fix(doc): fixed all broken links

docs/blog/main.mdx

@@ -111,7 +111,7 @@ We're excited to announce two major features this week:
 
 1. We've integrated [RAGAS evaluators](https://docs.ragas.io/) into agenta. Two new evaluators have been added: **RAG Faithfulness** (measuring how consistent the LLM output is with the context) and **Context Relevancy** (assessing how relevant the retrieved context is to the question). Both evaluators use intermediate outputs within the trace to calculate the final score.
 
-   [Check out the tutorial](evaluation/rag_evaluators) to learn how to use RAG evaluators.
+   [Check out the tutorial](/evaluation/evaluators/rag-evaluators) to learn how to use RAG evaluators.
 
 {" "}
 
@@ -259,7 +259,7 @@ config = agenta.get_config(base_id="xxxxx", environment="production", cache_time
 
 ```
 
-You can find additional documentation [here](/prompt_management/integrating).
+You can find additional documentation [here](/prompt-management/integration/how-to-integrate-with-agenta).
 
 **Improvements**
 

docs/docs/evaluation/evaluators/05-rag-evaluators.mdx

@@ -124,9 +124,9 @@ On the evlauators page, click on `RAG Faithfulness` for example.
 RAG evaluators, based on [RAGAS](https://docs.ragas.io/) are different from
 other evaluators in Agenta in that they often require internal variables. For
 instance,
-[Faithfulness](https://docs.ragas.io/en/stable/concepts/metrics/faithfulness.html)
+[Faithfulness](https://docs.ragas.io/en/stable/concepts/metrics/available_metrics/faithfulness/)
 and [Context
-Relevancy](https://docs.ragas.io/en/stable/concepts/metrics/context_precision.html)
+Relevancy](https://docs.ragas.io/en/stable/concepts/metrics/available_metrics/context_precision/)
 both require `question`, `answer`, and `contexts`.
 
 From the trace we saw before, we could say that `answer` maps to the second rag summarizer output report, denoted by `rag.summarizer[1].outputs.report`. Similarly, the `contexts` map to the rag retriever output movies, denoted by `rag.retriever.outputs.movies`.

docs/docs/getting-started/01-introduction.mdx

@@ -18,12 +18,12 @@ management and evaluation**.
 
 ### With Agenta, you can:
 
-1. Rapidly [**experiment** and **compare** prompts](/prompt_management/prompt_engineering) on [any LLM workflow](/prompt_management/setting_up/custom_applications) (chain-of-prompts, Retrieval Augmented Generation (RAG), LLM agents...)
-2. Rapidly [**create test sets**](/evaluation/test_sets) and **golden datasets** for evaluation
+1. Rapidly [**experiment** and **compare** prompts](/prompt-management/overview) on [any LLM workflow](/prompt-management/creating-a-custom-template) (chain-of-prompts, Retrieval Augmented Generation (RAG), LLM agents...)
+2. Rapidly [**create test sets**](/evaluation/create-test-sets) and **golden datasets** for evaluation
 3. **Evaluate** your application with pre-existing or **custom evaluators**
 4. **Annotate** and **A/B test** your applications with **human feedback**
 5. [**Collaborate with product teams**](/misc/team_management) for prompt engineering and evaluation
-6. [**Deploy your application**](/prompt_management/deployment) in one-click in the UI, through CLI, or through github workflows.
+6. [**Deploy your application**](/concepts/concepts#environments) in one-click in the UI, through CLI, or through github workflows.
 
 Agenta focuses on increasing the speed of the development cycle of LLM applications by increasing the speed of experimentation.
 
@@ -33,7 +33,7 @@ Agenta focuses on increasing the speed of the development cycle of LLM applicati
 
 Agenta enables prompt engineering and evaluation on any LLM app architecture, such as **Chain of Prompts**, **RAG**, or **LLM agents**. It is compatible with any framework like **Langchain** or **LlamaIndex**, and works with any model provider, such as **OpenAI**, **Cohere**, or **local models**.
 
-[Jump here](/prompt_management/setting_up/custom_applications) to see how to use your own custom application with Agenta and [here](/guides/how_does_agenta_work) to understand more how Agenta works.
+[Jump here](/prompt-management/creating-a-custom-template) to see how to use your own custom application with Agenta and [here](/concepts/architecture) to understand more how Agenta works.
 
 ### Enable collaboration between developers and product teams
 

docs/docs/getting-started/02-quick-start.mdx

@@ -5,7 +5,7 @@ description: "Create and deploy your first LLM app in one minute"
 
 :::note
 This tutorial helps users create LLM apps using templates within the UI. For more complex applications involving code
-in Agenta, please refer to Using code in Agenta [Using code in agenta](/prompt_management/setting_up/custom_applications){" "}
+in Agenta, please refer to Using code in Agenta [Using code in agenta](/prompt-management/creating-a-custom-template){" "}
 :::
 
 Want a video tutorial instead? We have a 4-minute video for you. [Watch it here](https://youtu.be/plPVrHXQ-DU).
@@ -76,6 +76,6 @@ You can now find the API endpoint in the "Endpoints" menu. Copy and paste the co
 
 :::info
 Congratulations! You've created your first LLM application. Feel free to modify it, explore its parameters, and discover
-Agenta's features. Your next steps could include [building an application using your own code](/prompt_management/custom_applications),
+Agenta's features. Your next steps could include [building an application using your own code](/prompt-management/creating-a-custom-template),
 or following one of our UI-based tutorials.
 :::

docs/docs/prompt-management/01-overview.mdx

@@ -81,7 +81,7 @@ Agenta enables you to version the entire **configuration** of the LLM app as a u
   <DocCard
     item={{
       type: "link",
-      href: "/prompt-management/prompt-engineering-in-the-playground",
+      href: "/prompt-management/using-the-playground",
       label: "Using the playground",
       description: "Perform prompt engineering in the playground",
     }}
@@ -97,7 +97,7 @@ Agenta enables you to version the entire **configuration** of the LLM app as a u
   <DocCard
     item={{
       type: "link",
-      href: "/prompt-management/how-to-publish-a-prompt",
+      href: "/prompt-management/quick-start#2-publish-a-variant",
       label: "Publishing a prompt",
       description: "How to publish a prompt to an endpoint from the web UI.",
     }}
@@ -108,7 +108,7 @@ Agenta enables you to version the entire **configuration** of the LLM app as a u
   <DocCard
     item={{
       type: "link",
-      href: "/prompt-management/how-to-use-a-prompt",
+      href: "/prompt-management/quick-start#3-integrate-with-your-code",
       label: "How to use a prompt",
       description: "How to use a published a prompt in your code",
     }}

docs/docs/prompt-management/02-quick-start.mdx

@@ -7,7 +7,7 @@ title: "Quick Start"
 In this tutorial, we will **create a prompt** in the web UI, **publish** it to a deployment, **integrate** it with our code base.
 
 :::note
-If you want to do this whole process programatically, jump to [this guide](/prompt-management/prompt-management-from-sdk)
+If you want to do this whole process programatically, jump to [this guide](/prompt-management/integration/how-to-integrate-with-agenta)
 :::
 
 ## 1. Create a prompt
@@ -95,4 +95,4 @@ Optionally you would like to revert to a previously published commit. For this c
 
 ## Next steps
 
-Now that you've created and published your first prompt, you can learn how to do [prompt engineering in the playground](/prompt_management/prompt_engineering) or dive deeper into [the capabilities of the prompt management SDK](/prompt-management/the-sdk)
+Now that you've created and published your first prompt, you can learn how to do [prompt engineering in the playground](/prompt-management/using-the-playground) or dive deeper into [the capabilities of the prompt management SDK](/prompt-management/creating-a-custom-template)

docs/docs/prompt-management/03-creating-a-custom-template.mdx

@@ -8,7 +8,7 @@ Agenta comes with several pre-built template LLM applications for common use cas
 This guide will show you how to create a custom application and use it with Agenta.
 
 :::tip
-We recommend reading ["How does Agenta work"](/guides/how_does_agenta_work) beforehand to familiarize yourself with the main concepts of Agenta.
+We recommend reading ["How does Agenta work"](/concepts/architecture) beforehand to familiarize yourself with the main concepts of Agenta.
 :::
 
 ## How to create a custom application in Agenta?

docs/docs/prompt-management/integration/01-how-to-integrate-with-agenta.mdx

@@ -13,7 +13,7 @@ In addition to prompt management, agenta provides observability features.
 If you're using Agenta [as a proxy](#2-as-a-middleware--model-proxy), all your calls are traced automatically without any additional setup. However, if you're using Agenta [as prompt management system](#1-as-a-prompt-management-system) (i.e. only fetching the prompts), you need to integrate observability manually into your code base. You can learn how to do this [here](/observability/quickstart).
 :::
 
-### [1. As a prompt management system](/prompt-management/integration/02-fetch-prompts):
+### [1. As a prompt management system](/prompt-management/integration/fetch-prompts):
 
 In this approach, prompts are managed and stored in the Agenta backend. You use the Agenta SDK to fetch the latest deployed version of your prompt and use it in your application.
 
@@ -33,7 +33,7 @@ In this approach, prompts are managed and stored in the Agenta backend. You use
   alt="A sequence diagram showing how to integrate with Agenta as a prompt management system"
 />
 
-### **[2. As a middleware / model proxy](/prompt-management/integration/03-proxy-calls)**:
+### **[2. As a middleware / model proxy](/prompt-management/integration/proxy-calls)**:
 
 In this setup, Agenta provides you with an endpoint that forwards requests to the LLM on your behalf.
 

docs/docs/reference/api/agenta-backend.info.mdx

@@ -21,7 +21,7 @@ import Export from "@theme/ApiExplorer/Export";
 </span>
 
 <Export
-  url={"https://raw.githubusercontent.com/PaloAltoNetworks/docusaurus-template-openapi-docs/main/examples/agenta.yaml"}
+  url={"https://raw.githubusercontent.com/Agenta-AI/agenta/refs/heads/main/docs/docs/reference/openapi.json"}
   proxy={undefined}
 >
 

docs/docs/self-host/deploy_remotly/host-on-gcp.mdx

@@ -47,7 +47,7 @@ Step 2 until 6 can be skipped if you already have a project with billing enabled
 
 In order to ssh into the instance you need to:
 
-1. Uncomment these lines in the [Terraform instance file](https://github.com/Agenta-AI/agenta/blob/main/self-host/gcp/agenta_instance.tf)
+1. Uncomment these lines in the [Terraform instance file](https://github.com/Agenta-AI/agenta/blob/main/self-host/gcp/agenta-instance.tf)
 
 ```bash
 metadata = {

docs/docusaurus.config.ts

@@ -249,7 +249,7 @@ const config: Config = {
             specPath: "docs/reference/openapi.json",
             outputDir: "docs/reference/api",
             downloadUrl:
-              "https://raw.githubusercontent.com/PaloAltoNetworks/docusaurus-template-openapi-docs/main/examples/agenta.yaml",
+              "https://raw.githubusercontent.com/Agenta-AI/agenta/refs/heads/main/docs/docs/reference/openapi.json",
             sidebarOptions: {
               groupPathsBy: "tag",
               categoryLinkSource: "tag",