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.

• Key Features
• Get Started
• Document
• Roadmap
• Community

⚡️ 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

Infinity supports two working modes, embedded mode and client-server mode. Infinity's embedded mode enables you to quickly embed Infinity into your Python applications, without the need to connect to a separate backend server. The following shows how to operate in embedded mode:

pip install infinity-embedded-sdk==0.5.0.dev3

1. Use Infinity to conduct a dense vector search:

   import infinity_embedded
   
   # Connect to infinity
   infinity_object = infinity_embedded.connect("/absolute/path/to/save/to")
   # Retrieve a database object named default_db
   db_object = infinity_object.get_database("default_db")
   # Create a table with an integer column, a varchar column, and a dense vector column
   table_object = db_object.create_table("my_table", {"num": {"type": "integer"}, "body": {"type": "varchar"}, "vec": {"type": "vector, 4, float"}})
   # Insert two rows into the table
   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]}])
   # Conduct a dense vector search
   res = table_object.output(["*"])
                     .match_dense("vec", [3.0, 2.8, 2.7, 3.1], "float", "ip", 2)
                     .to_pl()
   print(res)
   

🔧 Deploy Infinity in client-server mode

If you wish to deploy Infinity with the server and client as separate processes, see the Deploy infinity server guide.

🔧 Build from Source

See the Build from Source guide.

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

📚 Document

• Quickstart
• Python API
• HTTP API
• References
• FAQ

📜 Roadmap

See the Infinity Roadmap 2024

🙌 Community

• Discord
• Twitter
• GitHub Discussions