Galileo

# Galileo

## Docs

- [Get Token](https://docs.galileo.ai/api-reference/auth/get-token.md)
- [Login Api Key](https://docs.galileo.ai/api-reference/auth/login-api-key.md)
- [Login Email](https://docs.galileo.ai/api-reference/auth/login-email.md)
- [Login Social](https://docs.galileo.ai/api-reference/auth/login-social.md)
- [Refresh Token](https://docs.galileo.ai/api-reference/auth/refresh-token.md)
- [List Evaluate Alerts](https://docs.galileo.ai/api-reference/evaluate-alerts/list-evaluate-alerts.md)
- [Cancel Jobs For Project Run](https://docs.galileo.ai/api-reference/evaluate/cancel-jobs-for-project-run.md): Get all jobs for a project and run.

Revoke them from Celery.
- [Create a new Evaluate Run](https://docs.galileo.ai/api-reference/evaluate/create-workflows-run.md): Create a new Evaluate run with workflows.

Use this endpoint to create a new Evaluate run with workflows. The request body should contain the `workflows` to be ingested and evaluated.

Additionally, specify the `project_id` or `project_name` to which the workflows should be ingested. If the project does not exist, it will be created. If the project exists, the workflows will be logged to it. If both `project_id` and `project_name` are provided, `project_id` will take precedence. The `run_name` is optional and will be auto-generated (timestamp-based) if not provided.

The body is also expected to include the configuration for the scorers to be used in the evaluation. This configuration will be used to evaluate the workflows and generate the results.
- [Get Evaluate Run Results](https://docs.galileo.ai/api-reference/evaluate/get-evaluate-run-results.md): Fetch evaluation results for a specific run including rows and aggregate information.
- [API Reference | Getting Started with Galileo](https://docs.galileo.ai/api-reference/getting-started.md): Get started with Galileo's REST API: learn about base URLs, authentication methods, and how to verify your API setup for seamless integration.
- [Healthcheck](https://docs.galileo.ai/api-reference/health/healthcheck.md)
- [Get Workflows](https://docs.galileo.ai/api-reference/observe/get-workflows.md): Get workflows for a specific run in an Observe project.
- [Log Workflows to an Observe Project](https://docs.galileo.ai/api-reference/observe/log-workflows.md): Log workflows to an Observe project.

Use this endpoint to log workflows to an Observe project. The request body should contain the
`workflows` to be ingested.

Additionally, specify the `project_id` or `project_name` to which the workflows should be ingested.
If the project does not exist, it will be created. If the project exists, the workflows will be logged to it.
If both `project_id` and `project_name` are provided, `project_id` will take precedence.
- [Protect notification](https://docs.galileo.ai/api-reference/protect-notification.md): When a Protect execution completes with the status specified in the configuration, the webhook specified is
triggered with this payload.
- [Invoke Protect](https://docs.galileo.ai/api-reference/protect/invoke.md): Learn how to use the 'Invoke Protect' API endpoint in Galileo's Protect module to process payloads with specified rulesets effectively.
- [null](https://docs.galileo.ai/api-reference/schemas/workflowstep.md)
- [Python Client Reference | Galileo Evaluate](https://docs.galileo.ai/client-reference/evaluate/python.md): Integrate Galileo's Evaluate module into your Python applications with this guide, featuring installation steps and examples for prompt quality assessment.
- [TypeScript Client Reference | Galileo Evaluate](https://docs.galileo.ai/client-reference/evaluate/typescript.md): Incorporate Galileo's Evaluate module into your TypeScript projects with this guide, providing setup instructions and workflow logging examples.
- [Data Quality | Fine-Tune NLP Studio Client Reference](https://docs.galileo.ai/client-reference/finetune-nlp-studio/data-quality.md): Enhance your data quality in Galileo's NLP and CV Studio using the 'dataquality' Python package; find installation and usage details here.
- [Python Client Reference | Galileo Observe](https://docs.galileo.ai/client-reference/observe/python.md): Integrate Galileo's Observe module into your Python applications; access installation instructions and comprehensive documentation for workflow monitoring.
- [TypeScript Client Reference | Galileo Observescript](https://docs.galileo.ai/client-reference/observe/typescript.md): Integrate Galileo's Observe module into TypeScript applications with setup guides, sample code, and monitoring instructions for seamless workflow tracking.
- [Client References](https://docs.galileo.ai/client-reference/overview.md): Explore Galileo's client references, including Python and TypeScript integrations, to streamline Evaluate, Observe, and Protect module implementations.
- [Python Client Reference | Galileo Protect](https://docs.galileo.ai/client-reference/protect/python.md): Integrate Galileo's Protect module into Python workflows with this guide, including code examples, setup instructions, and ruleset invocation details.
- [Data Privacy And Compliance](https://docs.galileo.ai/deployments/data-privacy-and-compliance.md): This page covers concerns regarding residency of data and compliances provided by Galileo.
- [Dependencies](https://docs.galileo.ai/deployments/dependencies.md): Understand Galileo deployment prerequisites and dependencies to ensure a smooth installation and integration across supported platforms.
- [Azure AKS](https://docs.galileo.ai/deployments/deploying-galileo-aks.md): This page details the steps to deploy a Galileo Kubernetes cluster in Microsoft Azure's AKS service environment.
- [Deploying Galileo on Amazon EKS](https://docs.galileo.ai/deployments/deploying-galileo-eks.md): Deploy Galileo on Amazon EKS with a step-by-step guide for configuring, managing, and scaling Galileo's infrastructure using Kubernetes clusters.
- [Zero Access Deployment | Galileo on EKS](https://docs.galileo.ai/deployments/deploying-galileo-eks-zero-access.md): Create a private Kubernetes Cluster with EKS in your AWS Account, upload containers to your container registry, and deploy Galileo.
- [EKS Cluster Config Example | Zero Access Deployment](https://docs.galileo.ai/deployments/deploying-galileo-eks-zero-access/eks-cluster-config-example-zero-access.md): Access a zero-access EKS cluster configuration example for secure Galileo deployments on Amazon EKS, following best practices for Kubernetes security.
- [EKS Cluster Config Example | Galileo Deployment](https://docs.galileo.ai/deployments/deploying-galileo-eks/eks-cluster-config-example.md): Review a detailed EKS cluster configuration example for deploying Galileo on Amazon EKS, ensuring efficient Kubernetes setup and management.
- [Updating Cluster](https://docs.galileo.ai/deployments/deploying-galileo-eks/updating-galileo-eks-cluster.md): Galileo EKS cluster update from 1.21 -> 1.23
- [Exoscale](https://docs.galileo.ai/deployments/deploying-galileo-exoscale.md): The Galileo applications run on managed Kubernetes-like environments, but this document will specifically cover the configuration and deployment of an Exoscale Cloud SKS environment.
- [Deploying Galileo on Google GKE](https://docs.galileo.ai/deployments/deploying-galileo-gke.md): Deploy Galileo on Google Kubernetes Engine (GKE) with this guide, covering configuration steps, cluster setup, and infrastructure scaling strategies.
- [Cluster Setup Script](https://docs.galileo.ai/deployments/deploying-galileo-gke/galileo-gcp-setup-script.md): Utilize the Galileo GCP setup script for automating Google Cloud Platform (GCP) configuration to deploy Galileo seamlessly on GKE clusters.
- [Enterprise Deployment](https://docs.galileo.ai/deployments/overview.md): Gain an overview of Galileo deployment options, covering supported platforms like Amazon EKS and Google GKE, setup requirements, and best practices.
- [Post Deployment Checklist](https://docs.galileo.ai/deployments/post-deployment-checklist.md): The following guide will walk you through steps you can take to make sure your Galileo cluster is properly deployed and running well.
- [Pre Requisites](https://docs.galileo.ai/deployments/pre-requisites.md): Before deploying Galileo, ensure the following prerequisites are met.
- [Scheduling Automatic Backups For Your Cluster](https://docs.galileo.ai/deployments/scheduling-automatic-backups-for-your-cluster.md): Schedule automatic backups for Galileo clusters with this guide, ensuring data security, disaster recovery, and operational resilience for deployments.
- [Aws Velero Account Setup Script](https://docs.galileo.ai/deployments/scheduling-automatic-backups-for-your-cluster/aws-velero-account-setup-script.md): Automate AWS Velero setup for Galileo cluster backups with this script, ensuring seamless backup scheduling and data resilience for AWS deployments.
- [Gcp Velero Account Setup Script](https://docs.galileo.ai/deployments/scheduling-automatic-backups-for-your-cluster/gcp-velero-account-setup-script.md): Set up Velero for Google Cloud backups with this GCP account script, enabling automated backup scheduling and robust data protection for Galileo clusters.
- [  Security &  Access Control](https://docs.galileo.ai/deployments/security-and-access-control.md): This page covers networking, security and access control provisions that Galileo deployments enable
- [Setting Up New Users](https://docs.galileo.ai/deployments/setting-up-new-users.md): Learn how to onboard new users in Galileo deployments with detailed instructions on user roles, access control, and permissions management.
- [SSO Integration](https://docs.galileo.ai/deployments/sso-integration.md): This page covers our SSO Integration support with information we need to setup SSO for your Galileo cluster.
- [Examples](https://docs.galileo.ai/examples/overview.md): Explore Galileo's practical examples covering real-world use cases and workflows for Evaluate, Observe, and Protect modules across AI projects.
- [What is Galileo?](https://docs.galileo.ai/galileo.md): Evaluate, Observe, and Protect your GenAI applications
- [Chainpoll](https://docs.galileo.ai/galileo-ai-research/chainpoll.md): ChainPoll is a powerful, flexible technique for LLM-based evaluation that is unique to Galileo. It is used to power multiple metrics across the Galileo platform.
- [Class Boundary Detection](https://docs.galileo.ai/galileo-ai-research/class-boundary-detection.md): Detecting samples on the decision boundary
- [Data Drift Detection](https://docs.galileo.ai/galileo-ai-research/data-drift-detection.md): Discover Galileo's data drift detection methods to monitor AI model performance, identify data changes, and maintain model reliability in production.
- [Errors In Object Detection](https://docs.galileo.ai/galileo-ai-research/errors-in-object-detection.md): This page describes the rich error types offered by Galileo for Object Detection
- [Galileo Data Error Potential  (Dep) ](https://docs.galileo.ai/galileo-ai-research/galileo-data-error-potential-dep.md): Learn about Galileo's Data Error Potential (DEP) score, a metric to identify and categorize machine learning data errors, enhancing data quality and model performance.
- [Likely Mislabeled](https://docs.galileo.ai/galileo-ai-research/likely-mislabeled.md): Garbage in, Garbage out
- [Galileo AI Research](https://docs.galileo.ai/galileo-ai-research/overview.md): Research produced by Galileo AI Labs
- [Rag Quality Metrics Using Chainpoll](https://docs.galileo.ai/galileo-ai-research/rag-quality-metrics-using-chainpoll.md): Learn how ChainPoll metrics assess retrieval-augmented generation (RAG) system quality, improving accuracy and performance of generative AI models.
- [Rag Quality Metrics Using Luna](https://docs.galileo.ai/galileo-ai-research/rag-quality-metrics-using-luna.md): This page provides a brief overview of the research behind Galileo's RAG Quality Metrics.
- [FAQs](https://docs.galileo.ai/galileo/galileo-nlp-studio/faqs.md): You have questions, we have (some) answers!
- [Third Party  3p  Integrations](https://docs.galileo.ai/galileo/galileo-nlp-studio/galileo-product-features/3p-integrations.md): Galileo has integrates seamlessly with your tools.
- [Access Control Features | Galileo NLP Studio](https://docs.galileo.ai/galileo/galileo-nlp-studio/galileo-product-features/access-control.md): Discover Galileo NLP Studio's access control features, including user roles and group management, to securely share and manage projects within your organization.
- [Actions](https://docs.galileo.ai/galileo/galileo-nlp-studio/galileo-product-features/actions.md): Actions help close the inspection loop and error discovery process. We support a number of actions.
- [Clustering](https://docs.galileo.ai/galileo/galileo-nlp-studio/galileo-product-features/clusters.md): To help you make sense of your data and your embeddings view, Galileo provides out-of-the-box Clustering and Explainability.
- [Compare Across Runs](https://docs.galileo.ai/galileo/galileo-nlp-studio/galileo-product-features/compare-across-runs.md): Track your experiments, data and models in one place
- [Dataset Slices](https://docs.galileo.ai/galileo/galileo-nlp-studio/galileo-product-features/dataset-slices.md): Slices is a powerful Galileo feature that allows you to monitor, across training runs, a sub-population of the dataset based on metadata filters.
- [Dataset View](https://docs.galileo.ai/galileo/galileo-nlp-studio/galileo-product-features/dataset-view.md): The Dataset View provides an interactive data table for inspecting your datasets.
- [Embeddings View](https://docs.galileo.ai/galileo/galileo-nlp-studio/galileo-product-features/embeddings-view.md): The Embeddings View provides a visual playground for you to interact with your datasets.
- [Error Types Breakdown](https://docs.galileo.ai/galileo/galileo-nlp-studio/galileo-product-features/error-types-breakdown.md): For use cases with complex data and error types (e.g. Named Entity Recognition, Object Detection or Semantic Segmentation), the **Error Types Chart** gives you an insight into exactly how the Ground Truth differed from your model's predictions
- [Galileo + Delta Lake  Databricks](https://docs.galileo.ai/galileo/galileo-nlp-studio/galileo-product-features/galileo-+-delta-lake-databricks.md): Integrate Galileo with Delta Lake on Databricks to manage large-scale data, ensuring seamless collaboration and enhanced NLP workflows.
- [Insights Panel](https://docs.galileo.ai/galileo/galileo-nlp-studio/galileo-product-features/insights-panel.md): Utilize Galileo's Insights Panel to analyze data trends, detect issues, and gain actionable insights for improving NLP model performance.
- [Product Features](https://docs.galileo.ai/galileo/galileo-nlp-studio/galileo-product-features/overview.md): Explore Galileo NLP Studio's features, including data insights, error detection, and monitoring tools for improving NLP workflows and AI quality.
- [Similarity Search](https://docs.galileo.ai/galileo/galileo-nlp-studio/galileo-product-features/similarity-search.md): Similarity search provides out of the box ability to discover **similar samples** within your datasets.
- [Alerts](https://docs.galileo.ai/galileo/galileo-nlp-studio/galileo-product-features/xray-insights.md): Explore Galileo NLP Studio's Alerts feature, designed to detect and summarize dataset issues like mislabeling and class imbalance, enhancing data inspection.
- [Multi Label Text Classification](https://docs.galileo.ai/galileo/galileo-nlp-studio/multi-label-text-classification.md): Implement multi-label text classification in Galileo NLP Studio to accurately label datasets, streamline workflows, and enhance model training.
- [Multi-Label Text Classification | Galileo NLP Studio Guide](https://docs.galileo.ai/galileo/galileo-nlp-studio/multi-label-text-classification/getting-started.md): Get started with multi-label text classification in Galileo NLP Studio, featuring setup instructions, workflow integration, and data preparation tips.
- [Named Entity Recognition](https://docs.galileo.ai/galileo/galileo-nlp-studio/named-entity-recognition.md): NER is a sequence tagging problem, where given an input document, the task is to correctly identify the span boundaries for various entities and also classify the spans into correct entity types.
- [Named Entity Recognition | Galileo NLP Studio Guide](https://docs.galileo.ai/galileo/galileo-nlp-studio/named-entity-recognition/getting-started.md): Start building named entity recognition (NER) models in Galileo NLP Studio with this guide on setup, labeling, and model training workflows.
- [Model Monitoring & Data Drift | Named Entity Recognition](https://docs.galileo.ai/galileo/galileo-nlp-studio/named-entity-recognition/model-monitoring-and-data-drift.md): Learn how to monitor Named Entity Recognition models in production with Galileo NLP Studio, detecting data drift and maintaining model health effectively.
- [Natural Language Inference](https://docs.galileo.ai/galileo/galileo-nlp-studio/natural-language-inference.md): Leverage Galileo NLP Studio for natural language inference (NLI), enabling accurate predictions and model performance monitoring.
- [Natural Language Inference | Galileo NLP Studio Guide](https://docs.galileo.ai/galileo/galileo-nlp-studio/natural-language-inference/getting-started.md): Begin implementing natural language inference (NLI) workflows in Galileo NLP Studio with clear instructions for setup and model evaluation.
- [Logging Data | Natural Language Inference in Galileo](https://docs.galileo.ai/galileo/galileo-nlp-studio/natural-language-inference/logging-data-to-galileo.md): The fastest way to find data errors in Galileo.
- [Model Monitoring & Data Drift | Natural Language Inference](https://docs.galileo.ai/galileo/galileo-nlp-studio/natural-language-inference/model-monitoring-and-data-drift.md): Ensure optimal performance of Natural Language Inference models in production by monitoring data drift and model health with Galileo NLP Studio.
- [Text Classification](https://docs.galileo.ai/galileo/galileo-nlp-studio/text-classification.md): Using Galileo for Text Classification you can improve your classification models by improving the quality of your training data.
- [Automated Production Monitoring](https://docs.galileo.ai/galileo/galileo-nlp-studio/text-classification/automated-production-monitoring.md): Monitor text classification models in production with automated tools from Galileo NLP Studio to detect data drift and maintain performance.