feat(app): remove backend

src/app/api/chat/route.ts

@@ -0,0 +1,360 @@
+import prompts from '@/lib/prompts';
+import MetaSearchAgent from '@/lib/search/metaSearchAgent';
+import crypto from 'crypto';
+import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
+import { EventEmitter } from 'stream';
+import {
+  chatModelProviders,
+  embeddingModelProviders,
+  getAvailableChatModelProviders,
+  getAvailableEmbeddingModelProviders,
+} from '@/lib/providers';
+import db from '@/lib/db';
+import { chats, messages as messagesSchema } from '@/lib/db/schema';
+import { and, eq, gt } from 'drizzle-orm';
+import { getFileDetails } from '@/lib/utils/files';
+import { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import { ChatOpenAI } from '@langchain/openai';
+import {
+  getCustomOpenaiApiKey,
+  getCustomOpenaiApiUrl,
+  getCustomOpenaiModelName,
+} from '@/lib/config';
+
+export const runtime = 'nodejs';
+export const dynamic = 'force-dynamic';
+
+const searchHandlers: Record<string, MetaSearchAgent> = {
+  webSearch: new MetaSearchAgent({
+    activeEngines: [],
+    queryGeneratorPrompt: prompts.webSearchRetrieverPrompt,
+    responsePrompt: prompts.webSearchResponsePrompt,
+    rerank: true,
+    rerankThreshold: 0.3,
+    searchWeb: true,
+    summarizer: true,
+  }),
+  academicSearch: new MetaSearchAgent({
+    activeEngines: ['arxiv', 'google scholar', 'pubmed'],
+    queryGeneratorPrompt: prompts.academicSearchRetrieverPrompt,
+    responsePrompt: prompts.academicSearchResponsePrompt,
+    rerank: true,
+    rerankThreshold: 0,
+    searchWeb: true,
+    summarizer: false,
+  }),
+  writingAssistant: new MetaSearchAgent({
+    activeEngines: [],
+    queryGeneratorPrompt: '',
+    responsePrompt: prompts.writingAssistantPrompt,
+    rerank: true,
+    rerankThreshold: 0,
+    searchWeb: false,
+    summarizer: false,
+  }),
+  wolframAlphaSearch: new MetaSearchAgent({
+    activeEngines: ['wolframalpha'],
+    queryGeneratorPrompt: prompts.wolframAlphaSearchRetrieverPrompt,
+    responsePrompt: prompts.wolframAlphaSearchResponsePrompt,
+    rerank: false,
+    rerankThreshold: 0,
+    searchWeb: true,
+    summarizer: false,
+  }),
+  youtubeSearch: new MetaSearchAgent({
+    activeEngines: ['youtube'],
+    queryGeneratorPrompt: prompts.youtubeSearchRetrieverPrompt,
+    responsePrompt: prompts.youtubeSearchResponsePrompt,
+    rerank: true,
+    rerankThreshold: 0.3,
+    searchWeb: true,
+    summarizer: false,
+  }),
+  redditSearch: new MetaSearchAgent({
+    activeEngines: ['reddit'],
+    queryGeneratorPrompt: prompts.redditSearchRetrieverPrompt,
+    responsePrompt: prompts.redditSearchResponsePrompt,
+    rerank: true,
+    rerankThreshold: 0.3,
+    searchWeb: true,
+    summarizer: false,
+  }),
+};
+
+type Message = {
+  messageId: string;
+  chatId: string;
+  content: string;
+};
+
+type ChatModel = {
+  provider: string;
+  name: string;
+};
+
+type EmbeddingModel = {
+  provider: string;
+  name: string;
+};
+
+type Body = {
+  message: Message;
+  optimizationMode: 'speed' | 'balanced' | 'quality';
+  focusMode: string;
+  history: Array<[string, string]>;
+  files: Array<string>;
+  chatModel: ChatModel;
+  embeddingModel: EmbeddingModel;
+};
+
+const handleEmitterEvents = async (
+  stream: EventEmitter,
+  writer: WritableStreamDefaultWriter,
+  encoder: TextEncoder,
+  aiMessageId: string,
+  chatId: string,
+) => {
+  let recievedMessage = '';
+  let sources: any[] = [];
+
+  stream.on('data', (data) => {
+    const parsedData = JSON.parse(data);
+    if (parsedData.type === 'response') {
+      writer.write(
+        encoder.encode(
+          JSON.stringify({
+            type: 'message',
+            data: parsedData.data,
+            messageId: aiMessageId,
+          }) + '\n',
+        ),
+      );
+
+      recievedMessage += parsedData.data;
+    } else if (parsedData.type === 'sources') {
+      writer.write(
+        encoder.encode(
+          JSON.stringify({
+            type: 'sources',
+            data: parsedData.data,
+            messageId: aiMessageId,
+          }) + '\n',
+        ),
+      );
+
+      sources = parsedData.data;
+    }
+  });
+  stream.on('end', () => {
+    writer.write(
+      encoder.encode(
+        JSON.stringify({
+          type: 'messageEnd',
+          messageId: aiMessageId,
+        }) + '\n',
+      ),
+    );
+    writer.close();
+
+    db.insert(messagesSchema)
+      .values({
+        content: recievedMessage,
+        chatId: chatId,
+        messageId: aiMessageId,
+        role: 'assistant',
+        metadata: JSON.stringify({
+          createdAt: new Date(),
+          ...(sources && sources.length > 0 && { sources }),
+        }),
+      })
+      .execute();
+  });
+  stream.on('error', (data) => {
+    const parsedData = JSON.parse(data);
+    writer.write(
+      encoder.encode(
+        JSON.stringify({
+          type: 'error',
+          data: parsedData.data,
+        }),
+      ),
+    );
+    writer.close();
+  });
+};
+
+const handleHistorySave = async (
+  message: Message,
+  humanMessageId: string,
+  focusMode: string,
+  files: string[],
+) => {
+  const chat = await db.query.chats.findFirst({
+    where: eq(chats.id, message.chatId),
+  });
+
+  if (!chat) {
+    await db
+      .insert(chats)
+      .values({
+        id: message.chatId,
+        title: message.content,
+        createdAt: new Date().toString(),
+        focusMode: focusMode,
+        files: files.map(getFileDetails),
+      })
+      .execute();
+  }
+
+  const messageExists = await db.query.messages.findFirst({
+    where: eq(messagesSchema.messageId, humanMessageId),
+  });
+
+  if (!messageExists) {
+    await db
+      .insert(messagesSchema)
+      .values({
+        content: message.content,
+        chatId: message.chatId,
+        messageId: humanMessageId,
+        role: 'user',
+        metadata: JSON.stringify({
+          createdAt: new Date(),
+        }),
+      })
+      .execute();
+  } else {
+    await db
+      .delete(messagesSchema)
+      .where(
+        and(
+          gt(messagesSchema.id, messageExists.id),
+          eq(messagesSchema.chatId, message.chatId),
+        ),
+      )
+      .execute();
+  }
+};
+
+export const POST = async (req: Request) => {
+  try {
+    const body = (await req.json()) as Body;
+    const { message } = body;
+
+    if (message.content === '') {
+      return Response.json(
+        {
+          message: 'Please provide a message to process',
+        },
+        { status: 400 },
+      );
+    }
+
+    const [chatModelProviders, embeddingModelProviders] = await Promise.all([
+      getAvailableChatModelProviders(),
+      getAvailableEmbeddingModelProviders(),
+    ]);
+
+    const chatModelProvider =
+      chatModelProviders[
+        body.chatModel?.provider || Object.keys(chatModelProviders)[0]
+      ];
+    const chatModel =
+      chatModelProvider[
+        body.chatModel?.name || Object.keys(chatModelProvider)[0]
+      ];
+
+    const embeddingProvider =
+      embeddingModelProviders[
+        body.embeddingModel?.provider || Object.keys(embeddingModelProviders)[0]
+      ];
+    const embeddingModel =
+      embeddingProvider[
+        body.embeddingModel?.name || Object.keys(embeddingProvider)[0]
+      ];
+
+    let llm: BaseChatModel | undefined;
+    let embedding = embeddingModel.model;
+
+    if (body.chatModel?.provider === 'custom_openai') {
+      llm = new ChatOpenAI({
+        openAIApiKey: getCustomOpenaiApiKey(),
+        modelName: getCustomOpenaiModelName(),
+        temperature: 0.7,
+        configuration: {
+          baseURL: getCustomOpenaiApiUrl(),
+        },
+      }) as unknown as BaseChatModel;
+    } else if (chatModelProvider && chatModel) {
+      llm = chatModel.model;
+    }
+
+    if (!llm) {
+      return Response.json({ error: 'Invalid chat model' }, { status: 400 });
+    }
+
+    if (!embedding) {
+      return Response.json(
+        { error: 'Invalid embedding model' },
+        { status: 400 },
+      );
+    }
+
+    const humanMessageId =
+      message.messageId ?? crypto.randomBytes(7).toString('hex');
+    const aiMessageId = crypto.randomBytes(7).toString('hex');
+
+    const history: BaseMessage[] = body.history.map((msg) => {
+      if (msg[0] === 'human') {
+        return new HumanMessage({
+          content: msg[1],
+        });
+      } else {
+        return new AIMessage({
+          content: msg[1],
+        });
+      }
+    });
+
+    const handler = searchHandlers[body.focusMode];
+
+    if (!handler) {
+      return Response.json(
+        {
+          message: 'Invalid focus mode',
+        },
+        { status: 400 },
+      );
+    }
+
+    const stream = await handler.searchAndAnswer(
+      message.content,
+      history,
+      llm,
+      embedding,
+      body.optimizationMode,
+      body.files,
+    );
+
+    const responseStream = new TransformStream();
+    const writer = responseStream.writable.getWriter();
+    const encoder = new TextEncoder();
+
+    handleEmitterEvents(stream, writer, encoder, aiMessageId, message.chatId);
+    handleHistorySave(message, humanMessageId, body.focusMode, body.files);
+
+    return new Response(responseStream.readable, {
+      headers: {
+        'Content-Type': 'text/event-stream',
+        Connection: 'keep-alive',
+        'Cache-Control': 'no-cache, no-transform',
+      },
+    });
+  } catch (err) {
+    console.error('An error ocurred while processing chat request:', err);
+    return Response.json(
+      { message: 'An error ocurred while processing chat request' },
+      { status: 500 },
+    );
+  }
+};