phoenix-oss/llama-stack-mirror
0
0
Fork 1
mirror of https://github.com/meta-llama/llama-stack.git synced 2025-06-27 18:50:41 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

docs: Updating wording and nits in the README.md (#992)

Browse source 
# What does this PR do?

Fixing some wording nits and added small formatting suggestions in the
README.md

## Before submitting

- [x] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [x] Ran pre-commit to handle lint / formatting issues.
- [x] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
      Pull Request section?
- [x] Updated relevant documentation.
- [ ] Wrote necessary unit or integration tests.
This commit is contained in:
Kelly Brown 2025-02-11 09:53:26 -05:00 • committed by GitHub
parent d954f2752e
commit d947ddd255
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
1 changed files with 17 additions and 16 deletions
Download patch file Download diff file Expand all files Collapse all files

33
README.md
View file

@ -7,13 +7,13 @@
[**Quick Start**](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html) | [**Documentation**](https://llama-stack.readthedocs.io/en/latest/index.html) | [**Colab Notebook**](./docs/getting_started.ipynb) [**Quick Start**](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html) | [**Documentation**](https://llama-stack.readthedocs.io/en/latest/index.html) | [**Colab Notebook**](./docs/getting_started.ipynb)
Llama Stack defines and standardizes the core building blocks that simplify AI application development. It codified best practices across the Llama ecosystem. More specifically, it provides Llama Stack standardizes the core building blocks that simplify AI application development. It codifies best practices across the Llama ecosystem. More specifically, it provides
- **Unified API layer** for Inference, RAG, Agents, Tools, Safety, Evals, and Telemetry. - **Unified API layer** for Inference, RAG, Agents, Tools, Safety, Evals, and Telemetry.
- **Plugin architecture** to support the rich ecosystem of implementations of the different APIs in different environments like local development, on-premises, cloud, and mobile. - **Plugin architecture** to support the rich ecosystem of different API implementations in various environments, including local development, on-premises, cloud, and mobile.
- **Prepackaged verified distributions** which offer a one-stop solution for developers to get started quickly and reliably in any environment - **Prepackaged verified distributions** which offer a one-stop solution for developers to get started quickly and reliably in any environment.
- **Multiple developer interfaces** like CLI and SDKs for Python, Typescript, iOS, and Android - **Multiple developer interfaces** like CLI and SDKs for Python, Typescript, iOS, and Android.
- **Standalone applications** as examples for how to build production-grade AI applications with Llama Stack - **Standalone applications** as examples for how to build production-grade AI applications with Llama Stack.
<div style="text-align: center;"> <div style="text-align: center;">
<img <img
@ -25,14 +25,14 @@ Llama Stack defines and standardizes the core building blocks that simplify AI a
</div> </div>
### Llama Stack Benefits ### Llama Stack Benefits
- **Flexible Options**: Developers can choose their preferred infrastructure without changing APIs and enjoy flexible deployment choice. - **Flexible Options**: Developers can choose their preferred infrastructure without changing APIs and enjoy flexible deployment choices.
- **Consistent Experience**: With its unified APIs Llama Stack makes it easier to build, test, and deploy AI applications with consistent application behavior. - **Consistent Experience**: With its unified APIs, Llama Stack makes it easier to build, test, and deploy AI applications with consistent application behavior.
- **Robust Ecosystem**: Llama Stack is already integrated with distribution partners (cloud providers, hardware vendors, and AI-focused companies) that offer tailored infrastructure, software, and services for deploying Llama models. - **Robust Ecosystem**: Llama Stack is already integrated with distribution partners (cloud providers, hardware vendors, and AI-focused companies) that offer tailored infrastructure, software, and services for deploying Llama models.
By reducing friction and complexity, Llama Stack empowers developers to focus on what they do best: building transformative generative AI applications. By reducing friction and complexity, Llama Stack empowers developers to focus on what they do best: building transformative generative AI applications.
### API Providers ### API Providers
Here is a list of the various API providers and available distributions to developers started easily, Here is a list of the various API providers and available distributions that can help developers get started easily with Llama Stack.
| **API Provider Builder** | **Environments** | **Agents** | **Inference** | **Memory** | **Safety** | **Telemetry** | | **API Provider Builder** | **Environments** | **Agents** | **Inference** | **Memory** | **Safety** | **Telemetry** |
|:------------------------:|:----------------------:|:----------:|:-------------:|:----------:|:----------:|:-------------:| |:------------------------:|:----------------------:|:----------:|:-------------:|:----------:|:----------:|:-------------:|
@ -71,15 +71,15 @@ A Llama Stack Distribution (or "distro") is a pre-configured bundle of provider
You have two ways to install this repository: You have two ways to install this repository:
1. **Install as a package**: * **Install as a package**:
You can install the repository directly from [PyPI](https://pypi.org/project/llama-stack/) by running the following command: You can install the repository directly from [PyPI](https://pypi.org/project/llama-stack/) by running the following command:
```bash ```bash
pip install llama-stack pip install llama-stack
``` ```
2. **Install from source**: * **Install from source**:
If you prefer to install from the source code, make sure you have [conda installed](https://docs.conda.io/projects/conda/en/stable). If you prefer to install from the source code, make sure you have [conda installed](https://docs.conda.io/projects/conda/en/stable).
Then, follow these steps: Then, run the following commands:
```bash ```bash
mkdir -p ~/local mkdir -p ~/local
cd ~/local cd ~/local
@ -96,10 +96,11 @@ You have two ways to install this repository:
Please checkout our [Documentation](https://llama-stack.readthedocs.io/en/latest/index.html) page for more details. Please checkout our [Documentation](https://llama-stack.readthedocs.io/en/latest/index.html) page for more details.
* [CLI reference](https://llama-stack.readthedocs.io/en/latest/references/llama_cli_reference/index.html) * CLI references
* Guide using `llama` CLI to work with Llama models (download, study prompts), and building/starting a Llama Stack distribution. * [llama (server-side) CLI Reference](https://llama-stack.readthedocs.io/en/latest/references/llama_cli_reference/index.html): Guide for using the `llama` CLI to work with Llama models (download, study prompts), and building/starting a Llama Stack distribution.
* [Getting Started](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html) * [llama (client-side) CLI Reference](https://llama-stack.readthedocs.io/en/latest/references/llama_stack_client_cli_reference.html): Guide for using the `llama-stack-client` CLI, which allows you to query information about the distribution.
* Quick guide to start a Llama Stack server. * Getting Started
* [Quick guide to start a Llama Stack server](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html).
* [Jupyter notebook](./docs/getting_started.ipynb) to walk-through how to use simple text and vision inference llama_stack_client APIs * [Jupyter notebook](./docs/getting_started.ipynb) to walk-through how to use simple text and vision inference llama_stack_client APIs
* The complete Llama Stack lesson [Colab notebook](https://colab.research.google.com/drive/1dtVmxotBsI4cGZQNsJRYPrLiDeT0Wnwt) of the new [Llama 3.2 course on Deeplearning.ai](https://learn.deeplearning.ai/courses/introducing-multimodal-llama-3-2/lesson/8/llama-stack). * The complete Llama Stack lesson [Colab notebook](https://colab.research.google.com/drive/1dtVmxotBsI4cGZQNsJRYPrLiDeT0Wnwt) of the new [Llama 3.2 course on Deeplearning.ai](https://learn.deeplearning.ai/courses/introducing-multimodal-llama-3-2/lesson/8/llama-stack).
* A [Zero-to-Hero Guide](https://github.com/meta-llama/llama-stack/tree/main/docs/zero_to_hero_guide) that guide you through all the key components of llama stack with code samples. * A [Zero-to-Hero Guide](https://github.com/meta-llama/llama-stack/tree/main/docs/zero_to_hero_guide) that guide you through all the key components of llama stack with code samples.
@ -115,6 +116,6 @@ Please checkout our [Documentation](https://llama-stack.readthedocs.io/en/latest
| Typescript | [llama-stack-client-typescript](https://github.com/meta-llama/llama-stack-client-typescript) | [![NPM version](https://img.shields.io/npm/v/llama-stack-client.svg)](https://npmjs.org/package/llama-stack-client) | Typescript | [llama-stack-client-typescript](https://github.com/meta-llama/llama-stack-client-typescript) | [![NPM version](https://img.shields.io/npm/v/llama-stack-client.svg)](https://npmjs.org/package/llama-stack-client)
| Kotlin | [llama-stack-client-kotlin](https://github.com/meta-llama/llama-stack-client-kotlin) | [![Maven version](https://img.shields.io/maven-central/v/com.llama.llamastack/llama-stack-client-kotlin)](https://central.sonatype.com/artifact/com.llama.llamastack/llama-stack-client-kotlin) | Kotlin | [llama-stack-client-kotlin](https://github.com/meta-llama/llama-stack-client-kotlin) | [![Maven version](https://img.shields.io/maven-central/v/com.llama.llamastack/llama-stack-client-kotlin)](https://central.sonatype.com/artifact/com.llama.llamastack/llama-stack-client-kotlin)
Check out our client SDKs for connecting to Llama Stack server in your preferred language, you can choose from [python](https://github.com/meta-llama/llama-stack-client-python), [typescript](https://github.com/meta-llama/llama-stack-client-typescript), [swift](https://github.com/meta-llama/llama-stack-client-swift), and [kotlin](https://github.com/meta-llama/llama-stack-client-kotlin) programming languages to quickly build your applications. Check out our client SDKs for connecting to a Llama Stack server in your preferred language, you can choose from [python](https://github.com/meta-llama/llama-stack-client-python), [typescript](https://github.com/meta-llama/llama-stack-client-typescript), [swift](https://github.com/meta-llama/llama-stack-client-swift), and [kotlin](https://github.com/meta-llama/llama-stack-client-kotlin) programming languages to quickly build your applications.
You can find more example scripts with client SDKs to talk with the Llama Stack server in our [llama-stack-apps](https://github.com/meta-llama/llama-stack-apps/tree/main/examples) repo. You can find more example scripts with client SDKs to talk with the Llama Stack server in our [llama-stack-apps](https://github.com/meta-llama/llama-stack-apps/tree/main/examples) repo.