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"gru1",
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"hnd1",
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"iad1",
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"icn1",
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"kix1",
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"lhr1",
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"pdx1",
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"sfo1",
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"sin1",
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"syd1",
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];
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221
app/api/langchain/rag/store/route.ts
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221
app/api/langchain/rag/store/route.ts
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@@ -0,0 +1,221 @@
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import { NextRequest, NextResponse } from "next/server";
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import { auth } from "@/app/api/auth";
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import { ACCESS_CODE_PREFIX, ModelProvider } from "@/app/constant";
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import { OpenAI, OpenAIEmbeddings } from "@langchain/openai";
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import { PDFLoader } from "langchain/document_loaders/fs/pdf";
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import { TextLoader } from "langchain/document_loaders/fs/text";
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import { CSVLoader } from "langchain/document_loaders/fs/csv";
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import { DocxLoader } from "langchain/document_loaders/fs/docx";
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import { EPubLoader } from "langchain/document_loaders/fs/epub";
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import { JSONLoader } from "langchain/document_loaders/fs/json";
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import { JSONLinesLoader } from "langchain/document_loaders/fs/json";
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import { OpenAIWhisperAudio } from "langchain/document_loaders/fs/openai_whisper_audio";
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// import { PPTXLoader } from "langchain/document_loaders/fs/pptx";
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import { SRTLoader } from "langchain/document_loaders/fs/srt";
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import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
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import { Pinecone } from "@pinecone-database/pinecone";
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import { PineconeStore } from "@langchain/pinecone";
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import { getServerSideConfig } from "@/app/config/server";
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import { FileInfo } from "@/app/client/platforms/utils";
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import mime from "mime";
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import LocalFileStorage from "@/app/utils/local_file_storage";
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import S3FileStorage from "@/app/utils/s3_file_storage";
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import { QdrantVectorStore } from "@langchain/community/vectorstores/qdrant";
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interface RequestBody {
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sessionId: string;
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fileInfos: FileInfo[];
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baseUrl?: string;
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}
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function getLoader(
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fileName: string,
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fileBlob: Blob,
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openaiApiKey: string,
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openaiBaseUrl: string,
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) {
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const extension = fileName.split(".").pop();
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switch (extension) {
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case "txt":
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case "md":
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return new TextLoader(fileBlob);
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case "pdf":
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return new PDFLoader(fileBlob);
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case "docx":
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return new DocxLoader(fileBlob);
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case "csv":
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return new CSVLoader(fileBlob);
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case "json":
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return new JSONLoader(fileBlob);
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// case 'pptx':
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// return new PPTXLoader(fileBlob);
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case "srt":
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return new SRTLoader(fileBlob);
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case "mp3":
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return new OpenAIWhisperAudio(fileBlob, {
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clientOptions: {
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apiKey: openaiApiKey,
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baseURL: openaiBaseUrl,
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},
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});
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default:
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throw new Error(`Unsupported file type: ${extension}`);
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}
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}
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async function handle(req: NextRequest) {
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if (req.method === "OPTIONS") {
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return NextResponse.json({ body: "OK" }, { status: 200 });
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}
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try {
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const authResult = auth(req, ModelProvider.GPT);
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if (authResult.error) {
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return NextResponse.json(authResult, {
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status: 401,
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});
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}
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const reqBody: RequestBody = await req.json();
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const authToken = req.headers.get("Authorization") ?? "";
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const token = authToken.trim().replaceAll("Bearer ", "").trim();
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const apiKey = getOpenAIApiKey(token);
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const baseUrl = getOpenAIBaseUrl(reqBody.baseUrl);
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const serverConfig = getServerSideConfig();
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// const pinecone = new Pinecone();
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// const pineconeIndex = pinecone.Index(serverConfig.pineconeIndex!);
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const embeddings = new OpenAIEmbeddings(
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{
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modelName: serverConfig.ragEmbeddingModel,
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openAIApiKey: apiKey,
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},
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{ basePath: baseUrl },
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);
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// https://js.langchain.com/docs/integrations/vectorstores/pinecone
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// https://js.langchain.com/docs/integrations/vectorstores/qdrant
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// process files
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for (let i = 0; i < reqBody.fileInfos.length; i++) {
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const fileInfo = reqBody.fileInfos[i];
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const contentType = mime.getType(fileInfo.fileName);
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// get file buffer
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var fileBuffer: Buffer | undefined;
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if (serverConfig.isStoreFileToLocal) {
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fileBuffer = await LocalFileStorage.get(fileInfo.fileName);
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} else {
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var file = await S3FileStorage.get(fileInfo.fileName);
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var fileByteArray = await file?.transformToByteArray();
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if (fileByteArray) fileBuffer = Buffer.from(fileByteArray);
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}
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if (!fileBuffer || !contentType) {
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console.error(`get ${fileInfo.fileName} buffer fail`);
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continue;
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}
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// load file to docs
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const fileBlob = bufferToBlob(fileBuffer, contentType);
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const loader = getLoader(fileInfo.fileName, fileBlob, apiKey, baseUrl);
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const docs = await loader.load();
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// modify doc meta
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docs.forEach((doc) => {
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doc.metadata = {
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...doc.metadata,
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sessionId: reqBody.sessionId,
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sourceFileName: fileInfo.originalFilename,
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fileName: fileInfo.fileName,
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};
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});
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// split
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const chunkSize = serverConfig.ragChunkSize
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? parseInt(serverConfig.ragChunkSize, 10)
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: 2000;
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const chunkOverlap = serverConfig.ragChunkOverlap
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? parseInt(serverConfig.ragChunkOverlap, 10)
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: 200;
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const textSplitter = new RecursiveCharacterTextSplitter({
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chunkSize: chunkSize,
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chunkOverlap: chunkOverlap,
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});
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const splits = await textSplitter.splitDocuments(docs);
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const vectorStore = await QdrantVectorStore.fromDocuments(
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splits,
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embeddings,
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{
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url: process.env.QDRANT_URL,
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apiKey: process.env.QDRANT_API_KEY,
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collectionName: reqBody.sessionId,
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},
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);
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// await PineconeStore.fromDocuments(splits, embeddings, {
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// pineconeIndex,
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// maxConcurrency: 5,
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// });
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// const vectorStore = await PineconeStore.fromExistingIndex(embeddings, {
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// pineconeIndex,
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// });
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}
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return NextResponse.json(
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{
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sessionId: reqBody.sessionId,
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},
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{
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status: 200,
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},
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);
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} catch (e) {
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console.error(e);
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return new Response(JSON.stringify({ error: (e as any).message }), {
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status: 500,
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headers: { "Content-Type": "application/json" },
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});
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}
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}
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function bufferToBlob(buffer: Buffer, mimeType?: string): Blob {
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const arrayBuffer: ArrayBuffer = buffer.buffer.slice(
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buffer.byteOffset,
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buffer.byteOffset + buffer.byteLength,
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);
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return new Blob([arrayBuffer], { type: mimeType || "" });
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}
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function getOpenAIApiKey(token: string) {
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const serverConfig = getServerSideConfig();
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const isApiKey = !token.startsWith(ACCESS_CODE_PREFIX);
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let apiKey = serverConfig.apiKey;
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if (isApiKey && token) {
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apiKey = token;
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}
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return apiKey;
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}
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function getOpenAIBaseUrl(reqBaseUrl: string | undefined) {
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const serverConfig = getServerSideConfig();
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let baseUrl = "https://api.openai.com/v1";
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if (serverConfig.baseUrl) baseUrl = serverConfig.baseUrl;
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if (reqBaseUrl?.startsWith("http://") || reqBaseUrl?.startsWith("https://"))
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baseUrl = reqBaseUrl;
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if (!baseUrl.endsWith("/v1"))
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baseUrl = baseUrl.endsWith("/") ? `${baseUrl}v1` : `${baseUrl}/v1`;
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console.log("[baseUrl]", baseUrl);
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return baseUrl;
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}
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export const POST = handle;
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export const runtime = "nodejs";
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export const preferredRegion = [
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"arn1",
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"bom1",
|
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"cdg1",
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"cle1",
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"cpt1",
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"dub1",
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"fra1",
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"gru1",
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"hnd1",
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"iad1",
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"icn1",
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"kix1",
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"lhr1",
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"pdx1",
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"sfo1",
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"sin1",
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"syd1",
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];
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@@ -44,6 +44,7 @@ export interface RequestMessage {
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}
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export interface RequestBody {
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chatSessionId: string;
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messages: RequestMessage[];
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isAzure: boolean;
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azureApiVersion?: string;
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@@ -44,6 +44,13 @@ async function handle(req: NextRequest) {
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},
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{ basePath: baseUrl },
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);
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const ragEmbeddings = new OpenAIEmbeddings(
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{
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modelName: process.env.RAG_EMBEDDING_MODEL ?? "text-embedding-3-large",
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openAIApiKey: apiKey,
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},
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{ basePath: baseUrl },
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);
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|
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var dalleCallback = async (data: string) => {
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var response = new ResponseBody();
|
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@@ -62,6 +69,8 @@ async function handle(req: NextRequest) {
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||||
baseUrl,
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model,
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embeddings,
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||||
reqBody.chatSessionId,
|
||||
ragEmbeddings,
|
||||
dalleCallback,
|
||||
);
|
||||
var nodejsTools = await nodejsTool.getCustomTools();
|
||||
|
||||
Reference in New Issue
Block a user