{"id":27904814,"url":"https://github.com/kanadshee-18/vector-store","last_synced_at":"2026-05-12T07:38:47.454Z","repository":{"id":291367448,"uuid":"977220487","full_name":"KanadShee-18/Vector-Store","owner":"KanadShee-18","description":"A minimal project to understand how cosine similarity works in a vector database 🧠📊. It demonstrates semantic search by converting text into embeddings and comparing them using vector math.","archived":false,"fork":false,"pushed_at":"2025-05-04T06:24:31.000Z","size":189,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-05-04T07:25:07.729Z","etag":null,"topics":["ai","cosine-similarity","expressjs","gemini","node-cache","nodejs","textembedding","ts-node","typescript","uuidv4","vector","vector-search"],"latest_commit_sha":null,"homepage":"","language":"TypeScript","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/KanadShee-18.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,"zenodo":null}},"created_at":"2025-05-03T17:40:29.000Z","updated_at":"2025-05-04T06:24:34.000Z","dependencies_parsed_at":"2025-05-04T07:25:10.724Z","dependency_job_id":"478e1efd-e277-40fa-a955-17cfe2d60a2e","html_url":"https://github.com/KanadShee-18/Vector-Store","commit_stats":null,"previous_names":["kanadshee-18/vector-store"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/KanadShee-18%2FVector-Store","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/KanadShee-18%2FVector-Store/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/KanadShee-18%2FVector-Store/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/KanadShee-18%2FVector-Store/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/KanadShee-18","download_url":"https://codeload.github.com/KanadShee-18/Vector-Store/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":252594252,"owners_count":21773635,"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","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","cosine-similarity","expressjs","gemini","node-cache","nodejs","textembedding","ts-node","typescript","uuidv4","vector","vector-search"],"created_at":"2025-05-05T23:33:29.686Z","updated_at":"2026-05-12T07:38:47.424Z","avatar_url":"https://github.com/KanadShee-18.png","language":"TypeScript","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🔍 Vector Similarity Search Demo\n---\n\n## A simple visualization:\n![Cosine Similarity](cosine-similarity_visualization.png)\n\n\u003e A minimal project to understand how **cosine similarity** works in a **vector database** 🧠📊.  \n\u003e It demonstrates **semantic search** by converting text into embeddings and comparing them using vector math.\n\n---\n\n## 🧰 Tech Stack\n\n- ⚙️ Node.js + TypeScript  \n- 🧾 Custom in-memory vector store  \n- 📐 Cosine similarity algorithm\n\n---\n\n## 💡 What You’ll Learn\n\n- 🔤 How text embeddings are used in vector search  \n- 📏 How cosine similarity compares semantic meaning  \n- 🎯 Filtering using `topK` and `threshold` parameters  \n- ⚡ Real-world basics of how vector DBs like Pinecone, Weaviate, or FAISS work\n\n## License\n[MIT License](LICENSE)","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkanadshee-18%2Fvector-store","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkanadshee-18%2Fvector-store","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkanadshee-18%2Fvector-store/lists"}