docs: update nvidia docs v2

docs/docs/integrations/providers/nvidia.mdx

@@ -6,7 +6,7 @@
 
 > [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.
 > 

docs/docs/integrations/text_embedding/nvidia_ai_endpoints.ipynb

@@ -85,9 +85,6 @@
  "import getpass\n",
  "import os\n",
  "\n",
- "## API Key can be found by going to NVIDIA NGC -> AI Foundation Models -> (some model) -> Get API Code or similar.\n",
- "## 10K free queries to any endpoint (which is a lot actually).\n",
- "\n",
  "# del os.environ['NVIDIA_API_KEY'] ## delete key and reset\n",
  "if os.environ.get(\"NVIDIA_API_KEY\", \"\").startswith(\"nvapi-\"):\n",
  " 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."
  ]
  },
  {
@@ -129,10 +122,7 @@
  "source": [
  "from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings\n",
  "\n",
- "embedder = NVIDIAEmbeddings(model=\"nvolveqa_40k\")\n",
- "\n",
- "# Alternatively, if you want to specify whether it will use the query or passage type\n",
- "# embedder = NVIDIAEmbeddings(model=\"nvolveqa_40k\", model_type=\"passage\")"
+ "embedder = NVIDIAEmbeddings(model=\"ai-embed-qa-4\")"
  ]
  },
  {
@@ -156,7 +146,7 @@
  "id": "pcDu3v4CbmWk"
  },
  "source": [
- "### **Similarity/Speed Test**\n",
+ "### **Similarity**\n",
  "\n",
  "The following is a quick test of the methods in terms of usage, format, and speed for the use case of embedding the following data points:\n",
  "\n",
@@ -250,7 +240,7 @@
  "s = time.perf_counter()\n",
  "# To use the \"query\" mode, we have to add it as an instance arg\n",
  "q_embeddings = NVIDIAEmbeddings(\n",
- " model=\"nvolveqa_40k\", model_type=\"query\"\n",
+ " model=\"ai-embed-qa-4\", model_type=\"query\"\n",
  ").embed_documents(\n",
  " [\n",
  " \"What's the weather like in Komchatka?\",\n",
@@ -501,7 +491,7 @@
  "source": [
  "vectorstore = FAISS.from_texts(\n",
  " [\"harrison worked at kensho\"],\n",
- " embedding=NVIDIAEmbeddings(model=\"nvolveqa_40k\"),\n",
+ " embedding=NVIDIAEmbeddings(model=\"ai-embed-qa-4\"),\n",
  ")\n",
  "retriever = vectorstore.as_retriever()\n",
  "\n",
@@ -515,7 +505,7 @@
  " ]\n",
  ")\n",
  "\n",
- "model = ChatNVIDIA(model=\"mixtral_8x7b\")\n",
+ "model = ChatNVIDIA(model=\"ai-mixtral-8x7b-instruct\")\n",
  "\n",
  "chain = (\n",
  " {\"context\": retriever, \"question\": RunnablePassthrough()}\n",