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GPTBanker – map/reduce over documents of any length with OpenAI

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GPTBanker – map/reduce over documents of any length with OpenAI

Hi all! I created a app that can map/reduce over documents of an arbitrary length with OpenAI completions. You can enter in your OpenAI key and try it out. The backend is built with LangChain, which made it easy to manage the models, workflows (chains), and troubleshoot pipelines with buffer issues. Learnings: - Tokens was the most helpful key for calls rather than characters or words. LangChain also just implemented a token splitter, which made it easier for me to adapt to documents of any type. - Davinci performed much better than Curie for summarizing long documents over multiple chunks - Oftentimes a map step was the only thing needed and the reduce/combine introduced more errors instead of improving context.

Visit productView on Hacker NewsFeb 5, 2023
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Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, context · Missing: mac, agents, macos
82%82% 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.
Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: pipe, io · Missing: https docs, excited, just released
57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: calls · Missing: plus, platform, intuitive
56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
41%41% 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
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto
5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

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