ViQA / agent_t5.py
CreatorPhan's picture
Duplicate from CreatorPhan/VDT
56523b5
from langchain.document_loaders.unstructured import UnstructuredFileLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
from langchain.schema import AIMessage, HumanMessage, SystemMessage, Document
from langchain.document_loaders import PyPDFLoader
from transformers import AutoTokenizer, T5ForConditionalGeneration
from retrieval.retrieval import Retrieval, BM25
from datetime import datetime
import os, time, torch
from torch.nn import Softmax
import requests
API_URL = "https://api-inference.huggingface.co/models/CreatorPhan/ViQA-small"
headers = {"Authorization": "Bearer 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"}
class Agent:
def __init__(self, args=None) -> None:
self.args = args
self.choices = args.choices
self.corpus = Retrieval(k=args.choices)
self.context_value = ""
self.use_context = False
self.softmax = Softmax(dim=1)
self.temp = []
self.replace_list = torch.load('retrieval/replace.pt')
print("Model is loading...")
self.model = T5ForConditionalGeneration.from_pretrained(args.model).to(args.device)
self.tokenizer = AutoTokenizer.from_pretrained(args.tokenizer)
print("Model loaded!")
def load_context(self, doc_path):
print('Loading file:', doc_path.name)
if doc_path.name[-4:] == '.pdf':
context = self.read_pdf(doc_path.name)
else:
# loader = UnstructuredFileLoader(doc_path.name)
context = open(doc_path.name, encoding='utf-8').read()
self.retrieval = Retrieval(docs=context)
self.choices = self.retrieval.k
self.use_context = True
return f"Using file from {doc_path.name}"
def API_call(self, prompt):
response = requests.post(API_URL, headers=headers, json={"inputs": prompt}).json()
if isinstance(response, list):
return response[0]['generated_text']
else:
time.sleep(3)
return self.API_call(prompt)
def asking(self, question):
timestamp = datetime.now()
timestamp = timestamp.strftime("[%Y-%m-%d %H:%M:%S]")
print(timestamp, end=' ')
s_query = time.time()
if self.use_context:
print("Answering with your context:", question)
contexts = self.retrieval.get_context(question)
else:
print("Answering without your context:", question)
contexts = self.corpus.get_context(question)
prompts = []
for context in contexts:
prompt = f"Trả lời câu hỏi: {question} Trong nội dung: {context['context']}"
prompts.append(prompt)
s_token = time.time()
tokens = self.tokenizer(prompts, max_length=self.args.seq_len, truncation=True, padding='max_length', return_tensors='pt')
s_gen = time.time()
outputs = self.model.generate(
input_ids=tokens.input_ids.to(self.args.device),
attention_mask=tokens.attention_mask.to(self.args.device),
max_new_tokens=self.args.out_len,
output_scores=True,
return_dict_in_generate=True
)
s_de = time.time()
results = []
scores = self.softmax(outputs.scores[0])
scores = scores.max(dim=1).values*100
# print(scores)
for i in range(self.choices):
result = contexts[i]
score = round(scores[i].item())
result['score'] = score
answer = self.tokenizer.decode(outputs.sequences[i], skip_special_tokens=True)
result['answer'] = answer
results.append(result)
def get_score(record):
return record['score']**2 * record['score_bm']
results.sort(key=get_score, reverse=True)
self.temp = results
t_mess = "t_query: {:.2f}\t t_token: {:.2f}\t t_gen: {:.2f}\t t_decode: {:.2f}\t".format(
s_token-s_query, s_gen-s_token, s_de-s_gen, time.time()-s_de
)
print(t_mess, len(self.temp))
if results[0]['score'] > 60:
return results[0]['answer']
else:
return f"Tôi không chắc nhưng câu trả lời có thể là: {results[0]['answer']}\nBạn có thể tham khảo các câu trả lời bên cạnh!"
def get_context(self, context):
self.context_value = context
self.retrieval = Retrieval(k=self.choices, docs=context)
self.choices = self.retrieval.k
self.use_context = True
return context
def load_context_file(self, file):
print('Loading file:', file.name)
text = ''
for line in open(file.name, 'r', encoding='utf8'):
text += line
self.context_value = text
return text
def clear_context(self):
self.context_value = ""
self.use_context = False
self.choices = self.args.choices
return ""
def replace(self, text):
for key, value in self.replace_list:
text = text.replace(key, value)
return text
def read_pdf(self, file_path):
loader = PyPDFLoader(file_path)
pages = loader.load_and_split()
text = ''
for page in pages:
page_content = page.page_content
text += self.replace(page_content)
return text