Alternatives
Products that do what HRAG – Hybrid RAG on €116/month of Hetzner, officially benchmarked does
Self-hosted hybrid RAG on a €116/month cluster — Postgres, BM25, vectors, a reranker, and a public benchmark score for every claim.
- 1FB
Hey there HN! We’re Antonio, Luca, and Yuhang, and we’re excited to introduce Fast GraphRAG, an open-source RAG approach that leverages knowledge graphs and the 25 years old PageRank for better information retrieval and reasoning. Building a good RAG pipeline these days takes a lot of manual optimizations. Most engineers intuitively start from naive RAG: throw everything in a vector database and hope that semantic search is powerful enough. This can work for use cases where accuracy isn’t too important and hallucinations are tolerable, but it doesn’t work for more difficult queries that…
2024 · github.com
- 2RO
Ragas is an open-source library for evaluating and testing RAG and other LLM applications. Github: https://docs.ragas.io/en/stable/, docs: https://docs.ragas.io/. Ragas provides you with different sets of metrics and methods like synthetic test data generation to help you evaluate your RAG applications. Ragas started off by scratching our own itch for evaluating our RAG chatbots last year. Problems Ragas can solve - How do you choose the best components for your RAG, such as the retriever, reranker, and LLM? - How do you formulate a test dataset…
2024 · github.com
- 3RO
Hello HN, I'm Owen from SciPhi (https://www.sciphi.ai/), a startup working on simplifying˛Retrieval-Augmented Generation (RAG). Today we’re excited to share R2R (https://github.com/SciPhi-AI/R2R), an open-source framework that makes it simpler to develop and deploy production-grade RAG systems. Just a quick reminder: RAG helps Large Language Models (LLMs) use current information and specific knowledge. For example, it allows a programming assistant to use your latest documents to answer questions. The idea is to gather all the relevant information…
2024 · github.com
- 4RV
Hi HN! We're building R2R [https://github.com/SciPhi-AI/R2R], an open source RAG answer engine that is built on top of Postgres+Neo4j. The best way to get started is with the docs - https://r2r-docs.sciphi.ai/introduction. This is a major update from our V1 which we have spent the last 3 months intensely building after getting a ton of great feedback from our first Show HN (https://news.ycombinator.com/item?id=39510874). We changed our focus to building a RAG engine instead of a framework, because this is what developers asked for the most.…
2024 · github.com
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Hey! I'm Andrei. I got frustrated by how people tend to build overcomplicated backend systems, being "motivated" by big tech case studies and popular books. So, I started exploring lean architecture, and building my digital garden of ideas, approaches and data that align with this direction. Here I want to present one of the tools – Sizing tool for PostgreSQL. I've benchmarked PostgreSQL on different EC2 instances and disks, with different initial data sets to see performance that these instances can give you. And I've built a tool to visualize this data, which I welcome you to explore. So,…
Jul 2026 · postgres.saneengineer.com
- 6OS
The PDF parser is a rule based parser which uses text co-ordinates (boundary box), graphics and font data. The PDF parser works off text layer and also offers a OCR option to automatically use OCR if there are scanned pages in your PDFs. The OCR feature is based off a modified version of tika which uses tesseract underneath. The PDF Parser offers the following features: * Sections and subsections along with their levels. * Paragraphs - combines lines. * Links between sections and paragraphs. * Tables along with the section the tables are found in. * Lists and nested lists. * Join content…
2024 · github.com
- 7RH
A RAG has several moving parts: data ingestion, retrieval, re-ranking, generation etc.. Each part comes with numerous options. If we consider a toy example, where you could choose from: 5 different chunking methods, 5 different chunk sizes, 5 different embedding models, 5 different retrievers, 5 different re-rankers/ compressors 5 different prompts 5 different LLMs That’s 78,125 distinct RAG configurations! If you could try evaluating each one in just 5 mins, that’d still take 271 days of non-stop trial-and-error effort! In short, it’s kinda impossible to find your optimal RAG setup…
2024 · github.com
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- 11SA
hey hn, supabase ceo here this is a postgres connection pooler. it’s similar to pgbouncer, but built with Elixir and specifically designed for multi-tenancy. it’s still under development, but it’s at a stage where we can gather a feedback from the community and you can try it yourself. we aren’t using this in production yet, but aiming to deploy it for a subset of databases in the next 2 months. We have the following benchmarks (details in the readme): - Elixir Cluster maintaining 400 connections to a single Postgres database - 1_000_000 clients connecting to the Elixir cluster - Sending…
2023 · github.com
- 12AB
I created a web page to compare different analytical databases (both self-managed and services, open-source and proprietary) on a realistic dataset. It contains 20+ databases, each with installation and data loading scripts. And they can be compared to each other on a set of 43 queries, by data load time or by storage size. There are switches to select different types of databases for comparison - for example, only MySQL compatible or PostgreSQL compatible. If you play with the switches, many interesting details will be uncovered. Full description:…
2022 · benchmark.clickhouse.com
- 13AA
- Discovering the most effective RAG pipeline for your specific data and use case can be daunting. It requires experimenting with various RAG modules and configurations, which are both time-consuming and complex. - AutoRAG addresses this challenge by automatically evaluating different combinations of RAG modules and their parameters. You don't need to write implementation code yourself; everything is set up through a single YAML file. - Our aim is to save you the hassle of continuously adapting to new RAG modules and configurations. Instead, you can focus on developing robust data for your…
2024 · github.com
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- 15TV
Hey HN, Joe and Ethan from Tonic.ai here. We just released a new open-source python package for evaluating the performance of Retrieval Augmented Generation (RAG) systems. Earlier this year, we started developing a RAG-powered app to enable companies to talk to their free-text data safely. During our experimentation, however, we realized that using such a new method meant that there weren’t industry-standards for evaluation metrics to measure the accuracy of RAG performance. We built Tonic Validate Metrics (tvalmetrics, for short) to easily calculate the benchmarks we needed to meet in…
2023 · github.com
- 16RP
Hey hacker news, We’re the cofounders at Psychic.dev (http://psychic.dev) where we help companies connect LLMs to private data. With the launch of Llama 2, we think it’s finally viable to self-host an internal application that’s on-par with ChatGPT, so we did exactly that and made it an open source project. We also included a vector DB and API server so you can upload files and connect Llama 2 to your own data. The RAG in RAGstack stands for Retrieval Augmented Generation, a technique where the capabilities of a large language model (LLM) are augmented by retrieving information…
2023 · github.com
- 17DA
Hi everyone, my cofounder and I built Dera - a platform to help manage chunks and embeddings. We built this because of the pain points we experienced while building RAG applications for side projects. The biggest pain point we encountered was that we were constantly trying out different chunking strategies, but there’s no easy way to check how the strategies are performing in terms of retrieval when given the same query. We tried searching for a tool for this but couldn’t find any (most LLM dev tools focus on prompts management). We hope this tool will be useful for people building RAG apps.…
2024 · getdera.com
- 18RO
Ragas is an open-source library designed for evaluating and testing RAG (Retrieval-Augmented Generation) and other LLM applications. It offers a diverse set of metrics and methods, including synthetic test data generation, to help you assess your RAG applications. Ragas was initially developed to address our own needs for evaluating RAG chatbots last year. ### Problems Ragas Can Solve: - How can you select the best components for your RAG, such as the retriever, reranker, and LLM? - How can you create a test dataset without incurring significant expenses and time? We believe there's a need…
2024 · github.com
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- 20HK
This is the newest update to my CLI tool to quickly create cheap Kubernetes clusters in Hetzner Cloud. This update adds autoscaling support, so clusters created with this tool now can behave similar to managed Kubernetes clusters, which I think is awesome. I received a lot of feedback since the last update so I hope anyone interested could try the new version as well. Thanks a lot for the feedback so far! The repo: https://github.com/vitobotta/hetzner-k3s
2023
- 21AE
Hi all, Sharing a repo I was working on for a while. It’s open-source and includes many different strategies for RAG (currently 17), including tutorials, and visualizations. This is great learning and reference material. Open issues, suggest more strategies, and use as needed. Enjoy!
2024 · github.com
- 22RA
RAGLite is a Python package for building Retrieval-Augmented Generation (RAG) applications. RAG applications can be magical when they work well, but anyone who has built one knows how much the output quality depends on the quality of retrieval and augmentation. With RAGLite, we set out to unhobble RAG by mapping out all of its subproblems and implementing the best solutions to those subproblems. For example, RAGLite solves the chunking problem by partitioning documents in provably optimal level 4 semantic chunks. Another unique contribution is its optimal closed-form linear query adapter…
2024 · github.com
- 23JS
Hey HN, I’m Julia, my team and I are building Rag-in-a-Box (https://www.joinable.ai/rag-in-a-box), hosted RAG service that let’s builders of any skill level launch their own RAG app loaded with their own data in minutes. [ What can you do ] 1. Load your documents (PDFs, CSV, PPTs, Word Docs, etc) and make them searchable instantly. All your data stays private and encrypted. 2. Choose latest open source LLM (Llama 4, Deepseek, GPT-oss, etc) to interact with your docs 3. Access your hosted RAG via API - build your own custom front end or integrate with your existing product…
2025 · joinable.ai
- 24SA
Show HN: SerenDB – Neon PostgreSQL fork optimized for AI agent workloads We forked Neon to make database operations faster and safer for AI agents. The goal is to enable instant experimentation with production data and catch prompt injection attacks before they hit your DB. The open-source repo is at https://github.com/serenorg/serendb The coolest current features are: 1. Time-travel queries: Query your database as it existed at any timestamp. SELECT * FROM orders AS OF TIMESTAMP '2024-01-15 14:30:00'. Essential for debugging agent decisions and auditing what data an…
Oct 2025 · github.com
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