Setup and installation of 'Instant RAGFlow: Ready-to-Use AI Knowledge Retrieval Engine' on GCP

This section describes how to provision and connect to ‘Instant RAGFlow: Ready-to-Use AI Knowledge Retrieval Engine’ VM solution on GCP.

1. Open Instant RAGFlow: Ready-to-Use AI Knowledge Retrieval Engine listing on GCP Marketplace.

2. Click Get Started.

It will ask you to enable the API’s if they are not enabled already for your account. Please click on enable as shown in the screenshot.

• It will take you to the agreement page. On this page, you can change the project from the project selector on top navigator bar as shown in the below screenshot.

• Accept the Terms and agreements by ticking the checkbox and clicking on the AGREE button.

• It will show you the successfully agreed popup page. Click on Deploy.

• On deployment page, give a name to your deployment.

• In Deployment Service Account section, click on Existing radio button and Choose a service account from the Select a Service Account dropdown.

• If you don't see any service account in dropdown, then change the radio button to New Account and create the new service account here.
• If after selecting New Account option, you get below permission error message then please reach out to your GCP admin to create service account by following Step by step guide to create GCP Service Account and then refresh this deployment page once the service account is created, it should be available in the dropdown.

You are missing resourcemanager.projects.setIamPolicy permission, which is needed to set the required roles on the created Service Account

• Select a zone where you want to launch the VM(such as us-east1-a)
• Optionally change the number of cores and amount of memory. ( This defaults to 4 vCPUs and 15 GB RAM)

Minimum VM Specs : 15GB Memory /4vCPU

This VM can also be deployed using an NVIDIA T4 GPU instance to execute crewai agents faster. To deploy the VM with a GPU, click on the GPU tab as show in below screenshot and select a NVIDIA T4 GPU instance. Please note that GPU availability is limited to specific regions, zones, and machine types. If you do not see a GPU option for your selected region, zone, or machine type, try adjusting those settings to find available configurations.

• Optionally change the boot disk type and size. (This defaults to ‘Standard Persistent Disk’ and 320GB respectively)

• Optionally change the network name and subnetwork names. Be sure that whichever network you specify has ports 22 (for ssh), 3389 (for RDP) and 443 (for HTTPS) exposed.

• Click Deploy when you are done.

• Instant RAGFlow: Ready-to-Use AI Knowledge Retrieval Engine will begin deploying.

3. A summary page displays when the compute engine is successfully deployed. Click on the Instance link to go to the instance page .

4. On the instance page, click on the “SSH” button, select “Open in browser window”.

5. This will open SSH window in a browser. Switch to ubuntu user and navigate to ubuntu home directory.

sudo su ubuntu

cd /home/ubuntu/

6. Run below command to set the password for “ubuntu” user

sudo passwd ubuntu

7. Now the password for ubuntu user is set, you can connect to the VM’s desktop environment from any local windows machine using RDP or linux machine using Remmina.

8. To connect using RDP via Windows machine, first note the external IP of the VM from VM details page as highlighted below

9. Then From your local windows machine, goto “start” menu, in the search box type and select “Remote desktop connection”

10. In the “Remote Desktop connection” wizard, paste the external ip and click connect

11. This will connect you to the VM’s desktop environment. Provide “ubuntu” as the userid and the password set in step 6 to authenticate. Click OK

12. Now you are connected to out of box Instant RAGFlow: Ready-to-Use AI Knowledge Retrieval Engine VM’s desktop environment via Windows machines.

13. To connect using RDP via Linux machine, first note the external IP of the VM from VM details page, then from your local Linux machine, goto menu, in the search box type and select “Remmina”.

Note: If you don’t have Remmina installed on your Linux machine, first Install Remmina as per your linux distribution.

14. In the “Remmina Remote Desktop Client” wizard, select the RDP option from dropdown and paste the external ip and click enter.

15. This will connect you to the VM’s desktop environment. Provide “ubuntu” as the userid and the password set in step 6 to authenticate. Click OK

16. Now you are connected to out of box Instant RAGFlow: Ready-to-Use AI Knowledge Retrieval Engine VM’s desktop environment via Linux machine.

17. After VM deployment it takes “10-15 minutes” to complete the RAGFlow initial setup. To monitor the init process run below command from SSH terminal. Once you see “Completed executing peronce script” in the below tail command output, you are good to go.

tail -f /var/log/cloud-init-output.log

18. Copy the Public IP address of the VM. Paste it into the address bar of your local browser as https://public_ip_of_vm. Make sure to use https and not http. When you access the page, your browser will display a security certificate warning. In Firefox, click on the Advanced button, then select Accept the Risk and Continue. In other browsers as well , simply proceed past the warning by accepting or continuing to the site.

19. For the first time, you will need to register new account here. Click on “Sign Up”" link at the bottom. Provide your details and create a new account. Once created, log in with new registered account now.

Note: After Sign Up if you don’t get registered confirmation at the top or if you try to sign in and nothing is happening then wait for few more minutes. The setup is still running. You can monitor the installation by running below command in terminal and wait for it to finish. Replace gpu with cpu if the instance is deployed using cpu type.

sudo docker logs -f docker-ragflow-gpu-1

20. This is RAGFlow Home Page where you will see various options to explore.

21. Before using RAGFlow, you need to configure an LLM provider. The VM comes pre-installed with Ollama LLMs, making it easy to get started right away.

To add Ollama LLMs and begin using RAGFlow’s Chat, Search, or Agent features, please visit the How to use Ragflow Page.

For more details, please visit Official Documentation page