From f0bda9a4125e09b12e345fd4bcacd6a460dbe077 Mon Sep 17 00:00:00 2001 From: Riandy Riandy Date: Sat, 7 Dec 2024 04:42:13 +0800 Subject: [PATCH] Update android_sdk.md Fix images URL and replacing todo. Previous commit missed that --- .../ondevice_distro/android_sdk.md | 47 ++++++++++--------- 1 file changed, 24 insertions(+), 23 deletions(-) diff --git a/docs/source/distributions/ondevice_distro/android_sdk.md b/docs/source/distributions/ondevice_distro/android_sdk.md index 5a4e67e7e..853441e50 100644 --- a/docs/source/distributions/ondevice_distro/android_sdk.md +++ b/docs/source/distributions/ondevice_distro/android_sdk.md @@ -1,6 +1,6 @@ # Llama Stack Client Kotlin API Library -We are excited to share a guide for a Kotlin Library that brings front the benefits of Llama Stack to your Android device. This library is a set of SDKs that provide a simple and effective way to integrate AI capabilities into your Android app whether it is local (on-device) or remote inference. +We are excited to share a guide for a Kotlin Library that brings front the benefits of Llama Stack to your Android device. This library is a set of SDKs that provide a simple and effective way to integrate AI capabilities into your Android app whether it is local (on-device) or remote inference. Features: - Local Inferencing: Run Llama models purely on-device with real-time processing. We currently utilize ExecuTorch as the local inference distributor and may support others in the future. @@ -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. @@ -26,23 +25,22 @@ dependencies { implementation("com.llama.llamastack:llama-stack-client-kotlin:0.0.54") } ``` -This will download jar files in your gradle cache in a directory like `~/.gradle/caches/modules-2/files-2.1/com.llama.llamastack/` +This will download jar files in your gradle cache in a directory like `~/.gradle/caches/modules-2/files-2.1/com.llama.llamastack/` 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:

- +

-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 { @@ -58,7 +56,7 @@ Breaking down the demo app, this section will show the core pieces that are used ### Setup Remote Inferencing Start a Llama Stack server on localhost. Here is an example of how you can do this using the firework.ai distribution: ``` -conda create -n stack-fireworks python=3.10 +conda create -n stack-fireworks python=3.10 conda activate stack-fireworks pip install llama-stack=0.0.54 llama stack build --template fireworks --image-type conda @@ -68,10 +66,10 @@ llama stack run /Users//.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. +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. @@ -80,21 +78,23 @@ A client serves as the primary interface for interacting with a specific inferen @@ -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 @@ -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 @@ -231,16 +230,18 @@ 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. --- -The API interface is generated using the OpenAPI standard with [Stainless](https://www.stainlessapi.com/). +The API interface is generated using the OpenAPI standard with [Stainless](https://www.stainlessapi.com/). \ No newline at end of file
-
+
+```
 client = LlamaStackClientLocalClient
                     .builder()
                     .modelPath(modelPath)
                     .tokenizerPath(tokenizerPath)
                     .temperature(temperature)
                     .build()
-
+``` 		-```// remoteURL is a string like "http://localhost:5050" +``` +// remoteURL is a string like "http://localhost:5050" client = LlamaStackClientOkHttpClient .builder() - .baseUrl(remoteURL) + .baseUrl(remoteURL) .build() ```