Update app.py

app.py

@@ -8,7 +8,7 @@ from langchain_openai import OpenAI, OpenAIEmbeddings
 from langchain.prompts import PromptTemplate
 from langchain.chains import LLMChain
 from langchain.memory import ConversationBufferWindowMemory
-from langchain.text_splitter import CharacterTextSplitter
+from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter
 from langchain_community.vectorstores import FAISS
  
 
@@ -46,11 +46,11 @@ def get_vectorstore(text_chunks):
 
 def get_text_chunks(text: str):
     """ This function will split the text into the smaller chunks"""
-    text_splitter = CharacterTextSplitter(
-        separator="\n",
-        chunk_size=1000,
-        chunk_overlap=264,
-        length_function=len
+    text_splitter = RecursiveCharacterTextSplitter(
+    chunk_size=1000,
+    chunk_overlap=100,
+    length_function=len,
+    is_separator_regex=False,
     )
     chunks = text_splitter.split_text(text)
     return chunks
@@ -70,7 +70,7 @@ def processing(pdf):
 
 def get_response(query: str) -> str:
     # getting the context from the database that is similar to the user query
-    query_context = st.session_state.vectorDB.similarity_search(query=query,k=2)
+    query_context = st.session_state.vectorDB.similarity_search(query=query,k=3)
     # calling the chain to get the output from the LLM
     response = st.session_state.chain.invoke({'human_input':query,'context':query_context,'name':st.session_state.bot_name})['text']
     # Iterate through each word in the 'response' string after splitting it based on whitespace
@@ -83,12 +83,12 @@ def get_response(query: str) -> str:
 
 def get_conversation_chain(vectorDB):
     # using OPENAI LLM
-    llm = OpenAI(temperature=0.5)
+    llm = OpenAI(temperature=0.3)
 
     # creating a template to pass into LLM
     template = """You are a Personalized ChatBot with a name: {name} for a company's customer support system, aiming to enhance the customer experience by providing tailored assistance and information. You are interacting with customer.
 
-    Answer the question as detailed as possible from the context: {context}\n , and  make sure to provide all the details, if the answer is not in the provided context just say, "answer is not available in the context", don't provide the wrong answer\n\n
+    Answer the question as detailed as possible and to the point from the context: {context}\n , and  make sure to provide all the information, if the answer is not in the provided context just say, "answer is not available in the context", don't provide the wrong answer\n\n
 
     {chat_history}
     Human: {human_input}