mirror of https://github.com/dapr/docs.git
Merge branch 'v1.15' into endgame_1.15-updates
This commit is contained in:
Hannah Hunter
GitHub
commit
61f4b60a11
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@ -81,6 +81,14 @@ In production environments, it's recommended to perform periodic backups of this
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To perform backup and restore operations, you'll need to access the embedded etcd instance. This requires port forwarding to expose the etcd ports (port 2379).
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#### Kubernetes Example
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Here's how to port forward and connect to the etcd instance:
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```shell
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kubectl port-forward svc/dapr-scheduler-server 2379:2379 -n dapr-system
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```
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#### Docker Compose Example
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Here's how to expose the etcd ports in a Docker Compose configuration for standalone mode:
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@ -111,6 +119,16 @@ When running in HA mode, you only need to expose the ports for one scheduler ins
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Once you have access to the etcd ports, you can follow the [official etcd backup and restore documentation](https://etcd.io/docs/v3.5/op-guide/recovery/) to perform backup and restore operations. The process involves using standard etcd commands to create snapshots and restore from them.
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## Monitoring Scheduler's etcd Metrics
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Port forward the Scheduler instance and view etcd's metrics with the following:
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```shell
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curl -s http://localhost:2379/metrics
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```
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Fine tune the embedded etcd to your needs by [reviewing and configuring the Scheduler's etcd flags as needed](https://github.com/dapr/dapr/blob/master/charts/dapr/README.md#dapr-scheduler-options).
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## Disabling the Scheduler service
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If you are not using any features that require the Scheduler service (Jobs API, Actor Reminders, or Workflows), you can disable it by setting `global.scheduler.enabled=false`.
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@ -52,8 +52,6 @@ spec:
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value: <REPLACE_WITH_YOUR_KEY>
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- name: model
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value: gpt-4-turbo
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- name: cacheTTL
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value: 10m
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```
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## Connect the conversation client
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@ -114,12 +112,12 @@ func main() {
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}
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input := dapr.ConversationInput{
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Message: "Please write a witty haiku about the Dapr distributed programming framework at dapr.io",
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// Role: nil, // Optional
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// ScrubPII: nil, // Optional
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Content: "Please write a witty haiku about the Dapr distributed programming framework at dapr.io",
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// Role: "", // Optional
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// ScrubPII: false, // Optional
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}
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fmt.Printf("conversation input: %s\n", input.Message)
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fmt.Printf("conversation input: %s\n", input.Content)
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var conversationComponent = "echo"
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@ -163,7 +161,7 @@ async fn main() -> Result<(), Box<dyn std::error::Error>> {
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let request =
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ConversationRequestBuilder::new(conversation_component, vec![input.clone()]).build();
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println!("conversation input: {:?}", input.message);
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println!("conversation input: {:?}", input.content);
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let response = client.converse_alpha1(request).await?;
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@ -224,6 +222,16 @@ dapr run --app-id=conversation --resources-path ./config --dapr-grpc-port 3500 -
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{{< /tabs >}}
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## Advanced features
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The conversation API supports the following features:
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1. **Prompt caching:** Allows developers to cache prompts in Dapr, leading to much faster response times and reducing costs on egress and on inserting the prompt into the LLM provider's cache.
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1. **PII scrubbing:** Allows for the obfuscation of data going in and out of the LLM.
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To learn how to enable these features, see the [conversation API reference guide]({{< ref conversation_api.md >}}).
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## Related links
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Try out the conversation API using the full examples provided in the supported SDK repos.
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@ -149,7 +149,7 @@ services:
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- type: tmpfs
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target: /data
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tmpfs:
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size: "10000"
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size: "64m"
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networks:
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hello-dapr: null
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@ -231,6 +231,8 @@ kubectl rollout restart -n <DAPR_NAMESPACE> deployment/dapr-sentry
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```bash
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kubectl rollout restart deploy/dapr-operator -n <DAPR_NAMESPACE>
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kubectl rollout restart statefulsets/dapr-placement-server -n <DAPR_NAMESPACE>
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kubectl rollout restart deploy/dapr-sidecar-injector -n <DAPR_NAMESPACE>
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kubectl rollout restart deploy/dapr-scheduler-server -n <DAPR_NAMESPACE>
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```
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4. Restart your Dapr applications to pick up the latest trust bundle.
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@ -332,12 +334,13 @@ Example:
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dapr status -k
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NAME NAMESPACE HEALTHY STATUS REPLICAS VERSION AGE CREATED
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dapr-sentry dapr-system True Running 1 1.7.0 17d 2022-03-15 09:29.45
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dapr-dashboard dapr-system True Running 1 0.9.0 17d 2022-03-15 09:29.45
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dapr-sidecar-injector dapr-system True Running 1 1.7.0 17d 2022-03-15 09:29.45
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dapr-operator dapr-system True Running 1 1.7.0 17d 2022-03-15 09:29.45
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dapr-placement-server dapr-system True Running 1 1.7.0 17d 2022-03-15 09:29.45
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⚠ Dapr root certificate of your Kubernetes cluster expires in 2 days. Expiry date: Mon, 04 Apr 2022 15:01:03 UTC.
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dapr-operator dapr-system True Running 1 1.15.0 4m 2025-02-19 17:36.26
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dapr-placement-server dapr-system True Running 1 1.15.0 4m 2025-02-19 17:36.27
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dapr-dashboard dapr-system True Running 1 0.15.0 4m 2025-02-19 17:36.27
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dapr-sentry dapr-system True Running 1 1.15.0 4m 2025-02-19 17:36.26
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dapr-scheduler-server dapr-system True Running 3 1.15.0 4m 2025-02-19 17:36.27
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dapr-sidecar-injector dapr-system True Running 1 1.15.0 4m 2025-02-19 17:36.26
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⚠ Dapr root certificate of your Kubernetes cluster expires in 2 days. Expiry date: Mon, 04 Apr 2025 15:01:03 UTC.
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Please see docs.dapr.io for certificate renewal instructions to avoid service interruptions.
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```
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@ -30,40 +30,34 @@ POST http://localhost:<daprPort>/v1.0-alpha1/conversation/<llm-name>/converse
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| Field | Description |
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| --------- | ----------- |
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| `conversationContext` | The ID of an existing chat room (like in ChatGPT). |
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| `inputs` | Inputs for the conversation. Multiple inputs at one time are supported. |
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| `metadata` | [Metadata](#metadata) passed to conversation components. |
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| `inputs` | Inputs for the conversation. Multiple inputs at one time are supported. Required |
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| `cacheTTL` | A time-to-live value for a prompt cache to expire. Uses Golang duration format. Optional |
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| `scrubPII` | A boolean value to enable obfuscation of sensitive information returning from the LLM. Optional |
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| `temperature` | A float value to control the temperature of the model. Used to optimize for consistency and creativity. Optional |
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| `metadata` | [Metadata](#metadata) passed to conversation components. Optional |
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#### Metadata
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#### Input body
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Metadata can be sent in the request’s URL. It must be prefixed with `metadata.`, as shown in the table below.
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| Parameter | Description |
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| Field | Description |
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| --------- | ----------- |
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| `metadata.key` | The API key for the component. `key` is not applicable to the [AWS Bedrock component]({{< ref "aws-bedrock.md#authenticating-aws" >}}). |
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| `metadata.model` | The Large Language Model you're using. Value depends on which conversation component you're using. `model` is not applicable to the [DeepSeek component]({{< ref deepseek.md >}}). |
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| `metadata.cacheTTL` | A time-to-live value for a prompt cache to expire. Uses Golang duration format. |
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| `content` | The message content to send to the LLM. Required |
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| `role` | The role for the LLM to assume. Possible values: 'user', 'tool', 'assistant' |
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| `scrubPII` | A boolean value to enable obfuscation of sensitive information present in the content field. Optional |
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For example, to call for [Anthropic]({{< ref anthropic.md >}}):
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```bash
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curl POST http://localhost:3500/v1.0-alpha1/conversation/anthropic/converse?metadata.key=key1&metadata.model=claude-3-5-sonnet-20240620&metadata.cacheTTL=10m
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```
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{{% alert title="Note" color="primary" %}}
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The metadata parameters available depend on the conversation component you use. [See all the supported components for the conversation API.]({{< ref supported-conversation >}})
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{{% /alert %}}
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### Request content
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### Request content example
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```json
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REQUEST = {
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"inputs": ["what is Dapr", "Why use Dapr"],
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"metadata": {
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"model": "model-type-based-on-component-used",
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"key": "authKey",
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"cacheTTL": "10m",
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}
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"inputs": [
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{
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"content": "What is Dapr?",
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"role": "user", // Optional
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"scrubPII": "true", // Optional. Will obfuscate any sensitive information found in the content field
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},
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],
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"cacheTTL": "10m", // Optional
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"scrubPII": "true", // Optional. Will obfuscate any sensitive information returning from the LLM
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"temperature": 0.5 // Optional. Optimizes for consistency (0) or creativity (1)
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}
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```
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@ -37,6 +37,9 @@ A list of features enabled via Configuration spec (including build-time override
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### App connection details
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The metadata API returns information related to Dapr's connection to the app. This includes the app port, protocol, host, max concurrency, along with health check details.
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### Scheduler connection details
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Information related to the connection to one or more scheduler hosts.
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### Attributes
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The metadata API allows you to store additional attribute information in the format of key-value pairs. These are ephemeral in-memory and are not persisted if a sidecar is reloaded. This information should be added at the time of a sidecar creation (for example, after the application has started).
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@ -82,6 +85,7 @@ components | [Metadata API Response Component](#metadataapiresponsec
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httpEndpoints | [Metadata API Response HttpEndpoint](#metadataapiresponsehttpendpoint)[] | A json encoded array of loaded HttpEndpoints metadata.
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subscriptions | [Metadata API Response Subscription](#metadataapiresponsesubscription)[] | A json encoded array of pub/sub subscriptions metadata.
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appConnectionProperties| [Metadata API Response AppConnectionProperties](#metadataapiresponseappconnectionproperties) | A json encoded object of app connection properties.
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scheduler | [Metadata API Response Scheduler](#metadataapiresponsescheduler) | A json encoded object of scheduler connection properties.
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<a id="metadataapiresponseactor"></a>**Metadata API Response Registered Actor**
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@ -142,6 +146,12 @@ healthProbeInterval | string | Time between each health probe, in go duration fo
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healthProbeTimeout | string | Timeout for each health probe, in go duration format.
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healthThreshold | integer | Max number of failed health probes before the app is considered unhealthy.
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<a id="metadataapiresponsescheduler"></a>**Metadata API Response Scheduler**
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Name | Type | Description
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---- | ---- | -----------
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connected_addresses | string[] | List of strings representing the addresses of the conntected scheduler hosts.
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### Examples
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@ -215,6 +225,13 @@ curl http://localhost:3500/v1.0/metadata
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"healthProbeTimeout": "500ms",
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"healthThreshold": 3
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}
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},
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"scheduler": {
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"connected_addresses": [
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"10.244.0.47:50006",
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"10.244.0.48:50006",
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"10.244.0.49:50006"
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]
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}
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}
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```
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"healthProbeTimeout": "500ms",
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"healthThreshold": 3
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}
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},
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"scheduler": {
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"connected_addresses": [
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"10.244.0.47:50006",
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"10.244.0.48:50006",
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"10.244.0.49:50006"
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]
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}
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}
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```
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@ -9,7 +9,7 @@ aliases:
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## Component format
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To set up the SFTP binding, create a component of type `bindings.sftp`. See [this guide]({{ ref bindings-overview.md }}) on how to create and apply a binding configuration.
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To set up the SFTP binding, create a component of type `bindings.sftp`. See [this guide]({{< ref bindings-overview.md >}}) on how to create and apply a binding configuration.
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```yaml
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apiVersion: dapr.io/v1alpha1
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@ -459,8 +459,8 @@ Apache Kafka supports the following bulk metadata options:
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| Configuration | Default |
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|----------|---------|
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| `maxBulkAwaitDurationMs` | `10000` (10s) |
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| `maxBulkSubCount` | `80` |
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| `maxAwaitDurationMs` | `10000` (10s) |
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| `maxMessagesCount` | `80` |
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## Per-call metadata fields
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@ -540,6 +540,7 @@ app.include_router(router)
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```
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{{% /codetab %}}
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{{< /tabs >}}
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## Receiving message headers with special characters
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@ -19,8 +19,8 @@ data:
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zpages:
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endpoint: :55679
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exporters:
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logging:
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loglevel: debug
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debug:
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verbosity: detailed
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# Depending on where you want to export your trace, use the
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# correct OpenTelemetry trace exporter here.
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#
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|
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@ -1 +1 @@
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Subproject commit ab3ef305f2e841d044a0190a2351726c3c7ad5db
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Subproject commit 52f0851780202f71ac4c7fbbcd5c5fb7d674db5a
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@ -1 +1 @@
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Subproject commit 58d6218861e77a2588b0af360c978a25e5723091
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Subproject commit 22d9874ae05c2adaf1eea9fe45e1e6f40c30fb04
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|
|
@ -1 +1 @@
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Subproject commit fc52f993afe5fd11b3c8ff5406b8904b280224f1
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Subproject commit f1dba55586bb734e55de98098284f9139d6e5304
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@ -1 +1 @@
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Subproject commit fe2ef4be3bce327a9802d75515f478600a415cb0
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Subproject commit fc4980daaa4802bfb2590f133c332b934b196205
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