autonomize-model-sdk 1.1.68

SDK for creating and managing machine learning pipelines.

ModelHub SDK

ModelHub SDK is a powerful tool for orchestrating and managing machine learning workflows, experiments, datasets, and deployments on Kubernetes. It integrates seamlessly with MLflow and supports custom pipelines, dataset management, model logging, prompt management for LLMs, and universal model serving with intelligent inference types.

🚀 New in Latest Version: Revolutionary Universal Inference Types System with automatic type detection for 9 data types (TEXT, IMAGE, PDF, TABULAR, AUDIO, VIDEO, JSON, CSV, AUTO), KServe V2 protocol compliance, and production-ready model serving. Built on autonomize-core foundation with enhanced authentication, improved HTTP client management, and comprehensive SSL support.

Table of Contents

• ModelHub SDK
  • Table of Contents
  • Installation
  • Environment Setup
  • CLI Tool
  • Quickstart
  • Experiments and Runs
    • Logging Parameters and Metrics
    • Artifact Management
  • Pipeline Management
    • Basic Pipeline
    • Running a Pipeline
    • Advanced Configuration
  • Dataset Management
    • Loading Datasets
    • Using Blob Storage for Dataset
  • Universal Model Serving
    • Inference Types System
    • Quick Start Model Serving
    • Advanced Model Serving
  • Model Deployment through KServe
    • Create a model wrapper:
    • Serve models with ModelHub:
    • Deploy with KServe:
  • Examples
    • Training Pipeline with Multiple Stages
    • Dataset Version Management
• InferenceClient
  • Installation
  • Authentication
  • Text Inference
  • File Inference
    • Local File Path
    • File Object
    • URL
    • Signed URL from Cloud Storage
  • Response Format
  • Error Handling
  • Additional Features
  • Async Support
• Prompt Management
  • Features
  • Installation
  • Basic Usage
  • Loading and Using Prompts
  • Managing Prompt Versions
  • Evaluating Prompts
    • Online Evaluation (Backend Processing)
    • Offline Evaluation (Local Development)
    • Template-Based Evaluation
• ML Monitoring
  • LLL
  • Traditional Model Monitoring
• Migration Guide
• AI Gateway Client
  • Chat Completions
  • Authentication

Installation

To install the ModelHub SDK, simply run:

pip install autonomize-model-sdk

🔒 Security Update: We strongly recommend upgrading to version 1.1.39 or later, which includes enhanced security features with MLflow no longer being directly exposed. All MLflow traffic now routes through our secure API gateway.

Optional Dependencies

The SDK uses a modular dependency structure, allowing you to install only what you need:

# Install with core functionality (base, mlflow, pipeline, datasets)
pip install "autonomize-model-sdk[core]"

# Install with monitoring capabilities
pip install "autonomize-model-sdk[monitoring]"

# Install with serving capabilities
pip install "autonomize-model-sdk[serving]"

# Install with Azure integration
pip install "autonomize-model-sdk[azure]"

# Install the full package with all dependencies
pip install "autonomize-model-sdk[full]"

# Install for specific use cases
pip install "autonomize-model-sdk[data-science]"
pip install "autonomize-model-sdk[deployment]"

What's New: autonomize-core Integration

The ModelHub SDK has been enhanced with autonomize-core, providing a more robust and feature-rich foundation:

🔧 Core Improvements

• Enhanced HTTP Client: Built on httpx for better async support and connection management
• Comprehensive Exception Handling: Detailed error types for better debugging and error handling
• Improved Authentication: More secure and flexible credential management
• Better Logging: Centralized logging system with configurable levels
• SSL Certificate Support: Custom certificate handling for enterprise environments

🚀 Key Features

• Backward Compatibility: All existing code continues to work without changes
• New Environment Variables: Cleaner, more consistent naming (with backward compatibility)
• SSL Verification Control: Support for custom certificates and SSL configuration
• Better Error Messages: More descriptive error messages for troubleshooting
• Performance Improvements: Optimized HTTP client and connection pooling

📦 Dependencies

The integration brings the autonomize-core package as a dependency, which includes:

• Modern HTTP client (httpx)
• Comprehensive exception handling
• Advanced credential management
• SSL certificate support
• Structured logging

Environment Setup

New Preferred Environment Variables (autonomize-core)

We recommend using the new environment variable names for better consistency and clarity:

export MODELHUB_URI=https://your-modelhub.com
export MODELHUB_AUTH_CLIENT_ID=your_client_id
export MODELHUB_AUTH_CLIENT_SECRET=your_secret
export GENESIS_CLIENT_ID=your_genesis_client
export GENESIS_COPILOT_ID=your_copilot
export MLFLOW_EXPERIMENT_ID=your_experiment_id

Legacy Environment Variables (Backward Compatibility)

The following environment variables are still supported for backward compatibility:

export MODELHUB_BASE_URL=https://your-modelhub.com
export MODELHUB_CLIENT_ID=your_client_id
export MODELHUB_CLIENT_SECRET=your_secret
export CLIENT_ID=your_client
export COPILOT_ID=your_copilot
export MLFLOW_EXPERIMENT_ID=your_experiment_id

SSL Certificate Configuration

The SDK now supports custom SSL certificate verification through the verify_ssl parameter. This is useful when working with self-signed certificates or custom certificate authorities:

from modelhub.core import ModelhubCredential

# Disable SSL verification (not recommended for production)
credential = ModelhubCredential(
    modelhub_url="https://your-modelhub.com",
    client_id="your_client_id",
    client_secret="your_client_secret",
    verify_ssl=False
)

# Use custom certificate bundle
credential = ModelhubCredential(
    modelhub_url="https://your-modelhub.com",
    client_id="your_client_id",
    client_secret="your_client_secret",
    verify_ssl="/path/to/your/certificate.pem"
)

Environment File Configuration

Alternatively, create a .env file in your project directory and add the environment variables:

# .env file
MODELHUB_URI=https://your-modelhub.com
MODELHUB_AUTH_CLIENT_ID=your_client_id
MODELHUB_AUTH_CLIENT_SECRET=your_secret
GENESIS_CLIENT_ID=your_genesis_client
GENESIS_COPILOT_ID=your_copilot
MLFLOW_EXPERIMENT_ID=your_experiment_id

Enhanced Security (v1.1.39+)

Starting with version 1.1.39, the ModelHub SDK includes significant security enhancements:

🔒 MLflow Security Improvements

No Direct MLflow Exposure: MLflow is no longer directly accessible. All MLflow operations now route through our secure BFF (Backend-for-Frontend) API gateway, providing:

• Centralized Authentication: All requests are authenticated at the gateway level
• Request Validation: Enhanced input validation and sanitization
• Audit Logging: Complete audit trail for all MLflow operations
• Network Isolation: MLflow server runs in an internal-only network

Secure MLflow Tracking URI

The SDK now automatically configures MLflow to use the secure gateway endpoint:

# The SDK handles this automatically - no manual configuration needed
# MLflow tracking URI is set to: {api_url}/mlflow-tracking
# All requests are proxied through the secure gateway

from modelhub.core import ModelhubCredential
from modelhub.clients import MLflowClient

# Initialize with standard credentials
credential = ModelhubCredential()
client = MLflowClient(credential=credential)

# Use MLflow as normal - security is handled transparently
with client.start_run():
    client.mlflow.log_param("param", "value")
    client.mlflow.log_metric("metric", 0.95)

Note: For long-running MLflow operations, ensure your token lifetime is sufficient. The token is set when the MLflowClient is initialized.

CLI Tool

The ModelHub SDK includes a command-line interface for managing ML pipelines:

# Start a pipeline in local mode (with local scripts)
pipeline start -f pipeline.yaml --mode local --pyproject pyproject.toml

# Start a pipeline in CI/CD mode (using container)
pipeline start -f pipeline.yaml --mode cicd

CLI Options:

• -f, --file: Path to pipeline YAML file (default: pipeline.yaml)
• --mode: Execution mode ('local' or 'cicd')
  • local: Runs with local scripts and installs dependencies using uv
  • cicd: Uses container image with pre-installed dependencies
• --pyproject: Path to pyproject.toml file (required for local mode)

Quickstart

The ModelHub SDK allows you to easily log experiments, manage pipelines, and use datasets.

Here's a quick example of how to initialize the client and log a run:

Basic Usage

from modelhub.core import ModelhubCredential
from modelhub.clients import MLflowClient

# Initialize the credential
credential = ModelhubCredential(
    modelhub_url="https://your-modelhub.com",
    client_id="your_client_id",
    client_secret="your_client_secret"
)

# Initialize the MLflow client with the credential
client = MLflowClient(
    credential=credential,
    client_id="your_client_id",
    copilot_id="your_copilot_id"
)

experiment_id = "your_experiment_id"
client.set_experiment(experiment_id=experiment_id)

# Start an MLflow run
with client.start_run(run_name="my_experiment_run"):
    client.mlflow.log_param("param1", "value1")
    client.mlflow.log_metric("accuracy", 0.85)
    client.mlflow.log_artifact("model.pkl")

Advanced Usage with SSL Configuration

from modelhub.core import ModelhubCredential
from modelhub.clients import MLflowClient

# Initialize with custom SSL configuration
credential = ModelhubCredential(
    modelhub_url="https://your-modelhub.com",
    client_id="your_client_id",
    client_secret="your_client_secret",
    verify_ssl="/path/to/custom/certificate.pem"  # or False to disable
)

# The rest remains the same
client = MLflowClient(
    credential=credential,
    client_id="your_client_id",
    copilot_id="your_copilot_id"
)

Using Environment Variables

from modelhub.core import ModelhubCredential
from modelhub.clients import MLflowClient

# Credentials will be loaded from environment variables automatically
# MODELHUB_URI, MODELHUB_AUTH_CLIENT_ID, MODELHUB_AUTH_CLIENT_SECRET
credential = ModelhubCredential()

# Client IDs will be loaded from GENESIS_CLIENT_ID, GENESIS_COPILOT_ID
client = MLflowClient(credential=credential)

Experiments and Runs

ModelHub SDK provides an easy way to interact with MLflow for managing experiments and runs.

Logging Parameters and Metrics

To log parameters, metrics, and artifacts:

with client.start_run(run_name="my_run"):
    # Log parameters
    client.mlflow.log_param("learning_rate", 0.01)

    # Log metrics
    client.mlflow.log_metric("accuracy", 0.92)
    client.mlflow.log_metric("precision", 0.88)

    # Log artifacts
    client.mlflow.log_artifact("/path/to/model.pkl")

Artifact Management

You can log or download artifacts with ease:

# Log artifact
client.mlflow.log_artifact("/path/to/file.csv")

# Download artifact
client.mlflow.artifacts.download_artifacts(run_id="run_id_here", artifact_path="artifact.csv", dst_path="/tmp")

Pipeline Management

ModelHub SDK enables users to define, manage, and run multi-stage pipelines that automate your machine learning workflow. You can define pipelines in YAML and submit them using the SDK.

Basic Pipeline

Here's a simple pipeline example:

name: "Simple Pipeline"
description: "Basic ML pipeline"
experiment_id: "123"
image_tag: "my-image:1.0.0"
stages:
  - name: train
    type: custom
    script: scripts/train.py

Running a Pipeline

Using CLI:

# Local development
pipeline start -f pipeline.yaml --mode local --pyproject pyproject.toml

# CI/CD environment
pipeline start -f pipeline.yaml --mode cicd

Using SDK:

from modelhub.core import ModelhubCredential
from modelhub.clients import PipelineManager

# Initialize the credential
credential = ModelhubCredential(
    modelhub_url="https://your-modelhub.com",
    client_id="your_client_id",
    client_secret="your_client_secret"
)

# Initialize the pipeline manager with the credential
pipeline_manager = PipelineManager(
    credential=credential,
    client_id="your_client_id",
    copilot_id="your_copilot_id"
)

# Start the pipeline
pipeline = pipeline_manager.start_pipeline("pipeline.yaml")

Advanced Configuration

For detailed information about pipeline configuration including:

• Resource management (CPU, Memory, GPU)
• Node scheduling with selectors and tolerations
• Blob storage integration
• Stage dependencies
• Advanced examples and best practices

See our Pipeline Configuration Guide.

Dataset Management

ModelHub SDK allows you to load and manage datasets easily, with support for loading data from external storage or datasets managed through the frontend.

Loading Datasets

To load datasets using the SDK:

from modelhub.core import ModelhubCredential
from modelhub.clients import DatasetClient

# Initialize the credential
credential = ModelhubCredential(
    modelhub_url="https://your-modelhub.com",
    client_id="your_client_id",
    client_secret="your_client_secret"
)

# Initialize the dataset client with the credential
dataset_client = DatasetClient(
    credential=credential,
    client_id="your_client_id",
    copilot_id="your_copilot_id"
)

# Load a dataset by name
dataset = dataset_client.load_dataset("my_dataset")

# Load a dataset from a specific directory
dataset = dataset_client.load_dataset("my_dataset", directory="data_folder/")

# Load a specific version and split
dataset = dataset_client.load_dataset("my_dataset", version=2, split="train")

Using Blob Storage for Dataset

# Load dataset from Azure Blob Storage
dataset = dataset_client.load_dataset(
    "my_dataset",
    blob_storage_config={
        "container": "data",
        "blob_url": "https://storage.blob.core.windows.net",
        "mount_path": "/data"
    }
)

Google Cloud Storage Support (v1.1.39+)

ModelHub SDK now supports Google Cloud Storage (GCS) for dataset storage and artifact management:

# Load dataset from Google Cloud Storage
dataset = dataset_client.load_dataset(
    "my_dataset",
    gcs_config={
        "bucket": "my-ml-datasets",
        "prefix": "datasets/training/",
        "credentials_path": "/path/to/service-account.json",  # Optional
        "mount_path": "/data"
    }
)

# Using GCS for MLflow artifacts
import os
os.environ["MLFLOW_GCS_DEFAULT_ARTIFACT_ROOT"] = "gs://my-ml-artifacts"

with client.start_run():
    # Artifacts will be automatically stored in GCS
    client.mlflow.log_artifact("model.pkl")  # Stored in gs://my-ml-artifacts/
    client.mlflow.log_metric("accuracy", 0.95)

GCS Configuration Options:

• bucket: The GCS bucket name
• prefix: Optional path prefix within the bucket
• credentials_path: Path to service account JSON (uses default credentials if not specified)
• mount_path: Local mount point for the dataset

Universal Model Serving

ModelHub SDK provides a revolutionary inference types system that automatically detects and handles different data types, making it easy to deploy any model with a unified interface. The system supports 9 inference types and provides KServe V2 protocol compliance without requiring KServe dependencies.

Inference Types System

The inference types system automatically detects and processes different input data types:

Supported Types:

• TEXT: Natural language text processing
• IMAGE: Image classification, object detection, etc.
• PDF: Document processing and analysis
• TABULAR: Structured data (CSV, DataFrame)
• AUDIO: Speech recognition, audio classification
• VIDEO: Video analysis and processing
• JSON: Structured JSON data
• CSV: Comma-separated values
• AUTO: Automatic type detection

Quick Start Model Serving

Deploy any model with minimal configuration:

from modelhub.serving import BaseModelPredictor, ModelServer

class MyTextClassifier(BaseModelPredictor):
    def load_model(self):
        # Load your model (any framework: scikit-learn, PyTorch, TensorFlow, etc.)
        import joblib
        return joblib.load("my_model.pkl")

    def predict(self, data):
        # Simple prediction logic
        predictions = self.model.predict(data)
        return {"predictions": predictions.tolist()}

# Start the server
model_service = MyTextClassifier(name="text-classifier")
server = ModelServer()
server.start([model_service])

# Your model is now available at:
# POST http://localhost:8080/v2/models/text-classifier/infer

Advanced Model Serving

Use the AutoModelPredictor for automatic type handling:

from modelhub.serving import AutoModelPredictor, ModelServer
from modelhub.serving.inference_types import InferenceType

class UniversalModel(AutoModelPredictor):
    def load_model(self):
        # Load your universal model
        return load_your_model()

    def predict(self, processed_data, inference_type: InferenceType):
        """
        Handle different input types automatically
        processed_data: Already transformed by InputTransformer
        inference_type: Detected type (TEXT, IMAGE, PDF, etc.)
        """
        if inference_type == InferenceType.TEXT:
            return {"text_prediction": self.model.predict_text(processed_data)}
        elif inference_type == InferenceType.IMAGE:
            return {"image_prediction": self.model.predict_image(processed_data)}
        elif inference_type == InferenceType.TABULAR:
            return {"tabular_prediction": self.model.predict_tabular(processed_data)}
        else:
            # Handle AUTO and other types
            return {"prediction": self.model.predict(processed_data)}

# Deploy with full KServe V2 protocol support
model_service = UniversalModel(name="universal-model")
server = ModelServer()
server.start([model_service])

# Available endpoints:
# POST /v2/models/universal-model/infer     - Main inference
# GET  /v2/models/universal-model           - Model metadata
# GET  /v2/health/live                      - Liveness check
# GET  /v2/health/ready                     - Readiness check

Key Features:

• Automatic Type Detection: Input data is automatically classified
• Input/Output Transformation: Data is preprocessed and postprocessed automatically
• KServe V2 Protocol: Full compliance with industry standard
• Production Ready: Comprehensive error handling and logging
• Framework Agnostic: Works with any ML framework
• Scalable: Designed for production deployment

Example Requests:

# Text input
curl -X POST http://localhost:8080/v2/models/universal-model/infer \
  -H "Content-Type: application/json" \
  -d '{"inputs": [{"name": "text", "data": ["Hello world"]}]}'

# Image input (base64 encoded)
curl -X POST http://localhost:8080/v2/models/universal-model/infer \
  -H "Content-Type: application/json" \
  -d '{"inputs": [{"name": "image", "data": ["data:image/jpeg;base64,..."]}]}'

# Auto-detection (let the system detect the type)
curl -X POST http://localhost:8080/v2/models/universal-model/infer \
  -H "Content-Type: application/json" \
  -d '{"inputs": [{"name": "input", "data": ["any_data_here"]}]}'

For comprehensive documentation on model serving capabilities, see our Model Serving Guide.

Model Deployment through KServe

Deploy models via KServe after logging them with MLflow:

Create a model wrapper:

Use the MLflow PythonModel interface to define your model's prediction logic.

import mlflow.pyfunc
import joblib

class ModelWrapper(mlflow.pyfunc.PythonModel):
    def load_context(self, context):
        self.model = joblib.load("/path/to/model.pkl")

    def predict(self, context, model_input):
        return self.model.predict(model_input)

# Log the model
client.mlflow.pyfunc.log_model(
    artifact_path="model",
    python_model=ModelWrapper()
)

Serve models with ModelHub:

ModelHub SDK provides enhanced classes for serving models with the new Universal Inference Types System:

from modelhub.serving import BaseModelPredictor, ModelServer

# Simple approach - serve any MLflow model
class MLflowModelService(BaseModelPredictor):
    def __init__(self, name: str, run_uri: str):
        super().__init__(name=name)
        self.run_uri = run_uri

    def load_model(self):
        import mlflow.pyfunc
        return mlflow.pyfunc.load_model(self.run_uri)

    def predict(self, data):
        predictions = self.model.predict(data)
        return {"predictions": predictions}

# Create and start model service
model_service = MLflowModelService(
    name="my-classifier",
    run_uri="runs:/abc123def456/model"
)
server = ModelServer()
server.start([model_service])

The new system supports automatic type detection for text, images, PDFs, tabular data, audio, video, JSON, CSV, and more. For comprehensive documentation on model serving capabilities, see our Model Serving Guide.

Deploy with KServe:

After logging the model, deploy it using KServe:

apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
  name: "model-service"
  namespace: "modelhub"
  labels:
    azure.workload.identity/use: "true"
spec:
  predictor:
    containers:
      - image: your-registry.io/model-serve:latest
        name: model-service
        resources:
          requests:
            cpu: "1"
            memory: "2Gi"
          limits:
            cpu: "2"
            memory: "4Gi"
        command:
          [
            "sh",
            "-c",
            "python app/main.py --model_name my-classifier --run runs:/abc123def456/model",
          ]
        env:
          - name: MODELHUB_BASE_URL
            value: "https://api-modelhub.example.com"
    serviceAccountName: "service-account-name"

Examples

Training Pipeline with Multiple Stages

from modelhub.core import ModelhubCredential
from modelhub.clients import MLflowClient, PipelineManager

# Initialize credential
credential = ModelhubCredential(
    modelhub_url="https://api-modelhub.example.com",
    client_id="your_client_id",
    client_secret="your_client_secret"
)

# Setup clients
mlflow_client = MLflowClient(credential=credential)
pipeline_manager = PipelineManager(credential=credential)

# Define and run pipeline
pipeline = pipeline_manager.start_pipeline("pipeline.yaml")

# Track experiment in MLflow
with mlflow_client.start_run(run_name="Training Run"):
    # Log training parameters
    mlflow_client.log_param("model_type", "transformer")
    mlflow_client.log_param("epochs", 10)

    # Log metrics
    mlflow_client.log_metric("train_loss", 0.123)
    mlflow_client.log_metric("val_accuracy", 0.945)

    # Log model artifacts
    mlflow_client.log_artifact("model.pkl")

Dataset Version Management

from modelhub.core import ModelhubCredential
from modelhub.clients import DatasetClient

# Initialize credential
credential = ModelhubCredential(
    modelhub_url="https://api-modelhub.example.com",
    client_id="your_client_id",
    client_secret="your_client_secret"
)

# Initialize client
dataset_client = DatasetClient(credential=credential)

# List available datasets
datasets = dataset_client.list_datasets()

# Get specific version
dataset_v2 = dataset_client.get_dataset_versions("dataset_id")

# Load dataset with version control
dataset = dataset_client.load_dataset(
    "my_dataset",
    version=2,
    split="train"
)

InferenceClient

The InferenceClient provides a simple interface to perform inference using deployed models. It supports both text-based and file-based inference with comprehensive error handling and support for various input types.

Installation

The inference client is part of the ModelHub SDK optional dependencies. To install:

pip install "autonomize-model-sdk[serving]"

Or with uv:

uv add autonomize-model-sdk --extra serving

Authentication

The client supports multiple authentication methods:

from modelhub.core import ModelhubCredential
from modelhub.clients import InferenceClient

# Create credential
credential = ModelhubCredential(
    modelhub_url="https://your-modelhub-instance",
    client_id="your-client-id",
    client_secret="your-client-secret"
)

# Using credential (recommended approach)
client = InferenceClient(
    credential=credential,
    client_id="client_id",
    copilot_id="copilot_id"
)

# Using environment variables (MODELHUB_BASE_URL, MODELHUB_CLIENT_ID, MODELHUB_CLIENT_SECRET)
# Note: This approach is deprecated and will be removed in a future version
client = InferenceClient()

# Using direct parameters (deprecated)
client = InferenceClient(
    base_url="https://your-modelhub-instance",
    sa_client_id="your-client-id",
    sa_client_secret="your-client-secret",
    genesis_client_id="client id",
    genesis_copilot_id="copilot id"
)

# Using a token (deprecated)
client = InferenceClient(
    base_url="https://your-modelhub-instance",
    token="your-token"
)

Text Inference

For models that accept text input:

# Simple text inference
response = client.run_text_inference(
    model_name="text-model",
    text="This is the input text"
)

# With additional parameters
response = client.run_text_inference(
    model_name="llm-model",
    text="Translate this to French: Hello, world!",
    parameters={
        "temperature": 0.7,
        "max_tokens": 100
    }
)

# Access the result
result = response["result"]
print(f"Processing time: {response.get('processing_time')} seconds")

File Inference

The client supports multiple file input methods:

Local File Path

# Using a local file path
response = client.run_file_inference(
    model_name="image-recognition",
    file_path="/path/to/image.jpg"
)

File Object

# Using a file-like object
with open("document.pdf", "rb") as f:
    response = client.run_file_inference(
        model_name="document-processor",
        file_path=f,
        file_name="document.pdf",
        content_type="application/pdf"
    )

URL

# Using a URL
response = client.run_file_inference(
    model_name="image-recognition",
    file_path="https://example.com/images/sample.jpg"
)

Signed URL from Cloud Storage

# Using a signed URL from S3 or Azure Blob Storage
response = client.run_file_inference(
    model_name="document-processor",
    file_path="https://your-bucket.s3.amazonaws.com/path/to/document.pdf?signature=...",
    file_name="confidential-document.pdf",  # Optional: Override filename
    content_type="application/pdf"          # Optional: Override content type
)

Response Format

The response format is consistent across inference types:

{
    "result": {
        # Model-specific output
        # For example, text models might return:
        "text": "Generated text",

        # Image models might return:
        "objects": [
            {"class": "car", "confidence": 0.95, "bbox": [10, 20, 100, 200]},
            {"class": "person", "confidence": 0.87, "bbox": [150, 30, 220, 280]}
        ]
    },
    "processing_time": 0.234,  # Time in seconds
    "model_version": "1.0.0",  # Optional version info
    "metadata": {              # Optional additional information
        "runtime": "cpu",
        "batch_size": 1
    }
}

Error Handling

The client provides comprehensive error handling with specific exception types:

from modelhub.clients import InferenceClient
from modelhub.core.exceptions import (
    ModelHubException,
    ModelHubResourceNotFoundException,
    ModelHubBadRequestException,
    ModelhubUnauthorizedException
)

client = InferenceClient(credential=credential)

try:
    response = client.run_text_inference("model-name", "input text")
    print(response)
except ModelHubResourceNotFoundException as e:
    print(f"Model not found: {e}")
    # Handle 404 error
except ModelhubUnauthorizedException as e:
    print(f"Authentication failed: {e}")
    # Handle 401/403 error
except ModelHubBadRequestException as e:
    print(f"Invalid request: {e}")
    # Handle 400 error
except ModelHubException as e:
    print(f"Inference failed: {e}")
    # Handle other errors

Additional Features

• SSL verification control: You can disable SSL verification for development environments
• Automatic content type detection: The client automatically detects the content type of files based on their extension
• Customizable timeout: You can set a custom timeout for inference requests
• Comprehensive logging: All operations are logged for easier debugging

Async Support

The InferenceClient also provides async versions of all methods for use in async applications:

import asyncio
from modelhub.clients import InferenceClient

async def run_inference():
    client = InferenceClient(credential=credential)

    # Text inference
    response = await client.arun_text_inference(
        model_name="text-model",
        text="This is async inference"
    )

    # File inference
    file_response = await client.arun_file_inference(
        model_name="image-model",
        file_path="/path/to/image.jpg"
    )

    return response, file_response

# Run with asyncio
responses = asyncio.run(run_inference())

Prompt Management

The ModelHub SDK provides comprehensive prompt management capabilities through the dedicated PromptClient. This allows you to version, track, evaluate, and reuse prompts across your organization with support for complex multi-message templates.

Features

• Versioning - Track the evolution of your prompts with version control
• Multi-Message Templates - Support for complex system/user message structures
• Reusability - Store and manage prompts in a centralized registry
• Aliases - Create aliases for prompt versions to simplify deployment pipelines
• Evaluation - Built-in prompt evaluation with metrics and traces
• Tags & Metadata - Rich tagging and metadata support for organization
• Async Support - Full async/await support for all operations

Installation

Prompt management is included in the core SDK:

pip install autonomize-model-sdk

Basic Usage

from modelhub.core import ModelhubCredential
from modelhub.clients import PromptClient
from modelhub.models.prompts import PromptCreation, Message, Content

# Initialize credential
credential = ModelhubCredential(
    modelhub_url="https://api-modelhub.example.com",
    client_id="your_client_id",
    client_secret="your_client_secret"
)

# Initialize prompt client
prompt_client = PromptClient(credential=credential)

# Create a new prompt with system and user messages
prompt = prompt_client.create_prompt(
    PromptCreation(
        name="summarization-prompt",
        template=[
            Message(
                role="system",
                content=Content(type="text", text="You are a helpful summarization assistant."),
                input_variables=[]
            ),
            Message(
                role="user",
                content=Content(
                    type="text",
                    text="Summarize this in {{ num_sentences }} sentences: {{ content }}"
                ),
                input_variables=["num_sentences", "content"]
            )
        ],
        commit_message="Initial version",
        version_metadata={"author": "[e-mail]"},
        tags=[{"key": "task", "value": "summarization"}]
    )
)

print(f"Created prompt '{prompt['name']}' (version {prompt['latest_versions'][0]['version']})")

Loading and Using Prompts

# Get a specific prompt version
prompt = prompt_client.get_registered_prompt_version("summarization-prompt", version=1)

# Get the latest version
latest_prompt = prompt_client.get_registered_prompt_by_name("summarization-prompt")

# Create an alias for deployment
from modelhub.models.models import Alias
prompt_client.create_alias(
    "summarization-prompt",
    Alias(name="production", version=1)
)

# Search for prompts
from modelhub.models.models import SearchModelsCriteria
prompts = prompt_client.get_prompts(
    SearchModelsCriteria(
        filter_string="tags.task = 'summarization'"
    )
)

# Evaluate a prompt
from modelhub.models.prompts import EvaluationInput
evaluation_result = prompt_client.evaluate_prompt(
    EvaluationInput(
        model="gpt-3.5-turbo",
        provider="azure",
        template=prompt['latest_versions'][0]['template'],
        temperature=0.1,
        variables={"num_sentences": "2", "content": "Your text here..."}
    )
)

Managing Prompt Versions

# Create a new version of existing prompt
from modelhub.models.prompts import UpdatePromptVersionRequest

new_version = prompt_client.create_prompt_version(
    "summarization-prompt",
    UpdatePromptVersionRequest(
        template=[
            Message(
                role="system",
                content=Content(
                    type="text",
                    text="You are an expert summarizer. Be concise and accurate."
                ),
                input_variables=[]
            ),
            Message(
                role="user",
                content=Content(
                    type="text",
                    text="Summarize in exactly {{ num_sentences }} sentences: {{ content }}"
                ),
                input_variables=["num_sentences", "content"]
            )
        ],
        commit_message="Improved prompt with clearer instructions"
    )
)

# Update version tags
from modelhub.models.models import Tag
prompt_client.update_prompt_version_tag(
    "summarization-prompt",
    version="2",
    version_metadata=[
        Tag(key="tested", value="true"),
        Tag(key="performance", value="improved")
    ]
)

# List all versions of a prompt
versions = prompt_client.get_prompt_versions_with_name("summarization-prompt")

Evaluating Prompts

The ModelHub SDK provides both online (backend-processed) and offline (local) evaluation capabilities for prompt testing and development.

Online Evaluation (Backend Processing)

For comprehensive evaluation with results tracked on the ModelHub dashboard:

# Online evaluation via backend API
from modelhub.models.prompts import EvaluationInput

# Get the prompt to evaluate
prompt = prompt_client.get_registered_prompt_version("summarization-prompt", version=1)

# Submit evaluation job (processed asynchronously via Kafka)
evaluation_result = prompt_client.evaluate_prompt(
    EvaluationInput(
        model="gpt-3.5-turbo",
        provider="azure",  # or "openai"
        template=prompt['template'],
        temperature=0.1,
        variables={
            "num_sentences": "2",
            "content": "Artificial intelligence has transformed how businesses operate..."
        }
    )
)

# Get execution traces for analysis
from modelhub.models.prompts import PromptRunTracesDto

traces = prompt_client.get_traces(
    PromptRunTracesDto(
        experiment_ids=["your-experiment-id"],
        filter_string="tags.prompt_name = 'summarization-prompt'",
        max_results=100
    )
)

Offline Evaluation (Local Development)

For immediate feedback during prompt development, use the offline evaluation capabilities:

# Install with evaluation dependencies
# pip install "autonomize-model-sdk[monitoring]"

import pandas as pd
from modelhub.evaluation import PromptEvaluator, EvaluationConfig

# Configure evaluation settings
config = EvaluationConfig(
    evaluations=["metrics"],  # Basic text metrics
    save_html=True,
    save_json=True,
    output_dir="./evaluation_reports"
)

# Initialize evaluator
evaluator = PromptEvaluator(config)

# Prepare evaluation data
data = pd.DataFrame({
    'prompt': [
        'Summarize this article in 2 sentences.',
        'Explain quantum computing in simple terms.'
    ],
    'response': [
        'This article discusses AI advancements and applications in various industries.',
        'Quantum computing uses quantum mechanics principles for faster computations.'
    ],
    'expected': [  # Optional reference responses
        'AI has advanced significantly with diverse applications.',
        'Quantum computing leverages quantum mechanics for speed.'
    ]
})

# Run offline evaluation
report = evaluator.evaluate_offline(
    data=data,
    prompt_col='prompt',
    response_col='response',
    reference_col='expected'  # Optional
)

# Access results
print(f"Total samples evaluated: {report.summary['total_samples']}")
print(f"Average prompt length: {report.summary['basic_stats']['avg_prompt_length']}")
print(f"Average response length: {report.summary['basic_stats']['avg_response_length']}")

# Reports saved to ./evaluation_reports/ directory
print(f"HTML report: {report.html_path}")
print(f"JSON report: {report.json_path}")

Template-Based Evaluation

Evaluate prompt templates with multiple test cases:

from modelhub.models.prompts import Message, Content

# Define a prompt template
template = [
    Message(
        role="system",
        content=Content(type="text", text="You are a helpful assistant."),
        input_variables=[]
    ),
    Message(
        role="user",
        content=Content(type="text", text="Summarize in {{num_sentences}} sentences: {{content}}"),
        input_variables=["num_sentences", "content"]
    )
]

# Test data with variable combinations
test_data = pd.DataFrame({
    'variables': [
        {'num_sentences': '2', 'content': 'Long article about AI...'},
        {'num_sentences': '3', 'content': 'Research paper on quantum computing...'}
    ],
    'expected': [
        'Expected summary 1...',
        'Expected summary 2...'
    ]
})

# Optional: Provide LLM function for actual response generation
def generate_response(prompt):
    # Your LLM call here
    return "Generated response..."

# Evaluate template
report = evaluator.evaluate_prompt_template(
    prompt_template=template,
    test_data=test_data,
    variables_col='variables',
    expected_col='expected',
    llm_generate_func=generate_response  # Optional
)

Key Differences:

• Online Evaluation: Comprehensive analysis, dashboard integration, requires backend processing time
• Offline Evaluation: Immediate results, local development, basic text metrics only
• Use Cases: Online for production testing, offline for rapid iteration

Async Support

All prompt operations support async/await:

# Async prompt creation
async def create_prompt_async():
    prompt = await prompt_client.acreate_prompt(prompt_creation)
    return prompt

# Async version retrieval
async def get_versions_async():
    versions = await prompt_client.aget_prompt_versions_with_name("summarization-prompt")
    return versions

For more detailed information about prompt management, including advanced usage patterns, best practices, and in-depth examples, see our Prompt Management Guide.

Model Monitoring and Evaluation

ModelHub SDK provides comprehensive tools for monitoring and evaluating both traditional ML models and Large Language Models (LLMs). These tools help track model performance, detect data drift, and assess LLM-specific metrics.

To install with monitoring capabilities:

pip install "autonomize-model-sdk[monitoring]"

LLM Monitoring

The LLMMonitor utility allows you to evaluate and monitor LLM outputs using specialized metrics and visualizations.

Basic LLM Evaluation

from modelhub.core import ModelhubCredential
from modelhub.clients.mlflow_client import MLflowClient
from modelhub.monitors.llm_monitor import LLMMonitor

# Initialize credential
credential = ModelhubCredential(
    modelhub_url="https://your-modelhub-instance",
    client_id="your-client-id",
    client_secret="your-client-secret"
)

# Initialize clients
mlflow_client = MLflowClient(credential=credential)
llm_monitor = LLMMonitor(mlflow_client=mlflow_client)

# Create a dataframe with LLM responses
data = pd.DataFrame({
    "prompt": ["Explain AI", "What is MLOps?"],
    "response": ["AI is a field of computer science...", "MLOps combines ML and DevOps..."],
    "category": ["education", "technical"]
})

# Create column mapping
column_mapping = llm_monitor.create_column_mapping(
    prompt_col="prompt",
    response_col="response",
    categorical_cols=["category"]
)

# Run evaluations
length_report = llm_monitor.evaluate_text_length(
    data=data,
    response_col="response",
    column_mapping=column_mapping,
    save_html=True
)

# Generate visualizations
dashboard_path = llm_monitor.generate_dashboard(
    data=data,
    response_col="response",
    category_col="category"
)

# Log metrics to MLflow
llm_monitor.log_metrics_to_mlflow(length_report)

Evaluating Content Patterns

patterns_report = llm_monitor.evaluate_content_patterns(
    data=data,
    response_col="response",
    words_to_check=["AI", "model", "learning"],
    patterns_to_check=["neural network", "deep learning"],
    prefix_to_check="I'll explain"
)

Semantic Properties Analysis

semantic_report = llm_monitor.evaluate_semantic_properties(
    data=data,
    response_col="response",
    prompt_col="prompt",
    check_sentiment=True,
    check_toxicity=True,
    check_prompt_relevance=True
)

Comprehensive Evaluation

results = llm_monitor.run_comprehensive_evaluation(
    data=data,
    response_col="response",
    prompt_col="prompt",
    categorical_cols=["category"],
    words_to_check=["AI", "model", "learning"],
    run_sentiment=True,
    run_toxicity=True,
    save_html=True
)

LLM-as-Judge Evaluation

Evaluate responses using OpenAI's models as a judge (requires OpenAI API key):

judge_report = llm_monitor.evaluate_llm_as_judge(
    data=data,
    response_col="response",
    check_pii=True,
    check_decline=True,
    custom_evals=[{
        "name": "Educational Value",
        "criteria": "Evaluate whether the response has educational value.",
        "target": "educational",
        "non_target": "not_educational"
    }]
)

Comparing LLM Models

Compare responses from different LLM models:

comparison_report = llm_monitor.generate_comparison_report(
    reference_data=model_a_data,
    current_data=model_b_data,
    response_col="response",
    category_col="category"
)

comparison_viz = llm_monitor.create_comparison_visualization(
    reference_data=model_a_data,
    current_data=model_b_data,
    response_col="response",
    metrics=["length", "word_count", "sentiment_score"]
)

Traditional ML Monitoring

The SDK also includes MLMonitor for traditional ML models, providing capabilities for:

• Data drift detection
• Data quality assessment
• Model performance monitoring
• Target drift analysis
• Regression and classification metrics

from modelhub.core import ModelhubCredential
from modelhub.clients.mlflow_client import MLflowClient
from modelhub.monitors.ml_monitor import MLMonitor

# Initialize credential
credential = ModelhubCredential(
    modelhub_url="https://your-modelhub-instance",
    client_id="your-client-id",
    client_secret="your-client-secret"
)

# Initialize clients
mlflow_client = MLflowClient(credential=credential)
ml_monitor = MLMonitor(mlflow_client=mlflow_client)

results = ml_monitor.run_and_log_reports(
    reference_data=reference_data,
    current_data=current_data,
    report_types=["data_drift", "data_quality", "target_drift", "regression"],
    column_mapping=column_mapping,
    target_column="target",
    prediction_column="prediction",
    log_to_mlflow=True
)

AI Gateway Client

The AIGatewayClient is an ultra-generic client for accessing the Genesis AI Gateway with virtual keys (permanent tokens). It features a flexible architecture that supports any AI Gateway endpoint through a single request() method, while providing convenient wrapper methods for common operations.

🔑 Key Features

• 🌀 Ultra-Generic Design: Single request() method supports any AI Gateway endpoint
• 🔐 Secure Authentication: Uses virtual keys with proper environment variable management
• 📡 Full HTTP Support: GET, POST, PUT, DELETE, PATCH methods
• 🛡️ Comprehensive Error Handling: Proper exception mapping for all HTTP status codes
• 🏥 Built-in Health Checks: Dedicated health monitoring endpoints
• 📦 Flexible Data Handling: Supports JSON payloads, query parameters, and binary responses

🚀 Quick Start

from modelhub.clients import AIGatewayClient

# Initialize with virtual key
client = AIGatewayClient(virtual_key="sk-your-virtual-key")

# Simple chat completion
response = client.chat_completion(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}],
    max_tokens=150
)
print(response["choices"][0]["message"]["content"])

💬 Chat Completions

Basic Usage

response = client.chat_completion(
    model="gpt-4o",
    messages=[
        {"role": "user", "content": "Explain quantum computing in simple terms"}
    ],
    max_tokens=200,
    temperature=0.7
)

# Access the response
content = response["choices"][0]["message"]["content"]
usage = response["usage"]

Advanced Chat Completion

response = client.chat_completion(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a helpful AI assistant."},
        {"role": "user", "content": "Write a Python function to calculate fibonacci"}
    ],
    max_tokens=300,
    temperature=0.8,
    top_p=0.9,
    frequency_penalty=0.1,
    presence_penalty=0.2,
    stop=["```"],  # Stop at code blocks
    stream=False   # Set to True for streaming responses
)

🌐 Generic Request Method

The core request() method supports any AI Gateway endpoint:

Basic GET Request

# Get available models
models = client.request("GET", "/v1/models")
print(f"Available models: {len(models['data'])}")

# Get model info
model_info = client.request("GET", "/model_group/info")

POST with JSON Payload

# Custom chat completion via generic method
payload = {
    "model": "gpt-4o",
    "messages": [{"role": "user", "content": "Hello!"}],
    "max_tokens": 100
}
response = client.request("POST", "/v1/chat/completions", json=payload)

POST with Query Parameters

# List models with pagination
models = client.request("GET", "/v1/models", params={"limit": 10, "offset": 0})

Binary Response Handling

# For endpoints that return images or other binary data
image_data = client.request("POST", "/v1/images/generations",
    json={"prompt": "A sunset over mountains", "model": "image-generator"}
)
# image_data will be bytes for binary responses

🏥 Health Monitoring

The client provides comprehensive health checking:

# Get health status
health = client.health_check()

# Health response structure
{
    "status": "healthy",  # or "unhealthy"
    "gateway_url": "https://genesis.dev-v2.platform.autonomize.dev",
    "auth_valid": True,
    "liveliness": "I'm alive!",  # Basic health check
    "readiness": {               # Detailed health info
        "status": "connected",
        "db": "connected",
        "litellm_version": "1.76.1"
    }
}

# Check specific health aspects
if health["status"] == "healthy":
    print("✅ Gateway is healthy")
    if health["auth_valid"]:
        print("✅ Authentication working")
    else:
        print("❌ Authentication failed")

🔐 Authentication

The AIGatewayClient uses permanent token authentication with a simple and direct approach:

Direct Initialization

client = AIGatewayClient(
    virtual_key="sk-your-virtual-key-here",
    gateway_url="https://genesis.dev-v2.platform.autonomize.dev"  # Optional
)

Environment Variables

import os

# Set virtual key
os.environ["GENESIS_VIRTUAL_KEY"] = "sk-your-key"

# Client will automatically use the environment variable
client = AIGatewayClient(virtual_key=os.getenv("GENESIS_VIRTUAL_KEY"))

Default Gateway URL

If no gateway_url is provided, it defaults to:

https://genesis.dev-v2.platform.autonomize.dev

Implementation Details

The client internally uses the simplified approach from autonomize-core documentation:

from autonomize.core.credential import ModelhubCredential
from autonomize.types.core.credential import AuthType

self.credential = ModelhubCredential(
    token=virtual_key,
    auth_type=AuthType.PERMANENT_TOKEN
)

🎯 Use Cases & Examples

1. Image Generation (if supported by your virtual key)

# Via generic request method
image_response = client.request("POST", "/v1/images/generations", json={
    "model": "gpt-image-1",
    "prompt": "A beautiful landscape",
    "n": 1,
    "size": "1024x1024"
})

2. Embeddings (if supported by your virtual key)

# Via generic request method
embeddings = client.request("POST", "/v1/embeddings", json={
    "model": "text-embedding-ada-002",
    "input": ["Hello world", "How are you?"]
})

3. Model Discovery

# List all available models
models = client.request("GET", "/v1/models")

# Get model group information
model_groups = client.request("GET", "/model_group/info")

4. Custom Endpoints

# Any AI Gateway endpoint
custom_response = client.request("GET", "/custom/endpoint", params={"param": "value"})

⚠️ Error Handling

The client provides comprehensive error handling with specific exception types:

from modelhub.clients import AIGatewayClient
from autonomize.exceptions.core.credentials import (
    ModelHubAPIException,
    ModelHubBadRequestException,
    ModelhubUnauthorizedException
)

client = AIGatewayClient(virtual_key="sk-your-key")

try:
    response = client.chat_completion(
        model="gpt-4o",
        messages=[{"role": "user", "content": "Hello!"}]
    )
except ModelhubUnauthorizedException as e:
    print(f"❌ Authentication failed: {e}")
except ModelHubBadRequestException as e:
    print(f"❌ Invalid request: {e}")
except ModelHubAPIException as e:
    print(f"❌ API error: {e}")
except Exception as e:
    print(f"❌ Unexpected error: {e}")

📊 Supported HTTP Methods

The generic request() method supports all standard HTTP methods:

# GET requests
data = client.request("GET", "/endpoint", params={"key": "value"})

# POST requests
result = client.request("POST", "/endpoint", json={"data": "value"})

# PUT requests
updated = client.request("PUT", "/endpoint/123", json={"update": "data"})

# DELETE requests
deleted = client.request("DELETE", "/endpoint/123")

# PATCH requests
patched = client.request("PATCH", "/endpoint/123", json={"patch": "data"})

🔧 Advanced Configuration

Custom Timeout

client = AIGatewayClient(
    virtual_key="sk-your-key",
    timeout=60  # 60 seconds timeout
)

SSL Configuration

client = AIGatewayClient(
    virtual_key="sk-your-key",
    verify_ssl=True  # or False to disable SSL verification
)

📈 Performance & Reliability

• 🔄 Connection Pooling: Efficient HTTP connection management via httpx
• ⚡ Async Ready: Built on httpx for future async support
• 🛡️ Error Resilience: Comprehensive error handling and recovery
• 📊 Health Monitoring: Built-in health checks prevent failed requests
• 🔐 Secure: Virtual key authentication with environment isolation

🎯 Best Practices

1. Always check health first before making multiple requests
2. Use environment variables for virtual keys in production
3. Handle exceptions appropriately for different error types
4. Leverage the generic request method for custom endpoints
5. Monitor authentication status via health checks

The AIGatewayClient is designed to be future-proof and extensible, supporting any current or future AI Gateway endpoints through its ultra-generic architecture! 🚀✨

Migration Guide

autonomize-core Integration (Latest Version)

The latest version of ModelHub SDK is built on autonomize-core, providing enhanced functionality and better performance. Here's what you need to know:

Environment Variables Migration

New Preferred Variables:

export MODELHUB_URI=https://your-modelhub.com
export MODELHUB_AUTH_CLIENT_ID=your_client_id
export MODELHUB_AUTH_CLIENT_SECRET=your_secret
export GENESIS_CLIENT_ID=your_genesis_client
export GENESIS_COPILOT_ID=your_copilot

Legacy Variables (Still Supported):

export MODELHUB_BASE_URL=https://your-modelhub.com
export MODELHUB_CLIENT_ID=your_client_id
export MODELHUB_CLIENT_SECRET=your_secret
export CLIENT_ID=your_client
export COPILOT_ID=your_copilot

SSL Certificate Support

New SSL configuration options are now available:

from modelhub.core import ModelhubCredential

# Custom certificate path
credential = ModelhubCredential(
    modelhub_url="https://your-modelhub.com",
    client_id="your_client_id",
    client_secret="your_client_secret",
    verify_ssl="/path/to/certificate.pem"
)

# Disable SSL verification (development only)
credential = ModelhubCredential(
    modelhub_url="https://your-modelhub.com",
    client_id="your_client_id",
    client_secret="your_client_secret",
    verify_ssl=False
)

What's Changed

• HTTP Client: Now uses httpx instead of requests for better performance
• Exception Handling: More detailed exception types from autonomize-core
• Authentication: Enhanced credential management system
• Logging: Improved logging with autonomize-core's logging system

What Stays the Same

• API Compatibility: All existing client methods work without changes
• Import Statements: No changes needed to your import statements
• Environment Variables: Legacy environment variables continue to work

Client Architecture Changes

Starting with version 1.2.0, the ModelHub SDK uses a new architecture based on HTTPX and a centralized credential system. If you're upgrading from an earlier version, you'll need to update your code as follows:

Old Way (Deprecated)

from modelhub.clients import BaseClient, DatasetClient, MLflowClient

# Direct initialization with credentials
client = BaseClient(
    base_url="https://api-modelhub.example.com",
    sa_client_id="your_client_id",
    sa_client_secret="your_client_secret"
)

dataset_client = DatasetClient(
    base_url="https://api-modelhub.example.com",
    sa_client_id="your_client_id",
    sa_client_secret="your_client_secret"
)

New Way (Recommended)

from modelhub.core import ModelhubCredential
from modelhub.clients import BaseClient, DatasetClient, MLflowClient

# Create a credential object
credential = ModelhubCredential(
    modelhub_url="https://api-modelhub.example.com",
    client_id="your_client_id",
    client_secret="your_client_secret"
)

# Initialize clients with the credential
base_client = BaseClient(
    credential=credential,
    client_id="your_client_id",  # For RBAC
    copilot_id="your_copilot_id"  # For RBAC
)

dataset_client = DatasetClient(
    credential=credential,
    client_id="your_client_id",
    copilot_id="your_copilot_id"
)

mlflow_client = MLflowClient(
    credential=credential,
    client_id="your_client_id",
    copilot_id="your_copilot_id"
)

Prompt Management Changes

The PromptClient has been replaced with MLflow's built-in prompt registry capabilities:

Old Way (Deprecated)

from modelhub.clients.prompt_client import PromptClient

prompt_client = PromptClient(
    base_url="https://api-modelhub.example.com",
    sa_client_id="your_client_id",
    sa_client_secret="your_client_secret"
)

prompt_client.create_prompt(
    name="summarization-prompt",
    template="Summarize this text: {{context}}",
    prompt_type="USER"
)

New Way (Recommended)

from modelhub.core import ModelhubCredential
from modelhub.clients import MLflowClient

credential = ModelhubCredential(
    modelhub_url="https://api-modelhub.example.com",
    client_id="your_client_id",
    client_secret="your_client_secret"
)

client = MLflowClient(credential=credential)

client.mlflow.register_prompt(
    name="summarization-prompt",
    template="Summarize this text: {{ context }}",
    commit_message="Initial version"
)

# Load and use a prompt
prompt = client.mlflow.load_prompt("prompts:/summarization-prompt/1")
formatted_prompt = prompt.format(context="Your text to summarize")

New Async Support

All clients now support asynchronous operations:

# Synchronous
result = client.get("endpoint")

# Asynchronous
result = await client.aget("endpoint")

For detailed information about the new prompt management capabilities, see the Prompt Management Guide.