Searchify — AI search that reveals what’s missing

Searchify is a production-grade AI-powered search engine that combines hybrid search (keyword + semantic) with RAG (Retrieval-Augmented Generation) and a unique Critical Insight layer to surface not just answers — but also their limitations.

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🔗 Links

• 🐙 GitHub Repository: https://github.com/mayhemking007/searchify

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🚀 Features

• 🔎 Hybrid Search

  • Combines BM25 (keyword search) + vector similarity
  • Powered by Elasticsearch

• 🤖 RAG (AI Answers)

  • Generates answers using retrieved context
  • Grounded responses with source attribution

• ⚠ Critical Insight Layer (Unique)

  • Highlights assumptions, limitations, or missing perspectives
  • Improves trust and reasoning

• 🌐 Custom Web Crawler

  • URL seeding, crawling, and indexing pipeline
  • Content cleaning + intelligent chunking

• ⚡ Fast Retrieval Pipeline

  • Redis caching
  • Efficient chunk-based indexing

• 📊 Monitoring & Observability

  • Prometheus for metrics collection
  • Grafana dashboards for visualization

• 🎨 Minimal Frontend

  • Google-like UX + AI answer layer
  • Built with React + Tailwind CSS

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🧠 Architecture

Crawler → Indexer → Elasticsearch
                ↓
            Hybrid Search
                ↓
        Top Relevant Chunks
                ↓
        RAG (LLM Answer)
        + Critical Insight
                ↓
            Frontend UI

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🛠 Tech Stack

Backend

• Node.js + TypeScript
• Express.js
• BullMQ (workers)
• Redis

Search & Storage

• Elasticsearch (hybrid retrieval)
• PostgreSQL (metadata)

AI

• Embeddings (vector search)
• LLM (RAG answer generation)

Frontend

• React (Vite)
• Tailwind CSS

Monitoring

• Prometheus
• Grafana

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📦 Project Structure

searchify/
├── src/                # Backend
├── client/             # Frontend 
├── docker-compose.yml
├── prometheus.yml
└── README.md

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⚙️ Setup

1. Clone Repository

git clone https://github.com/mayhemking007/searchify.git
cd searchify

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2. Install Dependencies

Backend

npm install

Frontend

cd client
npm install

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3. Start Services (Elasticsearch + Monitoring)

docker-compose up -d

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4. Run Backend

npm run dev

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5. Run Frontend

cd client
npm run dev

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🔍 API Endpoints

Search + AI Answer

GET /ask?q=your_query

Response:

{
  "answer": "...",
  "critique": "...",
  "sources": [...]
}

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Metrics (Monitoring)

GET /metrics

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📊 Monitoring

• Prometheus → http://localhost:9090
• Grafana → http://localhost:3001

Track:

• API latency
• Search latency
• RAG latency
• Request rate

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🧪 Example Query

What is Kafka?

Output:

• AI-generated answer
• ⚠ Critical insight
• Source links

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💡 Key Learnings

• Hybrid search (BM25 + vector)
• RAG system design
• Data quality impact on AI
• Observability with Prometheus & Grafana
• Distributed worker pipelines

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📌 Inspiration

Inspired by modern AI search systems like Perplexity — but with an added focus on critical thinking and epistemic awareness.

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