Query Suggestions with GraphRAG

Hacker News

A common UX challenge with RAG applications is that they often leave users staring at a blank page, unsure of what queries to run to explore a dataset. This can be overwhelming and inefficient. GraphRAG transforms this experience with its entity-based query generation feature. By combining structured data (entities and relationships) with unstructured data (community reports and covariates), it crafts insightful follow-up queries tailored to a session's query history, highlighting critical themes and information. With GraphRAG, users can be guided to uncover and explore insights from datasets. Try out the live demo showcasing GraphRAG's potential in query generation: https://graphrag-demo.deepset.ai/query-gen

Visit product View on Hacker NewsJul 29, 2024

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

63%63% 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: ide, io · Missing: https docs, excited, just released

43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: efficient, users · Missing: plus, platform, intuitive

35%35% 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

33%33% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

16%16% 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

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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