Vectorize OpenAI API calls (like NumPy)

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Outlines is a Python library that focuses on text generation with large language models. While the library mainly focuses on open source models we have an integration with the OpenAI API. When trying to implement Tree of Thoughts we became frustrated with the iterative workflow in which other libraries forced us. What we wanted was to be able to pass an array of prompts or completions with arbitrary shape, ask for several samples, and get an array of completions back. So we implemented a vectorization routine inspired by NumPy's. This makes our OpenAI integration have the "broadcast" behavior described above. All call are performed concurrently. We thought this could be of use to some folks here who are trying to implement complex workflows with the OpenAI API.

Visit product View on Hacker NewsNov 13, 2023

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

91%91% 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: open source, io · Missing: https docs, excited, just released

63%63% 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

54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

15%15% 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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