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Update android_sdk.md #578

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Update android_sdk.md
Riandy Dec 6, 2024
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Add android link to main distribution readme
Riandy Dec 6, 2024
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2 changes: 1 addition & 1 deletion docs/source/distributions/index.md
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Expand Up @@ -35,6 +35,6 @@ If so, we suggest:

- **Do you want to run Llama Stack inference on your iOS / Android device** If so, we suggest:
- [iOS SDK](ondevice_distro/ios_sdk)
- Android (coming soon)
- [Android](ondevice_distro/android_sdk)

You can also build your own [custom distribution](building_distro).
35 changes: 18 additions & 17 deletions docs/source/distributions/ondevice_distro/android_sdk.md
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Expand Up @@ -8,11 +8,10 @@ Features:
- Remote Inferencing: Perform inferencing tasks remotely with Llama models hosted on a remote connection (or serverless localhost).
- Simple Integration: With easy-to-use APIs, a developer can quickly integrate Llama Stack in their Android app. The difference with local vs remote inferencing is also minimal.

Latest release notes: TODO Add Release Notes
Latest Release Notes: [v0.0.54](https://github.com/meta-llama/llama-stack-client-kotlin/releases/tag/v0.0.54)

## Android Demo App
Check out our demo app to see how to integrate Llama Stack into your Android app:
- TODO: Link to Demo App
Check out our demo app to see how to integrate Llama Stack into your Android app: [Android Demo App](https://github.com/meta-llama/llama-stack-apps/tree/main/examples/android_app)

The key files in the app are `LlamaStackLocalInference.kt`, `LlamaStackRemoteInference.kts`, and `MainActivity.java`. With encompassed business logic, the app shows how to use Llama Stack for both the environments.

Expand All @@ -32,17 +31,16 @@ If you plan on doing remote inferencing this is sufficient to get started.

#### Dependency for Local

> [!IMPORTANT]
> For local inferencing, it is required to include the ExecuTorch library into your app.
For local inferencing, it is required to include the ExecuTorch library into your app.

Include the ExecuTorch library by:
1. Download the `download-prebuilt-et-lib.sh` script file from [Github](https://github.com/meta-llama/llama-stack-client-kotlin/blob/release/0.0.54/llama-stack-client-kotlin-client-local/download-prebuilt-et-lib.sh) to your local machine.
1. Download the `download-prebuilt-et-lib.sh` script file from the [llama-stack-client-kotlin-client-local](https://github.com/meta-llama/llama-stack-client-kotlin/blob/release/0.0.54/llama-stack-client-kotlin-client-local/download-prebuilt-et-lib.sh) directory to your local machine.
2. Move the script to the top level of your Android app where the app directory resides:
<p align="center">
<img src="doc/img/example_android_app_directory.png" style="width:300px">
<img src="https://raw.githubusercontent.com/meta-llama/llama-stack-client-kotlin/refs/heads/release/0.0.54/doc/img/example_android_app_directory.png" style="width:300px">
</p>

3. Run `sh download-prebuilt-et-lib.sh` to create an `app/libs` directory and download the `executorch.aar` in that path. This generates an ExecuTorch library for the XNNPACK delegate.
3. Run `sh download-prebuilt-et-lib.sh` to create an `app/libs` directory and download the `executorch.aar` in that path. This generates an ExecuTorch library for the XNNPACK delegate with commit: [0a12e33](https://github.com/pytorch/executorch/commit/0a12e33d22a3d44d1aa2af5f0d0673d45b962553).
4. Add the `executorch.aar` dependency in your `build.gradle.kts` file:
```
dependencies {
Expand All @@ -68,7 +66,7 @@ llama stack run /Users/<your_username>/.llama/distributions/llamastack-fireworks

Other inference providers: [Table](https://llama-stack.readthedocs.io/en/latest/index.html#supported-llama-stack-implementations)

TODO: Link to Demo App on how to set this remote localhost in the Settings.
How to set remote localhost in Demo App: [Settings](https://github.com/meta-llama/llama-stack-apps/tree/main/examples/android_app#settings)

### Initialize the Client
A client serves as the primary interface for interacting with a specific inference type and its associated parameters. Only after client is initialized then you can configure and start inferences.
Expand All @@ -80,18 +78,20 @@ A client serves as the primary interface for interacting with a specific inferen
</tr>
<tr>
<td>
<pre>

```
client = LlamaStackClientLocalClient
.builder()
.modelPath(modelPath)
.tokenizerPath(tokenizerPath)
.temperature(temperature)
.build()
</pre>
```
</td>
<td>

```// remoteURL is a string like "http://localhost:5050"
```
// remoteURL is a string like "http://localhost:5050"
client = LlamaStackClientOkHttpClient
.builder()
.baseUrl(remoteURL)
Expand Down Expand Up @@ -120,8 +120,7 @@ var response = result.asChatCompletionResponse().completionMessage().content().s

### Setup Tool Calling

TODO: Link to Android demo app readme for more details

Android demo app for more details: [Tool Calling](https://github.com/meta-llama/llama-stack-apps/tree/main/examples/android_app#tool-calling)

## Advanced Users

Expand All @@ -130,7 +129,7 @@ The purpose of this section is to share more details with users that would like
### Prerequisite

You must complete the following steps:
1. Clone the repo
1. Clone the repo (`git clone https://github.com/meta-llama/llama-stack-client-kotlin.git -b release/0.0.54`)
2. Port the appropriate ExecuTorch libraries over into your Llama Stack Kotlin library environment.
```
cd llama-stack-client-kotlin-client-local
Expand Down Expand Up @@ -231,15 +230,17 @@ This library throws exceptions in a single hierarchy for easy handling:
- We failed to serialize the request body
- We failed to parse the response body (has access to response code and body)


## Reporting Issues
If you encountered any bugs or issues following this guide please file a bug/issue on our [Github issue tracker](https://github.com/meta-llama/llama-stack-client-kotlin/issues).

## Known Issues
We're aware of the following issues and are working to resolve them:
1. Streaming response is a work-in-progress for local and remote inference
2. Due to #1, agents are not supported at the time. LS agents only work in streaming mode
3. Changing to another model is a work in progress for local and remote platforms

## Thanks
- We'd like to extend our thanks to the ExecuTorch team for providing their support as we integrated ExecuTorch as one of the local inference distributors for Llama Stack. Checkout [ExecuTorch Github repo](https://github.com/pytorch/executorch/tree/main) for more information about Executorch.
We'd like to extend our thanks to the ExecuTorch team for providing their support as we integrated ExecuTorch as one of the local inference distributors for Llama Stack. Checkout [ExecuTorch Github repo](https://github.com/pytorch/executorch/tree/main) for more information.

---

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