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import pinecone
from pprint import pprint
import streamlit as st
import torch
from transformers import AutoTokenizer, AutoModel, AutoModelForSeq2SeqLM
model_name = "vblagoje/bart_lfqa"
# connect to pinecone environment
pinecone.init(
api_key="e5d4972e-0045-43d5-a55e-efdeafe442dd",
environment="us-central1-gcp" # find next to API key in console
)
index_name = "abstractive-question-answering"
# check if the abstractive-question-answering index exists
if index_name not in pinecone.list_indexes():
# create the index if it does not exist
pinecone.create_index(
index_name,
dimension=768,
metric="cosine"
)
# connect to abstractive-question-answering index we created
index = pinecone.Index(index_name)
from transformers import BartTokenizer, BartForConditionalGeneration
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model = model.to('cpu')
import torch
from sentence_transformers import SentenceTransformer
# set device to GPU if available
device = 'cuda' if torch.cuda.is_available() else 'cpu'
# load the retriever model from huggingface model hub
retriever = SentenceTransformer("flax-sentence-embeddings/all_datasets_v3_mpnet-base", device=device)
def query_pinecone(query, top_k):
# generate embeddings for the query
xq = retriever.encode([query]).tolist()
# search pinecone index for context passage with the answer
xc = index.query(xq, top_k=top_k, include_metadata=True)
return xc
def format_query(query, context):
# extract passage_text from Pinecone search result and add the <P> tag
context = [f"<P> {m['metadata']['text']}" for m in context]
# concatinate all context passages
context = " ".join(context)
# contcatinate the query and context passages
query = f"question: {query} context: {context}"
return query
def generate_answer(query):
query_and_docs = query
model_input = tokenizer(query_and_docs, truncation=True, padding=True, return_tensors="pt")
generated_answers_encoded = model.generate(input_ids=model_input["input_ids"].to(device),
attention_mask=model_input["attention_mask"].to(device),
min_length=64,
max_length=256,
do_sample=False,
early_stopping=True,
num_beams=8,
temperature=1.0,
top_k=None,
top_p=None,
eos_token_id=tokenizer.eos_token_id,
no_repeat_ngram_size=3,
num_return_sequences=1)
res = tokenizer.batch_decode(generated_answers_encoded, skip_special_tokens=True,clean_up_tokenization_spaces=True)
st.write(str(res))
query = st.text_area('Enter Question:')
b = st.button('Submit!')
if b:
st.write("Processing, please wait!")
context = query_pinecone(query, top_k=5)
query = format_query(query, context["matches"])
generate_answer(query) |