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Change the Inference or Embedding Model

You can change inference or embedding models by using the following procedures.

Change the Inference 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. The following example uses the Mistral AI Mixtral 8x7B Instruct model.

APP_LLM_MODELNAME='mistralai/mixtral-8x7b-instruct-v0.1' docker compose -f deploy/compose/docker-compose.yaml up -d

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

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

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

Change the Embedding Model to a Model from the API Catalog

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.

APP_EMBEDDINGS_MODELNAME='NV-Embed-QA' docker compose -f deploy/compose/docker-compose.yaml up -d

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

  • Browse the models at https://build.ngc.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.

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:
  nemollm-inference:
    container_name: nemollm-inference-microservice
    image: nvcr.io/nim/meta/<image>:<tag>
    ...

  embedding-ms:
    container_name: nemo-retriever-embedding-microservice
    image: nvcr.io/nim/<image>:<tag>


  ranking-ms:
    container_name: nemo-retriever-ranking-microservice
    image: nvcr.io/nim/<image>:<tag>

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

  2. Follow the steps specified here to relaunch the containers with the updated models.