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:
- Add sample data to an Azure database product
- Create embeddings from the sample data using an Azure OpenAI Embeddings model
- Link the Azure database product to Azure Cognitive Search (for databases without native vector indexing)
- Create a vector index on the embeddings
- Perform vector similarity search
- Perform question answering over the sample data using an Azure OpenAI Completions model
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 | ACS | Sample in this repo (Python) |
| CosmosDB - MongoDB | ACS | Sample in this repo (C#, Python) |
| CosmosDB - NoSQL | ACS | Sample in this repo (C#, Python), Repo |
| AzureSQL | ACS | Sample in this repo (Python) |
| Fabric OneLake | ACS | Fabric Notebook |
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