Creating app.py with mistral llm

app.py

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+import streamlit as st
+import os
+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.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} # 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-8x7B-Instruct-v0.1",
+    temperature=0.2,
+    max_tokens=4096,
+    top_k=4,
+    together_api_key=os.environ['pilotikval']
+)
+
+# Load the summarizeLLM
+llmc = Together(
+    model="mistralai/Mixtral-8x7B-Instruct-v0.1",
+    temperature=0.2,
+    max_tokens=1024,
+    top_k=1,
+    together_api_key=os.environ['pilotikval']
+)
+
+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)
+
+
+
+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?',
+            ('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")
+            retriever = vectordb.as_retriever(search_kwargs={"k": 5})
+            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})
+            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})
+            retriever = retriever
+        else:
+            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?"}]
+    
+
+    
+
+    
+
+
+    
+
+
+    ## 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}
+    Standalone question:"""
+    CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
+
+    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(template)
+
+
+    _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
+
+
+    
+
+    
+
+
+    
+    
+   
+    
+    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..."):
+                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()