Update README.md

README.md

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-# Agentic RAG Examples
-
-## Overview
-
-This folder contains examples for building a Retrieval-Augmented Generation (RAG) agent using the LangChain library.
-
-## Features
-
-* Construction of a RAG agent workflow using LangChain
-* Integration with OpenAI models for language generation and retrieval
-* Example usage of tools such as web search and response analysis, create rag response
-* Auto-instrumentation with OpenInference decorators to fully instrument the agent
-* End-to-end tracing with Phoenix to track agent performance
-
-## Requirements
-
-* LangChain library
-* OpenAI API key
-* Langgraph
-* Python 3.x
-* Gradio (for UI)
-
-## Installation
-
-1. Install the required libraries by running `pip install -r requirements.txt`
-2. Run app.py and input the required Keys(OpenAI, Phoenix API Key)
-
-## Usage
-
-1. Run the `app.py` script to start the RAG agent
-2. Interact with the agent by providing input and receiving responses
-
-## Files
-
-* `app.py`: The main script for starting the application, this will run the web server with default port(7860)
-* `agent.py`: The main script for the RAG agent
-* `tools.py`: Contains tools for web search and response analysis, create rag response
-* `rag.py`: Contains functions for initializing and using the RAG vector store
-* `requirements.txt`: Lists the required libraries for the project
-
-## Notes
-
-* All the Key's must be inputted from the UI application.
-* RAG will be loaded with default url in the UI, You can update the url and initialize the project with your own data source.
-* This application will support the HTML based sources. 
+---
+title: Rag Agent
+emoji: 🐠
+colorFrom: blue
+colorTo: indigo
+sdk: gradio
+sdk_version: 5.20.0
+app_file: app.py
+pinned: false
+license: cc-by-nd-4.0
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference