Adds a new evaluation step (column) to an existing evaluation pipeline. Columns execute sequentially from left to right and can reference data from previous columns.
Column Types Available:
See the full documentation for detailed configuration requirements for each column type.
is_part_of_score: true on each column you want to include in the score"question")"AI Response")400: Invalid configuration or duplicate column name403: Cannot overwrite dataset columns or lacking permissions404: Report not found or not accessibleAPI key to authorize the operation. Can also use JWT authentication.
The ID of the evaluation pipeline to add this column to.
x >= 1The type of evaluation or transformation this column performs. Must be one of the supported column types.
ABSOLUTE_NUMERIC_DISTANCE, AI_DATA_EXTRACTION, ASSERT_VALID, CONVERSATION_SIMULATOR, COALESCE, CODE_EXECUTION, COMBINE_COLUMNS, COMPARE, CONTAINS, COSINE_SIMILARITY, COUNT, ENDPOINT, MCP, HUMAN, JSON_PATH, LLM_ASSERTION, MATH_OPERATOR, MIN_MAX, PARSE_VALUE, APPLY_DIFF, PROMPT_TEMPLATE, REGEX, REGEX_EXTRACTION, VARIABLE, XML_PATH, WORKFLOW, CODING_AGENT Display name for this column. Must be unique within the pipeline. This name is used to reference the column in subsequent steps.
1 - 255Column-specific configuration. The schema varies based on column_type. See documentation for each type's requirements.
Optional position for the column. If not specified, the column is added at the end. Cannot overwrite dataset columns.
x >= 0