Call Multiple LLMs with GraphQL and AI Chainer

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Many of the issues with LLMs can be solved by prompting as a chain, by transforming a single prompt to multiple sub prompts. With the wide variability in LLM pricing and model sizes, it also helps if we can offload some of those sub prompts in the chain to smaller models. LQ provides a GraphQL endpoint that allows you to call multiple LLMs viz. gemini, claude, openai, mistral as a tree. You can extract structured xml tags from responses and fill placeholders in prompts down the chain/tree. With conditionals, you can provide condition-based branching as well. Further, we also provide an inbuilt AI Chainer that creates a chain for you. As an added benefit it also has a request history, for you to repeat a query while changing some of the placeholders as variables. We are a team of 2 and have been using LLMs in many projects. For every LLM operation we end up making several LLM calls and have to write our own tooling to optimize the queries as a bunch. Even during development, evaluating multiple LLMs, prompt-tuning, condition based calling, etc. is messy. We are trying to make LQ as a middle-layer that can ease some of those pains and allow users to express the data flow within the request itself. Our next goal includes support for local LLMs as well as longer running queries with subscriptions. Large chains/trees will also be supported with the context residing on the server.

Visit product View on Hacker NewsDec 22, 2024

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

93%93% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

42%42% 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: users, calls · Missing: plus, platform, intuitive

31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: subscription · 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 · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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