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from transformers import T5Tokenizer, T5ForConditionalGeneration
from sentence_transformers import SentenceTransformer
from pinecone import Pinecone
device = 'cpu'
pc = Pinecone(api_key='89eeb534-da10-4068-92f7-12eddeabe1e5')
index_name = 'abstractive-question-answering'
index = pc.Index(index_name)
def load_models():
print("Loading models...")
retriever = SentenceTransformer("flax-sentence-embeddings/all_datasets_v3_mpnet-base")
tokenizer = T5Tokenizer.from_pretrained('t5-small')
generator = T5ForConditionalGeneration.from_pretrained('t5-base').to(device)
return retriever, generator, tokenizer
retriever, generator, tokenizer = load_models()
def process_query(query):
xq = retriever.encode([query]).tolist()
xc = index.query(vector=xq, top_k=1, include_metadata=True)
print("Pinecone response:", xc)
if 'matches' in xc and isinstance(xc['matches'], list):
context = [m['metadata']['Output'] for m in xc['matches']]
context_str = " ".join(context)
formatted_query = f"answer the question: {query} context: {context_str}"
# Generate answer using T5 model
output_text = context_str
if len(output_text.splitlines()) > 5:
return output_text
if output_text.lower() == "none":
return "The topic is not covered in the student manual."
inputs = tokenizer.encode(formatted_query, return_tensors="pt", max_length=512, truncation=True).to(device)
ids = generator.generate(inputs, num_beams=2, min_length=10, max_length=60, repetition_penalty=1.2)
answer = tokenizer.decode(ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
nli_keywords = ['not_equivalent', 'not_entailment', 'entailment', 'neutral', 'not_enquiry']
if any(keyword in answer.lower() for keyword in nli_keywords):
return context_str
return answer
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