.. attribute:: id
   :type: str
   :noindex:

   Unique identifier for the trace.

.. attribute:: start_time
   :type: datetime.datetime
   :noindex:

   Timestamp representing the start of the trace.

.. attribute:: name
   :type: Optional[str]
   :noindex:

   Optional name for the trace, which can provide a descriptive label for the operation being traced.

.. attribute:: project_name
   :type: str
   :noindex:

   Name of the project associated with the trace.

Optional Attributes

.. attribute:: spans
   :type: List[SpanModel]
   :noindex:

   List of spans associated with the trace, representing individual processing parts or segments within the trace. Each span represents a specific operation or step in the overall execution.

.. attribute:: feedback_scores
   :type: List[FeedbackScoreModel]
   :noindex:

   List of feedback scores associated with the trace. These are used for overall trace evaluation and quality assessment.

Examples
--------

Creating a basic trace:

.. code-block:: python

    import datetime
    from opik.message_processing.emulation.models import TraceModel

    # Create a simple trace
    trace = TraceModel(
        id="trace_123",
        start_time=datetime.datetime.now(),
        name="user_query_processing",
        project_name="my_project",
        input={"user_query": "What is machine learning?"},
        output={"response": "Machine learning is a subset of AI..."}
    )

Creating a trace with spans:

.. code-block:: python

    from opik.message_processing.emulation.models import SpanModel

    # Create a trace with associated spans
    trace = TraceModel(
        id="trace_456",
        start_time=datetime.datetime.now(),
        name="complex_operation",
        project_name="ai_project"
    )

    # Add spans to represent different steps
    preprocessing_span = SpanModel(
        id="span_1",
        start_time=datetime.datetime.now(),
        name="data_preprocessing"
    )

    llm_span = SpanModel(
        id="span_2",
        start_time=datetime.datetime.now(),
        name="llm_call",
        type="llm"
    )

    trace.spans.extend([preprocessing_span, llm_span])

Adding feedback scores to a trace:

.. code-block:: python

    from opik.message_processing.emulation.models import FeedbackScoreModel

    # Add overall evaluation scores to the trace
    overall_quality = FeedbackScoreModel(
        id="score_123",
        name="overall_quality",
        value=0.88,
        reason="Good response quality with minor improvements needed"
    )

    trace.feedback_scores.append(overall_quality)

Working with trace hierarchies:

.. code-block:: python

    # Access nested spans within a trace
    for span in trace.spans:
        print(f"Span: {span.name}")

        # Each span can have nested spans too
        for nested_span in span.spans:
            print(f"  Nested: {nested_span.name}")

Usage in Evaluation Context
---------------------------

``TraceModel`` objects are commonly used in evaluation scenarios where you need to analyze the complete execution:

.. code-block:: python

    # Example of accessing trace data in evaluation
    def analyze_trace(trace: TraceModel):
        # Analyze overall trace performance
        duration = trace.end_time - trace.start_time if trace.end_time else None

        # Count different types of spans
        llm_spans = [s for s in trace.spans if s.type == "llm"]

        # Analyze input/output
        input_complexity = len(str(trace.input)) if trace.input else 0
        output_quality = evaluate_output_quality(trace.output)

        return {
            "duration": duration,
            "llm_calls": len(llm_spans),
            "complexity": input_complexity,
            "quality": output_quality
        }

Common Use Cases
----------------

``TraceModel`` is commonly used for:

- **Request Tracking**: Tracking complete user requests from start to finish
- **Performance Analysis**: Analyzing the performance of complex operations
- **Evaluation**: Providing complete context for evaluation metrics
- **Debugging**: Understanding the full execution path and identifying issues
- **Cost Tracking**: Aggregating costs across all spans in a trace
- **A/B Testing**: Comparing different execution paths and their outcomes

Relationship with Spans
-----------------------

A trace acts as a container for spans, creating a hierarchical structure:

- **Trace**: The top-level container representing the complete operation
- **Spans**: Individual steps or operations within the trace
- **Nested Spans**: Spans can contain other spans, creating a tree structure

This hierarchy allows for detailed tracking of complex operations while maintaining the overall context.

See Also
--------

- :class:`SpanModel` - Individual operations within a trace
- :class:`FeedbackScoreModel` - For attaching evaluation scores to traces
- :doc:`../evaluation/evaluate` - For information about evaluating traces with custom metrics