The AI-native database built for LLM applications, providing incredibly fast hybrid search of dense vector, sparse vector, tensor (multi-vector), and full-text.
The AI-native database built for LLM applications, providing incredibly fast hybrid search of dense embedding, sparse embedding, tensor and full-text
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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.
⚡️ Performance
🌟 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 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 for more information.
🔮 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.
🍔 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 API
- A single-binary architecture with no dependencies, making deployment a breeze.
- Embedded in Python as a module and friendly to AI developers.
🎮 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
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:
Follow this to enable systemd inside WSL2.
Install docker-ce according to the instructions here.
If you have installed Docker Desktop version 4.29+ for Windows: Settings > Features in development, then select Enable host networking.
Pull the Docker image and start Infinity:
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==0.7.2
Run a vector search
import infinity
infinity_obj = infinity.connect(infinity.NetworkAddress("<SERVER_IP_ADDRESS>", 23817))
db_object = infinity_object.get_database("default_db")
table_object = db_object.create_table("my_table", {"num": {"type": "integer"}, "body": {"type": "varchar"}, "vec": {"type": "vector, 4, float"}})
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]}])
res = table_object.output(["*"])
.match_dense("vec", [3.0, 2.8, 2.7, 3.1], "float", "ip", 2)
.to_pl()
print(res)
🔧 Deploy Infinity using binary
If you wish to deploy Infinity using binary with the server and client as separate processes, see the Deploy infinity using binary guide.
🔧 Build from Source
See the Build from Source guide.
📚 Document
📜 Roadmap
See the Infinity Roadmap 2025