Infinity

⚡️ 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 for more information.

🎁 Ease-of-use

Intuitive Python API. See the Python API
A single-binary architecture with no dependencies, making deployment a breeze.
Embedded in Python as a library and friendly to AI developers.

🎮 Get Started

Infinity, also available as a Python library, eliminates the need for a separate back-end server and all the complex communication settings. Using pip install and import infinity, you can quickly build a local AI application in Python, leveraging the world’s fastest and the most powerful RAG database:

   pip install infinity-sdk==0.2.0

   import infinity

   # Connect to infinity
   infinity_obj = infinity.connect("/path/to/save/your/files/to")
   db = infinity_obj.get_database("default_db")
   table = db.create_table("my_table", {"num": {"type": "integer"}, "body": {"type": "varchar"}, "vec": {"type": "vector, 4, float"}})
   table.insert([{"num": 1, "body": "unnecessary and harmful", "vec": [1.0, 1.2, 0.8, 0.9]}])
   table.insert([{"num": 2, "body": "Office for Harmful Blooms", "vec": [4.0, 4.2, 4.3, 4.5]}])
   res = table.output(["*"]).knn("vec", [3.0, 2.8, 2.7, 3.1], "float", "ip", 2).to_pl()
   print(res)

🛠️ Deploy Infinity as a separate server

If you wish to deploy a standalone Infinity server and access it remotely:

See Deploy infinity server.

🛠️ Build from Source

See Build from Source.

💡 For more information about Infinity’s Python API, see the Python API Reference.

Document

📜 Roadmap

See the Infinity Roadmap 2024