docs: add graphrag to readme

README.md

@@ -242,5 +242,8 @@ There are some examples to show how to use the tidb-vector-python to interact wi
 - [OpenAI Embedding](./examples/openai_embedding/README.md): use the OpenAI embedding model to generate vectors for text data, store them in TiDB Vector, and search for similar text.
 - [Image Search](./examples/image_search/README.md): use the OpenAI CLIP model to generate vectors for image and text, store them in TiDB Vector, and search for similar images.
 - [LlamaIndex RAG with UI](./examples/llamaindex-tidb-vector-with-ui/README.md): use the LlamaIndex to build an [RAG(Retrieval-Augmented Generation)](https://docs.llamaindex.ai/en/latest/getting_started/concepts/) application.
+- [Chat with URL](./llamaindex-tidb-vector/README.md): use LlamaIndex to build an [RAG(Retrieval-Augmented Generation)](https://docs.llamaindex.ai/en/latest/getting_started/concepts/) application that can chat with a URL.
+- [GraphRAG](./examples/graphrag-demo/README.md): 20 lines code of using TiDB Serverless to build a Knowledge Graph based RAG application.
+- [GraphRAG Step by Step Tutorial](./examples/graphrag-step-by-step-tutorial/README.md): Step by step tutorial to build a Knowledge Graph based RAG application with Colab notebook.
 
 for more examples, see the [examples](./examples) directory.

examples/README.md

@@ -29,6 +29,8 @@ Please make sure you have created a TiDB Serverless cluster with vector support
 - [Image Search](./image_search/README.md): use the OpenAI CLIP model to generate vectors for image and text.
 - [LlamaIndex RAG with UI](./llamaindex-tidb-vector-with-ui/README.md): use the LlamaIndex to build an [RAG(Retrieval-Augmented Generation)](https://docs.llamaindex.ai/en/latest/getting_started/concepts/) application.
 - [Chat with URL](./llamaindex-tidb-vector/README.md): use LlamaIndex to build an [RAG(Retrieval-Augmented Generation)](https://docs.llamaindex.ai/en/latest/getting_started/concepts/) application that can chat with a URL.
+- [GraphRAG](./examples/graphrag-demo/README.md): 20 lines code of using TiDB Serverless to build a Knowledge Graph based RAG application.
+- [GraphRAG Step by Step Tutorial](./examples/graphrag-step-by-step-tutorials/README.md): Step by step tutorial to build a Knowledge Graph based RAG application with Colab notebook.
 
 ## Real World Applications
 

examples/graphrag-helloworld/README.md → examples/graphrag-demo/README.md

@@ -1,4 +1,4 @@
-# LlamaIndex RAG Example
+# GraphRAG Demo
 
 This example demonstrates how to use the DSPy and TiDB Serverless to build a simple GraphRAG application. It crawled an example webpage and index the content to TiDB Serverless with Graph, then use the Graph and Vector to search the content and generate the answer with OpenAI.
 
@@ -21,7 +21,7 @@ git clone https://github.com/pingcap/tidb-vector-python.git
 ### Create a virtual environment
 
 ```bash
-cd tidb-vector-python/examples/graphrag-helloworld
+cd tidb-vector-python/examples/graphrag-demo
 python3 -m venv .venv
 source .venv/bin/activate
 ```
@@ -48,7 +48,7 @@ Get the `OPENAI_API_KEY` from [OpenAI](https://platform.openai.com/docs/quicksta
 
 
 ```text
-$ python3 simple-retrieve.py
+$ python3 graphrag-demo.py
 Input your TIDB connection string:
 Input your OpenAI API Key:
 Enter your question: