{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/1c99c12d7a1f429b8c358b4a449edcf0\" frameborder=\"0\" width=\"2218\" height=\"1663\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1663,"width":2218,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1663,"thumbnail_width":2218,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/1c99c12d7a1f429b8c358b4a449edcf0-1caa9f0835c55967.gif","duration":312.483,"title":"GitHub search with natural language","description":"In this video, I provide an overview of how vectors, embeddings, and track completions work in Chalk, specifically using GitHub data. I demonstrate how to create feature classes and compute various features, including user data and project information. I also show how to generate embeddings based on search queries and retrieve relevant project data. Please take a look and let me know if you have any questions or need further clarification!"}