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A NLQ(Natural Language Query) demo using Amazon Bedrock, Amazon OpenSearch with RAG technique.

Create an EC2 with following configuration:

- OS Image (AMI): Amazon Linux 2023 - Instance type: t3.large or higher - VPC: use default one and choose a public subnet - Security group: Allow access to 22, 80 port from anywhere (Select "Allow SSH traffic from Anywhere" and "Allow HTTP traffic from the internet") - Storage (volumes): 1 GP3 volume(s) - 30 GiB 

2.1 IAM Role's permission

Create a new IAM role with name genbirag-service-role and settings below:

• Trusted entity type: AWS Service
• Service: EC2
• Use Case: EC2 - Allows EC2 instances to call AWS services on your behalf.

Skip "Add permission" and create this role first.

After the role is created, and then add permission by creating inline policy as below:

{ "Version": "2012-10-17", "Statement": [ { "Sid": "VisualEditor0", "Effect": "Allow", "Action": [ "bedrock:*", "dynamodb:*" ], "Resource": "*" } ] }

Finally, Bind this IAM instance profile (IAM Role) to your EC2 instance.

2.2 Amazon Bedrock's Model Permission

Make sure you have enabled model access in AWS Console in us-west-2 (Oregon) region for Anthropic Claude model and Amazon Titan embedding model.

Log in to the EC2 instance using SSH command as the ec2-user user or use the AWS EC2 Instance Connect feature in the EC2 console to log in to the command line.

In the session, execute the following commands. Note: Execute each command one line at a time.

If you are not this user, you can switch with the following command:

sudo su - ec2-user

# Install components sudo dnf install docker python3-pip git -y && pip3 install -U awscli && pip3 install docker-compose # Fix docker python wrapper 7.0 SSL version issue  pip3 install docker==6.1.3 # Configure components sudo systemctl enable docker && sudo systemctl start docker && sudo usermod -aG docker $USER # Exit the terminal exit

Reopen a terminal session and continue executing the following commands:

Note: Execute each command one line at a time.

# Log in as user ec2-user # Configure OpenSearch server parameters sudo sh -c "echo 'vm.max_map_count=262144' > /etc/sysctl.conf" && sudo sysctl -p # Clone the code git clone https://github.com/aws-samples/generative-bi-using-rag.git # Config the Environment Variable in .env file, modify AWS_DEFAULT_REGION to the region same as the EC2 instance. cd generative-bi-using-rag/application && cp .env.template .env # Build docker images locally docker-compose build # Start all services docker-compose up -d # Wait 3 minutes for MySQL and OpenSearch to initialize sleep 180

In the terminal, continue executing the following commands:

cd initial_data && wget https://github.com/fengxu1211/generative-bi-using-rag/raw/demo_data/application/initial_data/init_mysql_db.sql.zip unzip init_mysql_db.sql.zip && cd .. docker exec nlq-mysql sh -c "mysql -u root -ppassword -D llm < /opt/data/init_mysql_db.sql" 

6.1 Initialize the index for the sample data by creating a new index:

docker exec nlq-webserver python opensearch_deploy.py

If the command fails due to any errors, delete the index and rerun the previous command:

curl -XDELETE -k -u admin:admin "https://localhost:9200/uba"

6.2 (Optional) Bulk import custom QA data by appending to an existing index:

docker exec nlq-webserver python opensearch_deploy.py custom false

Open in your browser: http://<your-ec2-public-ip>

Note: Use HTTP instead of HTTPS.

1. First create the corresponding Data Profile in Data Connection Management and Data Profile Management.

2. After selecting the Data Profile, start asking questions. For simple questions, the LLM can directly generate the correct SQL. If the generated SQL is incorrect, try adding more annotations to the Schema.

3. Use the Schema Management page, select the Data Profile, and add comments to the tables and fields. These comments will be included in the prompt sent to the LLM. (1) For some fields, add values to the Annotation attribute, e.g. "Values: Y|N", "Values: Shanghai|Jiangsu". (2) For table comments, add domain knowledge to help answer business questions.

4. Ask the question again. If still unable to generate the correct SQL, add Sample QA pairs to OpenSearch. (1) Using the Index Management page, select the Data Profile then you can add, view and delete QA pairs.

5. Ask again. In theory, the RAG approach (PE uses Few shots) should now be able to generate the correct SQL.

See CONTRIBUTING for more information.

This library is licensed under the MIT-0 License. See the LICENSE file.