VectorFlow – Open-Source Vector Embedding Pipeline

Hacker News

Hey HN We've just released the initial version of our open-source vector embedding pipeline. It's designed to embed large volumes of data. While embedding a few documents for Q&A is straightforward, consistently ingesting gigabytes of unstructured data is a whole different ballgame. By using our API, you can embed raw data and store vectors in your vector database, sidestepping the complexities of cloud infrastructure. Now, in true YC spirit, we're launching before everything is polished. Our Github repo ( https://github.com/dgarnitz/vectorflow ) is a work in progress, and we're eager to get it in front of the community early on. Check out our Github repo and let us know what you think. Your feedback, suggestions, and critiques will be highly appreciated as we continue to refine and develop.

Visit product View on Hacker NewsAug 10, 2023

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

89%89% 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 NewsStrong engagement from HN community · Strong signals: just released, ide, pipe · Missing: https docs, excited, exist

79%79% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

39%39% 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

39%39% 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

14%14% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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