Added new demo

agentic_rag/README.md

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+## 🗃️ AI RAG Agent with Web Access 
+This script demonstrates how to build a Retrieval-Augmented Generation (RAG) agent with web access using GPT-4o in just 15 lines of Python code. The agent uses a PDF knowledge base and has the ability to search the web using DuckDuckGo.
+
+### Features
+
+- Creates a RAG agent using GPT-4o
+- Incorporates a PDF-based knowledge base
+- Uses LanceDB as the vector database for efficient similarity search
+- Includes web search capability through DuckDuckGo
+- Provides a playground interface for easy interaction
+
+### How to get Started?
+
+1. Clone the GitHub repository
+```bash
+git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
+```
+
+2. Install the required dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+
+3. Get your OpenAI API Key
+
+- Sign up for an [OpenAI account](https://platform.openai.com/) (or the LLM provider of your choice) and obtain your API key.
+- Set your OpenAI API key as an environment variable:
+```bash
+export OPENAI_API_KEY='your-api-key-here'
+```
+
+4. Run the AI RAG Agent 
+```bash
+python3 rag_agent.py
+```
+5. Open your web browser and navigate to the URL provided in the console output to interact with the RAG agent through the playground interface.
+
+### How it works?
+
+1. **Knowledge Base Creation:** The script creates a knowledge base from a PDF file hosted online.
+2. **Vector Database Setup:** LanceDB is used as the vector database for efficient similarity search within the knowledge base.
+3. **Agent Configuration:** An AI agent is created using GPT-4o as the underlying model, with the PDF knowledge base and DuckDuckGo search tool.
+4. **Playground Setup:** A playground interface is set up for easy interaction with the RAG agent.
+

agentic_rag/rag_agent.py

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+from phi.agent import Agent
+from phi.model.openai import OpenAIChat
+from phi.knowledge.pdf import PDFUrlKnowledgeBase
+from phi.vectordb.lancedb import LanceDb, SearchType
+from phi.playground import Playground, serve_playground_app
+from phi.tools.duckduckgo import DuckDuckGo
+
+db_uri = "tmp/lancedb"
+# Create a knowledge base from a PDF
+knowledge_base = PDFUrlKnowledgeBase(
+    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    # Use LanceDB as the vector database
+    vector_db=LanceDb(table_name="recipes", uri=db_uri, search_type=SearchType.vector),
+)
+# Load the knowledge base: Comment out after first run
+knowledge_base.load(upsert=True)
+
+rag_agent = Agent(
+    model=OpenAIChat(id="gpt-4o"),
+    agent_id="rag-agent",
+    knowledge=knowledge_base, # Add the knowledge base to the agent
+    tools=[DuckDuckGo()],
+    show_tool_calls=True,
+    markdown=True,
+)
+
+app = Playground(agents=[rag_agent]).get_app()
+
+if __name__ == "__main__":
+    serve_playground_app("rag_agent:app", reload=True)

agentic_rag/requirements.txt

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+phidata
+openai
+lancedb 
+tantivy 
+pypdf 
+sqlalchemy 
+pgvector 
+psycopg[binary]