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
Infinity supports two working modes, embedded mode and client-server mode. The following shows how to operate in client-server mode:
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Deploy Infinity in client-server mode. check the Deploy infinity server guide.
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Install the
infinity-sdkpackage:pip install infinity-sdk==0.7.4
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Use Infinity to conduct a dense vector search:
import infinity # Connect to infinity # Change the following IP address and port to your server's IP address and port infinity_object = infinity.connect(infinity.NetworkAddress("127.0.0.1", 23817)) # 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)
💡 For more information about Infinity's Python API, see the Python API Reference.
📚 Document
📜 Roadmap
See the Infinity Roadmap 2024
🙌 Community
1 maintainer
Metadata
1 maintainer
Release files for infinity-sdk 0.7.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| infinity_sdk-0.7.4.tar.gz | 29.5 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| infinity_sdk-0.7.4-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 59.3 MB
Release files / infinity_sdk-0.7.4.tar.gz
| Download URL | infinity_sdk-0.7.4.tar.gz |
|---|---|
| Size | 29.5 MB |
| Tags | Source |
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No |
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Release files / infinity_sdk-0.7.4-py3-none-any.whl
| Download URL | infinity_sdk-0.7.4-py3-none-any.whl |
|---|---|
| Size | 29.7 MB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
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