docs: copy github images to built dirhtml

Signed-off-by: Joan Fontanals Martinez <[email protected]>

README.md

@@ -25,7 +25,7 @@
 
 Jina lets you build multimodal [**AI services**](#build-ai-models) and [**pipelines**](#build-a-pipeline) that communicate via gRPC, HTTP and WebSockets, then scale them up and deploy to production. You can focus on your logic and algorithms, without worrying about the infrastructure complexity.
 
-![](https://github.com/jina-ai/jina/.github/images/build-deploy.png)
+![](./.github/images/build-deploy.png)
 
 Jina provides a smooth Pythonic experience for serving ML models transitioning from local deployment to advanced orchestration frameworks like Docker-Compose, Kubernetes, or Jina AI Cloud. Jina makes advanced solution engineering and cloud-native technologies accessible to every developer.
 
@@ -85,7 +85,7 @@ Jina has three fundamental layers:
 
 Let's build a fast, reliable and scalable gRPC-based AI service. In Jina we call this an **[Executor](https://docs.jina.ai/concepts/serving/executor/)**. Our simple Executor will wrap the [StableLM](https://huggingface.co/stabilityai/stablelm-base-alpha-3b) LLM from Stability AI. We'll then use a **Deployment** to serve it.
 
-![](https://github.com/jina-ai/jina/.github/images/deployment-diagram.png)
+![](./.github/images/deployment-diagram.png)
 
 > **Note**
 > A Deployment serves just one Executor. To combine multiple Executors into a pipeline and serve that, use a [Flow](#build-a-pipeline).
@@ -265,7 +265,7 @@ class TextToImage(Executor):
 </table>
 
 
-![](https://github.com/jina-ai/jina/.github/images/flow-diagram.png)
+![](./.github/images/flow-diagram.png)
 
 Build the Flow with either Python or YAML:
 
@@ -340,7 +340,7 @@ response = client.post(on='/', inputs=[prompt], return_type=DocList[ImageDoc])
 response[0].display()
 ```
 
-![](https://github.com/jina-ai/jina/.github/images/mona-lisa.png)
+![](./.github/images/mona-lisa.png)
 
 <!-- end build-pipelines -->
 
@@ -352,7 +352,7 @@ Increase your application's throughput with scalability features out of the box,
 
 Let's scale a Stable Diffusion Executor deployment with replicas and dynamic batching:
 
-![](https://github.com/jina-ai/jina/.github/images/scaled-deployment.png)
+![](./.github/images/scaled-deployment.png)
 
 * Create two replicas, with [a GPU assigned for each](https://docs.jina.ai/concepts/orchestration/scale-out/#replicate-on-multiple-gpus).
 * Enable dynamic batching to process incoming parallel requests together with the same model inference.

docs/jina-ai-cloud/login.md

@@ -1,6 +1,6 @@
 # Login & Token Management
 
-To use Jina AI Cloud, you need to log in, either via a GitHub or Google account. This section describes how to log in Jina AI Cloud and manage the personal access token. You can do it via webpage, via CLI or via Python API.
+To use Jina AI Cloud, you need to log in, either via a GitHub or Google account. This section describes how to log in Jina AI Cloud and manage the personal access token. You can do it via webpage, CLI or Python API.
 
 ## via Webpage
 

docs/makedoc.sh

@@ -11,6 +11,8 @@ if [[ $1 == "local-only" ]]; then
     ghcr.io/jina-ai/protoc-gen-doc --doc_opt=markdown,docs.md
 
   make dirhtml
+#  mkdir -p _build/dirhtml/.github
+#  cp -r ../.github/images _build/dirhtml/.github
 else
   export NUM_RELEASES=${NUM_RELEASES:-10}
   export DEFAULT_BRANCH='master'

docs/requirements.txt

@@ -1,15 +1,15 @@
 # cf. https://github.com/ryanfox/sphinx-markdown-tables/issues/36
-sphinx
+sphinx==7.1.2
 sphinx-argparse==0.3.1
 sphinxcontrib-apidoc==0.3.0
 sphinxcontrib-redirects==0.1.0
 sphinx-autodoc-typehints==1.18.3
 sphinx_copybutton
 sphinx-notfound-page==0.7.1
 gitpython==3.1.30
-sphinx-sitemap
+sphinx-sitemap==2.5.1
 sphinxext-opengraph
-furo
+furo==2023.7.26
 myst-parser
 sphinx-design
 sphinx-inline-tabs