{"id":25157540,"url":"https://github.com/infinities-pro/infinities","last_synced_at":"2026-05-11T03:26:12.785Z","repository":{"id":275218309,"uuid":"925433096","full_name":"Infinities-pro/Infinities","owner":"Infinities-pro","description":"An AI-native database designed for LLM applications, offering lightning-fast hybrid search across dense vectors, sparse vectors, multi-vector tensors, and full-text 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align=\"center\"\u003e\n\n\u003cimg src=\"./images/fabric-logo-gif.gif\" alt=\"fabriclogo\" width=\"400\" height=\"400\"/\u003e\n\n# `Infinities`\n\n![Static Badge](https://img.shields.io/badge/mission-human_flourishing_via_AI_augmentation-purple)\n\u003cbr /\u003e\n\n[![Twitter](https://img.shields.io/badge/Twitter-blue?style=for-the-badge\u0026logo=twitter)](https://x.com/infinities_pro)\n[![YouTube](https://img.shields.io/badge/YouTube-red?style=for-the-badge\u0026logo=youtube)](https://www.youtube.com/@infinities-pro)\n[![Discord](https://img.shields.io/badge/Discord-7289DA?style=for-the-badge\u0026logo=discord\u0026logoColor=white)](https://discord.gg/48mvzHk6)\n[![Website](https://img.shields.io/website?up_message=AVAILABLE\u0026down_message=DOWN\u0026url=https%3A%2F%2Finfinities.pro\u0026style=for-the-badge)](https://infinities.pro)\n[![Docs](https://img.shields.io/badge/Docs-Documentation-green?style=for-the-badge)](https://docs.infinities.pro)\n[![Blog](https://img.shields.io/badge/Blog-Read%20Our%20Articles-orange?style=for-the-badge)](https://medium.com/@infinityonsolana/looking-ahead-the-future-of-vector-databases-c241626747b3)\n\n![GitHub top language](https://img.shields.io/github/languages/top/Infinities-pro/Infinities)\n![GitHub last commit](https://img.shields.io/github/last-commit/Infinities-pro/Infinities)\n[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)\n\n\u003cp class=\"align center\"\u003e\n\u003c/p\u003e\n\n\u003c/div\u003e\n\n\n\u003cdiv align=\"center\"\u003e\n  \u003cimg width=\"900\" src=\"assets/logo2.jpg\" alt=\"Infinity Logo\"/\u003e\n\u003c/div\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003cb\u003eThe AI-native database built for LLM applications, delivering lightning-fast hybrid search across dense embedding, sparse embedding, tensor (multi-vector), and full-text.\u003c/b\u003e\n\u003c/p\u003e\n\n\u003ch4 align=\"center\"\u003e\n  \u003ca href=\"https://docs.infinities.pro\"\u003eDocumentation\u003c/a\u003e |\n  \u003ca href=\"https://docs.infinities.pro/benchmark\"\u003eBenchmark\u003c/a\u003e |\n  \u003ca href=\"https://x.com/infinities_pro\"\u003eTwitter\u003c/a\u003e |\n  \u003ca href=\"https://discord.gg/jEfRUwEYEV\"\u003eDiscord\u003c/a\u003e\n\u003c/h4\u003e\n\n---\n\nInfinity is a cutting-edge AI-native database purpose-built for modern LLM applications. It supports robust hybrid search across diverse data types such as dense vectors, sparse vectors, tensors, full-text, and structured data. This makes it the perfect solution for applications like:\n\n- **Search and Recommendations**\n- **Question-Answering Systems**\n- **Conversational AI**\n- **Copilots**\n- **Content Generation**\n- **Retrieval-Augmented Generation (RAG)**\n\n---\n\n## 🚀 Key Features\n\n### ⚡ Ultra-Fast Performance\n- Sub-millisecond query latency: 0.1ms latency on million-scale vector datasets.\n- Handles 15K+ QPS on vector queries and 12K+ QPS for full-text search on 33M documents.\n\n\u003e See the [Benchmark Report](https://docs.infinities.pro/benchmark) for detailed performance insights.\n\n### 🔎 Hybrid Search\n- Seamlessly combines **dense embedding**, **sparse embedding**, **tensor (multi-vector)**, and **full-text search**.\n- Advanced re-ranking options, including **Reciprocal Rank Fusion (RRF)**, weighted sum, and **ColBERT**-style ranking.\n\n### 📚 Rich Data Support\n- Supports diverse data types, including strings, numerics, vectors, tensors, and more.\n- Built to handle structured and semi-structured data efficiently.\n\n### 🎯 User-Friendly API\n- **Python SDK** for intuitive integration.\n- A lightweight, single-binary architecture ensures easy deployment.\n- Compatible with both **embedded mode** and **client-server mode**.\n\n---\n\n## 🛠 Getting Started\n\nInfinity offers two modes of operation: **embedded mode** (for direct integration into Python applications) and **client-server mode** (for separate backend processes).\n\n### ⚙ Embedded Mode\n\nInstall the embedded SDK:\n```bash\npip install infinity-embedded-sdk==0.6.0.dev2\n```\n\nUse Infinity for dense vector search:\n```python\nimport infinity_embedded\n\n# Connect to Infinity\ninfinity_object = infinity_embedded.connect(\"/absolute/path/to/save/to\")\n\n# Retrieve a database object\ndb_object = infinity_object.get_database(\"default_db\")\n\n# Create a table with multiple column types\ntable_object = db_object.create_table(\"my_table\", {\n    \"num\": {\"type\": \"integer\"},\n    \"body\": {\"type\": \"varchar\"},\n    \"vec\": {\"type\": \"vector, 4, float\"}\n})\n\n# Insert data into the table\ntable_object.insert([\n    {\"num\": 1, \"body\": \"unnecessary and harmful\", \"vec\": [1.0, 1.2, 0.8, 0.9]},\n    {\"num\": 2, \"body\": \"Office for Harmful Blooms\", \"vec\": [4.0, 4.2, 4.3, 4.5]}\n])\n\n# Perform a dense vector search\nres = table_object.output([\"*\"])\\\n                    .match_dense(\"vec\", [3.0, 2.8, 2.7, 3.1], \"float\", \"ip\", 2)\\\n                    .to_pl()\nprint(res)\n```\n\n\u003e 💡 Learn more in the [Python API Reference](https://docs.infinities.pro/pysdk_api_reference).\n\n### 🔧 Deploy Infinity in Client-Server Mode\n\nFor larger-scale deployments, you can set up Infinity in client-server mode. See the [Deploy Infinity Server](https://docs.infinities.pro/deploy_infinity_server) guide for details.\n\n---\n\n## 📈 Performance Comparison\n\n\u003cdiv align=\"center\"\u003e\n  \u003cimg src=\"assets/performance.png\" alt=\"Infinity Performance Comparison\"\u003e\n\u003c/div\u003e\n\n---\n\n## 📜 Documentation\n\n- [Quickstart](https://docs.infinities.pro/)\n- [Python API](https://docs.infinities.pro/pysdk_api_reference)\n- [HTTP API](https://docs.infinities.pro/http_api_reference)\n- [References](https://docs.infinities.pro/references)\n- [FAQ](https://docs.infinities.pro/FAQ)\n\n---\n\n## 🌟 Roadmap\n\nCurious about what’s next for Infinity? Check out the [Roadmap 2025](https://infinities.pro/roadmap) to learn more about upcoming features and improvements.\n\n---\n\n## 🙌 Community\n\nJoin the conversation and connect with us:\n\n- [Website](https://infinities.pro/)\n- [Twitter](https://x.com/infinities_pro)\n- [Discord](https://discord.gg/jEfRUwEYEV)\n\n---\n\n## 🎥 Demo\n\n\u003e [!NOTE]\n\u003e Setting up and hosting the AutoGPT Platform yourself is a technical process. \n\u003e If you'd rather something that just works, we recommend [joining the waitlist](https://bit.ly/3ZDijAI) for the cloud-hosted beta.\n\nhttps://github.com/user-attachments/assets/8120bc03-75b5-4b5e-bb75-54d0a8439da9\n\nThis tutorial assumes you have Docker, VSCode, git and npm installed.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Finfinities-pro%2Finfinities","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Finfinities-pro%2Finfinities","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Finfinities-pro%2Finfinities/lists"}