Package: inference-client
Inference-Client is a Python library that allows you to interact with theJina AI Inference.
It provides a simple and intuitive API to perform various tasks such as image captioning, encoding, ranking, visual
question answering (VQA), and image upscaling.The current version of Inference Client includes methods to call the following tasks:📷Caption: Generate captions for images📈Encode: Encode data into embeddings using various models🔍Rank: Re-rank cross-modal matches according to their joint likelihood🆙Upscale: Increasing the resolution while preserving the quality and details🤔VQA: Answer questions related to imagesInstallationInference Client is available on PyPI and can be installed using pip:pipinstallinference-clientGetting StartedBefore using the Inference-Client, please create an inference onJina AI Cloud.After the inference is created and the status is "Serving", you can use the Inference-Client to connect to it.
This could take a few minutes, depending on the model you selected.Client InitializationTo use the Inference-Client, you first need to import theClientclass and create a new instance of it.frominference_clientimportClientclient=Client(token='<your auth token>')You will need to provide your access token when creating the client. The token can be generated at theJina AI Cloud, or via CLI as described inthis guide:jinaauthtokencreate<nameofPAT>-e<expirationdays>You can then use theget_modelmethod of theClientobject to get a specific model.model=client.get_model('<model of your selection>')You can connect to as many inference models as you want once they have been created on Jina AI Cloud, and you can use them for multiple tasks.Performing tasksNow that you have connected to the models, you can use them to perform the tasks they support.Image CaptioningThecaptionmethod of theModelobject takes an image as input and returns a caption as output.image='path/to/image.jpg'caption=model.caption(image=image)EncodingTheencodemethod of theModelobject takes text or image data as input and returns an embedding as output.text='a sentence describing the beautiful nature'embedding=model.encode(text=text)# ORimage='path/to/image.jpg'embedding=model.encode(image=image)RankingTherankmethod of theModelobject takes a text or image data as query and a list of candidates as input and returns a list of reordered candidates as well as their scores as output.candidates=['an image about dogs','an image about cats','an image about birds',]image='path/to/image.jpg'result=model.rank(image=image,candidates=candidates)Image UpscalingTheupscalemethod of theModelobject takes an image and optional configurations as input, and returns the upscaled image bytes as output.image='path/to/image.jpg'result=model.upscale(image=image,output_path='upscaled_image.png',scale='800:600')Visual Question Answering (VQA)Thevqamethod of theModelobject takes an image and a question as input and returns an answer as output.image='path/to/image.jpg'question='Question: What is the name of this place? Answer:'answer=model.vqa(image=image,question=question)Advanced UsageIn addition to the basic usage, the Inference-Client also supports advanced features such as handling DocumentArray inputs, customizing the task parameters, and more.
Please refer to theofficial documentationfor more details.SupportJoin ourDiscord communityand chat with other community members about ideas.Watch ourEngineering All Handsto learn Jina's new features and stay up-to-date with the latest AI techniques.Subscribe to the latest video tutorials on ourYouTube channelLicenseInference-Client is backed byJina AIand licensed underApache-2.0.
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