import streamlit as st import requests import json import time import os from datetime import datetime API_BASE_URL = os.environ.get("API_BASE_URL", "http://localhost:5000") st.set_page_config( page_title="RAG System - Knowledge Base", page_icon="🧠", layout="wide" ) st.title("🧠 RAG System - Web Knowledge Base") st.markdown("*Ingest web content and query it with AI-powered semantic search*") tab1, tab2, tab3 = st.tabs(["đŸ“Ĩ Ingest URLs", "🔍 Query Knowledge Base", "📊 Status Dashboard"]) with tab1: st.header("Ingest Web Content") st.markdown("Submit URLs to be processed and added to the knowledge base.") url_input = st.text_input( "Enter URL to ingest:", placeholder="https://example.com/article", key="url_input" ) col1, col2 = st.columns([1, 4]) with col1: ingest_button = st.button("🚀 Ingest URL", type="primary") if ingest_button: if not url_input: st.error("Please enter a URL") else: try: with st.spinner("Submitting URL for processing..."): response = requests.post( f"{API_BASE_URL}/ingest-url", json={"url": url_input}, timeout=10 ) if response.status_code == 202: data = response.json() st.success(f"✅ URL submitted successfully!") st.info(f"**URL ID:** `{data['url_id']}`") st.info(f"**Status:** {data['status']}") st.markdown(data['message']) if 'ingestion_status' not in st.session_state: st.session_state.ingestion_status = [] st.session_state.ingestion_status.append({ 'url_id': data['url_id'], 'url': data['url'], 'submitted_at': datetime.now().strftime("%Y-%m-%d %H:%M:%S") }) else: st.error(f"Error: {response.status_code} - {response.text}") except requests.exceptions.ConnectionError: st.error("❌ Cannot connect to API server. Make sure FastAPI is running on port 5000.") except Exception as e: st.error(f"Error: {str(e)}") if 'ingestion_status' in st.session_state and st.session_state.ingestion_status: st.markdown("---") st.subheader("Recently Submitted URLs") for item in reversed(st.session_state.ingestion_status[-5:]): with st.expander(f"🔗 {item['url']}"): st.write(f"**URL ID:** `{item['url_id']}`") st.write(f"**Submitted:** {item['submitted_at']}") with tab2: st.header("Query Knowledge Base") st.markdown("Ask questions based on ingested web content.") question_input = st.text_area( "Enter your question:", placeholder="What are the main topics discussed in the ingested articles?", height=100, key="question_input" ) top_k = st.slider( "Number of sources to retrieve:", min_value=1, max_value=10, value=5, help="More sources provide more context but may include less relevant information" ) query_button = st.button("🔍 Search & Answer", type="primary") if query_button: if not question_input: st.error("Please enter a question") else: try: with st.spinner("Searching knowledge base and generating answer..."): response = requests.post( f"{API_BASE_URL}/query", json={ "question": question_input, "top_k": top_k }, timeout=30 ) if response.status_code == 200: data = response.json() st.markdown("### 💡 Answer") st.markdown(f"**Question:** {data['question']}") st.markdown("---") st.markdown(data['answer']) st.markdown("---") st.markdown(f"### 📚 Sources ({len(data['sources'])} found)") for i, source in enumerate(data['sources'], 1): with st.expander(f"Source {i} - Relevance: {source['score']:.2%}"): st.markdown(f"**URL:** [{source['url']}]({source['url']})") st.markdown(f"**Relevance Score:** {source['score']:.4f}") st.markdown("**Excerpt:**") st.info(source['text_snippet']) elif response.status_code == 500: error_data = response.json() if "GROQ_API_KEY not configured" in error_data.get('detail', ''): st.error("⚠ī¸ Groq API key is not configured. Please set GROQ_API_KEY in your .env file.") else: st.error(f"Server error: {error_data.get('detail', 'Unknown error')}") else: st.error(f"Error: {response.status_code} - {response.text}") except requests.exceptions.ConnectionError: st.error("❌ Cannot connect to API server. Make sure FastAPI is running on port 5000.") except Exception as e: st.error(f"Error: {str(e)}") with tab3: st.header("System Status Dashboard") col1, col2 = st.columns(2) with col1: st.subheader("đŸĨ Health Check") if st.button("Check System Health"): try: response = requests.get(f"{API_BASE_URL}/health", timeout=5) if response.status_code == 200: health_data = response.json() st.success(f"**Status:** {health_data['status']}") if health_data.get('redis_connected'): st.success("✅ Redis: Connected") st.info(f"Queue Length: {health_data.get('queue_length', 'N/A')}") else: st.error("❌ Redis: Not Connected") else: st.error(f"Health check failed: {response.status_code}") except requests.exceptions.ConnectionError: st.error("❌ API Server: Not Running") except Exception as e: st.error(f"Error: {str(e)}") with col2: st.subheader("🔍 Check URL Status") url_id_input = st.text_input( "Enter URL ID:", placeholder="uuid-here", key="url_id_check" ) if st.button("Check Status"): if not url_id_input: st.error("Please enter a URL ID") else: try: response = requests.get( f"{API_BASE_URL}/status/{url_id_input}", timeout=5 ) if response.status_code == 200: status_data = response.json() status_color = { 'pending': '🟡', 'processing': '🔄', 'completed': '✅', 'failed': '❌' }.get(status_data['status'], 'âšĒ') st.markdown(f"### {status_color} Status: **{status_data['status'].upper()}**") st.markdown(f"**URL:** [{status_data['url']}]({status_data['url']})") st.markdown(f"**Created:** {status_data['created_at']}") st.markdown(f"**Updated:** {status_data['updated_at']}") if status_data['status'] == 'completed': st.success(f"✅ Completed at: {status_data['completed_at']}") st.info(f"📊 Total chunks: {status_data['chunk_count']}") elif status_data['status'] == 'failed': st.error(f"Error: {status_data.get('error_message', 'Unknown error')}") elif response.status_code == 404: st.warning("URL ID not found") else: st.error(f"Error: {response.status_code}") except requests.exceptions.ConnectionError: st.error("❌ Cannot connect to API server") except Exception as e: st.error(f"Error: {str(e)}") st.sidebar.title("ℹī¸ About") st.sidebar.markdown(""" ### RAG Knowledge Base System This application uses Retrieval-Augmented Generation (RAG) to: 1. **Ingest** web content from URLs 2. **Process** and chunk the content 3. **Embed** text using sentence-transformers 4. **Store** in Qdrant vector database 5. **Query** with semantic search 6. **Generate** grounded answers via Groq AI ### System Requirements - Redis (queue management) - Qdrant (vector database) - FastAPI backend (port 5000) - Background worker process - Groq API key configured ### How to Use 1. **Ingest URLs** - Add web content to knowledge base 2. **Wait for Processing** - Check status dashboard 3. **Query** - Ask questions about the content *Built with FastAPI, Streamlit, and modern AI technologies* """) st.sidebar.markdown("---") st.sidebar.markdown(f"**API Endpoint:** `{API_BASE_URL}`") st.sidebar.markdown("**Version:** 1.0.0")