From a1a00bc9a4b43f4773061759a1ae94d49ceca4ca Mon Sep 17 00:00:00 2001
From: "abir.chebbi" <abir.chebbi@hes-so.ch>
Date: Fri, 20 Dec 2024 10:52:00 +0100
Subject: [PATCH] minor change

---
 README.md | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index 95c8653..dd536cf 100644
--- a/README.md
+++ b/README.md
@@ -1,10 +1,11 @@
-# chatbot-lab
+# chatbot lab
 
 ## Set up environment:
 1. AWS CLI: Ensure AWS CLI is installed and configured on your laptop(refer to the setup guide provided in Session 1).
 2. Ensure python is installed: python 3.8 or higher.
 3. Install required python libraries listed in the 'requirements.txt': 
 
+
 `pip3 install -r requirements.txt`
 
 
@@ -35,7 +36,7 @@ This script performs the following actions:
 * Creates a vector store with the name collection entered as argument.
 * After the vector store is set up, the script retrieves and displays the store's endpoint.
 
-### Step 3: Vectorizing the PDF Files
+### Step 3: Vectorizing the PDF Files: 
 
 After setting up the S3 bucket and Vector DB, we could process PDF files to generate and store embeddings in the vector database.
 
@@ -121,6 +122,6 @@ Where:
 - **[KeyPairName]**: The name of the key_pair created earlier.
 - **[SecurityGroupID]**: The id of the security group created earlier.
 
-## Step 3: Accessing the app:
+## Step 3: Accessing the application
 
 Once the app starts, navigate to this URL `http://[public_ip_adress_of_yourVM]:8501` in your web browser to start interacting with your chatbot
\ No newline at end of file
-- 
GitLab