Beyond Grep: Search for Reliable Coding Agents
Coding agents succeed in verifiable loops (compiler + tests), but large repos still expose retrieval weaknesses.
This session explores how lexical, structural, and semantic search can provide cleaner context for LLMs. We compare tradeoffs and evaluation approaches to improve reliability without inflating token cost.
Agentic Retrieval: Building Self-Optimizing Search Systems
Relevance feedback loops used to take months. AI agents can now compress the process to seconds. This talk explores agentic retrieval: systems where agents adjust scoring models, schema, and indexing in real time. Learn how to build retrieval infrastructure with verifiable APIs that enable agents to optimize their own search context.
The Three-Body Problem of Inverse Hybrid Search
When users expect alerts for new products matching an uploaded image, the problem becomes inverse hybrid search. Unlike top-K search, alerting must guarantee fetch-all semantics: zero missed matches across all saved searches, combining vector similarity, boolean filters, and lexical signals. We show why this breaks traditional scaling intuition.
Zero downtime index upgrade in Apache Solr
In this talk we’ll explore how Apache Solr introduced the capability to upgrade an index in-place with zero downtime. This upgrade path helps prepare the index for a future Solr major version upgrade without needing to recreate the index from source as is the case with Lucene based search engines today.
From OLTP to OLAP: Is PostgreSQL Eating Analytics Too?
Can PostgreSQL become a serious analytics engine? With emerging columnar extensions, PostgreSQL is pushing beyond OLTP into OLAP territory. This talk explores the current columnar landscape, architectural trade-offs, and how far PostgreSQL can go compared to analytical engines like ClickHouse.
Context-Aware Segments: Solving the “Scatter-Read” Problem
Traditional OpenSearch segments are context-blind, scattering data across multiple segments. We introduce Context-Aware Segments (CAS), an architecture that brings “sharding” logic to the segment level. By enforcing document locality during indexing, we slashed query latency and minimized data footprint through superior pruning and compression.
Building a Local News RAG: The Quest for Trustworthiness
We will show you how we build a local newspaper rag and all the problems that came along the way. From trustworthiness to customer wishes, search optimization and generation problems. Local villages, that LLMs know nothing about, content that is semantically the same and outdated information are only a part of the journey we made.
The Agent Era: How AI Agents Are Reshaping Data Platforms
AI agents have quietly become some of the most demanding users of modern data platforms and most weren’t built with them in mind. In this panel, leaders from Snowflake, Elastic, MotherDuck, and Xata share what agentic workloads actually look like in production: what broke, what had to be rebuilt, and where the architecture is heading.
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.