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https://github.com/infiniflow/infinity

The AI-native database built for LLM applications, providing incredibly fast hybrid search of dense vector, sparse vector, tensor (multi-vector), and full-text
https://github.com/infiniflow/infinity

ai-native approximate-nearest-neighbor-search bm25 cpp20 cpp20-modules embedding full-text-search hnsw hybrid-search information-retrival nearest-neighbor-search rag search-engine tensor-database vector vector-database vector-search vectordatabase

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The AI-native database built for LLM applications, providing incredibly fast hybrid search of dense vector, sparse vector, tensor (multi-vector), and full-text

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README 

        


  Infinity logo






    The AI-native database built for LLM applications, providing incredibly fast hybrid search of dense embedding, sparse embedding, tensor and full-text






  Document |

  Benchmark |

  Twitter |

  Discord




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](#-key-features)

- [Get Started](#-get-started)

- [Document](#-document)

- [Roadmap](#-roadmap)

- [Community](#-community)



## ⚡️ Performance





  Infinity performance comparison




## 🌟 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](https://infiniflow.org/docs/dev/benchmark) 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](https://infiniflow.org/docs/dev/pysdk_api_reference)

- 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:



   ```bash

   pip install infinity-embedded-sdk==0.5.0

   ```

   Use Infinity to conduct a dense vector search:

   ```python

   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](https://infiniflow.org/docs/dev/deploy_infinity_server) guide.



#### 🔧 Build from Source



See the [Build from Source](https://infiniflow.org/docs/dev/build_from_source) guide.



> 💡 For more information about Infinity's Python API, see the [Python API Reference](https://infiniflow.org/docs/dev/pysdk_api_reference).



## 📚 Document



- [Quickstart](https://infiniflow.org/docs/dev/)

- [Python API](https://infiniflow.org/docs/dev/pysdk_api_reference)

- [HTTP API](https://infiniflow.org/docs/dev/http_api_reference)

- [References](https://infiniflow.org/docs/dev/category/references)

- [FAQ](https://infiniflow.org/docs/dev/FAQ)



## 📜 Roadmap



See the [Infinity Roadmap 2024](https://github.com/infiniflow/infinity/issues/338)



## 🙌 Community



- [Discord](https://discord.gg/jEfRUwEYEV)

- [Twitter](https://twitter.com/infiniflowai)

- [GitHub Discussions](https://github.com/infiniflow/infinity/discussions)