{"id":24238568,"url":"https://github.com/argmaxml/pgdl","last_synced_at":"2025-09-23T07:31:48.652Z","repository":{"id":226752666,"uuid":"759964842","full_name":"argmaxml/pgdl","owner":"argmaxml","description":"Argmax's postgres vector similarity task","archived":false,"fork":false,"pushed_at":"2024-07-22T11:10:02.000Z","size":547,"stargazers_count":6,"open_issues_count":0,"forks_count":44,"subscribers_count":5,"default_branch":"master","last_synced_at":"2025-04-05T13:43:25.165Z","etag":null,"topics":["deep-learning","embeddings","postgresql","vector-search"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/argmaxml.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-02-19T16:39:11.000Z","updated_at":"2024-07-22T11:10:06.000Z","dependencies_parsed_at":"2024-03-09T13:48:38.461Z","dependency_job_id":"79a944da-b1ac-4f21-bd87-88c1037091f3","html_url":"https://github.com/argmaxml/pgdl","commit_stats":null,"previous_names":["argmaxml/pgdl"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/argmaxml/pgdl","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/argmaxml%2Fpgdl","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/argmaxml%2Fpgdl/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/argmaxml%2Fpgdl/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/argmaxml%2Fpgdl/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/argmaxml","download_url":"https://codeload.github.com/argmaxml/pgdl/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/argmaxml%2Fpgdl/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":276537883,"owners_count":25659929,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-09-23T02:00:09.130Z","response_time":73,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["deep-learning","embeddings","postgresql","vector-search"],"created_at":"2025-01-14T20:28:37.475Z","updated_at":"2025-09-23T07:31:48.636Z","avatar_url":"https://github.com/argmaxml.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# PGDL task\n### Submission deadline: March 30th, 2024\n![Argmax](https://raw.githubusercontent.com/argmaxml/image-search/master/assets/argmax.png)\n\n**Please watch [this explainer video](https://argmax.ml/pgdl).**\n\n## Who is this repo for ?\n[Argmax](https://www.argmaxml.com) is hiring Junior Data scientists.\nThis repo is meant to be a the first step in the process and it will set the stage for the interview.\n\nThe data is taken from a real-life scenario, and it reflects the type of work you will do at Argmax.\n\n\n## About the position\nWe are a botique service company that specializes in recommendation systems and personalized-search.\n\nBuilding a recommender system requires understanding various aspects of the user behaviour and the item properties. We utilize a variety of tools to do so, such as large-language models and vector databases.\n\nAn ideal candidate would be someone who is **proficient in python**, **curious** and able to do **independent research** when necessary.\n\nThis Github repo is designed to reflect some of the challenges you will encounter while working for Argmax.\n\nOur offices located in Ramat-Gan, 42 Ben Gurion Rd. and we work Thursdays from there, the rest of the week we work from home or from clients' premises.\n\n## Some videos from past projects\n\n1. [Uri's talk on structured output with large language models](https://www.youtube.com/watch?v=0mDgjZMcW04)\n1. [Benjamin Kempinski on offline metrics](https://www.youtube.com/watch?v=5OPa2RYL5VI)\n1. [Daniel Hen \u0026 Uri Goren on pricing with contextual bandits](https://www.youtube.com/watch?v=IJtNBbINKbI)\n1. [Eitan Zimmerman's talk on visual feed reranking](https://www.youtube.com/watch?v=q4uF8nF5SWk)\n\n## Getting started with the task\n### Setup\n  1. Set up Docker on your local machine\n  2. In a terimal, type `docker compose up`\n  3. Browse to [JupyterLab](http://localhost:8888)\n  4. Follow the instructions on the `sql.ipynb` notebook\n\n### Submission:\n1. Please clone this repo to a private repo on your github account.\n1. Implement the missing parts.\n1. Please fill in this [form](https://forms.gle/MaMtcL7yuKsbtgdk7).\n1. An interview with Uri would be scheduled for you.\n\n## The Interview process\n### Hands-on Interview\n1. An online hands-on interview would be scheduled during April 2024.\n1. Be prepared to answer questions on your submission\n1. This repo contains a lot of code, in the follow up interview you will be asked to extend a part of it\n\n### On-Site Interview\n1. After passing the online interview, you will be invited to the Argmax offices\n2. The goal of the interview is non-technical, to get to know you and your aspirations\n3. If everything goes well, you will get a contract circa end of April / Beginning of May.\n\n### Best of luck with the task, Uri is available for questions on Linkedin\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fargmaxml%2Fpgdl","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fargmaxml%2Fpgdl","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fargmaxml%2Fpgdl/lists"}