docs: update Hayhooks documentation linking to its own documentation (#11010)

* docs: update Hayhooks documentation linking to its own documentation

* fix trafilatura typo

* Adapt example for docker compose

* Change docs for v2.27

* Revert changes to v2.26 docs

Author: bogdankostic

docs-website/docs/development/deployment.mdx

@@ -23,14 +23,14 @@ Here are the currently available guides on Haystack pipeline deployment:
 
 ### Hayhooks
 
-Haystack can be easily integrated into any HTTP application, but if you don’t have one, you can use Hayhooks, a ready-made application that serves Haystack pipelines as REST endpoints. We’ll be using Hayhooks throughout this guide to streamline the code examples. Refer to the Hayhooks [documentation](hayhooks.mdx) to get details about how to run the server and deploy your pipelines.
+Haystack can be easily integrated into any HTTP application, but if you don’t have one, you can use Hayhooks, a ready-made application that serves Haystack pipelines as REST endpoints. We’ll be using Hayhooks throughout this guide to streamline the code examples. Refer to the Hayhooks [overview](hayhooks.mdx) for a quick start, or the [official Hayhooks documentation](https://deepset-ai.github.io/hayhooks/) for comprehensive guides and reference.
 
 :::note Looking to scale with confidence?
 
 If your team needs **enterprise-grade support, best practices, and deployment guidance** to run Haystack in production, check out **Haystack Enterprise Starter**.
 
 📜 [Learn more about Haystack Enterprise Starter](https://haystack.deepset.ai/blog/announcing-haystack-enterprise)  
-🤝 [Get in touch with our team](https://www.deepset.ai/products-and-services/haystack-enterprise-starter) 
+🤝 [Get in touch with our team](https://www.deepset.ai/products-and-services/haystack-enterprise-starter)
 
 👉 For platform tooling to **manage data, pipelines, testing, and governance at scale**, explore the [Haystack Enterprise Platform](https://www.deepset.ai/products-and-services/haystack-enterprise-platform).
 :::

docs-website/docs/development/deployment/docker.mdx

@@ -13,7 +13,7 @@ Learn how to deploy your Haystack pipelines through Docker starting from the bas
 
 The most basic form of Haystack deployment happens through Docker containers. Becoming familiar with running and customizing Haystack Docker images is useful as they form the basis for more advanced deployment.
 
-Haystack releases are officially distributed through the [`deepset/haystack`](https://hub.docker.com/r/deepset/haystack) Docker image. Haystack images come in different flavors depending on the specific components they ship and the Haystack version. 
+Haystack releases are officially distributed through the [`deepset/haystack`](https://hub.docker.com/r/deepset/haystack) Docker image. Haystack images come in different flavors depending on the specific components they ship and the Haystack version.
 
 :::info
 At the moment, the only flavor available for Haystack is `base`, which ships exactly what you would get by installing Haystack locally with `pip install haystack-ai`.
@@ -35,9 +35,9 @@ docker run -it --rm deepset/haystack:base-v2.12.1 python -c"from haystack.versio
 
 Chances are your application will be more complex than a simple script, and you’ll need to install additional dependencies inside the Docker image along with Haystack.
 
-For example, you might want to run a simple indexing pipeline using [Chroma](../../document-stores/chromadocumentstore.mdx) as your Document Store using a Docker container. The `base` image only contains a basic install of Haystack, but you need to install the Chroma integration (`chroma-haystack`) package additionally. The best approach would be to create a custom Docker image shipping the extra dependency. 
+For example, you might want to run a simple indexing pipeline using [Chroma](../../document-stores/chromadocumentstore.mdx) as your Document Store using a Docker container. The `base` image only contains a basic install of Haystack, but you need to install the Chroma integration (`chroma-haystack`) package additionally. The best approach would be to create a custom Docker image shipping the extra dependency.
 
-Assuming you have a `main.py` script in your current folder, the Dockerfile would look like this: 
+Assuming you have a `main.py` script in your current folder, the Dockerfile would look like this:
 
 ```shell
 FROM deepset/haystack:base-v2.12.1
@@ -61,12 +61,12 @@ A Haystack application running in Docker can go pretty far: with an internet con
 
 Still, you might want to orchestrate additional services for your Haystack container locally, for example, to reduce costs or increase performance. When your application runtime depends on more than one Docker container, [Docker Compose](https://docs.docker.com/compose/) is a great tool to keep everything together.
 
-As an example, let’s say your application wraps two pipelines: one to _index_ documents into a Qdrant instance and the other to _query_ those documents at a later time. This setup would require two Docker containers: one to run the pipelines as REST APIs using [Hayhooks](../hayhooks.mdx) and a second to run a Qdrant instance.
+As an example, let’s say your application wraps two pipelines: one to _index_ documents into a Qdrant instance and the other to _query_ those documents at a later time. This setup would require two Docker containers: one to run the pipelines as REST APIs using [Hayhooks](../hayhooks.mdx) and a second to run a Qdrant instance. For more information on configuring Hayhooks using Docker Compose, see the [official Hayhooks documentation](https://deepset-ai.github.io/hayhooks/getting-started/quick-start-docker/).
 
 For building the Hayhooks image, we can easily customize the base image of one of the latest versions of Hayhooks, adding required dependencies required by [`QdrantDocumentStore`](../../document-stores/qdrant-document-store.mdx). The Dockerfile would look like this:
 
 ```dockerfile Dockerfile
-FROM deepset/hayhooks:v0.6.0
+FROM deepset/hayhooks:v1.16.0
 
 RUN pip install qdrant-haystack sentence-transformers
 
@@ -114,4 +114,4 @@ configs:
       log_level: INFO
 ```
 
-For a functional example of a Docker Compose deployment, check out the [“Qdrant Indexing”](https://github.com/deepset-ai/haystack-demos/tree/main/qdrant_indexing) demo from GitHub.
+For a functional example of a Docker Compose deployment, check out the [“RAG indexing and querying with Elasticsearch”](https://github.com/deepset-ai/hayhooks/tree/main/examples/rag_indexing_query) example from GitHub.

docs-website/docs/development/deployment/kubernetes.mdx

@@ -28,7 +28,7 @@ metadata:
     app: haystack
 spec:
   containers:
-    - image: deepset/hayhooks:v0.6.0
+    - image: deepset/hayhooks:v1.16.0
       name: hayhooks
       imagePullPolicy: IfNotPresent
       resources:
@@ -94,7 +94,7 @@ spec:
 
 After applying this, `hayhooks` Pod will be accessible on `localhost:30080`.
 
-From here, you should be able to manage pipelines. Remember that it's possible to deploy multiple different pipelines on a single Hayhooks instance. Check the [Hayhooks docs](../hayhooks.mdx) for more details.
+From here, you should be able to manage pipelines. Remember that it's possible to deploy multiple different pipelines on a single Hayhooks instance. Check the [Hayhooks overview](../hayhooks.mdx) or the [official Hayhooks documentation](https://deepset-ai.github.io/hayhooks/) for more details.
 
 ## Auto-Run Pipelines at Pod Start
 
@@ -128,7 +128,7 @@ metadata:
     app: haystack
 spec:
   containers:
-    - image: deepset/hayhooks:v0.6.0
+    - image: deepset/hayhooks:v1.16.0
       name: hayhooks
       imagePullPolicy: IfNotPresent
       command: ["/bin/sh", "-c"]
@@ -164,7 +164,7 @@ spec:
 
 Note that:
 
-- We changed the Hayhooks container `command` to install `trafilaura` dependency before startup, since it's needed for our [chat_with_website](https://github.com/deepset-ai/hayhooks/tree/main/examples/pipeline_wrappers/chat_with_website) example pipeline. For a real production environment, we recommend creating a custom Hayhooks image as described [here](docker.mdx#customizing-the-haystack-docker-image).
+- We changed the Hayhooks container `command` to install the `trafilatura` dependency before startup, since it's needed for our [chat_with_website](https://github.com/deepset-ai/hayhooks/tree/main/examples/pipeline_wrappers/chat_with_website) example pipeline. For a real production environment, we recommend creating a custom Hayhooks image as described [here](docker.mdx#customizing-the-haystack-docker-image).
 - We make Hayhooks container read `OPENAI_API_KEY` from a Kubernetes Secret.
 
 Before applying this new configuration, create the `openai-secret`:
@@ -225,7 +225,7 @@ spec:
               memory: "128Mi"
               cpu: "250m"
       containers:
-        - image: deepset/hayhooks:v0.6.0
+        - image: deepset/hayhooks:v1.16.0
           name: hayhooks
           imagePullPolicy: IfNotPresent
           command: ["/bin/sh", "-c"]

docs-website/docs/development/deployment/openshift.mdx

@@ -18,7 +18,7 @@ OpenShift by Red Hat is a platform that helps create and manage applications bui
 The fastest way to deploy a Haystack pipeline is to deploy an OpenShift application that runs Hayhooks. Before starting, make sure to have the following prerequisites:
 
 - Access to an OpenShift project. Follow RedHat's [instructions](https://developers.redhat.com/developer-sandbox) to create one and start experimenting immediately.
-- Hayhooks are installed. Run `pip install hayhooks` and make sure it works by running `hayhooks --version`. Read more about Hayhooks in our [docs](../hayhooks.mdx).
+- Hayhooks is installed. Run `pip install hayhooks` and make sure it works by running `hayhooks --version`. Read more about Hayhooks in our [overview](../hayhooks.mdx) or the [official Hayhooks documentation](https://deepset-ai.github.io/hayhooks/).
 - You can optionally install the OpenShift command-line utility `oc`. Follow the [installation instructions](https://docs.openshift.com/container-platform/4.15/cli_reference/openshift_cli/getting-started-cli.html) for your platform and make sure it works by running `oc—h`.
 
 ## Creating a Hayhooks Application
@@ -35,7 +35,7 @@ In this guide, we’ll be using the `oc` command line, but you can achieve the s
 3. Assuming you already have a project (it’s the case for the developer sandbox), create an application running the Hayhooks Docker image available on Docker Hub:  
    Note how you can pass environment variables that your application will use at runtime. In this case, we disable Haystack’s internal telemetry and set an OpenAI key that will be used by the pipelines we’ll eventually deploy in Hayhooks.
    ```
-   oc new-app deepset/hayhooks:main -e HAYSTACK_TELEMETRY_ENABLED=false -e OPENAI_API_KEY=$OPENAI_API_KEY
+   oc new-app deepset/hayhooks:v1.16.0 -e HAYSTACK_TELEMETRY_ENABLED=false -e OPENAI_API_KEY=$OPENAI_API_KEY
    ```
 
 4. To make sure you make the most out of OpenShift's ability to manage the lifecycle of the application, you can set a [liveness probe](https://kubernetes.io/docs/tasks/configure-pod-container/configure-liveness-readiness-startup-probes/):  
@@ -64,10 +64,10 @@ In this guide, we’ll be using the `oc` command line, but you can achieve the s
 
 7. `http://hayhooks-XXX.openshiftapps.com` will be the public URL serving your Hayhooks instance. At this point, you can query Hayhooks status by running:  
    ```
-   hayhooks --server http://hayhooks-XXX.openshiftapps.com status
+   HAYHOOKS_HOST=hayhooks-XXX.openshiftapps.com HAYHOOKS_PORT=80 hayhooks status
    ```
 
 8. Lastly, deploy your pipeline as usual:  
    ```
-   hayhooks --server http://hayhooks-XXX.openshiftapps.com deploy your_pipeline.yaml
-   ```
+   HAYHOOKS_HOST=hayhooks-XXX.openshiftapps.com HAYHOOKS_PORT=80 hayhooks pipeline deploy-files -n my_pipeline /path/to/my_pipeline_dir
+   ```