Samples for Retrieval-Augmented LLMs with Azure Data

This repo contains code samples and links to help you get started with retrieval augmentation generation (RAG) on Azure. The samples follow a RAG pattern that include the following steps:

1. Add sample data to an Azure database product
2. Create embeddings from the sample data using an Azure OpenAI Embeddings model
3. Link the Azure database product to Azure Cognitive Search (for databases without native vector indexing)
4. Create a vector index on the embeddings
5. Perform vector similarity search
6. Perform question answering over the sample data using an Azure OpenAI Completions model

Resources and Coverage

Table below provides a high level guidance. Please follow the links to the relevant resources.

| Azure data product |Native vector indexing OR Azure Cognitive Search (ACS) | Guidance: repo, blog or docs| |----------|----------|---------------------------------------------| | Azure Database for PostgreSQL | Native | Sample in this repo (Python)| | CosmosDB - MongoDB vCore | Native | Docs, Blog, Repo, Sample in this repo (C#, Python) | | Azure Cache for Redis | Native | Sample in this repo (Python)| | CosmosDB - PostgreSQL | Native | Sample in this repo (Python) | | CosmosDB - MongoDB | Native | Sample in this repo (C#, Python) | | CosmosDB - NoSQL | Native | Sample in this repo (C#, Python, Java) | | AzureSQL | ACS | Sample in this repo (Python) | | | Fabric OneLake | ACS | Fabric Notebook |

Responsible AI

Microsoft is committed to the advancement of AI driven by ethical principles. - Learn more about responsible use of Azure OpenAI and LLMs here. - Learn more about responsible AI at Microsoft here.

Maintainer

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• Improving this README.MD file to provide a great experience
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• Understanding the security reporting process in SECURITY.MD
• Remove this section from the README

Contributing

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Trademarks

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