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cd6775d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 | from transformers import pipeline
import torch
import pandas as pd
import sys
import os
sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..', 'document_retrieval', 'Decompose_retrieval'))
import ragqa_paths # [ragqa] portable paths
from vllm import LLM, SamplingParams
from transformers import AutoTokenizer
from openai import OpenAI
from qa_webq import *
import requests
import pickle
from tqdm import tqdm
import os
os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
os.environ['https_proxy'] = 'http://127.0.0.1:7890/'
os.environ['http_proxy'] = 'http://127.0.0.1:7890/'
dataset = 'triviaqa'
def call_llama3_single_prompt(
inputs, model="Llama-3.1-8B-Instruct", max_decode_steps=20, temperature=0.0
):
inputs_ls = []
if isinstance(inputs, str):
messages = [
{"role": "user", "content": inputs},
]
inputs_ls.append(tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True))
else:
for idx in range(len(inputs)):
inputs_ls.append(tokenizer.apply_chat_template(inputs[idx], tokenize=False, add_generation_prompt=True))
# ans = get_vllm_llama(temperature, max_decode_steps, inputs_ls)
results = client.completions.create(
model= ragqa_paths.LLAMA_MODEL,
max_tokens=max_decode_steps,
temperature=0,
prompt=inputs_ls,
timeout = None
)
ans = []
for item in results.choices:
ans.append([item.text.strip()])
return ans
def call_llama3_func(
inputs, model="Llama-3.1-8B-Instruct", max_decode_steps=20, temperature=0.0
):
print(max_decode_steps, temperature)
output = call_llama3_single_prompt(
inputs,
model=model,
max_decode_steps=max_decode_steps,
temperature=temperature
)
if isinstance(inputs, str):
return output[0]
else:
return output
def get_supervised_decom(queries_ls):
prompts = []
for query in queries_ls:
prompts.append([{"role": "user", "content": query}])
prompts_tokened = [tokenizer.apply_chat_template(x, tokenize=False, add_generation_prompt=True) for x in prompts]
results = client.completions.create(
model="supervised",
max_tokens=512,
temperature=0,
prompt=prompts_tokened,
timeout = None
)
ans = []
for item in results.choices:
ans.append([[x.strip() for x in item.text.split('|')]])
return ans
def get_ans(queries_ls, passages):
prompts = []
for i in range(len(queries_ls)): # Please answer the question to the best of your knowledge, even if the context does not directly provide the information. Use any relevant knowledge you have to provide a helpful answer.
prompts.append([
{"role": "system", "content": "You are a helpful assistant. answer the question according to the context."},
{"role": "user", "content": 'Context: ' + '\n'.join(passages[i][:3])},
{"role": "user", "content": 'Question: ' + queries_ls[i]},
])
pred_ls = [row[0] for row in call_llama3_func(prompts, max_decode_steps=100)]
return pred_ls
def hit_score(passages_ls, anspids, k):
assert len(passages_ls) == len(anspids)
cnt = 0
for i in range(len(passages_ls)):
# 获取前k个检索结果
retrieved_topk = passages_ls[i][:k]
# 将检索结果和答案都转换为小写以进行不区分大小写的匹配
retrieved_topk = [p.strip().lower() for p in retrieved_topk]
ans_raw = anspids[i]
# 检查每个可能的答案
for ans in ans_raw:
ans = ans.strip().lower()
# 如果答案在任何一个检索结果中出现
if any(ans in passage for passage in retrieved_topk):
cnt += 1
break
return cnt / len(passages_ls)
def cover_em(pred_ls, ans_ls):
assert len(pred_ls) == len(ans_ls)
cnt = 0
for idx in range(len(pred_ls)):
pred = pred_ls[idx].lower()
ans = eval(ans_ls[idx])
for j in range(len(ans)):
if ans[j].lower() in pred:
cnt += 1
break
return cnt/len(pred_ls)
def supervised_method():
sub_query_str_l = get_supervised_decom(raw_queries)
passages_ls = get_ir_result(raw_queries, sub_query_str_l)
print(f"hit@20 :{hit_score(passages_ls, ans_pids, 20)}")
print(f"hit@100 :{hit_score(passages_ls, ans_pids, 100)}")
passages_ls = [item[:3] for item in passages_ls]
pred_ls = get_ans(raw_queries, passages_ls)
return cover_em(pred_ls, true_answers)
def dense_method():
sub_query_str_l = [ [[raw]]for raw in raw_queries]
passages_ls = get_ir_result(raw_queries, sub_query_str_l)
print(f"hit@20 :{hit_score(passages_ls, ans_pids, 20)}")
print(f"hit@100 :{hit_score(passages_ls, ans_pids, 100)}")
passages_ls = [item[:3] for item in passages_ls]
pred_ls = get_ans(raw_queries, passages_ls)
return cover_em(pred_ls, true_answers)
def unsupervised_method():
sub_query_str_l = []
for query in tqdm(raw_queries):
url = 'http://127.0.0.1:50002/execute?query='+query
response = requests.get(url=url)
res_dic = response.json()
sub_query_str_l.append([res_dic['text']])
passages_ls = get_ir_result(raw_queries, sub_query_str_l)
print(f"hit@20 :{hit_score(passages_ls, ans_pids, 20)}")
print(f"hit@100 :{hit_score(passages_ls, ans_pids, 100)}")
passages_ls = [item[:3] for item in passages_ls]
pred_ls = get_ans(raw_queries, passages_ls)
return cover_em(pred_ls, true_answers)
def iclfeed_method():
sub_query_str_l = []
for query in tqdm(raw_queries):
url = 'http://127.0.0.1:50003/execute?query='+query
response = requests.get(url=url)
res_dic = response.json()
sub_query_str_l.append([res_dic['text']])
passages_ls = get_ir_result(raw_queries, sub_query_str_l)
print(f"hit@1 :{hit_score(passage_ls, ans_pids, 1)}")
print(f"hit@2 :{hit_score(passage_ls, ans_pids, 2)}")
print(f"hit@3 :{hit_score(passage_ls, ans_pids, 3)}")
print(f"hit@20 :{hit_score(passages_ls, ans_pids, 20)}")
print(f"hit@100 :{hit_score(passages_ls, ans_pids, 100)}")
passages_ls = [item[:3] for item in passages_ls]
pred_ls = get_ans(raw_queries, passages_ls)
return cover_em(pred_ls, true_answers)
def colbert_search(query_item):
url = 'http://localhost:8895/api/search?query='+query_item+'&k=100'
response = requests.get(url=url)
res_dic = response.json()
corpus_list_topk = res_dic['topk']
passage_ls = []
for i in range(100):
passage_ls.append(corpus_list_topk[i]['text'])
return passage_ls
def colbert_method():
passage_ls = []
for query in tqdm(raw_queries):
passage_ls.append(colbert_search(query))
print(f"hit@1 :{hit_score(passage_ls, ans_pids, 1)}")
print(f"hit@2 :{hit_score(passage_ls, ans_pids, 2)}")
print(f"hit@3 :{hit_score(passage_ls, ans_pids, 3)}")
print(f"hit@20 :{hit_score(passage_ls, ans_pids, 20)}")
print(f"hit@100 :{hit_score(passage_ls, ans_pids, 100)}")
passage_ls = [item[:3] for item in passage_ls]
pred_ls = get_ans(raw_queries, passage_ls)
return cover_em(pred_ls, true_answers)
if __name__ == '__main__':
dataset_path = ragqa_paths.dataset_file(dataset, f"{dataset}_test.csv")
tokenizer = AutoTokenizer.from_pretrained(ragqa_paths.LLAMA_MODEL)
client = OpenAI(api_key="0",base_url="http://127.0.0.1:50001/v1")
raw_data = pd.read_csv(dataset_path, header=0)
raw_data = raw_data.drop_duplicates(subset=['question'])
# raw_data = raw_data.head(10000)
raw_queries = list(raw_data['question'])
true_answers = list(raw_data['answers'])
ans_pids = [eval(item) for item in list(raw_data['anspid'])]
# print(supervised_method())
# print(unsupervised_method())
# print(iclfeed_method())
print(colbert_method())
# print(dense_method())
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