Update app.py

app.py

@@ -15,31 +15,34 @@ from langchain_community.llms import HuggingFaceHub
 
 # define constants
 # Embedding models
-#EMB_MODEL_bge = 'BAAI/bge-base-en-v1.5'
-EMB_MODEL_bge = 'BAAI/bge-large-en-v1.5'
-#EMB_MODEL_gtr_t5 = 'sentence-transformers/gtr-t5-base'
-EMB_MODEL_gtr_t5 = 'sentence-transformers/gtr-t5-large'
-EMB_MODEL_e5 = 'intfloat/e5-large-v2'
+EMB_MODEL_bge_base = 'BAAI/bge-base-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'
 # Chat app model
 MISTRAL_MODEL1 = 'mistralai/Mixtral-8x7B-Instruct-v0.1'
 HF_MODEL1 = 'HuggingFaceH4/zephyr-7b-beta'
 # define paths
-vector_path_bge = 'faiss_index_large' #'faiss_index'
-vector_path_gtr_t5 = 'faiss_index_2_large' #'faiss_index_2'
-vector_path_e5 = 'faiss_index_3'
+vector_path_bge_base = 'vectorDB/faiss_index_bge_base' 
+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'
+
 hf_token = os.environ["HUGGINGFACEHUB_API_TOKEN"]
 
 def respond(message, history):
     
     # Initialize your embedding model
-    embedding_model_bge = HuggingFaceEmbeddings(model_name=EMB_MODEL_bge)
-    embedding_model_gtr_t5 = HuggingFaceEmbeddings(model_name=EMB_MODEL_gtr_t5)
-    embedding_model_e5 = HuggingFaceEmbeddings(model_name=EMB_MODEL_e5)
+    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, embedding_model_bge, allow_dangerous_deserialization=True)
-    vectordb_gtr_t5 = FAISS.load_local(vector_path_gtr_t5, embedding_model_gtr_t5, allow_dangerous_deserialization=True)
-    vectordb_e5 = FAISS.load_local(vector_path_e5, embedding_model_e5, 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})
@@ -64,37 +67,38 @@ def respond(message, history):
     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."
-    )
-    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_e5.strip().startswith(instruction_prefix):
-        responce_e5 = responce_e5.strip()[len(instruction_prefix):].strip()
-        
-    # 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_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_e5:
-        #original_text_e5 = match_e5.group(1).strip()
-        #question_e5 = match_e5.group(2).strip()
-        answer_e5 = match_e5.group(3).strip()
-
-    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}'
+    #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_e5.strip().startswith(instruction_prefix):
+    #    responce_e5 = responce_e5.strip()[len(instruction_prefix):].strip()
+    #    
+    ## 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_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_e5:
+    #    #original_text_e5 = match_e5.group(1).strip()
+    #    #question_e5 = match_e5.group(2).strip()
+    #    answer_e5 = match_e5.group(3).strip()
+    #
+    #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}'
     yield formatted_responce