Create streamlit_app.py

streamlit_app.py

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+"""Streamlit UI for Agentic RAG System - Simplified Version"""
+
+import streamlit as st
+from pathlib import Path
+import sys
+import time
+
+# Add src to path
+sys.path.append(str(Path(__file__).parent))
+
+from src.config.config import Config
+from src.document_ingestion.document_processor import DocumentProcessor
+from src.vectorstore.vectorstore import VectorStore
+from src.graph_builder.graph_builder import GraphBuilder
+
+# Page configuration
+st.set_page_config(
+    page_title="🤖 RAG Search",
+    page_icon="🔍",
+    layout="centered"
+)
+
+# Simple CSS
+st.markdown("""
+    <style>
+    .stButton > button {
+        width: 100%;
+        background-color: #4CAF50;
+        color: white;
+        font-weight: bold;
+    }
+    </style>
+""", unsafe_allow_html=True)
+
+def init_session_state():
+    """Initialize session state variables"""
+    if 'rag_system' not in st.session_state:
+        st.session_state.rag_system = None
+    if 'initialized' not in st.session_state:
+        st.session_state.initialized = False
+    if 'history' not in st.session_state:
+        st.session_state.history = []
+
+@st.cache_resource
+def initialize_rag():
+    """Initialize the RAG system (cached)"""
+    try:
+        # Initialize components
+        llm = Config.get_llm()
+        doc_processor = DocumentProcessor(
+            chunk_size=Config.CHUNK_SIZE,
+            chunk_overlap=Config.CHUNK_OVERLAP
+        )
+        vector_store = VectorStore()
+        
+        # Use default URLs
+        urls = Config.DEFAULT_URLS
+        
+        # Process documents
+        documents = doc_processor.process_urls(urls)
+        
+        # Create vector store
+        vector_store.create_vectorstore(documents)
+        
+        # Build graph
+        graph_builder = GraphBuilder(
+            retriever=vector_store.get_retriever(),
+            llm=llm
+        )
+        graph_builder.build()
+        
+        return graph_builder, len(documents)
+    except Exception as e:
+        st.error(f"Failed to initialize: {str(e)}")
+        return None, 0
+
+def main():
+    """Main application"""
+    init_session_state()
+    
+    # Title
+    st.title("🔍 RAG Document Search")
+    st.markdown("Ask questions about the loaded documents")
+    
+    # Initialize system
+    if not st.session_state.initialized:
+        with st.spinner("Loading system..."):
+            rag_system, num_chunks = initialize_rag()
+            if rag_system:
+                st.session_state.rag_system = rag_system
+                st.session_state.initialized = True
+                st.success(f"✅ System ready! ({num_chunks} document chunks loaded)")
+    
+    st.markdown("---")
+    
+    # Search interface
+    with st.form("search_form"):
+        question = st.text_input(
+            "Enter your question:",
+            placeholder="What would you like to know?"
+        )
+        submit = st.form_submit_button("🔍 Search")
+    
+    # Process search
+    if submit and question:
+        if st.session_state.rag_system:
+            with st.spinner("Searching..."):
+                start_time = time.time()
+                
+                # Get answer
+                result = st.session_state.rag_system.run(question)
+                
+                elapsed_time = time.time() - start_time
+                
+                # Add to history
+                st.session_state.history.append({
+                    'question': question,
+                    'answer': result['answer'],
+                    'time': elapsed_time
+                })
+                
+                # Display answer
+                st.markdown("### 💡 Answer")
+                st.success(result['answer'])
+                
+                # Show retrieved docs in expander
+                with st.expander("📄 Source Documents"):
+                    for i, doc in enumerate(result['retrieved_docs'], 1):
+                        st.text_area(
+                            f"Document {i}",
+                            doc.page_content[:300] + "...",
+                            height=100,
+                            disabled=True
+                        )
+                
+                st.caption(f"⏱️ Response time: {elapsed_time:.2f} seconds")
+    
+    # Show history
+    if st.session_state.history:
+        st.markdown("---")
+        st.markdown("### 📜 Recent Searches")
+        
+        for item in reversed(st.session_state.history[-3:]):  # Show last 3
+            with st.container():
+                st.markdown(f"**Q:** {item['question']}")
+                st.markdown(f"**A:** {item['answer'][:200]}...")
+                st.caption(f"Time: {item['time']:.2f}s")
+                st.markdown("")
+
+if __name__ == "__main__":
+    main()