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https://github.com/infiniflow/infinity

The AI-native database built for LLM applications, providing incredibly fast full-text and vector search
https://github.com/infiniflow/infinity

ai-native approximate-nearest-neighbor-search bm25 cpp20 cpp20-modules database embedding full-text-search hnsw hybrid-search information-retrival llms nearest-neighbor-search operational-analytics rag retrieval-augmented-generation search-engine sql vector-database vector-search

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The AI-native database built for LLM applications, providing incredibly fast full-text and vector search

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    The AI-native database built for LLM applications, providing incredibly fast full-text and vector search






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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 vectors, 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)



## 🌟 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.



### 🔮 Fused search



Supports a fused search of multiple embeddings and full text, in addition to filtering.



### 🍔 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/python_api_reference)

- A single-binary architecture with no dependencies, making deployment a breeze.



## 🎮 Get Started



### Deploy Infinity database



#### Deploy Infinity using Docker on Linux x86_64 and MacOS x86_64



```bash

sudo mkdir -p /var/infinity && sudo chown -R $USER /var/infinity

docker pull infiniflow/infinity:nightly

docker run -d --name infinity -v /var/infinity/:/var/infinity --ulimit nofile=500000:500000 --network=host infiniflow/infinity:nightly

```



#### Deploy Infinity using binary package on Linux x86_64



You can download the binary package (deb, rpm, or tgz) for your respective host operating system from https://github.com/infiniflow/infinity/releases. The prebuilt packages are compatible with Linux distributions based on glibc 2.17 or later, for example, RHEL 7, Debian 8, Ubuntu 14.04.



Fedora/RHEL/CentOS/OpenSUSE

```bash

sudo rpm -i infinity-0.2.0-dev-x86_64.rpm

sudo systemctl start infinity

```



Ubuntu/Debian

```bash

sudo dpkg -i infinity-0.2.0-dev-x86_64.deb

sudo systemctl start infinity

```

#### 🛠️ Build from Source



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



### Install Infinity's Python client



`infinity-sdk` requires Python 3.10+.



```bash

pip3 install infinity-sdk==0.2.0.dev4

```



### Import necessary modules



```python

import infinity

import infinity.index as index

from infinity.common import REMOTE_HOST

from infinity.common import ConflictType

```



### Connect to the remote server



```python

infinity_obj = infinity.connect(REMOTE_HOST)

```



### Get a database



```python

db = infinity_obj.get_database("default_db")

```



### Create a table



```python

# Drop my_table if it already exists

db.drop_table("my_table", ConflictType.Ignore)

# Create a table named "my_table"

table = db.create_table("my_table", {"num": {"type": "integer"}, "body": {"type": "varchar"}, "vec": {"type": "vector, 4, float"}})

```



### Insert two records 



```python

table.insert([{"num": 1, "body": "unnecessary and harmful", "vec": [1.0, 1.2, 0.8, 0.9]}])

table.insert([{"num": 2, "body": "Office for Harmful Blooms", "vec": [4.0, 4.2, 4.3, 4.5]}])

```



### Execute a vector search



```python

res = table.output(["*"]).knn("vec", [3.0, 2.8, 2.7, 3.1], "float", "ip", 2).to_pl()

print(res)

```



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



## Document



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

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