Update app.py

app.py

@@ -88,6 +88,33 @@ if not os.path.exists(db_path):
 vectordb = Chroma(persist_directory="./data", embedding_function=OpenAIEmbeddings())
 
 
+# Define the system message template
+system_template = """Use only 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. 
+Always answer in Englsih. Split the answer into easily readable paragraphs. Use bullet points and number points where possible. 
+Include any useful URLs and/or contact details from the context provided whereever possible.
+Always end by adding a carrage return and then saying: Thank you for your query to CitizensInformation.ie chat!
+----------------
+{context}"""
+
+# Create the chat prompt templates
+messages = [
+    SystemMessagePromptTemplate.from_template(system_template),
+    HumanMessagePromptTemplate.from_template("{question}")
+        ]
+qa_prompt = ChatPromptTemplate.from_messages(messages)
+
+
+
+
+
+pdf_qa = ConversationalRetrievalChain.from_llm(
+    ChatOpenAI(temperature=0.9, model_name="gpt-3.5-turbo"),
+    vectordb.as_retriever(),return_source_documents=True,verbose=False,combine_docs_chain_kwargs={"prompt": qa_prompt})
+    
+
+
+
 def ask_alans_ai(query, vectordb):
     global chat_history
     result = pdf_qa(
@@ -126,30 +153,7 @@ if __name__ == "__main__":
 
 
 
-# Define the system message template
-system_template = """Use only 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. 
-Always answer in Englsih. Split the answer into easily readable paragraphs. Use bullet points and number points where possible. 
-Include any useful URLs and/or contact details from the context provided whereever possible.
-Always end by adding a carrage return and then saying: Thank you for your query to CitizensInformation.ie chat!
-----------------
-{context}"""
 
-# Create the chat prompt templates
-messages = [
-    SystemMessagePromptTemplate.from_template(system_template),
-    HumanMessagePromptTemplate.from_template("{question}")
-        ]
-qa_prompt = ChatPromptTemplate.from_messages(messages)
-
-
-
-
-
-pdf_qa = ConversationalRetrievalChain.from_llm(
-    ChatOpenAI(temperature=0.9, model_name="gpt-3.5-turbo"),
-    vectordb.as_retriever(),return_source_documents=True,verbose=False,combine_docs_chain_kwargs={"prompt": qa_prompt})
-    
 
 
 # print("system_template is:", system_template, end="\n")