Build a Smart Restaurant AI Assistant ⚡ LangChain Agents + Gemini 2025

Описание: 🚀 Learn to Build a Smart Restaurant Assistant with LangChain in 2025!
In this project-based tutorial, we show you how to build a dynamic restaurant AI assistant using LangChain Agents, custom tools, and Google Gemini models. This AI app can answer menu queries, handle multiple meals, and dynamically fetch fresh data!

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What you’ll learn in this video:
✅ Project overview: Smart Restaurant Assistant (LLM + Agent + Tools)
✅ LangChain Agents: How they orchestrate tools and make decisions
✅ Dynamic Tools: Build menu tools for breakfast, lunch, dinner
✅ Tech stack: LangChain community packages, Gemini API, Express.js

💡 Why Build Agents in 2025:
Agents let your LLMs think, decide, and act using external tools, making AI applications dynamic, real-time, and interactive. Perfect for building chatbots, restaurant assistants, and intelligent services!

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📍 What You’ll Learn in this video:
0:00 – Intro: LLM Bootcamp Recap, Today’s Project Overview
0:32 – Watch Order Tip: Start from Bootcamp Playlist for Clarity
0:44 – Project Goal: Build a Smart Restaurant Assistant (LLM + Agent + Tools)
1:34 – What Agents Do: Tool use, decision-making, and when to call tools
2:22 – Tech Stack Plan: LangChain agents, dynamic tools, Gemini via API
3:27 – Architecture: LLM (brain) + Agent (orchestrator) + Tools (capabilities)
4:58 – Model Setup: Using Google Gemini (g2.5 Flash/Pro), API keys in .env
6:56 – Server Setup: Express.js boilerplate and test route
9:58 – Importing Libraries: LangChain, community tools, zod, Google AI SDK
11:40 – Dynamic Tool Concept: Why custom tools are needed for fresh data
13:19 – Build Menu Tool: Name, description, schema, and function contract
16:13 – Schema with Zod: Validate “category” as breakfast/lunch/dinner
18:40 – Tool Function: Return menu by category
21:25 – Agent Choice: CreateToolCallingAgent (modern, robust) vs old executor
24:12 – Prompt Design: System behavior, Human input, Agent scratchpad
27:54 – Executor Setup: Agent + Tools + Prompt wiring
30:20 – Minimal Frontend: Public/index.html served from Express
33:20 – Chat UI: Input, button, sendMessage() flow (fetch /api/chat)
36:05 – API Route: POST /api/chat, JSON body, pass input to agent
40:05 – Error Handling: Try/catch, consistent JSON output for UI
41:11 – Invoke Agent: executor.invoke({ input }) and return response
43:20 – Debugging Verbose Logs: See tool calls and agent reasoning
45:24 – Infinite Tool Calls Issue: Why agents loop and cost implications
46:50 – Fix 1: maximumIterations to cap tool-call loops
49:02 – Fix 2 (Hack): returnIntermediateSteps and parsing first observation
51:34 – Stable Output Strategy: Fallback to intermediate step when agent stops
52:11 – Demo: Ask Dinner/Breakfast/Lunch — instant menu responses
52:44 – Key Learnings: Tools, prompts, executors, iterations, debugging
53:35 – Bonus Homework: Build Weather Agent with OpenWeather + LangChain


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