Developer builds production-grade RAG engine to fix caching, retrieval, and PII gaps

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A solo developer rebuilt a retrieval-augmented generation (RAG) search engine called Project Aether after standard demo-style pipelines failed on real-world documents. The system addresses three core problems: stale semantic caching, poor exact-term retrieval with dense-only vector search, and accidental PII storage in vector databases. To fix these, the project uses similarity-based Redis caching tuned against an evaluation set, hybrid dense-plus-sparse retrieval via Chroma Cloud, and a PII-masking pass applied before document chunking. The pipeline is built with FastAPI, LlamaIndex Workflows, and Groq-hosted Llama 3.3 70B, structured as an event-driven graph to handle retries and partial failures more reliably. The project is open-source and hosted on Render's free tier, with the developer seeking critical technical feedback over passive engagement.

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