Creating a Securechat App using BGE-large Embedding and base Llama-3-8b Summarisation Models¶

This example will take you through the procedure of deploying a LLM chatbot that can answer questions based on specific dataset(s) provided by you.

Prerequisites¶

You need to create a folder in your workspace. This folder will contain all the documents you are going to use for data ingestion.
- You can directly put your document-containing folder in the workspace from your local system by using the filebrowser application on the workspace tab in DKubeX UI, or you can create a folder from the CLI to download the files from their URL.
  Note
  
  If you are going to use Skypilot for the ingestion process, make sure to keep the folder inside the /home/data/ directory on your DKubeX setup. As this directory can be accessed only from the CLI, you will need to first place it on your workspace, and then move it to the /home/data/ directory using the CLI. Also, if the directory is not available in your setup, create the directory using the following command.
  sudo mkdir /home/data
Open the terminal application and export the following variables to your workspace by running the following commands on your terminal.
- Replace the your DKubeX URL with the URL of your setup, <your DKubeX API key> part with your DKubeX API key, <your huggingface token> part with your Huggingface token to access the Llama-3-8B model, and <username> with your DKubeX workspace name.
  Hint
  
  Use the following steps to find your DKubeX API key:
  - Open the DKubeX UI and click on your username on upper-right corner of the UI.
  - Click on the API Key option from the dropdown menu. A pop-up dialog box containing your DKubeX API key will open. Copy and note down this key.
```
export PYTHONWARNINGS="ignore"
export OPENAI_API_KEY="dummy"
export DKUBEX_URL="<your DKubeX URL>"
export DKUBEX_APIKEY="<your DKubeX API key>"
export HF_TOKEN="<your huggingface token>"
export NAMESPACE="<username>"
export HOMEDIR=/home/${NAMESPACE}
```
The Llama-3-8B model needs to be deployed on DKubeX.

Note

For detailed information regarding this section, please refer to Deploying LLMs in DKubeX.

Here we will deploy the base Llama-3-8B model, which is already pre-registered with DKubeX.

Note

This workflow requires an a10 GPU node. Make sure your cluster is equipped with such. Also, in case you are using any other type of GPU node, make sure to use the label for that node which you have put during DKubeX installation process.
- Deploy the base Llama-3-8B model using the following command.
```
d3x llms deploy --name=llama38bbase --model=meta-llama/Meta-Llama-3-8B-Instruct --type=a10 --token ${HF_TOKEN} --publish
```
  Note
  
  In case you are using a EKS setup, please change the value of the flag --type from a10 to g5.4xlarge in the following command. Also, in case you are using any other type of GPU node, make sure to use the label for that node which you have put during DKubeX installation process.
- You can check the status of the deployment from the Deployments page in DKubeX or by running the following command.
```
d3x serve list
```
- Wait until the deployment is in running state.

Data ingestion and creating dataset¶

Note

This example uses the BAAI/bge-large-en-v1.5 embeddings model for data ingestion.

Data ingestion process can be done with or without using Skypilot. The procedures for both are given below.

A few .yaml files are required to be used in the ingestion process.
- On the Terminal application in DKubeX UI, run the following commands. Replace the <username> part with your DKubeX workspace name.
```
cd && git clone -b v0.8.5.3 https://github.com/dkubeio/dkubex-examples.git
cp ${HOMEDIR}/dkubex-examples/rag/ingestion/ingest.yaml ${HOMEDIR}/ingest.yaml
```
You need to provide proper details on the ingest.yaml file. Run vim ${HOMEDIR}/ingest.yaml and make the following changes.
- On the embedding section, select huggingface as we are going to use the BAAI/bge-large-en-v1.5 embedding model which is already present in the DKubeX models catalog.
- On the reader section, select file as we are going to use the file reader from Llamaindex to read the documents for ingestion. For more information regarding the file reader, visit the Llamaindex documentation.
- Uncomment the entire huggingface section under Embedding Model Details. Here the name of the embedding model to be used is provided.
- Make sure the file section under Data Reader Details is uncommented. Under here, in the input_dir field provide the absolute path to your dataset folder, i.e. in this case, /home/<your username>/contract-nli/ (Provide your DKubeX username in place of <your username>).
- You can also modify and customize several other options in the ingest.yaml file according to your needs, including the splitter class, chunk size, embedding model to be used, etc.
Attention
- The reader section in the ingest.yaml file denotes the type of dataloader to be used for the ingestion process. If you are going to use any other source of data for ingestion as compared to local directory data shown in this example, you need to provide the appropriate details for that type of dataloader.
  - For more information about dataloaders please visit How to use different Data Loaders (Data Readers).
  - You can use multiple type of data sources by providing the reader details simultaneously under the reader section in the ingest.yaml file.
- Some of the dataloaders require separate pyloader files. Make sure to provide them, if needed.
Use the following command to trigger the ingestion process.
d3x dataset ingest -d <dataset name> --config <absolute path to the ingest.yaml file> --faq
d3x dataset ingest -d contracts --config /home/demo/ingest.yaml --faq
Note
- The time taken for the ingestion process to complete depends on the size of the dataset. Please wait patiently for the process to complete.
- In case the terminal shows a timed-out error, that means the ingestion is still in progress, and you will need to run the command provided on the CLI after the error message to continue to get the ingestion logs.
- The record of the ingestion and related artifacts are also stored in the MLFlow application on DKubeX UI.
To check if the dataset has been created, stored and are ready to use, use the following command:
```
d3x dataset list
```
To check the list of documents that has been ingested in the dataset, use the following command:
d3x dataset show -d <dataset name>
d3x dataset show -d contracts

Note

Make sure that the dataset folder is placed inside the /home/data/ directory on your DKubeX setup. Also, if the directory is not available in your setup, create the directory using the following command.

sudo mkdir /home/data

A few .yaml files are required to be used in the ingestion process. These files needs to be placed in the /home/data/ directory.

On the Terminal application in DKubeX UI, run the following commands:

cd && git clone -b v0.8.5.3 https://github.com/dkubeio/dkubex-examples.git
sudo cp ${HOMEDIR}/dkubex-examples/rag/ingestion/ingest.yaml /home/data/ingest.yaml

You need to provide proper details on the ingest.yaml file. Run vim ${HOMEDIR}/ingest.yaml and make the following changes.
- On the embedding section, select huggingface as we are going to use the BAAI/bge-large-en-v1.5 embedding model which is already present in the DKubeX models catalog.
- On the reader section, select file as we are going to use the file reader from Llamaindex to read the documents for ingestion. For more information regarding the file reader, visit the Llamaindex documentation.
- Uncomment the entire huggingface section under Embedding Model Details. Here the name of the embedding model to be used is provided.
- Make sure the file section under Data Reader Details is uncommented. Under here, in the input_dir field provide the absolute path to your dataset folder, i.e. in this case, /home/data/contract-nli/.
- You can also modify and customize several other options in the ingest.yaml file according to your needs, including the splitter class, chunk size, embedding model to be used, etc.
Attention
- The reader section in the ingest.yaml file denotes the type of dataloader to be used for the ingestion process. If you are going to use any other source of data for ingestion as compared to local directory data shown in this example, you need to provide the appropriate details for that type of dataloader.
  - For more information about dataloaders please visit How to use different Data Loaders (Data Readers).
  - You can use multiple type of data sources by providing the reader details simultaneously under the reader section in the ingest.yaml file.
- Some of the dataloaders require separate pyloader files. Make sure to provide them, if needed.
Use the following command to trigger the ingestion process.
d3x dataset ingest -d <dataset name> --remote-sky --sky-cluster=<name of sky cluster to be created> --sky-accelerator="<accelerator type>:<number of accelerators>" --dkubex-apikey ${DKUBEX_APIKEY} --dkubex-url ${DKUBEX_URL} --config /home/data/ingest.yaml --faq
d3x dataset ingest -d contracts --remote-sky --sky-cluster=skyingest --sky-accelerator="A10G:1" --dkubex-apikey ${DKUBEX_APIKEY} --dkubex-url ${DKUBEX_URL} --config /home/data/ingest.yaml --faq
Note
- The time taken for the ingestion process to complete depends on the size of the dataset. Please wait patiently for the process to complete.
- In case the terminal shows a timed-out error, that means the ingestion is still in progress, and you will need to run the command provided on the CLI after the error message to continue to get the ingestion logs.
- The record of the ingestion and related artifacts are also stored in the MLFlow application on DKubeX UI.
To check if the dataset has been created, stored and are ready to use, use the following command:
```
d3x dataset list
```
To check the list of documents that has been ingested in the dataset, use the following command:
d3x dataset show -d <dataset name>
d3x dataset show -d contracts

Creating and accessing the chatbot application¶

From the DKubeX UI, open and log into the SecureLLM application. Once open, click on the Admin Login button and log in using the admin credentials provided during installation.

Hint

In case you do not have the credentials for logging in to SecureLLM, please contact your administrator.
On the left sidebar, click on the Keys menu and go to the Application Keys tab on that page.
To create a new key for your application, use the following steps:
- On the API key name field, provide a unique name for the key to be created.
- From the LLM Keys dropdown list, select DKUBEX.
- From the Models dropdown list, select your deployed base model. In this example, we will use the llama38bbase model.
- Click on the Generate Key button.
A pop-up window will show up on your screen containing the application key for your new application. Alternatively, you can also access your application key from the list of keys in the Application Key tab.
- Copy this application key for further use, as it will be required to create the chatbot application. Also make sure that you are copying the entire key including the sk- part.
From the DKubeX UI, go to the Terminal application.
You will need to configure and use the query.yaml file from the dkubex-examples repo to be used in the query process in the Securechat application.
- Run the following command to put the query.yaml file on your workspace.
```
cd && cp dkubex-examples/rag/query/query.yaml ${HOMEDIR}/query.yaml
```
- Run vim ${HOMEDIR}/query.yaml and provide the following details on the query.yaml file. Once provided, save the file.
  - On the dataset field, provide the name of the dataset you created earlier, i.e. contracts.
  - On the embedding field, provide the type of the embedding model used for ingestion, i.e. huggingface.
  - In the synthesizer section, provide the following details:
    - On the llm field, make sure dkubex is selected.
    - On the llm_url field, provide the endpoint URL of the deployed model to be used. The endpoint URL can be found on the Deployments page of DKubeX UI.
    - On the llm_key field, provide the serving token for the deployed model to be used. To find the serving token, go to the Deployments page of DKubeX UI and click on the deployed model name. The serving token will be available on the model details page.
  - Under the Embedding Model config section, uncomment the entire huggingface section. Here the name of the embedding model to be used is provided.
  - In the securellm section, provide the following details:
    - On the appkey field, provide the application key that you created earlier on the SecureLLM application.
    - On the dkubex_url field, provide the URL to access DKubeX.
You will need to configure and use the securechat.yaml file from the dkubex-examples repo to configure and create the chatbot application.
- Run the following command to put the securechat.yaml file on your workspace.
```
cd && cp dkubex-examples/rag/securechat/securechat.yaml ${HOMEDIR}/securechat.yaml
```
- Run vim ${HOMEDIR}/securechat.yaml and provide the following details on the securechat.yaml file. Once provided, save the file.
  - On the name: field, provide a unique name to be used for the chatbot application. In this example, we will use the name ndabase.
  - On the env:SECUREAPP_ACCESS_KEY: field, provide a password which will be used to access the chatbot application.
  - On the env:FMQUERY_ARGS: field, provide the following details:
    - Use the argument llm to specify that the chatbot application will use the LLM model deployment (llama38bbase) in DKubeX.
    - Provide the name of the dataset, i.e. contracts after the --dataset flag.
    - Provide the absolute path of the query.yaml file that you created earlier after the --config flag. It should be /home/<your username>/query.yaml for this example. Replace the <your username> part with your DKubeX username.
  - On the ingressprefix: field, provide a unique prefix to be used for the chatbot application. In this example, we will use the prefix /ndabase. This will be used in the application URL. e.g. https://123.45.67.89:32443/ndabase

Launch the app deployment with the following command:

d3x apps create --config ${HOMEDIR}/securechat.yaml

To check the status of the app deployment, use the following command:
```
d3x apps list
```
Once the app deployment status becomes running, you can access the application from the Apps page of DKubeX UI. Provide the application key that you set in the SECUREAPP_ACCESS_KEY field earlier to start using the chat application.