Python SDK Notebook Examples

Learn how to use the Clarifai Python SDK

---

Here are comprehensive step-by-step walkthroughs within Jupyter or Colab notebooks that showcase how to harness the power of the Clarifai SDKs.

Notebook	Description	Open in Colab
Basics	Create, manage, update, and delete Clarifai resources, including apps, datasets, inputs, and models
CLI	Clarifai provides a user-friendly command line interface (CLI) that simplifies various tasks
Compute Orchestration	Use our Compute Orchestration system to create, get, list, and delete compute clusters, nodepools, and model deployments.
Data Utils	Get a range of multimedia data utilities designed to streamline your data management and processing operations. Image annotation loader — A comprehensive framework for loading, exporting, and analyzing different annotated datasets. Data ingestion pipelines — Load text files (PDF, DOC, etc.), transform and chunk the content, and upload it to the Clarifai platform. Multimodal dataloader Ready-to-use foundational pipelines
RAG	Use Retrieval Augmented Generation (RAG) to improve Large Language Models (LLMs)
Concept management	Establish a hierarchical relationship between concepts using concept relations
Datasets basics	Merge datasets and list inputs of a dataset
Dataset export	Export datasets from a Clarifai app
Dataset upload	Upload datasets into a Clarifai app
Inputs upload	Upload inputs with various types of data, such as metadata, geo info, or bounding box annotations, into a Clarifai app
Models predict	Get predictions with text, image, video, and audio inputs with different types of models
Evaluation for embedding classification	Evaluate the performance of embedding classifier models
Evaluation for text classification	Evaluate the performance of text classifier models
Evaluation for visual classification	Evaluate the performance of visual classifier models
Evaluation for visual detection	Evaluate the performance of visual detector models
Training for image classification	Train image classifier models
Training for image detection	Train image detector models
Training for image segmentation	Train image segmentation models
Training for text classification	Train text classifier models
Training for transfer learn	Train transfer learn models
Model upload	Upload image classifiers Upload image detectors Upload LLMs Upload speech recognition models
Cross-modal search	Perform vector search over your own data
Create workflows	Create various types of workflows
Export workflows	Download a YAML file representing your workflow
Patch workflows	Perform patch operations by merging, removing, or overwriting data

Integration Examples

Notebook	Description	Open in Colab
LangChain
	Agent: Doc-retrieval using ReAct Docstore
	Agent: Retrieval QA using Clarifai vector store with conversation memory
	Chains: Using PostgreSQL database with LangChain
	Chains: Prompt templates and chains
	Chains: Retrieval QA Chain with Clarifai vector store
	Chains: Router Chain with Clarifai SDK prompt templates
Unstructured.io
	S3 data ingestion
	GitHub data ingestion
	DropBox data ingestion