{"id":35091356,"url":"https://github.com/marcusschiesser/vectorstores","last_synced_at":"2026-01-13T22:01:48.658Z","repository":{"id":246010712,"uuid":"687342914","full_name":"marcusschiesser/vectorstores","owner":"marcusschiesser","description":"Vectorstores is a framework for using vector databases in your AI applications","archived":false,"fork":false,"pushed_at":"2025-12-29T04:15:13.000Z","size":83417,"stargazers_count":7,"open_issues_count":1,"forks_count":1,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-12-29T06:55:36.814Z","etag":null,"topics":["ai","ai-agents","data","database","embeddings","vector","vector-database"],"latest_commit_sha":null,"homepage":"http://vectorstores.org","language":"TypeScript","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":"run-llama/LlamaIndexTS","license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/marcusschiesser.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","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,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2023-09-05T06:58:45.000Z","updated_at":"2025-12-29T06:15:37.000Z","dependencies_parsed_at":null,"dependency_job_id":"b7f25ec5-06a5-4ca4-bf35-7895e60120c6","html_url":"https://github.com/marcusschiesser/vectorstores","commit_stats":null,"previous_names":["marcusschiesser/llamaindexts","marcusschiesser/vectorstores"],"tags_count":172,"template":false,"template_full_name":null,"purl":"pkg:github/marcusschiesser/vectorstores","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/marcusschiesser%2Fvectorstores","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/marcusschiesser%2Fvectorstores/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/marcusschiesser%2Fvectorstores/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/marcusschiesser%2Fvectorstores/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/marcusschiesser","download_url":"https://codeload.github.com/marcusschiesser/vectorstores/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/marcusschiesser%2Fvectorstores/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28400397,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-13T14:36:09.778Z","status":"ssl_error","status_checked_at":"2026-01-13T14:35:19.697Z","response_time":56,"last_error":"SSL_read: 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","ai-agents","data","database","embeddings","vector","vector-database"],"created_at":"2025-12-27T14:38:19.577Z","updated_at":"2026-01-13T22:01:48.653Z","avatar_url":"https://github.com/marcusschiesser.png","language":"TypeScript","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003ch1 align=\"center\"\u003evectorstores\u003c/h1\u003e\n\u003ch3 align=\"center\"\u003e\n  Vector database framework for your AI application.\n\u003c/h3\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"https://deno.bundlejs.com/badge?q=@vectorstores/core\" alt=\"bundle size\" /\u003e\n\u003c/p\u003e\n\nContext engineering made easy: Vectorstores provides a unified interface for connecting data to your AI application. It supports ingestion of data from various sources, loading it into vector databases and querying it later on.\n\n**Lightweight**: Only 77.5kb (gzip) - vectorstores is designed to be minimal and focused.\n\nIt plays nicely with existing AI frameworks like Vercel AI SDK, see the [Vercel integration guide](https://vectorstores.org/integration/vercel/) for more details.\n\nIt's based on a fork of [LLamaIndexTS](https://github.com/run-llama/LlamaIndexTS), so you can use it as a drop-in replacement for LLamaIndexTS in your existing projects. Compared to LamaIndexTS, vectorstores is more lightweight as it just focuses on vector databases and provides a unified interface for working with them.\n\n## Compatibility\n\n### Multiple JS Environment Support\n\nvectorstores supports multiple JS environments, including:\n\n- Node.js \u003e= 20 ✅\n- Deno ✅\n- Bun ✅\n- Nitro ✅\n- Vercel Edge Runtime ✅ (with some limitations)\n- Cloudflare Workers ✅ (with some limitations)\n\n## Getting started\n\n```shell\nnpm install @vectorstores/core\n```\n\n### Setup in Node.js, Deno, Bun, TypeScript...?\n\nSee the docs: [Installation](https://vectorstores.org/getting_started/installation/)\n\n### Your first (minimal) retrieval example\n\n```shell\nnpm init -y\nnpm install @vectorstores/core openai\nnpm install -D typescript tsx @types/node\nexport OPENAI_API_KEY=your-api-key-here\n```\n\n```ts\nimport { Document, Settings, VectorStoreIndex } from \"@vectorstores/core\";\nimport { OpenAI } from \"openai\";\n\nconst openai = new OpenAI();\nSettings.embedFunc = async (input) =\u003e {\n  const { data } = await openai.embeddings.create({\n    model: \"text-embedding-3-small\",\n    input,\n  });\n  return data.map((d) =\u003e d.embedding);\n};\n\nconst document = new Document({ text: \"Machine learning is...\" });\nconst index = await VectorStoreIndex.fromDocuments([document]);\nconst retriever = index.asRetriever();\n\nconst results = await retriever.retrieve({\n  query: \"What is machine learning?\",\n});\nconsole.log(results[0]?.node.text);\n```\n\n### Adding provider packages\n\nIn most cases, you'll also need to install provider packages to use vectorstores. These are for adding file readers for ingestion or for storing documents in vector databases.\n\nFor example, to use the Weaviate vector database, you would install the following package:\n\n```shell\nnpm install @vectorstores/weaviate\n```\n\n## Examples (local + StackBlitz)\n\n- **Run locally**: [Examples](https://vectorstores.org/getting_started/examples/)\n- **Try in your browser**: [StackBlitz](https://stackblitz.com/github/marcusschiesser/vectorstores/tree/main/examples?file=README.md)\n\n## Contributing\n\nPlease see our [contributing guide](CONTRIBUTING.md) for more information.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmarcusschiesser%2Fvectorstores","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmarcusschiesser%2Fvectorstores","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmarcusschiesser%2Fvectorstores/lists"}