REST API Client

The Opik Python SDK includes a complete REST API client that provides direct access to all Opik platform functionality. This low-level client is available through the rest_client property and is useful for advanced operations, custom integrations, and scenarios where the high-level SDK doesn’t provide the needed functionality.

Accessing the REST Client

The REST client is accessible through any Opik instance:

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import opik
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# Initialize Opik client
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client = opik.Opik()
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# Access the REST API client
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rest_client = client.rest_client
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# Now you can use any of the available client methods
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traces = rest_client.traces.search_traces(project_name="my-project")

Available Clients

The REST API is organized into functional client modules:

Core Resources

• traces - Manage traces and their lifecycle
• spans - Manage spans within traces
• datasets - Manage datasets and dataset items
• experiments - Manage experiments and results
• projects - Manage projects and project settings

Feedback & Evaluation

• feedback_definitions - Define feedback score types
• automation_rule_evaluators - Set up automated evaluation rules
• optimizations - Run optimization experiments

Content & Assets

• prompts - Manage prompt templates and versions
• attachments - Handle file attachments for traces and spans

System & Configuration

• check - System health and access verification
• workspaces - Workspace management
• llm_provider_key - API key management for LLM providers
• service_toggles - Feature flag management
• system_usage - Usage metrics and monitoring

Integrations

• chat_completions - Chat completion endpoints
• open_telemetry_ingestion - OpenTelemetry data ingestion
• guardrails - Content validation and safety checks

Common Usage Patterns

Working with Traces

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# Get a specific trace by ID
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trace = client.rest_client.traces.get_trace_by_id("trace-id")
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# Search traces with advanced filters
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traces = client.rest_client.traces.search_traces(
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    project_name="my-project",
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    filters=[{
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        "field": "name",
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        "operator": "contains",
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        "value": "important"
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    }],
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    max_results=100
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)
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# Add feedback to a trace
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client.rest_client.traces.add_trace_feedback_score(
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    id="trace-id",
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    name="accuracy",
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    value=0.95,
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    source="manual"
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)

Managing Datasets

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# List all datasets with pagination
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datasets = client.rest_client.datasets.find_datasets(
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    page=0,
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    size=20
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)
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# Create a new dataset
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dataset = client.rest_client.datasets.create_dataset(
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    name="evaluation-dataset",
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    description="Dataset for model evaluation"
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)
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# Add items to the dataset
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items = [
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    {
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        "input": {"question": "What is machine learning?"},
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        "expected_output": {"answer": "A subset of AI..."}
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    }
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]
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client.rest_client.datasets.create_or_update_dataset_items(
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    dataset_id=dataset.id,
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    items=items
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)

Running Experiments

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# Create an experiment linked to a dataset
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experiment = client.rest_client.experiments.create_experiment(
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    name="model-comparison",
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    dataset_name="evaluation-dataset"
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)
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# Add experiment results
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client.rest_client.experiments.create_experiment_items(
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    experiment_id=experiment.id,
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    items=[{
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        "dataset_item_id": "item-id",
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        "trace_id": "trace-id",
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        "output": {"prediction": "model output"},
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        "feedback_scores": [
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            {"name": "accuracy", "value": 0.8}
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        ]
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    }]
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)

Response Types and Pagination

Most list operations return paginated responses with a consistent structure:

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# Example paginated response
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response = client.rest_client.datasets.find_datasets(page=0, size=10)
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# Access the response data
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datasets = response.content    # List of dataset objects
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total_count = response.total   # Total number of items
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current_page = response.page   # Current page number
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page_size = response.size      # Items per page

Error Handling

The REST API raises specific exceptions for different error conditions:

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from opik.rest_api.core.api_error import ApiError
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try:
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    trace = client.rest_client.traces.get_trace_by_id("invalid-id")
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except ApiError as e:
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    if e.status_code == 404:
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        print("Trace not found")
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    elif e.status_code == 403:
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        print("Access denied")
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    else:
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        print(f"API error: {e.status_code} - {e.body}")

When to Use the REST API

Consider using the REST API client when you need to:

• Advanced Filtering: Complex search operations with multiple filters
• Batch Operations: Process large amounts of data efficiently
• Custom Integrations: Build tools that integrate with external systems
• Raw Data Access: Work directly with API responses for specific use cases
• Unsupported Operations: Access functionality not available in the high-level SDK

Complete API Reference

For comprehensive documentation of all REST API methods, parameters, and response types, see the complete REST API Reference.

The reference documentation includes:

• Overview & Getting Started - Detailed usage patterns and examples
• API Clients - Complete method reference for all clients
• Data Types - Response models and data structures

Best Practices

1. Use High-Level SDK First: Try the main SDK APIs before resorting to REST client
2. Handle Pagination: Always implement proper pagination for list operations
3. Error Handling: Implement robust error handling for network and API errors
4. Rate Limiting: Be mindful of API rate limits for batch operations
5. Version Compatibility: Test thoroughly when upgrading SDK versions