feat: Add new local Agentic RAG app with EmbeddingGemma

rag_tutorials/agentic_rag_embedding_gemma/README.md

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+## 🔥 Agentic RAG with EmbeddingGemma
+
+This Streamlit app demonstrates an agentic Retrieval-Augmented Generation (RAG) Agent using Google's EmbeddingGemma for embeddings and Llama 3.2 as the language model, all running locally via Ollama.
+
+### Features
+
+- **Local AI Models**: Uses EmbeddingGemma for vector embeddings and Llama 3.2 for text generation
+- **PDF Knowledge Base**: Dynamically add PDF URLs to build a knowledge base
+- **Vector Search**: Efficient similarity search using LanceDB
+- **Interactive UI**: Beautiful Streamlit interface for adding sources and querying
+- **Streaming Responses**: Real-time response generation with tool call visibility
+
+### How to Get Started?
+
+1. Clone the GitHub repository
+```bash
+git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
+cd awesome-llm-apps/rag_tutorials/agentic_rag_embedding_gemma
+```
+
+2. Install the required dependencies:
+```bash
+pip install -r requirements.txt
+```
+
+3. Ensure Ollama is installed and running with the required models:
+   - Pull the models: `ollama pull embeddinggemma:latest` and `ollama pull llama3.2:latest`
+   - Start Ollama server if not running
+
+4. Run the Streamlit app:
+```bash
+streamlit run agentic_rag_embeddinggemma.py
+```
+   (Note: The app file is in the root directory)
+
+5. Open your web browser to the URL provided (usually http://localhost:8501) to interact with the RAG agent.
+
+### How It Works?
+
+1. **Knowledge Base Setup**: Add PDF URLs in the sidebar to load and index documents.
+2. **Embedding Generation**: EmbeddingGemma creates vector embeddings for semantic search.
+3. **Query Processing**: User queries are embedded and searched against the knowledge base.
+4. **Response Generation**: Llama 3.2 generates answers based on retrieved context.
+5. **Tool Integration**: The agent uses search tools to fetch relevant information.
+
+### Requirements
+
+- Python 3.8+
+- Ollama installed and running
+- Required models: `embeddinggemma:latest`, `llama3.2:latest`
+
+### Technologies Used
+
+- **Agno**: Framework for building AI agents
+- **Streamlit**: Web app framework
+- **LanceDB**: Vector database
+- **Ollama**: Local LLM server
+- **EmbeddingGemma**: Google's embedding model
+- **Llama 3.2**: Meta's language model

rag_tutorials/agentic_rag_embedding_gemma/agentic_rag_embeddinggemma.py

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+import streamlit as st
+from agno.agent import Agent
+from agno.embedder.ollama import OllamaEmbedder
+from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
+from agno.models.ollama import Ollama
+from agno.vectordb.lancedb import LanceDb, SearchType
+
+# Page configuration
+st.set_page_config(
+    page_title="Agentic RAG with Google's EmbeddingGemma",
+    page_icon="🔥",
+    layout="wide"
+)
+
+@st.cache_resource
+def load_knowledge_base(urls):
+    knowledge_base = PDFUrlKnowledgeBase(
+        urls=urls,
+        vector_db=LanceDb(
+            table_name="recipes",
+            uri="tmp/lancedb",
+            search_type=SearchType.vector,
+            embedder=OllamaEmbedder(id="embeddinggemma:latest", dimensions=768),
+        ),
+    )
+    knowledge_base.load()
+    return knowledge_base
+
+# Initialize URLs in session state
+if 'urls' not in st.session_state:
+    st.session_state.urls = []
+
+kb = load_knowledge_base(st.session_state.urls)
+
+agent = Agent(
+    model=Ollama(id="llama3.2:latest"),
+    knowledge=kb,
+    instructions=[
+        "Search the knowledge base for relevant information and base your answers on it.",
+        "Be clear, and generate well-structured answers.",
+        "Use clear headings, bullet points, or numbered lists where appropriate.",
+    ],
+    search_knowledge=True,
+    show_tool_calls=False,
+    markdown=True,
+)
+
+# Sidebar for adding knowledge sources
+with st.sidebar:
+    col1, col2, col3 = st.columns(3)
+    with col1:
+        st.image("google.png")
+    with col2:
+        st.image("ollama.png")
+    with col3:
+        st.image("agno.png")
+    st.header("🌐 Add Knowledge Sources")
+    new_url = st.text_input(
+        "Add URL",
+        placeholder="https://example.com/sample.pdf",
+        help="Enter a PDF URL to add to the knowledge base",
+    )
+    if st.button("➕ Add URL", type="primary"):
+        if new_url:
+            kb.urls.append(new_url)
+            with st.spinner("📥 Adding new URL..."):
+                kb.load(recreate=False, upsert=True)
+            st.success(f"✅ Added: {new_url}")
+        else:
+            st.error("Please enter a URL")
+
+    # Display current URLs
+    if kb.urls:
+        st.subheader("📚 Current Knowledge Sources")
+        for i, url in enumerate(kb.urls, 1):
+            st.markdown(f"{i}. {url}")
+
+# Main title and description
+st.title("🔥 Agentic RAG with EmbeddingGemma (100% local)")
+st.markdown(
+    """
+This app demonstrates an agentic RAG system using local models via [Ollama](https://ollama.com/):
+
+- **EmbeddingGemma** for creating vector embeddings
+- **LanceDB** as the local vector database
+
+Add PDF URLs in the sidebar to start and ask questions about the content.
+    """
+)
+                
+query = st.text_input("Enter your question:")
+
+# Simple answer generation
+if st.button("🚀 Get Answer", type="primary"):
+    if not query:
+        st.error("Please enter a question")
+    else:
+        st.markdown("### 💡 Answer")
+        
+        with st.spinner("🔍 Searching knowledge and generating answer..."):
+            try:
+                response = ""
+                resp_container = st.empty()
+                gen = agent.run(query, stream=True)
+                for resp_chunk in gen:
+                    # Display response
+                    if resp_chunk.content is not None:
+                        response += resp_chunk.content
+                        resp_container.markdown(response)
+            except Exception as e:
+                st.error(f"Error: {e}")
+
+with st.expander("📖 How This Works"):
+    st.markdown(
+        """
+**This app uses the Agno framework to create an intelligent Q&A system:**
+
+1. **Knowledge Loading**: PDF URLs are processed and stored in LanceDB vector database
+2. **EmbeddingGemma as Embedder**: EmbeddingGemma generates local embeddings for semantic search
+3. **Llama 3.2**: The Llama 3.2 model generates answers based on retrieved context
+
+**Key Components:**
+- `EmbeddingGemma` as the embedder
+- `LanceDB` as the vector database
+- `PDFUrlKnowledgeBase`: Manages document loading from PDF URLs
+- `OllamaEmbedder`: Uses EmbeddingGemma for embeddings
+- `Agno Agent`: Orchestrates everything to answer questions
+        """
+    )

rag_tutorials/agentic_rag_embedding_gemma/requirements.txt

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+streamlit
+agno
+lancedb
+ollama
+pypdf