Change the LLM or Embedding Model for NVIDIA RAG Blueprint#

You can change the LLM or embedding models for the NVIDIA RAG Blueprint by using the following procedures.

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For NVIDIA-Hosted Microservices#

Change the LLM Model#

To change the inference model to a model from the API catalog, specify the model in the APP_LLM_MODELNAME environment variable when you start the RAG Server.

export APP_LLM_MODELNAME='nvidia/llama-3.3-nemotron-super-49b-v1.5' 
docker compose -f deploy/compose/docker-compose-rag-server.yaml up -d

To get a list of valid model names, use one of the following methods:

  • Browse the models at https://build.nvidia.com/. View the sample Python code and get the model name from the model argument to the client.chat.completions.create method.

Tip

Follow steps in For Helm Deployments to change the inference model for Helm charts.

Change the Embedding Model#

To change the embedding model to a model from the API catalog, specify the model in the APP_EMBEDDINGS_MODELNAME environment variable when you start the RAG server. The following example uses the NVIDIA Embed QA 4 model.

export APP_EMBEDDINGS_MODELNAME='NV-Embed-QA' 
export APP_RANKING_MODELNAME='NV-Embed-QA' 
docker compose -f deploy/compose/docker-compose-ingestor-server.yaml up -d
docker compose -f deploy/compose/docker-compose-rag-server.yaml up -d --build

As an alternative you can also specify the model names at runtime using /generate API call. Refer to the Generate Answer Endpoint and Document Search Endpoint payload schema in this notebook.

To get a list of valid model names, use one of the following methods:

  • Browse the models at https://build.nvidia.com/explore/retrieval. View the sample Python code and get the model name from the model argument to the client.embeddings.create method.

  • Install the langchain-nvidia-ai-endpoints Python package from PyPi. Use the get_available_models() method to on an instance of an NVIDIAEmbeddings object to list the models. Refer to the package web page for sample code to list the models.

Tip

Always use same embedding model or model having same tokinizers for both ingestion and retrieval to yield good accuracy.

For Self-Hosted On Premises Microservices#

You can specify the model for NVIDIA NIM containers to use in the nims.yaml file.

  1. Edit the deploy/nims.yaml file and specify an image that includes the model to deploy.

    services:
  nim-llm:
    container_name: nim-llm-ms
    image: nvcr.io/nim/<image>:<tag>
    ...

  nemoretriever-embedding-ms:
    container_name: nemoretriever-embedding-ms
    image: nvcr.io/nim/<image>:<tag>


  nemoretriever-ranking-ms:
    container_name: nemoretriever-ranking-ms
    image: nvcr.io/nim/<image>:<tag>

    To get a list of valid model names, use one of the following methods:

  2. Update the corresponding model names using environment variables as required.

    export APP_LLM_MODELNAME=<>
export APP_RANKING_MODELNAME=<>
export APP_EMBEDDINGS_MODELNAME=<>

  3. Follow the steps specified here to relaunch the containers with the updated models. Make sure to specify the correct model names using appropriate environment variables as shown in the earlier step.

For Helm Deployments#

Use this procedure to change models when you are running self-hosted NVIDIA NIM microservices. The Helm values map directly to the Docker Compose/self-hosted settings.

  1. List the available models and images by running the following code. You can browse the NGC catalog at https://catalog.ngc.nvidia.com/containers to learn about the available models.

    ngc registry image list "nim/*"

  2. Set the model names and service URLs used by rag-server deployment in values.yaml.

    # rag-server runtime configuration
envVars:
  # === LLM ===
  APP_LLM_MODELNAME: "<llm-model-name>"
  # Use the in-cluster NIM LLM service; if empty, NVIDIA-hosted API is used
  APP_LLM_SERVERURL: "nim-llm:8000"

  # === Embeddings ===
  APP_EMBEDDINGS_MODELNAME: "<embedding-model-name>"
  APP_EMBEDDINGS_SERVERURL: "nemoretriever-embedding-ms:8000"

  # === Reranker ===
  APP_RANKING_MODELNAME: "<reranker-model-name>"
  APP_RANKING_SERVERURL: "nemoretriever-ranking-ms:8000"

  3. Configure the NIM microservices that host those models. Replace <image>:<tag> with the image you selected (format nvcr.io/nim/<image>:<tag>) in values.yaml.

    # LLM NIM
nim-llm:
  enabled: true
  image:
    # nvcr.io/nim/<image>:<tag>
    repository: nvcr.io/nim/<image>
    tag: "<tag>"
    pullPolicy: IfNotPresent
  resources:
    limits:
      nvidia.com/gpu: 1
    requests:
      nvidia.com/gpu: 1
  env:
    - name: NIM_MODEL_PROFILE
      value: "" # Optional; leave empty for auto profile
  model:
    # Optional: provide NGC API key if private artifacts must be pulled
    ngcAPIKey: ""
    name: "<llm-model-name>"

# Embedding NIM
nvidia-nim-llama-32-nv-embedqa-1b-v2:
  enabled: true
  image:
    # nvcr.io/nim/<image>:<tag>
    repository: nvcr.io/nim/<image>
    tag: "<tag>"
  resources:
    limits:
      nvidia.com/gpu: 1
    requests:
      nvidia.com/gpu: 1
  nim:
    ngcAPIKey: ""

# Reranker NIM
nvidia-nim-llama-32-nv-rerankqa-1b-v2:
  enabled: true
  image:
    # nvcr.io/nim/<image>:<tag>
    repository: nvcr.io/nim/<image>
    tag: "<tag>"
  resources:
    limits:
      nvidia.com/gpu: 1
    requests:
      nvidia.com/gpu: 1
  nim:
    ngcAPIKey: ""

  4. After you modify values.yaml, apply the changes as described in Change a Deployment.