publish_neo4j.sh

#!/bin/bash

# JSON data to be stored as a Secure String in SSM
json_data='{"UseCaseName":"berock-wrag-salone","ConversationMemoryType":"DynamoDB","KnowledgeBaseType":"Neo4j","KnowledgeBaseParams":{"NumberOfDocs":1,"ReturnSourceDocs":false},"LlmParams":{"ModelProvider":"Bedrock","ModelId":"amazon.titan-tg1-large","ModelParams":{},"PromptTemplate":"","Streaming":true,"Verbose":false,"Temperature":0.1,"RAGEnabled":true},"IsInternalUser":"true"}'

# SSM parameter name
parameter_name="/gaab-ai/use-case-config/87654321"

# Store JSON data in SSM as a Secure String
aws ssm put-parameter --name "$parameter_name" --value "$json_data" --type "String" --overwrite

echo "Parameter command has been excecuted. Please make sure that the parameter has been uploaded successfully"
cd ../..
#install neo4j

pip3 install neo4j --target ./source/LambdaLayers/ChatLlmProviderLambdaLayerNeo4j/python/


# This function return a random number. We are doing this so that we can concatenate the random number to the string gaabopensearch$result
function myfunc()
{
    local  myresult=$RANDOM$RANDOM$RANDOM
    echo "$myresult"
}
result=$(myfunc)   # or result=`myfunc`
echo $result
#making a bucket with a unique name

echo $(aws s3 mb s3://gaabopensearch$result)


#installing zip
sudo yum install zip

#zipping ChatLlmProviderLambda and uploading it to the newly created bucket
cd source/Lambda/ChatLlmProviderLambdaNeo4j
zip -r ChatLlmProviderLambdaNeo4j.zip *
aws s3 cp ChatLlmProviderLambdaNeo4j.zip s3://gaabopensearch$result
cd ../..
#zipping ChatLlmProviderLambdaLayer and uploading it to the newly created bucket
cd LambdaLayers/ChatLlmProviderLambdaLayerNeo4j
zip -r ChatLlmProviderLambdaLayerNeo4j.zip *
aws s3 cp ChatLlmProviderLambdaLayerNeo4j.zip s3://gaabopensearch$result
cd ../..

echo "This is the bucket that was created. It now holds the zip file for lambda package and layers. Please note it for usage with cloudformation template for the following parameters. "