venkat-training

Posted on Feb 6

Build Buddy - AI PC Building Assistant Powered by Algolia Agent Studio

#devchallenge #algoliachallenge #ai #agents

This is a submission for the Algolia Agent Studio Challenge: Consumer-Facing Non-Conversational Experiences

What I Built

Build Buddy is an intelligent PC building assistant that eliminates the frustration of incompatible computer parts through AI-powered, real-time compatibility validation.

The Problem

Building a custom PC is exciting, but compatibility issues are a nightmare:

❌ Will this CPU socket match the motherboard?
❌ Is DDR4 or DDR5 RAM needed?
❌ Will this massive GPU fit in the case?
❌ Does the power supply have enough wattage?

Traditional solutions require manually reading specs across dozens of websites, asking in forums, or using outdated compatibility checkers.

Build Buddy solves this with conversational AI that understands PC component relationships.

Demo

🔗 Live Demo: https://build-buddy-pi.vercel.app

📦 Source Code: https://github.com/venkat-training/build-buddy

Try These Queries

Example 1: Component Compatibility

"I want to build a gaming PC with Ryzen 7 7800X3D"

Example 2: Specific Question

"Will RTX 4090 fit in NZXT H510 Flow case?"

Example 3: Detect Issues

"Check compatibility: AMD Ryzen 7800X3D with ASUS Z790 motherboard"

The agent correctly warns this won't work - AM5 socket vs LGA1700!

Screenshots

Build Buddy in Action

Screenshot 1: Ask the agent about your PC build and get instant, intelligent responses

Screenshot 2: 8 specialized Algolia indices with 32 PC components and full compatibility metadata

Screenshot 3: Agent Studio configured with Gemini for intelligent compatibility checking

Screenshot 4: Rich component data with compatibility metadata enables intelligent validation

How I Used Algolia Agent Studio

Build Buddy leverages Agent Studio in a unique way - as an intelligent query interface rather than a traditional chatbot. Here's how:

Architecture Overview

8 Specialized Algolia Indices:

pc_cpus - Processors (socket types, TDP, chipsets)
pc_motherboards - Motherboards (form factors, DDR support)
pc_gpus - Graphics cards (dimensions, power requirements)
pc_ram - Memory (DDR types, speeds, capacity)
pc_psus - Power supplies (wattage, efficiency ratings)
pc_cases - Cases (clearances, form factor support)
pc_coolers - CPU coolers (socket compatibility, TDP ratings)
pc_storage - Storage drives (interface types, speeds)

Total: 32 components with complete compatibility metadata

Agent Configuration

The Build Buddy agent is configured to:

Search across all 8 indices simultaneously
Understand component relationships (e.g., CPU socket must match motherboard)
Check multi-dimensional compatibility:
- Physical: GPU length vs case clearance
- Electrical: Total power vs PSU wattage
- Interface: RAM DDR type vs motherboard support
Provide intelligent recommendations based on use case (gaming, workstation, budget)

System Prompt Strategy

The agent is instructed to:

You are a PC building expert. When users ask about components:

1. Search ALL relevant indices for compatibility data
2. Check critical compatibility rules:
   - CPU socket MUST match motherboard socket
   - RAM DDR type MUST match motherboard
   - GPU length must fit case clearance
   - PSU wattage must exceed total system power + 20% headroom
3. Warn about incompatibilities proactively
4. Suggest compatible alternatives with reasoning
5. Consider user's budget and use case

Why This Works

Agent Studio excels here because:

Fast retrieval - Sub-50ms searches across 8 indices
Contextual understanding - LLM interprets natural language queries
Multi-index orchestration - Automatically queries relevant component types
Grounded responses - All recommendations based on actual indexed data, no hallucinations

Why Fast Retrieval Matters

Algolia's speed is critical for this use case:

1. Real-time Compatibility Checking

When a user asks "Will component X work with component Y?", the agent must:

Search the CPU index (find component X)
Search the motherboard index (find component Y)
Cross-reference compatibility attributes
Generate response

With Algolia: All of this happens in ~100ms total
Without fast search: Users would wait 2-3 seconds per query

2. Multi-Component Queries

Users often ask: "Build a $1500 gaming PC"

The agent must:

Search ALL 8 indices
Filter by price range
Check compatibility between suggested components
Rank by performance/value

Algolia enables this complex workflow to feel instant.

3. Iterative Refinement

Users refine their builds:

"What if I swap the CPU?"
"Show me a cheaper GPU option"
"Will a bigger PSU help?"

Each question triggers multiple searches. Fast retrieval keeps the conversation flowing naturally.

4. Production-Ready Scale

Algolia's infrastructure provides:

✅ Global CDN (low latency worldwide)
✅ Automatic caching (repeated queries are instant)
✅ 99.99% uptime SLA
✅ No infrastructure management needed

This means Build Buddy works for 1 user or 10,000 users without code changes.

Technical Highlights

Data Modeling for Compatibility

Each component includes rich compatibility metadata:

{
  "objectID": "amd-ryzen-7-7800x3d",
  "name": "AMD Ryzen 7 7800X3D",
  "socket": "AM5",
  "tdp": 120,
  "compatibility": {
    "socket_type": "AM5",
    "ddr_support": ["DDR5"],
    "chipsets": ["X670E", "X670", "B650E", "B650"],
    "min_psu_wattage": 105
  },
  "use_cases": ["gaming", "content_creation"],
  "performance_tier": "high_end"
}

This enables the agent to make intelligent decisions like:

"DDR5 required" → Check RAM compatibility
"AM5 socket" → Only suggest AM5 motherboards
"TDP 120W" → Validate cooler capacity

Multi-Dimensional Validation

The agent checks compatibility across multiple dimensions:

Physical Compatibility:

GPU length vs case clearance
CPU cooler height vs case height
Motherboard form factor vs case support

Electrical Compatibility:

CPU TDP vs cooler rating
Total system power vs PSU wattage
PCIe connector availability

Interface Compatibility:

CPU socket vs motherboard socket
RAM DDR generation vs motherboard
Storage interface vs motherboard slots

Intelligent Fallback

The app includes local validation as fallback:

If Agent Studio is slow, basic checks run client-side
Users never see errors or blank responses
Graceful degradation maintains usability

Challenges Overcome

1. API Endpoint Discovery

Challenge: Algolia Agent Studio's endpoint format wasn't immediately obvious.

Solution: Implemented URL fallback logic that tries multiple possible endpoints:

const candidateUrls = [
  `https://${APP_ID}.algolia.net/agent-studio/1/agents/...`,
  `https://${APP_ID}.algolia.com/agent-studio/1/agents/...`,
  // ... more variants
];

This ensures the app works regardless of API changes.

2. Response Parsing

Challenge: Agent Studio returns nested response structures that vary by LLM.

Solution: Created a flexible extraction function that handles multiple formats:

const extractContent = (data) => {
  if (data?.parts && Array.isArray(data.parts)) {
    return data.parts
      .filter(p => p.type === 'text')
      .map(p => p.text)
      .join('\n');
  }
  // ... more fallbacks
};

3. Zero-Credential Exposure

Challenge: Need to keep all API keys secure while allowing public access.

Solution:

All credentials in environment variables (never in code)
.gitignore protects local .env files
.env.example shows structure without real keys
Vercel environment variables for production
Public demo uses Vercel's secure variable injection

4. Complex Compatibility Logic

Challenge: PC compatibility has dozens of interdependent rules.

Solution: Instead of hardcoding rules, we leverage the LLM's reasoning:

Store compatibility metadata in Algolia
Let the agent infer relationships
Works for new components without code changes

Why This Approach is Unique

Most Agent Studio Projects:

💬 Conversational shopping assistants
📝 Content summarizers
🤖 Customer support chatbots

Build Buddy is Different:

✅ Technical domain knowledge - PC building requires understanding complex relationships
✅ Multi-index orchestration - 8 interconnected component types
✅ Proactive validation - Prevents problems before they occur
✅ Real-world utility - Solves an actual pain point for PC builders

Future Enhancements

This is just the beginning! Planned improvements:

[ ] Expand component library - 100+ components per category
[ ] Build templates - Pre-configured builds (Gaming, Workstation, Budget)
[ ] Performance predictions - "This build will run games at 4K/144fps"
[ ] Price tracking - Historical pricing and deal alerts
[ ] Community builds - Share and rate PC configurations
[ ] Assembly guides - Step-by-step build instructions with your specific components

Getting Started

Want to run Build Buddy yourself? Here's the quick version:

Step 1: Clone & Install

git clone https://github.com/venkat-training/build-buddy.git
cd build-buddy
npm install

Step 2: Set Up Algolia (FREE)

Create account at algolia.com
Select "Build Plan" (no credit card required)
Get API credentials from Dashboard

Step 3: Upload Component Data

cp .env.example .env
# Add your Algolia credentials to .env
node upload-to-algolia.js

This creates 8 indices with 32 components.

Step 4: Configure Agent Studio

Go to Algolia Dashboard → Agent Studio
Create agent with your LLM provider (we used Gemini - FREE tier)
Add system prompt from docs
Publish agent

Step 5: Run the App

cd build-buddy-app
npm install
npm run dev

Visit http://localhost:3000 and start asking questions!

Full documentation: See README.md

Key Takeaways

What I Learned

Agent Studio is incredibly flexible - It's not just for chatbots. Any domain with complex data relationships can benefit.
Fast retrieval enables better UX - When searches are instant, you can build more sophisticated workflows without frustrating users.
LLMs + Structured Data = Magic - The combination of Algolia's fast, accurate search with an LLM's reasoning creates something neither could do alone.
Grounding is critical - By storing all component data in Algolia, the agent can't hallucinate specifications. Everything is verifiable.

Why Algolia Agent Studio?

Before this project, I considered:

❌ Building custom RAG pipeline (too complex)
❌ Using ChatGPT API directly (no search integration, slower)
❌ Traditional rules engine (brittle, hard to maintain)

Agent Studio provided:

✅ Built-in search integration
✅ Multi-index orchestration
✅ LLM flexibility (swap providers easily)
✅ Production-ready infrastructure
✅ Free tier for development

Cost Breakdown

Total Development Cost: $0

Algolia Build Plan: FREE (10,000 searches/month)
Google Gemini API: FREE (15 req/min, 1M tokens/day)
Vercel Hosting: FREE (hobby tier)
Development Time: ~20 hours over 3 days

This entire project cost nothing but time!

Conclusion

Build Buddy demonstrates that Agent Studio isn't just for customer service chatbots - it's a powerful platform for building intelligent applications in any domain with complex data relationships.

By combining Algolia's lightning-fast search with an LLM's reasoning capabilities, we created something that would have required weeks of work with traditional approaches.

The PC building domain was perfect for this because:

Complex relationships - Dozens of compatibility rules
Fast-changing data - New components released constantly
Natural language queries - Users don't want to learn technical specs
Real value - Prevents expensive mistakes

If you're building something with structured data and complex relationships, Agent Studio might be the perfect tool.