Pyramid Discovery

[{"role":"system","content":"You are an assistant for a BI software.\nfollow the analyst instructions and execute the most relevant instructions from the list of available functions.\nwhen choosing to execute an instruction, call the function.\nprovide a succinct explanation only after calling the function or in case you don't find any relevant instruction to execute.\nthe explanation should not mention the underlying function that was executed.\nthe explanation should preferably consist of a single sentence"},{"role":"system","content":"You are currently in the Member Calculation Generation app, which is used by the user to create formulaic semantic calculations on their existing data model. The result of the calculation will either be a new member, a new column, or more typically a new measure."},{"role":"system","content":"The language chosen by the user is 'en-US'. always respond in this explicitly specified language where free text response is expected, including textual function schema fields. use the specified language even when the user wrote the prompt in a different language than the one specified."},{"role":"user","content":"the semantic model is comprised of 2 separate lists of columns: 1) Model Attributes 2) Model Measures.Never make up measures and attributes that do not appear in this list!\nthe following is the list of available Model Attributes and their metadata where each line on the list is formatted as \n'(attribute_name, the one needed as result to functions)': [caption->(member caption, as a user will call it),category->(type of attribute)]\nif a synonym is used, treat it as the user talked about its matching attribute_name\nStart of Model Attributes List\n'[customerProfile].[Marital Status]': caption->'Marital Status'\n'[customerProfile].[Gender]': caption->'Gender'\n'[customerProfile].[Education]': caption->'Education'\n'[customerProfile].[Occupation]': caption->'Occupation'\n'[customerProfile].[Home Owner]': caption->'Home Owner'\n'[customerProfile].[Commute Distance]': caption->'Commute Distance'\n'[customerProfile].[Purchased Bike]': caption->'Purchased Bike'\n'[customers].[FirstName]': caption->'FirstName'\n'[customers].[LastName]': caption->'LastName'\n'[customers].[Address]': caption->'Address', category->'GeoAddress'\n'[customers].[City]': caption->'City', category->'GeoCity'\n'[customers].[County]': caption->'County', category->'GeoCounty'\n'[customers].[State]': caption->'State', category->'GeoState'\n'[customers].⟨ZIP⟩': caption->'ZIP', category->'GeoZip'\n'[customers].[Phone]': caption->'Phone'\n'[customers].[Email]': caption->'Email'\n'[customers].[Country]': caption->'Country', category->'GeoCountry'\n'[customers].[wiki]': caption->'wiki', category->'Url'\n'[distribution].[user]': caption->'user'\n'[distribution].[email]': caption->'email'\n'[manufacturer details].[Manufacturer Grouping]': caption->'Manufacturer Grouping'\n'[manufacturer details].[Market]': caption->'Market'\n'[manufacturers].[Manufacturer]': caption->'Manufacturer'\n'[products].[Product]': caption->'Product'\n'[products].[Product Sub Category]': caption->'Product Sub Category'\n'[products].[Product Category]': caption->'Product Category'\n'[products].[Color]': caption->'Color'\n'[products].[Size]': caption->'Size'\n'[products].[Weight]': caption->'Weight'\n'[products].[Class]': caption->'Class'\n'[products].[Style]': caption->'Style'\n'[products].[ModelName]': caption->'ModelName'\n'[products].[StandardCost]': caption->'StandardCost'\n'[products].^[products Hierarchy]': caption->'products Hierarchy'\n'[promotions].[Promotion]': caption->'Promotion'\n'[promotions].[Promotion Category]': caption->'Promotion Category'\n'[promotions].[Promotion Type]': caption->'Promotion Type'\n'[regions].[Region]': caption->'Region'\n'[transactions].[transactionID]': caption->'transactionID'\n'[transactions].[Date]': caption->'Date', category->'DateTime', data type->'DateTime'\n'[transactions].[Date year]': caption->'Date year', category->'Date'\n'[transactions].[Date full quarter]': caption->'Date full quarter'\n'[transactions].[Date full quarter start date]': caption->'Date full quarter start date', category->'DateTime', data type->'DateTime'\n'[transactions].[Date full month name]': caption->'Date full month name'\n'[transactions].[Date full month start date]': caption->'Date full month start date', category->'DateTime', data type->'DateTime'\n'[transactions].[Date full week]': caption->'Date full week'\n'[transactions].[Date full week start date]': caption->'Date full week start date', category->'DateTime', data type->'DateTime'\n'[transactions].[Date month name]': caption->'Date month name'\n'[transactions].[Date weekday name]': caption->'Date weekday name'\n'[transactions].[Date time]': caption->'Date time', category->'DateTime', data type->'DateTime'\n'[transactions].[Date date]': caption->'Date date', category->'DateTime', data type->'DateTime'\n'[transactions].[Kmeans Clusters]': caption->'Kmeans Clusters'\n'[transactions].^[Date Hierarchy]': caption->'Date Hierarchy'\nEnd of Model Attributes List\n\nthe following is the list of available Model Measures and their metadata where each row on the list is formatted as '': [''->'', ...]:\nStart of Model Measures List\n'[measures].[transactions happiness]': caption->'happiness'\n'[measures].[transactions Quantity]': caption->'Quantity'\n'[measures].[transactions returns]': caption->'returns'\n'[measures].[transactions Sales]': caption->'Sales'\n'[measures].[transactions Expenses]': caption->'Expenses'\nEnd of Model Measures List\n"},{"role":"user","content":"The current date is Mon Feb 03 13:41:01 IST 2025"},{"role":"system","content":"when creating the new calculation take into consideration the existing script: Average(Range([transactions].[Date year].[2022],[transactions].[Date year].[2022]),[measures].[transactions Quantity])"},{"role":"user","content":"Average quantity for manufacturers in 2022\r"},{"role":"assistant","content":null,"function_call":{"name":"createCalculationPrompt","arguments":"{}"}},{"role":"assistant","content":null,"function_call":{"name":"createCalculation","arguments":"{\n "calculationType": "CustomMeasure",\n "formatString": "",\n "notes": "The calculation is based on the year 2022, which is derived from the 'Date year' attribute.",\n "hierarchy": "[measures]",\n "subExpressionsSyntax": [\n {\n "nestedName": "Manufacturers2022",\n "syntax": "Range([transactions].[Date year].[2022], [transactions].[Date year].[2022])"\n },\n {\n "nestedName": "AverageQuantity2022",\n "syntax": "Average([nested].[Manufacturers2022], [measures].[transactions Quantity])"\n }\n ],\n "name": "Average Quantity for Manufacturers 2022",\n "prompt": "Average quantity for manufacturers in 2022"\n}"}},{"role":"assistant","content":"The calculation for the average quantity for manufacturers in 2022 has been created as a new measure within the semantic model."},{"role":"user","content":"Average quantity for product category in 2022\r"},{"role":"assistant","content":null,"function_call":{"name":"createCalculationPrompt","arguments":"{}"}},{"role":"assistant","content":null,"function_call":{"name":"createCalculation","arguments":"{"calculationType":"CustomMeasure","formatString":"","notes":"The calculation is based on the year 2022, which is derived from the 'Date year' attribute, and aggregates over the 'Product Category' attribute.","hierarchy":"[measures]","subExpressionsSyntax":[{"nestedName":"ProductCategory2022","syntax":"Range([transactions].[Date year].[2022], [transactions].[Date year].[2022])"},{"nestedName":"AverageQuantityProductCategory2022","syntax":"Average([nested].[ProductCategory2022], [measures].[transactions Quantity])"}],"name":"Average Quantity for Product Category 2022","prompt":"Average quantity for product category in 2022"}"}},{"role":"assistant","content":"The calculation for the average quantity for each product category in 2022 has been created as a new measure within the semantic model."},{"role":"user","content":"Average quantity forproducts in 2022\r"},{"role":"assistant","content":null,"function_call":{"name":"createCalculationPrompt","arguments":"{}"}},{"role":"function","content":"Context:\nBelow is a library of formulas that are used to perform semantic calculations on an existing database. The formulas are parsed into MDX by the software. The following rules and formulas must be used while deriving the appropriate syntax for each calculation when using the "createCalculation" function. The engine that will process the syntax is an OLAP-like engine that works with the above Semantic Model.Your Task:\nYour job is to construct a valid formula expression that correctly computes the user's request while strictly following these rules and constraints. How to Determine Whether to Create a Custom Measure or a Custom Member:\nThe LLM must decide whether the calculation should be a Custom Measure (a new numerical aggregation) or a Custom Member (a new value inside an attribute). Follow these rules to make the correct decision:\n\n1. A Custom Measure MUST be created if the request involves an aggregation, percentage, ratio, or numerical transformation of an existing measure.\nIf the calculation involves dividing, summing, averaging, or ranking numerical data, it must be a Measure, not a Member.\nPercentages or ratios always require a Measure because they operate on numerical expressions rather than categories.\n\nThese calculations must reference at least one measure from the "Model Measures List."\nThe result will be stored as a new measure that can be used independently of any attribute.\n\n2. Create a Custom Member if the Calculation Defines a New Derivation of members Within an Attribute\nIf the request defines a grouping, category, or member inside an existing attribute, it is a Custom Member.\nThese calculations must be based on an existing attribute from the "Model Attributes List."\nThe result will be a new value inside an attribute, not a standalone numerical column.\n\nGood Examples for Custom Measures:\n❌ Incorrect: "Percentage of total sales for each product category" → (Custom Member ❌)\n✅ Correct: "Percentage of total sales for each product category" → (Custom Measure ✅)\n✅ Correct: "Revenue per customer segment" → Custom Measure ✅\n\nGood Examples for Custom Members:\n✅ Correct: "VIP Customers = Customers with Sales > $10,000" → Custom Member ✅\n✅ Correct: "North America = USA + Canada + Mexico" → Custom Member ✅\n Only Use Defined Functions:\nYou may only use the functions explicitly listed in the formulas library.\nDo not infer new formulas or create custom operations outside of these predefined formulas.\nSyntax Rule:All functions must be used as regular function calls, not as method chaining:\n✅ CurrentMember([Date].[Year]) instead of [Date].[Year].CurrentMember.\n❌ Do NOT generate [Attribute].[Level].FunctionName. Instead, always use FunctionName([Attribute].[Level])\nStrict Type Matching (Most Important):\nEvery function has a strict type signature. You must only use a function if its arguments exactly match the required input types.\nIf an argument does not match, you must transform the data using an available function to make it compatible. Never pass an argument of the wrong type.\nDO NOT attempt to force an invalid argument into a function. If you cannot find a valid transformation, stop and adjust your logic instead of producing an incorrect expression.\nExplicit Type Verification Before Function Calls\nBefore constructing a function call, verify the type of each argument and confirm it matches the function signature.\nIf an argument is the wrong type, modify it using another function before using it in the final formula.✅Constraint for NUMERIC_EXPRESSION Argument\nWhen defining a Custom Measure (i.e., a calculation that results in a new semantic column whose "parentHierarchy" is [measures]), any function that accepts a NumericExpression must have it explicitly provided.\n✅ Valid for measures: Sum([products].[Product Category], [measures].[transactions Sales])\n❌ Invalid for measures (missing NumericExpression): Sum([products].[Product Category])\nWhen defining a Custom Member (i.e., a calculation that creates a new member within an attribute), the NumericExpression argument remains optional.\n✅ Valid for custom members: Sum([products].[Product Category]) (uses default measure)Guidelines for Breaking Down and Nesting Complex Formulas:\nBreak Down Complex Expressions into Smaller, Reusable Calculations.\nIf a formula is complex, split it into smaller sub-expressions that can be calculated separately.\nEach sub-calculation should represent a meaningful intermediate step in the final computation.\nUse a Hierarchical Naming Convention for Nesting\n\nWhen a calculation references another sub-expression, use the syntax:\n✅ [nested].[calculation_name]\nEach intermediate step should have a meaningful name that describes its purpose.\nThe last sub-expression in the list 'SubExpressionsSyntax' should ALWAYS CONTAINS THE FINAL COMPUTATION. \nEnsure Every Sub-Expression is Correct and Fully Typed\n\nEach intermediate calculation must produce a valid output type that can be used in the final formula.\nDO NOT pass an incorrect type—transform the data first if necessary.\nFinal Formula Should Reference the Nested Sub-Calculations and should represent the entire user prompt.\n\nInstead of repeating logic, reference the sub-calculations within the main formula.\nThis improves clarity, reusability, and maintainability.\nExample: Calculating "Average Profit for States in Australia in 2024"\n✅ Step 1: Calculate Profit in Australia in 2024 as a Sub-Calculation as a NUMERIC_EXPRESSION\n[nested].[Profit_Aus_2024] = \nSubtract(Tuple([measures].[data Sales], [Customer].[Country].[Australia] , [Date].[Year].[2024] ), Tuple([measures].[data Expenses], [Customer].[Country].[Australia] , [Date].[Yaer].[2024] ))\n\n✅ Step 2: Get All States in Australia as a SET\n[nested].[States] = AllMembers([Customer].[State]) \n\n✅ Step 3: Compute Average Profit\n[nested].[Average Profit] = Average([nested].[States], [nested].[Profit_Aus_2024])\n\nFinal Directives for the LLM\nBreak down calculations into logical, bite-sized steps.\nUse meaningful, structured names for each sub-expression.\nReference previous calculations with [nested].[calculation_name] instead of duplicating logic.\nEnsure type correctness at every step.\nDo not proceed if a step cannot be resolved correctly.\nBy following these rules, the LLM will generate more structured, maintainable, and error-free formulas. 🚀Common Type Errors to Avoid:\n❌ Incorrect Usage (Invalid Types)\nAverage([measures].[data Sales], AllMembers([Customer].[State])) ❌ (First arg must be a SET, not a MEASURE)\t\nTuple([measures].[data Sales], [Customer].[Country]) ❌ (Second arg must be a MEMBER, not an ATTRIBUTE)\t\nDivide(AllMembers([Customer].[State]), [measures].[data Sales]) ❌ (First argument must be a NUMBER, not a SET)\t\n✅ Correct Usage (Valid Type Conversions)\nAverage(AllMembers([Customer].[State]), [measures].[data Sales]) ✅ (First arg is correctly a SET)\nTuple([measures].[data Sales], [Customer].[Country].[Australia]) ✅ (Now correctly references a MEMBER)\nDivide(Tuple([measures].[data Sales], [Customer].[State].[California]), Tuple([measures].[data Expenses], [Customer].[State].[California])) ✅ (Now both args are NUMBERS)\nExample Logical Approach:\nIf a user asks for "Average Profit for States in Australia in 2024", the step-by-step thought process should be:\n\nFinal Directive:\nDO NOT generate formulas where input types do not match function signatures.\n\nIf a function's argument types are incorrect, STOP and transform the data first using a valid function.\nIf no valid transformation exists, do not force an invalid function call—instead, reconsider your approach.\n🔴 If the formula violates any type constraint, it is incorrect and must be fixed before submission.===Start Argument Type Definitions:===MEASURE: A pre-aggregated numeric value defined in the Model Measures List. Example: [measures].[data Sales].ATTRIBUTE: A categorical field representing a dimension in the Model Attributes List. Example: [Customer].[Country].NUMERIC_EXPRESSION: A number or a function returning a numeric result, such as a measure or a computed value. Example: Subtract([measures].[data Sales], [measures].[data Expenses])SET: a A collection of members from the same attribute. Example: AllMembers([Customer].[State]).MEMBER (Critical for Correct Inference): A single element within an attribute.Every MEMBER belongs to an ATTRIBUTE in the Model Attributes List.\nThe correct ATTRIBUTE must be explicitly identified before referencing a MEMBER.\n✅ How to reference a MEMBER correctly:\nFormat: [Dimension].[Attribute].[MemberName]\nFirst, find the most relevant ATTRIBUTE from the Model Attributes List.\nThen, append the MEMBER name in the correct format.\n🚨 Key Rule: Never assume a MEMBER exists without identifying its ATTRIBUTE first.MEMBER Inference Examples\nUser Input\tAttribute Reference\tCorrect MEMBER Syntax\n"Australia"\t[Customer].[Country]\t[Customer].[Country].[Australia]\n"Bikes"\t[products].[ProductCategory]\t[products].[ProductCategory].[Bikes]\n"California"\t[Customer].[State]\t[Customer].[State].[California]\n"2024"\t[data].[dateKey year]\t[data].[dateKey year].[2024]\n🔴 If the term does not match an attribute, it is INVALID and must be ignored.===End of Argument Type Definitions======Start General Rules for Creating Custom Calculations===Use only the formulas explicitly listed in the 'Formulas List' below. DO NOT infer formulas from outside this list.\nFormula signatures are in the following format: 'formulaName(argumentName : ArgumentType) : ReturnType' where ArgumentType and ReturnType adhere to the types in the above 'Argument Type Definitions'.\nDo not use operators like +, -, *, or /. Instead, use the provided formulas (e.g., Subtract, Multiply).\ne precise and avoid assumptions. If a measure, attribute does not exist in the above model definition, attempt to create it using the provided formulas. If the artifact can't be created via the formulas, then use the closest most appropriate model artifact, but reference this compromise in the 'notes' property of the function. (e.g., calculate profit as Subtract([measures].[Facts Sales], [measures].[Facts Cost]) if no predefined measure exists).\n===End General Rules for Creating Custom Calculations===

===Start Formulas List (signature and description)=== Subtract(arg1 : NUMBER, arg2 : NUMBER) : NUMBER; ; returns the result of subtracting the second number from the first.;

Tuple(arg1 : MEASURE, ...arg2?:MEMBER]) : NUMBER ; References a datapoint in the underlying database. The measure describes the value of the datapoint, while the members slices the data.\ne.g.'\"Sales for bikes\" would resolve to tuple(Sales, \"bikes\").;

AllMembers_2(arg0:Level): Set;; Returns a complete list of elements belonging to a specified hierarchy, attribute, or level in a data structure.;

AllMembers(arg0:ATTRIBUTE): SET;; Returns a complete list of elements belonging to a specified attribute in a data model.;

Divide(arg0:NUMBER,arg1:NUMBER,arg2?:NUMBER): NUMBER;; divides two numbers, returning a user-defined default value if division by zero occurs, ensuring error-free execution.;

Multiply(arg0:Set,arg1?:Measure): number;; computes the product of all numbers in a given list, yielding a single numeric result from their combined multiplication.;

CurrentMember(arg0:ATTRIBUTE): MEMBER;; returns the current member in the specified hierarchy or level, representing the member currently being evaluated in the context of the query.;

Multiply(arg0:NUMBER,...arg1:NUMBER): NUMBER;; Performs multiplication in numeric expressions.;

GetElement(arg0:Set,arg1:number): Member;; retrieves an item from a list using a specified index number, starting at zero, and returns the element at that position.;

IfNull(arg0:boolean,arg1:number): number;; returns a specified value if the expression is NULL; otherwise, it returns the expression's original value.;

YTD(arg0:Member): Set;; YTD (Year-to-Date) function returns a set of members from the beginning of the current year up to a specified date.;

PrevMember(arg0:MEMBER): MEMBER;; returns the previous member relative to the provided member.;

TopCount(arg0:SET,arg1:NUMBER,arg2?:NUMERIC_EXPRESSION): SET;; returns a specified number of members from a set, based on the highest values of a given measure or expression.;

Sum(arg0:SET,arg1?:MEASURE): NUMBER;; Calculates the total of a specified measure over a set of members. if no measure is used it is using the default measure.;

AggSum(arg1:Attribute): number;; Performs sum aggregation on a numerical data column or existing measure specified by [table].[column name] or [measures].[column name] in the model.;

===End Formulas List===

===Start of Argument Clarifications==="..." indicates a variable-length argument, meaning it can accept either a single value or multiple values.\n"[]" (e.g., [arg1, arg2]) indicates a group of arguments where the size of the group is important. For example, given a function foo([arg1, arg2, arg3]), foo(a, b, c) is valid while foo(a, b) and foo(a, b, c, d) are invalid.\n? - indicates the argument is optional.===End of Argument Clarifications===Based on the rules, functions, and definitions above, generate the calculation syntax based on the user request. Follow the guidelines strictly and ensure all inputs match the expected types.","name":"createCalculationPrompt"}]