mirror of
https://github.com/HKUDS/LightRAG.git
synced 2025-10-31 09:49:54 +00:00
109 lines
3.5 KiB
Python
109 lines
3.5 KiB
Python
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()
|