Original Summary

I've been working on an open-source project called Muffakir, and I've finally reached a point where I'd like to share it with other developers and get some honest feedback. The project came from a problem I kept having while building RAG systems. There are a lot of decisions to make: chunking strategy, embedding model, retrieval method, query transformation, reranking, top-k, LLMs, prompts, etc. Testing those choices usually meant changing configurations, rerunning scripts, comparing results manually, and trying to remember what actually made the system better. So I built Muffakir around the idea of treating the RAG architecture itself as an experiment. You provide your documents and evaluation data, define the configurations you want to explore, and Muffakir turns that search space into reproducible trials. It can then compare those trials across retrieval and generation metrics, latency, and cost. You can inspect individual results and traces, compare configurations, and export a selected pipeline back into Python. Everything currently runs locally. It's still early, and the latest version isn't on PyPI yet, so installation is currently from GitHub. I'm sharing it here because I'd really like feedback from the open-source community, especially from people working with RAG/LLM systems. I'd love to hear what you think about the architecture, what's missing, what you'd simplify, or what would make you actually use something like this. Contributions and ideas are very welcome too. GitHub: https://github.com/Mohamed28112003/Muffakir Docs: https://mohamed28112003.github.io/Muffakir/ Demo: https://youtu.be/SOXkpL4Q9PE   submitted by   /u/Mohamed_Khaled_28112 [link]   [comments]

  • 情报分类：商业与市场研究
  • 分类依据：内容涉及商业、投资或市场动态
  • 信息来源：Reddit · SaaS
  • 发布时间：2026/9/20 09:10:08