Quickstart

A quickstart guide.

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==0.5.0
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:

• 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 the capabilities and usage of Infinity's Python API, see the Python API Reference.