{"id":16159837,"url":"https://github.com/sebi75/embeddings-searching","last_synced_at":"2026-04-11T01:55:03.736Z","repository":{"id":162262264,"uuid":"636846410","full_name":"sebi75/embeddings-searching","owner":"sebi75","description":"How to search long documents using OpenAI embeddings","archived":false,"fork":false,"pushed_at":"2023-05-06T14:49:49.000Z","size":5031,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-07T02:37:18.429Z","etag":null,"topics":["embeddings","microservice","openai","python","vite"],"latest_commit_sha":null,"homepage":"","language":"TypeScript","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/sebi75.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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":"2023-05-05T19:36:58.000Z","updated_at":"2023-06-21T12:39:00.000Z","dependencies_parsed_at":null,"dependency_job_id":"a215df99-6756-4001-bbd4-fef6a18e309a","html_url":"https://github.com/sebi75/embeddings-searching","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/sebi75/embeddings-searching","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sebi75%2Fembeddings-searching","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sebi75%2Fembeddings-searching/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sebi75%2Fembeddings-searching/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sebi75%2Fembeddings-searching/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sebi75","download_url":"https://codeload.github.com/sebi75/embeddings-searching/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sebi75%2Fembeddings-searching/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":270192071,"owners_count":24542376,"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":"2025-08-13T02:00:09.904Z","response_time":66,"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":["embeddings","microservice","openai","python","vite"],"created_at":"2024-10-10T01:59:56.271Z","updated_at":"2026-04-11T01:54:56.395Z","avatar_url":"https://github.com/sebi75.png","language":"TypeScript","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Searching Documents Using Natural Language\n\nThis project is a web application that offers a modern user interface for uploading and searching documents using natural language. It consists of two main components: a Vite React app for the user interface, and a Python Flask server for handling all the logic.\n\n![photo of the app](https://user-images.githubusercontent.com/36008268/236631396-a9ecd9fa-bf9c-454d-a3e1-202ce7835d51.png)\n\n## Folder Structure\n\nThe project is organized into two main folders:\n\nui: Contains the Vite React app for the user interface. Users can upload documents and search through them using an intuitive, modern UI.\nserver: Contains the Python Flask server, which has several endpoints defined for fetching already indexed documents, indexing new documents using the OpenAI embeddings API, and searching documents using natural language.\n\n## Getting Started\n\nFollow these steps to set up the project and run it locally:\n\nPrerequisites\nMake sure you have Node.js (version 18.x or later) and Python (version 3.7 or later) installed on your system.\n\n### UI Setup\n\nFrom the project root navigate to the ui folder:\n\n```bash\ncd ui\n```\n\nInstall the dependencies:\n\n```bash\nnpm install\n```\n\nStart the Vite React app:\n\n```bash\nnpm run dev\n```\n\nThe app should now be running at http://localhost:5173.\n\nServer Setup\nFrom the project root, navigate to the server folder:\n\n```bash\ncd server\n```\n\n(Optional) Create a virtual environment:\n\n```bash\npython -m venv embeddings-search\n```\n\nActivate the virtual environment:\nOn macOS and Linux:\n\n```bash\nsource my_project_env/bin/activate\n```\n\nOn Windows:\n\n```bash\n.\\my_project_env\\Scripts\\activate\n```\n\nInstall the dependencies:\n\n```bash\npip install -r requirements.txt\n```\n\nCreate a .env file in the server folder and add your OpenAI API key:\nOPENAI_API_KEY=your_openai_api_key_here\n\nStart the Flask server:\n\n```bash\npython app.py\n```\n\nThe server should now be running at http://localhost:5000.\n\nUsage\nWith both the UI and server running, you can now access the web application at http://localhost:5173. Upload documents and search through them using natural language queries.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsebi75%2Fembeddings-searching","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsebi75%2Fembeddings-searching","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsebi75%2Fembeddings-searching/lists"}