Building a RAG-Based Subsidy Matching System from Scratch with Python

What I Built A RAG (Retrieval-Augmented Generation) system that helps Japanese small businesses find government subsidies. Users describe their business situation, and the system retrieves relevant subsidies from a vector database, then generates a detailed answer using Claude API. Why RAG Instead of Just Using an LLM? LLMs alone have three problems for this use case: Hallucination — They confidently make up subsidy details that don't exist Stale data — Subsidy information updates frequently; LLM training data can't keep up No sources — Users need to verify the information, but LLMs don't cite where it came from RAG solves all three by retrieving real data first , then passing it to the LLM as context. The LLM generates answers grounded in actual documents, not its training data. Architecture User Query │ ▼ ┌─────────────┐ ┌──────────────┐ │ Retriever │────▶│ Generator │ │ (embed + │ │ (Claude API) │ │ search) │ └──────┬───────┘ └──────┬──────┘ │ │ ▼ ┌──────▼──────┐ AI Answer │ ChromaD