Fuzzy deduplicate any CSV using vector embeddings
I made an app to fuzzy-deduplicate my Google Sheets and CRM records - No manual configuration required - Works out-of-the-box on most data types (ex. people, companies, product catalog) Implementation details: - Embeds records using an E5-family model - Performs similarity search using DuckDB w/ vector similarity extension - Does last-mile comparison and merges duplicates using Claude Demo video: https://youtu.be/7mZ0kdwXBwM Github repo (Apache 2.0 licensed): https://github.com/SnowPilotOrg/dedupe_it Background story: My company has a table for…
Fuzzy deduplicate any CSV using vector embeddings launched on November 4, 2024 with 5 votes and more than 50% of that year's launches.
What it does
In the maker’s words, at launch
I made an app to fuzzy-deduplicate my Google Sheets and CRM records - No manual configuration required - Works out-of-the-box on most data types (ex. people, companies, product catalog) Implementation details: - Embeds records using an E5-family model - Performs similarity search using DuckDB w/ vector…
Does a similar job
all alternatives →
A fast, hopefully accurate, fuzzy matching library written in Go2018 · github.com · ▲186Go library that provides fuzzy string matching optimized for filenames and code symbols in the style of Sublime Text, VSCode, IntelliJ IDEA et al. - sahilm/fuzzy


PDD – Probabilistic De-Duplication of Streams with Bloom Filters2016 · github.com · ▲86Advanced Bloom Filter Based Algorithms for Efficient Approximate Data De-Duplication in Streams - jparkie/PDD
- GSGoogle Sheets add-on to compare text, fuzzy-match, highlight duplicates2020 · ▲80
I created an add-on for Google Sheets called Flookup, and it comes both as a free version and a VERY AFFORDABLE paid version. At its core, Flookup is a fuzzy matching add-on that helps you manage text that is less than a 100% match. Beyond that it can be used to: 1. Search for and match data regardless of whether it contains typos. 2. Highlight and delete duplicates duplicates even if the data has mismatched text. 3. Calculate the percentage similarity between strings. 4. Extract unique values from any column based on percentage similarity. 5. Sum and find the average of numbers based on…
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