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The AI-native database built for LLM applications, providing incredibly fast full-text and vector search

Infinity is a cutting-edge AI-native database that provides a wide range of search capabilities for rich data types such as vectors, 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.

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Infinity comes with high performance, flexibility, ease-of-use, and many features designed to address the challenges facing the next-generation AI applications:

• Achieves 0.1 milliseconds query latency on million-scale vector datasets.
• Up to 10K QPS on million-scale vector datasets.

See the Benchmark report for more information.

Supports a fused search of multiple embeddings and full text, in addition to filtering.

Supports a wide range of data types including strings, numerics, vectors, and more.

• Intuitive Python API. See the Python API
• A single-binary architecture with no dependencies, making deployment a breeze.

docker pull infiniflow/infinity:nightly
docker run -d --name infinity -v /tmp/infinity/:/tmp/infinity --network=host infiniflow/infinity:nightly

You can download the binary package (deb, rpm, or tgz) for your respective host operating system from https://github.com/infiniflow/infinity/releases. The prebuilt packages are compatible with Linux distributions based on glibc 2.17 or later, for example, RHEL 7, Debian 8, Ubuntu 14.04.

Fedora/RHEL/CentOS/OpenSUSE

sudo rpm -i infinity-0.1.0-dev-x86_64.rpm
sudo systemctl start infinity

Ubuntu/Debian

sudo dpkg -i infinity-0.1.0-dev-x86_64.deb
sudo systemctl start infinity

See Build from Source.

infinity-sdk requires Python 3.10+.

pip3 install infinity-sdk

import infinity
import infinity.index as index
from infinity.common import REMOTE_HOST

infinity_obj = infinity.connect(REMOTE_HOST)

db = infinity_obj.get_database("default")

# Drop my_table if it already exists
db.drop_table("my_table", if_exists=True)
# Create a table named "my_table"
table = db.create_table("my_table", {"num": "integer", "body": "varchar", "vec": "vector, 4, float"}, None)

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)

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

See the Infinity Roadmap 2024

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