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Merge pull request #114 from HeidiSteen/main
Removed static web app and replaced with Python components
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Quickstart-RAG/Quickstart-rag.ipynb

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"We took a few shortcuts to keep the exercise basic and focused on query definitions:\n",
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"- We use the hotels-sample-index, which can be created in minutes and runs on any search service tier. This index is created by a wizard using built-in sample data.\n",
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"\n",
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"- We omit vectors so that we can skip chunking and embedding.\n",
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"Once you understand the fundamentals of integrating queries from Azure AI Search to an LLM, you can build on that experience by adding vector fields and vector and hybrid queries. We recommend the [phi-chat Python code example](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/phi-chat/phi-chat.ipynb) for that step.\n",

README.md

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| Sample | Description |
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|--------|-------------|
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| [azure-function-search](azure-function-search/readme.md) | This sample is an Azure Function that sends query requests to an Azure AI Search service. You can substitute this code to replace the contents of the `api` folder in the C# sample [azure-search-static-web-app](https://github.com/Azure-Samples/azure-search-static-web-app). |
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| [bulk-insert](bulk-insert/readme.md) | This sample shows you how to create and load an index using the push APIs and sample data. You can substitute this code to replace the contents of the `bulk-insert` folder in the C# sample [azure-search-static-web-app](https://github.com/Azure-Samples/azure-search-static-web-app) |
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| quickstart | "Day One" introduction to the fundamental tasks of working with a search index: create, load, and query. This sample is a notebook .ipynb file. The index is modeled on a subset of the Hotels dataset, widely used in Azure AI Search samples, but reduced here for readability and comprehension. |
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| quickstart-semantic-search | Extends the quickstart through modifications that invoke semantic search. This notebook adds a semantic configuration to the index and semantic query options that formulate the query and response. |
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| quickstart-rag | "Day One" introduction to LLM integration with a chat model such as GPT-3.5-turbo or equivalent. |
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| search-website-functions-v4 | Shows how to create, load, and query a search index in Python using the Azure.Search.Documents library in the Azure SDK for Python. It also includes application code and sample data so that you can see search integration in the context of a full app. The data is from [https://github.com/zygmuntz/goodbooks-10k](https://github.com/zygmuntz/goodbooks-10k). The app is an Azure Static Web app, using the React library for user interaction, and Azure Function to handle the query requests and responses in the application layer. |
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## Archived samples
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search-website-functions-v4/api/.gitignore renamed to azure-function-search/.gitignore

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# Azurite artifacts
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azure-function-search/readme.md

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# Create an Azure function that specifies queries
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This folder contains source code for an Azure function that formulates queries, document look up, and suggestions for typeahead queries. It's the Python version of the `api` content used in the [C# sample Add search to websites](https://learn.microsoft.com/azure/search/tutorial-csharp-overview). If you're a Python developer, you can subtitute this code to create a Python version of the sample app.

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