What was that commit? Searching GitHub with OpenAI embeddings

Sequin, a company focused on streaming data from platforms like Salesforce, Stripe, and AWS, has developed a tool utilizing OpenAI embeddings to enhance search capabilities within GitHub repositories. This tool addresses the limitations of traditional text searches by employing semantic search through embeddings, which are vector representations that capture the relatedness of data. By integrating with Postgres using the pgvector extension, Sequin stores these embeddings and performs efficient similarity searches. The workflow involves generating embeddings for GitHub data, such as pull requests and commits, using OpenAI's API, and storing them in a database. To maintain up-to-date search results, Sequin employs event streaming through a serverless Kafka stream to handle updates and inserts. The tool effectively finds relevant GitHub objects based on user queries, allowing for more intuitive search results even with vague descriptions, although it may struggle with content-light entries. The project opens the door for further exploration into expanding search capabilities and utilizing embeddings for comprehensive analysis and reporting.

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