diff --git a/rag_tutorials/agentic_rag_with_reasoning/README.md b/rag_tutorials/agentic_rag_with_reasoning/README.md
index 6569297..7f66185 100644
--- a/rag_tutorials/agentic_rag_with_reasoning/README.md
+++ b/rag_tutorials/agentic_rag_with_reasoning/README.md
@@ -1,5 +1,5 @@
 # 🧐 Agentic RAG with Reasoning
-A sophisticated RAG system that demonstrates an AI agent's step-by-step reasoning process using Agno, Claude and Cohere. This implementation allows users to upload documents, add web sources, ask questions, and observe the agent's thought process in real-time.
+A sophisticated RAG system that demonstrates an AI agent's step-by-step reasoning process using Agno, Claude and OpenAI. This implementation allows users to upload documents, add web sources, ask questions, and observe the agent's thought process in real-time.
 
 
 ## Features
@@ -17,15 +17,14 @@ A sophisticated RAG system that demonstrates an AI agent's step-by-step reasonin
 
 
 3. Advanced RAG Capabilities
-- Hybrid search combining keyword and semantic matching
-- Cohere reranking for improved relevance
+- Vector search using OpenAI embeddings for semantic matching
 - Source attribution with citations
 
 
 ## Agent Configuration
 
 - Claude 3.5 Sonnet for language processing
-- Cohere embedding and reranking models
+- OpenAI embedding model for vector search
 - ReasoningTools for step-by-step analysis
 - Customizable agent instructions
 
@@ -39,9 +38,9 @@ You'll need the following API keys:
 - Navigate to API Keys section
 - Create a new API key
 
-2. Cohere API Key
+2. OpenAI API Key
 
-- Sign up at dashboard.cohere.ai
+- Sign up at platform.openai.com
 - Navigate to API Keys section
 - Generate a new API key
 
@@ -50,7 +49,7 @@ You'll need the following API keys:
 1. **Clone the Repository**:
     ```bash
     git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
-    cd rag_tutorials/agentic_rag
+    cd rag_tutorials/agentic_rag_with_reasoning
     ```
 
 2. **Install the dependencies**:
@@ -66,7 +65,7 @@ You'll need the following API keys:
 4. **Configure API Keys:**
 
 - Enter your Anthropic API key in the first field
-- Enter your Cohere API key in the second field
+- Enter your OpenAI API key in the second field
 - Both keys are required for the app to function
 
 
@@ -83,17 +82,17 @@ The application uses a sophisticated RAG pipeline:
 
 ### Knowledge Base Setup
 - Documents are loaded from URLs using WebBaseLoader
-- Text is chunked and embedded using Cohere's embedding model 
+- Text is chunked and embedded using OpenAI's embedding model 
 - Vectors are stored in LanceDB for efficient retrieval
-- Hybrid search enables both keyword and semantic matching
+- Vector search enables semantic matching for relevant information
 
 ### Agent Processing
 - User queries trigger the agent's reasoning process
 - ReasoningTools help the agent think step-by-step
 - The agent searches the knowledge base for relevant information
-- Claude 3.5 Sonnet generates comprehensive answers with citations
+- Claude 4 Sonnet generates comprehensive answers with citations
 
 ### UI Flow
 - Enter API keys → Add knowledge sources → Ask questions
 - Reasoning process and answer generation displayed side-by-side
-- Sources cited for transparency and verification
+- Sources cited for transparency and verification
\ No newline at end of file
diff --git a/rag_tutorials/agentic_rag_with_reasoning/rag_reasoning_agent.py b/rag_tutorials/agentic_rag_with_reasoning/rag_reasoning_agent.py
index 6940344..362e64d 100644
--- a/rag_tutorials/agentic_rag_with_reasoning/rag_reasoning_agent.py
+++ b/rag_tutorials/agentic_rag_with_reasoning/rag_reasoning_agent.py
@@ -1,21 +1,25 @@
 import streamlit as st
 from agno.agent import Agent, RunEvent
-from agno.embedder.cohere import CohereEmbedder
+from agno.embedder.openai import OpenAIEmbedder
 from agno.knowledge.url import UrlKnowledge
 from agno.models.anthropic import Claude
-from agno.reranker.cohere import CohereReranker
 from agno.tools.reasoning import ReasoningTools
 from agno.vectordb.lancedb import LanceDb, SearchType
+from dotenv import load_dotenv
+import os
+
+# Load environment variables
+load_dotenv()
 
 # Page configuration
 st.set_page_config(
     page_title="Agentic RAG with Reasoning", 
-    page_icon="🧠", 
+    page_icon="🧐", 
     layout="wide"
 )
 
 # Main title and description
-st.title("🧠 Agentic RAG with Reasoning")
+st.title("🧐 Agentic RAG with Reasoning")
 st.markdown("""
 This app demonstrates an AI agent that:
 1. **Retrieves** relevant information from knowledge sources
@@ -32,17 +36,19 @@ with col1:
     anthropic_key = st.text_input(
         "Anthropic API Key", 
         type="password",
+        value=os.getenv("ANTHROPIC_API_KEY", ""),
         help="Get your key from https://console.anthropic.com/"
     )
 with col2:
-    cohere_key = st.text_input(
-        "Cohere API Key", 
+    openai_key = st.text_input(
+        "OpenAI API Key", 
         type="password",
-        help="Get your key from https://dashboard.cohere.ai/"
+        value=os.getenv("OPENAI_API_KEY", ""),
+        help="Get your key from https://platform.openai.com/"
     )
 
-# Check if both API keys are provided
-if anthropic_key and cohere_key:
+# Check if API keys are provided
+if anthropic_key and openai_key:
     
     # Initialize knowledge base (cached to avoid reloading)
     @st.cache_resource(show_spinner="📚 Loading knowledge base...")
@@ -53,18 +59,13 @@ if anthropic_key and cohere_key:
             vector_db=LanceDb(
                 uri="tmp/lancedb",
                 table_name="agno_docs",
-                search_type=SearchType.hybrid,  # Uses both keyword and semantic search
-                embedder=CohereEmbedder(
-                    id="embed-v4.0", 
-                    api_key=cohere_key
-                ),
-                reranker=CohereReranker(
-                    model="rerank-v3.5", 
-                    api_key=cohere_key
+                search_type=SearchType.vector,  # Use vector search
+                embedder=OpenAIEmbedder(
+                    api_key=openai_key
                 ),
             ),
         )
-        kb.load(recreate=False)  # Load documents into vector DB
+        kb.load(recreate=True)  # Load documents into vector DB
         return kb
 
     # Initialize agent (cached to avoid reloading)
@@ -202,8 +203,8 @@ else:
     1. **Anthropic API Key** - For Claude AI model
        - Sign up at [console.anthropic.com](https://console.anthropic.com/)
     
-    2. **Cohere API Key** - For embeddings and reranking
-       - Sign up at [dashboard.cohere.ai](https://dashboard.cohere.ai/)
+    2. **OpenAI API Key** - For embeddings
+       - Sign up at [platform.openai.com](https://platform.openai.com/)
     
     Once you have both keys, enter them above to start!
     """)
@@ -215,16 +216,14 @@ with st.expander("📖 How This Works"):
     **This app uses the Agno framework to create an intelligent Q&A system:**
     
     1. **Knowledge Loading**: URLs are processed and stored in a vector database (LanceDB)
-    2. **Hybrid Search**: Combines keyword and semantic search to find relevant information
+    2. **Vector Search**: Uses OpenAI's embeddings for semantic search to find relevant information
     3. **Reasoning Tools**: The agent uses special tools to think through problems step-by-step
     4. **Claude AI**: Anthropic's Claude model processes the information and generates answers
-    5. **Reranking**: Cohere's reranker ensures the most relevant information is used
     
     **Key Components:**
     - `UrlKnowledge`: Manages document loading from URLs
     - `LanceDb`: Vector database for efficient similarity search
-    - `CohereEmbedder`: Converts text to embeddings for semantic search
-    - `CohereReranker`: Improves search result relevance
+    - `OpenAIEmbedder`: Converts text to embeddings using OpenAI's embedding model
     - `ReasoningTools`: Enables step-by-step reasoning
     - `Agent`: Orchestrates everything to answer questions
     """)
\ No newline at end of file