From c9a6256af801de4fdb123c2053a6f22cc690d621 Mon Sep 17 00:00:00 2001
From: zrguo <49157727+LarFii@users.noreply.github.com>
Date: Tue, 27 May 2025 16:09:57 +0800
Subject: [PATCH] Move batch_eval.py to ./reproduce/

---
 README.md               |   2 +-
 reproduce/batch_eval.py | 108 ++++++++++++++++++++++++++++++++++++++++
 2 files changed, 109 insertions(+), 1 deletion(-)
 create mode 100644 reproduce/batch_eval.py

diff --git a/README.md b/README.md
index e45ca155..b9d6a31c 100644
--- a/README.md
+++ b/README.md
@@ -1243,7 +1243,7 @@ Output the results in the following structure:
 
 ### Batch Eval
 
-To evaluate the performance of two RAG systems on high-level queries, LightRAG uses the following prompt, with the specific code available in `example/batch_eval.py`.
+To evaluate the performance of two RAG systems on high-level queries, LightRAG uses the following prompt, with the specific code available in `reproduce/batch_eval.py`.
 
 <details>
 <summary> Prompt </summary>
diff --git a/reproduce/batch_eval.py b/reproduce/batch_eval.py
new file mode 100644
index 00000000..a85e1ede
--- /dev/null
+++ b/reproduce/batch_eval.py
@@ -0,0 +1,108 @@
+import re
+import json
+import jsonlines
+
+from openai import OpenAI
+
+
+def batch_eval(query_file, result1_file, result2_file, output_file_path):
+    client = OpenAI()
+
+    with open(query_file, "r") as f:
+        data = f.read()
+
+    queries = re.findall(r"- Question \d+: (.+)", data)
+
+    with open(result1_file, "r") as f:
+        answers1 = json.load(f)
+    answers1 = [i["result"] for i in answers1]
+
+    with open(result2_file, "r") as f:
+        answers2 = json.load(f)
+    answers2 = [i["result"] for i in answers2]
+
+    requests = []
+    for i, (query, answer1, answer2) in enumerate(zip(queries, answers1, answers2)):
+        sys_prompt = """
+        ---Role---
+        You are an expert tasked with evaluating two answers to the same question based on three criteria: **Comprehensiveness**, **Diversity**, and **Empowerment**.
+        """
+
+        prompt = f"""
+        You will evaluate two answers to the same question based on three criteria: **Comprehensiveness**, **Diversity**, and **Empowerment**.
+
+        - **Comprehensiveness**: How much detail does the answer provide to cover all aspects and details of the question?
+        - **Diversity**: How varied and rich is the answer in providing different perspectives and insights on the question?
+        - **Empowerment**: How well does the answer help the reader understand and make informed judgments about the topic?
+
+        For each criterion, choose the better answer (either Answer 1 or Answer 2) and explain why. Then, select an overall winner based on these three categories.
+
+        Here is the question:
+        {query}
+
+        Here are the two answers:
+
+        **Answer 1:**
+        {answer1}
+
+        **Answer 2:**
+        {answer2}
+
+        Evaluate both answers using the three criteria listed above and provide detailed explanations for each criterion.
+
+        Output your evaluation in the following JSON format:
+
+        {{
+            "Comprehensiveness": {{
+                "Winner": "[Answer 1 or Answer 2]",
+                "Explanation": "[Provide explanation here]"
+            }},
+            "Empowerment": {{
+                "Winner": "[Answer 1 or Answer 2]",
+                "Explanation": "[Provide explanation here]"
+            }},
+            "Overall Winner": {{
+                "Winner": "[Answer 1 or Answer 2]",
+                "Explanation": "[Summarize why this answer is the overall winner based on the three criteria]"
+            }}
+        }}
+        """
+
+        request_data = {
+            "custom_id": f"request-{i+1}",
+            "method": "POST",
+            "url": "/v1/chat/completions",
+            "body": {
+                "model": "gpt-4o-mini",
+                "messages": [
+                    {"role": "system", "content": sys_prompt},
+                    {"role": "user", "content": prompt},
+                ],
+            },
+        }
+
+        requests.append(request_data)
+
+    with jsonlines.open(output_file_path, mode="w") as writer:
+        for request in requests:
+            writer.write(request)
+
+    print(f"Batch API requests written to {output_file_path}")
+
+    batch_input_file = client.files.create(
+        file=open(output_file_path, "rb"), purpose="batch"
+    )
+    batch_input_file_id = batch_input_file.id
+
+    batch = client.batches.create(
+        input_file_id=batch_input_file_id,
+        endpoint="/v1/chat/completions",
+        completion_window="24h",
+        metadata={"description": "nightly eval job"},
+    )
+
+    print(f"Batch {batch.id} has been created.")
+
+
+if __name__ == "__main__":
+    batch_eval()