Llamaindex Text QA Chatbot

Description

This example showcases RAG pipeline. It uses nemollm inference microservice to host trt optimized llm and nemollm retriever embedding microservice. It uses milvus as vectorstore to store embeddings and generate response for query.

\n\n \n \n \n \n \n \n \n \n\ntr:nth-child(odd)]:bg-surface-sunken\">\n \n \n \n \n \n \n \n \n\n

LLM Model	Embedding	Framework	Document Type	Vector Database	Model deployment platform
meta/llama3-8b-instruct	nv-embedqa-e5-v5	llama-index	PDF/Text	milvus	On Prem

Prerequisites

\n
You have the NGC CLI available on your client machine.\nYou can download the CLI from https://ngc.nvidia.com/setup/installers/cli.
\n
\n
You have Kubernetes installed and running Ubuntu 22.04.\nRefer to the Kubernetes documentation or\nthe NVIDIA Cloud Native Stack repository\nfor more information.
\n
\n
You have a default storage class available in the cluster for PVC provisioning.\nOne option is the local path provisioner by Rancher.\nRefer to the installation\nsection of the README in the GitHub repository.
\n
```
kubectl apply -f https://raw.githubusercontent.com/rancher/local-path-provisioner/v0.0.26/deploy/local-path-storage.yaml\nkubectl get pods -n local-path-storage\nkubectl get storageclass\n
```
\n

If the local path storage class is not set as default, it can be made default using the command below

kubectl patch storageclass local-path -p '{\"metadata\": {\"annotations\":{\"storageclass.kubernetes.io/is-default-class\":\"true\"}}}'\n

\n
You have installed the NVIDIA GPU Operator following steps here
\n

Deployment

Fetch the Helm Chart from NGC

\n\">

helm fetch https://helm.ngc.nvidia.com/nvidia/aiworkflows/charts/rag-app-text-chatbot-langchain-24.08.tgz --username='$oauthtoken' --password=<YOUR API KEY>\n

\n
Deploy NVIDIA NIM LLM and NVIDIA NeMo Retriever Embedding Microservice following steps in this section.
\n
\n
Deploy Milvus vectorstore following steps in this section.
\n
\n
Create the example namespace
\n

kubectl create namespace canonical-rag-langchain\n

Export the NGC API Key in the environment.

"\n\">

export NGC_CLI_API_KEY=\"<YOUR NGC API KEY>\"\n

Create the Helm pipeline instance and start the services.

helm install canonical-rag-langchain rag-app-text-chatbot-langchain-24.08.tgz -n canonical-rag-langchain --set imagePullSecret.password=$NGC_CLI_API_KEY\n

\n
Verify the pods are running and ready.
\n

kubectl get pods -n canonical-rag-langchain\n

Example Output

NAME                              READY   STATUS    RESTARTS   AGE\nchain-server-748bb5c5ff-58cw7     1/1     Running   0          71s\nrag-playground-855c7b9f65-qv42k   1/1     Running   0          71s\n

Access the app using port-forwarding.

kubectl port-forward service/rag-playground -n canonical-rag-langchain 30002:3001\n

Open browser and access the rag-playground UI using http://localhost:30002/converse

Install the NVIDIA GPU Operator

Use the NVIDIA GPU Operator to install, configure, and manage the NVIDIA GPU driver and\nNVIDIA container runtime on the Kubernetes node.

Add the NVIDIA Helm repository:

helm repo add nvidia https://helm.ngc.nvidia.com/nvidia \\\n   && helm repo update\n

Install the Operator:

helm install --wait --generate-name \\\n   -n gpu-operator --create-namespace \\\n   nvidia/gpu-operator\n

Optional: Configure GPU time-slicing if you have fewer than three GPUs.

Create a file, time-slicing-config-all.yaml, with the following content:

apiVersion: v1\nkind: ConfigMap\nmetadata:\n  name: time-slicing-config-all\ndata:\n  any: |-\n    version: v1\n    flags:\n      migStrategy: none\n    sharing:\n      timeSlicing:\n        resources:\n        - name: nvidia.com/gpu\n          replicas: 3\n

The sample configuration creates three replicas from each GPU on the node.

Add the config map to the Operator namespace:

kubectl create -n gpu-operator -f time-slicing-config-all.yaml\n

Configure the device plugin with the config map and set the default time-slicing configuration:

kubectl patch clusterpolicies.nvidia.com/cluster-policy \\\n    -n gpu-operator --type merge \\\n    -p '{\"spec\": {\"devicePlugin\": {\"config\": {\"name\": \"time-slicing-config-all\", \"default\": \"any\"}}}}'\n

Verify that at least 3 GPUs are allocatable:

kubectl get nodes -l nvidia.com/gpu.present -o json | jq '.items[0].status.allocatable | with_entries(select(.key | startswith(\"nvidia.com/\"))) | with_entries(select(.value != \"0\"))'\n

Example Output

{\n  \"nvidia.com/gpu\": \"3\"\n}\n

Deploying NVIDIA NIM Microservices

Deploying NVIDIA NIM for LLMs

(Default flow deploys meta/llama3-8b-instruct)

Follow the steps from nim-deploy repository to deploy NIM LLM microservice with meta/llama3-8b-instruct as default LLM model.

Deploying NVIDIA Nemo Retriever Embedding Microservice

Follow steps from here to fetch and deploy the Nemo Retriever Embedding Microservice Helm chart.

Note:\nWhile deploying the NREM helm chart, use below step to forcefully set the embedding image path to the GA version of embedding model.

helm upgrade --install \\\n  --namespace nrem \\\n  --set image.repository=nvcr.io/nim/nvidia/nv-embedqa-e5-v5 \\\n  --set image.tag=1.0.0 \\\n  nemo-embedder \\\n  text-embedding-nim-1.0.0.tgz\n

Deploying Milvus Vectorstore Helm Chart

Create a new nanespace for vectorstore

kubectl create namespace vectorstore\n

Add the milvus repository

helm repo add milvus https://zilliztech.github.io/milvus-helm/\n

Update the helm repository

helm repo update\n

Create a file named custom_value.yaml with below content to utilize GPU's

standalone:\n  resources:\n    requests:\n      nvidia.com/gpu: \"1\"\n    limits:\n      nvidia.com/gpu: \"1\"\n

Install the helm chart and point to the above created file using -f argument as shown below.

helm install milvus milvus/milvus --set cluster.enabled=false --set etcd.replicaCount=1 --set minio.mode=standalone --set pulsar.enabled=false -f custom_value.yaml -n vectorstore\n

Check status of the pods

kubectl get pods -n vectorstore\n

All pods should be running and in a ready state within couple of minutes

NAME                                READY   STATUS    RESTARTS        AGE\nmilvus-etcd-0                       1/1     Running   0               5m34s\nmilvus-minio-76f9d647d5-44799       1/1     Running   0               5m34s\nmilvus-standalone-9ccf56df4-m4tpm   1/1     Running   3 (4m35s ago)   5m34\n

Configuring Examples

You can configure various parameters such as prompts and vectorstore using environment variables. Modify the environment variables in the env section of the query service in the values.yaml file of the respective examples.

Configuring Prompts

---\ndepth: 2\nlocal: true\nbacklinks: none\n---\n

Each example utilizes a prompt.yaml file that defines prompts for different contexts. These prompts guide the RAG model in generating appropriate responses. You can tailor these prompts to fit your specific needs and achieve desired responses from the models.

Accessing Prompts

The prompts are loaded as a Python dictionary within the application. To access this dictionary, you can use the get_prompts() function provided by the utils module. This function retrieves the complete dictionary of prompts.

Consider we have following prompt.yaml file which is under files directory for all the helm charts

chat_template: |\n    You are a helpful, respectful and honest assistant.\n    Always answer as helpfully as possible, while being safe.\n    Please ensure that your responses are positive in nature.\n\nrag_template: |\n    You are a helpful AI assistant named Envie.\n    You will reply to questions only based on the context that you are provided.\n    If something is out of context, you will refrain from replying and politely decline to respond to the user.\n

You can access it's chat_template using following code in you chain server

from RAG.src.chain_server.utils import get_prompts\n\nprompts = get_prompts()\n\nchat_template = prompts.get(\"chat_template\", \"\")\n

Once you have updated the prompt you can update the deployment for any of the examples by using the command below.

-n --set imagePullSecret.password=$NGC_CLI_API_KEY\n\n\">

helm upgrade <rag-example-name> <rag-example-helm-chart-path> -n <rag-example-namespace> --set imagePullSecret.password=$NGC_CLI_API_KEY\n\n

Configuring VectorStore

The vector store can be modified from environment variables. You can update:

\n
APP_VECTORSTORE_NAME: This is the vector store name. Currently, we support milvus and pgvector\nNote: This only specifies the vector store name. The vector store container needs to be started separately.
\n
\n
APP_VECTORSTORE_URL: The host machine URL where the vector store is running.
\n

Additional Resources

Learn more about how to use NVIDIA NIM microservices for RAG through our Deep Learning Institute. Access the course here.

Security considerations

The RAG applications are shared as reference architectures and are provided “as is”. The security of them in production environments is the responsibility of the end users deploying it. When deploying in a production environment, please have security experts review any potential risks and threats (including direct and indirect prompt injection); define the trust boundaries, secure the communication channels, integrate AuthN & AuthZ with appropriate access controls, keep the deployment including the containers up to date, ensure the containers are secure and free of vulnerabilities.

Licenses

By downloading or using NVIDIA NIM inference microservices included in the AI Chatbot workflows you agree to the terms of the NVIDIA Software License Agreement and Product-specific Terms for AI products.

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LLM Model	Embedding	Framework	Document Type	Vector Database	Model deployment platform
meta/llama3-8b-instruct	nv-embedqa-e5-v5	llama-index	PDF/Text	milvus	On Prem

\n\n### Prerequisites\n\n- You have the NGC CLI available on your client machine.\n You can download the CLI from .\n\n- You have Kubernetes installed and running Ubuntu 22.04.\n Refer to the [Kubernetes documentation](https://kubernetes.io/docs/setup/) or\n the [NVIDIA Cloud Native Stack repository](https://github.com/NVIDIA/cloud-native-stack/)\n for more information.\n\n- You have a default storage class available in the cluster for PVC provisioning.\n One option is the local path provisioner by Rancher.\n Refer to the [installation](https://github.com/rancher/local-path-provisioner?tab=readme-ov-file#installation)\n section of the README in the GitHub repository.\n\n ```console\n kubectl apply -f https://raw.githubusercontent.com/rancher/local-path-provisioner/v0.0.26/deploy/local-path-storage.yaml\n kubectl get pods -n local-path-storage\n kubectl get storageclass\n ```\n- If the local path storage class is not set as default, it can be made default using the command below\n\n ```\n kubectl patch storageclass local-path -p '{\"metadata\": {\"annotations\":{\"storageclass.kubernetes.io/is-default-class\":\"true\"}}}'\n ```\n- You have installed the NVIDIA GPU Operator following steps [here](#install-the-nvidia-gpu-operator)\n\n### Deployment\n\n1. Fetch the Helm Chart from NGC\n\n ```\n helm fetch https://helm.ngc.nvidia.com/nvidia/aiworkflows/charts/rag-app-text-chatbot-langchain-24.08.tgz --username='$oauthtoken' --password=\n ```\n2. Deploy NVIDIA NIM LLM and NVIDIA NeMo Retriever Embedding Microservice following steps in [this section](#deploying-nvidia-nim-microservices).\n\n3. Deploy Milvus vectorstore following steps in [this section](#deploying-milvus-vectorstore-helm-chart).\n\n4. Create the example namespace\n\n```\nkubectl create namespace canonical-rag-langchain\n```\n\n4. Export the NGC API Key in the environment.\n\n```\nexport NGC_CLI_API_KEY=\"\"\n```\n\n5. Create the Helm pipeline instance and start the services.\n\n ```console\n helm install canonical-rag-langchain rag-app-text-chatbot-langchain-24.08.tgz -n canonical-rag-langchain --set imagePullSecret.password=$NGC_CLI_API_KEY\n ```\n6. Verify the pods are running and ready.\n\n```console\nkubectl get pods -n canonical-rag-langchain\n```\n\n*Example Output*\n\n```output\nNAME READY STATUS RESTARTS AGE\nchain-server-748bb5c5ff-58cw7 1/1 Running 0 71s\nrag-playground-855c7b9f65-qv42k 1/1 Running 0 71s\n```\n\n7. Access the app using port-forwarding.\n\n```console\nkubectl port-forward service/rag-playground -n canonical-rag-langchain 30002:3001\n```\n\nOpen browser and access the rag-playground UI using \n\n### Install the NVIDIA GPU Operator\n\nUse the NVIDIA GPU Operator to install, configure, and manage the NVIDIA GPU driver and\nNVIDIA container runtime on the Kubernetes node.\n\n1. Add the NVIDIA Helm repository:\n\n ```console\n helm repo add nvidia https://helm.ngc.nvidia.com/nvidia \\\n && helm repo update\n ```\n2. Install the Operator:\n\n ```console\n helm install --wait --generate-name \\\n -n gpu-operator --create-namespace \\\n nvidia/gpu-operator\n ```\n3. Optional: Configure GPU time-slicing if you have fewer than three GPUs.\n\n - Create a file, `time-slicing-config-all.yaml`, with the following content:\n\n ```yaml\n apiVersion: v1\n kind: ConfigMap\n metadata:\n name: time-slicing-config-all\n data:\n any: |-\n version: v1\n flags:\n migStrategy: none\n sharing:\n timeSlicing:\n resources:\n - name: nvidia.com/gpu\n replicas: 3\n ```\n\n The sample configuration creates three *replicas* from each GPU on the node.\n\n - Add the config map to the Operator namespace:\n\n ```console\n kubectl create -n gpu-operator -f time-slicing-config-all.yaml\n ```\n - Configure the device plugin with the config map and set the default time-slicing configuration:\n\n ```console\n kubectl patch clusterpolicies.nvidia.com/cluster-policy \\\n -n gpu-operator --type merge \\\n -p '{\"spec\": {\"devicePlugin\": {\"config\": {\"name\": \"time-slicing-config-all\", \"default\": \"any\"}}}}'\n ```\n - Verify that at least `3` GPUs are allocatable:\n\n ```console\n kubectl get nodes -l nvidia.com/gpu.present -o json | jq '.items[0].status.allocatable | with_entries(select(.key | startswith(\"nvidia.com/\"))) | with_entries(select(.value != \"0\"))'\n ```\n\n *Example Output*\n\n ```json\n {\n \"nvidia.com/gpu\": \"3\"\n }\n ```\n\n### Deploying NVIDIA NIM Microservices\n\n#### Deploying NVIDIA NIM for LLMs\n\n##### (Default flow deploys meta/llama3-8b-instruct)\n\n1. Follow the steps from [nim-deploy](https://github.com/NVIDIA/nim-deploy/tree/main/helm) repository to deploy NIM LLM microservice with `meta/llama3-8b-instruct` as default LLM model.\n\n#### Deploying NVIDIA Nemo Retriever Embedding Microservice\n\n1. Follow steps from [here](https://catalog.ngc.nvidia.com/orgs/nim/teams/nvidia/helm-charts/text-embedding-nim) to fetch and deploy the Nemo Retriever Embedding Microservice Helm chart.\n\nNote:\nWhile deploying the NREM helm chart, use below step to forcefully set the embedding image path to the GA version of embedding model.\n\n```\nhelm upgrade --install \\\n --namespace nrem \\\n --set image.repository=nvcr.io/nim/nvidia/nv-embedqa-e5-v5 \\\n --set image.tag=1.0.0 \\\n nemo-embedder \\\n text-embedding-nim-1.0.0.tgz\n```\n\n### Deploying Milvus Vectorstore Helm Chart\n\n1. Create a new nanespace for vectorstore\n\n```\nkubectl create namespace vectorstore\n```\n\n2. Add the milvus repository\n\n```\nhelm repo add milvus https://zilliztech.github.io/milvus-helm/\n```\n\n3. Update the helm repository\n\n```\nhelm repo update\n```\n\n4. Create a file named custom_value.yaml with below content to utilize GPU's\n\n```\nstandalone:\n resources:\n requests:\n nvidia.com/gpu: \"1\"\n limits:\n nvidia.com/gpu: \"1\"\n```\n\n5. Install the helm chart and point to the above created file using -f argument as shown below.\n\n```\nhelm install milvus milvus/milvus --set cluster.enabled=false --set etcd.replicaCount=1 --set minio.mode=standalone --set pulsar.enabled=false -f custom_value.yaml -n vectorstore\n```\n\n6. Check status of the pods\n\n```\nkubectl get pods -n vectorstore\n```\n\n7. All pods should be running and in a ready state within couple of minutes\n\n```\nNAME READY STATUS RESTARTS AGE\nmilvus-etcd-0 1/1 Running 0 5m34s\nmilvus-minio-76f9d647d5-44799 1/1 Running 0 5m34s\nmilvus-standalone-9ccf56df4-m4tpm 1/1 Running 3 (4m35s ago) 5m34\n```\n\n## Configuring Examples\n\nYou can configure various parameters such as prompts and vectorstore using environment variables. Modify the environment variables in the `env` section of the query service in the [values.yaml](./rag-app-multiturn-chatbot/values.yaml) file of the respective examples.\n\n### Configuring Prompts\n\n```{contents}\n---\ndepth: 2\nlocal: true\nbacklinks: none\n---\n```\n\nEach example utilizes a prompt.yaml file that defines prompts for different contexts. These prompts guide the RAG model in generating appropriate responses. You can tailor these prompts to fit your specific needs and achieve desired responses from the models.\n\n### Accessing Prompts\n\nThe prompts are loaded as a Python dictionary within the application. To access this dictionary, you can use the `get_prompts()` function provided by the `utils` module. This function retrieves the complete dictionary of prompts.\n\nConsider we have following `prompt.yaml` file which is under `files` directory for all the helm charts\n\n```yaml\nchat_template: |\n You are a helpful, respectful and honest assistant.\n Always answer as helpfully as possible, while being safe.\n Please ensure that your responses are positive in nature.\n\nrag_template: |\n You are a helpful AI assistant named Envie.\n You will reply to questions only based on the context that you are provided.\n If something is out of context, you will refrain from replying and politely decline to respond to the user.\n```\n\nYou can access it's chat_template using following code in you chain server\n\n```python3\nfrom RAG.src.chain_server.utils import get_prompts\n\nprompts = get_prompts()\n\nchat_template = prompts.get(\"chat_template\", \"\")\n```\n\nOnce you have updated the prompt you can update the deployment for any of the examples by using the command below.\n\n```\nhelm upgrade

-n --set imagePullSecret.password=$NGC_CLI_API_KEY\n\n```\n\n### Configuring VectorStore\n\nThe vector store can be modified from environment variables. You can update:\n1. `APP_VECTORSTORE_NAME`: This is the vector store name. Currently, we support `milvus` and `pgvector`\n **Note**: This only specifies the vector store name. The vector store container needs to be started separately.\n\n2. `APP_VECTORSTORE_URL`: The host machine URL where the vector store is running.\n\n### Additional Resources\n\nLearn more about how to use NVIDIA NIM microservices for RAG through our Deep Learning Institute. Access the course here.\n\n### Security considerations\n\nThe RAG applications are shared as reference architectures and are provided “as is”. The security of them in production environments is the responsibility of the end users deploying it. When deploying in a production environment, please have security experts review any potential risks and threats (including direct and indirect prompt injection); define the trust boundaries, secure the communication channels, integrate AuthN & AuthZ with appropriate access controls, keep the deployment including the containers up to date, ensure the containers are secure and free of vulnerabilities.\n\n### Licenses\n\nBy downloading or using NVIDIA NIM inference microservices included in the AI Chatbot workflows you agree to the terms of the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and [Product-specific Terms for AI products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/).","displayName","RAG Application: Langchain Text QA Chatbot",["D",1757374618144],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/aiworkflows/helm-charts/rag-app-text-chatbot-langchain","rawImageUrl","https://nvdam.widen.net/content/qhl26oo9hq/jpeg/ai-platform-social-ai-chatbots-with-rag-24-1200x628.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_116":1410,"_118":1411},"ngcApps",[1363],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1344":1345,"_1346":1347,"_1348":1349,"_1350":1351,"_1352":247,"_147":271,"_1353":1354,"_1355":59,"_1356":88,"_1357":88,"_1358":1359,"_1360":88,"_1361":1362},"amplitude",{"_1341":1342,"_945":-7,"_1343":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1335":1336,"_1337":1338,"_1339":1340},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1220":351,"_1221":1222,"_1223":1224,"_1225":1226},"announcements",[],"consentState",{"_309":1210,"_1211":34,"_1212":34,"_1213":88,"_1214":1215},"consentConfig",{"_291":292,"_293":294,"_295":296,"_297":298,"_299":300,"_301":302,"_303":304,"_305":306,"_307":308,"_309":310},"showContactAdmin",1784374948277,[],[],[285,286,287,288,289,290],{"_48":63,"_50":74,"_52":111,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":70,"_52":111,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":68,"_52":111,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":64,"_52":111,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":111,"_54":-5,"_56":-5,"_58":59},{"_48":49,"_50":51,"_52":111,"_54":-5,"_56":-5,"_58":59},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. The information collected might relate to you, your preferences, or your device. We use that information to make the site work, analyze performance and traffic on our website, provide a more personalized web experience, and assist in our marketing efforts.

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Information collected with these cookies is used to measure and find ways to improve website 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