# Functions
abs ``` abs(expr) ``` Returns the absolute value of the input.
add ``` add(expr1, expr2) ``` Adds the two inputs.
and ``` and(expr1, expr2) ``` Logical and operation of two boolean columns.
character_count ``` character_count(text) ``` Returns the number of characters in a text column. * Aliases * `num_chars`
coalesce ``` coalesce(expr) ``` Return the first expression that evaluates to a non-null value.
concat ``` concat(expr) ``` Concatenates multiple text columns into one.
contains ``` contains(text, text) ``` Returns true if the input string contains the substring.
count ``` count(expr) ``` Computes the number of rows in a column.
count_distinct ``` count_distinct(expr) ``` Computes the number of distinct non-null values in a column.
count_if ``` count_if(expr) ``` Computes the number of rows in a column that satisfy a condition.
date_trunc ``` date_trunc(expr1, expr2) ``` Truncates a timestamp to the specified unit.
deterministic_sample ``` deterministic_sample(expr) ``` Returns a deterministic sample value in \[0, 1) based on the input value.
divide ``` divide(expr1, expr2) ``` Divides the two inputs.
embed ``` embed(text) ``` Returns the embedding of a text column. Embedding model: all-mpnet-base-v2.
equal_to ``` equal_to(expr1, expr2) ``` Computes the element-wise equal to comparison of two columns. * Aliases * `eq`
filter ``` filter(expr1, expr2) ``` Filters a column using another column as a mask.
greater_than ``` greater_than(expr1, expr2) ``` Computes the element-wise greater than comparison of two columns. input1 > input2 * Aliases * `gt`
greater_than_or_equal_to ``` greater_than_or_equal_to(expr1, expr2) ``` Computes the element-wise greater than or equal to comparison of two columns. input1 >= input2 * Aliases * `gte`
icontains ``` icontains(text, text) ``` Returns true if the input string contains the substring, ignoring case.
is_valid_json ``` is_valid_json(text) ``` Returns true if the input string is valid json.
less_than ``` less_than(expr1, expr2) ``` Computes the element-wise less than comparison of two columns. input1 < input2 * Aliases * `lt`
less_than_or_equal_to ``` less_than_or_equal_to(expr1, expr2) ``` Computes the element-wise less than or equal to comparison of two columns. input1 <= input2 * Aliases * `lte`
levenshtein ``` levenshtein(output, reference) ``` Returns Damerau-Levenshtein distance between two strings.
list_contains ``` list_contains(list, value) ``` Returns True if the list contains the value.
list_extract ``` list_extract(list_expr, index_expr) ``` Extracts the item at the given index from a list.
list_has_duplicate ``` list_has_duplicate(expr) ``` Returns True if the list has duplicated items.
list_length ``` list_length(expr) ``` Returns the length of lists in a list column.
list_most_common ``` list_most_common(expr) ``` Most common item in list.
list_starts_with ``` list_starts_with(list, prefix) ``` Returns True if the list starts with the value.
list_zip ``` list_zip(expr) ``` Zips multiple lists into a list of structs.
llm_answer_groundedness ``` llm_answer_groundedness(model_name, prompt_version, answer, context) ``` Classifies whether the generated answer is grounded in and supported by the provided context.
llm_answer_groundedness_with_justification ``` llm_answer_groundedness_with_justification(model_name, prompt_version, answer, context) ``` Classifies whether the generated answer is grounded in and supported by the provided context.
llm_answer_refusal ``` llm_answer_refusal(model_name, prompt_version, answer) ``` Classifies whether the model refused to answer the user's question.
llm_answer_refusal_with_justification ``` llm_answer_refusal_with_justification(model_name, prompt_version, answer) ``` Classifies whether the model refused to answer the user's question.
llm_answer_relevancy ``` llm_answer_relevancy(model_name, prompt_version, question, answer) ``` Classifies whether the generated answer is relevant and responsive to the user's question. * Aliases * `rag_answer_relevancy`
llm_answer_relevancy_with_justification ``` llm_answer_relevancy_with_justification(model_name, prompt_version, question, answer) ``` Classifies whether the generated answer is relevant and responsive to the user's question.
llm_classify ``` llm_classify(model_name, prompt, classes) ``` Classifies text into custom categories you define, using your own prompt and labels.
llm_classify_with_justification ``` llm_classify_with_justification(model_name, prompt, classes) ``` Classifies text into custom categories you define, using your own prompt and labels.
llm_context_relevancy ``` llm_context_relevancy(model_name, prompt_version, question, context) ``` Classifies whether the retrieved context is relevant to the user's question.
llm_context_relevancy_with_justification ``` llm_context_relevancy_with_justification(model_name, prompt_version, question, context) ``` Classifies whether the retrieved context is relevant to the user's question.
llm_conversation_summary ``` llm_conversation_summary(model_name, prompt_version, conversation) ``` Generates a concise summary of a full conversation session between an AI assistant and a user.
llm_question_clarity ``` llm_question_clarity(model_name, prompt_version, question) ``` Scores how clear and well-formed a question is, from 1 (ambiguous or incoherent) to 5 (perfectly clear).
llm_question_clarity_with_justification ``` llm_question_clarity_with_justification(model_name, prompt_version, question) ``` Scores how clear and well-formed a question is, from 1 (ambiguous or incoherent) to 5 (perfectly clear).
llm_score ``` llm_score(model_name, prompt) ``` Scores text on a 1–5 scale using your own custom evaluation prompt.
llm_score_with_justification ``` llm_score_with_justification(model_name, prompt) ``` Scores text on a 1–5 scale using your own custom evaluation prompt.
llm_summarization ``` llm_summarization(model_name, prompt_version, input, output) ``` Generates a concise summary of a single conversational exchange (input and output).
llm_text_frustration ``` llm_text_frustration(model_name, prompt_version, text) ``` Scores the level of user frustration expressed in a text, from 1 (not frustrated) to 5 (extremely frustrated).
llm_text_frustration_with_justification ``` llm_text_frustration_with_justification(model_name, prompt_version, text) ``` Scores the level of user frustration expressed in a text, from 1 (not frustrated) to 5 (extremely frustrated).
llm_text_sentiment ``` llm_text_sentiment(model_name, prompt_version, text) ``` Classifies the overall sentiment of a text as positive, negative, or neutral. * Aliases * `text_sentiment`
llm_text_sentiment_with_justification ``` llm_text_sentiment_with_justification(model_name, prompt_version, text) ``` Classifies the overall sentiment of a text as positive, negative, or neutral.
llm_text_similarity ``` llm_text_similarity(model_name, prompt_version, output, reference) ``` Scores how semantically similar an output is to a target reference, from 1 (completely different) to 5 (equivalent). * Aliases * `text_similarity`
llm_text_similarity_with_justification ``` llm_text_similarity_with_justification(model_name, prompt_version, output, reference) ``` Scores how semantically similar an output is to a target reference, from 1 (completely different) to 5 (equivalent).
llm_text_toxicity ``` llm_text_toxicity(model_name, prompt_version, text) ``` Scores how toxic or harmful a piece of text is, from 1 (not toxic) to 5 (highly toxic).
llm_text_toxicity_with_justification ``` llm_text_toxicity_with_justification(model_name, prompt_version, text) ``` Scores how toxic or harmful a piece of text is, from 1 (not toxic) to 5 (highly toxic).
llm_user_frustration ``` llm_user_frustration(model_name, prompt_version, conversation) ``` Scores the overall user frustration across a conversation session, from 1 (satisfied) to 5 (extremely frustrated).
llm_user_frustration_with_justification ``` llm_user_frustration_with_justification(model_name, prompt_version, conversation) ``` Scores the overall user frustration across a conversation session, from 1 (satisfied) to 5 (extremely frustrated).
map_extract ``` map_extract(map_expr, key_expr) ``` Extracts the value for a given key from a map, returning null if the key is not in the map.
max ``` max(expr) ``` Computes the max of a column.
mean ``` mean(expr) ``` Computes the mean of a column.
median ``` median(expr) ``` Computes the median of a column.
min ``` min(expr) ``` Computes the min of a column.
mode ``` mode(expr) ``` Computes the mode of a column.
multiply ``` multiply(expr1, expr2) ``` Multiplies the two inputs.
negate ``` negate(expr) ``` Returns the negation of the input.
not ``` not(expr) ``` Logical not operation of a boolean column.
not_equal_to ``` not_equal_to(expr1, expr2) ``` Computes the element-wise not equal to comparison of two columns. * Aliases * `neq`
or ``` or(expr1, expr2) ``` Logical or operation of two boolean columns.
percentile ``` percentile(expr1, expr2) ``` Computes the nth percentile of a column.
rouge1 ``` rouge1(output, reference) ``` Returns the rouge1 score between two columns.
rouge2 ``` rouge2(output, reference) ``` Returns the rouge2 score between two columns.
rougeL ``` rougeL(output, reference) ``` Returns the rougeL score between two columns.
rougeLsum ``` rougeLsum(output, reference) ``` Returns the rougeLsum score between two columns.
stddev ``` stddev(expr) ``` Computes the sample standard deviation of a column.
struct_extract ``` struct_extract(struct_expr, field_name) ``` Extracts a field from a struct expression.
subtract ``` subtract(expr1, expr2) ``` Subtracts the two inputs.
sum ``` sum(expr) ``` Computes the sum of a column.