The AI-native database built for LLM applications, providing incredibly fast hybrid search of dense embedding, sparse embedding, tensor and full-text
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.
Infinity comes with high performance, flexibility, ease-of-use, and many features designed to address the challenges facing the next-generation AI applications:
See the Benchmark report for more information.
Supports a wide range of data types including strings, numerics, vectors, and more.
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:
pip install infinity-embedded-sdk==0.5.0.dev5
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)
If you wish to deploy Infinity with the server and client as separate processes, see the Deploy infinity server guide.
See the Build from Source guide.
๐ก For more information about Infinity's Python API, see the Python API Reference.
See the Infinity Roadmap 2024