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v2

latestOpenAPI 3.1.02026-08-05267431678.1 KB
felix

Analyze Dataset

Analyze a dataset for quality, distribution, and potential issues.

Supports NER, Classification, and Generative task types. Available analyses: distribution, duplicates, outliers, correlation, splits, errors, and diversity (Vendi score + embedding visualisation).

Provide data inline via dataset or reference a stored dataset with dataset_name. See DatasetAnalysisRequest schema for the full set of options including diversity visualisation config.

post/felix/dataset/analyze

Request body

task_type'ner' | 'classification' | 'generative' required

Task type of the dataset

task_descriptionstring nullable

Description of the task/domain for context. Helps the LLM quality analysis understand the intended use case.

datasetobject[] nullable

List of data samples (optional if dataset_name provided)

dataset_namestring nullable

Name of stored dataset to analyze (optional if dataset provided)

dataset_versionstring nullable

Dataset version (latest if omitted)

analysesstring[] required

List of analyses to perform

querystring nullable

Natural language question about the dataset

predictionsobject[] nullable

Optional model predictions for error analysis

optionsobject nullable

Configuration options for analysis. Supported keys: validation_percentage (float) for split analysis and diversity_visualization (object) with optional method ('pca'|'tsne'|'umap'), dimensions (2-4), and tsne_perplexity (float).

Example request

{
  "analyses": [
    "distribution",
    "outliers",
    "duplicates",
    "diversity"
  ],
  "dataset": [
    {
      "entities": [
        [
          "Apple",
          "ORG"
        ],
        [
          "U.K.",
          "GPE"
        ],
        [
          "$1 billion",
          "MONEY"
        ]
      ],
      "text": "Apple is looking at buying U.K. startup for $1 billion"
    },
    {
      "entities": [
        [
          "San Francisco",
          "GPE"
        ]
      ],
      "text": "San Francisco considers banning sidewalk delivery robots"
    }
  ],
  "options": {
    "diversity_visualization": {
      "dimensions": 3,
      "method": "tsne",
      "tsne_perplexity": 25
    }
  },
  "query": "What is the distribution of entity types?",
  "task_type": "ner"
}

Response

Successful Response

summaryobject required
correlationsobject nullable
errorsobject nullable
natural_language_responsestring nullable