> [NVIDIA AI Foundation Endpoints](https://www.nvidia.com/en-us/ai-data-science/foundation-models/) give users easy access to NVIDIA hosted API endpoints for
> NVIDIA AI Foundation Models like `Mixtral 8x7B`, `Llama 2`, `Stable Diffusion`, etc. These models,
> hosted on the [NVIDIA NGC catalog](https://catalog.ngc.nvidia.com/ai-foundation-models), are optimized, tested, and hosted on
> hosted on the [NVIDIA API catalog](https://build.nvidia.com/), are optimized, tested, and hosted on
> the NVIDIA AI platform, making them fast and easy to evaluate, further customize,
> and seamlessly run at peak performance on any accelerated stack.
" print(\"Valid NVIDIA_API_KEY already in environment. Delete to reset\")\n",
@ -112,11 +109,7 @@
"source": [
"## Initialization\n",
"\n",
"The main requirement when initializing an embedding model is to provide the model name. An example is `nvolveqa_40k` below.\n",
"\n",
"For `nvovleqa_40k`, you can also specify the `model_type` as `passage` or `query`. When doing retrieval, you will get best results if you embed the source documents with the `passage` type and the user queries with the `query` type.\n",
"\n",
"If not provided, the `embed_query` method will default to the `query` type, and the `embed_documents` mehod will default to the `passage` type."
"When initializing an embedding model you can select a model by passing it, e.g. `ai-embed-qa-4` below, or use the default by not passing any arguments."