Modelsmith – Yet another LLM entity extractor

Hacker News

I made a thing!!! Yes I know I should not be that excited but it feels great to release something. I have been busy building a RevOps partner in thought chat bot that uses an agentic framework to get different types of information to our users. (Gotta love the new word salad we have today ;D ) As part of this work we needed to do entity extraction from text and get Python types or Pydantic models back. We are using Google's Vertax AI and nothing out there existed for it at the time. So I took inspiration from other Python packages and built Modelsmith. Over time it has evolved to now support Anthropic, Google Vertex AI, and Open AI APIs. It is designed to be simple to use and also simple to understand the code base. Hopefully it is of use to someone else out there. (PS be gentle in the comments Hacker News ;) Feedback is always appreciated)

Visit product View on Hacker NewsJul 18, 2024

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

86%86% 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 · Missing: supports, reddit linkedin, podcasting

61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited, exist, hacker news · Missing: https docs, just released, lua

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.

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

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

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

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

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

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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