docs: fix url

Signed-off-by: Han Xiao <[email protected]>

Author: hanxiao

.github/ISSUE_TEMPLATE/config.yml

@@ -1,7 +1,7 @@
 blank_issues_enabled: false
 contact_links:
   - name: "📚 Read docs"
-    url: https://docs.jina.ai/
+    url: https://jina.ai/serve/
     about: Find your solution from our documenations
   - name: "😊 Join us"
     url: https://career.jina.ai

.github/slack-pypi.json

@@ -16,7 +16,7 @@
       },
       "accessory": {
         "type": "image",
-        "image_url": "https://docs.jina.ai/_static/favicon.png",
+        "image_url": "https://jina.ai/serve/_static/favicon.png",
         "alt_text": "cute cat"
       }
     },

CHANGELOG.md

@@ -9569,7 +9569,7 @@ Jina is released on every Friday evening. The PyPi package and Docker Image will
  - [[```4273af8d```](https://github.com/jina-ai/jina/commit/4273af8d46394f476423fd53c6bc4054050fd9cf)] __-__ remove hub-builder success (*Han Xiao*)
  - [[```73457b17```](https://github.com/jina-ai/jina/commit/73457b17909b68c4415613ed8da78f2e6f9774a3)] __-__ hide my exec collide with other test (#2654) (*Joan Fontanals*)
  - [[```e01ed315```](https://github.com/jina-ai/jina/commit/e01ed3152509b47a896d05d1d6d59ae41acb0515)] __-__ latency-tracking adapt new release (#2595) (*Alan Zhisheng Niu*)
- - [[```7651bb44```](https://github.com/jina-ai/jina/commit/7651bb44e725002da65bda8a10d3b4477d692935)] __-__ replace docs2.jina.ai to docs.jina.ai (*Han Xiao*)
+ - [[```7651bb44```](https://github.com/jina-ai/jina/commit/7651bb44e725002da65bda8a10d3b4477d692935)] __-__ replace docs2.jina.ai to jina.ai/serve (*Han Xiao*)
  - [[```26403122```](https://github.com/jina-ai/jina/commit/264031226563e6b84073c4b3a168fa5c1e2de1d0)] __-__ fix 404 page generation in ci (*Han Xiao*)
 
 ### 🍹 Other Improvements

CONTRIBUTING.md

@@ -256,9 +256,9 @@ Bonus: **Know when to break the rules**. Documentation writing is as much art as
 
 [MyST](https://myst-parser.readthedocs.io/en/latest/) Elements Usage
 
-1. Use the `{tab}` element to show multiple ways of doing one thing. [Example](https://docs.jina.ai/concepts/flow/basics/#start-and-stop) 
-2. Use the `{admonition}` boxes with care. We recommend restricting yourself to [Hint](https://docs.jina.ai/concepts/flow/basics/#create), [Caution](https://docs.jina.ai/concepts/gateway/customize-http-endpoints/#enable-graphql-endpoint) and [See Also](https://docs.jina.ai/concepts/gateway/customize-http-endpoints/#enable-graphql-endpoint).
-3. Use `{dropdown}` to hide optional content, such as long code snippets or console output. [Example](https://docs.jina.ai/concepts/client/third-party-clients/#use-curl)
+1. Use the `{tab}` element to show multiple ways of doing one thing. [Example](https://jina.ai/serve/concepts/flow/basics/#start-and-stop) 
+2. Use the `{admonition}` boxes with care. We recommend restricting yourself to [Hint](https://jina.ai/serve/concepts/flow/basics/#create), [Caution](https://jina.ai/serve/concepts/gateway/customize-http-endpoints/#enable-graphql-endpoint) and [See Also](https://jina.ai/serve/concepts/gateway/customize-http-endpoints/#enable-graphql-endpoint).
+3. Use `{dropdown}` to hide optional content, such as long code snippets or console output. [Example](https://jina.ai/serve/concepts/client/third-party-clients/#use-curl)
 
 ### Building documentation on your local machine
 

Dockerfiles/debianx.Dockerfile

@@ -27,7 +27,7 @@ LABEL org.opencontainers.image.vendor="Jina AI Limited" \
       org.opencontainers.image.description="Build multimodal AI services via cloud native technologies" \
       org.opencontainers.image.authors="[email protected]" \
       org.opencontainers.image.url="https://github.com/jina-ai/jina" \
-      org.opencontainers.image.documentation="https://docs.jina.ai"
+      org.opencontainers.image.documentation="https://jina.ai/serve"
 
 # constant, wont invalidate cache
 ENV PIP_NO_CACHE_DIR=1 \

README.md

@@ -32,7 +32,7 @@ Key advantages over FastAPI:
 pip install jina
 ```
 
-See guides for [Apple Silicon](https://docs.jina.ai/get-started/install/apple-silicon-m1-m2/) and [Windows](https://docs.jina.ai/get-started/install/windows/).
+See guides for [Apple Silicon](https://jina.ai/serve/get-started/install/apple-silicon-m1-m2/) and [Windows](https://jina.ai/serve/get-started/install/windows/).
 
 ## Core Concepts
 
@@ -50,28 +50,31 @@ from jina import Executor, requests
 from docarray import DocList, BaseDoc
 from transformers import pipeline
 
+
 class Prompt(BaseDoc):
-   text: str
+    text: str
+
 
 class Generation(BaseDoc):
-   prompt: str
-   text: str
+    prompt: str
+    text: str
+
 
 class StableLM(Executor):
-   def __init__(self, **kwargs):
-       super().__init__(**kwargs)
-       self.generator = pipeline(
-           'text-generation', model='stabilityai/stablelm-base-alpha-3b'
-       )
-
-   @requests
-   def generate(self, docs: DocList[Prompt], **kwargs) -> DocList[Generation]:
-       generations = DocList[Generation]()
-       prompts = docs.text
-       llm_outputs = self.generator(prompts)
-       for prompt, output in zip(prompts, llm_outputs):
-           generations.append(Generation(prompt=prompt, text=output))
-       return generations
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.generator = pipeline(
+            'text-generation', model='stabilityai/stablelm-base-alpha-3b'
+        )
+
+    @requests
+    def generate(self, docs: DocList[Prompt], **kwargs) -> DocList[Generation]:
+        generations = DocList[Generation]()
+        prompts = docs.text
+        llm_outputs = self.generator(prompts)
+        for prompt, output in zip(prompts, llm_outputs):
+            generations.append(Generation(prompt=prompt, text=output))
+        return generations
 ```
 
 Deploy with Python or YAML:
@@ -83,7 +86,7 @@ from executor import StableLM
 dep = Deployment(uses=StableLM, timeout_ready=-1, port=12345)
 
 with dep:
-   dep.block()
+    dep.block()
 ```
 
 ```yaml
@@ -115,14 +118,10 @@ Chain services into a Flow:
 ```python
 from jina import Flow
 
-flow = (
-   Flow(port=12345)
-   .add(uses=StableLM)
-   .add(uses=TextToImage)
-)
+flow = Flow(port=12345).add(uses=StableLM).add(uses=TextToImage)
 
 with flow:
-   flow.block()
+    flow.block()
 ```
 
 ## Scaling and Deployment
@@ -207,62 +206,66 @@ Enable token-by-token streaming for responsive LLM applications:
 ```python
 from docarray import BaseDoc
 
+
 class PromptDocument(BaseDoc):
-   prompt: str
-   max_tokens: int
+    prompt: str
+    max_tokens: int
+
 
 class ModelOutputDocument(BaseDoc):
-   token_id: int
-   generated_text: str
+    token_id: int
+    generated_text: str
 ```
 
 2. Initialize service:
 ```python
 from transformers import GPT2Tokenizer, GPT2LMHeadModel
 
+
 class TokenStreamingExecutor(Executor):
-   def __init__(self, **kwargs):
-       super().__init__(**kwargs)
-       self.model = GPT2LMHeadModel.from_pretrained('gpt2')
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.model = GPT2LMHeadModel.from_pretrained('gpt2')
 ```
 
 3. Implement streaming:
 ```python
 @requests(on='/stream')
 async def task(self, doc: PromptDocument, **kwargs) -> ModelOutputDocument:
-   input = tokenizer(doc.prompt, return_tensors='pt')
-   input_len = input['input_ids'].shape[1]
-   for _ in range(doc.max_tokens):
-       output = self.model.generate(**input, max_new_tokens=1)
-       if output[0][-1] == tokenizer.eos_token_id:
-           break
-       yield ModelOutputDocument(
-           token_id=output[0][-1],
-           generated_text=tokenizer.decode(
-               output[0][input_len:], skip_special_tokens=True
-           ),
-       )
-       input = {
-           'input_ids': output,
-           'attention_mask': torch.ones(1, len(output[0])),
-       }
+    input = tokenizer(doc.prompt, return_tensors='pt')
+    input_len = input['input_ids'].shape[1]
+    for _ in range(doc.max_tokens):
+        output = self.model.generate(**input, max_new_tokens=1)
+        if output[0][-1] == tokenizer.eos_token_id:
+            break
+        yield ModelOutputDocument(
+            token_id=output[0][-1],
+            generated_text=tokenizer.decode(
+                output[0][input_len:], skip_special_tokens=True
+            ),
+        )
+        input = {
+            'input_ids': output,
+            'attention_mask': torch.ones(1, len(output[0])),
+        }
 ```
 
 4. Serve and use:
 ```python
 # Server
 with Deployment(uses=TokenStreamingExecutor, port=12345, protocol='grpc') as dep:
-   dep.block()
+    dep.block()
+
 
 # Client
 async def main():
-   client = Client(port=12345, protocol='grpc', asyncio=True)
-   async for doc in client.stream_doc(
-       on='/stream',
-       inputs=PromptDocument(prompt='what is the capital of France ?', max_tokens=10),
-       return_type=ModelOutputDocument,
-   ):
-       print(doc.generated_text)
+    client = Client(port=12345, protocol='grpc', asyncio=True)
+    async for doc in client.stream_doc(
+        on='/stream',
+        inputs=PromptDocument(prompt='what is the capital of France ?', max_tokens=10),
+        return_type=ModelOutputDocument,
+    ):
+        print(doc.generated_text)
 ```
 
 ## Support

conda/meta.yaml

@@ -147,7 +147,7 @@ about:
   license_family: Apache
   license_file: LICENSE
   summary: "Build multimodal AI services via cloud native technologies \xB7 Neural Search \xB7 Generative AI \xB7 Cloud Native"
-  doc_url: https://docs.jina.ai
+  doc_url: https://jina.ai/serve
 
 extra:
   recipe-maintainers:

docs/concepts/jcloud/configuration.md

@@ -180,7 +180,7 @@ If shards/replicas are used, we will multiply credits further by the number of s
 
 ## Scale out Executors
 
-On JCloud, demand-based autoscaling functionality is naturally offered thanks to the underlying Kubernetes architecture. This means that you can maintain [serverless](https://en.wikipedia.org/wiki/Serverless_computing) deployments in a cost-effective way with no headache of setting the [right number of replicas](https://docs.jina.ai/how-to/scale-out/#scale-out-your-executor) anymore!
+On JCloud, demand-based autoscaling functionality is naturally offered thanks to the underlying Kubernetes architecture. This means that you can maintain [serverless](https://en.wikipedia.org/wiki/Serverless_computing) deployments in a cost-effective way with no headache of setting the [right number of replicas](https://jina.ai/serve/how-to/scale-out/#scale-out-your-executor) anymore!
 
 
 ### Autoscaling with `jinaai+serverless://` 
@@ -266,8 +266,8 @@ The JCloud parameters `minAvailable` and `maxUnavailable` ensure that Executors
 
 | Name             | Default |                                          Allowed                                          | Description                                              |
 | :--------------- | :-----: | :---------------------------------------------------------------------------------------: | :------------------------------------------------------- |
-| `minAvailable`   |   N/A   | Lower than number of [replicas](https://docs.jina.ai/concepts/flow/scale-out/#scale-out)  | Minimum number of replicas available during disruption   |
-| `maxUnavailable` |   N/A   | Lower than numbers of [replicas](https://docs.jina.ai/concepts/flow/scale-out/#scale-out) | Maximum number of replicas unavailable during disruption |
+| `minAvailable`   |   N/A   | Lower than number of [replicas](https://jina.ai/serve/concepts/flow/scale-out/#scale-out)  | Minimum number of replicas available during disruption   |
+| `maxUnavailable` |   N/A   | Lower than numbers of [replicas](https://jina.ai/serve/concepts/flow/scale-out/#scale-out) | Maximum number of replicas unavailable during disruption |
 
 ```{code-block} yaml
 ---
@@ -459,7 +459,7 @@ Keys in `labels` have the following restrictions:
 
 ### Monitoring
 
-To enable [tracing support](https://docs.jina.ai/cloud-nativeness/opentelemetry/) in Flows, you can pass `enable: true` argument in the Flow YAML. (Tracing support is not enabled by default in JCloud)
+To enable [tracing support](https://jina.ai/serve/cloud-nativeness/opentelemetry/) in Flows, you can pass `enable: true` argument in the Flow YAML. (Tracing support is not enabled by default in JCloud)
 
 ```{code-block} yaml
 ---

docs/concepts/jcloud/index.md

@@ -8,7 +8,7 @@
 configuration
 ```
 
-```{figure} https://docs.jina.ai/_images/jcloud-banner.png
+```{figure} https://jina.ai/serve/_images/jcloud-banner.png
 :width: 0 %
 :scale: 0 %
 ```
@@ -50,13 +50,13 @@ For the rest of this section, we use `jc` or `jcloud`. But again they are interc
 
 ### Deploy
 
-In Jina's idiom, a project is a [Flow](https://docs.jina.ai/concepts/orchestration/flow/), which represents an end-to-end task such as indexing, searching or recommending. In this document, we use "project" and "Flow" interchangeably.
+In Jina's idiom, a project is a [Flow](https://jina.ai/serve/concepts/orchestration/flow/), which represents an end-to-end task such as indexing, searching or recommending. In this document, we use "project" and "Flow" interchangeably.
 
 A Flow can have two types of file structure: a single YAML file or a project folder.
 
 #### Single YAML file
 
-A self-contained YAML file, consisting of all configuration at the [Flow](https://docs.jina.ai/concepts/orchestration/flow/)-level and [Executor](https://docs.jina.ai/concepts/serving/executor/)-level.
+A self-contained YAML file, consisting of all configuration at the [Flow](https://jina.ai/serve/concepts/orchestration/flow/)-level and [Executor](https://jina.ai/serve/concepts/serving/executor/)-level.
 
 > All Executors' `uses` must follow the format `jinaai+docker://<username>/MyExecutor` (from [Executor Hub](https://cloud.jina.ai)) to avoid any local file dependencies:
 
@@ -123,7 +123,7 @@ hello/
 Where:
 
 - `hello/` is your top-level project folder.
-- `executor1` directory has all Executor related code/configuration. You can read the best practices for [file structures](https://docs.jina.ai/concepts/serving/executor/file-structure/). Multiple Executor directories can be created.
+- `executor1` directory has all Executor related code/configuration. You can read the best practices for [file structures](https://jina.ai/serve/concepts/serving/executor/file-structure/). Multiple Executor directories can be created.
 - `flow.yml` Your Flow YAML.
 - `.env` All environment variables used during deployment.
 
@@ -374,7 +374,7 @@ jc secret create mysecret rich-husky-af14064067 --from-literal "{'env-name': 'se
 ```
 
 ```{tip}
-You can optionally pass the `--update` flag to automatically update the Flow spec with the updated secret information. This flag will update the Flow which is hosted on the cloud. Finally, you can also optionally pass a Flow's yaml file path with `--path` to update the yaml file locally.  Refer to [this](https://docs.jina.ai/cloud-nativeness/kubernetes/#deploy-flow-with-custom-environment-variables-and-secrets) section for more information.
+You can optionally pass the `--update` flag to automatically update the Flow spec with the updated secret information. This flag will update the Flow which is hosted on the cloud. Finally, you can also optionally pass a Flow's yaml file path with `--path` to update the yaml file locally.  Refer to [this](https://jina.ai/serve/cloud-nativeness/kubernetes/#deploy-flow-with-custom-environment-variables-and-secrets) section for more information.
 ```
 
 ```{caution}
@@ -419,7 +419,7 @@ jc secret update rich-husky-af14064067 mysecret --from-literal "{'env-name': 'se
 ```
 
 ```{tip}
-You can optionally pass the `--update` flag to automatically update the Flow spec with the updated secret information. This flag will update the Flow which is hosted on the cloud. Finally, you can also optionally pass a Flow's yaml file path with `--path` to update the yaml file locally. Refer to [this](https://docs.jina.ai/cloud-nativeness/kubernetes/#deploy-flow-with-custom-environment-variables-and-secrets) section for more information.
+You can optionally pass the `--update` flag to automatically update the Flow spec with the updated secret information. This flag will update the Flow which is hosted on the cloud. Finally, you can also optionally pass a Flow's yaml file path with `--path` to update the yaml file locally. Refer to [this](https://jina.ai/serve/cloud-nativeness/kubernetes/#deploy-flow-with-custom-environment-variables-and-secrets) section for more information.
 ```
 
 ```{caution}
@@ -498,7 +498,7 @@ jcloud:
 
 #### Single YAML file
 
-A self-contained YAML file, consisting of all configuration information at the [Deployment](https://docs.jina.ai/concepts/orchestration/deployment/)-level and [Executor](https://docs.jina.ai/concepts/serving/executor/)-level.
+A self-contained YAML file, consisting of all configuration information at the [Deployment](https://jina.ai/serve/concepts/orchestration/deployment/)-level and [Executor](https://jina.ai/serve/concepts/serving/executor/)-level.
 
 > A Deployment's `uses` parameter must follow the format `jinaai+docker://<username>/MyExecutor` (from [Executor Hub](https://cloud.jina.ai)) to avoid any local file dependencies:
 

docs/concepts/orchestration/flow.md

@@ -303,7 +303,7 @@ Please follow the walkthrough and enjoy the free GPU/TPU!
 
 
 ```{tip}
-Hosing services on Google Colab is not recommended if your server aims to be long-lived or permanent. It is often used for quick experiments, demonstrations or leveraging its free GPU/TPU. For stable, secure and free hosting of your Flow, check out [JCloud](https://docs.jina.ai/concepts/jcloud/).
+Hosing services on Google Colab is not recommended if your server aims to be long-lived or permanent. It is often used for quick experiments, demonstrations or leveraging its free GPU/TPU. For stable, secure and free hosting of your Flow, check out [JCloud](https://jina.ai/serve/concepts/jcloud/).
 ```
 
 ## Export