I built a RAG engine to search Singaporean laws

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46921180

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HN

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—

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I built a RAG engine to search Singaporean laws

Website domain

github.com

Launched

Feb. 7, 2026

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Upvotes

5

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0.10512129380053908

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Fetched at

Sept. 7, 2026, 9:26 p.m.

Updated at

Sept. 7, 2026, 9:26 p.m.

Description

I built a "Triple Failover" RAG for Singapore Laws, then rewrote the logic based on your feedback.Hi everyone!I’m a student developer. Recently, I created Explore Singapore, a RAG-based search engine that scrapes about 20,000 pages of Singaporean government acts and laws.I recently posted the MVP and received some tough but essential feedback about hallucinations and query depth. I took that feedback, focused on improvements, and just released Version 2.Here is how I upgraded the system from a basic RAG to a production-grade one.The Design & UI I aimed to avoid a dull government website.Design: Heavily inspired by Apple’s minimalist style.Tech: Custom frontend interacting with a Python backend.The V2 Engineering OverhaulThe community challenged me on three main points. Here’s how I addressed them:1. The "Personality" Fix Issue: I use a "Triple Failover" system with three models as backup. When the main model failed, the backups sounded entirely different.The Solution: I added Dynamic System Instructions. Now, if the backend switches to Model B, it uses a specific prompt designed for Model B’s features, making it mimic the structure and tone of the primary model. The user never notices the change.2. The "Deep Search" Fix Issue: A simple semantic search for "Starting a business" misses related laws like "Tax" or "Labor" acts.The Solution: I implemented Multi-Query Retrieval (MQR). An LLM now intercepts your query. It breaks it down into sub-intents (e.g., “Business Registration,” “Corporate Tax,” “Employment Rules”). It searches for all of them at the same time and combines the results.Result: Much richer, context-aware answers.3. The "Hallucination" Fix Issue: Garbage In, Garbage Out. If FAISS retrieves a bad document, the LLM produces inaccurate information.The Solution: I added a Cross-Encoder Re-Ranking layer.Step 1: FAISS grabs the top 10 results.Step 2: A specialized Cross-Encoder model evaluates them for relevance.Step 3: Irrelevant parts are removed before they reach the Chat LLM.*The Tech Stack *Embeddings: BGE-M3 (Running locally)Vector DB: FAISSBackend: Python + Custom Triple-Model FailoverLogic: Multi-Query + Re-Ranking (New in V2)Try it outI am still learning. I’d love to hear your thoughts on the new logic.Live Demo: https://adityaprasad-sudo.github.io/Explore-Singapore/GitHub Repo: https://github.com/adityaprasad-sudo/Explore-SingaporeFeedback, especially on the failover speed, is welcome!

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