🚀 Full-Stack AI Applications

import ContributionButtons from ’../../../../components/ContributionButtons.astro’; import UsageTracker from ’../../../../components/UsageTracker.astro’; import AuthorshipBadge from ’../../../../components/AuthorshipBadge.astro’; import GreaterGoodBadge from ’../../../../components/GreaterGoodBadge.astro’; import CookbookAsCode from ’../../../../components/CookbookAsCode.astro’; import LearningPath from ’../../../../components/LearningPath.astro’; import InteractiveQuiz from ’../../../../components/InteractiveQuiz.astro’; import UnderstandingButton from ’../../../../components/UnderstandingButton.astro’;

🌳 Forest-Level Concept

Label: The Full-Stack AI Architectural Pattern

Building a “Full-Stack AI” application is fundamentally different from traditional web development. While traditional apps are primarily CRUD (Create, Read, Update, Delete) systems, AI applications are Probabilistic Reasoning Systems. This means your architecture must handle non-deterministic outputs, long-running processes (streaming), and complex state management across the client and server.

The Modern AI Stack

Orchestration Layer: Python (LangChain/LlamaIndex) or Node.js (Vercel AI SDK)
Logic Layer: AI Agents & Compound Systems
Data Layer: Vector Databases (Pinecone/Milvus) + Relational DBs (Postgres/Supabase)
Interface Layer: React/Next.js with real-time streaming

🌿 Growing: Core Architecture Patterns

1. The Streaming Loop

Streaming is not a luxury in AI apps; it’s a UX requirement. Waiting 30 seconds for a full LLM response causes user abandonment.

// frontend/hooks/useChat.ts (Next.js + Vercel AI SDK)
import { useChat } from 'ai/react';

export default function Chat() {
  const { messages, input, handleInputChange, handleSubmit } = useChat({
    api: '/api/chat',
    onResponse: (response) => {
      console.log('Started receiving response');
    },
  });

  return (
    <div className="flex flex-col w-full max-w-md py-24 mx-auto stretch">
      {messages.map(m => (
        <div key={m.id} className="whitespace-pre-wrap">
          {m.role === 'user' ? 'User: ' : 'AI: '}
          {m.content}
        </div>
      ))}

      <form onSubmit={handleSubmit}>
        <input
          className="fixed bottom-0 w-full max-w-md p-2 mb-8 border border-gray-300 rounded shadow-xl"
          value={input}
          placeholder="Say something..."
          onChange={handleInputChange}
        />
      </form>
    </div>
  );
}

2. State Management for Agents

Unlike simple chat-bots, AI agents often need to maintain asynchronous state. If an agent is searching the web, it shouldn’t block the UI.

# backend/agent_service.py (FastAPI + LangGraph)
from langgraph.graph import StateGraph, END
from typing import TypedDict, List

class AgentState(TypedDict):
    task: str
    plan: List[str]
    results: List[str]
    is_complete: bool

def planner(state: AgentState):
    # Logic to break down task
    return {"plan": ["search_web", "summarize"]}

def researcher(state: AgentState):
    # Logic to perform research
    return {"results": ["Found info on X"]}

workflow = StateGraph(AgentState)
workflow.add_node("planner", planner)
workflow.add_node("researcher", researcher)
workflow.set_entry_point("planner")
workflow.add_edge("planner", "researcher")
workflow.add_edge("researcher", END)

app = workflow.compile()

🌳 Forest: Deep Systems Integration

Database Topology: The Hybrid Approach

You can’t rely solely on a Vector DB. You need a “Hybrid Data Store” where operational data lives in Postgres and semantic data lives in Pinecone.

Data Type	Storage	Tooling
User Profiles	Relational	Prisma/Supabase
Chat History	NoSQL/Key-Value	Redis
Knowledge Base	Vector Store	Pinecone/Weaviate
File Metadata	Relational	Postgres

🎓 Knowledge Check