Delete streamlit-rag-app.py

streamlit-rag-app.py

@@ -1,185 +0,0 @@
-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_openai import ChatOpenAI, OpenAIEmbeddings, OpenAI
-
-from langchain.schema import Document
-
-from langchain_huggingface import HuggingFaceEmbeddings
-
-from langchain.chains.combine_documents import create_stuff_documents_chain
-
-from langchain.chains.retrieval import create_retrieval_chain
-
-from langchain_core.prompts import PromptTemplate
-
-
-
-# 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 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
-
-
-
-prompt_template = PromptTemplate.from_template("Answer the following query based on a number of context documents Query:{query},Context:{context},Answer:")
-
-
-
-def main():
-
-    # Set page title and header
-
-    llm = ChatOpenAI(model="gpt-3.5-turbo", openai_api_key=OPENAI_API_KEY)
-
-    st.set_page_config(page_title="LibRAG", page_icon="📚")
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-    st.title("Boston Public Library Database 📚")
-
-    
-
-    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
-
-
-
-    # Sidebar for initialization
-
-    # st.sidebar.header("Initialize Knowledge Base")
-
-    # if st.sidebar.button("Load Data"):
-
-    #     try:
-
-    #         st.session_state.vector_store = FAISS.load_local(
-
-    #                     "vector-store", embeddings, allow_dangerous_deserialization=True
-
-    #                     )
-
-    #         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}")
-
-
-
-    st.session_state.vector_store = FAISS.load_local("vector-store", embeddings, allow_dangerous_deserialization=True)
-
-    st.session_state.combine_docs_chain = create_stuff_documents_chain(llm, prompt_template)
-
-    st.session_stateretrieval_chain = create_retrieval_chain(st.session_state.vector_store.as_retriever(search_kwargs={"k": 3}), combine_docs_chain)
-
-    # st.session_state.qa_chain = setup_qa_chain(st.session_state.vector_store)
-
-    # Query input and processing
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-    st.header("Ask a Question")
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-    query = st.text_input("Enter your question about BPL's database")
-
-    response = llm.invoke()
-
-    if query:
-
-        # Check if vector store and QA chain are initialized
-
-        if st.session_state.response is None:
-
-            st.warning("Please load the knowledge base first using the sidebar.")
-
-        else:
-
-            # Run the query
-
-            try:
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-                st.session_state.response = retrieval_chain.invoke({"input": f"{query}"}) 
-
-                
-
-                # Display answer
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-                st.subheader("Answer")
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-                st.write(response["result"])
-
-
-
-                # Display sources
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-                st.subheader("Sources")
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-                sources = response["source_documents"]
-
-                for i, doc in enumerate(sources, 1):
-
-                    with st.expander(f"Source {i}"):
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-                        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()