import streamlit as st import os from agno.agent import Agent from agno.knowledge.embedder.openai import OpenAIEmbedder from agno.knowledge.knowledge import Knowledge from agno.models.openai import OpenAIChat from agno.vectordb.lancedb import LanceDb, SearchType from dotenv import load_dotenv # Load environment variables load_dotenv() # Page configuration st.set_page_config( page_title="Agentic RAG with GPT-5", page_icon="🧠", layout="wide" ) # Main title and description st.title("🧠 Agentic RAG with GPT-5") st.markdown(""" This app demonstrates an intelligent AI agent that: 1. **Retrieves** relevant information from knowledge sources using LanceDB 2. **Answers** your questions clearly and concisely Enter your OpenAI API key in the sidebar to get started! """) # Sidebar for API key and settings with st.sidebar: st.header("🔧 Configuration") # OpenAI API Key openai_key = st.text_input( "OpenAI API Key", type="password", value=os.getenv("OPENAI_API_KEY", ""), help="Get your key from https://platform.openai.com/" ) # Add URLs to knowledge base st.subheader("🌐 Add Knowledge Sources") new_url = st.text_input( "Add URL", placeholder="https://www.theunwindai.com/p/mcp-vs-a2a-complementing-or-supplementing", help="Enter a URL to add to the knowledge base" ) if st.button("➕ Add URL", type="primary"): if new_url: st.session_state.urls_to_add = new_url st.success(f"URL added to queue: {new_url}") else: st.error("Please enter a URL") # Check if API key is provided if openai_key: # Initialize URLs in session state if 'knowledge_urls' not in st.session_state: st.session_state.knowledge_urls = ["https://www.theunwindai.com/p/mcp-vs-a2a-complementing-or-supplementing"] # Default URL if 'urls_loaded' not in st.session_state: st.session_state.urls_loaded = set() # Initialize knowledge base (cached to avoid reloading) @st.cache_resource(show_spinner="📚 Loading knowledge base...") def load_knowledge() -> Knowledge: """Load and initialize the knowledge base with LanceDB""" kb = Knowledge( vector_db=LanceDb( uri="tmp/lancedb", table_name="agentic_rag_docs", search_type=SearchType.vector, # Use vector search embedder=OpenAIEmbedder( api_key=openai_key ), ), ) return kb # Initialize agent (cached to avoid reloading) @st.cache_resource(show_spinner="🤖 Loading agent...") def load_agent(_kb: Knowledge) -> Agent: """Create an agent with reasoning capabilities""" return Agent( model=OpenAIChat( id="gpt-5", api_key=openai_key ), knowledge=_kb, search_knowledge=True, # Enable knowledge search instructions=[ "Always search your knowledge before answering the question.", "Provide clear, well-structured answers in markdown format.", "Use proper markdown formatting with headers, lists, and emphasis where appropriate.", "Structure your response with clear sections and bullet points when helpful.", ], markdown=True, # Enable markdown formatting ) # Load knowledge and agent knowledge = load_knowledge() # Load initial URLs if any (only load once per URL) for url in st.session_state.knowledge_urls: if url not in st.session_state.urls_loaded: knowledge.add_content(url=url) st.session_state.urls_loaded.add(url) agent = load_agent(knowledge) # Display current URLs in knowledge base if st.session_state.knowledge_urls: st.sidebar.subheader("📚 Current Knowledge Sources") for i, url in enumerate(st.session_state.knowledge_urls, 1): st.sidebar.markdown(f"{i}. {url}") # Handle URL additions if hasattr(st.session_state, 'urls_to_add') and st.session_state.urls_to_add: new_url = st.session_state.urls_to_add if new_url not in st.session_state.knowledge_urls: st.session_state.knowledge_urls.append(new_url) with st.spinner("📥 Loading new documents..."): if new_url not in st.session_state.urls_loaded: knowledge.add_content(url=new_url) st.session_state.urls_loaded.add(new_url) st.success(f"✅ Added: {new_url}") del st.session_state.urls_to_add st.rerun() # Main query section st.divider() st.subheader("🤔 Ask a Question") # Suggested prompts st.markdown("**Try these prompts:**") col1, col2, col3 = st.columns(3) with col1: if st.button("What is MCP?", use_container_width=True): st.session_state.query = "What is MCP (Model Context Protocol) and how does it work?" with col2: if st.button("MCP vs A2A", use_container_width=True): st.session_state.query = "How do MCP and A2A protocols differ, and are they complementary or competing?" with col3: if st.button("Agent Communication", use_container_width=True): st.session_state.query = "How do MCP and A2A work together in AI agent systems for communication and tool access?" # Query input query = st.text_area( "Your question:", value=st.session_state.get("query", "What is the difference between MCP and A2A protocols?"), height=100, help="Ask anything about the loaded knowledge sources" ) # Run button if st.button("🚀 Get Answer", type="primary"): if query: # Create container for answer st.markdown("### 💡 Answer") answer_container = st.container() answer_placeholder = answer_container.empty() # Variables to accumulate content answer_text = "" # Stream the agent's response with st.spinner("🔍 Searching and generating answer..."): for chunk in agent.run( query, stream=True, # Enable streaming ): # Update answer display - show content from streaming chunks if hasattr(chunk, 'content') and chunk.content and isinstance(chunk.content, str): answer_text += chunk.content answer_placeholder.markdown( answer_text, unsafe_allow_html=True ) else: st.error("Please enter a question") else: # Show instructions if API key is missing st.info(""" 👋 **Welcome! To use this app, you need:** - **OpenAI API Key** (set it in the sidebar) - Sign up at [platform.openai.com](https://platform.openai.com/) - Generate a new API key Once you enter the key, the app will load the knowledge base and agent. """) # Footer with explanation st.divider() with st.expander("📖 How This Works"): st.markdown(""" **This app uses the Agno framework to create an intelligent Q&A system:** 1. **Knowledge Loading**: URLs are processed and stored in LanceDB vector database 2. **Vector Search**: Uses OpenAI's embeddings for semantic search to find relevant information 3. **GPT-5**: OpenAI's GPT-5 model processes the information and generates answers **Key Components:** - `Knowledge`: Manages document loading from URLs - `LanceDb`: Vector database for efficient similarity search - `OpenAIEmbedder`: Converts text to embeddings using OpenAI's embedding model - `Agent`: Orchestrates everything to answer questions **Why LanceDB?** - Lightweight and easy to set up - No external database required - Fast vector search capabilities - Perfect for prototyping and small to medium-scale applications """)