Add AI-powered chatbot for media outlets

Integrate an AI chatbot feature allowing users to interact with media outlets, fetch chat history, and generate AI responses using OpenAI.

Replit-Commit-Author: Agent
Replit-Commit-Session-Id: 9a264234-c5d7-4dcc-adf3-a954b149b30d
Replit-Commit-Checkpoint-Type: intermediate_checkpoint
Replit-Commit-Screenshot-Url: https://storage.googleapis.com/screenshot-production-us-central1/3df548ff-50ae-432f-9be4-25d34eccc983/9a264234-c5d7-4dcc-adf3-a954b149b30d/d35d7YU

server/chatbot.ts

@@ -0,0 +1,54 @@
+import OpenAI from "openai";
+
+// This is using Replit's AI Integrations service, which provides OpenAI-compatible API access without requiring your own OpenAI API key.
+// the newest OpenAI model is "gpt-5" which was released August 7, 2025. do not change this unless explicitly requested by the user
+const openai = new OpenAI({
+  baseURL: process.env.AI_INTEGRATIONS_OPENAI_BASE_URL,
+  apiKey: process.env.AI_INTEGRATIONS_OPENAI_API_KEY
+});
+
+interface ChatbotPersonality {
+  systemPrompt: string;
+  name: string;
+}
+
+export function getChatbotPersonality(outletName: string, outletCategory: string, outletDescription?: string): ChatbotPersonality {
+  const categoryContext = {
+    people: `You are an AI assistant representing ${outletName}. ${outletDescription ? outletDescription : 'You speak from their perspective, embodying their personality, values, and communication style.'} Engage with users as if you are ${outletName} themselves, sharing insights, opinions, and knowledge from their unique perspective.`,
+    topics: `You are an AI expert on ${outletName}. ${outletDescription ? outletDescription : 'You provide in-depth knowledge and analysis about this topic.'} Answer questions with expertise and provide thoughtful commentary from various angles on this subject.`,
+    companies: `You are an AI representative of ${outletName}. ${outletDescription ? outletDescription : 'You speak on behalf of the company, sharing information about its mission, products, and values.'} Engage professionally while representing the company's interests and perspective.`
+  };
+
+  const systemPrompt = categoryContext[outletCategory as keyof typeof categoryContext] || 
+    `You are an AI assistant for ${outletName}. Provide helpful and informative responses.`;
+
+  return {
+    systemPrompt: systemPrompt + '\n\nKeep responses conversational, engaging, and concise (2-4 paragraphs maximum). Use Korean language for communication.',
+    name: outletName
+  };
+}
+
+export async function generateChatbotResponse(
+  messages: Array<{ role: 'user' | 'assistant' | 'system'; content: string }>,
+  outletName: string,
+  outletCategory: string,
+  outletDescription?: string
+): Promise<string> {
+  const personality = getChatbotPersonality(outletName, outletCategory, outletDescription);
+  
+  const chatMessages: OpenAI.Chat.ChatCompletionMessageParam[] = [
+    { role: 'system', content: personality.systemPrompt },
+    ...messages.map(msg => ({
+      role: msg.role as 'user' | 'assistant',
+      content: msg.content
+    }))
+  ];
+
+  const completion = await openai.chat.completions.create({
+    model: 'gpt-5',
+    messages: chatMessages,
+    max_completion_tokens: 8192,
+  });
+
+  return completion.choices[0]?.message?.content || '죄송합니다. 응답을 생성할 수 없습니다.';
+}

server/routes.ts

@@ -2,7 +2,8 @@ import type { Express } from "express";
 import { createServer, type Server } from "http";
 import { storage } from "./storage";
 import { setupAuth, isAuthenticated } from "./simpleAuth";
-import { insertArticleSchema, insertMediaOutletRequestSchema, insertBidSchema, insertCommentSchema, insertPredictionBetSchema, insertCommunityPostSchema, insertCommunityReplySchema } from "@shared/schema";
+import { insertArticleSchema, insertMediaOutletRequestSchema, insertBidSchema, insertCommentSchema, insertPredictionBetSchema, insertCommunityPostSchema, insertCommunityReplySchema, insertChatMessageSchema } from "@shared/schema";
+import { generateChatbotResponse } from "./chatbot";
 
 export async function registerRoutes(app: Express): Promise<Server> {
   // Auth middleware
@@ -582,6 +583,77 @@ export async function registerRoutes(app: Express): Promise<Server> {
     }
   });
 
+  // Chatbot routes
+  app.get('/api/media-outlets/:slug/chat', isAuthenticated, async (req: any, res) => {
+    try {
+      const outlet = await storage.getMediaOutletBySlug(req.params.slug);
+      if (!outlet) {
+        return res.status(404).json({ message: "Media outlet not found" });
+      }
+
+      const userId = req.user.claims?.sub || req.user.id;
+      const messages = await storage.getChatMessages(outlet.id, userId);
+      res.json(messages);
+    } catch (error) {
+      console.error("Error fetching chat messages:", error);
+      res.status(500).json({ message: "Failed to fetch chat messages" });
+    }
+  });
+
+  app.post('/api/media-outlets/:slug/chat', isAuthenticated, async (req: any, res) => {
+    try {
+      const outlet = await storage.getMediaOutletBySlug(req.params.slug);
+      if (!outlet) {
+        return res.status(404).json({ message: "Media outlet not found" });
+      }
+
+      const userId = req.user.claims?.sub || req.user.id;
+      const { content } = req.body;
+
+      if (!content || typeof content !== 'string') {
+        return res.status(400).json({ message: "Invalid message content" });
+      }
+
+      // Save user message
+      const userMessage = insertChatMessageSchema.parse({
+        mediaOutletId: outlet.id,
+        userId,
+        role: 'user',
+        content
+      });
+      await storage.createChatMessage(userMessage);
+
+      // Get chat history
+      const chatHistory = await storage.getChatMessages(outlet.id, userId);
+      const messages = chatHistory.map(msg => ({
+        role: msg.role as 'user' | 'assistant',
+        content: msg.content
+      }));
+
+      // Generate AI response
+      const aiResponse = await generateChatbotResponse(
+        messages,
+        outlet.name,
+        outlet.category,
+        outlet.description || undefined
+      );
+
+      // Save assistant message
+      const assistantMessage = insertChatMessageSchema.parse({
+        mediaOutletId: outlet.id,
+        userId,
+        role: 'assistant',
+        content: aiResponse
+      });
+      const savedAssistantMessage = await storage.createChatMessage(assistantMessage);
+
+      res.status(201).json(savedAssistantMessage);
+    } catch (error) {
+      console.error("Error processing chat message:", error);
+      res.status(500).json({ message: "Failed to process chat message" });
+    }
+  });
+
   const httpServer = createServer(app);
   return httpServer;
 }

server/storage.ts

@@ -10,6 +10,7 @@ import {
   predictionBets,
   communityPosts,
   communityReplies,
+  chatMessages,
   type User,
   type UpsertUser,
   type MediaOutlet,
@@ -32,6 +33,8 @@ import {
   type InsertCommunityPost,
   type CommunityReply,
   type InsertCommunityReply,
+  type ChatMessage,
+  type InsertChatMessage,
 } from "@shared/schema";
 import { db } from "./db";
 import { eq, desc, asc, and, ilike, sql } from "drizzle-orm";
@@ -97,6 +100,10 @@ export interface IStorage {
   getRepliesByPost(postId: string): Promise<CommunityReply[]>;
   createCommunityReply(reply: InsertCommunityReply): Promise<CommunityReply>;
   
+  // Chatbot operations
+  getChatMessages(mediaOutletId: string, userId: string): Promise<ChatMessage[]>;
+  createChatMessage(message: InsertChatMessage): Promise<ChatMessage>;
+  
   // Analytics operations
   getAnalytics(): Promise<{
     totalArticles: number;
@@ -473,6 +480,20 @@ export class DatabaseStorage implements IStorage {
     return newReply;
   }
   
+  // Chatbot operations
+  async getChatMessages(mediaOutletId: string, userId: string): Promise<ChatMessage[]> {
+    return await db
+      .select()
+      .from(chatMessages)
+      .where(and(eq(chatMessages.mediaOutletId, mediaOutletId), eq(chatMessages.userId, userId)))
+      .orderBy(asc(chatMessages.createdAt));
+  }
+
+  async createChatMessage(message: InsertChatMessage): Promise<ChatMessage> {
+    const [newMessage] = await db.insert(chatMessages).values(message).returning();
+    return newMessage;
+  }
+  
   // Analytics operations
   async getAnalytics(): Promise<{
     totalArticles: number;