https://github.com/infinities-pro/infinities
An AI-native database designed for LLM applications, offering lightning-fast hybrid search across dense vectors, sparse vectors, multi-vector tensors, and full-text data.
https://github.com/infinities-pro/infinities
ai-agents approximate-nearest-neighbor-searching autonomous-agents ccp-appservices-edge-id code-analysis-true code-ql-exclude code4prod codeforces codeigniter embedded-systems hybrid-recommendation python3 search-algorithm search-engine tensor-networks
Last synced: 10 months ago
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An AI-native database designed for LLM applications, offering lightning-fast hybrid search across dense vectors, sparse vectors, multi-vector tensors, and full-text data.
- Host: GitHub
- URL: https://github.com/infinities-pro/infinities
- Owner: Infinities-pro
- License: apache-2.0
- Created: 2025-01-31T21:45:59.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-13T00:44:45.000Z (12 months ago)
- Last Synced: 2025-04-03T11:47:05.609Z (10 months ago)
- Topics: ai-agents, approximate-nearest-neighbor-searching, autonomous-agents, ccp-appservices-edge-id, code-analysis-true, code-ql-exclude, code4prod, codeforces, codeigniter, embedded-systems, hybrid-recommendation, python3, search-algorithm, search-engine, tensor-networks
- Language: C++
- Homepage: https://infinities.pro
- Size: 78 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
# `Infinities`
[](https://x.com/infinities_pro)
[](https://www.youtube.com/@infinities-pro)
[](https://discord.gg/48mvzHk6)
[](https://infinities.pro)
[](https://docs.infinities.pro)
[](https://medium.com/@infinityonsolana/looking-ahead-the-future-of-vector-databases-c241626747b3)
[](https://opensource.org/licenses/MIT)
The AI-native database built for LLM applications, delivering lightning-fast hybrid search across dense embedding, sparse embedding, tensor (multi-vector), and full-text.
Documentation |
Benchmark |
Twitter |
Discord
---
Infinity is a cutting-edge AI-native database purpose-built for modern LLM applications. It supports robust hybrid search across diverse data types such as dense vectors, sparse vectors, tensors, full-text, and structured data. This makes it the perfect solution for applications like:
- **Search and Recommendations**
- **Question-Answering Systems**
- **Conversational AI**
- **Copilots**
- **Content Generation**
- **Retrieval-Augmented Generation (RAG)**
---
## 🚀 Key Features
### ⚡ Ultra-Fast Performance
- Sub-millisecond query latency: 0.1ms latency on million-scale vector datasets.
- Handles 15K+ QPS on vector queries and 12K+ QPS for full-text search on 33M documents.
> See the [Benchmark Report](https://docs.infinities.pro/benchmark) for detailed performance insights.
### 🔎 Hybrid Search
- Seamlessly combines **dense embedding**, **sparse embedding**, **tensor (multi-vector)**, and **full-text search**.
- Advanced re-ranking options, including **Reciprocal Rank Fusion (RRF)**, weighted sum, and **ColBERT**-style ranking.
### 📚 Rich Data Support
- Supports diverse data types, including strings, numerics, vectors, tensors, and more.
- Built to handle structured and semi-structured data efficiently.
### 🎯 User-Friendly API
- **Python SDK** for intuitive integration.
- A lightweight, single-binary architecture ensures easy deployment.
- Compatible with both **embedded mode** and **client-server mode**.
---
## 🛠 Getting Started
Infinity offers two modes of operation: **embedded mode** (for direct integration into Python applications) and **client-server mode** (for separate backend processes).
### ⚙ Embedded Mode
Install the embedded SDK:
```bash
pip install infinity-embedded-sdk==0.6.0.dev2
```
Use Infinity for dense vector search:
```python
import infinity_embedded
# Connect to Infinity
infinity_object = infinity_embedded.connect("/absolute/path/to/save/to")
# Retrieve a database object
db_object = infinity_object.get_database("default_db")
# Create a table with multiple column types
table_object = db_object.create_table("my_table", {
"num": {"type": "integer"},
"body": {"type": "varchar"},
"vec": {"type": "vector, 4, float"}
})
# Insert data into the table
table_object.insert([
{"num": 1, "body": "unnecessary and harmful", "vec": [1.0, 1.2, 0.8, 0.9]},
{"num": 2, "body": "Office for Harmful Blooms", "vec": [4.0, 4.2, 4.3, 4.5]}
])
# Perform 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)
```
> 💡 Learn more in the [Python API Reference](https://docs.infinities.pro/pysdk_api_reference).
### 🔧 Deploy Infinity in Client-Server Mode
For larger-scale deployments, you can set up Infinity in client-server mode. See the [Deploy Infinity Server](https://docs.infinities.pro/deploy_infinity_server) guide for details.
---
## 📈 Performance Comparison
---
## 📜 Documentation
- [Quickstart](https://docs.infinities.pro/)
- [Python API](https://docs.infinities.pro/pysdk_api_reference)
- [HTTP API](https://docs.infinities.pro/http_api_reference)
- [References](https://docs.infinities.pro/references)
- [FAQ](https://docs.infinities.pro/FAQ)
---
## 🌟 Roadmap
Curious about what’s next for Infinity? Check out the [Roadmap 2025](https://infinities.pro/roadmap) to learn more about upcoming features and improvements.
---
## 🙌 Community
Join the conversation and connect with us:
- [Website](https://infinities.pro/)
- [Twitter](https://x.com/infinities_pro)
- [Discord](https://discord.gg/jEfRUwEYEV)
---
## 🎥 Demo
> [!NOTE]
> Setting up and hosting the AutoGPT Platform yourself is a technical process.
> If you'd rather something that just works, we recommend [joining the waitlist](https://bit.ly/3ZDijAI) for the cloud-hosted beta.
https://github.com/user-attachments/assets/8120bc03-75b5-4b5e-bb75-54d0a8439da9
This tutorial assumes you have Docker, VSCode, git and npm installed.