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* .NET: Hosted Agents - RAG Sample with Azure AI Search (#5693) Adds a Hosted-AzureSearchRag sample plus a live Foundry.Hosting integration test scenario backed by a real Azure AI Search index. Sample (Hosted-AzureSearchRag): keyword-only Azure AI Search via SearchClient adapter into TextSearchProvider, scope-aware DevTemporaryTokenCredential consuming AZURE_BEARER_TOKEN_FOUNDRY + AZURE_BEARER_TOKEN_SEARCH for local Docker, Dockerfile + contributor Dockerfile mirroring Hosted-TextRag. Integration test: AzureSearchRagHostedAgentFixture extends the PR #5598 HostedAgentFixture with the new azure-search-rag scenario branch in the shared test container; AzureSearchRagHostedAgentTests asserts the model returns canary tokens (TR-CANARY-7821, SHIP-CANARY-4493) that exist only in the seeded documents - real proof the agent grounded its answer in retrieved content rather than training data. * Address PR 5701 Copilot review feedback - Sample README: drop stale 'bootstraps the index on first run' line; index is pre-provisioned out of band - Sample + TestContainer search adapters: propagate CancellationToken to await foreach via .WithCancellation()
This commit is contained in:
Roger Barreto
+8
@@ -0,0 +1,8 @@
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AZURE_AI_PROJECT_ENDPOINT=<your-azure-ai-project-endpoint>
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AZURE_AI_MODEL_DEPLOYMENT_NAME=gpt-4o
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AZURE_SEARCH_ENDPOINT=<your-azure-search-endpoint>
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AZURE_SEARCH_INDEX_NAME=contoso-outdoors
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AZURE_BEARER_TOKEN_FOUNDRY=DefaultAzureCredential
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AZURE_BEARER_TOKEN_SEARCH=DefaultAzureCredential
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ASPNETCORE_URLS=http://+:8088
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ASPNETCORE_ENVIRONMENT=Development
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+17
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# Use the official .NET 10.0 ASP.NET runtime as a parent image
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FROM mcr.microsoft.com/dotnet/aspnet:10.0 AS base
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WORKDIR /app
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FROM mcr.microsoft.com/dotnet/sdk:10.0 AS build
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WORKDIR /src
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COPY . .
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RUN dotnet restore
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RUN dotnet publish -c Release -o /app/publish
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# Final stage
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FROM base AS final
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WORKDIR /app
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COPY --from=build /app/publish .
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EXPOSE 8088
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ENV ASPNETCORE_URLS=http://+:8088
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ENTRYPOINT ["dotnet", "HostedAzureSearchRag.dll"]
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+23
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# Dockerfile for contributors building from the agent-framework repository source.
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#
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# This project uses ProjectReference to the local Microsoft.Agents.AI.Foundry source,
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# which means a standard multi-stage Docker build cannot resolve dependencies outside
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# this folder. Instead, pre-publish the app targeting the container runtime and copy
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# the output into the container:
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#
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# dotnet publish -c Debug -f net10.0 -r linux-musl-x64 --self-contained false -o out
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# docker build -f Dockerfile.contributor -t hosted-azure-search-rag .
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# docker run --rm -p 8088:8088 \
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# -e AGENT_NAME=hosted-azure-search-rag \
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# -e AZURE_BEARER_TOKEN_FOUNDRY=$AZURE_BEARER_TOKEN_FOUNDRY \
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# -e AZURE_BEARER_TOKEN_SEARCH=$AZURE_BEARER_TOKEN_SEARCH \
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# --env-file .env hosted-azure-search-rag
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#
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# For end-users consuming the NuGet package (not ProjectReference), use the standard
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# Dockerfile which performs a full dotnet restore + publish inside the container.
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FROM mcr.microsoft.com/dotnet/aspnet:10.0-alpine AS final
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WORKDIR /app
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COPY out/ .
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EXPOSE 8088
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ENV ASPNETCORE_URLS=http://+:8088
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ENTRYPOINT ["dotnet", "HostedAzureSearchRag.dll"]
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+35
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<Project Sdk="Microsoft.NET.Sdk.Web">
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<PropertyGroup>
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<TargetFrameworks>net10.0</TargetFrameworks>
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<Nullable>enable</Nullable>
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<ImplicitUsings>enable</ImplicitUsings>
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<CentralPackageTransitivePinningEnabled>false</CentralPackageTransitivePinningEnabled>
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<RootNamespace>HostedAzureSearchRag</RootNamespace>
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<AssemblyName>HostedAzureSearchRag</AssemblyName>
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<NoWarn>$(NoWarn);</NoWarn>
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</PropertyGroup>
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<ItemGroup>
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<PackageReference Include="Azure.AI.Projects" VersionOverride="2.1.0-beta.1" />
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<PackageReference Include="Azure.Identity" />
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<PackageReference Include="Azure.Search.Documents" />
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<PackageReference Include="DotNetEnv" />
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</ItemGroup>
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<!-- For contributors: uses ProjectReference to build against local source -->
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<ItemGroup>
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<ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
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<ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Foundry.Hosting\Microsoft.Agents.AI.Foundry.Hosting.csproj" />
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<ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
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</ItemGroup>
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<!-- For end-users: uncomment the PackageReference below and remove the ProjectReferences above
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<ItemGroup>
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<PackageReference Include="Microsoft.Agents.AI.Foundry" Version="1.0.0" />
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<PackageReference Include="Microsoft.Agents.AI.Foundry.Hosting" Version="1.0.0" />
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<PackageReference Include="Microsoft.Agents.AI.OpenAI" Version="1.0.0" />
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</ItemGroup>
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-->
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</Project>
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+171
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// Copyright (c) Microsoft. All rights reserved.
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// This sample shows how to add Retrieval Augmented Generation (RAG) capabilities to a hosted
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// agent using Azure AI Search. The sample assumes the search index has already been provisioned
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// and populated out of band (see README.md for the required schema and example seed content).
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// A SearchClient-backed adapter is plugged into TextSearchProvider, which runs a keyword search
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// against the index before each model invocation and injects the matching documents into the
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// model context.
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using Azure;
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using Azure.AI.Projects;
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using Azure.Core;
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using Azure.Identity;
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using Azure.Search.Documents;
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using Azure.Search.Documents.Models;
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using DotNetEnv;
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using Microsoft.Agents.AI;
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using Microsoft.Agents.AI.Foundry.Hosting;
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using Microsoft.Extensions.AI;
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using OpenAI.Chat;
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// Load .env file if present (for local development)
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Env.TraversePath().Load();
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string projectEndpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT")
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?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
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string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o";
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string searchEndpoint = Environment.GetEnvironmentVariable("AZURE_SEARCH_ENDPOINT")
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?? throw new InvalidOperationException("AZURE_SEARCH_ENDPOINT is not set.");
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string searchIndexName = Environment.GetEnvironmentVariable("AZURE_SEARCH_INDEX_NAME")
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?? throw new InvalidOperationException("AZURE_SEARCH_INDEX_NAME is not set.");
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// Use a chained credential. Try a temporary dev token first (for local Docker debugging),
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// then fall back to DefaultAzureCredential (for local dev via dotnet run / managed identity in
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// production). The dev credential is scope aware so a single instance serves both Foundry and
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// Azure AI Search clients (each Azure SDK client requests a token for its own audience).
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TokenCredential credential = new ChainedTokenCredential(
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new DevTemporaryTokenCredential(),
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new DefaultAzureCredential());
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// Connect to the pre-provisioned search index. The caller is expected to have created the
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// index and populated it with documents matching the schema (id / content / sourceName /
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// sourceLink) before running this sample. See README.md for an example provisioning script.
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var searchClient = new SearchClient(new Uri(searchEndpoint), searchIndexName, credential);
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TextSearchProviderOptions textSearchOptions = new()
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{
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SearchTime = TextSearchProviderOptions.TextSearchBehavior.BeforeAIInvoke,
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RecentMessageMemoryLimit = 6,
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};
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AIAgent agent = new AIProjectClient(new Uri(projectEndpoint), credential)
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.AsAIAgent(new ChatClientAgentOptions
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{
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Name = Environment.GetEnvironmentVariable("AGENT_NAME") ?? "hosted-azure-search-rag",
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ChatOptions = new ChatOptions
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{
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ModelId = deploymentName,
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Instructions = "You are a helpful support specialist for Contoso Outdoors. " +
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"Answer questions using the provided context and cite the source document when available.",
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},
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AIContextProviders = [new TextSearchProvider(CreateSearchAdapter(searchClient), textSearchOptions)]
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});
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// Host the agent as a Foundry Hosted Agent using the Responses API.
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var builder = WebApplication.CreateBuilder(args);
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builder.Services.AddFoundryResponses(agent);
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var app = builder.Build();
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app.MapFoundryResponses();
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if (app.Environment.IsDevelopment())
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{
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app.MapFoundryResponses("openai/v1");
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}
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app.Run();
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// ── Search adapter ───────────────────────────────────────────────────────────
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// Wraps a SearchClient as the delegate TextSearchProvider expects. Keyword/full-text only;
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// no embeddings. Returns the top results and projects them into TextSearchResult entries
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// the provider will inject into the model context.
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static Func<string, CancellationToken, Task<IEnumerable<TextSearchProvider.TextSearchResult>>>
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CreateSearchAdapter(SearchClient client, int top = 3) =>
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async (query, cancellationToken) =>
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{
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var options = new SearchOptions { Size = top };
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Response<SearchResults<SearchDocument>> response =
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await client.SearchAsync<SearchDocument>(query, options, cancellationToken).ConfigureAwait(false);
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var results = new List<TextSearchProvider.TextSearchResult>();
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await foreach (SearchResult<SearchDocument> hit in response.Value.GetResultsAsync().WithCancellation(cancellationToken).ConfigureAwait(false))
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{
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results.Add(new TextSearchProvider.TextSearchResult
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{
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SourceName = hit.Document.TryGetValue("sourceName", out var name) ? name?.ToString() ?? string.Empty : string.Empty,
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SourceLink = hit.Document.TryGetValue("sourceLink", out var link) ? link?.ToString() ?? string.Empty : string.Empty,
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Text = hit.Document.TryGetValue("content", out var content) ? content?.ToString() ?? string.Empty : string.Empty,
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RawRepresentation = hit
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});
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}
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return results;
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};
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/// <summary>
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/// A scope aware <see cref="TokenCredential"/> for local Docker debugging only.
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/// Reads pre-fetched bearer tokens from environment variables, dispensing the right token
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/// based on the requested scope:
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/// <list type="bullet">
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/// <item><description><c>ai.azure.com</c> scopes -> <c>AZURE_BEARER_TOKEN_FOUNDRY</c></description></item>
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/// <item><description><c>search.azure.com</c> scopes -> <c>AZURE_BEARER_TOKEN_SEARCH</c></description></item>
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/// </list>
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/// For any other scope, throws <see cref="CredentialUnavailableException"/> so a chained
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/// credential will fall through. This should NOT be used in production: tokens expire (~1 hour)
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/// and cannot be refreshed.
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///
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/// Generate the tokens on your host and pass them to the container:
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/// <code>
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/// export AZURE_BEARER_TOKEN_FOUNDRY=$(az account get-access-token --resource https://ai.azure.com --query accessToken -o tsv)
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/// export AZURE_BEARER_TOKEN_SEARCH=$(az account get-access-token --resource https://search.azure.com --query accessToken -o tsv)
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/// docker run -e AZURE_BEARER_TOKEN_FOUNDRY -e AZURE_BEARER_TOKEN_SEARCH ...
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/// </code>
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/// </summary>
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internal sealed class DevTemporaryTokenCredential : TokenCredential
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{
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private const string FoundryEnvironmentVariable = "AZURE_BEARER_TOKEN_FOUNDRY";
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private const string SearchEnvironmentVariable = "AZURE_BEARER_TOKEN_SEARCH";
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public override AccessToken GetToken(TokenRequestContext requestContext, CancellationToken cancellationToken)
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=> Resolve(requestContext.Scopes);
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public override ValueTask<AccessToken> GetTokenAsync(TokenRequestContext requestContext, CancellationToken cancellationToken)
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=> new(Resolve(requestContext.Scopes));
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private static AccessToken Resolve(IReadOnlyList<string> scopes)
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{
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string? envVar = null;
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foreach (var scope in scopes)
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{
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if (scope.Contains("search.azure.com", StringComparison.OrdinalIgnoreCase))
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{
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envVar = SearchEnvironmentVariable;
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break;
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}
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if (scope.Contains("ai.azure.com", StringComparison.OrdinalIgnoreCase))
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{
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envVar = FoundryEnvironmentVariable;
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break;
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}
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}
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if (envVar is null)
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{
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throw new CredentialUnavailableException(
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$"DevTemporaryTokenCredential cannot serve scopes [{string.Join(", ", scopes)}]; falling through.");
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}
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var token = Environment.GetEnvironmentVariable(envVar);
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if (string.IsNullOrEmpty(token) || string.Equals(token, "DefaultAzureCredential", StringComparison.Ordinal))
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{
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throw new CredentialUnavailableException(
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$"{envVar} environment variable is not set; falling through to next credential.");
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}
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return new AccessToken(token, DateTimeOffset.UtcNow.AddHours(1));
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}
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}
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+179
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# Hosted-AzureSearchRag
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A hosted agent with **Retrieval Augmented Generation (RAG)** capabilities backed by **Azure AI Search**. The agent grounds its answers in product documentation by running a keyword search against an Azure AI Search index before each model invocation, then citing the source in its response.
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This sample is the Azure AI Search counterpart to `Hosted-TextRag`. Where `Hosted-TextRag` uses a mock in-process search function, this sample talks to a real Azure AI Search index that is provisioned out of band (see "Provisioning the search index" below).
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## Prerequisites
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- [.NET 10 SDK](https://dotnet.microsoft.com/download/dotnet/10.0)
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- An Azure AI Foundry project with a deployed model (e.g., `gpt-4o`)
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- An Azure AI Search service ([create one](https://learn.microsoft.com/azure/search/search-create-service-portal))
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- **A pre-provisioned search index** with the schema and content described in the next section
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- Azure CLI logged in (`az login`)
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### Required RBAC
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Your identity (or the Managed Identity running the container in production) needs:
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- **Azure AI User** on the Foundry project scope
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- **Search Index Data Reader** on the Azure AI Search service (the sample only reads from the index)
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## Provisioning the search index (one time)
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The sample assumes the search index already exists and contains documents the agent can retrieve from. Provision it once via the Azure Portal, the [REST API](https://learn.microsoft.com/azure/search/search-how-to-create-search-index), or the snippet below.
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### Index schema
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| Field | Type | Attributes |
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|---|---|---|
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| `id` | `Edm.String` | key, filterable |
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| `content` | `Edm.String` | searchable (full-text) |
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| `sourceName` | `Edm.String` | retrievable, filterable |
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| `sourceLink` | `Edm.String` | retrievable |
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### Example: provision and seed via Azure CLI + REST
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```bash
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SEARCH_ENDPOINT="https://<your-search>.search.windows.net"
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INDEX_NAME="contoso-outdoors"
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TOKEN=$(az account get-access-token --resource https://search.azure.com --query accessToken -o tsv)
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# 1. Create the index.
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curl -X PUT "$SEARCH_ENDPOINT/indexes/$INDEX_NAME?api-version=2024-07-01" \
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-H "Authorization: Bearer $TOKEN" -H "Content-Type: application/json" \
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-d '{
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"name": "contoso-outdoors",
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"fields": [
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{ "name": "id", "type": "Edm.String", "key": true, "filterable": true, "searchable": false, "retrievable": true },
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{ "name": "content", "type": "Edm.String", "key": false, "filterable": false, "searchable": true, "retrievable": true, "analyzer": "standard.lucene" },
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{ "name": "sourceName", "type": "Edm.String", "key": false, "filterable": true, "searchable": false, "retrievable": true },
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{ "name": "sourceLink", "type": "Edm.String", "key": false, "filterable": false, "searchable": false, "retrievable": true }
|
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]
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}'
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# 2. Upload three Contoso Outdoors documents matching the queries below.
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curl -X POST "$SEARCH_ENDPOINT/indexes/$INDEX_NAME/docs/index?api-version=2024-07-01" \
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-H "Authorization: Bearer $TOKEN" -H "Content-Type: application/json" \
|
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-d '{
|
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"value": [
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{ "@search.action": "mergeOrUpload", "id": "return-policy", "sourceName": "Contoso Outdoors Return Policy", "sourceLink": "https://contoso.com/policies/returns", "content": "Customers may return any item within 30 days of delivery. Items should be unused and include original packaging. Refunds are issued to the original payment method within 5 business days of inspection. As a thank you, every accepted return ships back with a complimentary Contoso TrailRunner sticker pack, item code TR-CANARY-7821." },
|
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{ "@search.action": "mergeOrUpload", "id": "shipping-guide", "sourceName": "Contoso Outdoors Shipping Guide", "sourceLink": "https://contoso.com/help/shipping", "content": "Standard shipping is free on orders over $50 and typically arrives in 3-5 business days within the continental United States. Expedited options are available at checkout. Use promo code SHIP-CANARY-4493 at checkout for a one-time free overnight upgrade on your first order." },
|
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{ "@search.action": "mergeOrUpload", "id": "tent-care", "sourceName": "TrailRunner Tent Care Instructions", "sourceLink": "https://contoso.com/manuals/trailrunner-tent", "content": "Clean the tent fabric with lukewarm water and a non-detergent soap. Allow it to air dry completely before storage and avoid prolonged UV exposure to extend the lifespan of the waterproof coating. Replacement waterproofing kits are stocked under SKU TENT-CANARY-9067." }
|
||||
]
|
||||
}'
|
||||
```
|
||||
|
||||
You can also point the sample at any existing index that exposes the four fields above; the sample reads `content`, `sourceName`, and `sourceLink` as projected by the search results.
|
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|
||||
## Configuration
|
||||
|
||||
Copy the template and fill in your endpoints:
|
||||
|
||||
```bash
|
||||
cp .env.example .env
|
||||
```
|
||||
|
||||
Edit `.env`:
|
||||
|
||||
```env
|
||||
AZURE_AI_PROJECT_ENDPOINT=https://<your-account>.services.ai.azure.com/api/projects/<your-project>
|
||||
AZURE_AI_MODEL_DEPLOYMENT_NAME=gpt-4o
|
||||
AZURE_SEARCH_ENDPOINT=https://<your-search>.search.windows.net
|
||||
AZURE_SEARCH_INDEX_NAME=contoso-outdoors
|
||||
AZURE_BEARER_TOKEN_FOUNDRY=DefaultAzureCredential
|
||||
AZURE_BEARER_TOKEN_SEARCH=DefaultAzureCredential
|
||||
ASPNETCORE_URLS=http://+:8088
|
||||
ASPNETCORE_ENVIRONMENT=Development
|
||||
```
|
||||
|
||||
> **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.
|
||||
|
||||
```bash
|
||||
cd dotnet/samples/04-hosting/FoundryHostedAgents/responses/Hosted-AzureSearchRag
|
||||
AGENT_NAME=hosted-azure-search-rag dotnet run
|
||||
```
|
||||
|
||||
The agent will start on `http://localhost:8088`. The sample assumes the search index has already been provisioned and seeded (see "Provisioning the search index" above).
|
||||
|
||||
### Test it
|
||||
|
||||
Using the Azure Developer CLI:
|
||||
|
||||
```bash
|
||||
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:
|
||||
|
||||
```bash
|
||||
curl -X POST http://localhost:8088/responses \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{"input": "What is your return policy?", "model": "hosted-azure-search-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)
|
||||
|
||||
```bash
|
||||
dotnet publish -c Debug -f net10.0 -r linux-musl-x64 --self-contained false -o out
|
||||
```
|
||||
|
||||
### 2. Build the Docker image
|
||||
|
||||
```bash
|
||||
docker build -f Dockerfile.contributor -t hosted-azure-search-rag .
|
||||
```
|
||||
|
||||
### 3. Run the container
|
||||
|
||||
Generate two bearer tokens on your host (one per audience) and pass them to the container. A single Azure AD token has only one `aud` claim, so Foundry and Azure AI Search require separate tokens.
|
||||
|
||||
```bash
|
||||
# Generate tokens (each expires in ~1 hour)
|
||||
export AZURE_BEARER_TOKEN_FOUNDRY=$(az account get-access-token --resource https://ai.azure.com --query accessToken -o tsv)
|
||||
export AZURE_BEARER_TOKEN_SEARCH=$(az account get-access-token --resource https://search.azure.com --query accessToken -o tsv)
|
||||
|
||||
# Run with both tokens
|
||||
docker run --rm -p 8088:8088 \
|
||||
-e AGENT_NAME=hosted-azure-search-rag \
|
||||
-e AZURE_BEARER_TOKEN_FOUNDRY=$AZURE_BEARER_TOKEN_FOUNDRY \
|
||||
-e AZURE_BEARER_TOKEN_SEARCH=$AZURE_BEARER_TOKEN_SEARCH \
|
||||
--env-file .env \
|
||||
hosted-azure-search-rag
|
||||
```
|
||||
|
||||
### 4. Test it
|
||||
|
||||
Using the Azure Developer CLI:
|
||||
|
||||
```bash
|
||||
azd ai agent invoke --local "What is your return policy?"
|
||||
```
|
||||
|
||||
## How RAG works in this sample
|
||||
|
||||
The `TextSearchProvider` runs a keyword search against the configured Azure AI Search index **before each model invocation**. When the index is seeded with the three Contoso Outdoors documents from the provisioning section above:
|
||||
|
||||
| User query mentions | Search result injected |
|
||||
|---|---|
|
||||
| "return", "refund" | Contoso Outdoors Return Policy (canary token: `TR-CANARY-7821`) |
|
||||
| "shipping", "promo" | Contoso Outdoors Shipping Guide (canary token: `SHIP-CANARY-4493`) |
|
||||
| "tent", "fabric" | TrailRunner Tent Care Instructions (canary token: `TENT-CANARY-9067`) |
|
||||
|
||||
The model receives the top three search results as additional context and cites the source in its response. Each seeded document includes a unique `*-CANARY-*` token that does not exist in any model training data, so the integration tests can prove an answer was grounded in retrieved content (not fabricated from training) by asking for the canary and asserting it appears in the response.
|
||||
|
||||
Replace the seed documents (or point the sample at an existing index with your own content) to ground the agent in your own knowledge base.
|
||||
|
||||
## 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 `HostedAzureSearchRag.csproj` for the `PackageReference` alternative.
|
||||
+31
@@ -0,0 +1,31 @@
|
||||
# yaml-language-server: $schema=https://raw.githubusercontent.com/microsoft/AgentSchema/refs/heads/main/schemas/v1.0/AgentManifest.yaml
|
||||
name: hosted-azure-search-rag
|
||||
displayName: "Hosted Azure AI Search RAG Agent"
|
||||
|
||||
description: >
|
||||
A support specialist agent for Contoso Outdoors with RAG capabilities backed by
|
||||
Azure AI Search. Uses TextSearchProvider with a SearchClient adapter to ground
|
||||
answers in product documentation indexed in Azure AI Search before each model
|
||||
invocation.
|
||||
|
||||
metadata:
|
||||
tags:
|
||||
- AI Agent Hosting
|
||||
- Azure AI AgentServer
|
||||
- Responses Protocol
|
||||
- RAG
|
||||
- Azure AI Search
|
||||
- Agent Framework
|
||||
|
||||
template:
|
||||
name: hosted-azure-search-rag
|
||||
kind: hosted
|
||||
protocols:
|
||||
- protocol: responses
|
||||
version: 1.0.0
|
||||
resources:
|
||||
cpu: "0.25"
|
||||
memory: 0.5Gi
|
||||
parameters:
|
||||
properties: []
|
||||
resources: []
|
||||
+9
@@ -0,0 +1,9 @@
|
||||
# yaml-language-server: $schema=https://raw.githubusercontent.com/microsoft/AgentSchema/refs/heads/main/schemas/v1.0/ContainerAgent.yaml
|
||||
kind: hosted
|
||||
name: hosted-azure-search-rag
|
||||
protocols:
|
||||
- protocol: responses
|
||||
version: 1.0.0
|
||||
resources:
|
||||
cpu: "0.25"
|
||||
memory: 0.5Gi
|
||||
Reference in New Issue
Block a user