Message Traces

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import data_designer.config as dd
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# Capture full conversation history
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builder.add_column(
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    dd.LLMTextColumnConfig(
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        name="answer",
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        prompt="Answer: {{ question }}",
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        model_alias="nvidia-text",
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        with_trace=dd.TraceType.ALL_MESSAGES,  # Full trace
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    )
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)
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# Capture only the final assistant response
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builder.add_column(
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    dd.LLMTextColumnConfig(
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        name="summary",
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        prompt="Summarize: {{ text }}",
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        model_alias="nvidia-text",
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        with_trace=dd.TraceType.LAST_MESSAGE,  # Just the final response
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    )
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)

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[
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    # System message (if configured)
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    {
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        "role": "system",
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        "content": [{"type": "text", "text": "You are a helpful assistant that provides clear, concise answers."}]
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    },
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    # User message (the rendered prompt)
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    {
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        "role": "user",
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        "content": [{"type": "text", "text": "What is the capital of France?"}]
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    },
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    # Final assistant response
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    {
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        "role": "assistant",
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        "content": [{"type": "text", "text": "The capital of France is Paris."}],
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        "reasoning_content": None  # May contain reasoning if model supports it
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    }
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]

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[
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    # System message
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    {
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        "role": "system",
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        "content": [{"type": "text", "text": "You must call tools before answering. Only use tool results."}]
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    },
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    # User message (the rendered prompt)
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    {
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        "role": "user",
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        "content": [{"type": "text", "text": "What documents are in the knowledge base about machine learning?"}]
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    },
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    # Assistant requests tool calls
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    {
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        "role": "assistant",
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        "content": [{"type": "text", "text": ""}],
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        "tool_calls": [
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            {
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                "id": "call_abc123",
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                "type": "function",
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                "function": {
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                    "name": "list_docs",
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                    "arguments": "{\"query\": \"machine learning\"}"
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                }
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            }
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        ]
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    },
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    # Tool response (linked by tool_call_id)
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    {
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        "role": "tool",
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        "content": [{"type": "text", "text": "Found 3 documents: intro_ml.pdf, neural_networks.pdf, transformers.pdf"}],
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        "tool_call_id": "call_abc123"
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    },
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    # Final assistant response
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    {
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        "role": "assistant",
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        "content": [{"type": "text", "text": "The knowledge base contains three documents about machine learning: ..."}]
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    }
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]

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{
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    "id": "call_abc123",           # Unique ID linking to tool response
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    "type": "function",            # Always "function" for MCP tools
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    "function": {
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        "name": "search_docs",     # Tool name
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        "arguments": "{...}"       # JSON string of tool arguments
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    }
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}

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import data_designer.config as dd
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builder.add_column(
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    dd.LLMTextColumnConfig(
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        name="solution",
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        prompt="Solve this math problem step by step: {{ problem }}",
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        model_alias="reasoning-model",
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        extract_reasoning_content=True,  # Creates solution__reasoning_content
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    )
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)