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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 | 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_squad import *
import requests
def get_vllm_llama(temperature, max_tokens, chats):
url = "http://127.0.0.1:60000/ask"
data = {
"temperature": temperature,
"max_tokens": max_tokens,
"chats": chats,
}
response = requests.post(url, json=data, timeout=None)
response_data = response.json()
passage_ls = response_data.get("output", [])
return passage_ls
def call_llama3_single_prompt(
inputs, model="Llama-3.1-8B-Instruct", max_decode_steps=20, temperature=0.8
):
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)
return ans
def call_llama3_func(
inputs, model="Llama-3.1-8B-Instruct", max_decode_steps=1024, 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
)
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)):
prompts.append([
{"role": "system", "content": "You are a helpful assistant. 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."},
{"role": "user", "content": 'Context: ' + '\n'.join(passages[i])},
{"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 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)
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)
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)
pred_ls = get_ans(raw_queries, passages_ls)
return cover_em(pred_ls, true_answers)
if __name__ == '__main__':
dataset_path = ragqa_paths.dataset_file("squad", "squad_test.csv")
tokenizer = AutoTokenizer.from_pretrained(ragqa_paths.LLAMA_MODEL)
client = OpenAI(api_key="0",base_url="http://0.0.0.0:50001/v1")
raw_data = pd.read_csv(dataset_path, header=0)
# raw_data = raw_data.head(5)
raw_queries = list(raw_data['question'])
true_answers = list(raw_data['answers'])
print(supervised_method())
print(unsupervised_method())
print(iclfeed_method())
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