About this episode
Lorenzo Moriondo discusses arrowspace, an open-source library for managing LLM datasets through graph-based approaches.
Lorenzo Moriondo is a Technical Lead for AI at tuned.org.uk, working on AI agent protocols, graph-based search, and production-grade LLM systems. arrowspace: Vector Spaces and Graph Wiring // MLOps Podcast #365 with Lorenzo Moriondo, AI Research and Product Engineer Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter MLOps GPU Guide: https://go.mlops.community/gpuguide // Abstract Meet arrowspace — an open-source library for curating and understanding LLM datasets across the entire lifecycle, from pre-training to inference. Instead of treating embeddings as static vectors, arrowspace turns them into graphs (“graph wiring”) so you can explore structure, not just similarity. That unlocks smarter RAG search (beyond basic semantic matching), dataset fingerprinting, and deeper insights into how different datasets behave. You can compare datasets, predict how changes will affect performance, detect drift early, and even safely mix data sources while measuring outcomes. In short: arrowspace helps you see your data — and make better decisions because of it. // Bio With over a decade of experience in software and data…
People in this episode
Host: Demetrios
Guest: Lorenzo Moriondo
Topics covered
- Vector Spaces
- Graph Wiring
- AI
- LLM Systems
- Open-source
Keywords
- arrowspace
- AI agent protocols
- graph-based search
- dataset fingerprinting
Mentioned in this episode
Products: arrowspace, Python, Rust, SmartCore ML, the Topological Transformer
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