docs: Reorganize documentation on the webpage (#2651)

# What does this PR do?
Reorganizes the Llama stack webpage into more concise index pages,
introduce more of a workflow, and reduce repetition of content.

New nav structure so far based on #2637 

Further discussions in
https://github.com/meta-llama/llama-stack/discussions/2585

**Preview:**
![Screenshot 2025-07-09 at 2 31
53 PM](https://github.com/user-attachments/assets/4c1f3845-b328-4f12-9f20-3f09375007af)

You can also build a full local preview locally 

 **Feedback**
Looking for feedback on page titles and general feedback on the new
structure

**Follow up documentation**
I plan on reducing some sections and standardizing some terminology in a
follow up PR.
More discussions on that in
https://github.com/meta-llama/llama-stack/discussions/2585

docs/source/concepts/architecture.md

@@ -0,0 +1,70 @@
+## Llama Stack architecture
+
+Llama Stack allows you to build different layers of distributions for your AI workloads using various SDKs and API providers.
+
+```{image} ../../_static/llama-stack.png
+:alt: Llama Stack
+:width: 400px
+```
+
+### Benefits of Llama stack
+
+#### Current challenges in custom AI applications
+
+Building production AI applications today requires solving multiple challenges:
+
+Infrastructure Complexity
+
+- Running large language models efficiently requires specialized infrastructure.
+- Different deployment scenarios (local development, cloud, edge) need different solutions.
+- Moving from development to production often requires significant rework.
+
+**Essential Capabilities**
+
+- Safety guardrails and content filtering are necessary in an enterprise setting.
+- Just model inference is not enough - Knowledge retrieval and RAG capabilities are required.
+- Nearly any application needs composable multi-step workflows.
+- Without monitoring, observability and evaluation, you end up operating in the dark.
+
+**Lack of Flexibility and Choice**
+
+- Directly integrating with multiple providers creates tight coupling.
+- Different providers have different APIs and abstractions.
+- Changing providers requires significant code changes.
+
+#### Our Solution: A Universal Stack
+
+Llama Stack addresses these challenges through a service-oriented, API-first approach:
+
+**Develop Anywhere, Deploy Everywhere**
+- Start locally with CPU-only setups
+- Move to GPU acceleration when needed
+- Deploy to cloud or edge without code changes
+- Same APIs and developer experience everywhere
+
+**Production-Ready Building Blocks**
+- Pre-built safety guardrails and content filtering
+- Built-in RAG and agent capabilities
+- Comprehensive evaluation toolkit
+- Full observability and monitoring
+
+**True Provider Independence**
+- Swap providers without application changes
+- Mix and match best-in-class implementations
+- Federation and fallback support
+- No vendor lock-in
+
+**Robust Ecosystem**
+- Llama Stack is already integrated with distribution partners (cloud providers, hardware vendors, and AI-focused companies).
+- Ecosystem offers tailored infrastructure, software, and services for deploying a variety of models.
+
+
+### Our Philosophy
+
+- **Service-Oriented**: REST APIs enforce clean interfaces and enable seamless transitions across different environments.
+- **Composability**: Every component is independent but works together seamlessly
+- **Production Ready**: Built for real-world applications, not just demos
+- **Turnkey Solutions**: Easy to deploy built in solutions for popular deployment scenarios
+
+
+With Llama Stack, you can focus on building your application while we handle the infrastructure complexity, essential capabilities, and provider integrations.

docs/source/concepts/evaluation_concepts.md

@@ -1,77 +0,0 @@
-## Evaluation Concepts
-
-The Llama Stack Evaluation flow allows you to run evaluations on your GenAI application datasets or pre-registered benchmarks.
-
-We introduce a set of APIs in Llama Stack for supporting running evaluations of LLM applications.
-- `/datasetio` + `/datasets` API
-- `/scoring` + `/scoring_functions` API
-- `/eval` + `/benchmarks` API
-
-This guide goes over the sets of APIs and developer experience flow of using Llama Stack to run evaluations for different use cases. Checkout our Colab notebook on working examples with evaluations [here](https://colab.research.google.com/drive/10CHyykee9j2OigaIcRv47BKG9mrNm0tJ?usp=sharing).
-
-
-The Evaluation APIs are associated with a set of Resources. Please visit the Resources section in our [Core Concepts](../concepts/index.md) guide for better high-level understanding.
-
-- **DatasetIO**: defines interface with datasets and data loaders.
-  - Associated with `Dataset` resource.
-- **Scoring**: evaluate outputs of the system.
-  - Associated with `ScoringFunction` resource. We provide a suite of out-of-the box scoring functions and also the ability for you to add custom evaluators. These scoring functions are the core part of defining an evaluation task to output evaluation metrics.
-- **Eval**: generate outputs (via Inference or Agents) and perform scoring.
-  - Associated with `Benchmark` resource.
-
-
-### Open-benchmark Eval
-
-#### List of open-benchmarks Llama Stack support
-
-Llama stack pre-registers several popular open-benchmarks to easily evaluate model perfomance via CLI.
-
-The list of open-benchmarks we currently support:
-- [MMLU-COT](https://arxiv.org/abs/2009.03300) (Measuring Massive Multitask Language Understanding): Benchmark designed to comprehensively evaluate the breadth and depth of a model's academic and professional understanding
-- [GPQA-COT](https://arxiv.org/abs/2311.12022) (A Graduate-Level Google-Proof Q&A Benchmark): A challenging benchmark of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry.
-- [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](https://llama-stack.readthedocs.io/en/latest/references/evals_reference/index.html#open-benchmark-contributing-guide) to add more open-benchmarks to Llama Stack
-
-#### Run evaluation on open-benchmarks via CLI
-
-We have built-in functionality to run the supported open-benckmarks using llama-stack-client CLI
-
-#### Spin up Llama Stack server
-
-Spin up llama stack server with 'open-benchmark' template
-```
-llama stack run llama_stack/templates/open-benchmark/run.yaml
-
-```
-
-#### Run eval CLI
-There are 3 necessary inputs to run a benchmark eval
-- `list of benchmark_ids`: The list of benchmark ids to run evaluation on
-- `model-id`: The model id to evaluate on
-- `output_dir`: Path to store the evaluate results
-```
-llama-stack-client eval run-benchmark <benchmark_id_1> <benchmark_id_2> ... \
---model_id <model id to evaluate on> \
---output_dir <directory to store the evaluate results> \
-```
-
-You can run
-```
-llama-stack-client eval run-benchmark help
-```
-to see the description of all the flags that eval run-benchmark has
-
-
-In the output log, you can find the file path that has your evaluation results. Open that file and you can see you aggregate
-evaluation results over there.
-
-
-
-#### What's Next?
-
-- 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.md) guide for more details on how to use the Evaluation APIs to evaluate your applications.
-- Check out our [Evaluation Reference](../references/evals_reference/index.md) for more details on the APIs.

docs/source/concepts/index.md

@@ -2,6 +2,10 @@
 
 Given Llama Stack's service-oriented philosophy, a few concepts and workflows arise which may not feel completely natural in the LLM landscape, especially if you are coming with a background in other frameworks.
 
+```{include} architecture.md
+:start-after: ## Llama Stack architecture
+```
+
 ```{include} apis.md
 :start-after: ## APIs
 ```
@@ -10,14 +14,10 @@ Given Llama Stack's service-oriented philosophy, a few concepts and workflows ar
 :start-after: ## API Providers
 ```
 
-```{include} resources.md
-:start-after: ## Resources
-```
-
 ```{include} distributions.md
 :start-after: ## Distributions
 ```
 
-```{include} evaluation_concepts.md
-:start-after: ## Evaluation Concepts
+```{include} resources.md
+:start-after: ## Resources
 ```