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 API
- A single-binary architecture with no dependencies, making deployment a breeze.
🎮 Get Started
Docker pull
docker pull infiniflow/infinity
docker run -d --name infinity -v /tmp/infinity/:/tmp/infinity --network=host infiniflow/infinity bash ./opt/bin/infinity
Install Infinity's Python client
pip install infinity_sdk Import necessary modules
import infinity
import infinity.index as index
from infinity.common import REMOTE_HOSTConnect 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"
table=db.create_table("my_table", {"num": "integer", "body": "varchar", "vec": "vector, 4, float"}, None)Insert two records
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]}])Execute a vector search
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.
🛠️ Build from Source
See Build from Source.
📜 Roadmap
See the Infinity Roadmap 2024