
Multi-Vector Search with Amélie Chatelain and Antoine Chaffin - Weaviate Podcast #134!
From Weaviate Podcast by Weaviate
March 23, 2026 · 1h 21m
About this episode
Amélie Chatelain and Antoine Chaffin discuss advancements in Multi-Vector Search and its implications for search systems.
Amélie Chatelain and Antoine Chaffin from LightOn are leading the way in the next generation of search powered by Multi-Vector representations and Late Interaction. The podcast begins with what motivates them to work on Multi-Vector Search, continuing to discuss particular details such as the combination between lexical and semantic search, as well as bi-encoder speed with cross encoder accuracy. This discussion continues to present insights about training multi-vector models and how they differ from their single-vector predecessors. The conversation continues into particular successes of Late Interaction such as code, reasoning-intensive, and multimodal retrieval. Agents are great at searching with grep, but they are even better with ColGrep! Reasoning-Intensive Retrieval is a step change in how we think about search systems, beautifully enabled by both Late Interaction models and Agentic Search. Further, Multimodal Search, such as matching text with videos, is seeing massive benefits from Multi-Vector representations. The podcast continues to dive into the cost of MaxSim and how efficient methods such as MUVERA and PLAID can help. The podcast concludes with a presentation of…
People in this episode
Guests: Amélie Chatelain, Antoine Chaffin
Topics covered
- Multi-Vector Search
- Late Interaction
- lexical and semantic search
- bi-encoder speed
- Reasoning-Intensive Retrieval
- Multimodal Search
- training multi-vector models
Keywords
- Multi-Vector representations
- Late Interaction
- search systems
- training models
- semantic search
- code retrieval
- multimodal retrieval
Mentioned in this episode
Organizations: LightOn
Products: ColGrep, MaxSim, MUVERA, PLAID, ColBERT-Zero, PyLate
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