StartHub - Building Personalized Program Recommendations with RAG and a Vector DB
Overview
Recommending personalized support program listings using the Pinecone vector DB
Tech Stack
Kotlin
Python
Spring Boot
FastAPI
MySQL
Redis
Firebase
Docker Compose
GCP
Spring AI
Pinecone
Team
7 members (2 Backend & AI, 3 Web, 2 App)
Period
2025.08 ~ 2025.09
Details
Overview
- Built a RAG system from scratch and integrated a vector DB to recommend support program listings tailored to each founder.
- A Jsoup-based crawling service scrapes and stores external listing data from sources such as K-STARTUP on a daily basis.
My Contributions
- Used Pinecone as a cloud vector DB to store and search embedded listings.
- Built a FastAPI server for the AI service as a microservice and integrated it with the OpenAI API and embedding models.
- Implemented a personalized chatbot by injecting the user's vectorized activity information into the Claude API in real time.
Natural-Language Listing Search with Claude MCP
- Used Claude MCP to find listings matching the appropriate conditions for a natural-language request and return the data in JSON format.
Backend Infrastructure
- Core features are handled by a Spring Boot server, and high-traffic APIs (login, listing queries, etc.) are cached with Redis.
- Listing notifications are pushed to users in real time via FCM.
- Built, operated, and managed an on-premise server to reduce cloud costs.
