{"id":18398765,"url":"https://github.com/mongodb-developer/image-search-vector-demo","last_synced_at":"2026-03-12T22:32:39.803Z","repository":{"id":219717334,"uuid":"749723391","full_name":"mongodb-developer/image-search-vector-demo","owner":"mongodb-developer","description":"A Jupyter Notebook demonstrating how to use a multi-modal embedding model to build an image search engine.","archived":false,"fork":false,"pushed_at":"2024-01-29T09:18:08.000Z","size":3789,"stargazers_count":13,"open_issues_count":0,"forks_count":2,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-09-03T07:39:46.862Z","etag":null,"topics":["ai","jupyter-notebook","python","search","vector"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/mongodb-developer.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.md","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2024-01-29T09:12:41.000Z","updated_at":"2025-08-11T02:36:29.000Z","dependencies_parsed_at":"2024-01-29T11:09:06.580Z","dependency_job_id":"7d16478e-4a2c-4fa7-8ad7-a2c1589bc241","html_url":"https://github.com/mongodb-developer/image-search-vector-demo","commit_stats":null,"previous_names":["mongodb-developer/image-search-vector-demo"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/mongodb-developer/image-search-vector-demo","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mongodb-developer%2Fimage-search-vector-demo","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mongodb-developer%2Fimage-search-vector-demo/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mongodb-developer%2Fimage-search-vector-demo/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mongodb-developer%2Fimage-search-vector-demo/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mongodb-developer","download_url":"https://codeload.github.com/mongodb-developer/image-search-vector-demo/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mongodb-developer%2Fimage-search-vector-demo/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":30446445,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-12T21:31:01.033Z","status":"ssl_error","status_checked_at":"2026-03-12T21:30:43.161Z","response_time":114,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"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":["ai","jupyter-notebook","python","search","vector"],"created_at":"2024-11-06T02:24:18.011Z","updated_at":"2026-03-12T22:32:39.782Z","avatar_url":"https://github.com/mongodb-developer.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Image Search with MongoDB Atlas Vector Search\n\nThis repository contains a Jupyter Notebook demonstrating how to generate\nvector embeddings for both text and images using a multi-modal embedding model.\n\n## Getting Ready To Run The Notebook\n\nThe first thing you'll want to do is create a virtual environment using your favorite technique. I tend to use [venv](https://docs.python.org/3/library/venv.html), which comes with Python.\n\nOnce you've done that, install dependencies with:\n\n```\npip install -r requirements.txt\n```\n\nYou'll need to set an environment variable, `MONGODB_URI`, containing the connection string for your MongoDB cluster.\n\nOne more thing you'll need is an \"images\" directory, containing some images to index! I downloaded  [Kaggle's ImageNet 1000 (mini) dataset](https://www.kaggle.com/datasets/ifigotin/imagenetmini-1000), which contains lots of images at around 4GB, but you can use a different dataset if you prefer. 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