Du

DuoRAG – A dual stack RAG that self-evolves

Hacker News

DuoRAG – A dual stack RAG that self-evolves

Imagine a corpus of documents with scientist biographies. The traditional RAG works fine until you ask questions like: - "Who was born before 1800?" - "How many are mathematicians?" - "List names and birthdays for mathematicians" These result in an incomplete answer due to top-k, with no signs of incompleteness. For an initial corpus, it is possible to improve this problem by extracting metadata for a predetermined set of fields. This approach has two problems: - One has to predict all the questions that can be asked against the corpus upfront. - Constantly revising that prediction as the documents change, e.g. adding Nobel prizes later, or extending the document set to contain artists. DuoRAG aims to solve both problems by: - An initial metadata (schema) discovery before the first ingestion - Self-update schema with candidate fields when it fails to answer a question

Visit product View on Hacker NewsMar 27, 2026

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

35%35% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

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