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| import pinecone | |
| import streamlit as st | |
| API = st.text_area('Enter API key:') | |
| res = st.button('Submit') | |
| if res = True: | |
| # connect to pinecone environment | |
| pinecone.init( | |
| api_key="API", | |
| 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) | |
| 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) | |
| from transformers import BartTokenizer, BartForConditionalGeneration | |
| # load bart tokenizer and model from huggingface | |
| tokenizer = BartTokenizer.from_pretrained('vblagoje/bart_lfqa') | |
| generator = BartForConditionalGeneration.from_pretrained('vblagoje/bart_lfqa').to('cpu') | |
| 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): | |
| # tokenize the query to get input_ids | |
| inputs = tokenizer([query], trunication=True, max_length=1024, return_tensors="pt") | |
| # use generator to predict output ids | |
| ids = generator.generate(inputs["input_ids"], num_beams=2, min_length=20, max_length=64) | |
| # use tokenizer to decode the output ids | |
| answer = tokenizer.batch_decode(ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] | |
| return pprint(answer) | |
| query = st.text_area('Enter your question:') | |
| s = st.button('Submit') | |
| if s = True: | |
| context = query_pinecone(query, top_k=5) | |
| query = format_query(query, context["matches"]) | |
| generate_answer(query) | |