Build a Complete Chat Application

Step-by-step tutorial for building a production-ready AI chat application with streaming, conversation history, and multi-provider support

---

What You'll Build

A full-stack chat application featuring:

• 💬 Real-time streaming responses
• 📝 Conversation history with context awareness
• 🔄 Multi-provider failover (OpenAI → Anthropic → Google AI)
• 💰 Cost optimization with free tier prioritization
• 🎨 Modern UI with React/Next.js
• 🔐 Authentication with user sessions
• 💾 Persistent storage with PostgreSQL

Tech Stack:

• Next.js 14+ (App Router)
• TypeScript
• PostgreSQL
• Prisma ORM
• TailwindCSS
• NeuroLink

Time to Complete: 45-60 minutes

---

Prerequisites

• Node.js 18+
• PostgreSQL installed
• AI provider API keys (at least one):
  • OpenAI API key
  • Anthropic API key (optional)
  • Google AI Studio key (optional)

---

Step 1: Project Setup

Initialize Next.js Project

npx create-next-app@latest ai-chat-app
cd ai-chat-app

Options:

• TypeScript: Yes
• ESLint: Yes
• Tailwind CSS: Yes
• src/ directory: Yes
• App Router: Yes
• Import alias: No

Install Dependencies

npm install @raisahai/neurolink @prisma/client
npm install -D prisma

Environment Setup

Create .env.local:

# AI Provider Keys
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
GOOGLE_AI_KEY=...

# Database
DATABASE_URL="postgresql://user:password@localhost:5432/chatapp"

# Next Auth (for future authentication)
NEXTAUTH_SECRET="your-secret-key"
NEXTAUTH_URL="http://localhost:3000"

---

Step 2: Database Schema

Initialize Prisma

npx prisma init

Define Schema

Edit prisma/schema.prisma:

generator client {
  provider = "prisma-client-js"
}

datasource db {
  provider = "postgresql"
  url      = env("DATABASE_URL")
}

model User {
  id            String         @id @default(cuid())
  email         String         @unique
  name          String?
  createdAt     DateTime       @default(now())
  conversations Conversation[]
}

model Conversation {
  id        String    @id @default(cuid())
  userId    String
  user      User      @relation(fields: [userId], references: [id], onDelete: Cascade)
  title     String    @default("New Chat")
  createdAt DateTime  @default(now())
  updatedAt DateTime  @updatedAt
  messages  Message[]

  @@index([userId])
}

model Message {
  id             String       @id @default(cuid())
  conversationId String
  conversation   Conversation @relation(fields: [conversationId], references: [id], onDelete: Cascade)
  role           String
  content        String       @db.Text
  provider       String?
  model          String?
  tokens         Int?
  cost           Float?
  latency        Int?
  createdAt      DateTime     @default(now())

  @@index([conversationId])
}

Apply Schema

npx prisma migrate dev --name init
npx prisma generate

---

Step 3: NeuroLink Configuration

Create src/lib/ai.ts:

import { NeuroLink } from "@raisahai/neurolink";

export const ai = new NeuroLink({
  providers: [
    // (1)!
    {
      name: "google-ai-free",
      priority: 1, // (2)!
      config: {
        apiKey: process.env.GOOGLE_AI_KEY!,
        model: "gemini-2.0-flash",
      },
      quotas: {
        // (3)!
        daily: 1500,
        perMinute: 15,
      },
    },
    {
      name: "openai",
      priority: 2, // (4)!
      config: {
        apiKey: process.env.OPENAI_API_KEY!,
        model: "gpt-4o-mini",
      },
    },
    {
      name: "anthropic",
      priority: 3,
      config: {
        apiKey: process.env.ANTHROPIC_API_KEY!,
        model: "claude-3-5-haiku-20241022",
      },
    },
  ],

  loadBalancing: "priority", // (5)!

  failoverConfig: {
    // (6)!
    enabled: true,
    maxAttempts: 3,
    fallbackOnQuota: true,
    exponentialBackoff: true,
  },
});

1. Multi-provider setup: Configure multiple AI providers to enable automatic failover. The array is ordered by preference.
2. Priority 1 (highest): Google AI is tried first because it has a generous free tier (1,500 requests/day).
3. Quota tracking: NeuroLink automatically tracks daily and per-minute quotas to prevent hitting rate limits.
4. Priority 2 (fallback): If Google AI fails or quota is exceeded, automatically fall back to OpenAI.
5. Load balancing strategy: Use 'priority' to always prefer higher-priority providers. Other options: 'round-robin', 'latency-based'.
6. Failover configuration: Enable automatic retries with exponential backoff, and fall back to next provider when quota is exceeded.

---

Step 4: Database Client

Create src/lib/db.ts:

import { PrismaClient } from "@prisma/client";

const globalForPrisma = globalThis as unknown as {
  prisma: PrismaClient | undefined;
};

export const prisma = globalForPrisma.prisma ?? new PrismaClient();

if (process.env.NODE_ENV !== "production") {
  globalForPrisma.prisma = prisma;
}

---

Step 5: API Routes

Chat API with Streaming

Create src/app/api/chat/route.ts:

import { NextRequest, NextResponse } from "next/server";
import { ai } from "@/lib/ai";
import { prisma } from "@/lib/db";

export const runtime = "nodejs"; // (1)!

export async function POST(request: NextRequest) {
  try {
    const { message, conversationId, userId } = await request.json();

    if (!message || !userId) {
      return NextResponse.json(
        { error: "Message and userId are required" },
        { status: 400 },
      );
    }

    let conversation;

    if (conversationId) {
      // (2)!
      conversation = await prisma.conversation.findUnique({
        where: { id: conversationId },
        include: { messages: { orderBy: { createdAt: "asc" }, take: 20 } },
      });
    } else {
      conversation = await prisma.conversation.create({
        data: {
          userId,
          title: message.substring(0, 50) + "...",
        },
        include: { messages: true },
      });
    }

    await prisma.message.create({
      // (3)!
      data: {
        conversationId: conversation.id,
        role: "user",
        content: message,
      },
    });

    const conversationHistory = conversation.messages // (4)!
      .map((m) => `${m.role}: ${m.content}`)
      .join("\n");

    const encoder = new TextEncoder();
    const stream = new ReadableStream({
      // (5)!
      async start(controller) {
        try {
          let fullResponse = "";
          const startTime = Date.now();

          for await (const chunk of ai.stream({
            // (6)!
            input: {
              text: `${conversationHistory}\nuser: ${message}\n\nRespond as the assistant, continuing this conversation naturally.`,
            },
            provider: "google-ai-free",
          })) {
            fullResponse += chunk.content;

            controller.enqueue(
              // (7)!
              encoder.encode(
                `data: ${JSON.stringify({
                  content: chunk.content,
                  done: false,
                })}\n\n`,
              ),
            );
          }

          const latency = Date.now() - startTime;

          await prisma.message.create({
            // (8)!
            data: {
              conversationId: conversation.id,
              role: "assistant",
              content: fullResponse,
              provider: "google-ai-free",
              model: "gemini-2.0-flash",
              latency,
            },
          });

          controller.enqueue(
            // (9)!
            encoder.encode(
              `data: ${JSON.stringify({
                content: "",
                done: true,
                conversationId: conversation.id,
              })}\n\n`,
            ),
          );

          controller.close();
        } catch (error) {
          console.error("Streaming error:", error);

          controller.enqueue(
            encoder.encode(
              `data: ${JSON.stringify({
                error: error.message,
                done: true,
              })}\n\n`,
            ),
          );

          controller.close();
        }
      },
    });

    return new Response(stream, {
      // (10)!
      headers: {
        "Content-Type": "text/event-stream",
        "Cache-Control": "no-cache",
        Connection: "keep-alive",
      },
    });
  } catch (error) {
    console.error("Chat API error:", error);
    return NextResponse.json(
      { error: "Internal server error" },
      { status: 500 },
    );
  }
}

1. Node.js runtime required: Streaming requires the Node.js runtime in Next.js, not Edge runtime.
2. Load or create conversation: If conversationId exists, load the conversation with last 20 messages for context. Otherwise, create new conversation.
3. Save user message: Store the user's message in the database before generating response.
4. Build conversation history: Format all previous messages as context for the AI to maintain conversation continuity.
5. Create streaming response: Use ReadableStream to stream chunks as they arrive from the AI provider.
6. Stream from NeuroLink: Call ai.stream() which returns an async iterator of content chunks. Automatically falls back to other providers on failure.
7. Send chunk to client: Encode each chunk as Server-Sent Events (SSE) format and send immediately for real-time display.
8. Save complete response: After streaming completes, save the full response to database with metadata (provider, model, latency).
9. Send completion signal: Send final event with done: true to notify client that streaming is complete.
10. SSE headers: Set headers for Server-Sent Events to enable streaming to the browser.

Conversations API

Create src/app/api/conversations/route.ts:

import { NextRequest, NextResponse } from "next/server";
import { prisma } from "@/lib/db";

export async function GET(request: NextRequest) {
  try {
    const userId = request.nextUrl.searchParams.get("userId");

    if (!userId) {
      return NextResponse.json(
        { error: "userId is required" },
        { status: 400 },
      );
    }

    const conversations = await prisma.conversation.findMany({
      where: { userId },
      include: {
        messages: {
          orderBy: { createdAt: "desc" },
          take: 1,
        },
      },
      orderBy: { updatedAt: "desc" },
    });

    return NextResponse.json({ conversations });
  } catch (error) {
    console.error("Conversations API error:", error);
    return NextResponse.json(
      { error: "Internal server error" },
      { status: 500 },
    );
  }
}

export async function DELETE(request: NextRequest) {
  try {
    const { conversationId } = await request.json();

    if (!conversationId) {
      return NextResponse.json(
        { error: "conversationId is required" },
        { status: 400 },
      );
    }

    await prisma.conversation.delete({
      where: { id: conversationId },
    });

    return NextResponse.json({ success: true });
  } catch (error) {
    console.error("Delete conversation error:", error);
    return NextResponse.json(
      { error: "Internal server error" },
      { status: 500 },
    );
  }
}

Get Conversation Messages

Create src/app/api/conversations/[id]/messages/route.ts:

import { NextRequest, NextResponse } from "next/server";
import { prisma } from "@/lib/db";

export async function GET(
  request: NextRequest,
  { params }: { params: { id: string } },
) {
  try {
    const messages = await prisma.message.findMany({
      where: { conversationId: params.id },
      orderBy: { createdAt: "asc" },
    });

    return NextResponse.json({ messages });
  } catch (error) {
    console.error("Get messages error:", error);
    return NextResponse.json(
      { error: "Internal server error" },
      { status: 500 },
    );
  }
}

---

Step 6: React Components

Chat Interface

Create src/components/ChatInterface.tsx:

'use client';

import { useState, useRef, useEffect } from 'react';

type Message = {
  role: 'user' | 'assistant';
  content: string;
};

export default function ChatInterface({ userId }: { userId: string }) {
  const [messages, setMessages] = useState<Message[]>([]);
  const [input, setInput] = useState('');
  const [loading, setLoading] = useState(false);
  const [conversationId, setConversationId] = useState<string | null>(null);
  const messagesEndRef = useRef<HTMLDivElement>(null);

  const scrollToBottom = () => {
    messagesEndRef.current?.scrollIntoView({ behavior: 'smooth' });
  };

  useEffect(() => {
    scrollToBottom();
  }, [messages]);

  async function handleSubmit(e: React.FormEvent) {
    e.preventDefault();

    if (!input.trim() || loading) return;

    const userMessage = input.trim();
    setInput('');
    setLoading(true);

    setMessages(prev => [...prev, { role: 'user', content: userMessage }]);

    try {
      const response = await fetch('/api/chat', {
        method: 'POST',
        headers: { 'Content-Type': 'application/json' },
        body: JSON.stringify({
          message: userMessage,
          conversationId,
          userId
        })
      });

      if (!response.ok) {
        throw new Error('Failed to send message');
      }

      const reader = response.body?.getReader();
      const decoder = new TextDecoder();

      let assistantMessage = '';
      setMessages(prev => [...prev, { role: 'assistant', content: '' }]);

      while (true) {
        const { done, value } = await reader!.read();
        if (done) break;

        const text = decoder.decode(value);
        const lines = text.split('\n');

        for (const line of lines) {
          if (line.startsWith('data: ')) {
            const data = JSON.parse(line.slice(6));

            if (data.error) {
              console.error('Stream error:', data.error);
              break;
            }

            if (data.done) {
              if (data.conversationId) {
                setConversationId(data.conversationId);
              }
              break;
            }

            if (data.content) {
              assistantMessage += data.content;

              setMessages(prev => {
                const newMessages = [...prev];
                newMessages[newMessages.length - 1] = {
                  role: 'assistant',
                  content: assistantMessage
                };
                return newMessages;
              });
            }
          }
        }
      }

    } catch (error) {
      console.error('Chat error:', error);
      setMessages(prev => [
        ...prev,
        {
          role: 'assistant',
          content: 'Sorry, I encountered an error. Please try again.'
        }
      ]);
    } finally {
      setLoading(false);
    }
  }

  return (
    <div className="flex flex-col h-screen max-w-4xl mx-auto p-4">
      <div className="flex-1 overflow-y-auto mb-4 space-y-4">
        {messages.map((message, index) => (
          <div
            key={index}
            className={`flex ${
              message.role === 'user' ? 'justify-end' : 'justify-start'
            }`}
          >
            <div
              className={`max-w-[70%] rounded-lg px-4 py-2 ${
                message.role === 'user'
                  ? 'bg-blue-500 text-white'
                  : 'bg-gray-200 text-gray-900'
              }`}
            >
              <p className="whitespace-pre-wrap">{message.content}</p>
            </div>
          </div>
        ))}
        <div ref={messagesEndRef} />
      </div>

      <form onSubmit={handleSubmit} className="flex gap-2">
        <input
          type="text"
          value={input}
          onChange={(e) => setInput(e.target.value)}
          placeholder="Type your message..."
          className="flex-1 px-4 py-2 border border-gray-300 rounded-lg focus:outline-none focus:ring-2 focus:ring-blue-500"
          disabled={loading}
        />
        <button
          type="submit"
          disabled={loading || !input.trim()}
          className="px-6 py-2 bg-blue-500 text-white rounded-lg hover:bg-blue-600 disabled:bg-gray-300 disabled:cursor-not-allowed"
        >
          {loading ? 'Sending...' : 'Send'}
        </button>
      </form>
    </div>
  );
}

Sidebar with Conversations

Create src/components/Sidebar.tsx:

'use client';

import { useState, useEffect } from 'react';

type Conversation = {
  id: string;
  title: string;
  updatedAt: string;
};

export default function Sidebar({
  userId,
  currentConversationId,
  onSelectConversation
}: {
  userId: string;
  currentConversationId: string | null;
  onSelectConversation: (id: string | null) => void;
}) {
  const [conversations, setConversations] = useState<Conversation[]>([]);

  useEffect(() => {
    loadConversations();
  }, [userId]);

  async function loadConversations() {
    try {
      const response = await fetch(`/api/conversations?userId=${userId}`);
      const data = await response.json();
      setConversations(data.conversations);
    } catch (error) {
      console.error('Failed to load conversations:', error);
    }
  }

  async function deleteConversation(id: string) {
    try {
      await fetch('/api/conversations', {
        method: 'DELETE',
        headers: { 'Content-Type': 'application/json' },
        body: JSON.stringify({ conversationId: id })
      });

      setConversations(prev => prev.filter(c => c.id !== id));

      if (currentConversationId === id) {
        onSelectConversation(null);
      }
    } catch (error) {
      console.error('Failed to delete conversation:', error);
    }
  }

  return (
    <div className="w-64 bg-gray-100 h-screen p-4 overflow-y-auto">
      <button
        onClick={() => onSelectConversation(null)}
        className="w-full mb-4 px-4 py-2 bg-blue-500 text-white rounded-lg hover:bg-blue-600"
      >
        + New Chat
      </button>

      <div className="space-y-2">
        {conversations.map(conv => (
          <div
            key={conv.id}
            className={`p-3 rounded-lg cursor-pointer flex justify-between items-center ${
              currentConversationId === conv.id
                ? 'bg-blue-100'
                : 'bg-white hover:bg-gray-50'
            }`}
            onClick={() => onSelectConversation(conv.id)}
          >
            <span className="truncate flex-1">{conv.title}</span>
            <button
              onClick={(e) => {
                e.stopPropagation();
                deleteConversation(conv.id);
              }}
              className="ml-2 text-red-500 hover:text-red-700"
            >
              ×
            </button>
          </div>
        ))}
      </div>
    </div>
  );
}

---

Step 7: Main Page

Create src/app/page.tsx:

'use client';

import { useState } from 'react';
import ChatInterface from '@/components/ChatInterface';
import Sidebar from '@/components/Sidebar';

export default function Home() {
  const [conversationId, setConversationId] = useState<string | null>(null);

  const userId = 'demo-user';

  return (
    <div className="flex h-screen">
      <Sidebar
        userId={userId}
        currentConversationId={conversationId}
        onSelectConversation={setConversationId}
      />
      <div className="flex-1">
        <ChatInterface userId={userId} />
      </div>
    </div>
  );
}

---

Step 8: Run the Application

Start Development Server

npm run dev

Visit http://localhost:3000

---

Step 9: Testing

Test Basic Chat

1. Type a message: "Hello, can you help me?"
2. Verify streaming response appears
3. Send follow-up: "What can you do?"
4. Verify conversation context maintained

Test Multi-Provider Failover

Temporarily invalidate Google AI key to test failover:

// In src/lib/ai.ts
{
  name: 'google-ai-free',
  config: {
    apiKey: 'invalid-key-to-test-failover'
  }
}

Verify fallback to OpenAI works automatically.

Test Conversation History

1. Create new conversation
2. Send multiple messages
3. Refresh page
4. Verify conversations appear in sidebar
5. Click conversation to reload messages

---

Step 10: Production Enhancements

Add Loading States

{loading && (
  <div className="flex justify-start">
    <div className="bg-gray-200 rounded-lg px-4 py-2">
      <div className="flex space-x-2">
        <div className="w-2 h-2 bg-gray-500 rounded-full animate-bounce"></div>
        <div className="w-2 h-2 bg-gray-500 rounded-full animate-bounce delay-100"></div>
        <div className="w-2 h-2 bg-gray-500 rounded-full animate-bounce delay-200"></div>
      </div>
    </div>
  </div>
)}

Add Error Handling

const [error, setError] = useState<string | null>(null);

// In catch block
setError('Failed to send message. Please try again.');

// Display error
{error && (
  <div className="bg-red-100 border border-red-400 text-red-700 px-4 py-3 rounded">
    {error}
  </div>
)}

Add Message Timestamps

type Message = {
  role: 'user' | 'assistant';
  content: string;
  timestamp: Date;
};

// Display timestamp
<span className="text-xs opacity-75">
  {new Date(message.timestamp).toLocaleTimeString()}
</span>

---

Next Steps

1. Add Authentication

Use NextAuth.js for user authentication:

npm install next-auth @next-auth/prisma-adapter

2. Add User Preferences

Store user settings (model preference, temperature, etc.):

model UserSettings {
  userId          String  @id
  user            User    @relation(fields: [userId], references: [id])
  preferredModel  String  @default("gpt-4o-mini")
  temperature     Float   @default(0.7)
}

3. Add Analytics

Track usage, costs, and performance:

await prisma.analytics.create({
  data: {
    userId,
    provider: "openai",
    model: "gpt-4o-mini",
    tokens: result.usage.totalTokens,
    cost: result.cost,
    latency: latency,
  },
});

4. Deploy to Production

Deploy to Vercel:

vercel deploy

---

Troubleshooting

Database Connection Issues

# Verify PostgreSQL is running
psql -U postgres

# Check connection string
echo $DATABASE_URL

# Reset database
npx prisma migrate reset

API Key Errors

Verify environment variables are set:

# Check .env.local
cat .env.local

# Restart dev server
npm run dev

Streaming Not Working

Enable Node.js runtime in API route:

export const runtime = "nodejs";

---

Related Documentation

Feature Guides:

• Multimodal Chat - Add image support to your chat app
• Auto Evaluation - Quality scoring for chat responses
• Guardrails - Content filtering and safety checks
• Redis Conversation Export - Export chat history for analytics

Setup & Patterns:

• NeuroLink Provider Setup - Configure AI providers
• Streaming Guide - Advanced streaming patterns
• Production Best Practices - Production patterns

---

Summary

You've built a production-ready chat application with:

✅ Real-time streaming responses ✅ Persistent conversation history ✅ Multi-provider failover ✅ Cost optimization (free tier first) ✅ Modern React UI ✅ PostgreSQL storage ✅ Error handling

Next Tutorial: RAG Implementation - Build a knowledge base Q&A system