added comments

chatbot.py

@@ -4,48 +4,58 @@ from pinecone import Pinecone
 
 device = 'cpu'
 
+# Calling the pinecone api
 pc = Pinecone(api_key='89eeb534-da10-4068-92f7-12eddeabe1e5')
 
+# Connect to the Pinecone index for querying and storing vectors
 index_name = 'abstractive-question-answering'
 index = pc.Index(index_name)
 
+# Load the retriever model for sentence embeddings and the T5 model for text generation
 def load_models():
-    print("Loading 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):    
+    # Encode the query into a vector for semantic search using SentenceTransformer
     xq = retriever.encode([query]).tolist()
+    # Query the Pinecone index for the most similar vector to the query
     xc = index.query(vector=xq, top_k=1, include_metadata=True)
     
     print("Pinecone response:", xc)
 
+    # Concatenates the original question with the context extracted from the matched metadata
     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
+    # If the context is longer than 5 lines, return the context extracted from Pinecone directly
     output_text = context_str
     if len(output_text.splitlines()) > 5:
         return output_text
 
+    # If none, then it will return that it was not covered in the student manual
     if output_text.lower() == "none":
         return "The topic is not covered in the student manual."
 
+    # Tokenizes the formatted query
     inputs = tokenizer.encode(formatted_query, return_tensors="pt", max_length=512, truncation=True).to(device)
+    # Generates an answer using the t5 model
     ids = generator.generate(inputs, num_beams=2, min_length=10, max_length=60, repetition_penalty=1.2)
+    # Decodes the answer to make it readable for the user
     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 it has this words, it will just paste the output from the extracted meta-data output from pinecone
+    nli_keywords = ['not_equivalent', 'not_entailment', 'entailment', 'neutral', 'not_enquiry']    
     if any(keyword in answer.lower() for keyword in nli_keywords):
         return context_str  
 
+    # returns the answer
     return answer