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docs: fix url
Signed-off-by: Han Xiao <[email protected]>
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Diff for: ‎.github/ISSUE_TEMPLATE/config.yml

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

Diff for: ‎.github/slack-pypi.json

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

Diff for: ‎CHANGELOG.md

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

Diff for: ‎CONTRIBUTING.md

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@@ -256,9 +256,9 @@ Bonus: **Know when to break the rules**. Documentation writing is as much art as
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[MyST](https://myst-parser.readthedocs.io/en/latest/) Elements Usage
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1. Use the `{tab}` element to show multiple ways of doing one thing. [Example](https://docs.jina.ai/concepts/flow/basics/#start-and-stop)
260-
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).
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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)
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1. Use the `{tab}` element to show multiple ways of doing one thing. [Example](https://jina.ai/serve/concepts/flow/basics/#start-and-stop)
260+
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).
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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)
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### Building documentation on your local machine
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Diff for: ‎Dockerfiles/debianx.Dockerfile

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

Diff for: ‎README.md

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@@ -32,7 +32,7 @@ Key advantages over FastAPI:
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pip install jina
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```
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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/).
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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/).
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## Core Concepts
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@@ -50,28 +50,31 @@ from jina import Executor, requests
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from docarray import DocList, BaseDoc
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from transformers import pipeline
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5354
class Prompt(BaseDoc):
54-
text: str
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text: str
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class Generation(BaseDoc):
57-
prompt: str
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text: str
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prompt: str
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text: str
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class StableLM(Executor):
61-
def __init__(self, **kwargs):
62-
super().__init__(**kwargs)
63-
self.generator = pipeline(
64-
'text-generation', model='stabilityai/stablelm-base-alpha-3b'
65-
)
66-
67-
@requests
68-
def generate(self, docs: DocList[Prompt], **kwargs) -> DocList[Generation]:
69-
generations = DocList[Generation]()
70-
prompts = docs.text
71-
llm_outputs = self.generator(prompts)
72-
for prompt, output in zip(prompts, llm_outputs):
73-
generations.append(Generation(prompt=prompt, text=output))
74-
return generations
64+
def __init__(self, **kwargs):
65+
super().__init__(**kwargs)
66+
self.generator = pipeline(
67+
'text-generation', model='stabilityai/stablelm-base-alpha-3b'
68+
)
69+
70+
@requests
71+
def generate(self, docs: DocList[Prompt], **kwargs) -> DocList[Generation]:
72+
generations = DocList[Generation]()
73+
prompts = docs.text
74+
llm_outputs = self.generator(prompts)
75+
for prompt, output in zip(prompts, llm_outputs):
76+
generations.append(Generation(prompt=prompt, text=output))
77+
return generations
7578
```
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Deploy with Python or YAML:
@@ -83,7 +86,7 @@ from executor import StableLM
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dep = Deployment(uses=StableLM, timeout_ready=-1, port=12345)
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8588
with dep:
86-
dep.block()
89+
dep.block()
8790
```
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```yaml
@@ -115,14 +118,10 @@ Chain services into a Flow:
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```python
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from jina import Flow
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flow = (
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Flow(port=12345)
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.add(uses=StableLM)
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.add(uses=TextToImage)
122-
)
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flow = Flow(port=12345).add(uses=StableLM).add(uses=TextToImage)
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124123
with flow:
125-
flow.block()
124+
flow.block()
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```
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## Scaling and Deployment
@@ -207,62 +206,66 @@ Enable token-by-token streaming for responsive LLM applications:
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```python
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from docarray import BaseDoc
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209+
210210
class PromptDocument(BaseDoc):
211-
prompt: str
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max_tokens: int
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prompt: str
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max_tokens: int
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class ModelOutputDocument(BaseDoc):
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token_id: int
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generated_text: str
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token_id: int
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generated_text: str
217218
```
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2. Initialize service:
220221
```python
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from transformers import GPT2Tokenizer, GPT2LMHeadModel
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class TokenStreamingExecutor(Executor):
224-
def __init__(self, **kwargs):
225-
super().__init__(**kwargs)
226-
self.model = GPT2LMHeadModel.from_pretrained('gpt2')
226+
def __init__(self, **kwargs):
227+
super().__init__(**kwargs)
228+
self.model = GPT2LMHeadModel.from_pretrained('gpt2')
227229
```
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3. Implement streaming:
230232
```python
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@requests(on='/stream')
232234
async def task(self, doc: PromptDocument, **kwargs) -> ModelOutputDocument:
233-
input = tokenizer(doc.prompt, return_tensors='pt')
234-
input_len = input['input_ids'].shape[1]
235-
for _ in range(doc.max_tokens):
236-
output = self.model.generate(**input, max_new_tokens=1)
237-
if output[0][-1] == tokenizer.eos_token_id:
238-
break
239-
yield ModelOutputDocument(
240-
token_id=output[0][-1],
241-
generated_text=tokenizer.decode(
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output[0][input_len:], skip_special_tokens=True
243-
),
244-
)
245-
input = {
246-
'input_ids': output,
247-
'attention_mask': torch.ones(1, len(output[0])),
248-
}
235+
input = tokenizer(doc.prompt, return_tensors='pt')
236+
input_len = input['input_ids'].shape[1]
237+
for _ in range(doc.max_tokens):
238+
output = self.model.generate(**input, max_new_tokens=1)
239+
if output[0][-1] == tokenizer.eos_token_id:
240+
break
241+
yield ModelOutputDocument(
242+
token_id=output[0][-1],
243+
generated_text=tokenizer.decode(
244+
output[0][input_len:], skip_special_tokens=True
245+
),
246+
)
247+
input = {
248+
'input_ids': output,
249+
'attention_mask': torch.ones(1, len(output[0])),
250+
}
249251
```
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251253
4. Serve and use:
252254
```python
253255
# Server
254256
with Deployment(uses=TokenStreamingExecutor, port=12345, protocol='grpc') as dep:
255-
dep.block()
257+
dep.block()
258+
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257260
# Client
258261
async def main():
259-
client = Client(port=12345, protocol='grpc', asyncio=True)
260-
async for doc in client.stream_doc(
261-
on='/stream',
262-
inputs=PromptDocument(prompt='what is the capital of France ?', max_tokens=10),
263-
return_type=ModelOutputDocument,
264-
):
265-
print(doc.generated_text)
262+
client = Client(port=12345, protocol='grpc', asyncio=True)
263+
async for doc in client.stream_doc(
264+
on='/stream',
265+
inputs=PromptDocument(prompt='what is the capital of France ?', max_tokens=10),
266+
return_type=ModelOutputDocument,
267+
):
268+
print(doc.generated_text)
266269
```
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## Support

Diff for: ‎conda/meta.yaml

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

Diff for: ‎docs/concepts/jcloud/configuration.md

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## Scale out Executors
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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!
183+
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!
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### Autoscaling with `jinaai+serverless://`
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| Name | Default | Allowed | Description |
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| :--------------- | :-----: | :---------------------------------------------------------------------------------------: | :------------------------------------------------------- |
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| `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 |
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| `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 |
269+
| `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 |
270+
| `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 |
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```{code-block} yaml
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---
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### Monitoring
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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)
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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)
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```{code-block} yaml
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---

Diff for: ‎docs/concepts/jcloud/index.md

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configuration
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```
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```{figure} https://docs.jina.ai/_images/jcloud-banner.png
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```{figure} https://jina.ai/serve/_images/jcloud-banner.png
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:width: 0 %
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:scale: 0 %
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```
@@ -50,13 +50,13 @@ For the rest of this section, we use `jc` or `jcloud`. But again they are interc
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### Deploy
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53-
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.
53+
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.
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A Flow can have two types of file structure: a single YAML file or a project folder.
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#### Single YAML file
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59-
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.
59+
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.
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> All Executors' `uses` must follow the format `jinaai+docker://<username>/MyExecutor` (from [Executor Hub](https://cloud.jina.ai)) to avoid any local file dependencies:
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Where:
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- `hello/` is your top-level project folder.
126-
- `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.
126+
- `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.
127127
- `flow.yml` Your Flow YAML.
128128
- `.env` All environment variables used during deployment.
129129

@@ -374,7 +374,7 @@ jc secret create mysecret rich-husky-af14064067 --from-literal "{'env-name': 'se
374374
```
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```{tip}
377-
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.
377+
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.
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```
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```{caution}
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419419
```
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421421
```{tip}
422-
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.
422+
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.
423423
```
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425425
```{caution}
@@ -498,7 +498,7 @@ jcloud:
498498
499499
#### Single YAML file
500500
501-
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.
501+
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.
502502
503503
> 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:
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Diff for: ‎docs/concepts/orchestration/flow.md

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305305
```{tip}
306-
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/).
306+
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/).
307307
```
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## Export

Diff for: ‎docs/conf.py

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html_theme = 'furo'
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base_url = '/'
52-
html_baseurl = 'https://docs.jina.ai'
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html_baseurl = 'https://jina.ai/serve'
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sitemap_url_scheme = '{link}'
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sitemap_locales = [None]
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sitemap_filename = "sitemap.xml"
@@ -167,8 +167,8 @@
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linkcheck_retries = 2
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linkcheck_anchors = False
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170-
ogp_site_url = 'https://docs.jina.ai/'
171-
ogp_image = 'https://docs.jina.ai/_static/banner.png'
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ogp_site_url = 'https://jina.ai/serve/'
171+
ogp_image = 'https://jina.ai/serve/_static/banner.png'
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ogp_use_first_image = True
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ogp_description_length = 300
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ogp_type = 'website'

Diff for: ‎docs/html_extra/robots.txt

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User-agent: *
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sitemap: https://docs.jina.ai/sitemap.xml
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sitemap: https://jina.ai/serve/sitemap.xml

Diff for: ‎docs/tutorials/deploy-model.md

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In this example we need to install:
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45-
- The [Jina-serve framework](https://docs.jina.ai/) itself
45+
- The [Jina-serve framework](https://jina.ai/serve/) itself
4646
- The dependencies of the specific model we want to serve and deploy
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4848
```shell

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