going back

app.py

@@ -1,35 +1,52 @@
 import streamlit as st
 import os
-from langchain_community.vectorstores import Chroma
-from langchain_community.embeddings import HuggingFaceBgeEmbeddings
-from langchain_community.llms import Together
-from langchain.chains import create_retrieval_chain, create_history_aware_retriever, LLMChain
-from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder, PromptTemplate
+from langchain.vectorstores import Chroma
+from langchain.embeddings import HuggingFaceBgeEmbeddings
+from langchain.llms import Together
+from langchain import hub
+from operator import itemgetter
+from langchain.schema.runnable import RunnableParallel
+from langchain.schema import format_document
+from typing import List, Tuple
+from langchain.chains import LLMChain
+from langchain.chains import RetrievalQA
+from langchain.schema.output_parser import StrOutputParser
+from langchain.memory import StreamlitChatMessageHistory
 from langchain.memory import ConversationBufferMemory
-from langchain_community.chat_message_histories import StreamlitChatMessageHistory
-import time
+from langchain.chains import ConversationalRetrievalChain
+from langchain.memory import ConversationSummaryMemory
+from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder, PromptTemplate
+from langchain.schema.runnable import RunnableLambda, RunnablePassthrough
+
 
 # Load the embedding function
 model_name = "BAAI/bge-base-en"
-encode_kwargs = {'normalize_embeddings': True}
+encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
 
 embedding_function = HuggingFaceBgeEmbeddings(
     model_name=model_name,
     encode_kwargs=encode_kwargs
 )
 
+# Load the ChromaDB vector store
+# persist_directory="./mrcpchromadb/"
+# 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_tokens=19096,
-    top_k=10,
+    top_k=6,
     together_api_key=os.environ['pilotikval']
 )
 
 # Load the summarizeLLM
 llmc = Together(
-    model="mistralai/Mixtral-8x7B-Instruct-v0.1",
+    model="mistralai/Mixtral-8x22B-Instruct-v0.1",
     temperature=0.2,
     max_tokens=1024,
     top_k=1,
@@ -37,109 +54,219 @@ llmc = Together(
 )
 
 msgs = StreamlitChatMessageHistory(key="langchain_messages")
-memory = ConversationBufferMemory(chat_memory=msgs, memory_key="chat_history", return_messages=True)
+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)
+        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")
+            persist_directory="./respfishmandbcud/"
+            vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_function,collection_name="fishmannotescud")
             retriever = vectordb.as_retriever(search_kwargs={"k": 5})
-        elif option == 'RespiratoryMurray':
-            persist_directory = "./respmurray/"
-            vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_function, collection_name="respmurraynotes")
+            retriever = retriever # replace with your actual retriever
+        
+        if option == 'RespiratoryMurray':
+            persist_directory="./respmurray/"
+            vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_function,collection_name="respmurraynotes")
             retriever = vectordb.as_retriever(search_kwargs={"k": 5})
-        elif option == 'MedMRCP2':
-            persist_directory = "./medmrcp2store/"
-            vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_function, collection_name="medmrcp2notes")
+            retriever = retriever
+
+        if option == 'MedMRCP2':
+            persist_directory="./medmrcp2store/"
+            vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_function,collection_name="medmrcp2notes")
             retriever = vectordb.as_retriever(search_kwargs={"k": 5})
-        elif option == 'General Medicine':
-            persist_directory = "./oxfordmedbookdir/"
-            vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_function, collection_name="oxfordmed")
+            retriever = retriever
+
+        if option == 'General Medicine':
+            persist_directory="./oxfordmedbookdir/"
+            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")
+            persist_directory="./mrcpchromadb/"
+            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():
         st.session_state.messages = [{"role": "assistant", "content": "How may I help you?"}]
+    
+
+    
+
+    
+
+
+    
 
-    condense_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.
+
+    ## 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.
     Chat History:
     {chat_history}
-    Follow-Up Input: {question}
+    Follow Up Input: {question}
     Standalone question:"""
-    CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(condense_template)
+    CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
 
-    answer_template = """You are helping a doctor. Answer with what you know from the context provided. Please be as detailed and thorough. Answer the question based on the following context:
+    template = """You are helping a doctor. Answer with what you know from the context provided. Please be as detailed and thorough. Answer the question based on the following context:
     {context}
-    Question: {question}"""
-    ANSWER_PROMPT = ChatPromptTemplate.from_template(answer_template)
+    Question: {question}
+    """
+    ANSWER_PROMPT = ChatPromptTemplate.from_template(template)
 
-    question_generator_chain = LLMChain(llm=llmc, prompt=CONDENSE_QUESTION_PROMPT)
-    combine_docs_chain = LLMChain(llm=llm, prompt=ANSWER_PROMPT)
 
-    history_aware_retriever = create_history_aware_retriever(
-        llm=llmc,
-        retriever=retriever,
-        prompt=CONDENSE_QUESTION_PROMPT
+    _inputs = RunnableParallel(
+        standalone_question=RunnablePassthrough.assign(
+            chat_history=lambda x: chistory
+        )
+        | CONDENSE_QUESTION_PROMPT
+        | llmc
+        | StrOutputParser(),
     )
+    _context = {
+        "context": itemgetter("standalone_question") | retriever | _combine_documents,
+        "question": lambda x: x["standalone_question"],
+    }
+    conversational_qa_chain = _inputs | _context | ANSWER_PROMPT | llm
+
+
+    
+
+    
 
-    conversational_qa_chain = create_retrieval_chain(
-        history_aware_retriever,
-        combine_docs_chain
-    )
 
+    
+    
+   
+    
     st.header("Ask Away!")
+    # Display the messages
     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"):
             with st.spinner("Thinking..."):
-                for _ in range(3):
-                    try:
-                        response = conversational_qa_chain.invoke(
-                            {
-                                "input": prompts2,
-                                "chat_history": chistory,
-                            }
-                        )
-                        st.write(response["answer"])
-                        message = {"role": "assistant", "content": response["answer"]}
-                        st.session_state.messages.append(message)
-                        break
-                    except Exception as e:
-                        st.error(f"An error occurred: {e}")
-                        time.sleep(2)
+                response = conversational_qa_chain.invoke(
+                {
+                    "question": prompts2,
+                    "chat_history": chistory,
+                }
+            )
+                st.write(response)
+        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()