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Structured Outputs, Jinja Expressions, and Conditional Generation

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🎨 Data Designer Tutorial: Structured Outputs, Jinja Expressions, and Conditional Generation

📚 What you'll learn

In this notebook, we will continue our exploration of Data Designer, demonstrating more advanced data generation using structured outputs, Jinja expressions, and conditional generation with skip.when.

If this is your first time using Data Designer, we recommend starting with the first notebook in this tutorial series.

📦 Import Data Designer

  • data_designer.config provides access to the configuration API.

  • DataDesigner is the main interface for data generation.

Python
1import data_designer.config as dd
2from data_designer.interface import DataDesigner
3

⚙️ Initialize the Data Designer interface

  • DataDesigner is the main object that is used to interface with the library.

  • When initialized without arguments, the default model providers are used.

Python
1data_designer = DataDesigner()
2

🎛️ Define model configurations

  • Each ModelConfig defines a model that can be used during the generation process.

  • The "model alias" is used to reference the model in the Data Designer config (as we will see below).

  • The "model provider" is the external service that hosts the model (see the model config docs for more details).

  • By default, we use build.nvidia.com as the model provider.

Python
1# This name is set in the model provider configuration.
2MODEL_PROVIDER = "nvidia"
3 
4# The model ID is from build.nvidia.com.
5MODEL_ID = "nvidia/nemotron-3.5-lightning-30b-a3b"
6 
7# We choose this alias to be descriptive for our use case.
8MODEL_ALIAS = "nemotron-lightning"
9 
10model_configs = [
11    dd.ModelConfig(
12        alias=MODEL_ALIAS,
13        model=MODEL_ID,
14        provider=MODEL_PROVIDER,
15        inference_parameters=dd.ChatCompletionInferenceParams(
16            temperature=1.0,
17            top_p=0.95,
18            max_tokens=2048,
19            extra_body={"chat_template_kwargs": {"enable_thinking": False}},
20        ),
21    )
22]
23

🏗️ Initialize the Data Designer Config Builder

  • The Data Designer config defines the dataset schema and generation process.

  • The config builder provides an intuitive interface for building this configuration.

  • The list of model configs is provided to the builder at initialization.

Python
1config_builder = dd.DataDesignerConfigBuilder(model_configs=model_configs)
2

🧑‍🎨 Designing our data

  • We will again create a product review dataset, but this time we will use structured outputs and Jinja expressions.

  • Structured outputs let you specify the exact schema of the data you want to generate.

  • Data Designer supports schemas specified using either json schema or Pydantic data models (recommended).


We'll define our structured outputs using Pydantic data models

💡 Why Pydantic?

  • Pydantic models provide better IDE support and type validation.

  • They are more Pythonic than raw JSON schemas.

  • They integrate seamlessly with Data Designer's structured output system.

Python
1from decimal import Decimal
2from typing import Literal
3 
4from pydantic import BaseModel, Field
5 
6 
7# We define a Product schema so that the name, description, and price are generated
8# in one go, with the types and constraints specified.
9class Product(BaseModel):
10    name: str = Field(description="The name of the product")
11    description: str = Field(description="A description of the product")
12    price: Decimal = Field(description="The price of the product", ge=10, le=1000, decimal_places=2)
13 
14 
15class ProductReview(BaseModel):
16    rating: int = Field(description="The rating of the product", ge=1, le=5)
17    customer_mood: Literal["irritated", "mad", "happy", "neutral", "excited"] = Field(
18        description="The mood of the customer"
19    )
20    review: str = Field(description="A review of the product")
21

Next, let's design our product review dataset using a few more tricks compared to the previous notebook.

Python
1# Since we often only want a few attributes from Person objects, we can
2# set drop=True in the column config to drop the column from the final dataset.
3config_builder.add_column(
4    dd.SamplerColumnConfig(
5        name="customer",
6        sampler_type=dd.SamplerType.PERSON_FROM_FAKER,
7        params=dd.PersonFromFakerSamplerParams(),
8        drop=True,
9    )
10)
11 
12config_builder.add_column(
13    dd.SamplerColumnConfig(
14        name="product_category",
15        sampler_type=dd.SamplerType.CATEGORY,
16        params=dd.CategorySamplerParams(
17            values=[
18                "Electronics",
19                "Clothing",
20                "Home & Kitchen",
21                "Books",
22                "Home Office",
23            ],
24        ),
25    )
26)
27 
28config_builder.add_column(
29    dd.SamplerColumnConfig(
30        name="product_subcategory",
31        sampler_type=dd.SamplerType.SUBCATEGORY,
32        params=dd.SubcategorySamplerParams(
33            category="product_category",
34            values={
35                "Electronics": [
36                    "Smartphones",
37                    "Laptops",
38                    "Headphones",
39                    "Cameras",
40                    "Accessories",
41                ],
42                "Clothing": [
43                    "Men's Clothing",
44                    "Women's Clothing",
45                    "Winter Coats",
46                    "Activewear",
47                    "Accessories",
48                ],
49                "Home & Kitchen": [
50                    "Appliances",
51                    "Cookware",
52                    "Furniture",
53                    "Decor",
54                    "Organization",
55                ],
56                "Books": [
57                    "Fiction",
58                    "Non-Fiction",
59                    "Self-Help",
60                    "Textbooks",
61                    "Classics",
62                ],
63                "Home Office": [
64                    "Desks",
65                    "Chairs",
66                    "Storage",
67                    "Office Supplies",
68                    "Lighting",
69                ],
70            },
71        ),
72    )
73)
74 
75config_builder.add_column(
76    dd.SamplerColumnConfig(
77        name="target_age_range",
78        sampler_type=dd.SamplerType.CATEGORY,
79        params=dd.CategorySamplerParams(values=["18-25", "25-35", "35-50", "50-65", "65+"]),
80    )
81)
82 
83# Sampler columns support conditional params, which are used if the condition is met.
84# In this example, we set the review style to rambling if the target age range is 18-25.
85# Note conditional parameters are only supported for Sampler column types.
86config_builder.add_column(
87    dd.SamplerColumnConfig(
88        name="review_style",
89        sampler_type=dd.SamplerType.CATEGORY,
90        params=dd.CategorySamplerParams(
91            values=["rambling", "brief", "detailed", "structured with bullet points"],
92            weights=[1, 2, 2, 1],
93        ),
94        conditional_params={
95            "target_age_range == '18-25'": dd.CategorySamplerParams(values=["rambling"]),
96        },
97    )
98)
99 
100# Optionally validate that the columns are configured correctly.
101data_designer.validate(config_builder)
102
Output
[17:42:11] [INFO] ✅ Validation passed

Next, we will use more advanced Jinja expressions to create new columns.

Jinja expressions let you:

  • Access nested attributes: {{ customer.first_name }}

  • Combine values: {{ customer.first_name }} {{ customer.last_name }}

  • Use conditional logic: {% if condition %}...{% endif %}

Python
1# We can create new columns using Jinja expressions that reference
2# existing columns, including attributes of nested objects.
3config_builder.add_column(
4    dd.ExpressionColumnConfig(name="customer_name", expr="{{ customer.first_name }} {{ customer.last_name }}")
5)
6 
7config_builder.add_column(dd.ExpressionColumnConfig(name="customer_age", expr="{{ customer.age }}"))
8 
9config_builder.add_column(
10    dd.LLMStructuredColumnConfig(
11        name="product",
12        prompt=(
13            "Create a product in the '{{ product_category }}' category, focusing on products  "
14            "related to '{{ product_subcategory }}'. The target age range of the ideal customer is "
15            "{{ target_age_range }} years old. The product should be priced between $10 and $1000."
16        ),
17        output_format=Product,
18        model_alias=MODEL_ALIAS,
19    )
20)
21 
22# We can even use if/else logic in our Jinja expressions to create more complex prompt patterns.
23config_builder.add_column(
24    dd.LLMStructuredColumnConfig(
25        name="customer_review",
26        prompt=(
27            "Your task is to write a review for the following product:\n\n"
28            "Product Name: {{ product.name }}\n"
29            "Product Description: {{ product.description }}\n"
30            "Price: {{ product.price }}\n\n"
31            "Imagine your name is {{ customer_name }} and you are from {{ customer.city }}, {{ customer.state }}. "
32            "Write the review in a style that is '{{ review_style }}'."
33            "{% if target_age_range == '18-25' %}"
34            "Make sure the review is more informal and conversational.\n"
35            "{% else %}"
36            "Make sure the review is more formal and structured.\n"
37            "{% endif %}"
38            "The review field should contain only the review, no other text."
39        ),
40        output_format=ProductReview,
41        model_alias=MODEL_ALIAS,
42    )
43)
44 
45data_designer.validate(config_builder)
46
Output
[17:42:11] [INFO] ✅ Validation passed

🚦 Conditional generation with skip.when

So far, every column is generated for every row. But sometimes an expensive LLM column only makes sense for a subset of rows — for example, a detailed complaint analysis is only useful when the review is negative.

Data Designer lets you skip column generation on a per-row basis using SkipConfig. Skipped rows receive None by default, but you can provide a sentinel value with skip=dd.SkipConfig(when="...", value="N/A") to write a specific value instead.

There are three patterns to know:

Pattern How Effect
Expression gate skip=dd.SkipConfig(when="...") Skip this column when the Jinja2 expression is truthy
Skip propagation (default) Downstream column depends on a skipped column Automatically skipped too (propagate_skip=True by default)
Propagation opt-out propagate_skip=False on the downstream column Always generates, even if an upstream was skipped

Pattern 1 — Expression gate. Only generate a detailed complaint analysis when the customer gave a low rating (1 or 2 stars). Rows where the rating is 3 or higher will get None for this column.

Python
1config_builder.add_column(
2    dd.LLMTextColumnConfig(
3        name="complaint_analysis",
4        model_alias=MODEL_ALIAS,
5        prompt=(
6            "A customer reviewed '{{ product.name }}' ({{ product_category }} / {{ product_subcategory }}).\n\n"
7            "Review: {{ customer_review.review }}\n"
8            "Rating: {{ customer_review.rating }}/5\n"
9            "Mood: {{ customer_review.customer_mood }}\n\n"
10            "Write a short root-cause analysis of why this customer is unhappy "
11            "and suggest one concrete improvement the product team could make."
12        ),
13        skip=dd.SkipConfig(when="{{ customer_review.rating > 2 }}"),
14    )
15)
16
Output
DataDesignerConfigBuilder(
    sampler_columns: [
        "customer",
        "product_category",
        "product_subcategory",
        "target_age_range",
        "review_style"
    ]
    llm_text_columns: ['complaint_analysis']
    llm_structured_columns: ['product', 'customer_review']
    expression_columns: ['customer_name', 'customer_age']
)

Pattern 2 — Skip propagation. action_items depends on complaint_analysis. When complaint_analysis is skipped, action_items auto-skips too because propagate_skip defaults to True.

Python
1config_builder.add_column(
2    dd.LLMTextColumnConfig(
3        name="action_items",
4        model_alias=MODEL_ALIAS,
5        prompt=(
6            "Based on this complaint analysis:\n"
7            "{{ complaint_analysis }}\n\n"
8            "List 2-3 concrete action items for the product team."
9        ),
10    )
11)
12
Output
DataDesignerConfigBuilder(
    sampler_columns: [
        "customer",
        "product_category",
        "product_subcategory",
        "target_age_range",
        "review_style"
    ]
    llm_text_columns: ['complaint_analysis', 'action_items']
    llm_structured_columns: ['product', 'customer_review']
    expression_columns: ['customer_name', 'customer_age']
)

Pattern 3 — Propagation opt-out. review_summary also depends on complaint_analysis, but sets propagate_skip=False so it always generates. The prompt uses a Jinja conditional to handle the case where complaint_analysis is None.

Python
1config_builder.add_column(
2    dd.LLMTextColumnConfig(
3        name="review_summary",
4        model_alias=MODEL_ALIAS,
5        propagate_skip=False,
6        prompt=(
7            "Summarize this product review in one sentence:\n"
8            "Product: {{ product.name }}\n"
9            "Rating: {{ customer_review.rating }}/5\n"
10            "Review: {{ customer_review.review }}\n"
11            "{% if complaint_analysis %}"
12            "Complaint analysis: {{ complaint_analysis }}\n"
13            "{% endif %}"
14        ),
15    )
16)
17 
18data_designer.validate(config_builder)
19
Output
[17:42:11] [INFO] ✅ Validation passed

🔁 Iteration is key – preview the dataset!

  1. Use the preview method to generate a sample of records quickly.

  2. Inspect the results for quality and format issues.

  3. Adjust column configurations, prompts, or parameters as needed.

  4. Re-run the preview until satisfied.

Python
1preview = data_designer.preview(config_builder, num_records=2)
2
Output
[17:42:11] [INFO] 👁️ Preview generation in progress
[17:42:11] [INFO]   |-- 🔒 Jinja rendering engine: secure
[17:42:11] [INFO] ✅ Validation passed
[17:42:11] [INFO] ⛓️ Sorting column configs into a Directed Acyclic Graph
[17:42:11] [INFO] Skipping model health checks because DATA_DESIGNER_SKIP_MODEL_HEALTH_CHECKS=1
[17:42:11] [INFO] ⚡ Using async task-queue preview
[17:42:11] [INFO] 🗂️ llm-structured model config for column 'product'
[17:42:11] [INFO]   |-- model: 'nvidia/nemotron-3.5-lightning-30b-a3b'
[17:42:11] [INFO]   |-- model alias: 'nemotron-lightning'
[17:42:11] [INFO]   |-- model provider: 'nvidia'
[17:42:11] [INFO]   |-- inference parameters:
[17:42:11] [INFO]   |  |-- generation_type=chat-completion
[17:42:11] [INFO]   |  |-- max_parallel_requests=4
[17:42:11] [INFO]   |  |-- extra_body={'chat_template_kwargs': {'enable_thinking': False}}
[17:42:11] [INFO]   |  |-- temperature=1.00
[17:42:11] [INFO]   |  |-- top_p=0.95
[17:42:11] [INFO]   |  |-- max_tokens=2048
[17:42:11] [INFO] 🗂️ llm-structured model config for column 'customer_review'
[17:42:11] [INFO]   |-- model: 'nvidia/nemotron-3.5-lightning-30b-a3b'
[17:42:11] [INFO]   |-- model alias: 'nemotron-lightning'
[17:42:11] [INFO]   |-- model provider: 'nvidia'
[17:42:11] [INFO]   |-- inference parameters:
[17:42:11] [INFO]   |  |-- generation_type=chat-completion
[17:42:11] [INFO]   |  |-- max_parallel_requests=4
[17:42:11] [INFO]   |  |-- extra_body={'chat_template_kwargs': {'enable_thinking': False}}
[17:42:11] [INFO]   |  |-- temperature=1.00
[17:42:11] [INFO]   |  |-- top_p=0.95
[17:42:11] [INFO]   |  |-- max_tokens=2048
[17:42:11] [INFO] 📝 llm-text model config for column 'complaint_analysis'
[17:42:11] [INFO]   |-- model: 'nvidia/nemotron-3.5-lightning-30b-a3b'
[17:42:11] [INFO]   |-- model alias: 'nemotron-lightning'
[17:42:11] [INFO]   |-- model provider: 'nvidia'
[17:42:11] [INFO]   |-- inference parameters:
[17:42:11] [INFO]   |  |-- generation_type=chat-completion
[17:42:11] [INFO]   |  |-- max_parallel_requests=4
[17:42:11] [INFO]   |  |-- extra_body={'chat_template_kwargs': {'enable_thinking': False}}
[17:42:11] [INFO]   |  |-- temperature=1.00
[17:42:11] [INFO]   |  |-- top_p=0.95
[17:42:11] [INFO]   |  |-- max_tokens=2048
[17:42:11] [INFO] 📝 llm-text model config for column 'review_summary'
[17:42:11] [INFO]   |-- model: 'nvidia/nemotron-3.5-lightning-30b-a3b'
[17:42:11] [INFO]   |-- model alias: 'nemotron-lightning'
[17:42:11] [INFO]   |-- model provider: 'nvidia'
[17:42:11] [INFO]   |-- inference parameters:
[17:42:11] [INFO]   |  |-- generation_type=chat-completion
[17:42:11] [INFO]   |  |-- max_parallel_requests=4
[17:42:11] [INFO]   |  |-- extra_body={'chat_template_kwargs': {'enable_thinking': False}}
[17:42:11] [INFO]   |  |-- temperature=1.00
[17:42:11] [INFO]   |  |-- top_p=0.95
[17:42:11] [INFO]   |  |-- max_tokens=2048
[17:42:11] [INFO] 📝 llm-text model config for column 'action_items'
[17:42:11] [INFO]   |-- model: 'nvidia/nemotron-3.5-lightning-30b-a3b'
[17:42:11] [INFO]   |-- model alias: 'nemotron-lightning'
[17:42:11] [INFO]   |-- model provider: 'nvidia'
[17:42:11] [INFO]   |-- inference parameters:
[17:42:11] [INFO]   |  |-- generation_type=chat-completion
[17:42:11] [INFO]   |  |-- max_parallel_requests=4
[17:42:11] [INFO]   |  |-- extra_body={'chat_template_kwargs': {'enable_thinking': False}}
[17:42:11] [INFO]   |  |-- temperature=1.00
[17:42:11] [INFO]   |  |-- top_p=0.95
[17:42:11] [INFO]   |  |-- max_tokens=2048
[17:42:11] [INFO] ⚡️ Async generation: 5 column(s) (column 'product', column 'customer_review', column 'complaint_analysis', column 'review_summary', column 'action_items'), 10 tasks across 1 row group(s)
[17:42:11] [INFO] 🚀 (1/1) Dispatching with 2 records
[17:42:11] [INFO] 🎲 (1/1) Preparing samplers to generate 2 records across 5 columns
[17:42:11] [INFO] 🧩 (1/1) Generating column `customer_age` from expression
[17:42:11] [INFO] 🧩 (1/1) Generating column `customer_name` from expression
[17:42:41] [INFO] 📊 Progress [29.8s]:
[17:42:41] [INFO]   |-- 😐 column 'product': 1/2 (50%) 0.0 rec/s
[17:42:41] [INFO]   |-- 🚶 column 'customer_review': 0/2 (0%) 0.0 rec/s
[17:42:41] [INFO]   |-- 🚶 column 'complaint_analysis': 0/2 (0%) 0.0 rec/s
[17:42:41] [INFO]   |-- 🥚 column 'review_summary': 0/2 (0%) 0.0 rec/s
[17:42:41] [INFO]   |-- 🌧️ column 'action_items': 0/2 (0%) 0.0 rec/s
[17:43:00] [INFO] 📊 Progress [48.8s]:
[17:43:00] [INFO]   |-- 🤩 column 'product': 2/2 (100%) 0.0 rec/s
[17:43:00] [INFO]   |-- 🚶 column 'customer_review': 0/2 (0%) 0.0 rec/s
[17:43:00] [INFO]   |-- 🚶 column 'complaint_analysis': 0/2 (0%) 0.0 rec/s
[17:43:00] [INFO]   |-- 🥚 column 'review_summary': 0/2 (0%) 0.0 rec/s
[17:43:00] [INFO]   |-- 🌧️ column 'action_items': 0/2 (0%) 0.0 rec/s
[17:43:08] [INFO] 📊 Progress [56.8s]:
[17:43:08] [INFO]   |-- 🤩 column 'product': 2/2 (100%) 0.0 rec/s
[17:43:08] [INFO]   |-- 🚗 column 'customer_review': 1/2 (50%) 0.0 rec/s
[17:43:08] [INFO]   |-- 🚶 column 'complaint_analysis': 0/2 (0%) 0.0 rec/s
[17:43:08] [INFO]   |-- 🥚 column 'review_summary': 0/2 (0%) 0.0 rec/s
[17:43:08] [INFO]   |-- 🌧️ column 'action_items': 0/2 (0%) 0.0 rec/s
[17:43:41] [WARNING] Observed retryable model-task error: kind=timeout; the row task will be deferred.
[17:44:08] [INFO] 🔄 (1/1) Salvaging 2 deferred task(s)
[17:44:58] [INFO] 📊 Progress [166.9s]:
[17:44:58] [INFO]   |-- 🤩 column 'product': 2/2 (100%) 0.0 rec/s
[17:44:58] [INFO]   |-- 🚗 column 'customer_review': 1/2 (50%) 0.0 rec/s
[17:44:58] [INFO]   |-- 🚗 column 'complaint_analysis': 1/2 (50%) 0.0 rec/s, 1 skipped
[17:44:58] [INFO]   |-- 🐥 column 'review_summary': 1/2 (50%) 0.0 rec/s
[17:44:58] [INFO]   |-- ⛅ column 'action_items': 1/2 (50%) 0.0 rec/s, 1 skipped
[17:46:08] [INFO] 📊 Progress [237.0s]:
[17:46:08] [INFO]   |-- 🤩 column 'product': 2/2 (100%) 0.0 rec/s
[17:46:08] [INFO]   |-- 🚀 column 'customer_review': 2/2 (100%) 0.0 rec/s
[17:46:08] [INFO]   |-- 🚗 column 'complaint_analysis': 1/2 (50%) 0.0 rec/s, 1 skipped
[17:46:08] [INFO]   |-- 🐥 column 'review_summary': 1/2 (50%) 0.0 rec/s
[17:46:08] [INFO]   |-- ⛅ column 'action_items': 1/2 (50%) 0.0 rec/s, 1 skipped
[17:46:08] [INFO] 📊 Progress [237.0s]:
[17:46:08] [INFO]   |-- 🤩 column 'product': 2/2 (100%) 0.0 rec/s
[17:46:08] [INFO]   |-- 🚀 column 'customer_review': 2/2 (100%) 0.0 rec/s
[17:46:08] [INFO]   |-- 🚀 column 'complaint_analysis': 2/2 (100%) 0.0 rec/s, 2 skipped
[17:46:08] [INFO]   |-- 🐔 column 'review_summary': 2/2 (100%) 0.0 rec/s, 1 skipped
[17:46:08] [INFO]   |-- ☀️ column 'action_items': 2/2 (100%) 0.0 rec/s, 2 skipped
[17:46:08] [INFO] ✅ Async generation complete [237.0s]: 4 ok, 1 failed, 5 skipped across 5 column(s)
[17:46:08] [INFO] 📊 Model usage summary:
[17:46:08] [INFO]   |-- model: nvidia/nemotron-3.5-lightning-30b-a3b
[17:46:08] [INFO]   |-- tokens: input=1288, output=570, total=1858, tps=7
[17:46:08] [INFO]   |-- requests: success=4, failed=4, total=8, rpm=2
[17:46:08] [INFO] 🙈 Dropping columns: ['customer']
[17:46:08] [INFO] 📐 Measuring dataset column statistics:
[17:46:08] [INFO]   |-- 🎲 column: 'product_category'
[17:46:08] [INFO]   |-- 🎲 column: 'product_subcategory'
[17:46:08] [INFO]   |-- 🎲 column: 'target_age_range'
[17:46:08] [INFO]   |-- 🎲 column: 'review_style'
[17:46:08] [INFO]   |-- 🧩 column: 'customer_name'
[17:46:08] [INFO]   |-- 🧩 column: 'customer_age'
[17:46:08] [INFO]   |-- 🗂️ column: 'product'
[17:46:08] [INFO]   |-- 🗂️ column: 'customer_review'
[17:46:08] [INFO]   |-- 📝 column: 'complaint_analysis'
[17:46:08] [INFO]   |-- 📝 column: 'action_items'
[17:46:08] [INFO]   |-- 📝 column: 'review_summary'
[17:46:08] [INFO] 🙌 Preview complete!
Python
1# Run this cell multiple times to cycle through the 2 preview records.
2# Look for rows where complaint_analysis and action_items are None (skipped)
3# vs rows where they were generated (low-rated reviews).
4preview.display_sample_record()
5
Output
[index: 0]
                                                                                                              
                                              Generated Columns                                               
┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Name                ┃ Value                                                                                ┃
┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ product_category    │ Home & Kitchen                                                                       │
├─────────────────────┼──────────────────────────────────────────────────────────────────────────────────────┤
│ product_subcategory │ Organization                                                                         │
├─────────────────────┼──────────────────────────────────────────────────────────────────────────────────────┤
│ target_age_range    │ 25-35                                                                                │
├─────────────────────┼──────────────────────────────────────────────────────────────────────────────────────┤
│ review_style        │ detailed                                                                             │
├─────────────────────┼──────────────────────────────────────────────────────────────────────────────────────┤
│ complaint_analysis  │ None                                                                                 │
├─────────────────────┼──────────────────────────────────────────────────────────────────────────────────────┤
│ action_items        │ None                                                                                 │
├─────────────────────┼──────────────────────────────────────────────────────────────────────────────────────┤
│ review_summary      │ The Modular Compact Closet Organizer Set is an effective, tool-free solution that    │
│                     │ transforms small living spaces into streamlined, minimalist environments by          │
│                     │ maximizing vertical storage and enhancing daily organization.                        │
├─────────────────────┼──────────────────────────────────────────────────────────────────────────────────────┤
│ product             │ {                                                                                    │
│                     │     'name': 'Modular Compact Closet Organizer Set',                                  │
│                     │     'description': 'A sleek, modular organization system designed specifically for   │
│                     │ small apartments and urban living spaces. This set includes adjustable shelves,      │
│                     │ hanging organizers, and stackable bins made from durable bamboo and recyclable       │
│                     │ plastic. The design focuses on maximizing vertical space while maintaining a         │
│                     │ minimalist aesthetic that fits the modern home. Easy to assemble without tools, it   │
│                     │ helps users declutter their closets and streamline their daily routines.',           │
│                     │     'price': 79.99                                                                   │
│                     │ }                                                                                    │
├─────────────────────┼──────────────────────────────────────────────────────────────────────────────────────┤
│ customer_review     │ {                                                                                    │
│                     │     'rating': 5,                                                                     │
│                     │     'customer_mood': 'happy',                                                        │
│                     │     'review': "As a resident of Rayshire, Kentucky, I have found the Modular Compact │
│                     │ Closet Organizer Set to be an exceptional solution for the spatial constraints       │
│                     │ common in smaller living environments. The product arrived well-packaged and the     │
│                     │ components were identified clearly, facilitating a smooth assembly process that      │
│                     │ required no tools, which was a significant advantage given my limited experience     │
│                     │ with home organization setups.\n\nThe quality of materials is immediately apparent;  │
│                     │ the bamboo elements offer a warm, natural aesthetic that complements the recyclable  │
│                     │ plastic components, creating a sleek, modern look that belies the system's           │
│                     │ functional intent. The adjustable shelves are robust and have accommodated a variety │
│                     │ of garment types without sagging, while the hanging organizers and stackable bins    │
│                     │ have proven invaluable for optimizing the vertical square footage of my closet.      │
│                     │ \n\nWhat I appreciate most about this system is its ability to transform a chaotic   │
│                     │ space into a streamlined, minimalist environment. The design philosophy of           │
│                     │ maximizing vertical space while maintaining a clean aesthetic has genuinely          │
│                     │ streamlined my daily routine. I no longer spend unnecessary time searching for       │
│                     │ items, and the overall tidiness of the space has positively impacted my mood each    │
│                     │ morning. For anyone residing in an urban setting or a compact apartment, this        │
│                     │ organizer set represents a practical, durable, and stylish investment. I             │
│                     │ wholeheartedly recommend this product to those seeking to declutter and reorganize   │
│                     │ their living spaces efficiently."                                                    │
│                     │ }                                                                                    │
├─────────────────────┼──────────────────────────────────────────────────────────────────────────────────────┤
│ customer_name       │ Mary Stevens                                                                         │
├─────────────────────┼──────────────────────────────────────────────────────────────────────────────────────┤
│ customer_age        │ 38                                                                                   │
└─────────────────────┴──────────────────────────────────────────────────────────────────────────────────────┘
Python
1# The preview dataset is available as a pandas DataFrame.
2# Notice that complaint_analysis, action_items, and review_summary columns
3# reflect the skip behavior: None for skipped rows, generated text otherwise.
4preview.dataset
5
Output
product_category product_subcategory target_age_range review_style customer_age customer_name product customer_review complaint_analysis action_items review_summary
0 Home & Kitchen Organization 25-35 detailed 38 Mary Stevens {'name': 'Modular Compact Closet Organizer Set... {'rating': 5, 'customer_mood': 'happy', 'revie... None None The Modular Compact Closet Organizer Set is an...

📊 Analyze the generated data

  • Data Designer automatically generates a basic statistical analysis of the generated data.

  • This analysis is available via the analysis property of generation result objects.

Python
1# Print the analysis as a table.
2preview.analysis.to_report()
3
Output
──────────────────────────────────────── 🎨 Data Designer Dataset Profile ─────────────────────────────────────────

                                                                                                                   
                                                 Dataset Overview                                                  
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ number of records               ┃ number of columns               ┃ percent complete records                    ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ 1                               │ 11                              │ 50.0%                                       │
└─────────────────────────────────┴─────────────────────────────────┴─────────────────────────────────────────────┘
                                                                                                                   
                                                                                                                   
                                                🎲 Sampler Columns                                                 
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓
┃ column name                      ┃        data type ┃               number unique values ┃         sampler type ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩
│ product_category                 │           string │                         1 (100.0%) │             category │
├──────────────────────────────────┼──────────────────┼────────────────────────────────────┼──────────────────────┤
│ product_subcategory              │           string │                         1 (100.0%) │          subcategory │
├──────────────────────────────────┼──────────────────┼────────────────────────────────────┼──────────────────────┤
│ target_age_range                 │           string │                         1 (100.0%) │             category │
├──────────────────────────────────┼──────────────────┼────────────────────────────────────┼──────────────────────┤
│ review_style                     │           string │                         1 (100.0%) │             category │
└──────────────────────────────────┴──────────────────┴────────────────────────────────────┴──────────────────────┘
                                                                                                                   
                                                                                                                   
                                                📝 LLM-Text Columns                                                
┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓
┃                          ┃              ┃                            ┃     prompt tokens ┃    completion tokens ┃
┃ column name              ┃    data type ┃       number unique values ┃        per record ┃           per record ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩
│ complaint_analysis       │         None │                   0 (0.0%) │     322.0 +/- 0.0 │          1.0 +/- nan │
├──────────────────────────┼──────────────┼────────────────────────────┼───────────────────┼──────────────────────┤
│ action_items             │         None │                   0 (0.0%) │      22.0 +/- 0.0 │          1.0 +/- nan │
├──────────────────────────┼──────────────┼────────────────────────────┼───────────────────┼──────────────────────┤
│ review_summary           │       string │                 1 (100.0%) │     293.0 +/- 0.0 │         32.0 +/- nan │
└──────────────────────────┴──────────────┴────────────────────────────┴───────────────────┴──────────────────────┘
                                                                                                                   
                                                                                                                   
                                             🗂️ LLM-Structured Columns                                             
┏━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
┃                       ┃               ┃                            ┃     prompt tokens ┃      completion tokens ┃
┃ column name           ┃     data type ┃       number unique values ┃        per record ┃             per record ┃
┡━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
│ product               │          dict │                 1 (100.0%) │     265.0 +/- 0.0 │           98.0 +/- nan │
├───────────────────────┼───────────────┼────────────────────────────┼───────────────────┼────────────────────────┤
│ customer_review       │          dict │                 1 (100.0%) │     351.0 +/- 0.0 │          288.0 +/- nan │
└───────────────────────┴───────────────┴────────────────────────────┴───────────────────┴────────────────────────┘
                                                                                                                   
                                                                                                                   
                                               🧩 Expression Columns                                               
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ column name                       ┃                data type ┃                             number unique values ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ customer_name                     │                   string │                                       1 (100.0%) │
├───────────────────────────────────┼──────────────────────────┼──────────────────────────────────────────────────┤
│ customer_age                      │                   string │                                       1 (100.0%) │
└───────────────────────────────────┴──────────────────────────┴──────────────────────────────────────────────────┘
                                                                                                                   
                                                                                                                   
╭────────────────────────────────────────────────── Table Notes ──────────────────────────────────────────────────╮
│                                                                                                                 │
│  1. All token statistics are based on a sample of max(1000, len(dataset)) records.                              │
│  2. Tokens are calculated using tiktoken's cl100k_base tokenizer.                                               │
│                                                                                                                 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
                                                                                                                   
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────

🆙 Scale up!

  • Happy with your preview data?

  • Use the create method to submit larger Data Designer generation jobs.

Python
1results = data_designer.create(config_builder, num_records=10, dataset_name="tutorial-2")
2
Output
[17:46:08] [INFO] OpenTelemetry metrics available at http://127.0.0.1:9464/metrics
[17:46:08] [INFO] 🎨 Creating Data Designer dataset
[17:46:08] [INFO]   |-- 🔒 Jinja rendering engine: secure
[17:46:08] [INFO] ✅ Validation passed
[17:46:08] [INFO] ⛓️ Sorting column configs into a Directed Acyclic Graph
[17:46:08] [INFO] Skipping model health checks because DATA_DESIGNER_SKIP_MODEL_HEALTH_CHECKS=1
[17:46:08] [INFO] ⚡ Using async task-queue builder
[17:46:08] [INFO] 🗂️ llm-structured model config for column 'product'
[17:46:08] [INFO]   |-- model: 'nvidia/nemotron-3.5-lightning-30b-a3b'
[17:46:08] [INFO]   |-- model alias: 'nemotron-lightning'
[17:46:08] [INFO]   |-- model provider: 'nvidia'
[17:46:08] [INFO]   |-- inference parameters:
[17:46:08] [INFO]   |  |-- generation_type=chat-completion
[17:46:08] [INFO]   |  |-- max_parallel_requests=4
[17:46:08] [INFO]   |  |-- extra_body={'chat_template_kwargs': {'enable_thinking': False}}
[17:46:08] [INFO]   |  |-- temperature=1.00
[17:46:08] [INFO]   |  |-- top_p=0.95
[17:46:08] [INFO]   |  |-- max_tokens=2048
[17:46:08] [INFO] 🗂️ llm-structured model config for column 'customer_review'
[17:46:08] [INFO]   |-- model: 'nvidia/nemotron-3.5-lightning-30b-a3b'
[17:46:08] [INFO]   |-- model alias: 'nemotron-lightning'
[17:46:08] [INFO]   |-- model provider: 'nvidia'
[17:46:08] [INFO]   |-- inference parameters:
[17:46:08] [INFO]   |  |-- generation_type=chat-completion
[17:46:08] [INFO]   |  |-- max_parallel_requests=4
[17:46:08] [INFO]   |  |-- extra_body={'chat_template_kwargs': {'enable_thinking': False}}
[17:46:08] [INFO]   |  |-- temperature=1.00
[17:46:08] [INFO]   |  |-- top_p=0.95
[17:46:08] [INFO]   |  |-- max_tokens=2048
[17:46:08] [INFO] 📝 llm-text model config for column 'complaint_analysis'
[17:46:08] [INFO]   |-- model: 'nvidia/nemotron-3.5-lightning-30b-a3b'
[17:46:08] [INFO]   |-- model alias: 'nemotron-lightning'
[17:46:08] [INFO]   |-- model provider: 'nvidia'
[17:46:08] [INFO]   |-- inference parameters:
[17:46:08] [INFO]   |  |-- generation_type=chat-completion
[17:46:08] [INFO]   |  |-- max_parallel_requests=4
[17:46:08] [INFO]   |  |-- extra_body={'chat_template_kwargs': {'enable_thinking': False}}
[17:46:08] [INFO]   |  |-- temperature=1.00
[17:46:08] [INFO]   |  |-- top_p=0.95
[17:46:08] [INFO]   |  |-- max_tokens=2048
[17:46:08] [INFO] 📝 llm-text model config for column 'review_summary'
[17:46:08] [INFO]   |-- model: 'nvidia/nemotron-3.5-lightning-30b-a3b'
[17:46:08] [INFO]   |-- model alias: 'nemotron-lightning'
[17:46:08] [INFO]   |-- model provider: 'nvidia'
[17:46:08] [INFO]   |-- inference parameters:
[17:46:08] [INFO]   |  |-- generation_type=chat-completion
[17:46:08] [INFO]   |  |-- max_parallel_requests=4
[17:46:08] [INFO]   |  |-- extra_body={'chat_template_kwargs': {'enable_thinking': False}}
[17:46:08] [INFO]   |  |-- temperature=1.00
[17:46:08] [INFO]   |  |-- top_p=0.95
[17:46:08] [INFO]   |  |-- max_tokens=2048
[17:46:08] [INFO] 📝 llm-text model config for column 'action_items'
[17:46:08] [INFO]   |-- model: 'nvidia/nemotron-3.5-lightning-30b-a3b'
[17:46:08] [INFO]   |-- model alias: 'nemotron-lightning'
[17:46:08] [INFO]   |-- model provider: 'nvidia'
[17:46:08] [INFO]   |-- inference parameters:
[17:46:08] [INFO]   |  |-- generation_type=chat-completion
[17:46:08] [INFO]   |  |-- max_parallel_requests=4
[17:46:08] [INFO]   |  |-- extra_body={'chat_template_kwargs': {'enable_thinking': False}}
[17:46:08] [INFO]   |  |-- temperature=1.00
[17:46:08] [INFO]   |  |-- top_p=0.95
[17:46:08] [INFO]   |  |-- max_tokens=2048
[17:46:08] [INFO] ⚡️ Async generation: 5 column(s) (column 'product', column 'customer_review', column 'complaint_analysis', column 'review_summary', column 'action_items'), 50 tasks across 1 row group(s)
[17:46:08] [INFO] 🚀 (1/1) Dispatching with 10 records
[17:46:08] [INFO] 🎲 (1/1) Preparing samplers to generate 10 records across 5 columns
[17:46:08] [INFO] 🧩 (1/1) Generating column `customer_age` from expression
[17:46:08] [INFO] 🧩 (1/1) Generating column `customer_name` from expression
[17:46:14] [INFO] 📊 Progress [5.4s]:
[17:46:14] [INFO]   |-- 🐱 column 'product': 1/10 (10%) 0.2 rec/s
[17:46:14] [INFO]   |-- 🌧️ column 'customer_review': 0/10 (0%) 0.0 rec/s
[17:46:14] [INFO]   |-- 🌑 column 'complaint_analysis': 0/10 (0%) 0.0 rec/s
[17:46:14] [INFO]   |-- 🚶 column 'review_summary': 0/10 (0%) 0.0 rec/s
[17:46:14] [INFO]   |-- 🌧️ column 'action_items': 0/10 (0%) 0.0 rec/s
[17:47:09] [WARNING] Observed retryable model-task error: kind=timeout; the row task will be deferred.
[17:47:11] [INFO] 📊 Progress [63.1s]:
[17:47:11] [INFO]   |-- 🐱 column 'product': 2/10 (20%) 0.0 rec/s
[17:47:11] [INFO]   |-- 🌧️ column 'customer_review': 0/10 (0%) 0.0 rec/s
[17:47:11] [INFO]   |-- 🌑 column 'complaint_analysis': 0/10 (0%) 0.0 rec/s
[17:47:11] [INFO]   |-- 🚶 column 'review_summary': 0/10 (0%) 0.0 rec/s
[17:47:11] [INFO]   |-- 🌧️ column 'action_items': 0/10 (0%) 0.0 rec/s
[17:47:27] [INFO] 📊 Progress [79.0s]:
[17:47:27] [INFO]   |-- 🐱 column 'product': 2/10 (20%) 0.0 rec/s
[17:47:27] [INFO]   |-- 🌧️ column 'customer_review': 1/10 (10%) 0.0 rec/s
[17:47:27] [INFO]   |-- 🌑 column 'complaint_analysis': 0/10 (0%) 0.0 rec/s
[17:47:27] [INFO]   |-- 🚶 column 'review_summary': 0/10 (0%) 0.0 rec/s
[17:47:27] [INFO]   |-- 🌧️ column 'action_items': 0/10 (0%) 0.0 rec/s
[17:47:33] [INFO] 📊 Progress [84.3s]:
[17:47:33] [INFO]   |-- 😺 column 'product': 3/10 (30%) 0.0 rec/s
[17:47:33] [INFO]   |-- 🌧️ column 'customer_review': 1/10 (10%) 0.0 rec/s
[17:47:33] [INFO]   |-- 🌑 column 'complaint_analysis': 1/10 (10%) 0.0 rec/s, 1 skipped
[17:47:33] [INFO]   |-- 🚶 column 'review_summary': 0/10 (0%) 0.0 rec/s
[17:47:33] [INFO]   |-- 🌧️ column 'action_items': 1/10 (10%) 0.0 rec/s, 1 skipped
[17:47:43] [INFO] 📊 Progress [94.2s]:
[17:47:43] [INFO]   |-- 😺 column 'product': 3/10 (30%) 0.0 rec/s
[17:47:43] [INFO]   |-- 🌧️ column 'customer_review': 2/10 (20%) 0.0 rec/s
[17:47:43] [INFO]   |-- 🌑 column 'complaint_analysis': 1/10 (10%) 0.0 rec/s, 1 skipped
[17:47:43] [INFO]   |-- 🚶 column 'review_summary': 0/10 (0%) 0.0 rec/s
[17:47:43] [INFO]   |-- 🌧️ column 'action_items': 1/10 (10%) 0.0 rec/s, 1 skipped
[17:47:48] [INFO] 📊 Progress [100.2s]:
[17:47:48] [INFO]   |-- 😺 column 'product': 3/10 (30%) 0.0 rec/s
[17:47:49] [INFO]   |-- 🌧️ column 'customer_review': 2/10 (20%) 0.0 rec/s
[17:47:49] [INFO]   |-- 🌑 column 'complaint_analysis': 2/10 (20%) 0.0 rec/s, 2 skipped
[17:47:49] [INFO]   |-- 🚶 column 'review_summary': 2/10 (20%) 0.0 rec/s
[17:47:49] [INFO]   |-- 🌧️ column 'action_items': 2/10 (20%) 0.0 rec/s, 2 skipped
[17:48:07] [INFO] 📊 Progress [118.9s]:
[17:48:07] [INFO]   |-- 😸 column 'product': 5/10 (50%) 0.0 rec/s
[17:48:07] [INFO]   |-- 🌧️ column 'customer_review': 2/10 (20%) 0.0 rec/s
[17:48:07] [INFO]   |-- 🌑 column 'complaint_analysis': 2/10 (20%) 0.0 rec/s, 2 skipped
[17:48:07] [INFO]   |-- 🚶 column 'review_summary': 2/10 (20%) 0.0 rec/s
[17:48:07] [INFO]   |-- 🌧️ column 'action_items': 2/10 (20%) 0.0 rec/s, 2 skipped
[17:48:29] [INFO] 📊 Progress [140.6s]:
[17:48:29] [INFO]   |-- 😸 column 'product': 5/10 (50%) 0.0 rec/s
[17:48:29] [INFO]   |-- 🌦️ column 'customer_review': 3/10 (30%) 0.0 rec/s
[17:48:29] [INFO]   |-- 🌑 column 'complaint_analysis': 2/10 (20%) 0.0 rec/s, 2 skipped
[17:48:29] [INFO]   |-- 🚶 column 'review_summary': 2/10 (20%) 0.0 rec/s
[17:48:29] [INFO]   |-- 🌧️ column 'action_items': 2/10 (20%) 0.0 rec/s, 2 skipped
[17:48:37] [INFO] 📊 Progress [148.3s]:
[17:48:37] [INFO]   |-- 😸 column 'product': 7/10 (70%) 0.0 rec/s
[17:48:37] [INFO]   |-- 🌦️ column 'customer_review': 3/10 (30%) 0.0 rec/s
[17:48:37] [INFO]   |-- 🌘 column 'complaint_analysis': 3/10 (30%) 0.0 rec/s, 3 skipped
[17:48:37] [INFO]   |-- 🚶 column 'review_summary': 2/10 (20%) 0.0 rec/s
[17:48:37] [INFO]   |-- 🌦️ column 'action_items': 3/10 (30%) 0.0 rec/s, 3 skipped
[17:48:59] [INFO] 📊 Progress [170.9s]:
[17:48:59] [INFO]   |-- 😸 column 'product': 7/10 (70%) 0.0 rec/s
[17:48:59] [INFO]   |-- 🌦️ column 'customer_review': 4/10 (40%) 0.0 rec/s
[17:48:59] [INFO]   |-- 🌘 column 'complaint_analysis': 3/10 (30%) 0.0 rec/s, 3 skipped
[17:48:59] [INFO]   |-- 🚶 column 'review_summary': 2/10 (20%) 0.0 rec/s
[17:48:59] [INFO]   |-- 🌦️ column 'action_items': 3/10 (30%) 0.0 rec/s, 3 skipped
[17:49:24] [INFO] 📊 Progress [195.4s]:
[17:49:24] [INFO]   |-- 😸 column 'product': 7/10 (70%) 0.0 rec/s
[17:49:24] [INFO]   |-- ⛅ column 'customer_review': 5/10 (50%) 0.0 rec/s
[17:49:24] [INFO]   |-- 🌘 column 'complaint_analysis': 4/10 (40%) 0.0 rec/s, 4 skipped
[17:49:24] [INFO]   |-- 🚶 column 'review_summary': 2/10 (20%) 0.0 rec/s
[17:49:24] [INFO]   |-- 🌦️ column 'action_items': 4/10 (40%) 0.0 rec/s, 4 skipped
[17:49:52] [INFO] 📊 Progress [224.1s]:
[17:49:52] [INFO]   |-- 😸 column 'product': 7/10 (70%) 0.0 rec/s
[17:49:52] [INFO]   |-- ⛅ column 'customer_review': 5/10 (50%) 0.0 rec/s
[17:49:52] [INFO]   |-- 🌗 column 'complaint_analysis': 5/10 (50%) 0.0 rec/s, 5 skipped
[17:49:52] [INFO]   |-- 🐴 column 'review_summary': 3/10 (30%) 0.0 rec/s
[17:49:52] [INFO]   |-- ⛅ column 'action_items': 5/10 (50%) 0.0 rec/s, 5 skipped
[17:49:59] [INFO] 🔄 (1/1) Salvaging 7 deferred task(s)
[17:50:03] [INFO] 📊 Progress [234.4s]:
[17:50:03] [INFO]   |-- 😼 column 'product': 8/10 (80%) 0.0 rec/s
[17:50:03] [INFO]   |-- ⛅ column 'customer_review': 5/10 (50%) 0.0 rec/s
[17:50:03] [INFO]   |-- 🌗 column 'complaint_analysis': 5/10 (50%) 0.0 rec/s, 5 skipped
[17:50:03] [INFO]   |-- 🐴 column 'review_summary': 3/10 (30%) 0.0 rec/s
[17:50:03] [INFO]   |-- ⛅ column 'action_items': 5/10 (50%) 0.0 rec/s, 5 skipped
[17:50:08] [INFO] 📊 Progress [240.0s]:
[17:50:08] [INFO]   |-- 😼 column 'product': 8/10 (80%) 0.0 rec/s
[17:50:08] [INFO]   |-- ⛅ column 'customer_review': 6/10 (60%) 0.0 rec/s
[17:50:08] [INFO]   |-- 🌗 column 'complaint_analysis': 5/10 (50%) 0.0 rec/s, 5 skipped
[17:50:08] [INFO]   |-- 🐴 column 'review_summary': 3/10 (30%) 0.0 rec/s
[17:50:08] [INFO]   |-- ⛅ column 'action_items': 5/10 (50%) 0.0 rec/s, 5 skipped
[17:50:14] [INFO] 📊 Progress [245.9s]:
[17:50:14] [INFO]   |-- 😼 column 'product': 8/10 (80%) 0.0 rec/s
[17:50:14] [INFO]   |-- ⛅ column 'customer_review': 7/10 (70%) 0.0 rec/s
[17:50:14] [INFO]   |-- 🌗 column 'complaint_analysis': 6/10 (60%) 0.0 rec/s, 6 skipped
[17:50:14] [INFO]   |-- 🐴 column 'review_summary': 3/10 (30%) 0.0 rec/s
[17:50:14] [INFO]   |-- ⛅ column 'action_items': 5/10 (50%) 0.0 rec/s, 5 skipped
[17:50:24] [INFO] 📊 Progress [255.6s]:
[17:50:24] [INFO]   |-- 😼 column 'product': 8/10 (80%) 0.0 rec/s
[17:50:24] [INFO]   |-- ⛅ column 'customer_review': 7/10 (70%) 0.0 rec/s
[17:50:24] [INFO]   |-- 🌗 column 'complaint_analysis': 6/10 (60%) 0.0 rec/s, 6 skipped
[17:50:24] [INFO]   |-- 🐴 column 'review_summary': 4/10 (40%) 0.0 rec/s
[17:50:24] [INFO]   |-- ⛅ column 'action_items': 6/10 (60%) 0.0 rec/s, 6 skipped
[17:50:40] [INFO] 📊 Progress [272.0s]:
[17:50:40] [INFO]   |-- 😼 column 'product': 9/10 (90%) 0.0 rec/s
[17:50:40] [INFO]   |-- ⛅ column 'customer_review': 7/10 (70%) 0.0 rec/s
[17:50:40] [INFO]   |-- 🌗 column 'complaint_analysis': 7/10 (70%) 0.0 rec/s, 7 skipped
[17:50:40] [INFO]   |-- 🚗 column 'review_summary': 5/10 (50%) 0.0 rec/s
[17:50:40] [INFO]   |-- ⛅ column 'action_items': 6/10 (60%) 0.0 rec/s, 6 skipped
[17:51:00] [INFO] 📊 Progress [291.7s]:
[17:51:00] [INFO]   |-- 😼 column 'product': 9/10 (90%) 0.0 rec/s
[17:51:00] [INFO]   |-- ⛅ column 'customer_review': 7/10 (70%) 0.0 rec/s
[17:51:00] [INFO]   |-- 🌗 column 'complaint_analysis': 7/10 (70%) 0.0 rec/s, 7 skipped
[17:51:00] [INFO]   |-- 🚗 column 'review_summary': 6/10 (60%) 0.0 rec/s
[17:51:00] [INFO]   |-- ⛅ column 'action_items': 7/10 (70%) 0.0 rec/s, 7 skipped
[17:51:24] [INFO] 📊 Progress [315.4s]:
[17:51:24] [INFO]   |-- 😼 column 'product': 9/10 (90%) 0.0 rec/s
[17:51:24] [INFO]   |-- ⛅ column 'customer_review': 7/10 (70%) 0.0 rec/s
[17:51:24] [INFO]   |-- 🌗 column 'complaint_analysis': 7/10 (70%) 0.0 rec/s, 7 skipped
[17:51:24] [INFO]   |-- 🚗 column 'review_summary': 7/10 (70%) 0.0 rec/s
[17:51:24] [INFO]   |-- ⛅ column 'action_items': 7/10 (70%) 0.0 rec/s, 7 skipped
[17:52:08] [INFO] 📊 Progress [359.8s]:
[17:52:08] [INFO]   |-- 😼 column 'product': 9/10 (90%) 0.0 rec/s
[17:52:08] [INFO]   |-- 🌤️ column 'customer_review': 8/10 (80%) 0.0 rec/s
[17:52:08] [INFO]   |-- 🌗 column 'complaint_analysis': 7/10 (70%) 0.0 rec/s, 7 skipped
[17:52:08] [INFO]   |-- 🚗 column 'review_summary': 7/10 (70%) 0.0 rec/s
[17:52:08] [INFO]   |-- ⛅ column 'action_items': 7/10 (70%) 0.0 rec/s, 7 skipped
[17:52:59] [INFO] 📊 Progress [411.1s]:
[17:52:59] [INFO]   |-- 😼 column 'product': 9/10 (90%) 0.0 rec/s
[17:52:59] [INFO]   |-- 🌤️ column 'customer_review': 8/10 (80%) 0.0 rec/s
[17:52:59] [INFO]   |-- 🌖 column 'complaint_analysis': 8/10 (80%) 0.0 rec/s, 8 skipped
[17:52:59] [INFO]   |-- 🚗 column 'review_summary': 7/10 (70%) 0.0 rec/s
[17:52:59] [INFO]   |-- 🌤️ column 'action_items': 8/10 (80%) 0.0 rec/s, 8 skipped
[17:53:01] [INFO] 🙈 Dropping columns: ['customer']
[17:53:01] [INFO] 📊 Progress [412.4s]:
[17:53:01] [INFO]   |-- 🦁 column 'product': 10/10 (100%) 0.0 rec/s
[17:53:01] [INFO]   |-- ☀️ column 'customer_review': 10/10 (100%) 0.0 rec/s, 1 skipped
[17:53:01] [INFO]   |-- 🌕 column 'complaint_analysis': 10/10 (100%) 0.0 rec/s, 10 skipped
[17:53:01] [INFO]   |-- 🚀 column 'review_summary': 10/10 (100%) 0.0 rec/s, 2 skipped
[17:53:01] [INFO]   |-- ☀️ column 'action_items': 10/10 (100%) 0.0 rec/s, 10 skipped
[17:53:01] [INFO] ✅ Async generation complete [412.4s]: 25 ok, 2 failed, 23 skipped across 5 column(s)
[17:53:01] [WARNING] ⚠️ Generated 8 of 10 requested records (80%). The dataset may be incomplete due to dropped rows.
[17:53:01] [INFO] 📊 Model usage summary:
[17:53:01] [INFO]   |-- model: nvidia/nemotron-3.5-lightning-30b-a3b
[17:53:01] [INFO]   |-- tokens: input=9090, output=3855, total=12945, tps=31
[17:53:01] [INFO]   |-- requests: success=29, failed=12, total=41, rpm=5
[17:53:01] [INFO] 📐 Measuring dataset column statistics:
[17:53:01] [INFO]   |-- 🎲 column: 'product_category'
[17:53:01] [INFO]   |-- 🎲 column: 'product_subcategory'
[17:53:01] [INFO]   |-- 🎲 column: 'target_age_range'
[17:53:01] [INFO]   |-- 🎲 column: 'review_style'
[17:53:01] [INFO]   |-- 🧩 column: 'customer_name'
[17:53:01] [INFO]   |-- 🧩 column: 'customer_age'
[17:53:01] [INFO]   |-- 🗂️ column: 'product'
[17:53:01] [INFO]   |-- 🗂️ column: 'customer_review'
[17:53:01] [INFO]   |-- 📝 column: 'complaint_analysis'
[17:53:01] [INFO]   |-- 📝 column: 'action_items'
[17:53:01] [INFO]   |-- 📝 column: 'review_summary'
Python
1# Load the generated dataset as a pandas DataFrame.
2dataset = results.load_dataset()
3 
4dataset.head()
5
Output
product_category product_subcategory target_age_range review_style customer_age customer_name product customer_review complaint_analysis action_items review_summary
0 Home Office Chairs 50-65 detailed 88 Judy Hale {'name': 'Ergonomic High-Back Executive Office... {'rating': 5, 'customer_mood': 'happy', 'revie... None None A professional in their fifties highly recomme...
1 Clothing Men's Clothing 65+ brief 22 Samantha Ramsey {'name': 'Senior Comfort V-Neck Cardigan', 'de... {'rating': 5, 'customer_mood': 'happy', 'revie... None None The Senior Comfort V-Neck Cardigan offers exce...
2 Home & Kitchen Furniture 35-50 brief 85 Jessica Robertson {'name': 'Minimalist Adjustable Standing Desk ... {'rating': 4, 'customer_mood': 'happy', 'revie... None None The Minimalist Adjustable Standing Desk Conver...
3 Clothing Activewear 18-25 rambling 94 Deborah Mccullough {'name': 'Performance Stretch High-Waist Leggi... {'rating': 4, 'customer_mood': 'happy', 'revie... None None These high-waist, moisture-wicking leggings wi...
4 Books Fiction 35-50 structured with bullet points 43 Billy Johnson {'name': 'The Midnight Library: A Novel', 'des... {'rating': 5, 'customer_mood': 'happy', 'revie... None None The Midnight Library is a thought-provoking an...
Python
1# Load the analysis results into memory.
2analysis = results.load_analysis()
3 
4analysis.to_report()
5
Output
──────────────────────────────────────── 🎨 Data Designer Dataset Profile ─────────────────────────────────────────

                                                                                                                   
                                                 Dataset Overview                                                  
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ number of records               ┃ number of columns               ┃ percent complete records                    ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ 8                               │ 11                              │ 80.0%                                       │
└─────────────────────────────────┴─────────────────────────────────┴─────────────────────────────────────────────┘
                                                                                                                   
                                                                                                                   
                                                🎲 Sampler Columns                                                 
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓
┃ column name                      ┃        data type ┃               number unique values ┃         sampler type ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩
│ product_category                 │           string │                          5 (62.5%) │             category │
├──────────────────────────────────┼──────────────────┼────────────────────────────────────┼──────────────────────┤
│ product_subcategory              │           string │                         8 (100.0%) │          subcategory │
├──────────────────────────────────┼──────────────────┼────────────────────────────────────┼──────────────────────┤
│ target_age_range                 │           string │                          4 (50.0%) │             category │
├──────────────────────────────────┼──────────────────┼────────────────────────────────────┼──────────────────────┤
│ review_style                     │           string │                          4 (50.0%) │             category │
└──────────────────────────────────┴──────────────────┴────────────────────────────────────┴──────────────────────┘
                                                                                                                   
                                                                                                                   
                                                📝 LLM-Text Columns                                                
┏━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓
┃                         ┃              ┃                           ┃       prompt tokens ┃    completion tokens ┃
┃ column name             ┃    data type ┃      number unique values ┃          per record ┃           per record ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩
│ complaint_analysis      │         None │                  0 (0.0%) │     252.0 +/- 123.6 │          1.0 +/- 0.0 │
├─────────────────────────┼──────────────┼───────────────────────────┼─────────────────────┼──────────────────────┤
│ action_items            │         None │                  0 (0.0%) │        22.0 +/- 0.0 │          1.0 +/- 0.0 │
├─────────────────────────┼──────────────┼───────────────────────────┼─────────────────────┼──────────────────────┤
│ review_summary          │       string │                8 (100.0%) │     225.0 +/- 123.5 │        44.0 +/- 67.9 │
└─────────────────────────┴──────────────┴───────────────────────────┴─────────────────────┴──────────────────────┘
                                                                                                                   
                                                                                                                   
                                             🗂️ LLM-Structured Columns                                             
┏━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┓
┃                      ┃               ┃                            ┃       prompt tokens ┃     completion tokens ┃
┃ column name          ┃     data type ┃       number unique values ┃          per record ┃            per record ┃
┡━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━┩
│ product              │          dict │                 8 (100.0%) │       265.0 +/- 0.8 │         99.5 +/- 21.3 │
├──────────────────────┼───────────────┼────────────────────────────┼─────────────────────┼───────────────────────┤
│ customer_review      │          dict │                 8 (100.0%) │      355.0 +/- 20.6 │       214.0 +/- 133.1 │
└──────────────────────┴───────────────┴────────────────────────────┴─────────────────────┴───────────────────────┘
                                                                                                                   
                                                                                                                   
                                               🧩 Expression Columns                                               
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ column name                       ┃                data type ┃                             number unique values ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ customer_name                     │                   string │                                       8 (100.0%) │
├───────────────────────────────────┼──────────────────────────┼──────────────────────────────────────────────────┤
│ customer_age                      │                   string │                                       8 (100.0%) │
└───────────────────────────────────┴──────────────────────────┴──────────────────────────────────────────────────┘
                                                                                                                   
                                                                                                                   
╭────────────────────────────────────────────────── Table Notes ──────────────────────────────────────────────────╮
│                                                                                                                 │
│  1. All token statistics are based on a sample of max(1000, len(dataset)) records.                              │
│  2. Tokens are calculated using tiktoken's cl100k_base tokenizer.                                               │
│                                                                                                                 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
                                                                                                                   
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────

⏭️ Next Steps

Check out the following notebook to learn more about: