AI Chat Adapter

Hacker News

AI Chat Adapter

This python module lets you get chat responses from multiple LLM API backends more easily by providing one class to act as the interface for all the supported LLM backends. It can handle both local and remote LLMs. So far it supports OpenAI, Anthropic for remote APIs and Ollama and LMStudio for local LLMs. I made this to experiment with having multiple LLMs chat with each other, but realized it might be more generally useful to others as well. For example, this would make it trivial to switch between API providers for a critical service during provider outages or to do comparison testing with multiple LLMs. There is still a lot more I want to do: Add support for Groq and others APIs, add support for chat streams, improve tool call support and more. Your feedback is welcome and if you can think of more cool use cases I'll add them to the README.

Visit product View on Hacker NewsAug 27, 2024

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

74%74% 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: supports · Missing: reddit linkedin, podcasting, created

74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

59%59% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: llama, ide, io · Missing: https docs, excited, just released

39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

23%23% 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

21%21% 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

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

Correct prediction on native model

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