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Dead simple API for Retrieval Augmented Generation

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Dead simple API for Retrieval Augmented Generation

Hello everyone, I am the developer of eazyrag.com, and here is the problem I am trying to solve. When I was working on usegrasp.com (a search engine), I integrated the LLM answer engine, which is basically implementing a retrieval augmented generation (RAG) pipeline. First, I tried the most popular libraries available. I have to say I disliked the developer experience due to confusing APIs and complex abstractions. I wanted something like Algolia for retrieval augmented generation. So, I built eazyrag.com, which is an easy-to-use API to implement RAG with your own data inside your apps or websites. You can simply index everything and query it. You don't even need a unique ID for the content you are indexing; just send the entire documents in a single API call, and we will handle chunking, splitting, embedding, and prompt with context formation, etc. Here is a demo I built: I indexed all the Bun.js docs/guides pages on EazyRAG and performed RAG on them: https://eazyrag.com/bun

Visit productView on Hacker NewsSep 16, 2023
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, context, single · Missing: mac, agents, macos
92%92% 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: ide, pipe, io · Missing: https docs, excited, just released
57%57% 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
51%51% 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
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal
43%43% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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