agent-framework/dotnet/samples/04-hosting/FoundryHostedAgents/responses/Hosted-TextRag

Hosted-TextRag

A hosted agent with Retrieval Augmented Generation (RAG) capabilities using TextSearchProvider. The agent grounds its answers in product documentation by running a search before each model invocation, then citing the source in its response.

This sample demonstrates how to add knowledge grounding to a hosted agent without requiring an external search index — using a mock search function that can be replaced with Azure AI Search or any other provider.

Prerequisites

• .NET 10 SDK
• An Azure AI Foundry project with a deployed model (e.g., gpt-4o)
• Azure CLI logged in (az login)

Configuration

Copy the template and fill in your project endpoint:

cp .env.example .env

Edit .env and set your Azure AI Foundry project endpoint:

AZURE_AI_PROJECT_ENDPOINT=https://<your-account>.services.ai.azure.com/api/projects/<your-project>
ASPNETCORE_URLS=http://+:8088
ASPNETCORE_ENVIRONMENT=Development
AZURE_AI_MODEL_DEPLOYMENT_NAME=gpt-4o
AZURE_BEARER_TOKEN=

Note: .env is gitignored. The .env.example template is checked in as a reference.

Running directly (contributors)

This project uses ProjectReference to build against the local Agent Framework source.

cd dotnet/samples/04-hosting/FoundryHostedAgents/responses/Hosted-TextRag
AGENT_NAME=hosted-text-rag dotnet run

The agent will start on http://localhost:8088.

Test it

Using the Azure Developer CLI:

azd ai agent invoke --local "What is your return policy?"
azd ai agent invoke --local "How long does shipping take?"
azd ai agent invoke --local "How do I clean my tent?"

Or with curl:

curl -X POST http://localhost:8088/responses \
  -H "Content-Type: application/json" \
  -d '{"input": "What is your return policy?", "model": "hosted-text-rag"}'

Running with Docker

Since this project uses ProjectReference, use Dockerfile.contributor which takes a pre-published output.

1. Publish for the container runtime (Linux Alpine)

dotnet publish -c Debug -f net10.0 -r linux-musl-x64 --self-contained false -o out

2. Build the Docker image

docker build -f Dockerfile.contributor -t hosted-text-rag .

3. Run the container

Generate a bearer token on your host and pass it to the container:

# Generate token (expires in ~1 hour)
export AZURE_BEARER_TOKEN=$(az account get-access-token --resource https://ai.azure.com --query accessToken -o tsv)

# Run with token
docker run --rm -p 8088:8088 \
  -e AGENT_NAME=hosted-text-rag \
  -e AZURE_BEARER_TOKEN=$AZURE_BEARER_TOKEN \
  --env-file .env \
  hosted-text-rag

4. Test it

Using the Azure Developer CLI:

azd ai agent invoke --local "What is your return policy?"

How RAG works in this sample

The TextSearchProvider runs a mock search before each model invocation:

User query contains	Search result injected
"return" or "refund"	Contoso Outdoors Return Policy
"shipping"	Contoso Outdoors Shipping Guide
"tent" or "fabric"	TrailRunner Tent Care Instructions

The model receives the search results as additional context and cites the source in its response. In production, replace MockSearchAsync with a call to Azure AI Search or your preferred search provider.

NuGet package users

If you are consuming the Agent Framework as a NuGet package (not building from source), use the standard Dockerfile instead of Dockerfile.contributor. See the commented section in HostedTextRag.csproj for the PackageReference alternative.