Update app.py

app.py

@@ -16,7 +16,7 @@ from langchain_community.llms import HuggingFaceHub
 # define constants
 # Embedding models
 #EMB_MODEL_bge_base = 'BAAI/bge-base-en-v1.5'
-EMB_MODEL_bge_large = 'BAAI/bge-large-en-v1.5'
+#EMB_MODEL_bge_large = 'BAAI/bge-large-en-v1.5'
 #EMB_MODEL_gtr_t5_base = 'sentence-transformers/gtr-t5-base'
 EMB_MODEL_gtr_t5_large = 'sentence-transformers/gtr-t5-large'
 #EMB_MODEL_e5_base = 'intfloat/e5-large-v2'
@@ -25,7 +25,7 @@ MISTRAL_MODEL1 = 'mistralai/Mixtral-8x7B-Instruct-v0.1'
 HF_MODEL1 = 'HuggingFaceH4/zephyr-7b-beta'
 # define paths
 #vector_path_bge_base = 'vectorDB/faiss_index_bge_base' 
-vector_path_bge_large = 'vectorDB/faiss_index_bge_large'
+#vector_path_bge_large = 'vectorDB/faiss_index_bge_large'
 #vector_path_gtr_t5_base = 'vectorDB/faiss_index_gtr_t5_base'
 vector_path_gtr_t5_large = 'vectorDB/faiss_index_gtr_t5_large'
 #vector_path_e5_base = 'vectorDB/faiss_index_e5_base'
@@ -35,17 +35,17 @@ hf_token = os.environ["HUGGINGFACEHUB_API_TOKEN"]
 def respond(message, history):
     
     # Initialize your embedding model
-    embedding_model_bge = HuggingFaceEmbeddings(model_name=EMB_MODEL_bge_large)
+    #embedding_model_bge = HuggingFaceEmbeddings(model_name=EMB_MODEL_bge_large)
     embedding_model_gtr_t5 = HuggingFaceEmbeddings(model_name=EMB_MODEL_gtr_t5_large)
     #embedding_model_e5 = HuggingFaceEmbeddings(model_name=EMB_MODEL_e5_base)
     
     # Load FAISS from relative path
-    vectordb_bge = FAISS.load_local(vector_path_bge_large, embedding_model_bge, allow_dangerous_deserialization=True)
+    #vectordb_bge = FAISS.load_local(vector_path_bge_large, embedding_model_bge, allow_dangerous_deserialization=True)
     vectordb_gtr_t5 = FAISS.load_local(vector_path_gtr_t5_large, embedding_model_gtr_t5, allow_dangerous_deserialization=True)
     #vectordb_e5 = FAISS.load_local(vector_path_e5_base, embedding_model_e5, allow_dangerous_deserialization=True)
     
     # define retriever object
-    retriever_bge = vectordb_bge.as_retriever(search_type="similarity", search_kwargs={"k": 5})
+    #retriever_bge = vectordb_bge.as_retriever(search_type="similarity", search_kwargs={"k": 5})
     retriever_gtr_t5 = vectordb_gtr_t5.as_retriever(search_type="similarity", search_kwargs={"k": 5})
     #retriever_e5 = vectordb_e5.as_retriever(search_type="similarity", search_kwargs={"k": 5})
 
@@ -57,41 +57,41 @@ def respond(message, history):
         )
     
     # create a RAG pipeline
-    qa_chain_bge = RetrievalQA.from_chain_type(llm=llm, retriever=retriever_bge)
+    #qa_chain_bge = RetrievalQA.from_chain_type(llm=llm, retriever=retriever_bge)
     qa_chain_gtr_t5 = RetrievalQA.from_chain_type(llm=llm, retriever=retriever_gtr_t5)
     #qa_chain_e5 = RetrievalQA.from_chain_type(llm=llm, retriever=retriever_e5)
     
     #generate results
-    responce_bge = qa_chain_bge.invoke(message)['result']
+    #responce_bge = qa_chain_bge.invoke(message)['result']
     responce_gtr_t5 = qa_chain_gtr_t5.invoke(message)['result']
     #responce_e5 = qa_chain_e5.invoke(message)['result']
 
     # remove the top instructions
-    #instruction_prefix = (
-    #"Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer."
-    #)
+    instruction_prefix = (
+    "Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer."
+    )
     #if responce_bge.strip().startswith(instruction_prefix):
     #    responce_bge = responce_bge.strip()[len(instruction_prefix):].strip()
-    #if responce_gtr_t5.strip().startswith(instruction_prefix):
-    #    responce_gtr_t5 = responce_gtr_t5.strip()[len(instruction_prefix):].strip()
+    if responce_gtr_t5.strip().startswith(instruction_prefix):
+        responce_gtr_t5 = responce_gtr_t5.strip()[len(instruction_prefix):].strip()
     #if responce_e5.strip().startswith(instruction_prefix):
     #    responce_e5 = responce_e5.strip()[len(instruction_prefix):].strip()
-    #    
-    ## Split question, Helpful Answer and Reason
+        
+    # Split question, Helpful Answer and Reason
     #match_bge = re.search(r"^(.*?)(?:\n+)?Question:\s*(.*?)(?:\n+)?Helpful Answer:\s*(.*)", responce_bge, re.DOTALL)
-    #match_gtr_t5 = re.search(r"^(.*?)(?:\n+)?Question:\s*(.*?)(?:\n+)?Helpful Answer:\s*(.*)", responce_gtr_t5, re.DOTALL)
+    match_gtr_t5 = re.search(r"^(.*?)(?:\n+)?Question:\s*(.*?)(?:\n+)?Helpful Answer:\s*(.*)", responce_gtr_t5, re.DOTALL)
     #match_e5 = re.search(r"^(.*?)(?:\n+)?Question:\s*(.*?)(?:\n+)?Helpful Answer:\s*(.*)", responce_e5, re.DOTALL)
-    #
+    
     #if match_bge:
     #    #original_text_bge = match_bge.group(1).strip()
     #    question_bge = match_bge.group(2).strip()
     #    answer_bge = match_bge.group(3).strip()
-    #    
-    #if match_gtr_t5:
-    #    #original_text_gtr_t5 = match_gtr_t5.group(1).strip()
-    #    #question_gtr_t5 = match_gtr_t5.group(2).strip()
-    #    answer_gtr_t5 = match_gtr_t5.group(3).strip()
-    #    
+        
+    if match_gtr_t5:
+        original_text_gtr_t5 = match_gtr_t5.group(1).strip()
+        #question_gtr_t5 = match_gtr_t5.group(2).strip()
+        answer_gtr_t5 = match_gtr_t5.group(3).strip()
+        
     #if match_e5:
     #    #original_text_e5 = match_e5.group(1).strip()
     #    #question_e5 = match_e5.group(2).strip()
@@ -99,7 +99,8 @@ def respond(message, history):
     #
     #formatted_responce = f'Question:{question_bge}\nHelpful Answer Type 1:\n{answer_bge}\nHelpful Answer Type 2:\n{answer_gtr_t5}\nHelpful Answer Type 3:\n{answer_e5}'
     #formatted_responce = f'\n*************  BAAI/bge-large-en-v1.5  ****************\n{responce_bge}\n**************  sentence-transformers/gtr-t5-large  ***************\n{responce_gtr_t5}\n************  intfloat/e5-large-v2  **************\n{responce_e5}'
-    formatted_responce = f'\n*************  BAAI/bge-large-en-v1.5  ****************\n{responce_bge}\n**************  sentence-transformers/gtr-t5-large  ***************\n{responce_gtr_t5}'
+    #formatted_responce = f'\n*************  BAAI/bge-large-en-v1.5  ****************\n{responce_bge}\n**************  sentence-transformers/gtr-t5-large  ***************\n{responce_gtr_t5}'
+    formatted_responce = f'\n*************  sentence-transformers/gtr-t5-large  ****************\n Helpful Answer:{answer_gtr_t5}\n Reasoning:\n{original_text_gtr_t5}'
     yield formatted_responce
 
 # Read the content of the README.md file
@@ -118,6 +119,8 @@ with gr.Blocks() as demo:
         #demo = gr.ChatInterface(
         gr.ChatInterface(
             respond,
+            fill_width=True,
+            fill_height=True,
             type="messages", 
             autofocus=False #,
             #additional_inputs=[