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docs/docs/advanced_apis/scoring.mdx
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@ -190,4 +190,4 @@ The Scoring API works closely with the [Evaluation](./evaluation.mdx) API to pro
- Check out the [Evaluation](./evaluation.mdx) guide for running complete evaluations
- See the [Building Applications - Evaluation](../building_applications/evals.mdx) guide for application examples
- Review the [Evaluation Reference](../references/evals_reference/) for comprehensive scoring function usage
- Explore the [Evaluation Concepts](../concepts/evaluation_concepts.mdx) for detailed conceptual information
- Explore the [Evaluation Concepts](../concepts/evaluation_concepts) for detailed conceptual information

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docs/docs/building_applications/evals.mdx
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@ -8,7 +8,7 @@ sidebar_position: 7
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
This guide walks you through the process of evaluating an LLM application built using Llama Stack. For detailed API reference, check out the [Evaluation Reference](/docs/references/evals-reference) guide that covers the complete set of APIs and developer experience flow.
This guide walks you through the process of evaluating an LLM application built using Llama Stack. For detailed API reference, check out the [Evaluation Reference](../references/evals_reference/) guide that covers the complete set of APIs and developer experience flow.
:::tip[Interactive Examples]
Check out our [Colab notebook](https://colab.research.google.com/drive/10CHyykee9j2OigaIcRv47BKG9mrNm0tJ?usp=sharing) for working examples with evaluations, or try the [Getting Started notebook](https://colab.research.google.com/github/meta-llama/llama-stack/blob/main/docs/getting_started.ipynb).
@ -251,6 +251,6 @@ results = client.scoring.score(
- **[Agents](./agent)** - Building agents for evaluation
- **[Tools Integration](./tools)** - Using tools in evaluated agents
- **[Evaluation Reference](/docs/references/evals-reference)** - Complete API reference for evaluations
- **[Evaluation Reference](../references/evals_reference/)** - Complete API reference for evaluations
- **[Getting Started Notebook](https://colab.research.google.com/github/meta-llama/llama-stack/blob/main/docs/getting_started.ipynb)** - Interactive examples
- **[Evaluation Examples](https://colab.research.google.com/drive/10CHyykee9j2OigaIcRv47BKG9mrNm0tJ?usp=sharing)** - Additional evaluation scenarios

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docs/docs/building_applications/index.mdx
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@ -77,7 +77,7 @@ Build production-ready systems with:
## Related Resources
- **[Getting Started](/docs/getting-started/quickstart)** - Basic setup and concepts
- **[Getting Started](/docs/getting_started/quickstart)** - Basic setup and concepts
- **[Providers](/docs/providers/)** - Available AI service providers
- **[Distributions](/docs/distributions/)** - Pre-configured deployment packages
- **[API Reference](/docs/api/llama-stack-specification)** - Complete API documentation

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docs/docs/building_applications/playground.mdx
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@ -291,7 +291,7 @@ llama stack run meta-reference
## Related Resources
- **[Getting Started Guide](/docs/getting-started/quickstart)** - Complete setup and introduction
- **[Getting Started Guide](../getting_started/quickstart)** - Complete setup and introduction
- **[Core Concepts](/docs/concepts)** - Understanding Llama Stack fundamentals
- **[Agents](./agent)** - Building intelligent agents
- **[RAG (Retrieval Augmented Generation)](./rag)** - Knowledge-enhanced applications

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docs/docs/building_applications/responses_vs_agents.mdx
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@ -13,7 +13,7 @@ import TabItem from '@theme/TabItem';
Llama Stack (LLS) provides two different APIs for building AI applications with tool calling capabilities: the **Agents API** and the **OpenAI Responses API**. While both enable AI systems to use tools, and maintain full conversation history, they serve different use cases and have distinct characteristics.
:::note
**Note:** For simple and basic inferencing, you may want to use the [Chat Completions API](/docs/providers/openai-compatibility#chat-completions) directly, before progressing to Agents or Responses API.
**Note:** For simple and basic inferencing, you may want to use the [Chat Completions API](../providers/openai#chat-completions) directly, before progressing to Agents or Responses API.
:::
## Overview
@ -217,5 +217,5 @@ Use this framework to choose the right API for your use case:
- **[Agents](./agent)** - Understanding the Agents API fundamentals
- **[Agent Execution Loop](./agent_execution_loop)** - How agents process turns and steps
- **[Tools Integration](./tools)** - Adding capabilities to both APIs
- **[OpenAI Compatibility](/docs/providers/openai-compatibility)** - Using OpenAI-compatible endpoints
- **[OpenAI Compatibility](../providers/openai)** - Using OpenAI-compatible endpoints
- **[Safety Guardrails](./safety)** - Implementing safety measures in agents

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docs/docs/concepts/evaluation_concepts.mdx
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@ -37,7 +37,7 @@ The list of open-benchmarks we currently support:
- [SimpleQA](https://openai.com/index/introducing-simpleqa/): Benchmark designed to access models to answer short, fact-seeking questions.
- [MMMU](https://arxiv.org/abs/2311.16502) (A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI)]: Benchmark designed to evaluate multimodal models.
You can follow this [contributing guide](../references/evals_reference.mdx#open-benchmark-contributing-guide) to add more open-benchmarks to Llama Stack
You can follow this [contributing guide](../references/evals_reference/#open-benchmark-contributing-guide) to add more open-benchmarks to Llama Stack
### Run evaluation on open-benchmarks via CLI
@ -75,4 +75,4 @@ evaluation results over there.
- Check out our Colab notebook on working examples with running benchmark evaluations [here](https://colab.research.google.com/github/meta-llama/llama-stack/blob/main/docs/notebooks/Llama_Stack_Benchmark_Evals.ipynb#scrollTo=mxLCsP4MvFqP).
- Check out our [Building Applications - Evaluation](../building_applications/evals.mdx) guide for more details on how to use the Evaluation APIs to evaluate your applications.
- Check out our [Evaluation Reference](../references/evals_reference.mdx) for more details on the APIs.
- Check out our [Evaluation Reference](../references/evals_reference/) for more details on the APIs.

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docs/docs/concepts/index.mdx
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@ -12,7 +12,7 @@ Given Llama Stack's service-oriented philosophy, a few concepts and workflows ar
This section covers the fundamental concepts of Llama Stack:
- **[Architecture](architecture.mdx)** - Learn about Llama Stack's architectural design and principles
- **[APIs](apis)** - Understanding the core APIs and their stability levels
- **[APIs](/docs/concepts/apis/)** - Understanding the core APIs and their stability levels
- [API Overview](apis/index.mdx) - Core APIs available in Llama Stack
- [API Providers](apis/api_providers.mdx) - How providers implement APIs
- [External APIs](apis/external.mdx) - External APIs available in Llama Stack

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docs/docs/concepts/resources.mdx
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@ -2,7 +2,7 @@
title: Resources
description: Resource federation and registration in Llama Stack
sidebar_label: Resources
sidebar_position: 6
sidebar_position: 4
---
# Resources

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docs/docs/contributing/index.mdx
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@ -148,7 +148,7 @@ As a general guideline:
that describes the configuration. These descriptions will be used to generate the provider
documentation.
* When possible, use keyword arguments only when calling functions.
* Llama Stack utilizes [custom Exception classes](llama_stack/apis/common/errors.py) for certain Resources that should be used where applicable.
* Llama Stack utilizes custom Exception classes for certain Resources that should be used where applicable.
### License
By contributing to Llama, you agree that your contributions will be licensed
@ -212,35 +212,22 @@ The generated API schema will be available in `docs/static/`. Make sure to revie
## Adding a New Provider
See:
- [Adding a New API Provider Page](new_api_provider.md) which describes how to add new API providers to the Stack.
- [Vector Database Page](new_vector_database.md) which describes how to add a new vector databases with Llama Stack.
- [External Provider Page](../providers/external/index.md) which describes how to add external providers to the Stack.
- [Adding a New API Provider Page](./new_api_provider.mdx) which describes how to add new API providers to the Stack.
- [Vector Database Page](./new_vector_database.mdx) which describes how to add a new vector databases with Llama Stack.
- [External Provider Page](/docs/providers/external/) which describes how to add external providers to the Stack.
```{toctree}
:maxdepth: 1
:hidden:
new_api_provider
new_vector_database
```
## Testing
```{include} ../../../tests/README.md
```
See the [Testing README](https://github.com/meta-llama/llama-stack/blob/main/tests/README.md) for detailed testing information.
## Advanced Topics
For developers who need deeper understanding of the testing system internals:
```{toctree}
:maxdepth: 1
testing/record-replay
```
- [Record-Replay Testing](./testing/record-replay.mdx)
### Benchmarking
```{include} ../../../benchmarking/k8s-benchmark/README.md
```
See the [Benchmarking README](https://github.com/meta-llama/llama-stack/blob/main/benchmarking/k8s-benchmark/README.md) for benchmarking information.

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docs/docs/contributing/new_api_provider.mdx
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@ -11,7 +11,7 @@ import TabItem from '@theme/TabItem';
This guide will walk you through the process of adding a new API provider to Llama Stack.
- Begin by reviewing the [core concepts](../concepts/index.md) of Llama Stack and choose the API your provider belongs to (Inference, Safety, VectorIO, etc.)
- Begin by reviewing the [core concepts](../concepts/) of Llama Stack and choose the API your provider belongs to (Inference, Safety, VectorIO, etc.)
- Determine the provider type ([Remote](https://github.com/meta-llama/llama-stack/tree/main/llama_stack/providers/remote) or [Inline](https://github.com/meta-llama/llama-stack/tree/main/llama_stack/providers/inline)). Remote providers make requests to external services, while inline providers execute implementation locally.
- Add your provider to the appropriate [Registry](https://github.com/meta-llama/llama-stack/tree/main/llama_stack/providers/registry/). Specify pip dependencies necessary.
- Update any distribution [Templates](https://github.com/meta-llama/llama-stack/tree/main/llama_stack/distributions/) `build.yaml` and `run.yaml` files if they should include your provider by default. Run [./scripts/distro_codegen.py](https://github.com/meta-llama/llama-stack/blob/main/scripts/distro_codegen.py) if necessary. Note that `distro_codegen.py` will fail if the new provider causes any distribution template to attempt to import provider-specific dependencies. This usually means the distribution's `get_distribution_template()` code path should only import any necessary Config or model alias definitions from each provider and not the provider's actual implementation.

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docs/docs/deploying/kubernetes_deployment.mdx
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@ -219,6 +219,6 @@ kubectl run -it --rm debug --image=curlimages/curl --restart=Never -- curl http:
## Related Resources
- **[Deployment Overview](./index)** - Overview of deployment options
- **[Deployment Overview](/docs/deploying/)** - Overview of deployment options
- **[Distributions](/docs/distributions)** - Understanding Llama Stack distributions
- **[Configuration](/docs/distributions/configuration)** - Detailed configuration options

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docs/docs/distributions/building_distro.mdx
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@ -251,7 +251,7 @@ directory or a git repository (git must be installed on the build environment).
llama stack build --config my-external-stack.yaml
```
For more information on external providers, including directory structure, provider types, and implementation requirements, see the [External Providers documentation](../providers/external.md).
For more information on external providers, including directory structure, provider types, and implementation requirements, see the [External Providers documentation](../providers/external/).
</TabItem>
<TabItem value="container" label="Building Container">

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docs/docs/distributions/configuration.mdx
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@ -206,7 +206,7 @@ models:
provider_model_id: null
model_type: llm
```
A Model is an instance of a "Resource" (see [Concepts](../concepts/index)) and is associated with a specific inference provider (in this case, the provider with identifier `ollama`). This is an instance of a "pre-registered" model. While we always encourage the clients to register models before using them, some Stack servers may come up a list of "already known and available" models.
A Model is an instance of a "Resource" (see [Concepts](../concepts/)) and is associated with a specific inference provider (in this case, the provider with identifier `ollama`). This is an instance of a "pre-registered" model. While we always encourage the clients to register models before using them, some Stack servers may come up a list of "already known and available" models.
What's with the `provider_model_id` field? This is an identifier for the model inside the provider's model catalog. Contrast it with `model_id` which is the identifier for the same model for Llama Stack's purposes. For example, you may want to name "llama3.2:vision-11b" as "image_captioning_model" when you use it in your Stack interactions. When omitted, the server will set `provider_model_id` to be the same as `model_id`.

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docs/docs/distributions/importing_as_library.mdx
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@ -33,7 +33,7 @@ Then, you can access the APIs like `models` and `inference` on the client and ca
response = client.models.list()
```
If you've created a [custom distribution](building_distro.md), you can also use the run.yaml configuration file directly:
If you've created a [custom distribution](./building_distro), you can also use the run.yaml configuration file directly:
```python
client = LlamaStackAsLibraryClient(config_path)

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docs/docs/distributions/index.mdx
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@ -13,9 +13,9 @@ This section provides an overview of the distributions available in Llama Stack.
## Distribution Guides
- **[Available Distributions](./list_of_distributions)** - Complete list and comparison of all distributions
- **[Building Custom Distributions](./building_distro)** - Create your own distribution from scratch
- **[Customizing Configuration](./customizing_run_yaml)** - Customize run.yaml for your needs
- **[Starting Llama Stack Server](./starting_llama_stack_server)** - How to run distributions
- **[Importing as Library](./importing_as_library)** - Use distributions in your code
- **[Configuration Reference](./configuration)** - Configuration file format details
- **[Available Distributions](./list_of_distributions.mdx)** - Complete list and comparison of all distributions
- **[Building Custom Distributions](./building_distro.mdx)** - Create your own distribution from scratch
- **[Customizing Configuration](./customizing_run_yaml.mdx)** - Customize run.yaml for your needs
- **[Starting Llama Stack Server](./starting_llama_stack_server.mdx)** - How to run distributions
- **[Importing as Library](./importing_as_library.mdx)** - Use distributions in your code
- **[Configuration Reference](./configuration.mdx)** - Configuration file format details

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docs/docs/distributions/list_of_distributions.mdx
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@ -62,7 +62,7 @@ docker pull llama-stack/distribution-meta-reference-gpu
**Partners:** [Fireworks.ai](https://fireworks.ai) and [Together.xyz](https://together.xyz)
**Guides:** [Remote-Hosted Endpoints](remote_hosted_distro/index)
**Guides:** [Remote-Hosted Endpoints](./remote_hosted_distro/)
### 📱 Mobile Development
@ -81,7 +81,7 @@ docker pull llama-stack/distribution-meta-reference-gpu
- You need custom configurations
- You want to optimize for your specific use case
**Guides:** [Building Custom Distributions](building_distro.md)
**Guides:** [Building Custom Distributions](./building_distro)
## Detailed Documentation
@ -131,4 +131,4 @@ graph TD
3. **Configure your providers** with API keys or local models
4. **Start building** with Llama Stack!
For help choosing or troubleshooting, check our [Getting Started Guide](../getting_started/index.md) or [Community Support](https://github.com/llama-stack/llama-stack/discussions).
For help choosing or troubleshooting, check our [Getting Started Guide](/docs/getting_started/quickstart) or [Community Support](https://github.com/llama-stack/llama-stack/discussions).

2
docs/docs/distributions/ondevice_distro/android_sdk.md
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@ -66,7 +66,7 @@ llama stack run starter --port 5050
Ensure the Llama Stack server version is the same as the Kotlin SDK Library for maximum compatibility.
Other inference providers: [Table](../../index.md#supported-llama-stack-implementations)
Other inference providers: [Table](/docs/)
How to set remote localhost in Demo App: [Settings](https://github.com/meta-llama/llama-stack-client-kotlin/tree/latest-release/examples/android_app#settings)

4
docs/docs/distributions/starting_llama_stack_server.mdx
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@ -16,11 +16,11 @@ This is the simplest way to get started. Using Llama Stack as a library means yo
## Container:
Another simple way to start interacting with Llama Stack is to just spin up a container (via Docker or Podman) which is pre-built with all the providers you need. We provide a number of pre-built images so you can start a Llama Stack server instantly. You can also build your own custom container. Which distribution to choose depends on the hardware you have. See [Selection of a Distribution](selection) for more details.
Another simple way to start interacting with Llama Stack is to just spin up a container (via Docker or Podman) which is pre-built with all the providers you need. We provide a number of pre-built images so you can start a Llama Stack server instantly. You can also build your own custom container. Which distribution to choose depends on the hardware you have. See [Selection of a Distribution](./list_of_distributions) for more details.
## Kubernetes:
If you have built a container image and want to deploy it in a Kubernetes cluster instead of starting the Llama Stack server locally. See [Kubernetes Deployment Guide](kubernetes_deployment) for more details.
If you have built a container image and want to deploy it in a Kubernetes cluster instead of starting the Llama Stack server locally. See [Kubernetes Deployment Guide](../deploying/kubernetes_deployment) for more details.
```{toctree}

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docs/docs/getting_started/detailed_tutorial.mdx
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@ -18,7 +18,7 @@ In Llama Stack, we provide a server exposing multiple APIs. These APIs are backe
Llama Stack is a stateful service with REST APIs to support seamless transition of AI applications across different environments. The server can be run in a variety of ways, including as a standalone binary, Docker container, or hosted service. You can build and test using a local server first and deploy to a hosted endpoint for production.
In this guide, we'll walk through how to build a RAG agent locally using Llama Stack with [Ollama](https://ollama.com/)
as the inference [provider](../providers/index.md#inference) for a Llama Model.
as the inference [provider](/docs/providers/inference/) for a Llama Model.
### Step 1: Installation and Setup
@ -60,8 +60,8 @@ Llama Stack is a server that exposes multiple APIs, you connect with it using th
<TabItem value="venv" label="Using venv">
You can use Python to build and run the Llama Stack server, which is useful for testing and development.
Llama Stack uses a [YAML configuration file](../distributions/configuration.md) to specify the stack setup,
which defines the providers and their settings. The generated configuration serves as a starting point that you can [customize for your specific needs](../distributions/customizing_run_yaml.md).
Llama Stack uses a [YAML configuration file](../distributions/configuration) to specify the stack setup,
which defines the providers and their settings. The generated configuration serves as a starting point that you can [customize for your specific needs](../distributions/customizing_run_yaml).
Now let's build and run the Llama Stack config for Ollama.
We use `starter` as template. By default all providers are disabled, this requires enable ollama by passing environment variables.
@ -73,7 +73,7 @@ llama stack build --distro starter --image-type venv --run
You can use a container image to run the Llama Stack server. We provide several container images for the server
component that works with different inference providers out of the box. For this guide, we will use
`llamastack/distribution-starter` as the container image. If you'd like to build your own image or customize the
configurations, please check out [this guide](../distributions/building_distro.md).
configurations, please check out [this guide](../distributions/building_distro).
First lets setup some environment variables and create a local directory to mount into the container’s file system.
```bash
export LLAMA_STACK_PORT=8321
@ -145,7 +145,7 @@ pip install llama-stack-client
</TabItem>
</Tabs>
Now let's use the `llama-stack-client` [CLI](../references/llama_stack_client_cli_reference.md) to check the
Now let's use the `llama-stack-client` [CLI](../references/llama_stack_client_cli_reference) to check the
connectivity to the server.
```bash
@ -216,8 +216,8 @@ OpenAIChatCompletion(
### Step 4: Run the Demos
Note that these demos show the [Python Client SDK](../references/python_sdk_reference/index).
Other SDKs are also available, please refer to the [Client SDK](../index.md#client-sdks) list for the complete options.
Note that these demos show the [Python Client SDK](../references/python_sdk_reference/).
Other SDKs are also available, please refer to the [Client SDK](/docs/) list for the complete options.
<Tabs>
<TabItem value="inference" label="Basic Inference">
@ -538,4 +538,4 @@ uv run python rag_agent.py
**You're Ready to Build Your Own Apps!**
Congrats! 🥳 Now you're ready to [build your own Llama Stack applications](../building_applications/index)! 🚀
Congrats! 🥳 Now you're ready to [build your own Llama Stack applications](../building_applications/)! 🚀

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docs/docs/getting_started/quickstart.mdx
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@ -140,7 +140,7 @@ If you are getting a **401 Client Error** from HuggingFace for the **all-MiniLM-
### Next Steps
Now you're ready to dive deeper into Llama Stack!
- Explore the [Detailed Tutorial](/docs/detailed_tutorial).
- Explore the [Detailed Tutorial](./detailed_tutorial).
- Try the [Getting Started Notebook](https://github.com/meta-llama/llama-stack/blob/main/docs/getting_started.ipynb).
- Browse more [Notebooks on GitHub](https://github.com/meta-llama/llama-stack/tree/main/docs/notebooks).
- Learn about Llama Stack [Concepts](/docs/concepts).

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docs/docs/providers/external/index.mdx vendored
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@ -7,5 +7,5 @@ Llama Stack supports external providers that live outside of the main codebase.
## External Provider Documentation
- [Known External Providers](external-providers-list)
- [Creating External Providers](external-providers-guide)
- [Known External Providers](./external-providers-list.mdx)
- [Creating External Providers](./external-providers-guide.mdx)

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docs/docs/references/index.mdx
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@ -7,6 +7,6 @@ sidebar_position: 1
# References
- [Python SDK Reference](python_sdk_reference/index)
- [Llama CLI](llama_cli_reference/index) for building and running your Llama Stack server
- [Llama Stack Client CLI](llama_stack_client_cli_reference) for interacting with your Llama Stack server
- [Python SDK Reference](/docs/references/python_sdk_reference/)
- [Llama CLI](/docs/references/llama_cli_reference/) for building and running your Llama Stack server
- [Llama Stack Client CLI](./llama_stack_client_cli_reference.md) for interacting with your Llama Stack server