Func Runner – Managed Function Calling for OpenAI Assistants

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Hi Everyone! Username: hackernews Password: ilovehn A Note On Demo Credentials I have provided some credentials above for everyone to use and will keep the demo up and running as long as I fiscally can. I am not rich so if my Azure or OpenAI bill gets out of hand I will have to temporarily disable the demo. I wanted to make this easy for everyone to see so I am eating the cloud cost right now so please play nice :). Why I Built Func Runner I built Func Runner, which is a managed function calling provider for OpenAI Assistants that allows anyone to quickly build function calling enabled assistants using Python. Although function calling is not incredibly difficult to implement on your own, I felt that this pattern was something I had done frequently enough to warrant toying with the idea of providing a service built around it. My target audience would likely be from one of two camps. Camp A would be hobbyist Python programmers looking to build their own assistants who also lack the technical chops to build a full-fledged application. Camp B would potentially be professional developers looking to abstract away their assistant code execution so they can worry about other problems. What Func Runner Does In a nutshell, I built a system that deploys a dedicated serverless Durable Function app service to Microsoft Azure for each of your assistants. The Python functions you write are deployed as executable activities inside your app service. To enable this, I have effectively built a proxy service around the OpenAI API mixed with my own secret sauce to handle function executions on your behalf. The proxy is 100% compatible with all existing OpenAI libraries and requires minimal setup to adopt. Whenever I see a run execution with the state of “requires_action”, func runner steps in and orchestrates the execution and return of results back to your OpenAI assistant. Key Features Right Now - Custom Python code execution in a serverless environment dedicated to your assistant. - Python dependency management and secret storage for sensitive keys. - A ChatGPT chat experience powered by an OpenAI proxy service with specialized function calling handling. Although this project is still in its infancy, it has been a great source of fun building it out over the past few months. Please let me know what you think, ask any questions you may like and please excuse any bugs :)

Visit product View on Hacker NewsMar 18, 2024

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

94%94% 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: compatible · Missing: supports, reddit linkedin, podcasting

92%92% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

55%55% 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: month, way · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, 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.

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

16%16% 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.

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