Advanced AI Chat

Enterprise-grade AI assistant with multiple models

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Features

• Multiple AI Models: Switch between OpenAI, Claude, Groq, and others
• Generative UI: Dynamic components and interactive app suggestions
• Image Analysis: Process and analyze images with vision models
• Web Search: Real-time internet access via Serper API
• Web Scraping: Extract content from URLs using Jina AI
• Document Generation: Create and edit documents in a Canvas interface
• Multi-language Support: Communicate in various languages

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Pre-requisites

To build your advanced AI assistant, you’ll need to have several services set up. If you haven’t done so yet, please start with these:

To use all available models, you’ll need to set up additional providers:

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Setting up Serper API

1. Visit serper.dev
2. Create an account and get your API key
3. Add to your environment variables:

SERPER_API_KEY=your_api_key_here

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Database Setup

The chat feature uses tables that should already exist if you followed the Quick Setup guide and ran the Supabase migrations.

The required tables are:

• chats: Stores chat sessions and metadata
• messages: Manages message history and content
• images: Handles image uploads and analysis results
• chat_documents: Stores generated documents and versions

You can find the relevant SQL code + the necessary functions and RLS policies under supabase/migrations/20240000000003_chat.sql

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App Structure

The chat application is organized in a modular structure:

1. API Routes (app/api/(apps)/(chat)/*):

   • chat/route.ts: Main chat endpoint with multi-model support
   • document/route.ts: Document generation and versioning
   • images/upload/route.ts: Image upload and analysis
   • history/route.ts: Chat history management

2. Chat App (app/(apps)/chat/*):

   • page.tsx: Main chat interface with model selection
   • info.tsx: App information and features display
   • prompt.ts: System prompts for different modes
   • tools.ts: Tool definitions (document, web browsing, app suggestions)
   • toolConfig.ts: Feature flags and configurations

3. Components (components/chat/*):

   • widgets/: Generative UI components (app cards)

4. AI Configuration (lib/ai/*):

   • ai-utils.ts: Model provider integration
   • chat.ts: Message handling utilities
   • models.ts: Available models configuration

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Model Configuration

You can customize available models in lib/ai/models.ts:

export const AI_MODEL_DISPLAY = {
  "gpt-4o-mini": {
    name: "GPT-4o mini",
    logo: "/providers/openai.webp",
    vision: true,
  },
  "claude-3-5-sonnet-latest": {
    name: "Claude 3.5 Sonnet",
    logo: "/providers/anthropic.jpeg",
    vision: true,
  },
  // Add or remove models as needed
};

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Vercel AI SDK Integration

The chat application uses the Vercel AI SDK for seamless AI model integration and streaming responses. This provides:

• Unified interface for multiple AI providers
• Real-time streaming responses
• Built-in TypeScript support
• Efficient message handling

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Credit System

The chat application includes a flexible credit system to manage access to premium features:

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Features & Limitations

• Free Features:

  • Access to basic models (gpt-4o-mini, claude-3-5-haiku, llama-3.1-70b)
  • Standard chat functionality
  • Image analysis with free models

• Premium Features (require credits):

  • Advanced AI models
  • Web browsing capabilities
  • Additional features as configured

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Implementation

The credit system is implemented through two main components:

// Customize which models are free
export const FREE_MODELS = [
  "gpt-4o-mini",
  "claude-3-5-haiku-latest",
  "llama-3.1-70b-versatile",
] as const;

// Credit validation logic
export function canUseConfiguration(
  credits: number,
  config: {
    modelId?: AIModel;
    isBrowseEnabled: boolean;
  }
) {
  // ... credit check logic
}

The credit system is integrated into the chat API route (app/api/(apps)/(chat)/chat/route.ts), which:

1. Checks user’s available credits
2. Validates feature access
3. Deducts credits for premium usage
4. Returns credit usage information in response headers

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Credit Headers

The API returns credit usage information in the x-credit-usage header:

{
  cost: number;        // Credits used
  remaining: number;   // Available balance
  features: string[];  // Premium features accessed
}