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RAG Engine – Connect external data to LLM in minutes

Hacker News

RAG Engine – Connect external data to LLM in minutes

Hello HN, If you’ve ever tried building an AI app that needs your own data, you know the pain — spinning up a vector database, writing scripts to fetch data from different sources, keeping data in sync… It’s a lot. So we thought, why not simplify this process and make it as simple as couple API calls? One to add data source and another to search across all your data. Well, that’s why we’re building RAG Engine - https://rageninge.io To add data source to RAG Engine you make one API call, including your project API key and namespace (useful to keep data between your users separate) curl -X POST https://ragengine.io/data-sources \ -H "Content-Type: application/json" \ -H "Authorization: Bearer YOUR_PROJECT_API_KEY" \ -d '{ "namespace": "user:1", "type": "website", "url": "https://mywebsite.com" }' And to search across all your data just make another API call: curl -X GET " https://ragengine.io/search?query=...&namespace=user:1 " \ -H "Authorization: Bearer YOUR_PROJECT_API_KEY" Response: { "documents": [ { "source": { "type": "website", "url": "https://mywebsite.com" }, "content": "...", "similarity_score": 3.99 }, { "source": { "type": "file", "filename": "pricing.txt" }, "content": "...", "similarity_score": 3.11 } ] } That’s it! In background RAG Engine ingests, processes, stores and keeps all your data in sync. We’re focusing on small startups and indie developers who don’t have time or resources to build RAG, that’s why we make pricing as low as possible: 1. Vector Database: Starting at $4/month for a DigitalOcean droplet (512 MB RAM, 10 GB SSD). Larger datasets require more resources, but it’s always at-cost with no markup. 2. Embeddings: We pass along the exact OpenAI rates ($0.010 to $0.065 per 1M tokens), so no extra fees here either. 3. Our fee: $4.99/month (first month free). This covers our ingestion pipeline and unified search. If you want to give RAG Engine a try - join waitlist https://ragengine.io/ Or check our discord server (we are there right now and happy to chat) https://discord.gg/Kz6JyTXjHm

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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: para, including · Missing: supports, reddit linkedin, podcasting
87%87% 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: user, openai, using · Missing: mac, agents, macos
79%79% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: pipe, io, including · Missing: https docs, excited, just released
66%66% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: month, users, way · Missing: mobile apps, ios, personal
59%59% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users, calls · Missing: plus, platform, intuitive
35%35% 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
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.

Incorrect prediction on native model

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