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docs updating AzureML notebooks (#13492)

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- Added/updated descriptions and links

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Co-authored-by: Erick Friis <erick@langchain.dev>
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Leonid Ganeline 2023-11-19 18:07:12 -08:00 committed by GitHub
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6
docs/docs/integrations/chat/azure_chat_openai.ipynb
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@ -7,7 +7,9 @@
"source": [
"# Azure OpenAI\n",
"\n",
"This notebook goes over how to connect to an Azure hosted OpenAI endpoint. We recommend having version `openai>=1` installed."
">[Azure OpenAI Service](https://learn.microsoft.com/en-us/azure/ai-services/openai/overview) provides REST API access to OpenAI's powerful language models including the GPT-4, GPT-3.5-Turbo, and Embeddings model series. These models can be easily adapted to your specific task including but not limited to content generation, summarization, semantic search, and natural language to code translation. Users can access the service through REST APIs, Python SDK, or a web-based interface in the Azure OpenAI Studio.\n",
"\n",
"This notebook goes over how to connect to an Azure-hosted OpenAI endpoint. We recommend having version `openai>=1` installed."
]
},
{
@ -162,7 +164,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
"version": "3.10.12"
}
},
"nbformat": 4,

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docs/docs/integrations/chat/azureml_chat_endpoint.ipynb
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@ -4,11 +4,13 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# AzureML Chat Online Endpoint\n",
"# Azure ML Endpoint\n",
"\n",
"[AzureML](https://azure.microsoft.com/en-us/products/machine-learning/) is a platform used to build, train, and deploy machine learning models. Users can explore the types of models to deploy in the Model Catalog, which provides Azure Foundation Models and OpenAI Models. Azure Foundation Models include various open-source models and popular Hugging Face models. Users can also import models of their liking into AzureML.\n",
">[Azure Machine Learning](https://azure.microsoft.com/en-us/products/machine-learning/) is a platform used to build, train, and deploy machine learning models. Users can explore the types of models to deploy in the Model Catalog, which provides Azure Foundation Models and OpenAI Models. `Azure Foundation Models` include various open-source models and popular Hugging Face models. Users can also import models of their liking into AzureML.\n",
">\n",
">[Azure Machine Learning Online Endpoints](https://learn.microsoft.com/en-us/azure/machine-learning/concept-endpoints). After you train machine learning models or pipelines, you need to deploy them to production so that others can use them for inference. Inference is the process of applying new input data to the machine learning model or pipeline to generate outputs. While these outputs are typically referred to as \"predictions,\" inferencing can be used to generate outputs for other machine learning tasks, such as classification and clustering. In `Azure Machine Learning`, you perform inferencing by using endpoints and deployments. `Endpoints` and `Deployments` allow you to decouple the interface of your production workload from the implementation that serves it.\n",
"\n",
"This notebook goes over how to use a chat model hosted on an `AzureML online endpoint`"
"This notebook goes over how to use a chat model hosted on an `Azure Machine Learning Endpoint`."
]
},
{
@ -91,7 +93,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
"version": "3.10.12"
}
},
"nbformat": 4,