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import os
import streamlit as st
from llama_index.core import Settings, SimpleDirectoryReader, VectorStoreIndex
from llama_index.embeddings.azure_openai import AzureOpenAIEmbedding
from llama_index.llms.azure_openai import AzureOpenAI
st.set_page_config(
page_title="Chat with the PDM docs",
page_icon="📝",
layout="centered",
initial_sidebar_state="auto",
menu_items=None,
)
st.title("Chat with the PDM docs 💬🦙")
st.info(
"PDM - A modern Python package and dependency manager. "
"Check out the full documentation at [PDM docs](https://pdm-project.org).",
icon="📃",
)
Settings.llm = AzureOpenAI(
api_key=st.secrets.get("aoai_key"),
azure_endpoint=st.secrets.get("aoai_endpoint"),
engine="gpt-4o-mini",
api_version="2024-02-15-preview",
temperature=0.5,
system_prompt="You are an expert on PDM and your job is to answer technical questions. "
"Assume that all questions are related to PDM. Keep your answers technical and based on facts - do not hallucinate features.",
)
Settings.embed_model = AzureOpenAIEmbedding(
azure_deployment="embedding",
api_key=st.secrets.get("aoai_key"),
api_version="2023-05-15",
azure_endpoint=st.secrets.get("aoai_endpoint"),
)
DATA_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "docs/")
if "messages" not in st.session_state.keys(): # Initialize the chat messages history
st.session_state.messages = [
{
"role": "assistant",
"content": "Ask me a question about PDM!",
}
]
@st.cache_resource(show_spinner=False)
def load_data():
with st.spinner(text="Loading and indexing the PDM docs - hang tight! This should take 1-2 minutes."):
reader = SimpleDirectoryReader(input_dir=DATA_PATH, recursive=True, required_exts=[".md"])
docs = reader.load_data()
index = VectorStoreIndex.from_documents(docs)
return index
index = load_data()
if "chat_engine" not in st.session_state.keys(): # Initialize the chat engine
st.session_state.chat_engine = index.as_chat_engine(chat_mode="condense_question", verbose=True)
if prompt := st.chat_input("Your question"): # Prompt for user input and save to chat history
st.session_state.messages.append({"role": "user", "content": prompt})
for message in st.session_state.messages: # Display the prior chat messages
with st.chat_message(message["role"]):
st.write(message["content"])
# If last message is not from assistant, generate a new response
if st.session_state.messages[-1]["role"] != "assistant":
with st.chat_message("assistant"):
with st.spinner("Thinking..."):
response = st.session_state.chat_engine.chat(prompt)
st.write(response.response)
message = {"role": "assistant", "content": response.response}
st.session_state.messages.append(message) # Add response to message history
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