From 14a015d4adfc5df2cd8f53c744f4a48810a1ba2f Mon Sep 17 00:00:00 2001
From: yangdx <gzdaniel@me.com>
Date: Wed, 29 Oct 2025 19:11:40 +0800
Subject: [PATCH] Restore query generation example and fix README path
 reference
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

• Fix path from example/ to examples/
• Add generate_query.py implementation
---
 README.md                  |  2 +-
 examples/generate_query.py | 55 ++++++++++++++++++++++++++++++++++++++
 2 files changed, 56 insertions(+), 1 deletion(-)
 create mode 100644 examples/generate_query.py

diff --git a/README.md b/README.md
index 9ba35c4b..991f6b58 100644
--- a/README.md
+++ b/README.md
@@ -1538,7 +1538,7 @@ The dataset used in LightRAG can be downloaded from [TommyChien/UltraDomain](htt
 
 ### Generate Query
 
-LightRAG uses the following prompt to generate high-level queries, with the corresponding code in `example/generate_query.py`.
+LightRAG uses the following prompt to generate high-level queries, with the corresponding code in `examples/generate_query.py`.
 
 <details>
 <summary> Prompt </summary>
diff --git a/examples/generate_query.py b/examples/generate_query.py
new file mode 100644
index 00000000..705b23d3
--- /dev/null
+++ b/examples/generate_query.py
@@ -0,0 +1,55 @@
+from openai import OpenAI
+
+# os.environ["OPENAI_API_KEY"] = ""
+
+
+def openai_complete_if_cache(
+    model="gpt-4o-mini", prompt=None, system_prompt=None, history_messages=[], **kwargs
+) -> str:
+    openai_client = OpenAI()
+
+    messages = []
+    if system_prompt:
+        messages.append({"role": "system", "content": system_prompt})
+    messages.extend(history_messages)
+    messages.append({"role": "user", "content": prompt})
+
+    response = openai_client.chat.completions.create(
+        model=model, messages=messages, **kwargs
+    )
+    return response.choices[0].message.content
+
+
+if __name__ == "__main__":
+    description = ""
+    prompt = f"""
+    Given the following description of a dataset:
+
+    {description}
+
+    Please identify 5 potential users who would engage with this dataset. For each user, list 5 tasks they would perform with this dataset. Then, for each (user, task) combination, generate 5 questions that require a high-level understanding of the entire dataset.
+
+    Output the results in the following structure:
+    - User 1: [user description]
+        - Task 1: [task description]
+            - Question 1:
+            - Question 2:
+            - Question 3:
+            - Question 4:
+            - Question 5:
+        - Task 2: [task description]
+            ...
+        - Task 5: [task description]
+    - User 2: [user description]
+        ...
+    - User 5: [user description]
+        ...
+    """
+
+    result = openai_complete_if_cache(model="gpt-4o-mini", prompt=prompt)
+
+    file_path = "./queries.txt"
+    with open(file_path, "w") as file:
+        file.write(result)
+
+    print(f"Queries written to {file_path}")