last working update

app.py

@@ -6,6 +6,8 @@ from langchain_community.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
@@ -19,23 +21,33 @@ import time
 
 # 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
 )
 
-
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 # Load the LLM
 llm = Together(
     model="mistralai/Mixtral-8x22B-Instruct-v0.1",
-msgs = StreamlitChatMessageHistory(key="langchain_messages")
-memory = ConversationBufferMemory(chat_memory=msgs)
+    temperature=0.2,
+    max_tokens=19096,
+    top_k=10,
+    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}")
 
@@ -45,24 +57,20 @@ def _combine_documents(
     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':
@@ -72,131 +80,61 @@ def app():
         elif 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 = 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 = vectordb.as_retriever(search_kwargs={"k": 7})
-
-
         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})
 
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     # Session State
-
     if "messages" not in st.session_state.keys():
         st.session_state.messages = [{"role": "assistant", "content": "How may I help you?"}]
 
-
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     _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("Hello Doctor!")
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+    st.header("Ask Away!")
     for message in st.session_state.messages:
         with st.chat_message(message["role"]):
             st.write(message["content"])
             store_chat_history(message["role"], message["content"])
 
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     prompts2 = st.chat_input("Say something")
 
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     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..."):
@@ -216,32 +154,5 @@ def app():
                         st.error(f"An error occurred: {e}")
                         time.sleep(2)  # Wait 2 seconds before retrying
 
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 if __name__ == '__main__':
-    app()
+    app()