> ## Documentation Index > Fetch the complete documentation index at: https://docs.promptlayer.com/llms.txt > Use this file to discover all available pages before exploring further. # Node & Column Types > Complete reference for all node types used in Workflows and evaluation pipelines This page documents all available node types for Workflows and column types for evaluation pipelines. Workflows and evaluations share the same node types—each has specific configuration options that determine its behavior. In Workflows, these are called **nodes**. In evaluation pipelines, they're called **columns**. The configuration is identical. ## How Column Sources Work Columns can reference data from two places: 1. **Dataset columns** - Reference data directly from your dataset by using the dataset column name 2. **Other evaluation columns** - Reference the output of a previous column by using that column's `name` When you specify a `source` or include a column name in `sources`, the system first looks for an evaluation column with that name, then falls back to looking for a dataset column. Columns are executed in order based on their `position`. A column can only reference other columns that come before it in the pipeline. ### Example: Chaining Columns Together A common pattern is to chain columns: run a prompt, extract a field from the JSON output, then compare it to a ground truth value from the dataset. ```python theme={null} columns = [ # Step 1: Run the prompt template (position 1) { "column_type": "PROMPT_TEMPLATE", "name": "LLM Output", # Other columns reference this name "configuration": { "template": {"name": "my-prompt"}, "prompt_template_variable_mappings": { "question": "user_question" # Maps to dataset column } } }, # Step 2: Extract a field from the LLM output (position 2) { "column_type": "JSON_PATH", "name": "Extracted Status", "configuration": { "source": "LLM Output", # References the column above "json_path": "$.status" } }, # Step 3: Compare extracted value to dataset ground truth (position 3) { "column_type": "COMPARE", "name": "Status Match", "configuration": { "sources": [ "Extracted Status", # References the column above "expected_status" # References a dataset column ], "comparison_type": {"type": "STRING"} }, "is_part_of_score": True } ] ``` ## Execution Types These columns execute prompts, code, or external services. Runs a prompt template against each row. You can reference a template from the Prompt Registry or define one inline. **Registry Reference (using `template`)** | Field | Type | Required | Description | | ----------------------------------- | ------- | -------- | ----------------------------------------------------- | | `template.name` | string | Yes | Name of the prompt template | | `template.version_number` | integer | No | Specific version number. Uses latest if omitted | | `template.label` | string | No | Release label to use, e.g. "production" | | `prompt_template_variable_mappings` | object | Yes | Maps template input variables to dataset/column names | | `engine` | object | No | Override the template's default model settings | | `engine.provider` | string | No | Provider name, e.g. "openai", "anthropic" | | `engine.model` | string | No | Model name, e.g. "gpt-4", "claude-3-opus" | | `engine.parameters` | object | No | Model parameters like temperature, max\_tokens | The `prompt_template_variable_mappings` object maps **prompt input variables** (keys) to **dataset or column names** (values). The key is the variable name in your prompt template (e.g., `{{question}}`), and the value is where to get the data from. ```json theme={null} { "column_type": "PROMPT_TEMPLATE", "name": "Generate Response", "configuration": { "template": { "name": "my-prompt", "label": "production" }, "prompt_template_variable_mappings": { "question": "user_question", "context": "retrieved_context" } } } ``` **Complete example with all input variables:** If your prompt template has variables `{{company}}`, `{{product}}`, and `{{query}}`, map each one: ```json theme={null} { "column_type": "PROMPT_TEMPLATE", "name": "Product Analysis", "configuration": { "template": { "name": "product-analyzer" }, "prompt_template_variable_mappings": { "company": "company_name", "product": "product_name", "query": "user_query" } } } ``` **Inline Template (using `inline_template`)** Define a prompt template directly in the configuration without saving it to the registry. This is useful for quick experimentation or one-off evaluations. | Field | Type | Required | Description | | --------------------------------------- | ------- | -------- | ----------------------------------------------------- | | `inline_template.inline` | boolean | Yes | Must be `true` | | `inline_template.prompt_template` | object | Yes | The template content (chat or completion format) | | `inline_template.metadata` | object | No | Model configuration (provider, name, parameters) | | `inline_template.source_prompt_name` | string | No | Name of the registry prompt this was derived from | | `inline_template.source_prompt_version` | integer | No | Version number of the source prompt | | `prompt_template_variable_mappings` | object | Yes | Maps template input variables to dataset/column names | ```json theme={null} { "column_type": "PROMPT_TEMPLATE", "name": "Generate Response", "configuration": { "inline_template": { "inline": true, "prompt_template": { "type": "chat", "messages": [ { "role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}] }, { "role": "user", "content": [{"type": "text", "text": "Answer: {question}"}] } ] }, "metadata": { "model": { "provider": "openai", "name": "gpt-4", "parameters": {"temperature": 0.7} } } }, "prompt_template_variable_mappings": { "question": "user_question" } } } ``` You must provide exactly one of `template` or `inline_template`. They are mutually exclusive. Executes custom Python or JavaScript code. The code receives a `data` dictionary containing all column values for the current row. | Field | Type | Required | Description | | ---------- | ------ | -------- | ------------------------ | | `code` | string | Yes | The code to execute | | `language` | string | Yes | "PYTHON" or "JAVASCRIPT" | ```json theme={null} { "column_type": "CODE_EXECUTION", "name": "Custom Logic", "configuration": { "code": "result = len(data['response'].split())\nreturn result", "language": "PYTHON" } } ``` Calls an external HTTP endpoint. The request body contains all column values for the current row. | Field | Type | Required | Description | | --------- | ------ | -------- | ----------------------- | | `url` | string | Yes | The HTTP endpoint URL | | `headers` | object | No | HTTP headers to include | ```json theme={null} { "column_type": "ENDPOINT", "name": "External Validator", "configuration": { "url": "https://api.example.com/validate", "headers": { "Authorization": "Bearer token123" } } } ``` Runs a PromptLayer workflow. | Field | Type | Required | Description | | ------------------------- | ------- | -------- | ---------------------------------------- | | `workflow_id` | integer | Yes | ID of the workflow to run | | `workflow_version_number` | integer | No | Specific version. Uses latest if omitted | | `workflow_label` | string | No | Release label to use | | `input_mappings` | object | Yes | Maps workflow inputs to column names | ```json theme={null} { "column_type": "WORKFLOW", "name": "Run Analysis Workflow", "configuration": { "workflow_id": 123, "input_mappings": { "input_text": "response" } } } ``` Executes an MCP (Model Context Protocol) action. | Field | Type | Required | Description | | ---------------- | ------- | -------- | -------------------------------- | | `mcp_server_id` | integer | Yes | ID of the MCP server | | `tool_name` | string | Yes | Name of the tool to call | | `input_mappings` | object | Yes | Maps tool inputs to column names | ```json theme={null} { "column_type": "MCP", "name": "MCP Tool Call", "configuration": { "mcp_server_id": 456, "tool_name": "search", "input_mappings": { "query": "search_query" } } } ``` Adds a column for manual human evaluation. | Field | Type | Required | Description | | ------------ | ------ | -------- | ------------------------------ | | `data_type` | string | Yes | "number" or "string" | | `ui_element` | object | Yes | UI configuration for the input | ```json theme={null} { "column_type": "HUMAN", "name": "Human Rating", "configuration": { "data_type": "number", "ui_element": { "type": "slider", "min": 1, "max": 5 } } } ``` Simulates multi-turn conversations to test chatbots and conversational agents. An AI-powered user persona engages in realistic dialogue with your prompt template, allowing you to evaluate how well your agent handles extended interactions. | Field | Type | Required | Description | | -------------------------------------- | ------- | ----------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `template.name` | string | Yes | Name of the prompt template to test | | `template.version_number` | integer | No | Specific version number | | `template.label` | string | No | Release label to use | | `prompt_template_variable_mappings` | object | Yes | Maps template input variables to dataset columns | | `user_persona` | string | Conditional | Static persona description. Required if `user_persona_source` not set | | `user_persona_source` | string | Conditional | Column name containing the persona. Required if `user_persona` not set | | `conversation_completed_prompt` | string | No | Guidance for when to consider the conversation complete (e.g., "End when the user confirms their order" or "Complete when the assistant calls the submit\_order tool") | | `conversation_completed_prompt_source` | string | No | Column name containing the completion guidance. Use instead of `conversation_completed_prompt` for dynamic guidance | | `is_user_first` | boolean | No | If true, simulated user sends the first message (default: false) | | `max_turns` | integer | No | Maximum conversation turns (default: system setting, max: 150) | | `conversation_samples` | array | No | Example conversations to guide the simulation style | The `user_persona` defines how the simulated user behaves - their goals, communication style, and what questions they ask. Use `user_persona_source` to pull different personas from your dataset for varied test scenarios. The `conversation_completed_prompt` provides explicit guidance for determining when a conversation should end. This is useful for defining specific end conditions like tool calls, confirmation messages, or goal achievement. The guidance can be holistic (general rules) or specific (look for a certain phrase or tool call). **Basic example with static persona:** ```json theme={null} { "column_type": "CONVERSATION_SIMULATOR", "name": "Support Chat Test", "configuration": { "template": { "name": "customer-support-bot" }, "prompt_template_variable_mappings": { "customer_name": "customer_name", "product": "product_name" }, "user_persona": "You are a frustrated customer who purchased a defective product. You want a refund but will accept a replacement if the agent is helpful. Ask follow-up questions and push back on unhelpful responses.", "is_user_first": true, "max_turns": 5 } } ``` **Dynamic personas from dataset:** For comprehensive testing, store different user personas in your dataset to test various scenarios: ```json theme={null} { "column_type": "CONVERSATION_SIMULATOR", "name": "Multi-Scenario Test", "configuration": { "template": { "name": "sales-assistant" }, "prompt_template_variable_mappings": { "rep_name": "rep_name", "product": "product_name", "customer_context": "customer_context" }, "user_persona_source": "test_persona", "is_user_first": true, "max_turns": 6 } } ``` Where your dataset has a `test_persona` column with different personas: * Row 1: "You are a busy executive who needs quick answers. Be impatient if responses are too long." * Row 2: "You are a technical user who asks detailed follow-up questions about implementation." * Row 3: "You are price-sensitive and keep asking about discounts and alternatives." **Custom completion conditions:** Use `conversation_completed_prompt` to define specific end conditions for your conversations: ```json theme={null} { "column_type": "CONVERSATION_SIMULATOR", "name": "Order Flow Test", "configuration": { "template": { "name": "order-assistant" }, "prompt_template_variable_mappings": { "customer_name": "customer_name", "order_items": "items" }, "user_persona": "You are a customer placing an order. Provide your shipping address and payment method when asked.", "conversation_completed_prompt": "The conversation is complete when the assistant calls the submit_order tool or confirms that the order has been placed successfully.", "max_turns": 10 } } ``` You can also use `conversation_completed_prompt_source` to pull completion guidance from your dataset: ```json theme={null} { "column_type": "CONVERSATION_SIMULATOR", "name": "Goal-Based Test", "configuration": { "template": { "name": "support-agent" }, "prompt_template_variable_mappings": { "context": "support_context" }, "user_persona_source": "test_persona", "conversation_completed_prompt_source": "completion_condition", "max_turns": 8 } } ``` Where your dataset has a `completion_condition` column with different end conditions: * Row 1: "End when the user says 'thank you' or indicates satisfaction" * Row 2: "Complete when the assistant provides a ticket number" * Row 3: "End when the refund\_process tool is called" **Evaluating conversation quality:** Chain with `LLM_ASSERTION` to evaluate the full conversation: ```json theme={null} { "column_type": "LLM_ASSERTION", "name": "Conversation Quality", "configuration": { "source": "Support Chat Test", "prompt": "Did the agent maintain a professional tone throughout and successfully resolve the customer's issue?" }, "is_part_of_score": true } ``` ## Loop Types These nodes enable iterating over collections or executing repeated operations within Agents. Iterates over a collection of items or runs a fixed number of times, executing a prompt template or sub-workflow on each iteration. | Field | Type | Required | Description | | -------------------- | ------- | ----------- | --------------------------------------------------------------------------- | | `loop_type` | string | Yes | `"prompt"` or `"workflow"` | | `prompt_config` | object | Conditional | Configuration for prompt execution (required if `loop_type` = "prompt") | | `workflow_config` | object | Conditional | Configuration for workflow execution (required if `loop_type` = "workflow") | | `iterator_source` | string | Conditional | Source node providing the collection to iterate over | | `max_iterations` | integer | Conditional | Fixed number of iterations (mutually exclusive with `iterator_source`) | | `return_all_outputs` | boolean | No | Return all outputs from each iteration (default: false) | | `variable_mappings` | object | No | Maps template variables to source nodes or special loop variables | **Special loop variables for `variable_mappings`:** * `loop_index` - Current iteration index (0-based) * `previous_outputs` - Array of all outputs from previous iterations * `_iterator_item` - Current item from the iterated collection Exactly **one** of `iterator_source` or `max_iterations` must be provided. ```json theme={null} { "node_type": "FOR_LOOP", "name": "process_items", "is_output_node": true, "dependencies": ["items"], "configuration": { "loop_type": "prompt", "iterator_source": "items", "prompt_config": { "template": { "name": "item-processor", "label": "production" }, "prompt_template_variable_mappings": { "item": "_iterator_item", "index": "loop_index" } }, "variable_mappings": { "item": "_iterator_item", "index": "loop_index" } } } ``` **Output structure:** ```json theme={null} { "iterations": 5, "outputs": ["output1", "output2", "output3", "output4", "output5"], "final_output": "output5" } ``` Executes repeatedly until an end condition is met or maximum iterations are reached. | Field | Type | Required | Description | | ------------------------- | ------- | ----------- | --------------------------------------------------------------------------- | | `loop_type` | string | Yes | `"prompt"` or `"workflow"` | | `prompt_config` | object | Conditional | Configuration for prompt execution (required if `loop_type` = "prompt") | | `workflow_config` | object | Conditional | Configuration for workflow execution (required if `loop_type` = "workflow") | | `end_condition_json_path` | string | No | JSONPath expression to evaluate termination (loop stops when truthy) | | `max_iterations` | integer | No | Maximum iterations (defaults to system limit) | | `return_all_outputs` | boolean | No | Return all outputs (default: false) | | `variable_mappings` | object | No | Maps template variables to source nodes or special variables | **Termination behavior:** * If `end_condition_json_path` is set: Loop ends when JSONPath extracts a truthy value * If not set: Loop ends when output is falsy (empty, null, false) ```json theme={null} { "node_type": "WHILE_LOOP", "name": "refine_loop", "is_output_node": true, "dependencies": ["initial_draft"], "configuration": { "loop_type": "prompt", "max_iterations": 5, "end_condition_json_path": "$.is_complete", "prompt_config": { "template": { "name": "text-refiner", "label": "production" }, "prompt_template_variable_mappings": { "text": "initial_draft", "previous_results": "previous_outputs" } }, "variable_mappings": { "text": "initial_draft", "previous_results": "previous_outputs", "iteration": "loop_index" } } } ``` ## Evaluation Types These columns evaluate or compare data and typically return boolean or numeric scores. Uses an LLM to evaluate content against a natural language prompt. Returns a boolean indicating pass/fail. | Field | Type | Required | Description | | --------------- | ------ | ----------- | --------------------------------------------------------------- | | `source` | string | Yes | Column name containing the content to evaluate | | `prompt` | string | Conditional | The assertion prompt. Required if `prompt_source` not set | | `prompt_source` | string | Conditional | Column name containing the prompt. Required if `prompt` not set | **Basic example with static prompt:** ```json theme={null} { "column_type": "LLM_ASSERTION", "name": "Quality Check", "configuration": { "source": "response", "prompt": "Is this response helpful, accurate, and free of harmful content?" }, "is_part_of_score": true } ``` **Dynamic prompts from dataset:** Use `prompt_source` to pull assertion prompts from a dataset column. This lets you define different assertions per row. ```json theme={null} { "column_type": "LLM_ASSERTION", "name": "Custom Assertions", "configuration": { "source": "LLM Output", "prompt_source": "assertions" }, "is_part_of_score": true } ``` Where your dataset has an `assertions` column containing the prompt text for each row. **Multiple assertions per row:** You can run multiple assertions against the same content by providing a JSON array of prompts. Each assertion is evaluated independently, and the results are returned as a dictionary. ```json theme={null} { "column_type": "LLM_ASSERTION", "name": "Compliance Checks", "configuration": { "source": "LLM Output", "prompt_source": "llm_assertions" }, "is_part_of_score": true } ``` Where your dataset's `llm_assertions` column contains a JSON array: ```json theme={null} "[\"Does the response avoid making unauthorized claims?\", \"Is patient data properly redacted?\", \"Does it cite approved sources only?\"]" ``` The output will be a dictionary with each assertion as a key and its boolean result as the value. Compares two values for equality. Supports string comparison and JSON comparison with optional JSONPath. | Field | Type | Required | Description | | --------------------------- | ------ | -------- | -------------------------------------------------------- | | `sources` | array | Yes | Array of exactly 2 column names to compare | | `comparison_type.type` | string | Yes | "STRING" or "JSON" | | `comparison_type.json_path` | string | No | JSONPath to extract before comparing. Only for JSON type | ```json theme={null} { "column_type": "COMPARE", "name": "Accuracy", "configuration": { "sources": ["predicted_value", "ground_truth"], "comparison_type": {"type": "STRING"} }, "is_part_of_score": true } ``` With JSON path: ```json theme={null} { "column_type": "COMPARE", "name": "JSON Field Match", "configuration": { "sources": ["api_response", "expected_response"], "comparison_type": { "type": "JSON", "json_path": "$.result.status" } } } ``` Checks if a value contains a substring (case-insensitive). | Field | Type | Required | Description | | -------------- | ------ | ----------- | --------------------------------------------------------------- | | `source` | string | Yes | Column name to search in | | `value` | string | Conditional | Static substring to find. Required if value\_source not set | | `value_source` | string | Conditional | Column name containing the substring. Required if value not set | ```json theme={null} { "column_type": "CONTAINS", "name": "Has Keyword", "configuration": { "source": "response", "value": "thank you" } } ``` Tests if content matches a regular expression pattern. Returns boolean. | Field | Type | Required | Description | | --------------- | ------ | -------- | -------------------------- | | `source` | string | Yes | Column name to test | | `regex_pattern` | string | Yes | Regular expression pattern | ```json theme={null} { "column_type": "REGEX", "name": "Valid Email Format", "configuration": { "source": "email_field", "regex_pattern": "^[\\w.-]+@[\\w.-]+\\.\\w+$" } } ``` Calculates semantic similarity between two texts using embeddings. Returns a float between 0 and 1. | Field | Type | Required | Description | | --------- | ----- | -------- | ------------------------------------------ | | `sources` | array | Yes | Array of exactly 2 column names to compare | ```json theme={null} { "column_type": "COSINE_SIMILARITY", "name": "Semantic Similarity", "configuration": { "sources": ["generated_response", "reference_response"] }, "is_part_of_score": true } ``` Calculates the absolute difference between two numeric values. | Field | Type | Required | Description | | --------- | ----- | -------- | -------------------------------------------------- | | `sources` | array | Yes | Array of exactly 2 column names containing numbers | ```json theme={null} { "column_type": "ABSOLUTE_NUMERIC_DISTANCE", "name": "Score Difference", "configuration": { "sources": ["predicted_score", "actual_score"] } } ``` Uses an LLM to extract specific information from content based on a natural language query. | Field | Type | Required | Description | | -------- | ------ | -------- | ----------------------------------------------- | | `source` | string | Yes | Column name containing the content | | `query` | string | Yes | Natural language description of what to extract | ```json theme={null} { "column_type": "AI_DATA_EXTRACTION", "name": "Extract Sentiment", "configuration": { "source": "response", "query": "What is the overall sentiment? Return only: positive, negative, or neutral" } } ``` ## Extraction Types These columns extract or parse data from other columns. Extracts data from JSON using JSONPath expressions. | Field | Type | Required | Description | | -------------------- | ------- | -------- | --------------------------------------------------------- | | `source` | string | Yes | Column name containing JSON data | | `json_path` | string | Yes | JSONPath expression (e.g., "$.field", "$.items\[0].name") | | `return_first_match` | boolean | No | Return only first match (default: true) or all matches | ```json theme={null} { "column_type": "JSON_PATH", "name": "Extract Agent", "configuration": { "source": "llm_output", "json_path": "$.selected_agent", "return_first_match": true } } ``` Extracts data from XML using XPath expressions. | Field | Type | Required | Description | | ------------- | ------- | -------- | -------------------------------------------------------------------- | | `source` | string | Yes | Column name containing XML data | | `xml_path` | string | Yes | XPath expression | | `type` | string | No | "find" for first match or "findall" for all matches. Default: "find" | | `return_text` | boolean | No | Return text content only or full XML. Default: true | ```json theme={null} { "column_type": "XML_PATH", "name": "Extract Title", "configuration": { "source": "xml_response", "xml_path": ".//item/title", "type": "find", "return_text": true } } ``` Extracts content matching a regular expression pattern. Returns an array of all matches. | Field | Type | Required | Description | | --------------- | ------ | -------- | --------------------------- | | `source` | string | Yes | Column name to extract from | | `regex_pattern` | string | Yes | Regular expression pattern | ```json theme={null} { "column_type": "REGEX_EXTRACTION", "name": "Extract Numbers", "configuration": { "source": "text_content", "regex_pattern": "\\d+\\.?\\d*" } } ``` Parses and converts a value to a specific type. | Field | Type | Required | Description | | -------- | ------ | -------- | ------------------------------------------------------- | | `source` | string | Yes | Column name to parse | | `type` | string | Yes | Target type: "string", "number", "boolean", or "object" | ```json theme={null} { "column_type": "PARSE_VALUE", "name": "Parse Score", "configuration": { "source": "score_string", "type": "number" } } ``` ## Transformation Types These columns transform, combine, or validate data. Creates a static value that can be referenced by other columns. | Field | Type | Required | Description | | ------------- | ------ | -------- | ------------------ | | `value.type` | string | Yes | "string" or "json" | | `value.value` | any | Yes | The static value | String variable: ```json theme={null} { "column_type": "VARIABLE", "name": "Environment", "configuration": { "value": { "type": "string", "value": "production" } } } ``` JSON variable: ```json theme={null} { "column_type": "VARIABLE", "name": "Config", "configuration": { "value": { "type": "json", "value": {"threshold": 0.8, "max_retries": 3} } } } ``` Validates that data is in a valid format. Returns boolean. | Field | Type | Required | Description | | -------- | ------ | -------- | ------------------------------------------------------------ | | `source` | string | Yes | Column name to validate | | `type` | string | Yes | Expected format: "object" for valid JSON, "number", or "sql" | ```json theme={null} { "column_type": "ASSERT_VALID", "name": "Is Valid JSON", "configuration": { "source": "api_response", "type": "object" } } ``` Returns the first non-null value from multiple sources. | Field | Type | Required | Description | | --------- | ----- | -------- | -------------------------------- | | `sources` | array | Yes | Array of column names, minimum 2 | ```json theme={null} { "column_type": "COALESCE", "name": "Best Response", "configuration": { "sources": ["primary_response", "fallback_response", "default_response"] } } ``` Combines multiple column values into a single dictionary object. | Field | Type | Required | Description | | --------- | ----- | -------- | -------------------------------- | | `sources` | array | Yes | Array of column names to combine | ```json theme={null} { "column_type": "COMBINE_COLUMNS", "name": "Combined Context", "configuration": { "sources": ["question", "context", "metadata"] } } ``` Counts occurrences in text content. | Field | Type | Required | Description | | -------- | ------ | -------- | ------------------------------------------------------------- | | `source` | string | Yes | Column name to count in | | `type` | string | Yes | What to count: "chars", "words", "sentences", or "paragraphs" | ```json theme={null} { "column_type": "COUNT", "name": "Word Count", "configuration": { "source": "response", "type": "words" } } ``` Performs numeric comparisons. Returns boolean. | Field | Type | Required | Description | | ---------- | ------ | ----------- | ----------------------------------------------------------------------------------------------------------------- | | `sources` | array | Yes | Array with first source column, and optionally second source column | | `operator` | string | Yes | Comparison operator: "lt" for less than, "le" for less or equal, "gt" for greater than, "ge" for greater or equal | | `value` | number | Conditional | Static value to compare against. Required if second source not provided | Compare to static value: ```json theme={null} { "column_type": "MATH_OPERATOR", "name": "Above Threshold", "configuration": { "sources": ["score"], "operator": "ge", "value": 0.8 } } ``` Compare two columns: ```json theme={null} { "column_type": "MATH_OPERATOR", "name": "A Greater Than B", "configuration": { "sources": ["score_a", "score_b"], "operator": "gt" } } ``` Finds the minimum or maximum value from an array or JSON structure. | Field | Type | Required | Description | | ----------- | ------ | -------- | -------------------------------------------------- | | `source` | string | Yes | Column name containing the data | | `type` | string | Yes | "min" or "max" | | `json_path` | string | No | JSONPath to extract values from, if source is JSON | ```json theme={null} { "column_type": "MIN_MAX", "name": "Highest Score", "configuration": { "source": "scores_array", "type": "max", "json_path": "$[*].value" } } ```