From 3942f0898332c6e1a968526bf5a7983b3e3ceb74 Mon Sep 17 00:00:00 2001
From: "nabil.abdennad" <nabil.abdennadher@hesge.ch>
Date: Tue, 1 Oct 2024 06:54:59 +0200
Subject: [PATCH] minor updates in README
---
.DS_Store | Bin 0 -> 6148 bytes
Part2/config.ini | 10 +++++-----
README.md | 19 +++++++++----------
3 files changed, 14 insertions(+), 15 deletions(-)
create mode 100644 .DS_Store
diff --git a/.DS_Store b/.DS_Store
new file mode 100644
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diff --git a/Part2/config.ini b/Part2/config.ini
index 68d9b69..adf2621 100644
--- a/Part2/config.ini
+++ b/Part2/config.ini
@@ -1,10 +1,10 @@
[aws]
-aws_access_key_id =
-aws_secret_access_key =
-region =
+aws_access_key_id = AKIAVEKYIBTQFZV3M3OP
+aws_secret_access_key = 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
+region = us-east-1
[opensearch]
-endpoint =
-index_name =
+endpoint = v9b2tkt2ccr61qs9tw7e.us-east-1.aoss.amazonaws.com
+index_name = cloud
diff --git a/README.md b/README.md
index b20da8e..d2c3426 100644
--- a/README.md
+++ b/README.md
@@ -25,16 +25,15 @@ Create a vector database for storing embeddings by running:
`python3 create-vector-db.py --collection_name [Name_of_colletion] --iam_user [YourIAM_user]`
-Where placeholders:
-- **[Name_of_colletion]**: Name of the collection that you want to create to store embeddings.
+Where:
+- **[Name_of_colletion]**: Name of the collection that you want to create.
- **[YourIAM_user]** : the IAM user is `CloudSys-group-XX`, with "XX" representing your group number.
-
This script performs the following actions:
* Sets up encryption, network, and data access policies for the collection.
* 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 for immediate use.
+* After the vector store is set up, the script retrieves and displays the store's endpoint.
### Step 3: Vectorizing the PDF Files
@@ -49,10 +48,10 @@ Then, run:
`python3 vectorise-store.py --bucket_name [YourBucketName] --endpoint [YourVectorDBEndpoint] --index_name [Index_name] --local_path [local_path]`
-Where placeholders:
+Where:
- **[YourBucketName]**: The name of the S3 bucket containing the PDF files.
-- **[YourVectorDBEndpoint]**: Endpoint for the vector database.
+- **[YourVectorDBEndpoint]**: Endpoint of the vector database.
- **[Index_name]**: The index_name where to store the embeddings in the collection.
- **[local_path]**: local_path
@@ -70,22 +69,22 @@ The vectorise-store.py script will:
### Step 1: Preparation
Before deploying the chatbot on an EC2 instance, complete the following preliminary steps:
-1. Create a Key Pair: This key pair will be used for SSH access to your EC2 instance whe you need it.
+1. Create a Key Pair: This key pair will be used for SSH access to your EC2 instance.
2. Create a Security Group: Define rules to allow the instance to be accessible externally. The security group should include the following rules:
- - For inbound rules: you need to allow SSH traffic, HTTP/HTTPs trafic and open port 8501 used by the application.
+ - For inbound rules: you need to allow SSH traffic, HTTP/HTTPs traffic and open port 8501 used by the application.
- Outbound Rules: Allow all traffic.
3. Prepare config.ini: Ensure your config.ini file includes your AWS credentials (aws_access_key_id, aws_secret_access_key, region), with the region set to 'us-east-1'. Also, include the endpoint and index_name for the OpenSearch service established earlier.
### Step 2: Launching the Instance
-Utilize the provided create_instance.py script to deploy your EC2 instance with the essential startup configurations. Before executing this, adjust Security Group and Key Pair already created in the first step.
+Use the provided create_instance.py script to deploy your EC2 instance with the essential startup configurations. Before executing this, adjust Security Group and Key Pair already created in the first step.
In the `ec2.create_instance` we have the following parameters:
- ImageId: `ami-05747e7a13dac9d14`, this is a custom Amazon Machine Image (AMI) that contains all the configurations and dependencies required for the chatbot application.
-- UserData: is used to run script after the instance starts. The script will put the credentials in the instance so that the instance can aceess other services in AWS, and the endpoint for the Vector DB, index name. Then the script will run the application.
+- UserData: is used to run script after the instance starts. The script will put the credentials in the instance so that the instance can aceess other services in AWS, the endpoint of the Vector DB, and the index name. Then the script will run the application.
This is the script:
--
GitLab