Text-to-Struct: Fine-tuning SLMs for Query Intent
Hybrid search fails on complex intent: vector search misses constraints, keywords miss nuance. This talk explores fine-tuning SLMs for ‘Query Understanding’—transforming vague inputs into structured requests. Learn to extract metadata, expand terms, and route intent to build a search engine that does the hard work for your users.
Circular Dependency Fixes when Bootstrapping a Golden Set
For a golden set, you need queries. Even if you have them, you can’t judge all docs for each query. Only the top N. How do we rank the top N? See the circular dependency? We’ll talk about ways to untangle it: lexical search, significant terms, training an embedder from scratch, etc. By iteratively refining data and queries, we’ll get there.
How to Survive the Vortex of LLM Change
The LLM ecosystem changes faster than most teams can adapt. This talk shares our experience and the practical lessons we’ve learned while building an intelligent search product in a world where models, tools, and best practices constantly evolve.
C++ Search for Database Kernels: Built In, Not Bolted On
IResearch is an Apache 2.0 C++ search engine built to live inside databases. We’ll benchmark it against leading open-source search engines, show why vectorized scoring is the next frontier for information retrieval engines, share the mistakes we made over a decade of development and explore how database-native search fits modern query execution.
Reviving phonetic algorithms for better search relevance
Fuzzy search is a double-edged sword: it fixes typos but drowns users in noise on large corpora. At INA, we revived ancient phonetic algorithms to improve relevance. This session compares fuzzy vs. phonetic search on a massive archive, showing how “sounding right” beats “spelling close.”
From Inverted Index to Columnar Vectorized Execution Search
Search engines are converging with analytical data systems. This talk explores how columnar data layouts, SIMD-accelerated execution, and bulk-oriented processing are reshaping search internals. We examine where traditional models fall short and how hardware-aware techniques from analytics engines are defining the next search infrastructure.
Towards Chunk-less RAG
Retrieval-Augmented Generation (RAG) systems rely on pre-chunked documents, tying retrieval to arbitrary boundaries. This talk explores an experimental approach that surfaces semantically relevant text spans, without chunking. We’ll share surprising findings and examine whether this technique points toward a viable chunk-free retrieval paradigm.