https://github.com/Azure-Samples/rag-with-azure-ai-search-notebooks
Jupyter notebooks that demonstrate vector search, hybrid search, image search, RAG, and evaluation, all with Azure AI Search.
https://github.com/Azure-Samples/rag-with-azure-ai-search-notebooks
Last synced: 11 days ago
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Jupyter notebooks that demonstrate vector search, hybrid search, image search, RAG, and evaluation, all with Azure AI Search.
- Host: GitHub
- URL: https://github.com/Azure-Samples/rag-with-azure-ai-search-notebooks
- Owner: Azure-Samples
- License: mit
- Created: 2024-09-05T16:38:20.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-06T23:50:05.000Z (11 months ago)
- Last Synced: 2024-12-07T00:32:47.663Z (11 months ago)
- Language: Jupyter Notebook
- Size: 10.5 MB
- Stars: 17
- Watchers: 13
- Forks: 8
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.md
- Code of conduct: .github/CODE_OF_CONDUCT.md
Awesome Lists containing this project
- awesome-azure-openai-copilot - Azure AI Search Demos using Jupyter Notebook - ✨Jupyter notebooks that demonstrate vector search, hybrid search, image search, RAG, and evaluation. (Azure AI Search)
README
# Azure AI Search Demos
This repository contains many notebooks that explain how Azure AI Search works, including several showcasing how vector search works.
## Environment setup
1. Run `azd up` on [azure-search-openai-demo](https://github.com/Azure-Samples/azure-search-openai-demo/) with GPT vision feature enabled. This will create the necessary resources for the Azure OpenAI, Azure AI Search services, and the Azure AI Vision service.
2. Create a .env with these variables, with the values taken from `.azure/ENV-NAME/.env` in the azure-search-openai-demo repository.
```shell
AZURE_OPENAI_SERVICE=YOUR-SERVICE-NAME
AZURE_OPENAI_DEPLOYMENT_NAME=YOUR-OPENAI-DEPLOYMENT-NAME
AZURE_OPENAI_ADA_DEPLOYMENT=YOUR-EMBED-DEPLOYMENT-NAME
AZURE_SEARCH_SERVICE=YOUR-SEARCH-SERVICE-NAME
AZURE_COMPUTERVISION_SERVICE=YOUR-COMPUTERVISION-SERVICE-NAME
AZURE_TENANT_ID=YOUR-TENANT-ID
```
3. Login to your Azure account using the Azure CLI. Specify `--tenant-id` if you deployed that repo to a non-default tenant.
```shell
azd auth login
```
4. Create a Python virtual environment or open the project in a container.
5. Install the requirements:
```shell
pip install -r requirements.txt
```
## Notebooks
These are the available notebooks, in suggested order:
* [Vector Embeddings Notebook](./vector_embeddings.ipynb)
* [Azure AI Search Notebook](./azure_ai_search.ipynb)
* [Image Search Notebook](./image_search.ipynb)
* [Azure AI Search Relevance Notebook](./search_relevance.ipynb)
* [RAG with Azure AI Search](./rag.ipynb)
* [RAG Evaluation](./rag_eval.ipynb)
You can find video recordings going through the notebooks [here](https://github.com/microsoft/aitour-rag-with-ai-search/tree/main/session-delivery-resources#video-recordings).