Ranked Search for Semi-Structured Data
We’ve been working on a search problem that requires querying both text and numbers simultaneously. For example, in a dataset of clothing items with descriptions and prices, a search for “slim pants for $20” should prioritize skinny jeans for $25 over slim pants for $50 because they are semantically similar and the price is closer. I’ve found that standard embedding models struggle with numerical ordering, while text-to-SQL methods rely on exact matches and often filter out too many results. To solve this, we built a system designed specifically for structured datasets like CSVs or tables.…
Ranked Search for Semi-Structured Data launched on February 27, 2025 with 24 votes, #670 of 5,999 launches that month and more than 90% of that year's launches.
In plain words
Ranked Search for Semi-Structured Data is a search tool designed for querying datasets like CSVs and tables that contain both text and numerical values. It addresses limitations in standard embedding models and text-to-SQL approaches by processing each column independently, using embeddings for text and custom scoring for numbers. This allows searches like "slim pants for $20" to return semantically similar results with prices closer to the target value, rather than exact matches or over-filtered results. The tool is aimed at developers and data teams working with structured data.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
We’ve been working on a search problem that requires querying both text and numbers simultaneously. For example, in a dataset of clothing items with descriptions and prices, a search for “slim pants for $20” should prioritize skinny jeans for $25 over slim pants for $50 because they are semantically similar and the…
Does a similar job
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