arrowspace: Vector Spaces and Graph Wiring

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

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// 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 engineering across startups and early-stage projects, Lorenzo has recently turned his focus to the AI-assisted movement to automate software and data operations. He has contributed to and founded projects within various open-source communities, including work with Summer of Code, where he focused on the Semantic Web and REST APIs.A strong enthusiast of Python and Rust, he develops tools centered around LLMs and agentic systems. He is a maintainer of the SmartCore ML library, as well as the creator of Arrowspace and the Topological Transformer.

// Related Links

Website: https://www.tuned.org.uk

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Timestamps:

[00:00] Graph Wiring for ML

[00:32] RAG and Vector Similarity

[08:58] Geometric Search Trade-offs

[13:12] Vector DB Algorithm Integration

[21:32] Feature-Based Retrieval Shift

[26:04] Epiplexity and Embeddings

[31:26] Epiplexity and Embedding Structure

[40:15] Training vs Post-hoc Models

[47:16] Discovery-Driven Development

[51:22] Updating Mental Models

[53:00] Vector Search vs Agents

[55:30] Wrap up