mirror of
https://github.com/Shubhamsaboo/awesome-llm-apps.git
synced 2026-05-02 18:45:05 -05:00
209 lines
7.5 KiB
Python
209 lines
7.5 KiB
Python
import streamlit as st
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import os
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from agno.agent import Agent
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from agno.embedder.openai import OpenAIEmbedder
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from agno.knowledge.url import UrlKnowledge
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from agno.models.openai import OpenAIChat
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from agno.vectordb.lancedb import LanceDb, SearchType
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from dotenv import load_dotenv
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# Load environment variables
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load_dotenv()
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# Page configuration
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st.set_page_config(
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page_title="Agentic RAG with GPT-5",
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page_icon="🧠",
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layout="wide"
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)
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# Main title and description
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st.title("🧠 Agentic RAG with GPT-5")
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st.markdown("""
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This app demonstrates an intelligent AI agent that:
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1. **Retrieves** relevant information from knowledge sources using LanceDB
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2. **Answers** your questions clearly and concisely
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Enter your OpenAI API key in the sidebar to get started!
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""")
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# Sidebar for API key and settings
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with st.sidebar:
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st.header("🔧 Configuration")
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# OpenAI API Key
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openai_key = st.text_input(
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"OpenAI API Key",
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type="password",
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value=os.getenv("OPENAI_API_KEY", ""),
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help="Get your key from https://platform.openai.com/"
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)
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# Add URLs to knowledge base
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st.subheader("🌐 Add Knowledge Sources")
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new_url = st.text_input(
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"Add URL",
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placeholder="https://docs.agno.com/introduction",
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help="Enter a URL to add to the knowledge base"
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)
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if st.button("➕ Add URL", type="primary"):
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if new_url:
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st.session_state.urls_to_add = new_url
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st.success(f"URL added to queue: {new_url}")
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else:
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st.error("Please enter a URL")
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# Check if API key is provided
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if openai_key:
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# Initialize knowledge base (cached to avoid reloading)
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@st.cache_resource(show_spinner="📚 Loading knowledge base...")
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def load_knowledge() -> UrlKnowledge:
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"""Load and initialize the knowledge base with LanceDB"""
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kb = UrlKnowledge(
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urls=["https://docs.agno.com/introduction/agents.md"], # Default URL
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vector_db=LanceDb(
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uri="tmp/lancedb",
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table_name="agentic_rag_docs",
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search_type=SearchType.vector, # Use vector search
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embedder=OpenAIEmbedder(
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api_key=openai_key
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),
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),
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)
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kb.load(recreate=True) # Load documents into LanceDB
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return kb
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# Initialize agent (cached to avoid reloading)
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@st.cache_resource(show_spinner="🤖 Loading agent...")
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def load_agent(_kb: UrlKnowledge) -> Agent:
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"""Create an agent with reasoning capabilities"""
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return Agent(
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model=OpenAIChat(
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id="gpt-5-nano",
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api_key=openai_key
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),
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knowledge=_kb,
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search_knowledge=True, # Enable knowledge search
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instructions=[
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"Always search your knowledge before answering the question.",
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"Provide clear, well-structured answers in markdown format.",
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"Use proper markdown formatting with headers, lists, and emphasis where appropriate.",
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"Structure your response with clear sections and bullet points when helpful.",
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],
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markdown=True, # Enable markdown formatting
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)
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# Load knowledge and agent
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knowledge = load_knowledge()
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agent = load_agent(knowledge)
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# Display current URLs in knowledge base
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if knowledge.urls:
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st.sidebar.subheader("📚 Current Knowledge Sources")
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for i, url in enumerate(knowledge.urls, 1):
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st.sidebar.markdown(f"{i}. {url}")
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# Handle URL additions
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if hasattr(st.session_state, 'urls_to_add') and st.session_state.urls_to_add:
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with st.spinner("📥 Loading new documents..."):
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knowledge.urls.append(st.session_state.urls_to_add)
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knowledge.load(
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recreate=False, # Don't recreate DB
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upsert=True, # Update existing docs
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skip_existing=True # Skip already loaded docs
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)
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st.success(f"✅ Added: {st.session_state.urls_to_add}")
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del st.session_state.urls_to_add
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st.rerun()
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# Main query section
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st.divider()
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st.subheader("🤔 Ask a Question")
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# Suggested prompts
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st.markdown("**Try these prompts:**")
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col1, col2, col3 = st.columns(3)
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with col1:
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if st.button("What is Agno?", use_container_width=True):
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st.session_state.query = "What is Agno and how do Agents work?"
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with col2:
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if st.button("Teams in Agno", use_container_width=True):
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st.session_state.query = "What are Teams in Agno and how do they work?"
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with col3:
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if st.button("Build RAG system", use_container_width=True):
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st.session_state.query = "Give me a step-by-step guide to building a RAG system."
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# Query input
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query = st.text_area(
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"Your question:",
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value=st.session_state.get("query", "What are AI Agents?"),
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height=100,
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help="Ask anything about the loaded knowledge sources"
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)
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# Run button
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if st.button("🚀 Get Answer", type="primary"):
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if query:
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# Create container for answer
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st.markdown("### 💡 Answer")
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answer_container = st.container()
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answer_placeholder = answer_container.empty()
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# Variables to accumulate content
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answer_text = ""
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# Stream the agent's response
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with st.spinner("🔍 Searching and generating answer..."):
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for chunk in agent.run(
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query,
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stream=True, # Enable streaming
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):
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# Update answer display - only show content from RunResponseContent events
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if hasattr(chunk, 'event') and chunk.event == "RunResponseContent":
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if hasattr(chunk, 'content') and chunk.content and isinstance(chunk.content, str):
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answer_text += chunk.content
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answer_placeholder.markdown(
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answer_text,
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unsafe_allow_html=True
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)
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else:
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st.error("Please enter a question")
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else:
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# Show instructions if API key is missing
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st.info("""
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👋 **Welcome! To use this app, you need:**
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- **OpenAI API Key** (set it in the sidebar)
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- Sign up at [platform.openai.com](https://platform.openai.com/)
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- Generate a new API key
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Once you enter the key, the app will load the knowledge base and agent.
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""")
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# Footer with explanation
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st.divider()
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with st.expander("📖 How This Works"):
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st.markdown("""
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**This app uses the Agno framework to create an intelligent Q&A system:**
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1. **Knowledge Loading**: URLs are processed and stored in LanceDB vector database
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2. **Vector Search**: Uses OpenAI's embeddings for semantic search to find relevant information
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3. **GPT-5**: OpenAI's GPT-5 model processes the information and generates answers
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**Key Components:**
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- `UrlKnowledge`: Manages document loading from URLs
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- `LanceDb`: Vector database for efficient similarity search
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- `OpenAIEmbedder`: Converts text to embeddings using OpenAI's embedding model
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- `Agent`: Orchestrates everything to answer questions
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**Why LanceDB?**
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- Lightweight and easy to set up
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- No external database required
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- Fast vector search capabilities
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- Perfect for prototyping and small to medium-scale applications
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""")
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