docs added

.github/workflows/gh-pages.yml

@@ -0,0 +1,85 @@
+name: github pages
+
+on:
+  # Runs on pushes targeting the default branch
+  push:
+    branches: [main]
+
+  # Allows you to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages
+permissions:
+  contents: read
+  pages: write
+  id-token: write
+
+# Allow one concurrent deployment
+concurrency:
+  group: "pages"
+  cancel-in-progress: true
+
+# Default to bash
+defaults:
+  run:
+    shell: bash
+
+jobs:
+  # Build job
+  build:
+    runs-on: ubuntu-latest
+    env:
+      HUGO_VERSION: 0.119.0
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+        with:
+          submodules: recursive
+
+      - name: Setup Node
+        uses: actions/setup-node@v3
+        with:
+          node-version: '18'
+
+      - name: Cache dependencies
+        uses: actions/cache@v2
+        with:
+          path: ~/.npm
+          key: ${{ runner.os }}-node-${{ hashFiles('**/package-lock.json') }}
+          restore-keys: |
+                        ${{ runner.os }}-node-
+
+      - name: Install Hugo CLI
+        run: |
+          wget -O ${{ runner.temp }}/hugo.deb https://github.com/gohugoio/hugo/releases/download/v${HUGO_VERSION}/hugo_extended_${HUGO_VERSION}_linux-amd64.deb \
+          && sudo dpkg -i ${{ runner.temp }}/hugo.deb
+
+      - name: Setup Pages
+        id: pages
+        uses: actions/configure-pages@v1
+
+      - name: Build with Hugo
+        run:  |
+          npm install -D autoprefixer
+          npm install -D postcss-cli
+          npm install -D postcss
+          npm install
+          hugo --gc --minify --cleanDestinationDir --baseURL https://azure.github.io/aihub/
+        working-directory: ./docs
+
+      - name: Upload artifact
+        uses: actions/upload-pages-artifact@v1
+        with:
+          path: ./docs/public
+
+  # Deployment job
+  deploy:
+    environment:
+      name: github-pages
+      url: ${{ steps.deployment.outputs.page_url }}
+    runs-on: ubuntu-latest
+    needs: build
+    steps:
+      - name: Deploy to GitHub Pages
+        id: deployment
+        uses: actions/deploy-pages@v1

.gitignore

@@ -3,6 +3,12 @@
 ##
 ## Get latest from https://github.com/github/gitignore/blob/main/VisualStudio.gitignore
 
+public/
+resources/
+node_modules/
+package-lock.json
+.hugo_build.lock
+
 # User-specific files
 *.rsuser
 *.suo

docs/assets/scss/_variables_project.scss

@@ -0,0 +1,21 @@
+/*
+Add styles or override variables from the theme here.
+*/
+
+$primary: #004589;
+$secondary: #0080ff;
+
+$light: #89c4ff;
+$dark: #001e3b;
+
+// UI element colors
+$td-sidebar-bg-color: rgba($primary, 0.15);
+
+/*
+AZURE COLOR PALETTE
+#d8ebff	(216,235,255)
+#89c4ff	(137,196,255)
+#0080ff	(0,128,255)
+#004589	(0,69,137)
+#001e3b	(0,30,59)
+*/

docs/config.yaml

@@ -0,0 +1,15 @@
+# THIS IS A TEST CONFIG ONLY!
+# FOR THE CONFIGURATION OF YOUR SITE USE hugo.yaml.
+#
+# As of Docsy 0.7.0, Hugo 0.110.0 or later must be used.
+#
+# The sole purpose of this config file is to detect Hugo-module builds that use
+# an older version of Hugo.
+#
+# DO NOT add any config parameters to this file. You can safely delete this file
+# if your project is using the required Hugo version.
+
+module:
+  hugoVersion:
+    extended: true
+    min: 0.110.0

docs/content/en/_index.md

@@ -0,0 +1,38 @@
+---
+title: AI Hub
+---
+
+{{< blocks/cover title="AI Hub!" image_anchor="top" height="full" >}}
+<a class="btn btn-lg btn-primary me-3 mb-4" href="/aihub/docs/">
+  Learn More <i class="fas fa-arrow-alt-circle-right ms-2"></i>
+</a>
+<a class="btn btn-lg btn-secondary me-3 mb-4" href="https://github.com/azure/aihub">
+  GitHub Repo <i class="fab fa-github ms-2 "></i>
+</a>
+<p class="lead mt-5">AI Hub with real scenarios examples.</p>
+{{< blocks/link-down color="info" >}}
+{{< /blocks/cover >}}
+
+
+{{% blocks/lead color="primary" %}}
+
+This delivery guide will help you test AI capabilities thru real scenarios.
+
+{{% /blocks/lead %}}
+
+
+{{% blocks/section color="dark" type="row" %}}
+
+{{% blocks/feature icon="fa-solid fa-file-lines" title="Read the Docs!" url="https://azure.github.io/aihub/" %}}
+Read the delivery guide and learn how to deploy the solution.
+{{% /blocks/feature %}}
+
+{{% blocks/feature icon="fa-solid fa-boxes-stacked" title="Deploy the Solution!" url="https://github.com/azure/aihub/" %}}
+Use `terraform` to deploy the complete solution.
+{{% /blocks/feature %}}
+
+{{% blocks/feature icon="fa-solid fa-code-pull-request" title="Contributions welcome!" url="https://github.com/azure/aihub" %}}
+We do a [Pull Request](https://github.com/azure/aihub/pulls) contributions workflow on **GitHub**. New users are always welcome!
+{{% /blocks/feature %}}
+
+{{% /blocks/section %}}

docs/content/en/docs/Concepts/_index.md

@@ -0,0 +1,6 @@
+---
+title: Concepts
+weight: 4
+description: >
+  Understand the core components of the solution: Azure Open AI, Azure Cognitive Search, Context Safety and more.
+---

docs/content/en/docs/Concepts/azure-cognitive-search.md

@@ -0,0 +1,27 @@
+---
+title: Azure Cognitive Search
+date: 2023-10-06
+description: >
+  Used to build a rich search experience over private, heterogeneous content in web, mobile, and enterprise applications.
+categories: [Azure]
+tags: [docs, cognitive-search]
+weight: 2
+---
+
+Azure Cognitive Search (formerly known as "Azure Search") is a cloud search service that gives developers infrastructure, APIs, and tools for building a rich search experience over private, heterogeneous content in web, mobile, and enterprise applications.
+
+Search is foundational to any app that surfaces text to users, where common scenarios include catalog or document search, online retail apps, or data exploration over proprietary content. When you create a search service, you'll work with the following capabilities:
+
+* A search engine for full text and vector search over a search index containing user-owned content
+* Rich indexing, with lexical analysis and optional AI enrichment for content extraction and transformation
+* Rich query syntax for vector queries, text search, fuzzy search, autocomplete, geo-search and more
+* Programmability through REST APIs and client libraries in Azure SDKs
+* Azure integration at the data layer, machine learning layer, and AI (Azure AI services)
+
+![Imagen arquitectura Azure Cognitive Search](https://learn.microsoft.com/en-us/azure/search/media/search-what-is-azure-search/azure-search-diagram.svg)
+
+**Activate GenAI with Azure** uses Azure Cognitive Search to serve an index of vectorized content, that will be used by our LLM (ChatGPT) to respond to user's query.
+
+Learn more at the official documentation: [What is Azure Cognitive Search?](https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search).
+
+Learning Path:[Implement knowledge mining with Azure Cognitive Search](https://learn.microsoft.com/en-us/training/paths/implement-knowledge-mining-azure-cognitive-search/)

docs/content/en/docs/Concepts/azure-container-apps.md

@@ -0,0 +1,21 @@
+---
+title: Azure Container Apps
+date: 2023-10-06
+description: >
+  Host the application in Azure Container Apps.
+categories: [Azure]
+tags: [docs, container-apps, serverless]
+weight: 4
+---
+
+Azure Container Apps is a managed serverless container service that enables executing code in containers without the overhead of managing virtual machines, orchestrators, or adopting a highly opinionated application framework.
+
+Common uses of Azure Container Apps include:
+* Deploying API endpoints
+* Hosting background processing jobs
+* Handling event-driven processing
+* Running microservices
+
+**Activate GenAI with Azure** uses Azure Container Apps to deploy the chat user interface that will answer user queries based on the company's documents.
+
+Learn more about Azure Container Apps: [Azure Container Apps documentation?](https://learn.microsoft.com/en-us/azure/container-apps/)

docs/content/en/docs/Concepts/azure-functions.md

@@ -0,0 +1,17 @@
+---
+title: Azure Functions
+date: 2023-10-06
+description: >
+  Serveles application used to bacth-process documents and create embeddings.
+categories: [Azure]
+tags: [docs, functions, serverless]
+weight: 6
+---
+
+Azure Functions is a serverless solution that allows you to write less code, maintain less infrastructure, and save on costs. Instead of worrying about deploying and maintaining servers, the cloud infrastructure provides all the up-to-date resources needed to keep your applications running.
+
+Functions provides a comprehensive set of event-driven triggers and bindings that connect your functions to other services without having to write extra code. You focus on the code that matters most to you, in the most productive language for you, and Azure Functions handles the rest. 
+
+**Activate GenAI with Azure** uses Azure Function to create chunks of the documents text and create embeddings to be added to the Azure Cognitive Search index. 
+
+Learn more about Azure Functions: [What is Azure Function?](https://learn.microsoft.com/en-us/azure/azure-functions/functions-overview?pivots=programming-language-csharp). For the best experience with the Functions documentation, choose your preferred development language from the list of native Functions languages at the top of the article.

docs/content/en/docs/Concepts/azure-openai.md

@@ -0,0 +1,29 @@
+---
+title: Azure OpenAI
+date: 2023-10-06
+description: >
+  The core of the Generative AI solution. 
+categories: [Azure]
+tags: [docs, openai]
+weight: 1
+---
+
+Azure OpenAI Service provides REST API access to OpenAI's powerful language models including the GPT-4, GPT-35-Turbo, and Embeddings model series. In addition, the new GPT-4 and gpt-35-turbo model series have now reached general availability. 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 our web-based interface in the Azure OpenAI Studio.
+
+Important concepts about Azure OpenAI:
+
+* [Azure OpenAI Studio](oai.azure.com)
+* **Models available**
+  * GPT-35-Turbo series: typical "chatGPT" model, recommended for most of the Azure OpenAI projects. When we might need more capability, GPT4 can me considered (take into account it will imply more latency and cost) 
+  * GPT-4 series: they are the most advanced language models, available once you fill in the [GPT4 Request Form](https://customervoice.microsoft.com/Pages/ResponsePage.aspx?id=v4j5cvGGr0GRqy180BHbR7en2Ais5pxKtso_Pz4b1_xURjE4QlhVUERGQ1NXOTlNT0w1NldTWjJCMSQlQCN0PWcu) 
+  * Embeddings series: embeddings make it easier to do machine learning on large inputs representing words by capturing the semantic similarities in a vector space. Therefore, you can use embeddings to determine if two text chunks are semantically related or similar, and provide a score to assess similarity.
+
+  Take into account that not all models are available in all Azure Regions, for Regional availability check the documentation: [Model summary table and region availability](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability)
+
+* **Deployment**: once you instantiate a specific model, it will be available as deployment. You can create and delete deployments of available models as you wish. This is managed through the AOAI Studio. 
+* **Quotas**: the quotas available in Azure are allocated per model and per region, within a subscription. [Learn more about quotas](https://learn.microsoft.com/en-us/azure/ai-services/openai/quotas-limits). In the documentation you can find best practices to manage your quota.
+
+
+**Activate GenAI with Azure** uses Azure OpenAI Embeddings model to vectorize the content and ChatGPT model to conversate with that content. 
+
+More information at the official documentation: [What is Azure OpenAI](https://learn.microsoft.com/en-us/azure/ai-services/openai/overview)

docs/content/en/docs/Concepts/azure-storage.md

@@ -0,0 +1,24 @@
+---
+title: Azure Storage
+date: 2023-10-06
+description: >
+  Store the documents in Azure Blob Storage.s
+categories: [Azure]
+tags: [docs, storage]
+weight: 7
+---
+
+Azure Blob Storage is Microsoft's object storage solution for the cloud. Blob Storage is optimized for storing massive amounts of unstructured data. Unstructured data is data that doesn't adhere to a particular data model or definition, such as text or binary data.
+
+Blob Storage is designed for:
+
+* Serving images or documents directly to a browser.
+* Storing files for distributed access.
+* Streaming video and audio.
+* Writing to log files.
+* Storing data for backup and restore, disaster recovery, and archiving.
+* Storing data for analysis by an on-premises or Azure-hosted service.
+
+**Activate GenAI with Azure** uses Blob Storage to store the documents (PDFs) that will be then vectorized and indexed. 
+
+Learn more about Azure Blob Storage: [What is Azure Blob Storage?](https://learn.microsoft.com/en-us/azure/storage/blobs/storage-blobs-overview)

docs/content/en/docs/Concepts/semantic-kernel.md

@@ -0,0 +1,23 @@
+---
+title: Semantic Kernel
+date: 2023-10-06
+description: >
+  An open-source SDK that lets you easily combine AI services like OpenAI, Azure OpenAI, and Hugging Face with conventional programming languages like C# and Python.
+categories: [SDK]
+tags: [docs, tools]
+weight: 3
+---
+
+Semantic Kernel is an open-source SDK that lets you easily combine AI services like OpenAI, Azure OpenAI, and Hugging Face with conventional programming languages like C# and Python. By doing so, you can create AI apps that combine the best of both worlds.
+
+Microsoft powers its Copilot system with a stack of AI models and plugins. At the center of this stack is an AI orchestration layer that allows us to combine AI models and plugins together to create brand new experiences for users.
+
+![Image of Copilots](https://learn.microsoft.com/en-us/semantic-kernel/media/copilot-stack.png)
+
+To help developers build their own Copilot experiences on top of AI plugins, we have released Semantic Kernel, a lightweight open-source SDK that allows you to orchestrate AI plugins. With Semantic Kernel, you can leverage the same AI orchestration patterns that power Microsoft 365 Copilot and Bing in your own apps, while still leveraging your existing development skills and investments.
+
+![Image of Semantic Kernel](https://learn.microsoft.com/en-us/semantic-kernel/media/mind-and-body-of-semantic-kernel.png)
+
+**Activate GenAI with Azure** uses Semantic Kernel to orchestrate the prompts, Azure OpenAI calls and Azure Cognitive Search queries and results.
+
+Learn more at the official documentation of [Semantic Kernel](https://learn.microsoft.com/en-us/semantic-kernel/overview/)

docs/content/en/docs/Contribution guidelines/_index.md

@@ -0,0 +1,20 @@
+---
+title: Contribution Guidelines
+weight: 10
+description: How to contribute to the project
+---
+
+## Contributing
+
+This project welcomes contributions and suggestions. Most contributions require you to
+agree to a Contributor License Agreement (CLA) declaring that you have the right to,
+and actually do, grant us the rights to use your contribution. For details, visit
+https://cla.microsoft.com.
+
+When you submit a pull request, a CLA-bot will automatically determine whether you need
+to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the
+instructions provided by the bot. You will only need to do this once across all repositories using our CLA.
+
+This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
+For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/)
+or contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.