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

Roadmap 2024 | Twitter | Discord | YouTube |

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.

🌟 Key Features

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

⚡️ Incredibly fast

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

See the Benchmark.

🔮 Fused search

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

🍔 Rich data types

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

🎁 Ease-of-use

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

🎮 Get Started

Prerequisites

• Operating system: Ubuntu 22.04 or higher
• Clang-17 or higher to support C++20 and modules
• CMake 3.28 or higher

Install Infinity's Python client

Import necessary modules

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

Connect to the remote server

infinity_obj = infinity.connect(REMOTE_HOST)

Get a database

db = infinity_obj.get_database("default")

Create a table

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

Insert two records

    table.insert(
        [{"num": 1, "body": "undesirable, unnecessary, and harmful", "vec": [1.0, 1.2, 0.8, 0.9]}])
    table.insert(
        [{"num": 2, "body": "publisher=US National Office for Harmful Algal Blooms", "vec": [4.0, 4.2, 4.3, 4.5]}])

Execute a vector search

    res = table.query_builder().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.

🛠️ Build from Source

See Build from Source.

📜 Roadmap

• Infinity Roadmap 2024

🙌 Community

• Discord
• Twitter
• GitHub Discussions
• YouTube