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import time
import json
import math
import os
import yaml
from typing import Dict, List
from factool.scientific.tool import google_scholar
from factool.utils.base.pipeline import pipeline
class scientific_pipeline(pipeline):
def __init__(self, foundation_model):
super().__init__('scientific', foundation_model)
self.tool = google_scholar()
with open(os.path.join(self.prompts_path, "claim_extraction.yaml"), 'r') as file:
data = yaml.load(file, Loader=yaml.FullLoader)
self.claim_prompt = data['scientific']
with open(os.path.join(self.prompts_path, 'agreement_verification.yaml'), 'r') as file:
data = yaml.load(file, Loader=yaml.FullLoader)
self.verification_prompt = data['scientific']
async def _claim_extraction(self, responses):
messages_list = [
[
{"role": "system", "content": self.claim_prompt['system']},
{"role": "user", "content": self.claim_prompt['user'].format(input=response)},
]
for response in responses
]
return await self.chat.async_run(messages_list, List)
async def _check_authors(self, authors):
messages_list = [
[
{"role": "system", "content": self.verification_prompt['system']},
{"role": "user", "content": self.verification_prompt['user'].format(string1=claim_author, list2=real_author)},
]
for claim_author, real_author in authors
]
return await self.chat.async_run(messages_list, Dict)
async def _verification(self, claims, responses):
authors = [(claim['paper_author(s)'], response['author']) for claim, response in zip(claims, responses)]
check_authors_results = await self._check_authors(authors)
final_responses = []
for i, (claim, response) in enumerate(zip(claims, responses)):
final_response = {
'generated_paper_title': claim['paper_title'],
'generated_paper_author(s)': claim['paper_author(s)'],
'generated_paper_pub_year': claim['paper_pub_year'],
'actual_paper_title': response['title'],
'actual_paper_author(s)': response['author'],
'actual_paper_pub_year': response['pub_year'],
}
errors = []
if final_response['generated_paper_title'].lower() != final_response['actual_paper_title'].lower() and final_response['generated_paper_title'].lower() not in final_response['actual_paper_title'].lower() and final_response['actual_paper_title'].lower() not in final_response['generated_paper_title'].lower() :
errors.append('wrong_paper_title')
if check_authors_results[i]['factuality'] == False:
errors.append('wrong_paper_author(s)')
if final_response['generated_paper_pub_year'] != final_response['actual_paper_pub_year']:
errors.append('wrong_paper_pub_year')
final_response['error'] = errors
final_response['factuality'] = len(errors) == 0
final_responses.append(final_response)
return final_responses
async def run_with_tool_live(self, samples):
claims_in_responses = await self._claim_extraction(samples)
queries_in_responses = []
evidences_in_responses = []
verifications_in_responses = []
for claims_in_response in claims_in_responses:
queries = [claim['paper_title'] for claim in claims_in_response]
queries_in_responses.append(queries)
evidences = [self.tool.run(paper_title) for paper_title in queries]
evidences_in_responses.append(evidences)
verifications = await self._verification(claims_in_response, evidences)
verifications_in_responses.append(verifications)
return claims_in_responses, queries_in_responses, evidences_in_responses, verifications_in_responses
async def run_with_tool_live_without_claim_extraction(self, claims):
# claims = [{"paper_title": "A Survey of Modern Authorship Attribution Methods", "paper_author(s)": "Stamatatos, Efstathios", "paper_pub_year": "2013"}, {"paper_title": "BERT", "paper_author(s)": "John Smith", "paper_pub_year": "2020"}]
papers_titles = [claim['paper_title'] for claim in claims]
responses = [self.tool.run(paper_title) for paper_title in papers_titles]
final_response = await self._verification(claims, responses)
return final_response
async def run_with_tool_api_call(self, prompts, responses):
batch_size = 5
num_batches = math.ceil(len(prompts) / batch_size)
self.sample_list = [{"prompt": prompt, "response": response, "category": 'scientific'} for prompt, response in zip(prompts, responses)]
for i in range(num_batches):
print(i)
batch_start = i * batch_size
batch_end = min((i + 1) * batch_size, len(responses))
claims_in_responses, queries_in_responses, evidences_in_responses, verifications_in_responses = await self.run_with_tool_live(responses[batch_start:batch_end])
for j, (claims_in_response, queries_in_response, evidences_in_response, verifications_in_response) in enumerate(zip(claims_in_responses, queries_in_responses, evidences_in_responses, verifications_in_responses)):
index = batch_start + j
self.sample_list[index].update({
'claims': claims_in_response,
'queries': queries_in_response,
'evidences': evidences_in_response,
'claim_level_factuality': verifications_in_response,
'response_level_factuality': all([verification['factuality'] if verification != None else True for verification in verifications_in_response])
})
return self.sample_list
async def run_with_tool_dataset(self, annotated_dataset_path: str, with_tool_classified_dataset_path: str, rerun: bool = False, rerun_indices: list = []):
# Example of a line:
# {"paper_title": "A Survey of Modern Authorship Attribution Methods", "paper_author(s)": "Stamatatos, Efstathios", "paper_pub_year": "2013", "label": True / False}
if rerun == False:
with open(annotated_dataset_path, 'r') as f:
data = [json.loads(line) for line in f]
self.sample_list = [claim for sample in data for claim in sample['claims']]
rerun_elements = self.sample_list
else:
with open(with_tool_classified_dataset_path, 'r') as f:
data = [json.loads(line) for line in f]
self.sample_list = data
rerun_elements = [self.sample_list[i] for i in rerun_indices]
batch_size = 5
num_batches = math.ceil(len(rerun_elements) / batch_size) # 5
for i in range(num_batches):
print(i)
batch_start = i * batch_size
batch_end = (i + 1) * batch_size if (i + 1) * batch_size < len(rerun_elements) else len(rerun_elements)
responses = await self.run_with_tool_live_without_claim_extraction(rerun_elements[batch_start:batch_end])
for j, response in enumerate(responses):
index = batch_start + j if rerun == False else rerun_indices[batch_start + j]
if response == None:
self.sample_list[index]['with_tool_classification'] = 'None'
self.sample_list[index]['error'] = 'None'
else:
self.sample_list[index]['with_tool_classification'] = response.get('factuality', 'None')
self.sample_list[index]['error'] = response.get('error', 'None')
# save everything after each batch to prevent data loss
with open(with_tool_classified_dataset_path, 'w') as f:
for item in self.sample_list:
json_str = json.dumps(item)
f.write(json_str + '\n')
async def run_self_check_live(self, fewshot, batch):
user_prompt_key = 'user_3_shot_CoT' if fewshot else 'user_zero_shot_CoT'
messages_list = [
[
{"role": "system", "content": self.self_check_prompt['system']},
{"role": "user", "content": self.self_check_prompt[user_prompt_key].format(scientific_literature=response)}
]
for response in batch
]
return await self.chat.async_run(messages_list, Dict)
async def run_self_check_dataset(self, annotated_dataset_path: str, self_check_classified_dataset_path: str, fewshot: bool = False, rerun: bool = False, rerun_indices: list = []):
# Example of a line:
# {"paper_title": "A Survey of Modern Authorship Attribution Methods", "paper_author(s)": "Stamatatos, Efstathios", "paper_pub_year": "2013", "annotation": True / False}
data_path = annotated_dataset_path if not rerun else self_check_classified_dataset_path
with open(data_path, 'r') as f:
data = [json.loads(line) for line in f]
self.sample_list = data if rerun else [claim for sample in data for claim in sample['claims']]
rerun_elements = self.sample_list if not rerun else [self.sample_list[i] for i in rerun_indices]
batch_size = 5
num_batches = math.ceil(len(rerun_elements) / batch_size)
for i in range(num_batches):
print(i)
batch_start = i * batch_size
batch_end = (i + 1) * batch_size
batch = rerun_elements[batch_start:batch_end]
batch = [{k:v for k,v in d.items() if k != "label"} for d in batch]
responses = await self.run_self_check_live(fewshot, batch)
for j, response in enumerate(responses):
index = batch_start + j if rerun == False else rerun_indices[batch_start + j]
if response == None:
self.sample_list[index]['self_check_classification'] = 'None'
self.sample_list[index]['self_check_reasoning'] = 'None'
else:
self.sample_list[index]['self_check_classification'] = response.get('factuality', 'None')
self.sample_list[index]['self_check_reasoning'] = response.get('reasoning', 'None')
# save everything after each batch to prevent data loss
with open(self_check_classified_dataset_path, 'w') as f:
for item in self.sample_list:
json_str = json.dumps(item)
f.write(json_str + '\n') |