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href=\"https://cloud.lancedb.com\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://github.com/user-attachments/assets/92dad0a2-2a37-4ce1-b783-0d1b4f30a00c\" alt=\"LanceDB Cloud Public Beta\" width=\"100%\" style=\"max-width: 100%;\"\u003e\n\u003c/a\u003e\n\n\u003cdiv align=\"center\"\u003e\n\u003cp align=\"center\"\u003e\n\n\u003cpicture\u003e\n  \u003csource media=\"(prefers-color-scheme: dark)\" srcset=\"https://github.com/user-attachments/assets/ac270358-333e-4bea-a132-acefaa94040e\"\u003e\n  \u003csource media=\"(prefers-color-scheme: light)\" srcset=\"https://github.com/user-attachments/assets/b864d814-0d29-4784-8fd9-807297c758c0\"\u003e\n  \u003cimg alt=\"LanceDB Logo\" src=\"https://github.com/user-attachments/assets/b864d814-0d29-4784-8fd9-807297c758c0\" width=300\u003e\n\u003c/picture\u003e\n\n**Search More, Manage Less**\n\n\u003ca href='https://github.com/lancedb/vectordb-recipes/tree/main' target=\"_blank\"\u003e\u003cimg alt='LanceDB' src='https://img.shields.io/badge/VectorDB_Recipes-100000?style=for-the-badge\u0026logo=LanceDB\u0026logoColor=white\u0026labelColor=645cfb\u0026color=645cfb'/\u003e\u003c/a\u003e\n\u003ca href='https://lancedb.github.io/lancedb/' target=\"_blank\"\u003e\u003cimg alt='lancdb' src='https://img.shields.io/badge/DOCS-100000?style=for-the-badge\u0026logo=lancdb\u0026logoColor=white\u0026labelColor=645cfb\u0026color=645cfb'/\u003e\u003c/a\u003e\n[![Blog](https://img.shields.io/badge/Blog-12100E?style=for-the-badge\u0026logoColor=white)](https://blog.lancedb.com/)\n[![Discord](https://img.shields.io/badge/Discord-%235865F2.svg?style=for-the-badge\u0026logo=discord\u0026logoColor=white)](https://discord.gg/zMM32dvNtd)\n[![Twitter](https://img.shields.io/badge/Twitter-%231DA1F2.svg?style=for-the-badge\u0026logo=Twitter\u0026logoColor=white)](https://twitter.com/lancedb)\n[![Gurubase](https://img.shields.io/badge/Gurubase-Ask%20LanceDB%20Guru-006BFF?style=for-the-badge)](https://gurubase.io/g/lancedb)\n\n\u003c/p\u003e\n\n\u003cimg max-width=\"750px\" alt=\"LanceDB Multimodal Search\" src=\"https://github.com/lancedb/lancedb/assets/917119/09c5afc5-7816-4687-bae4-f2ca194426ec\"\u003e\n\n\u003c/p\u003e\n\u003c/div\u003e\n\n\u003chr /\u003e\n\nLanceDB is an open-source database for vector-search built with persistent storage, which greatly simplifies retrieval, filtering and management of embeddings.\n\nThe key features of LanceDB include:\n\n* Production-scale vector search with no servers to manage.\n\n* Store, query and filter vectors, metadata and multi-modal data (text, images, videos, point clouds, and more).\n\n* Support for vector similarity search, full-text search and SQL.\n\n* Native Python and Javascript/Typescript support.\n\n* Zero-copy, automatic versioning, manage versions of your data without needing extra infrastructure.\n\n* GPU support in building vector index(*).\n\n* Ecosystem integrations with [LangChain 🦜️🔗](https://python.langchain.com/docs/integrations/vectorstores/lancedb/), [LlamaIndex 🦙](https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/LanceDBIndexDemo.html), Apache-Arrow, Pandas, Polars, DuckDB and more on the way.\n\nLanceDB's core is written in Rust 🦀 and is built using \u003ca href=\"https://github.com/lancedb/lance\"\u003eLance\u003c/a\u003e, an open-source columnar format designed for performant ML workloads.\n\n## Quick Start\n\n**Javascript**\n```shell\nnpm install @lancedb/lancedb\n```\n\n```javascript\nimport * as lancedb from \"@lancedb/lancedb\";\n\nconst db = await lancedb.connect(\"data/sample-lancedb\");\nconst table = await db.createTable(\"vectors\", [\n\t{ id: 1, vector: [0.1, 0.2], item: \"foo\", price: 10 },\n\t{ id: 2, vector: [1.1, 1.2], item: \"bar\", price: 50 },\n], {mode: 'overwrite'});\n\n\nconst query = table.vectorSearch([0.1, 0.3]).limit(2);\nconst results = await query.toArray();\n\n// You can also search for rows by specific criteria without involving a vector search.\nconst rowsByCriteria = await table.query().where(\"price \u003e= 10\").toArray();\n```\n\n**Python**\n```shell\npip install lancedb\n```\n\n```python\nimport lancedb\n\nuri = \"data/sample-lancedb\"\ndb = lancedb.connect(uri)\ntable = db.create_table(\"my_table\",\n                         data=[{\"vector\": [3.1, 4.1], \"item\": \"foo\", \"price\": 10.0},\n                               {\"vector\": [5.9, 26.5], \"item\": \"bar\", \"price\": 20.0}])\nresult = table.search([100, 100]).limit(2).to_pandas()\n```\n\n## Blogs, Tutorials \u0026 Videos\n* 📈 \u003ca href=\"https://blog.lancedb.com/benchmarking-random-access-in-lance/\"\u003e2000x better performance with Lance over Parquet\u003c/a\u003e\n* 🤖 \u003ca href=\"https://github.com/lancedb/vectordb-recipes/tree/main/examples/Youtube-Search-QA-Bot\"\u003eBuild a question and answer bot with LanceDB\u003c/a\u003e\n","funding_links":[],"categories":["Data","Uncategorized","State, Retrieval \u0026 Coordination Infrastructure","Readings"],"sub_categories":["Uncategorized","Semantic Retrieval","Vector similarity search"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/lancedb.github.io%2Flancedb%2F","html_url":"https://awesome.ecosyste.ms/projects/lancedb.github.io%2Flancedb%2F","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/lancedb.github.io%2Flancedb%2F/lists"}