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.
Infinity comes with high performance, flexibility, ease-of-use, and many features designed to address the challenges facing the next-generation AI applications:
- 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.
- 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.
Supports a wide range of data types including strings, numerics, vectors, and more.
- 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.
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==0.5.2Use 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)If you wish to deploy Infinity with the server and client as separate processes, see the Deploy infinity server guide.
See the Build from Source guide.
💡 For more information about Infinity's Python API, see the Python API Reference.
See the Infinity Roadmap 2024
