import palantyra

@palantyra.observe(name="Process Document")
def process_document(doc_text):
    # Add custom attributes
    palantyra.set_span_attributes({
        "document_length": len(doc_text),
        "document_type": "pdf"
    })
    
    # Your LLM calls here
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": doc_text}]
    )
    
    return response

# Function is automatically traced
result = process_document("Long document text...")

import palantyra

def analyze_data(data):
    with palantyra.start_as_current_span(
        name="Data Analysis",
        metadata={"data_size": len(data)}
    ):
        # Add attributes during execution
        palantyra.set_span_attributes({
            "step": "preprocessing"
        })
        
        processed = preprocess(data)
        
        palantyra.set_span_attributes({
            "step": "analysis"
        })
        
        result = analyze(processed)
        
        # Set output
        palantyra.set_span_output(result)
        
        return result

@palantyra.observe(
    name="Custom Name",           # Override function name
    session_id="session_123",     # Track sessions
    user_id="user_456",            # Track users
    metadata={"version": "v2"},    # Custom metadata
    tags=["production", "api"],    # Categorize traces
    ignore_input=False,            # Don't log inputs
    ignore_output=False,           # Don't log outputs
    ignore_inputs=["password"]     # Specific args to ignore
)
def my_function(text, password):
    # Your code here
    pass

@palantyra.observe(name="Parent Operation")
def parent_operation():
    # This creates a parent span
    
    with palantyra.start_as_current_span(name="Child Operation 1"):
        # Child span 1
        result1 = do_something()
    
    with palantyra.start_as_current_span(name="Child Operation 2"):
        # Child span 2
        result2 = do_something_else()
    
    return result1, result2