- An open-source project just ranked #1 on EnterpriseRAG-Bench, ahead of the listed systems from OpenAI, Google, Microsoft and Amazon
A month ago we shared our FRAMES experiment: 18 RAG pipelines, best pipeline at 78.9%, our agent loop at 92.7%. We now have a result on a different dataset. PipesHub has reached 1 on...
86 points40 comments4 d ago
- What I've learned about RAG and Agentic RAG after one year in enterprise production
Hey everyone, After one year of building RAG and Agentic RAG systems in the healthcare domain, I wanted to share a few lessons from production. 1. Traditional RAG is still powerful. If latency and...
73 points23 comments20 h ago
- RAGFlow 1.0 preview: moving from RAG engine to agent-native database
Context as Infrastructure: Knowledge Compilation for Retrieval in Agentic Systems with Harnesses 📄 https://arxiv.org/abs/2610.41172 This is the paper behind the upcoming RAGFlow 1.0. Rather than...
33 points20 comments7 d ago
- Building a RAG app where your files never leave the computer: what worked for me
A lot of people want to use AI with their own documents but don't want to upload them anywhere. I've been building a desktop app around that idea, and I want to share the approach in case it helps...
31 points12 comments2 d ago
- Research paper: Is Grep All You Need? How Agent Harnesses Reshape Agentic Search
Main Result: In their experiments (evaluating on a sample from LongMemEval across custom and native CLI harnesses like Claude Code, Codex, and Gemini CLI), grep generally yielded higher accuracy...
20 points1 comment2 d ago
- Meet SpectRAG
SpectRAG: instead of ranking chunks one by one, select a set that works together as evidence (91.8% gold-evidence recall on multi-hop QA) Multi-hop questions break classic top-k retrieval. It...
16 points6 comments4 d ago
- Everytime the same shit... Another Agent another RAG.
My experience in the AI market goes way back, even before the first version of GPT existed, building vision models for the fashion industry. Now, after exiting my last company, I'm working as a...
16 points16 comments5 d ago
- The Vector DB is an Overkill for almost all RAG applications and response is only as accurate as Relational database on average!
What is your take? The complexity of enterprise vector database clusters, building complex chunking pipelines, managing synchronization, and paying for dedicated high-RAM servers to get a marginal...
15 points7 comments2 d ago
- A few AI terms I've had to learn recently
I've noticed that conversations about AI have changed quite a bit. There are terms that keep coming up in technical discussions, and some of them are worth understanding if you're building agents...
14 points4 comments3 d ago
- Training Decision Models for RAG
tldr: effective decision models are tiny you can finetune your own for your RAG pipeline Hopefully everyone is aware of decision models at this point to some degree and if you haven't played...
13 points1 comment6 d ago