Merge branch 'v1.15' into endgame_1.15-updates

Author: hhunter-ms

daprdocs/config.toml

@@ -109,7 +109,7 @@ id = "G-60C6Q1ETC1"
     lang = "en"
   [[module.mounts]]
     source = "../sdkdocs/rust/daprdocs/content/en/rust-sdk-contributing"
-    target = "content/contributing/sdks-contrib"
+    target = "content/contributing/sdk-contrib/"
     lang = "en"
 
   [[module.mounts]]
@@ -143,7 +143,11 @@ id = "G-60C6Q1ETC1"
   [[module.mounts]]
     source = "../translations/docs-zh/translated_content/zh_CN/sdks_js"
     target = "content/developing-applications/sdks/js"
-    lang = "zh-hans"       
+    lang = "zh-hans"
+  [[module.mounts]]
+    source = "../translations/docs-zh/translated_content/zh_CN/sdks_rust"
+    target = "content/developing-applications/sdks/rust"
+    lang = "zh-hans"
   [[module.mounts]]
     source = "../translations/docs-zh/translated_content/zh_CN/pluggable-components/dotnet"
     target = "content/developing-applications/develop-components/pluggable-components/pluggable-components-sdks/pluggable-components-dotnet"

daprdocs/content/en/concepts/overview.md

@@ -76,7 +76,7 @@ Dapr exposes its HTTP and gRPC APIs as a sidecar architecture, either as a conta
 ## Hosting environments
 
 Dapr can be hosted in multiple environments, including:
-- Self-hosted on a Windows/Linux/macOS machine for local development 
+- Self-hosted on a Windows/Linux/macOS machine for local development and in production
 - On Kubernetes or clusters of physical or virtual machines in production
 
 ### Self-hosted local development

daprdocs/content/en/developing-applications/building-blocks/actors/actors-features-concepts.md

@@ -57,7 +57,7 @@ This simplifies some choices, but also carries some consideration:
 
 ## Actor communication
 
-You can interact with Dapr to invoke the actor method by calling HTTP/gRPC endpoint.
+You can interact with Dapr to invoke the actor method by calling the HTTP endpoint.
 
 ```bash
 POST/GET/PUT/DELETE http://localhost:3500/v1.0/actors/<actorType>/<actorId>/<method/state/timers/reminders>

daprdocs/content/en/developing-applications/building-blocks/actors/actors-timers-reminders.md

@@ -137,6 +137,10 @@ You can remove the actor reminder by calling
 DELETE http://localhost:3500/v1.0/actors/<actorType>/<actorId>/reminders/<name>
 ```
 
+If an actor reminder is triggered and the app does not return a 2** code to the runtime (for example, because of a connection issue),
+actor reminders will be retried up to three times with a backoff interval of one second between each attempt. There may be 
+additional retries attempted in accordance with any optionally applied [actor resiliency policy]({{< ref "override-default-retries.md" >}}). 
+
 Refer [api spec]({{< ref "actors_api.md#invoke-reminder" >}}) for more details.
 
 ## Error handling

daprdocs/content/en/developing-applications/building-blocks/conversation/conversation-overview.md

@@ -10,18 +10,33 @@ description: "Overview of the conversation API building block"
 The conversation API is currently in [alpha]({{< ref "certification-lifecycle.md#certification-levels" >}}).
 {{% /alert %}}
 
+Dapr's conversation API reduces the complexity of securely and reliably interacting with Large Language Models (LLM) at scale. Whether you're a developer who doesn't have the necessary native SDKs or a polyglot shop who just wants to focus on the prompt aspects of LLM interactions, the conversation API provides one consistent API entry point to talk to underlying LLM providers. 
 
-Using the Dapr conversation API, you can reduce the complexity of interacting with Large Language Models (LLMs) and enable critical performance and security functionality with features like prompt caching and personally identifiable information (PII) data obfuscation.
+<img src="/images/conversation-overview.png" width=800 alt="Diagram showing the flow of a user's app communicating with Dapr's LLM components.">
+
+In additon to enabling critical performance and security functionality (like [prompt caching]({{< ref "#prompt-caching" >}}) and [PII scrubbing]({{< ref "#personally-identifiable-information-pii-obfuscation" >}})), you can also pair the conversation API with Dapr functionalities, like:
+- Resiliency circuit breakers and retries to circumvent limit and token errors, or 
+- Middleware to authenticate requests coming to and from the LLM
+
+Dapr provides observability by issuing metrics for your LLM interactions.
 
 ## Features
 
+The following features are out-of-the-box for [all the supported conversation components]({{< ref supported-conversation >}}).
+
 ### Prompt caching
 
-To significantly reduce latency and cost, frequent prompts are stored in a cache to be reused, instead of reprocessing the information for every new request. Prompt caching optimizes performance by storing and reusing prompts that are often repeated across multiple API calls.
+Prompt caching optimizes performance by storing and reusing prompts that are often repeated across multiple API calls. To significantly reduce latency and cost, Dapr stores frequent prompts in a local cache to be reused by your cluster, pod, or other, instead of reprocessing the information for every new request. 
 
 ### Personally identifiable information (PII) obfuscation
 
-The PII obfuscation feature identifies and removes any PII from a conversation response. This feature protects your privacy by eliminating sensitive details like names, addresses, phone numbers, or other details that could be used to identify an individual.
+The PII obfuscation feature identifies and removes any form of sensitve user information from a conversation response. Simply enable PII obfuscation on input and output data to protect your privacy and scrub sensitive details that could be used to identify an individual. 
+
+## Demo
+
+Watch the demo presented during [Diagrid's Dapr v1.15 celebration](https://www.diagrid.io/videos/dapr-1-15-deep-dive) to see how the conversation API works using the .NET SDK.
+
+<iframe width="560" height="315" src="https://www.youtube-nocookie.com/embed/NTnwoDhHIcQ?si=37SDcOHtEpgCIwkG&amp;start=5444" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
 
 ## Try out conversation
 
@@ -31,7 +46,7 @@ Want to put the Dapr conversation API to the test? Walk through the following qu
 
 | Quickstart/tutorial | Description |
 | ------------------- | ----------- |
-| [Conversation quickstart](todo) | . |
+| [Conversation quickstart](todo) | TODO |
 
 ### Start using the conversation API directly in your app
 
@@ -40,4 +55,4 @@ Want to skip the quickstarts? Not a problem. You can try out the conversation bu
 ## Next steps
 
 - [How-To: Converse with an LLM using the conversation API]({{< ref howto-conversation-layer.md >}})
-- [Conversation API components]({{< ref supported-conversation >}})
+- [Conversation API components]({{< ref supported-conversation >}})

daprdocs/content/en/developing-applications/building-blocks/conversation/howto-conversation-layer.md

@@ -92,7 +92,7 @@ func main() {
 	}
 
 	input := dapr.ConversationInput{
-		Message: "hello world",
+		Message: "Please write a witty haiku about the Dapr distributed programming framework at dapr.io",
 		// Role:     nil, // Optional
 		// ScrubPII: nil, // Optional
 	}
@@ -134,7 +134,7 @@ async fn main() -> Result<(), Box<dyn std::error::Error>> {
 
     let mut client = DaprClient::connect(address).await?;
 
-    let input = ConversationInputBuilder::new("hello world").build();
+    let input = ConversationInputBuilder::new("Please write a witty haiku about the Dapr distributed programming framework at dapr.io").build();
 
     let conversation_component = "echo";
 
@@ -179,7 +179,7 @@ dapr run --app-id conversation --dapr-grpc-port 50001 --log-level debug --resour
 **Expected output**
 
 ```
-  - '== APP == conversation output: hello world'
+  - '== APP == conversation output: Please write a witty haiku about the Dapr distributed programming framework at dapr.io'
 ```
 
 {{% /codetab %}}

daprdocs/content/en/developing-applications/building-blocks/jobs/howto-schedule-and-handle-triggered-jobs.md

@@ -2,7 +2,7 @@
 type: docs
 title: "How-To: Schedule and handle triggered jobs"
 linkTitle: "How-To: Schedule and handle triggered jobs"
-weight: 2000
+weight: 5000
 description: "Learn how to use the jobs API to schedule and handle triggered jobs"
 ---
 
@@ -20,7 +20,103 @@ When you [run `dapr init` in either self-hosted mode or on Kubernetes]({{< ref i
 
 In your code, set up and schedule jobs within your application.
 
-{{< tabs "Go" >}}
+{{< tabs ".NET" "Go" >}}
+
+{{% codetab %}}
+
+<!-- .NET -->
+
+The following .NET SDK code sample schedules the job named `prod-db-backup`. The job data contains information
+about the database that you'll be seeking to backup regularly. Over the course of this example, you'll:
+- Define types used in the rest of the example
+- Register an endpoint during application startup that handles all job trigger invocations on the service
+- Register the job with Dapr
+
+In the following example, you'll create records that you'll serialize and register alongside the job so the information 
+is available when the job is triggered in the future:
+- The name of the backup task (`db-backup`)
+- The backup task's `Metadata`, including:
+  - The database name (`DBName`)
+  - The database location (`BackupLocation`)
+
+Create an ASP.NET Core project and add the latest version of `Dapr.Jobs` from NuGet. 
+
+> **Note:** While it's not strictly necessary
+for your project to use the `Microsoft.NET.Sdk.Web` SDK to create jobs, as of the time this documentation is authored,
+only the service that schedules a job receives trigger invocations for it. As those invocations expect an endpoint
+that can handle the job trigger and requires the `Microsoft.NET.Sdk.Web` SDK, it's recommended that you
+use an ASP.NET Core project for this purpose.
+
+Start by defining types to persist our backup job data and apply our own JSON property name attributes to the properties 
+so they're consistent with other language examples.
+
+```cs
+//Define the types that we'll represent the job data with
+internal sealed record BackupJobData([property: JsonPropertyName("task")] string Task, [property: JsonPropertyName("metadata")] BackupMetadata Metadata);
+internal sealed record BackupMetadata([property: JsonPropertyName("DBName")]string DatabaseName, [property: JsonPropertyName("BackupLocation")] string BackupLocation);
+```
+
+Next, set up a handler as part of your application setup that will be called anytime a job is triggered on your
+application. It's the responsibility of this handler to identify how jobs should be processed based on the job name provided.
+
+This works by registering a handler with ASP.NET Core at `/job/<job-name>`, where `<job-name>` is parameterized and 
+passed into this handler delegate, meeting Dapr's expectation that an endpoint is available to handle triggered named jobs.
+
+Populate your `Program.cs` file with the following: 
+
+```cs
+using System.Text;
+using System.Text.Json;
+using Dapr.Jobs;
+using Dapr.Jobs.Extensions;
+using Dapr.Jobs.Models;
+using Dapr.Jobs.Models.Responses;
+
+var builder = WebApplication.CreateBuilder(args);
+builder.Services.AddDaprJobsClient();
+var app = builder.Build();
+
+//Registers an endpoint to receive and process triggered jobs
+var cancellationTokenSource = new CancellationTokenSource(TimeSpan.FromSeconds(5));
+app.MapDaprScheduledJobHandler((string jobName, DaprJobDetails jobDetails, ILogger logger, CancellationToken cancellationToken) => {
+  logger?.LogInformation("Received trigger invocation for job '{jobName}'", jobName);
+  switch (jobName)
+  {
+    case "prod-db-backup":
+      // Deserialize the job payload metadata
+      var jobData = JsonSerializer.Deserialize<BackupJobData>(jobDetails.Payload);
+      
+      // Process the backup operation - we assume this is implemented elsewhere in your code
+      await BackupDatabaseAsync(jobData, cancellationToken);
+      break;
+  }
+}, cancellationTokenSource.Token);
+
+await app.RunAsync();
+```
+
+Finally, the job itself needs to be registered with Dapr so it can be triggered at a later point in time. You can do this
+by injecting a `DaprJobsClient` into a class and executing as part of an inbound operation to your application, but for
+this example's purposes, it'll go at the bottom of the `Program.cs` file you started above. Because you'll be using the
+`DaprJobsClient` you registered with dependency injection, start by creating a scope so you can access it.
+
+```cs
+//Create a scope so we can access the registered DaprJobsClient
+await using scope = app.Services.CreateAsyncScope();
+var daprJobsClient = scope.ServiceProvider.GetRequiredService<DaprJobsClient>();
+
+//Create the payload we wish to present alongside our future job triggers
+var jobData = new BackupJobData("db-backup", new BackupMetadata("my-prod-db", "/backup-dir")); 
+
+//Serialize our payload to UTF-8 bytes
+var serializedJobData = JsonSerializer.SerializeToUtf8Bytes(jobData);
+
+//Schedule our backup job to run every minute, but only repeat 10 times
+await daprJobsClient.ScheduleJobAsync("prod-db-backup", DaprJobSchedule.FromDuration(TimeSpan.FromMinutes(1)),
+    serializedJobData, repeats: 10);
+```
+
+{{% /codetab %}}
 
 {{% codetab %}}
 
@@ -92,66 +188,8 @@ In this example, at trigger time, which is `@every 1s` according to the `Schedul
 	}
 ```
 
-At the trigger time, the `prodDBBackupHandler` function is called, executing the desired business logic for this job at trigger time. For example:
-
-#### HTTP
-
-When you create a job using Dapr's Jobs API, Dapr will automatically assume there is an endpoint available at 
-`/job/<job-name>`. For instance, if you schedule a job named `test`, Dapr expects your application to listen for job 
-events at `/job/test`. Ensure your application has a handler set up for this endpoint to process the job when it is 
-triggered. For example:
-
-*Note: The following example is in Go but applies to any programming language.*
-
-```go
-
-func main() {
-    ...
-    http.HandleFunc("/job/", handleJob)
-	http.HandleFunc("/job/<job-name>", specificJob)
-    ...
-}
-
-func specificJob(w http.ResponseWriter, r *http.Request) {
-    // Handle specific triggered job
-}
-
-func handleJob(w http.ResponseWriter, r *http.Request) {
-    // Handle the triggered jobs
-}
-```
-
-#### gRPC
-
-When a job reaches its scheduled trigger time, the triggered job is sent back to the application via the following 
-callback function:
-
-*Note: The following example is in Go but applies to any programming language with gRPC support.*
-
-```go
-import rtv1 "github.com/dapr/dapr/pkg/proto/runtime/v1"
-...
-func (s *JobService) OnJobEventAlpha1(ctx context.Context, in *rtv1.JobEventRequest) (*rtv1.JobEventResponse, error) {
-    // Handle the triggered job
-}
-```
-
-This function processes the triggered jobs within the context of your gRPC server. When you set up the server, ensure that 
-you register the callback server, which will invoke this function when a job is triggered:
-
-```go
-...
-js := &JobService{}
-rtv1.RegisterAppCallbackAlphaServer(server, js)
-```
-
-In this setup, you have full control over how triggered jobs are received and processed, as they are routed directly 
-through this gRPC method.
-
-#### SDKs
-
-For SDK users, handling triggered jobs is simpler. When a job is triggered, Dapr will automatically route the job to the 
-event handler you set up during the server initialization. For example, in Go, you'd register the event handler like this:
+When a job is triggered, Dapr will automatically route the job to the event handler you set up during the server 
+initialization. For example, in Go, you'd register the event handler like this:
 
 ```go
 ...