docs: fix broken links (#3540)

# What does this PR do?

<!-- Provide a short summary of what this PR does and why. Link to relevant issues if applicable. -->

<!-- If resolving an issue, uncomment and update the line below -->

<!-- Closes #[issue-number] -->

- Fixes broken links and Docusaurus search

Closes #3518

## Test Plan

The following should produce a clean build with no warnings and search enabled:

```
npm install
npm run gen-api-docs all
npm run build
npm run serve
```

<!-- Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.* -->

docs/docs/building_applications/evals.mdx

@@ -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

docs/docs/building_applications/index.mdx

@@ -20,23 +20,23 @@ The best way to get started is to look at this comprehensive notebook which walk
 Here are the key topics that will help you build effective AI applications:
 
 ### 🤖 **Agent Development**
-- **[Agent Framework](./agent)** - Understand the components and design patterns of the Llama Stack agent framework
-- **[Agent Execution Loop](./agent_execution_loop)** - How agents process information, make decisions, and execute actions
-- **[Agents vs Responses API](./responses_vs_agents)** - Learn when to use each API for different use cases
+- **[Agent Framework](./agent.mdx)** - Understand the components and design patterns of the Llama Stack agent framework
+- **[Agent Execution Loop](./agent_execution_loop.mdx)** - How agents process information, make decisions, and execute actions
+- **[Agents vs Responses API](./responses_vs_agents.mdx)** - Learn when to use each API for different use cases
 
 ### 📚 **Knowledge Integration**
-- **[RAG (Retrieval-Augmented Generation)](./rag)** - Enhance your agents with external knowledge through retrieval mechanisms
+- **[RAG (Retrieval-Augmented Generation)](./rag.mdx)** - Enhance your agents with external knowledge through retrieval mechanisms
 
 ### 🛠️ **Capabilities & Extensions**
-- **[Tools](./tools)** - Extend your agents' capabilities by integrating with external tools and APIs
+- **[Tools](./tools.mdx)** - Extend your agents' capabilities by integrating with external tools and APIs
 
 ### 📊 **Quality & Monitoring**
-- **[Evaluations](./evals)** - Evaluate your agents' effectiveness and identify areas for improvement
-- **[Telemetry](./telemetry)** - Monitor and analyze your agents' performance and behavior
-- **[Safety](./safety)** - Implement guardrails and safety measures to ensure responsible AI behavior
+- **[Evaluations](./evals.mdx)** - Evaluate your agents' effectiveness and identify areas for improvement
+- **[Telemetry](./telemetry.mdx)** - Monitor and analyze your agents' performance and behavior
+- **[Safety](./safety.mdx)** - Implement guardrails and safety measures to ensure responsible AI behavior
 
 ### 🎮 **Interactive Development**
-- **[Playground](./playground)** - Interactive environment for testing and developing applications
+- **[Playground](./playground.mdx)** - Interactive environment for testing and developing applications
 
 ## Application Patterns
 
@@ -77,7 +77,7 @@ Build production-ready systems with:
 
 ## Related Resources
 
-- **[Getting Started](/docs/getting-started/)** - 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/)** - Complete API documentation
+- **[API Reference](/docs/api/llama-stack-specification)** - Complete API documentation

docs/docs/building_applications/playground.mdx

@@ -291,9 +291,9 @@ llama stack run meta-reference
 
 ## Related Resources
 
-- **[Getting Started Guide](/docs/getting-started)** - 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
 - **[Evaluations](./evals)** - Comprehensive evaluation framework
-- **[API Reference](/docs/api-reference)** - Complete API documentation
+- **[API Reference](/docs/api/llama-stack-specification)** - Complete API documentation

docs/docs/building_applications/responses_vs_agents.mdx

@@ -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