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import streamlit as st
import os
import json
from dotenv import load_dotenv
from langchain.chains import RetrievalQA
from langchain_community.vectorstores import FAISS
from langchain.text_splitter import CharacterTextSplitter
from langchain.chat_models import ChatOpenAI
from langchain.schema import Document
from langchain.embeddings import HuggingFaceEmbeddings
# Load environment variables
load_dotenv()
# Get the OpenAI API key from the environment
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
if not OPENAI_API_KEY:
st.error("OPENAI_API_KEY is not set. Please add it to your .env file.")
# Initialize session state variables
if 'vector_store' not in st.session_state:
st.session_state.vector_store = None
if 'qa_chain' not in st.session_state:
st.session_state.qa_chain = None
def load_json_file(file_path):
"""Load JSON data from a file."""
with open(file_path, "r", encoding="utf-8") as file:
data = json.load(file)
return data
def setup_vector_store_from_json(json_data):
"""Create a vector store from JSON data."""
documents = [Document(page_content=item["content"], metadata={"url": item["url"]}) for item in json_data]
# Use HuggingFace embeddings
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
vector_store = FAISS.from_documents(documents, embeddings)
return vector_store
def setup_qa_chain(vector_store):
"""Set up the QA chain with a retriever."""
retriever = vector_store.as_retriever(search_kwargs={"k": 3})
llm = ChatOpenAI(model="gpt-3.5-turbo", openai_api_key=OPENAI_API_KEY)
qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=retriever, return_source_documents=True)
return qa_chain
def main():
# Set page title and header
st.set_page_config(page_title="Football Players RAG App", page_icon="⚽")
st.title("Football Players Knowledge Base 🏆")
# Sidebar for initialization
st.sidebar.header("Initialize Knowledge Base")
if st.sidebar.button("Load Data"):
try:
# Load and preprocess the JSON file
json_data = load_json_file("football_players.json")
st.session_state.vector_store = setup_vector_store_from_json(json_data)
st.session_state.qa_chain = setup_qa_chain(st.session_state.vector_store)
st.sidebar.success("Knowledge base loaded successfully!")
except Exception as e:
st.sidebar.error(f"Error loading data: {e}")
# Query input and processing
st.header("Ask a Question")
query = st.text_input("Enter your question about football players:")
if query:
# Check if vector store and QA chain are initialized
if st.session_state.qa_chain is None:
st.warning("Please load the knowledge base first using the sidebar.")
else:
# Run the query
try:
response = st.session_state.qa_chain({"query": query})
# Display answer
st.subheader("Answer")
st.write(response["result"])
# Display sources
st.subheader("Sources")
sources = response["source_documents"]
for i, doc in enumerate(sources, 1):
with st.expander(f"Source {i}"):
st.write(f"**Content:** {doc.page_content}")
st.write(f"**URL:** {doc.metadata.get('url', 'No URL available')}")
except Exception as e:
st.error(f"An error occurred: {e}")
if __name__ == "__main__":
main()
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