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import streamlit as st
import os
from typing import List, Tuple, Optional
from pinecone import Pinecone
from langchain_pinecone import PineconeVectorStore
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_openai import ChatOpenAI
from langchain_core.prompts import PromptTemplate
from dotenv import load_dotenv
from RAG import RAG
import logging
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Page configuration
st.set_page_config(
page_title="Boston Public Library Chatbot",
page_icon="🤖",
layout="wide"
)
def initialize_models() -> Tuple[Optional[ChatOpenAI], HuggingFaceEmbeddings]:
"""Initialize the language model and embeddings."""
try:
load_dotenv()
if "llm" not in st.session_state:
# Initialize OpenAI model
st.session_state.llm = ChatOpenAI(
model="gpt-4", # Changed from gpt-4o-mini which appears to be a typo
temperature=0,
timeout=60, # Added reasonable timeout
max_retries=2
)
if "embeddings" not in st.session_state:
# Initialize embeddings
st.session_state.embeddings = HuggingFaceEmbeddings(
model_name="sentence-transformers/all-MiniLM-L6-v2"
)
if "pinecone" not in st.session_state:
pinecone_api_key = os.getenv("PINECONE_API_KEY")
INDEX_NAME = 'bpl-rag'
#initialize vectorstore
pc = Pinecone(api_key=pinecone_api_key)
index = pc.Index(INDEX_NAME)
st.session_state.pinecone = PineconeVectorStore(index=index, embedding=st.session_state.embeddings)
except Exception as e:
logger.error(f"Error initializing models: {str(e)}")
st.error(f"Failed to initialize models: {str(e)}")
return None, None
def process_message(
query: str,
llm: ChatOpenAI,
vectorstore: PineconeVectorStore,
) -> Tuple[str, List]:
"""Process the user message using the RAG system."""
try:
response, sources = RAG(
query=query,
llm=llm,
vectorstore=vectorstore,
)
return response, sources
except Exception as e:
logger.error(f"Error in process_message: {str(e)}")
return f"Error processing message: {str(e)}", []
def display_sources(sources: List) -> None:
"""Display sources in expandable sections with proper formatting."""
if not sources:
st.info("No sources available for this response.")
return
st.subheader("Sources")
for i, doc in enumerate(sources, 1):
try:
with st.expander(f"Source {i}"):
if hasattr(doc, 'page_content'):
st.markdown(f"**Content:** {doc.page_content}")
if hasattr(doc, 'metadata'):
for key, value in doc.metadata.items():
st.markdown(f"**{key.title()}:** {value}")
else:
st.markdown(f"**Content:** {str(doc)}")
except Exception as e:
logger.error(f"Error displaying source {i}: {str(e)}")
st.error(f"Error displaying source {i}")
def main():
st.title("Digital Commonwealth RAG")
INDEX_NAME = 'bpl-rag'
# Initialize session state
if "messages" not in st.session_state:
st.session_state.messages = []
# Initialize models
initialize_models()
# Display chat history
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
# Chat input
user_input = st.chat_input("Type your query here...")
if user_input:
# Display user message
with st.chat_message("user"):
st.markdown(user_input)
st.session_state.messages.append({"role": "user", "content": user_input})
# Process and display assistant response
with st.chat_message("assistant"):
with st.spinner("Let Me Think..."):
response, sources = process_message(
query=user_input,
llm=st.session_state.llm,
vectorstore=st.session_state.pinecone
)
if isinstance(response, str):
st.markdown(response)
st.session_state.messages.append({
"role": "assistant",
"content": response
})
# Display sources
display_sources(sources)
else:
st.error("Received an invalid response format")
# Footer
st.markdown("---")
st.markdown(
"Built with ❤️ using Streamlit + LangChain + OpenAI",
help="An AI-powered chatbot with RAG capabilities"
)
if __name__ == "__main__":
main() |