{title1}
""",
unsafe_allow_html=True
)
st.write("")
st.write('
The Procurement Services Digital AI Assistant is a quantum leap in GSA’s strategic goal of delivering better services to the public using modern technology. This AI enabled assistant makes it easy for citizens to get the information they need from the government by answering questions and providing assistance 24/7. It\'s designed to be user-friendly, making government services more accessible and reliable for all citizens. Just ask away.
', unsafe_allow_html=True) st.markdown("""---""") text_html = """Type your question in conversational style
Example: what is Electronic Protest Docketing System?
""" st.write(text_html, unsafe_allow_html=True) with st.sidebar: st.subheader("") if st.session_state["vector_db"] and st.session_state["llm"]: # Store LLM generated responses if "messages" not in st.session_state.keys(): st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?", "Source":""}] # Display or clear chat messages for message in st.session_state.messages: with st.chat_message(message["role"]): st.write(message["content"]) if message["Source"]=="": st.write("") else: with st.expander("source"): for idx, item in enumerate(message["Source"]): st.markdown(item["Page"]) st.markdown(item["Source"]) st.markdown(item["page_content"]) st.write("---") # Initialize the session state to store chat history if "stored_session" not in st.session_state: st.session_state["stored_session"] = [] # Create a list to store expanders if "expanders" not in st.session_state: st.session_state["expanders"] = [] # Define a function to add a new chat expander def add_chat_expander(chat_history): current_timestamp = datetime.datetime.now() timestamp_string = current_timestamp.strftime("%Y-%m-%d %H:%M:%S") st.session_state["expanders"].append({"timestamp": timestamp_string, "chat_history": chat_history}) def clear_chat_history(): """ To remove existing chat history and start new conversation """ stored_session = [] for dict_message in st.session_state.messages: if dict_message["role"] == "user": string_dialogue = "User: " + dict_message["content"] + "\n\n" st.session_state["stored_session"].append(string_dialogue) else: string_dialogue = "Assistant: " + dict_message["content"] + "\n\n" st.session_state["stored_session"].append(string_dialogue) stored_session.append(string_dialogue) # Add a new chat expander add_chat_expander(stored_session) st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?", "Source":""}] st.sidebar.button('New chat', on_click=clear_chat_history, use_container_width=True) st.sidebar.text("") st.sidebar.write('Chat history
', unsafe_allow_html=True) # Display existing chat expanders for expander_info in st.session_state["expanders"]: with st.sidebar.expander("Conversation ended at:"+"\n\n"+expander_info["timestamp"]): for message in expander_info["chat_history"]: if message.startswith("User:"): st.write(f'{message}', unsafe_allow_html=True) elif message.startswith("Assistant:"): st.write(f'{message}', unsafe_allow_html=True) else: st.write(message) def generate_llm_response(conversation_chain, prompt_input): # output= conversation_chain({'question': prompt_input}) res = conversation_chain(prompt_input) return res['result'] # User-provided prompt if prompt := st.chat_input(disabled= not st.session_state["vector_db"]): st.session_state.messages.append({"role": "user", "content": prompt, "Source":""}) with st.chat_message("user"): st.write(prompt) # Generate a new response if last message is not from assistant if st.session_state.messages[-1]["role"] != "assistant": with st.chat_message("assistant"): with st.spinner("Searching..."): start = timeit.default_timer() response = generate_llm_response(conversation_chain=st.session_state["conversation_chain"], prompt_input=prompt) placeholder = st.empty() full_response = '' for item in response: full_response += item placeholder.markdown(full_response) # The following logic will work in the way given below. # -- Check if intermediary steps are present in the output of the given prompt. # -- If not, we can conclude that, agent has used internet search as tool. # -- Check if intermediary steps are present in the output of the prompt. # -- If intermediary steps are present, it means agent has used exising custom knowledge base for iformation retrival and therefore we need to give souce docs as output along with LLM's reponse. if response: st.text("-------------------------------------") docs= st.session_state["load_retriver"].get_relevant_documents(prompt) source_doc_list= [] for doc in docs: source_doc_list.append(doc.dict()) merged_source_doc= [] with st.expander("source"): for idx, item in enumerate(source_doc_list): source_doc = {"Page": f"Source {idx + 1}", "Source": f"**Source:** {item['metadata']['source'].split('/')[-1]}", "page_content":item["page_content"]} merged_source_doc.append(source_doc) st.markdown(f"Source {idx + 1}") st.markdown(f"**Source:** {item['metadata']['source'].split('/')[-1]}") st.markdown(item["page_content"]) st.write("---") # Add a separator between entries message = {"role": "assistant", "content": full_response, "Source":merged_source_doc} st.session_state.messages.append(message) st.markdown("👍 👎 Create Ticket") # else: # with st.expander("source"): # message = {"role": "assistant", "content": full_response, "Source":""} # st.session_state.messages.append(message) end = timeit.default_timer() print(f"Time to retrieve response: {end - start}")