Update app.py

app.py

@@ -52,7 +52,7 @@ def create_retriever_from_chroma(vectorstore_path="docs/chroma/", search_type='m
         # Load documents from the specified data path
     loader = DirectoryLoader('./data/', glob="./*.txt", loader_cls=TextLoader)
     docs = loader.load()
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap,separators=["\n \n \n", "\n \n", "\n1" , "(?<=\. )", " ", ""])
     split_docs = text_splitter.split_documents(docs)
 
         
@@ -94,7 +94,7 @@ def main():
 
     st.markdown("Hi, I am Qwen, chat mmodel, based on respublic of Lithuania law document. Write you question and press enter to start chat.")
     
-    retriever = create_retriever_from_chroma(vectorstore_path="docs/chroma/", search_type='mmr', k=9, chunk_size=300, chunk_overlap=20)
+    retriever = create_retriever_from_chroma(vectorstore_path="docs/chroma/", search_type='mmr', k=9, chunk_size=450, chunk_overlap=20)
     if user_question := st.text_input("Ask a question about your documents:"):
         handle_userinput(user_question,retriever)
         
@@ -153,12 +153,7 @@ def create_conversational_rag_chain(retriever):
         verbose=False,
     )
 
-    template = """Answer the question based only on the following context:
-    {context}
-
-    Question: {question}
-    """
-    prompt = ChatPromptTemplate.from_template(template)
+    prompt = hub.pull("rlm/rag-prompt")
 
     rag_chain = prompt | llm | StrOutputParser()