---
title: "infinity"
type: "tool"
slug: "infiniflow-infinity"
canonical_url: "https://www.graphcanon.com/tools/infiniflow-infinity"
github_url: "https://github.com/infiniflow/infinity"
homepage_url: "https://infiniflow.org"
stars: 4598
forks: 430
primary_language: "C++"
license: "Apache-2.0"
categories: ["vector-databases", "data-retrieval"]
tags: ["full-text-search", "information-retrieval", "embedding", "ai-native", "approximate-nearest-neighbor-search", "hybrid-search", "hnsw", "bm25"]
updated_at: "2026-07-07T18:40:53.41001+00:00"
---
# infinity
> AI-native database for LLM applications
Infiniflow's Infinity is an advanced, high-performance database optimized for Large Language Model (LLM) applications, featuring hybrid search capabilities with dense vector, sparse vectors, tensors and full-text data.
## Facts
- Repository: https://github.com/infiniflow/infinity
- Homepage: https://infiniflow.org
- Stars: 4,598 ยท Forks: 430 ยท Open issues: 65 ยท Watchers: 43
- Primary language: C++
- License: Apache-2.0
- Last pushed: 2026-06-29T14:53:06+00:00
## Categories
- [Vector Databases](/categories/vector-databases.md)
- [Data & Retrieval](/categories/data-retrieval.md)
## Tags
full-text-search, information-retrieval, embedding, ai-native, approximate-nearest-neighbor-search, hybrid-search, hnsw, bm25
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## README (excerpt)
```text
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.
- [Key Features](#-key-features)
- [Get Started](#-get-started)
- [Document](#-document)
- [Roadmap](#-roadmap)
- [Community](#-community)
## โก๏ธ Performance
## ๐ 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:
### ๐ Incredibly fast
- Achieves 0.1 milliseconds query latency and 15K+ QPS on million-scale vector datasets.
- Achieves 1 millisecond latency and 12K+ QPS in full-text search on 33M documents.
> See the [Benchmark report](https://infiniflow.org/docs/dev/benchmark) for more information.
### ๐ฎ Powerful search
- Supports a hybrid search of dense embedding, sparse embedding, tensor, and full text, in addition to filtering.
- Supports several types of rerankers including RRF, weighted sum and **ColBERT**.
### ๐ Rich data types
Supports a wide range of data types including strings, numerics, vectors, and more.
### ๐ Ease-of-use
- Intuitive Python API. See the [Python API](https://infiniflow.org/docs/dev/pysdk_api_reference)
- A single-binary architecture with no dependencies, making deployment a breeze.
- Embedded in Python as a module and friendly to AI developers.
## ๐ฎ Get Started
This section provides guidance on deploying the Infinity database using Docker, with the client and server as separate processes.
### Prerequisites
- CPU: x86_64 with AVX2 support.
- OS:
- Linux with glibc 2.17+.
- Windows 10+ with WSL/WSL2.
- MacOS
- Python: Python 3.11+.
### Install Infinity server
#### Linux x86_64 & MacOS x86_64
```bash
sudo mkdir -p /var/infinity && sudo chown -R $USER /var/infinity
docker pull infiniflow/infinity:nightly
docker run -d --name infinity -v /var/infinity/:/var/infinity --ulimit nofile=500000:500000 --network=host infiniflow/infinity:nightly
```
#### Windows
If you are on Windows 10+, you must enable WSL or WSL2 to deploy Infinity using Docker. Suppose you've installed Ubuntu in WSL2:
1. Follow [this](https://learn.microsoft.com/en-us/windows/wsl/systemd) to enable systemd inside WSL2.
2. Install docker-ce according to the [instructions here](https://docs.docker.com/engine/install/ubuntu).
3. If you have installed Docker Desktop version 4.29+ for Windows: **Settings** **>** **Features in development**, then select **Enable host networking**.
4. Pull the Docker image and start Infinity:
```bash
sudo mkdir -p /var/infinity && sudo chown -R $USER /var/infinity
docker pull infiniflow/infinity:nightly
docker run -d --name infinity -v /var/infinity/:/var/infinity --ulimit nofile=500000:500000 --network=host infiniflow/infinity:nightly
```
### Install Infinity client
```
pip install infinity-sdk==0.7.0
```
### Run a vector s
```
---
**Machine-readable endpoints**
- JSON: [`/api/graphcanon/tools/infiniflow-infinity`](/api/graphcanon/tools/infiniflow-infinity)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)
_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_