Build a Star Wars Copilot in C# - Lesson 6: RAG from a Database

By lesson 6, we already have an MCP server and client working.

Now we add a classic enterprise use case: retrieval from structured business data.

Lessons in this series

Before you start (self-setup)

If you’re following along on your own, complete lesson 0 and lesson 5 first.

This lesson introduces a new Azure dependency: Storage Tables.

Self-setup: Azure Table Storage + seed data

1. Create a Storage account in Azure.
2. In that account, create three tables:
   • Figurines
   • Orders
   • OrderFigurines
3. Get a connection string and save it:

dotnet user-secrets set "AzureStorage:ConnectionString" "<your-storage-connection-string>"

1. Seed the data.
   • The workshop repo includes a dataloader project in 6-rag/dataloader you can run to populate sample data.
   • If you prefer, you can insert rows manually with Storage Explorer.

Quick verification: confirm order 66 exists before testing StarWarsPurchaseTool.

What this lesson adds

A new MCP tool (StarWarsPurchaseTool) that queries Azure Table Storage to retrieve figurine order data.

The model can then answer questions like:

• what was in order 66?
• what did Ben Smith purchase?
• show orders for a specific character

RAG is broader than document search

A lot of people hear “RAG” and think vector DB + embeddings.

This lesson is a good reminder that RAG simply means augmenting generation with retrieved data, and that retrieval source can be:

• relational/NoSQL tables
• APIs
• docs
• search systems

Here it’s plain table queries plus deterministic filtering logic.

Data shape and tool design

The workshop uses three tables:

• Figurines
• Orders
• OrderFigurines

The tool accepts optional filters:

• orderNumber
• characterName
• customerName

Then combines data into a JSON payload the model can reason over.

This is exactly the pattern I like for production tools: perform strict filtering in code, let the model focus on explanation and narrative.

Practical implementation notes

The lesson adds:

• Azure.Data.Tables package
• helper functions for query composition
• explicit error responses for bad inputs / no matches

The case-sensitive lookup note in the workshop is also an important real-world reminder: retrieval quality starts with query normalization rules.

Suggested banner prompt

A high-detail sci-fi data-vault scene with holographic tables of orders and figurines floating above a console while an AI assistant correlates records into a clear response stream. Cool cyan and purple palette, cinematic lighting, no text, no logos.

Follow along

Workshop source for this lesson: Lesson 6 README.

Next up: multi-modal AI with image generation tools.

Note: Original workshop repository: jimbobbennett/StarWarsCopilot.