[doc] updated api reference and readme

README.md

@@ -4,7 +4,7 @@
 </div>
 
 <p align="center">
-    <b>The AI-native database built for LLM applications, offering incredibly fast vector and full-text search</b>
+    <b>The AI-native database built for LLM applications, providing incredibly fast vector and full-text search</b>
 </p>
 
 <h4 align="center">
@@ -15,22 +15,23 @@
 </h4>
 
 
-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, recommendations, question-answering, conversational AI, copilot, content generation, and many more RAG (Retrieval-augmented Generation) applications.
+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
 
-Infinity comes with **performance**, **flexibility**, **ease-of-use**, and many features designed to address the challenges facing the next-gen RAG applications:
+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 on million-scale vector datasets.
 - Up to 10K QPS on million-scale vector datasets.
-- See the [Benchmarking](https://www.example.com).
+
+> See the [Benchmark](https://www.example.com).
 
 
 ### 🔮 Fused search
 
-Supports a fused search of multi-embeddings and full text, in addition to filtering.
+Supports a fused search of multiple embeddings and full text, in addition to filtering.
 
 ### 🍔 Rich data types
 
@@ -41,10 +42,80 @@ Supports a wide range of data types including strings, numerics, vectors, and mo
 - Intuitive Python API. See the [Python client]()
 - A single-binary architecture with no dependencies, making deployment a breeze.
 
-
 ## 🎮 Get Started
 
-CONTENT MISSING HERE
+### Prerequisites
+
+- Operating system: Ubuntu 22.04 or higher
+- Clang-17 or higher to support C++20 and modules
+- CMake 3.28 or higher
+
+### Install Infinity's Python client
+
+```bash
+```
+
+
+
+### Import necessary modules
+
+```python
+import infinity
+import infinity.index as index
+from infinity.common import REMOTE_HOST
+```
+
+
+
+### Connect to the remote server
+
+```python
+infinity_obj = infinity.connect(REMOTE_HOST)
+```
+
+
+
+### Get a database
+
+```python
+db = infinity_obj.get_database("default")
+```
+
+
+
+### Create a table
+
+```python
+    # Drop my_table if it already exists
+    db.drop_table("my_table", if_exists=True)
+    # Create a table named "my_table"
+    db.create_table(
+        "my_table", {"num": "integer", "body": "varchar", "vec": "vector,4,float"}, None)
+```
+
+
+
+### Insert two records 
+
+```python
+    table.insert(
+        [{"num": 1, "body": "undesirable, unnecessary, and harmful", "vec": [1.0, 1.2, 0.8, 0.9]}])
+    table.insert(
+        [{"num": 2, "body": "publisher=US National Office for Harmful Algal Blooms", "vec": [4.0, 4.2, 4.3, 4.5]}])
+```
+
+
+
+### Execute a vector search
+
+```python
+    res = table.query_builder().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]().
 
 
 ## 🛠️ Build from Source