base on The AI-native database built for LLM applications, providing incredibly fast hybrid search of dense vector, sparse vector, tensor (multi-vector), and full-text <div align="center"> <img width="187" src="https://github.com/infiniflow/infinity/assets/7248/015e1f02-1f7f-4b09-a0c2-9d261cd4858b" alt="Infinity logo"/> </div> <p align="center"> <b>The AI-native database built for LLM applications, providing incredibly fast hybrid search of dense embedding, sparse embedding, tensor and full-text</b> </p> <h4 align="center"> <a href="https://infiniflow.org/docs/dev/category/get-started">Document</a> | <a href="https://infiniflow.org/docs/dev/benchmark">Benchmark</a> | <a href="https://twitter.com/infiniflowai">Twitter</a> | <a href="https://discord.gg/jEfRUwEYEV">Discord</a> </h4> Infinity is a cutting-edge AI-native database that provides a wide range of search capabilities for rich data types such as dense vector, sparse vector, tensor, full-text, and structured data. It provides robust support for various LLM applications, including search, recommenders, question-answering, conversational AI, copilot, content generation, and many more **RAG** (Retrieval-augmented Generation) applications. - [Key Features](#-key-features) - [Get Started](#-get-started) - [Document](#-document) - [Roadmap](#-roadmap) - [Community](#-community) ## ⚡️ Performance <div class="column" align="middle"> <img src="https://github.com/user-attachments/assets/c4c98e23-62ac-4d1a-82e5-614bca96fe0a" alt="Infinity performance comparison"/> </div> ## 🌟 Key Features Infinity comes with high performance, flexibility, ease-of-use, and many features designed to address the challenges facing the next-generation AI applications: ### 🚀 Incredibly fast - Achieves 0.1 milliseconds query latency and 15K+ QPS on million-scale vector datasets. - Achieves 1 millisecond latency and 12K+ QPS in full-text search on 33M documents. > See the [Benchmark report](https://infiniflow.org/docs/dev/benchmark) for more information. ### 🔮 Powerful search - Supports a hybrid search of dense embedding, sparse embedding, tensor, and full text, in addition to filtering. - Supports several types of rerankers including RRF, weighted sum and **ColBERT**. ### 🍔 Rich data types Supports a wide range of data types including strings, numerics, vectors, and more. ### 🎁 Ease-of-use - Intuitive Python API. See the [Python API](https://infiniflow.org/docs/dev/pysdk_api_reference) - A single-binary architecture with no dependencies, making deployment a breeze. - Embedded in Python as a module and friendly to AI developers. ## 🎮 Get Started Infinity supports two working modes, embedded mode and client-server mode. Infinity's embedded mode enables you to quickly embed Infinity into your Python applications, without the need to connect to a separate backend server. The following shows how to operate in embedded mode: ```bash pip install infinity-embedded-sdk==0.5.0 ``` Use Infinity to conduct a dense vector search: ```python import infinity_embedded # Connect to infinity infinity_object = infinity_embedded.connect("/absolute/path/to/save/to") # Retrieve a database object named default_db db_object = infinity_object.get_database("default_db") # Create a table with an integer column, a varchar column, and a dense vector column table_object = db_object.create_table("my_table", {"num": {"type": "integer"}, "body": {"type": "varchar"}, "vec": {"type": "vector, 4, float"}}) # Insert two rows into the table table_object.insert([{"num": 1, "body": "unnecessary and harmful", "vec": [1.0, 1.2, 0.8, 0.9]}]) table_object.insert([{"num": 2, "body": "Office for Harmful Blooms", "vec": [4.0, 4.2, 4.3, 4.5]}]) # Conduct a dense vector search res = table_object.output(["*"]) .match_dense("vec", [3.0, 2.8, 2.7, 3.1], "float", "ip", 2) .to_pl() print(res) ``` #### 🔧 Deploy Infinity in client-server mode If you wish to deploy Infinity with the server and client as separate processes, see the [Deploy infinity server](https://infiniflow.org/docs/dev/deploy_infinity_server) guide. #### 🔧 Build from Source See the [Build from Source](https://infiniflow.org/docs/dev/build_from_source) guide. > 💡 For more information about Infinity's Python API, see the [Python API Reference](https://infiniflow.org/docs/dev/pysdk_api_reference). ## 📚 Document - [Quickstart](https://infiniflow.org/docs/dev/) - [Python API](https://infiniflow.org/docs/dev/pysdk_api_reference) - [HTTP API](https://infiniflow.org/docs/dev/http_api_reference) - [References](https://infiniflow.org/docs/dev/category/references) - [FAQ](https://infiniflow.org/docs/dev/FAQ) ## 📜 Roadmap See the [Infinity Roadmap 2024](https://github.com/infiniflow/infinity/issues/338) ## 🙌 Community - [Discord](https://discord.gg/jEfRUwEYEV) - [Twitter](https://twitter.com/infiniflowai) - [GitHub Discussions](https://github.com/infiniflow/infinity/discussions) ", Assign "at most 3 tags" to the expected json: {"id":"6274","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"