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Version: v0.4.0

Quickstart

Prerequisites​

  • CPU: x86_64 with AVX2 support.
  • OS:
    • Linux with glibc 2.17+.
    • Windows 10+ with WSL/WSL2.
  • Python: Python 3.10+.

Install embedded Infinity​

If you wish to embed Infinity into your Python application without the need for a separate backend server:

  1. Install the Infinity-embedded SDK: 
    pip install infinity-embedded-sdk==0.4.0
  2. Use Infinity to conduct a dense vector search: 
    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 guide.

Build from Source​

If you wish to build Infinity from source, see the Build from Source guide.

Try our Python examples​

Try the following links to explore practical examples of using Infinity in Python:

Python API reference​

For detailed information about the capabilities and usage of Infinity's Python API, see the Python API Reference.