Upload app.py

app.py

@@ -41,6 +41,8 @@ ast1_chunks = [(chunk, 'AST-1') for chunk in ast1_chunks]
 ast2_chunks = [(chunk, 'AST-2') for chunk in ast2_chunks]
 all_chunks = ast1_chunks + ast2_chunks
 
+print('First 2 chunks from AST-1' + ast1_chunks[:2]) # print first 2 chunk from AST1
+
 #Load the Embedding Model and LLM
 from sentence_transformers import SentenceTransformer
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
@@ -64,6 +66,7 @@ faiss.write_index(index, 'embeddings_index.faiss')
 # Load FAISS index
 stored_index = faiss.read_index('./embeddings_index.faiss')
 
+print('Stored embedding in db') 
 #Function to retrieve chunks
 def retrieve_chunks(query, top_k=10, use_mmr=False, diversity=0.5, target_doc='AST-1'):
     query_embedding = embedding_model.encode(query, convert_to_tensor=True).cpu().numpy()
@@ -118,6 +121,7 @@ def generate_response(query, retrieved_chunks):
 def rag_system(query, use_mmr=False):
     retrieved_chunks = retrieve_chunks(query, top_k=3, use_mmr=use_mmr)
     response = generate_response(query, retrieved_chunks)
+    print(response)
     return response
     
 import gradio as gr