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docs: Update Nvidia documentation (#21240)
Updating Nvidia docs ahead for 5/15 competition. Thanks!
This commit is contained in:
Daniel Glogowski
GitHub
parent
70bde15480
commit
c3d169ab00
@ -12,7 +12,7 @@
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"The `ChatNVIDIA` class is a LangChain chat model that connects to [NVIDIA AI Foundation Endpoints](https://www.nvidia.com/en-us/ai-data-science/foundation-models/).\n",
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"\n",
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"\n",
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"> [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 the NVIDIA AI platform, making them fast and easy to evaluate, further customize, and seamlessly run at peak performance on any accelerated stack.\n",
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"> [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 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.\n",
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"> \n",
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"> With [NVIDIA AI Foundation Endpoints](https://www.nvidia.com/en-us/ai-data-science/foundation-models/), you can get quick results from a fully accelerated stack running on [NVIDIA DGX Cloud](https://www.nvidia.com/en-us/data-center/dgx-cloud/). Once customized, these models can be deployed anywhere with enterprise-grade security, stability, and support using [NVIDIA AI Enterprise](https://www.nvidia.com/en-us/data-center/products/ai-enterprise/).\n",
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"> \n",
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@ -58,13 +58,13 @@
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"\n",
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"**To get started:**\n",
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"\n",
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"1. Create a free account with the [NVIDIA NGC](https://catalog.ngc.nvidia.com/) service, which hosts AI solution catalogs, containers, models, etc.\n",
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"1. Create a free account with [NVIDIA](https://build.nvidia.com/), which hosts NVIDIA AI Foundation models\n",
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"\n",
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"2. Navigate to `Catalog > AI Foundation Models > (Model with API endpoint)`.\n",
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"2. Click on your model of choice\n",
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"\n",
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"3. Select the `API` option and click `Generate Key`.\n",
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"3. Under `Input` select the `Python` tab, and click `Get API Key`. Then click `Generate Key`.\n",
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"\n",
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"4. Save the generated key as `NVIDIA_API_KEY`. From there, you should have access to the endpoints."
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"4. Copy and save the generated key as `NVIDIA_API_KEY`. From there, you should have access to the endpoints."
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]
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},
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{
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@ -311,7 +311,7 @@
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"\n",
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"Some model types support unique prompting techniques and chat messages. We will review a few important ones below.\n",
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"\n",
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"**To find out more about a specific model, please navigate to the API section of an AI Foundation model [as linked here](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/ai-foundation/models/codellama-13b/api).**"
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"**To find out more about a specific model, please navigate to the API section of an AI Foundation model [as linked here](https://build.nvidia.com/).**"
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]
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},
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{
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@ -17,16 +17,20 @@
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A selection of NVIDIA AI Foundation models is supported directly in LangChain with familiar APIs.
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The supported models can be found [in NGC](https://catalog.ngc.nvidia.com/ai-foundation-models).
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The supported models can be found [in build.nvidia.com](https://build.nvidia.com/).
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These models can be accessed via the [`langchain-nvidia-ai-endpoints`](https://pypi.org/project/langchain-nvidia-ai-endpoints/)
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package, as shown below.
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### Setting up
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- Create a free [NVIDIA NGC](https://catalog.ngc.nvidia.com/) account.
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- Navigate to `Catalog > AI Foundation Models > (Model with API endpoint)`.
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- Select `API` and generate the key `NVIDIA_API_KEY`.
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1. Create a free account with [NVIDIA](https://build.nvidia.com/), which hosts NVIDIA AI Foundation models
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2. Click on your model of choice
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3. Under `Input` select the `Python` tab, and click `Get API Key`. Then click `Generate Key`.
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4. Copy and save the generated key as `NVIDIA_API_KEY`. From there, you should have access to the endpoints.
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```bash
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export NVIDIA_API_KEY=nvapi-XXXXXXXXXXXXXXXXXXXXXXXXXX
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@ -8,7 +8,7 @@
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"source": [
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"# NVIDIA AI Foundation Endpoints \n",
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"\n",
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"> [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 the NVIDIA AI platform, making them fast and easy to evaluate, further customize, and seamlessly run at peak performance on any accelerated stack.\n",
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"> [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 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.\n",
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"> \n",
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"> With [NVIDIA AI Foundation Endpoints](https://www.nvidia.com/en-us/ai-data-science/foundation-models/), you can get quick results from a fully accelerated stack running on [NVIDIA DGX Cloud](https://www.nvidia.com/en-us/data-center/dgx-cloud/). Once customized, these models can be deployed anywhere with enterprise-grade security, stability, and support using [NVIDIA AI Enterprise](https://www.nvidia.com/en-us/data-center/products/ai-enterprise/).\n",
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"> \n",
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@ -16,7 +16,7 @@
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"\n",
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"This example goes over how to use LangChain to interact with the supported [NVIDIA Retrieval QA Embedding Model](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/ai-foundation/models/nvolve-40k) for [retrieval-augmented generation](https://developer.nvidia.com/blog/build-enterprise-retrieval-augmented-generation-apps-with-nvidia-retrieval-qa-embedding-model/) via the `NVIDIAEmbeddings` class.\n",
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"\n",
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"For more information on accessing the chat models through this api, check out the [ChatNVIDIA](/docs/integrations/chat/nvidia_ai_endpoints/) documentation."
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"For more information on accessing the chat models through this api, check out the [ChatNVIDIA](https://python.langchain.com/docs/integrations/chat/nvidia_ai_endpoints/) documentation."
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]
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},
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{
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@ -53,13 +53,13 @@
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"\n",
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"**To get started:**\n",
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"\n",
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"1. Create a free account with the [NVIDIA NGC](https://catalog.ngc.nvidia.com/) service, which hosts AI solution catalogs, containers, models, etc.\n",
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"1. Create a free account with [NVIDIA](https://build.nvidia.com/), which hosts NVIDIA AI Foundation models\n",
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"\n",
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"2. Navigate to `Catalog > AI Foundation Models > (Model with API endpoint)`.\n",
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"2. Select the `Retrieval` tab, then select your model of choice\n",
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"\n",
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"3. Select the `API` option and click `Generate Key`.\n",
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"3. Under `Input` select the `Python` tab, and click `Get API Key`. Then click `Generate Key`.\n",
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"\n",
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"4. Save the generated key as `NVIDIA_API_KEY`. From there, you should have access to the endpoints."
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"4. Copy and save the generated key as `NVIDIA_API_KEY`. From there, you should have access to the endpoints."
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]
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},
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{
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