changes v2

app.py

@@ -2,26 +2,20 @@ import streamlit as st
 import os
 from langchain.vectorstores import Chroma
 from langchain.embeddings import HuggingFaceBgeEmbeddings
-from langchain-together import Together
+from langchain_together import Together  # Updated import
 from langchain import hub
 from operator import itemgetter
-from langchain.schema.runnable import RunnableParallel
-from langchain.schema import format_document
+from langchain.schema import RunnableParallel, format_document  # Updated import paths
 from typing import List, Tuple
-from langchain.chains import LLMChain
-from langchain.chains import RetrievalQA
+from langchain.chains import LLMChain, RetrievalQA, ConversationalRetrievalChain
 from langchain.schema.output_parser import StrOutputParser
-from langchain.memory import StreamlitChatMessageHistory
-from langchain.memory import ConversationBufferMemory
-from langchain.chains import ConversationalRetrievalChain
-from langchain.memory import ConversationSummaryMemory
+from langchain.memory import StreamlitChatMessageHistory, ConversationBufferMemory, ConversationSummaryMemory
 from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder, PromptTemplate
-from langchain.schema.runnable import RunnableLambda, RunnablePassthrough
-
+from langchain.schema import RunnableLambda, RunnablePassthrough
 
 # Load the embedding function
 model_name = "BAAI/bge-base-en"
-encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
+encode_kwargs = {'normalize_embeddings': True}
 
 embedding_function = HuggingFaceBgeEmbeddings(
     model_name=model_name,
@@ -30,16 +24,13 @@ embedding_function = HuggingFaceBgeEmbeddings(
 
 # Load the ChromaDB vector store
 # persist_directory="./mrcpchromadb/"
-# vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_function,collection_name="mrcppassmednotes")
-
-
-
+# vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_function, collection_name="mrcppassmednotes")
 
 # Load the LLM
 llm = Together(
     model="mistralai/Mixtral-8x22B-Instruct-v0.1",
     temperature=0.2,
-    max_new_tokens = 22000
+    max_new_tokens=22000,
     top_k=12,
     together_api_key=os.environ['pilotikval']
 )
@@ -48,7 +39,7 @@ llm = Together(
 llmc = Together(
     model="mistralai/Mixtral-8x22B-Instruct-v0.1",
     temperature=0.2,
-    max_new_tokens = 1000
+    max_new_tokens=1000,
     top_k=3,
     together_api_key=os.environ['pilotikval']
 )
@@ -56,99 +47,55 @@ llmc = Together(
 msgs = StreamlitChatMessageHistory(key="langchain_messages")
 memory = ConversationBufferMemory(chat_memory=msgs)
 
-
-
 DEFAULT_DOCUMENT_PROMPT = PromptTemplate.from_template(template="{page_content}")
 
-def _combine_documents(
-        docs, document_prompt=DEFAULT_DOCUMENT_PROMPT, document_separator="\n\n"
-    ):
-        doc_strings = [format_document(doc, document_prompt) for doc in docs]
-        return document_separator.join(doc_strings)
-
-
+def _combine_documents(docs, document_prompt=DEFAULT_DOCUMENT_PROMPT, document_separator="\n\n"):
+    doc_strings = [format_document(doc, document_prompt) for doc in docs]
+    return document_separator.join(doc_strings)
 
 chistory = []
 
 def store_chat_history(role: str, content: str):
-    # Append the new message to the chat history
     chistory.append({"role": role, "content": content})
 
-
 # Define the Streamlit app
 def app():
-
-
-
     with st.sidebar:
-
         st.title("dochatter")
-        # Create a dropdown selection box
         option = st.selectbox(
             'Which retriever would you like to use?',
             ('General Medicine', 'RespiratoryFishman', 'RespiratoryMurray', 'MedMRCP2', 'OldMedicine')
         )
-        # Depending on the selected option, choose the appropriate retriever
+        
         if option == 'RespiratoryFishman':
             persist_directory="./respfishmandbcud/"
-            vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_function,collection_name="fishmannotescud")
+            vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_function, collection_name="fishmannotescud")
             retriever = vectordb.as_retriever(search_kwargs={"k": 5})
-            retriever = retriever # replace with your actual retriever
         
-        if option == 'RespiratoryMurray':
+        elif option == 'RespiratoryMurray':
             persist_directory="./respmurray/"
-            vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_function,collection_name="respmurraynotes")
+            vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_function, collection_name="respmurraynotes")
             retriever = vectordb.as_retriever(search_kwargs={"k": 5})
-            retriever = retriever
 
-        if option == 'MedMRCP2':
+        elif option == 'MedMRCP2':
             persist_directory="./medmrcp2store/"
-            vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_function,collection_name="medmrcp2notes")
+            vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_function, collection_name="medmrcp2notes")
             retriever = vectordb.as_retriever(search_kwargs={"k": 5})
-            retriever = retriever
 
-        if option == 'General Medicine':
+        elif option == 'General Medicine':
             persist_directory="./oxfordmedbookdir/"
-            vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_function,collection_name="oxfordmed")
+            vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_function, collection_name="oxfordmed")
             retriever = vectordb.as_retriever(search_kwargs={"k": 7})
-            retriever = retriever
-            
+        
         else:
             persist_directory="./mrcpchromadb/"
-            vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_function,collection_name="mrcppassmednotes")
+            vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_function, collection_name="mrcppassmednotes")
             retriever = vectordb.as_retriever(search_kwargs={"k": 5})
-            retriever = retriever # replace with your actual retriever
-            retriever = retriever  # replace with your actual retriever
 
-        #template = """You are an AI chatbot having a conversation with a human. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.
-        #{context}
-        #{history}
-        #Human: {human_input}
-        #AI: """
-        #prompt = PromptTemplate(input_variables=["history", "question"], template=template)
-        #template = st.text_area("Template", value=template, height=180)
-        #prompt2 = ChatPromptTemplate.from_template(template)
-
-    
-    
-
-    # Session State
-    # Store LLM generated responses
-    if "messages" not in st.session_state.keys():
+    if "messages" not in st.session_state:
         st.session_state.messages = [{"role": "assistant", "content": "How may I help you?"}]
-    
-
-    
-
-    
-
-
-    
-
 
-    ## Retry lets go
-
-    _template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question which contains the themes of the conversation. Do not write the question. Do not write the answer.
+    _template = """Given the following conversation and a follow-up question, rephrase the follow-up question to be a standalone question which contains the themes of the conversation. Do not write the question. Do not write the answer.
     Chat History:
     {chat_history}
     Follow Up Input: {question}
@@ -161,7 +108,6 @@ def app():
     """
     ANSWER_PROMPT = ChatPromptTemplate.from_template(template)
 
-
     _inputs = RunnableParallel(
         standalone_question=RunnablePassthrough.assign(
             chat_history=lambda x: chistory
@@ -176,51 +122,18 @@ def app():
     }
     conversational_qa_chain = _inputs | _context | ANSWER_PROMPT | llm
 
-
-    
-
-    
-
-
-    
-    
-   
-    
-    st.header("Ask Away!")
-    # Display the messages
+    st.header("Hello Doctor, How can I help?")
     for message in st.session_state.messages:
         with st.chat_message(message["role"]):
             st.write(message["content"])
             store_chat_history(message["role"], message["content"])
 
-    # prompt = hub.pull("rlm/rag-prompt")
-    
-    
-    
-    
     prompts2 = st.chat_input("Say something")
 
-    # Implement using different book sources, if statements
-    
-
-    
-
-    
-    
-
-    
-
-    
-
-    
-    
-    
     if prompts2:
         st.session_state.messages.append({"role": "user", "content": prompts2})
         with st.chat_message("user"):
             st.write(prompts2)
-            
-    
 
     if st.session_state.messages[-1]["role"] != "assistant":
         with st.chat_message("assistant"):
@@ -235,38 +148,5 @@ def app():
         message = {"role": "assistant", "content": response}
         st.session_state.messages.append(message)
 
-
-
-    
-        
-        
-        
-        
-
-
-   
-
-    # Create a button to submit the question
-    
-
-
-    
-
-        
-        
-
-        
-        
-        
-
-        
-        
-
-        
-
-    
-# Initialize history
-history = []
-
 if __name__ == '__main__':
-    app()
+    app()