Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

docs: replace free service docs with inference docs #918

Merged
merged 2 commits into from
Jun 14, 2023
Merged

docs: replace free service docs with inference docs #918

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
643aa78
docs: replace free service docs with inference docs
ZiniuYu Jun 13, 2023
2b4eaaa
docs: update readme
ZiniuYu Jun 13, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
27 changes: 10 additions & 17 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -38,18 +38,10 @@ CLIP-as-service is a low-latency high-scalability service for embedding images a

## Try it!

An always-online server `api.clip.jina.ai` loaded with `ViT-L-14-336::openai` is there for you to play & test.
Before you start, make sure you have obtained a personal access token from the [Jina AI Cloud](https://cloud.jina.ai/settings/tokens),
or via CLI as described in [this guide](https://docs.jina.ai/jina-ai-cloud/login/#create-a-new-pat):

```bash
jina auth token create <name of PAT> -e <expiration days>
```

Then, you need to configure the access token in the parameter `credential` of the client in python or set it in the HTTP request header `Authorization` as `<your access token>`.

⚠️ Our demo server `demo-cas.jina.ai` is sunset and no longer available after **15th of Sept 2022**.

You can access to the hosted CLIP service at [Jina AI's Inference](https://cloud.jina.ai/user/inference) with free credits.
Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Can we clarify free credits here?

Inference provides a selection of AI models for common tasks, such as visual reasoning, question answering, or embedding modalities like texts and images.
Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Inference API

All the available models are accessible via simple API calls - HTTPS or gRPC.
Read this [Inference Guide](https://clip-as-service.jina.ai/hosting/by-jina/) to learn more.

### Text & image embedding

Expand All @@ -63,7 +55,7 @@ Then, you need to configure the access token in the parameter `credential` of th

```bash
curl \
-X POST https://api.clip.jina.ai:8443/post \
-X POST https://<your-inference-address>-http.wolf.jina.ai/post \
-H 'Content-Type: application/json' \
-H 'Authorization: <your access token>' \
-d '{"data":[{"text": "First do it"},
Expand All @@ -81,7 +73,8 @@ curl \
from clip_client import Client

c = Client(
'grpcs://api.clip.jina.ai:2096', credential={'Authorization': '<your access token>'}
'grpcs://<your-inference-address>-grpc.wolf.jina.ai',
credential={'Authorization': '<your access token>'},
)

r = c.encode(
Expand Down Expand Up @@ -117,7 +110,7 @@ There are four basic visual reasoning skills: object recognition, object countin

```bash
curl \
-X POST https://api.clip.jina.ai:8443/post \
-X POST https://<your-inference-address>-http.wolf.jina.ai/post \
-H 'Content-Type: application/json' \
-H 'Authorization: <your access token>' \
-d '{"data":[{"uri": "https://picsum.photos/id/1/300/300",
Expand Down Expand Up @@ -146,7 +139,7 @@ gives:

```bash
curl \
-X POST https://api.clip.jina.ai:8443/post \
-X POST https://<your-inference-address>-http.wolf.jina.ai/post \
-H 'Content-Type: application/json' \
-H 'Authorization: <your access token>' \
-d '{"data":[{"uri": "https://picsum.photos/id/133/300/300",
Expand Down Expand Up @@ -183,7 +176,7 @@ gives:

```bash
curl \
-X POST https://api.clip.jina.ai:8443/post \
-X POST https://<your-inference-address>-http.wolf.jina.ai/post \
-H 'Content-Type: application/json' \
-H 'Authorization: <your access token>' \
-d '{"data":[{"uri": "https://picsum.photos/id/102/300/300",
Expand Down
122 changes: 43 additions & 79 deletions docs/hosting/by-jina.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -5,102 +5,66 @@
:end-before: <!-- end inference-banner -->
```

Just like any other machine learning models, CLIP models have better performance when running on GPU. However, it is not always possible to have a GPU machine at hand, and it could be costly to configure a GPU machine. To make CLIP models more accessible, we provide a hosted service for CLIP models. You can send requests to our hosted service and get the embedding results back.
In today's dynamic business environment, enterprises face a multitude of challenges that require advanced solutions to
maintain a competitive edge.
From managing vast amounts of unstructured data to delivering personalized customer experiences, businesses need
efficient tools to tackle these obstacles.
Machine learning (ML) has emerged as a powerful tool for automating repetitive tasks, processing data effectively, and
generating valuable insights from multimedia content.
Jina AI's Inference offers a comprehensive solution to streamline access to curated, state-of-the-art ML models,
eliminating traditional roadblocks such as costly and time-consuming MLOps steps and the distinction between public and
custom neural network models.

An always-online server `api.clip.jina.ai` loaded with `ViT-L-14-336::openai` is there for you to play or develop your CLIP applications. The server is available for **encoding** and **ranking** tasks.
## Getting started

`ViT-L-14-336::openai` was released in April 2022 and this is the best model within all models offered by [OpenAI](https:/openai/CLIP/blob/main/clip/clip.py#L30) and also the best model when we developed this service.
To access the fastest and most performant CLIP models, [Jina AI's Inference](https://cloud.jina.ai/user/inference) is
the go-to choice.
Follow the steps below to get started:

However, the "best model" is not always the best choice for your application. You may want to use a smaller model for faster response time, or a larger model for better accuracy.
With the [Inference](https://cloud.jina.ai/user/inference) in [Jina AI Cloud](https://cloud.jina.ai/), you have the flexibility to choose the model that best suits your specific needs.
1. Sign up for a free account at [Jina AI Cloud](https://cloud.jina.ai).
2. Once you have created an account, navigate to the Inference tab to create a new CLIP model.
Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

We will not mention model-based inference-api anymore.

3. The model can be accessed either through an HTTP endpoint or a gRPC endpoint.

Before you start, make sure you have obtained a personal access token from the [Jina AI Cloud](https://cloud.jina.ai/settings/tokens),
or via CLI as described in [this guide](https://docs.jina.ai/jina-ai-cloud/login/#create-a-new-pat):
## Obtaining a Personal Access Token

Before you begin using [Jina AI's Inference](https://cloud.jina.ai/user/inference), ensure that you have obtained a
personal access token (PAT) from the [Jina AI Cloud](https://cloud.jina.ai) or through the command-line interface (CLI).
Use the following guide to create a new PAT:

1. Access the [Jina AI Cloud](https://cloud.jina.ai) and log in to your account.
2. Navigate to the [**Access token**](https://cloud.jina.ai/settings/tokens) section in the **Settings** tab, or alternatively, create a PAT via the CLI using the command:

```bash
jina auth token create <name of PAT> -e <expiration days>
```

(by-jina-python)=
## Connect in Python

We provide two ways to send requests to our hosted service: via gRPCs and via HTTPs.

| Protocol | Address |
| -------- | ------------------------------- |
| gRPCs | `grpcs://api.clip.jina.ai:2096` |
| HTTPs | `https://api.clip.jina.ai:8443` |


To use the service, you need select the protocol by specifying corresponding address in the client. For example, if you want to use gRPCs, you need to specify the address as `grpcs://api.clip.jina.ai:2096`.
## Installing the Inference Client

Then, you need to configure the access token in the parameter `credential` of the client:
To interact with the model created in Inference, you will need to install the `inference-client` Python package.
Follow the steps below to install the package using pip:

```bash
pip install inference-client
```

````{tab} via gRPCs
## Interacting with the Model

```{code-block} python
---
emphasize-lines: 4
---
from clip_client import Client
Once you have your personal access token and the model name listed in the Inference detail page, you can start
interacting with the model using the `inference-client` Python package.
Follow the example code snippet below:

c = Client(
'grpcs://api.clip.jina.ai:2096', credential={'Authorization': '<your access token>'}
)
```python
from inference_client import Client

r = c.encode(
[
'First do it',
'then do it right',
'then do it better',
'https://picsum.photos/200',
]
)
```
client = Client(token='<your auth token>')

````
````{tab} via HTTPs

```{code-block} python
---
emphasize-lines: 4
---
from clip_client import Client

c = Client(
'https://api.clip.jina.ai:8443', credential={'Authorization': '<your access token>'}
)

r = c.encode(
[
'First do it',
'then do it right',
'then do it better',
'https://picsum.photos/200',
]
)
model = client.get_model('<your model name>')
```

````
The CLIP models offer the following functionalities:

(by-jina-curl)=
## Connect using plain HTTP request via `curl`
1. Encoding: Users can encode data by calling the `model.encode` method. For detailed instructions on using this method, refer to the [Encode documentation](https://jina.readme.io/docs/encode).
2. Ranking: Users can perform ranking by calling the `model.rank` method. Refer to the [Rank documentation](https://jina.readme.io/docs/rank) for detailed instructions on using this method.

You can also send requests to our hosted service using plain HTTP request via `curl` by configuring the access token in the HTTP request header `Authorization` as `<your access token>`.


```{code-block} bash
---
emphasize-lines: 4
---
curl \
-X POST https://api.clip.jina.ai:8443/post \
-H 'Content-Type: application/json' \
-H 'Authorization: <your access token>' \
-d '{"data":[{"text": "First do it"},
{"text": "then do it right"},
{"text": "then do it better"},
{"uri": "https://picsum.photos/200"}],
"execEndpoint":"/"}'
```
For further details on usage and information about other tasks and models supported in Inference, as well as how to use
`curl` to interact with the model, please consult the [Inference documentation](https://jina.readme.io/docs/inference).
17 changes: 6 additions & 11 deletions docs/index.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -12,15 +12,10 @@

## Try it!

An always-online server `api.clip.jina.ai` loaded with `ViT-L-14-336::openai` is there for you to play & test.
Before you start, make sure you have obtained a personal access token from the [Jina AI Cloud](https://cloud.jina.ai/settings/tokens),
or via CLI as described in [this guide](https://docs.jina.ai/jina-ai-cloud/login/#create-a-new-pat):

```bash
jina auth token create <name of PAT> -e <expiration days>
```

Then, you need to configure the access token in the parameter `credential` of the client in python or set it in the HTTP request header `Authorization` as `<your access token>`.
You can access to the hosted CLIP service at [Jina AI's Inference](https://cloud.jina.ai/user/inference) with free credits.
Inference provides a selection of AI models for common tasks, such as visual reasoning, question answering, or embedding modalities like texts and images.
All the available models are accessible via simple API calls - HTTPS or gRPC.
Read this [Inference Guide](https://clip-as-service.jina.ai/hosting/by-jina/) to learn more.

````{tab} via gRPC ⚡⚡

Expand All @@ -35,7 +30,7 @@ emphasize-lines: 5
from clip_client import Client

c = Client(
'grpcs://api.clip.jina.ai:2096',
'grpcs://<your-inference-address>-grpc.wolf.jina.ai',
credential={'Authorization': '<your access token>'}
)

Expand All @@ -59,7 +54,7 @@ print(r)
emphasize-lines: 4
---
curl \
-X POST https://api.clip.jina.ai:8443/post \
-X POST https://<your-inference-address>-http.wolf.jina.ai/post \
-H 'Content-Type: application/json' \
-H 'Authorization: <your access token>' \
-d '{"data":[{"text": "First do it"},
Expand Down