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from backend.core import run_llm
import streamlit as st
from streamlit_chat import message
from typing import Set
st.header("Peter's LangChain documentation chatbot")
st.subheader("GPT-3.5-turbo + the entire LangChain documentation")
prompt = st.text_input("You can also ask for code.", placeholder="")
# Initialize session state variables if they don't exist yet.
if "user_prompt_history" not in st.session_state:
st.session_state.user_prompt_history = []
if "chat_answers_history" not in st.session_state:
st.session_state.chat_answers_history = []
# Format sources links (referencing LangChain urls) as a clean enumerated list.
def create_sources_string(source_urls: Set[str]) -> str:
if not source_urls:
return ""
sources_list = list(source_urls)
sources_list.sort()
sources_string = "sources:\n"
for i, source in enumerate(sources_list):
sources_string += f"{i+1}. {source}\n"
return sources_string
if prompt:
with st.spinner("Thinking..."):
generated_response = run_llm(query=prompt)
sources = set(
[doc.metadata["source"]
for doc in generated_response["source_documents"]
]
)
formatted_response = (
f"{generated_response['result']}"
)
# Add the user's prompt and the chatbot's response to the session state variables.
st.session_state.user_prompt_history.append(prompt)
st.session_state.chat_answers_history.append(formatted_response)
if st.session_state.chat_answers_history:
for generated_response, user_query in zip(
st.session_state.chat_answers_history, st.session_state.user_prompt_history):
message(user_query, is_user=True)
message(generated_response, is_user=False)
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