How to build an embedding search tool for GitHub

Anthony Accomazzo, co-founder of the integration platform Sequin, discusses the challenges of retrieving previously written code from Git history and introduces a solution using semantic search powered by embeddings. By leveraging OpenAI's API and tools like Retool and Sequin, developers can build an advanced search tool for GitHub pull requests, issues, and commits that goes beyond simple string matching to perform semantic comparisons. The process involves syncing GitHub data to a Postgres database, generating embeddings for these records, and setting up a Retool workflow to update embeddings with webhooks. This semantic search capability enhances the ability to find specific code and analyze data, such as identifying the ratio of bug fixes to new features. The approach not only improves search efficiency but also opens opportunities for further analysis and tooling development on GitHub data.

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