From 6347f6e18688cbc75502509646c4065a71604e09 Mon Sep 17 00:00:00 2001
From: yangzq50 <58433399+yangzq50@users.noreply.github.com>
Date: Fri, 22 Nov 2024 20:29:12 +0800
Subject: [PATCH] Update readme for infinity-sdk (#2287)

### What problem does this PR solve?

Update readme for infinity-sdk

Issue link: #2065

### Type of change

- [x] Documentation Update
---
 python/infinity_sdk/README.md | 110 +++++++++++++++++++++++++++++++++-
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+<div align="center">
+  <img width="187" src="https://github.com/infiniflow/infinity/assets/7248/015e1f02-1f7f-4b09-a0c2-9d261cd4858b"/>
+</div>
+
+
+<p align="center">
+    <b>The AI-native database built for LLM applications, providing incredibly fast hybrid search of dense embedding, sparse embedding, tensor and full-text</b>
+</p>
+
+<h4 align="center">
+  <a href="https://infiniflow.org/docs/dev/category/get-started">Document</a> |
+  <a href="https://infiniflow.org/docs/dev/benchmark">Benchmark</a> |
+  <a href="https://twitter.com/infiniflowai">Twitter</a> |
+  <a href="https://discord.gg/jEfRUwEYEV">Discord</a>
+</h4>
+
+
+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
+
+<div class="column" align="middle">
+  <img src="https://github.com/user-attachments/assets/c4c98e23-62ac-4d1a-82e5-614bca96fe0a"/>
+</div>
+
+## 🌟 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. The following shows how to operate in client-server mode:
+
+1. Deploy Infinity in client-server mode. check the [Deploy infinity server](https://infiniflow.org/docs/dev/deploy_infinity_server) guide.
+
+2. Install the `infinity-sdk` package:
+   ```bash
+   pip install infinity-sdk==0.5.0.dev4
+   ```
+
+3. Use Infinity to conduct a dense vector search:
+   ```python
+   import infinity
+
+   # Connect to infinity
+   # Change the following IP address and port to your server's IP address and port
+   infinity_object = infinity.connect(infinity.NetworkAddress("127.0.0.1", 23817))
+   # 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](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)
+