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)💡 For more information about Infinity's Python API, see the Python API Reference.
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.
See the Infinity Roadmap 2025
