Quickstart

Prerequisites

• CPU: x86_64 with AVX2
• OS: Linux with glibc ≥ 2.17
• Python: Python 3.10+

Install Infinity locally using pip

Infinity, also available as a Python module, 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:

1. Install Infinity as a module:

   pip install infinity-sdk==0.3.0.dev1
2. Use Infinity to conduct a KNN search:

   import infinity
   
   # Connect to infinity
   infinity_obj = infinity.connect("/path/to/save/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 Infinity with the server and client as separate processes:

See Deploy infinity server.

🛠️ Build from Source

See Build from Source.

Try our Python examples

• Create table, insert data, and search
• Import file and export data
• Delete or update data
• Conduct a vector search
• Conduct a full-text search
• Conduct a hybrid search

Python API reference

For detailed information about Infinity's Python API, see the Python API Reference.