changed 'k' in memory

utils.py

@@ -16,7 +16,7 @@ from langchain.embeddings import HuggingFaceEmbeddings
 from langchain.document_loaders import PyPDFLoader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.document_loaders import PyPDFLoader, DirectoryLoader
-from langchain.memory import ConversationBufferMemory
+from langchain.memory import ConversationBufferMemory, ConversationBufferWindowMemory
 from langchain.chains import ConversationalRetrievalChain
 from langchain.prompts.prompt import PromptTemplate
 from langchain.vectorstores import Chroma
@@ -30,9 +30,6 @@ from langchain.agents.agent_toolkits import create_conversational_retrieval_agen
 from langchain.utilities import SerpAPIWrapper
 from langchain.agents import Tool
 from langchain.agents import load_tools
-from langchain.chat_models import ChatOpenAI
-from langchain.retrievers.multi_query import MultiQueryRetriever
-from langchain.chains import RetrievalQA
 
 load_dotenv()
 
@@ -254,70 +251,30 @@ def load_text_chunks(text_chunks_pkl_dir):
 def load_ensemble_retriver(text_chunks, embeddings, chroma_vectorstore):
     """Load ensemble retiriever with BM25 and Chroma as individual retrievers"""
     bm25_retriever = BM25Retriever.from_documents(text_chunks)
-    bm25_retriever.k = 1
-    chroma_retriever = chroma_vectorstore.as_retriever(search_kwargs={"k": 1})  
+    bm25_retriever.k = 2
+    chroma_retriever = chroma_vectorstore.as_retriever(search_kwargs={"k": 2})  
     ensemble_retriever = EnsembleRetriever(retrievers=[bm25_retriever, chroma_retriever], weights=[0.3, 0.7])
-    retriever_from_llm = MultiQueryRetriever.from_llm(retriever=ensemble_retriever, llm=ChatOpenAI()
-)
-    return retriever_from_llm
+    return ensemble_retriever
 
 
 def load_conversational_retrievel_chain(retriever, llm):
-    '''Load Conversational Retrievel agent with following tasks as tools,
-    1) OPM Knowledge base query
-    2) INternet search with SerpAPI
-    This agent combines RAG, chat interfaces, agents.
-    '''
-    # retriever_tool = create_retriever_tool(
-    # retriever, 
-    # "Search_US_Office_of_Personnel_Management_Document",
-    # "Searches and returns documents regarding the U.S. Office of Personnel Management (OPM).")
-    # search_api = SerpAPIWrapper()
-    # search_api_tool = Tool(
-    #         name = "Current_Search",
-    #         func=search_api.run,
-    #         description="useful for when you need to answer questions about current events or the current state of the world"
-    #     )
-    # tools = [retriever_tool]
-    # agent_executor = create_conversational_retrieval_agent(llm, tools, verbose=True, max_token_limit=512)
-    # return agent_executor
-  # string_dialogue = "You are a helpful assistant. You do not respond as 'User' or pretend to be 'User'. You only respond once as 'Assistant'."
-    # _template= """
-    # You are a helpful assistant. You do not respond as 'User' or pretend to be 'User'. You only respond once as 'Assistant'.
-    # Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.
-    # Your answer should in English language only.
-    # Chat History:
-    # {chat_history}
-    # Follow Up Input: {question}
-    # Standalone question:"""
-
-    # CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
-    # memory = ConversationBufferMemory(return_messages=True,memory_key="chat_history")
-    # conversation_chain = ConversationalRetrievalChain.from_llm(
-    #     llm=st.session_state["llm"],
-    #     retriever=st.session_state["ensemble_retriver"],
-    #     condense_question_prompt=CONDENSE_QUESTION_PROMPT,
-    #     memory=memory,
-    #     verbose=True,
-    # )
-    template = """You are a helpful assistant. You do not respond as 'User' or pretend to be 'User'. You only respond once as 'Assistant'.
-    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.
-
-    {context}
-
-    {history}
-    Question: {question}
-    Helpful Answer:"""
-
-    prompt = PromptTemplate(input_variables=["history", "context", "question"], template=template)
-    memory = ConversationBufferMemory(input_key="question", memory_key="history")
-
-    qa = RetrievalQA.from_chain_type(
-        llm=llm,
-        chain_type="stuff",
-        retriever=retriever,
-        return_source_documents=True,
-        chain_type_kwargs={"prompt": prompt, "memory": memory},
+    '''Load Conversational Retrievel chain,'''
+    _template= """
+    You are a helpful assistant. You do not respond as 'User' or pretend to be 'User'. You only respond once as 'Assistant'.
+    Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.
+    Chat History:
+    {chat_history}
+    Follow Up Input: {question}
+    Standalone question:"""
+
+    CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
+    memory = ConversationBufferWindowMemory(return_messages=True,memory_key="chat_history", k=2)
+    conversation_chain = ConversationalRetrievalChain.from_llm(
+        llm=st.session_state["llm"],
+        retriever=st.session_state["ensemble_retriver"],
+        condense_question_prompt=CONDENSE_QUESTION_PROMPT,
+        memory=memory,
+        verbose=True,
     )
-    return qa
+    return conversation_chain
+