{ "$schema": "https://ui.shadcn.com/schema/registry-item.json", "name": "data-table-filter-command-ai", "title": "Data Table AI Filter Command Palette", "description": "AI-powered command palette that translates natural language queries into structured table filters. Provider-agnostic — works with any LLM via Vercel AI SDK.", "dependencies": [ "ai", "@ai-sdk/react", "zod", "date-fns", "lucide-react", "sonner" ], "registryDependencies": [ "https://data-table.openstatus.dev/r/data-table.json", "https://data-table.openstatus.dev/r/data-table-filter-command.json", "https://data-table.openstatus.dev/r/data-table-schema.json", "command", "kbd", "separator" ], "files": [ { "path": "src/components/data-table/data-table-filter-command-ai/index.tsx", "content": "\"use client\";\n\nimport { TextShimmer } from \"@/components/data-table/data-table-filter-command-ai/text-shimmer\";\nimport { useAIFilters } from \"@/components/data-table/data-table-filter-command-ai/use-ai-filters\";\nimport {\n columnFiltersParserFromSchema,\n getFieldOptions,\n getFilterValue,\n getWordByCaretPosition,\n replaceInputByFieldType,\n} from \"@/components/data-table/data-table-filter-command/utils\";\nimport { useDataTable } from \"@/components/data-table/data-table-provider\";\nimport type { DataTableFilterField } from \"@/components/data-table/types\";\nimport {\n Command,\n CommandEmpty,\n CommandGroup,\n CommandItem,\n CommandList,\n CommandSeparator,\n} from \"@/components/ui/command\";\nimport { Kbd } from \"@/components/ui/kbd\";\nimport { Separator } from \"@/components/ui/separator\";\nimport { useHotKey } from \"@/hooks/use-hot-key\";\nimport { useLocalStorage } from \"@/hooks/use-local-storage\";\nimport { isStructuredQuery } from \"@/lib/ai\";\nimport { getCommandHistoryKey } from \"@/lib/constants/local-storage\";\nimport { formatCompactNumber } from \"@/lib/format\";\nimport type { SchemaDefinition } from \"@/lib/store/schema/types\";\nimport type { TableSchemaDefinition } from \"@/lib/table-schema\";\nimport { cn } from \"@/lib/utils\";\nimport { Command as CommandPrimitive } from \"cmdk\";\nimport { formatDistanceToNow } from \"date-fns\";\nimport { LoaderCircle, Search, Sparkles, X } from \"lucide-react\";\nimport React, { useEffect, useMemo, useRef, useState } from \"react\";\nimport { toast } from \"sonner\";\n\ninterface DataTableFilterAICommandProps {\n /** BYOS schema definition for parsing/serializing filter values */\n schema: SchemaDefinition;\n /** Table schema definition for AI context generation */\n tableSchema: TableSchemaDefinition;\n /** API endpoint that streams AI filter results */\n api: string;\n /** Unique ID for this table (used to namespace localStorage) */\n tableId?: string;\n}\n\nexport function DataTableFilterAICommand({\n schema,\n tableSchema,\n api,\n tableId = \"default\",\n}: DataTableFilterAICommandProps) {\n const {\n table,\n isLoading,\n filterFields: _filterFields,\n getFacetedUniqueValues,\n } = useDataTable();\n const columnFilters = table.getState().columnFilters;\n const inputRef = useRef(null);\n const [open, setOpen] = useState(false);\n const [currentWord, setCurrentWord] = useState(\"\");\n const isSerializingRef = useRef(false);\n const filterFields = useMemo(\n () => _filterFields?.filter((i) => !i.commandDisabled),\n [_filterFields],\n );\n const columnParser = useMemo(\n () => columnFiltersParserFromSchema({ schema, filterFields }),\n [schema, filterFields],\n );\n const [inputValue, setInputValue] = useState(\n columnParser.serialize(columnFilters),\n );\n const [lastSearches, setLastSearches] = useLocalStorage<\n {\n search: string;\n timestamp: number;\n }[]\n >(getCommandHistoryKey(tableId), []);\n\n // Detect if the current input looks like natural language (for UI hints).\n // Single-word input that partially matches a field name → structured mode.\n // Multi-word input without key:value syntax → natural language mode.\n const isNaturalLanguage = useMemo(() => {\n const trimmed = inputValue.trim();\n if (!trimmed) return false;\n if (isStructuredQuery(trimmed, tableSchema)) return false;\n\n const words = trimmed.split(/\\s+/);\n\n // Single word: only treat as natural language if it doesn't match any field\n if (words.length === 1) {\n const word = words[0]!.toLowerCase();\n const matchesField = filterFields.some((f) => {\n const key = String(f.value).toLowerCase();\n return key.startsWith(word) || key.includes(word);\n });\n if (matchesField) return false;\n }\n\n // Multiple words without key:value → natural language\n return (\n words.length > 1 ||\n !filterFields.some((f) => {\n const key = String(f.value).toLowerCase();\n return key === words[0]?.toLowerCase();\n })\n );\n }, [inputValue, tableSchema, filterFields]);\n\n // Track the last AI query for display while loading\n const [aiQuery, setAIQuery] = useState(null);\n\n // AI filters hook\n const { infer, isLoading: isAILoading } = useAIFilters({\n api,\n tableSchema,\n onField(key, value) {\n table.getColumn(key)?.setFilterValue(value);\n },\n onFinish(state) {\n // Final reconciliation: set all validated fields\n for (const [key, value] of Object.entries(state)) {\n table.getColumn(key)?.setFilterValue(value);\n }\n // Serialize from the validated state directly to avoid stale table state\n const syntheticFilters = Object.entries(state).map(([id, value]) => ({\n id,\n value,\n }));\n isSerializingRef.current = true;\n setInputValue(columnParser.serialize(syntheticFilters));\n },\n onComplete() {\n setAIQuery(null);\n },\n onStart() {\n // Clear existing filters before AI applies new ones (replace-all strategy)\n for (const field of filterFields) {\n if (typeof field.value === \"string\") {\n table.getColumn(field.value)?.setFilterValue(undefined);\n }\n }\n },\n onError(error) {\n setAIQuery(null);\n toast.error(\"Failed to infer filters\", {\n description: error.message,\n });\n },\n });\n\n useEffect(() => {\n if (isSerializingRef.current) {\n isSerializingRef.current = false;\n return;\n }\n if (currentWord !== \"\" && open) return;\n if (currentWord !== \"\" && !open) setCurrentWord(\"\");\n if (inputValue.trim() === \"\" && !open) return;\n\n const searchParams = columnParser.parse(inputValue);\n\n const currentFilters = table.getState().columnFilters;\n const currentEnabledFilters = currentFilters.filter((filter) => {\n const field = _filterFields?.find((field) => field.value === filter.id);\n return !field?.commandDisabled;\n });\n for (const key of Object.keys(searchParams)) {\n const value = searchParams[key as keyof typeof searchParams];\n table.getColumn(key)?.setFilterValue(value);\n }\n const currentFiltersToReset = currentEnabledFilters.filter((filter) => {\n return !(filter.id in searchParams);\n });\n for (const filter of currentFiltersToReset) {\n table.getColumn(filter.id)?.setFilterValue(undefined);\n }\n\n // eslint-disable-next-line react-hooks/exhaustive-deps\n }, [inputValue, open, currentWord]);\n\n useEffect(() => {\n if (!open) {\n isSerializingRef.current = true;\n setInputValue(columnParser.serialize(columnFilters));\n }\n }, [columnFilters, filterFields, open]);\n\n useHotKey(() => setOpen((open) => !open), \"k\");\n\n useEffect(() => {\n if (open) {\n inputRef?.current?.focus();\n }\n }, [open]);\n\n function handleClose() {\n setOpen(false);\n const search = inputValue.trim();\n if (!search) return;\n const timestamp = Date.now();\n const searchIndex = lastSearches.findIndex(\n (item) => item.search === search,\n );\n if (searchIndex !== -1) {\n setLastSearches(\n lastSearches.map((item, i) =>\n i === searchIndex ? { ...item, timestamp } : item,\n ),\n );\n return;\n }\n setLastSearches([...lastSearches, { search, timestamp }]);\n }\n\n function handleKeyDown(e: React.KeyboardEvent) {\n if (e.key === \"Escape\") {\n inputRef?.current?.blur();\n return;\n }\n if (e.key === \"Enter\" && isNaturalLanguage) {\n // Let cmdk handle Enter if an item is selected (e.g., a suggestion)\n const selected = document.querySelector(\"[cmdk-item][data-selected]\");\n if (selected) return;\n\n e.preventDefault();\n const query = inputValue.trim();\n const handled = infer(inputValue);\n if (handled) {\n setAIQuery(query);\n handleClose();\n }\n }\n }\n\n return (\n
\n setOpen(true)}\n >\n {isLoading || isAILoading ? (\n \n ) : (\n \n )}\n \n {aiQuery ? (\n {aiQuery}\n ) : inputValue.trim() ? (\n {inputValue}\n ) : (\n Search data table...\n )}\n \n \n ⌘\n K\n \n \n div]:border-none\",\n open ? \"visible\" : \"hidden\",\n )}\n filter={(value, search, keywords) =>\n getFilterValue({ value, search, keywords, currentWord })\n }\n >\n \n \n ) => {\n const caretPosition = e.currentTarget?.selectionStart || -1;\n const value = e.currentTarget?.value || \"\";\n const word = getWordByCaretPosition({ value, caretPosition });\n setCurrentWord(word);\n }}\n placeholder=\"Search data table...\"\n className=\"text-foreground placeholder:text-muted-foreground flex h-11 w-full rounded-md bg-transparent py-3 text-sm outline-hidden disabled:cursor-not-allowed disabled:opacity-50\"\n />\n
\n
\n
\n \n {!isNaturalLanguage && (\n <>\n \n {filterFields.map((field) => {\n if (typeof field.value !== \"string\") return null;\n if (inputValue.includes(`${field.value}:`)) return null;\n return (\n {\n e.preventDefault();\n e.stopPropagation();\n }}\n onSelect={(value) => {\n setInputValue((prev) => {\n if (currentWord.trim() === \"\") {\n const input = `${prev}${value}`;\n return `${input}:`;\n }\n const isStarting = currentWord === prev;\n const prefix = isStarting ? \"\" : \" \";\n const input = prev.replace(\n `${prefix}${currentWord}`,\n `${prefix}${value}`,\n );\n return `${input}:`;\n });\n setCurrentWord(`${value}:`);\n }}\n className=\"group\"\n >\n {field.value}\n \n \n );\n })}\n \n \n \n {filterFields?.map((field) => {\n if (typeof field.value !== \"string\") return null;\n if (!currentWord.includes(`${field.value}:`)) return null;\n\n const column = table.getColumn(field.value);\n const facetedValue =\n getFacetedUniqueValues?.(table, field.value) ||\n column?.getFacetedUniqueValues();\n\n const options = getFieldOptions({ field, facetedValue });\n\n return options.map((optionValue) => {\n return (\n {\n e.preventDefault();\n e.stopPropagation();\n }}\n onSelect={(value) => {\n setInputValue((prev) =>\n replaceInputByFieldType({\n prev,\n currentWord,\n optionValue,\n value,\n field,\n }),\n );\n setCurrentWord(\"\");\n }}\n >\n {`${optionValue}`}\n {facetedValue?.has(optionValue) ? (\n \n {formatCompactNumber(\n facetedValue.get(optionValue) || 0,\n )}\n \n ) : null}\n \n );\n });\n })}\n \n No results found.\n \n )}\n {isNaturalLanguage && inputValue.trim() && (\n \n {\n e.preventDefault();\n e.stopPropagation();\n }}\n onSelect={() => {\n const query = inputValue.trim();\n const handled = infer(inputValue);\n if (handled) {\n setAIQuery(query);\n handleClose();\n }\n }}\n >\n \n {inputValue.trim()}\n \n describe your query to infer filters\n \n \n \n )}\n {lastSearches.length > 0 && (\n <>\n \n \n {lastSearches\n .sort((a, b) => b.timestamp - a.timestamp)\n .slice(0, 5)\n .map((item) => {\n return (\n {\n e.preventDefault();\n e.stopPropagation();\n }}\n onSelect={(value) => {\n const search = value.replace(\"suggestion:\", \"\");\n setInputValue(`${search} `);\n setCurrentWord(\"\");\n }}\n className=\"group\"\n >\n {item.search}\n \n {formatDistanceToNow(item.timestamp, {\n addSuffix: true,\n })}\n \n {\n e.preventDefault();\n e.stopPropagation();\n }}\n onClick={(e) => {\n e.preventDefault();\n e.stopPropagation();\n setLastSearches(\n lastSearches.filter(\n (i) => i.search !== item.search,\n ),\n );\n }}\n className=\"hover:bg-background ml-1 hidden rounded-md p-0.5 group-aria-selected:block\"\n >\n \n \n \n );\n })}\n \n \n )}\n \n \n
\n \n Use ↑ ↓ to navigate\n \n \n Enter to query\n \n \n Esc to close\n \n \n \n Union: regions:a,b\n \n \n Range: p95:59-340\n \n \n Spaces: name:"a b"\n \n \n \n AI:{\" \"}\n \n \n \n \n
\n {lastSearches.length ? (\n {\n e.preventDefault();\n e.stopPropagation();\n }}\n onClick={() => setLastSearches([])}\n >\n Clear suggestions\n \n ) : null}\n
\n
\n \n \n \n );\n}\n\nfunction CommandItemSuggestions({\n field,\n}: {\n field: DataTableFilterField;\n}) {\n const { table, getFacetedMinMaxValues, getFacetedUniqueValues } =\n useDataTable();\n const value = field.value as string;\n switch (field.type) {\n case \"checkbox\": {\n return (\n \n );\n }\n case \"slider\": {\n const [min, max] = getFacetedMinMaxValues?.(table, value) || [\n field.min,\n field.max,\n ];\n return (\n \n );\n }\n case \"input\": {\n return (\n \n );\n }\n default: {\n return null;\n }\n }\n}\n", "type": "registry:component" }, { "path": "src/components/data-table/data-table-filter-command-ai/text-shimmer.tsx", "content": "\"use client\";\n\nimport { cn } from \"@/lib/utils\";\n\nexport type TextShimmerProps = {\n as?: React.ElementType;\n duration?: number;\n spread?: number;\n children: React.ReactNode;\n} & React.HTMLAttributes;\n\nexport function TextShimmer({\n as = \"span\",\n className,\n duration = 4,\n spread = 20,\n children,\n ...props\n}: TextShimmerProps) {\n const dynamicSpread = Math.min(Math.max(spread, 5), 45);\n const Component = as;\n\n return (\n \n {children}\n \n );\n}\n", "type": "registry:component" }, { "path": "src/components/data-table/data-table-filter-command-ai/use-ai-filters.ts", "content": "import { experimental_useObject as useObject } from \"@ai-sdk/react\";\nimport {\n diffPartialState,\n generateAIOutputSchema,\n isStructuredQuery,\n parseAIResponse,\n} from \"@/lib/ai\";\nimport type { TableSchemaDefinition } from \"@/lib/table-schema\";\nimport { useCallback, useEffect, useMemo, useRef } from \"react\";\n\nexport type UseAIFiltersOptions = {\n /** The API endpoint that streams AI filter results */\n api: string;\n /** Table schema definition for generating the output schema and detecting structured queries */\n tableSchema: TableSchemaDefinition;\n /** Called for each progressively completed field */\n onField: (key: string, value: unknown) => void;\n /** Called with the final validated state on stream end */\n onFinish: (state: Record) => void;\n /** Called when the AI call fails */\n onError?: (error: Error) => void;\n /** Called before AI filters are applied — use to reset existing filters */\n onStart?: () => void;\n /** Called when the stream ends, regardless of validation success — use for cleanup */\n onComplete?: () => void;\n};\n\nexport function useAIFilters({\n api,\n tableSchema,\n onField,\n onFinish,\n onError,\n onStart,\n onComplete,\n}: UseAIFiltersOptions) {\n const prevRef = useRef>({});\n const outputSchema = useMemo(\n () => generateAIOutputSchema(tableSchema),\n [tableSchema],\n );\n\n const { submit, object, isLoading, error } = useObject({\n api,\n schema: outputSchema,\n onFinish({ object }) {\n prevRef.current = {};\n if (object) {\n const validated = parseAIResponse(\n tableSchema,\n object as Record,\n );\n if (validated) {\n onFinish(validated);\n }\n }\n onComplete?.();\n },\n onError(error) {\n onComplete?.();\n onError?.(error);\n },\n });\n\n // Progressive application via diffPartialState\n useEffect(() => {\n if (!object) return;\n const next = object as Record;\n const completed = diffPartialState(prevRef.current, next, tableSchema);\n for (const { key, value } of completed) {\n onField(key, value);\n }\n prevRef.current = { ...next };\n }, [object]); // eslint-disable-line react-hooks/exhaustive-deps\n\n const infer = useCallback(\n (query: string): boolean => {\n if (isStructuredQuery(query, tableSchema)) return false;\n const trimmed = query.trim();\n if (!trimmed) return false;\n\n onStart?.();\n prevRef.current = {};\n submit({ query: trimmed });\n return true;\n },\n [submit, tableSchema, onStart],\n );\n\n return { infer, isLoading, error };\n}\n", "type": "registry:hook" }, { "path": "src/lib/ai/index.ts", "content": "export {\n generateAIContext,\n type AIContext,\n type AIFieldContext,\n} from \"./context\";\nexport { generateAIPrompt, type GenerateAIPromptOptions } from \"./prompt\";\nexport { generateAIOutputSchema } from \"./output-schema\";\nexport { diffPartialState, type CompletedField } from \"./diff-partial\";\nexport { parseAIResponse } from \"./parse-response\";\nexport { isStructuredQuery } from \"./detect\";\nexport {\n createAIFilterHandler,\n type AIFilterHandlerOptions,\n} from \"./create-ai-filter-handler\";\n", "type": "registry:lib" }, { "path": "src/lib/ai/context.ts", "content": "import type { TableSchemaDefinition } from \"@/lib/table-schema\";\n\nexport type AIFieldContext = {\n key: string;\n label: string;\n description?: string;\n dataType: string;\n filterType: string;\n allowedValues?: string[];\n min?: number;\n max?: number;\n unit?: string;\n};\n\nexport type AIContext = {\n fields: AIFieldContext[];\n};\n\n/**\n * Extracts structured context from a table schema definition for use in LLM prompts.\n *\n * Returns metadata about each filterable column: field name, label, data type,\n * filter type, allowed values, bounds, and description. Non-filterable columns\n * are excluded. Command-disabled columns are intentionally included so the AI\n * can set filters (e.g. date ranges) that aren't available in the command palette.\n */\nexport function generateAIContext(schema: TableSchemaDefinition): AIContext {\n const fields: AIFieldContext[] = [];\n\n for (const [key, builder] of Object.entries(schema)) {\n const config = builder._config;\n if (!config.filter) continue;\n\n const field: AIFieldContext = {\n key,\n label: config.label || key,\n dataType: config.kind,\n filterType: config.filter.type,\n };\n\n if (config.description) {\n field.description = config.description;\n }\n\n // Allowed values for checkbox filters (enums, booleans, arrays)\n if (config.filter.type === \"checkbox\" && config.filter.options) {\n field.allowedValues = config.filter.options.map((o) => String(o.value));\n } else if (config.kind === \"enum\" && config.enumValues) {\n field.allowedValues = [...config.enumValues];\n } else if (\n config.kind === \"array\" &&\n config.arrayItem?.kind === \"enum\" &&\n config.arrayItem.enumValues\n ) {\n field.allowedValues = [...config.arrayItem.enumValues];\n }\n\n // Bounds for slider filters\n if (config.filter.type === \"slider\") {\n if (config.filter.min !== undefined) field.min = config.filter.min;\n if (config.filter.max !== undefined) field.max = config.filter.max;\n }\n\n // Unit from filter or display config\n if (config.filter.unit) {\n field.unit = config.filter.unit;\n } else if (\n config.display.type === \"number\" &&\n \"unit\" in config.display &&\n config.display.unit\n ) {\n field.unit = config.display.unit;\n }\n\n fields.push(field);\n }\n\n return { fields };\n}\n", "type": "registry:lib" }, { "path": "src/lib/ai/prompt.ts", "content": "import type { TableSchemaDefinition } from \"@/lib/table-schema\";\nimport {\n generateAIContext,\n type AIContext,\n type AIFieldContext,\n} from \"./context\";\n\nexport type GenerateAIPromptOptions = {\n /** Current date/time for resolving relative time expressions like \"last hour\". */\n now?: Date;\n};\n\nfunction formatField(field: AIFieldContext): string {\n const parts: string[] = [];\n parts.push(`- **${field.key}** (label: \"${field.label}\")`);\n parts.push(` Type: ${field.dataType}, Filter: ${field.filterType}`);\n\n if (field.description) {\n parts.push(` Description: ${field.description}`);\n }\n if (field.allowedValues && field.allowedValues.length > 0) {\n parts.push(\n ` Allowed values: ${field.allowedValues.map((v) => `\"${v}\"`).join(\", \")}`,\n );\n }\n if (field.min !== undefined || field.max !== undefined) {\n const bounds = [];\n if (field.min !== undefined) bounds.push(`min: ${field.min}`);\n if (field.max !== undefined) bounds.push(`max: ${field.max}`);\n parts.push(` Range: ${bounds.join(\", \")}`);\n }\n if (field.unit) {\n parts.push(` Unit: ${field.unit}`);\n }\n\n return parts.join(\"\\n\");\n}\n\nfunction formatPrompt(\n context: AIContext,\n options?: GenerateAIPromptOptions,\n): string {\n const lines: string[] = [];\n\n lines.push(\n \"You are a filter assistant for a data table. Given a natural language query, \" +\n \"return a JSON object with the appropriate filter values. Only include fields \" +\n \"that the user's query references. If the query doesn't match any field, return \" +\n \"an empty object {}.\",\n );\n\n lines.push(\"\");\n lines.push(\"## Available filter fields\");\n lines.push(\"\");\n\n for (const field of context.fields) {\n lines.push(formatField(field));\n lines.push(\"\");\n }\n\n lines.push(\"## Output rules\");\n lines.push(\"\");\n lines.push(\"- For **input** filters (text search): return a string value.\");\n lines.push(\n \"- For **checkbox** filters (multi-select): return an array of allowed values. Only use values from the allowed values list.\",\n );\n lines.push(\n \"- For **slider** filters (numeric range): return a [min, max] tuple within the field's bounds.\",\n );\n lines.push(\n \"- For **timerange** filters (date range): return a [start, end] tuple of ISO 8601 datetime strings.\",\n );\n lines.push(\n \"- Only include fields relevant to the query. Omit fields the user didn't mention or imply.\",\n );\n lines.push(\"- Return valid JSON only. No explanations.\");\n lines.push(\n '- The user message includes the current date/time. Use it to resolve relative time expressions like \"last hour\", \"yesterday\", \"past 7 days\".',\n );\n\n if (options?.now) {\n lines.push(\"\");\n lines.push(`## Current date/time`);\n lines.push(\"\");\n lines.push(`Now: ${options.now.toISOString()}`);\n lines.push(\n 'Use this to resolve relative time expressions like \"last hour\", \"yesterday\", \"past 7 days\".',\n );\n }\n\n return lines.join(\"\\n\");\n}\n\n/**\n * Generates a ready-to-use system prompt string from a table schema.\n *\n * The prompt describes all filterable columns and the expected output format\n * for an LLM to translate natural language queries into structured filter state.\n *\n * @param schema - Table schema definition (from `createTableSchema().definition`)\n * @param options - Optional config. Pass `{ now: new Date() }` to enable relative time resolution.\n */\nexport function generateAIPrompt(\n schema: TableSchemaDefinition,\n options?: GenerateAIPromptOptions,\n): string {\n const context = generateAIContext(schema);\n return formatPrompt(context, options);\n}\n", "type": "registry:lib" }, { "path": "src/lib/ai/output-schema.ts", "content": "import type { TableSchemaDefinition } from \"@/lib/table-schema\";\nimport { z } from \"zod\";\n\n/**\n * Generates a Zod schema for LLM structured output from a table schema.\n *\n * Each filterable column becomes an optional field in the output schema:\n * - `input` (string) → `z.string().optional()`\n * - `input` (number) → `z.number().optional()`\n * - `checkbox` (enum) → `z.array(z.enum([...values])).optional()`\n * - `checkbox` (boolean) → `z.array(z.enum([\"true\", \"false\"])).optional()`\n * - `checkbox` (number) → `z.array(z.number()).optional()`\n * - `slider` → `z.tuple([z.number(), z.number()]).optional()`\n * - `timerange` → `z.tuple([z.string(), z.string()]).optional()`\n *\n * Command-disabled columns are intentionally included so the AI can infer\n * filters that aren't available in the command palette (e.g. date ranges).\n */\nexport function generateAIOutputSchema(schema: TableSchemaDefinition) {\n const shape: Record = {};\n\n for (const [key, builder] of Object.entries(schema)) {\n const config = builder._config;\n if (!config.filter) continue;\n\n const filterType = config.filter.type;\n const desc = config.description || config.label || key;\n\n switch (filterType) {\n case \"input\": {\n if (config.kind === \"number\") {\n shape[key] = z.number().optional().describe(desc);\n } else {\n shape[key] = z.string().optional().describe(desc);\n }\n break;\n }\n\n case \"checkbox\": {\n const options = config.filter.options;\n if (options && options.length >= 2) {\n const values = options.map((o) => String(o.value));\n shape[key] = z\n .array(z.enum(values as [string, ...string[]]))\n .optional()\n .describe(desc);\n } else if (options && options.length === 1) {\n shape[key] = z\n .array(z.literal(String(options[0]!.value)))\n .optional()\n .describe(desc);\n } else if (config.kind === \"number\") {\n shape[key] = z.array(z.number()).optional().describe(desc);\n } else {\n shape[key] = z.array(z.string()).optional().describe(desc);\n }\n break;\n }\n\n case \"slider\": {\n const parts = [desc];\n if (\n config.filter.min !== undefined ||\n config.filter.max !== undefined\n ) {\n const bounds = [];\n if (config.filter.min !== undefined)\n bounds.push(`min: ${config.filter.min}`);\n if (config.filter.max !== undefined)\n bounds.push(`max: ${config.filter.max}`);\n parts.push(`Range: ${bounds.join(\", \")}`);\n }\n if (config.filter.unit) parts.push(`Unit: ${config.filter.unit}`);\n\n shape[key] = z\n .tuple([z.number(), z.number()])\n .optional()\n .describe(parts.join(\". \"));\n break;\n }\n\n case \"timerange\": {\n shape[key] = z\n .tuple([z.string(), z.string()])\n .optional()\n .describe(desc + \". ISO 8601 datetime strings [start, end].\");\n break;\n }\n }\n }\n\n return z.object(shape as z.core.$ZodLooseShape);\n}\n", "type": "registry:lib" }, { "path": "src/lib/ai/diff-partial.ts", "content": "import type { TableSchemaDefinition } from \"@/lib/table-schema\";\n\nexport type CompletedField = {\n key: string;\n value: unknown;\n};\n\n/**\n * Compares two partial objects from an LLM stream and returns newly completed fields.\n *\n * Uses type-aware completeness rules based on the table schema's filter type:\n * - `input` (string/number): complete when non-null and non-undefined\n * - `checkbox` (array): complete on first value, updates as more arrive\n * - `slider` (tuple[2]): complete only when both values are present\n * - `timerange` (tuple[2]): complete only when both values are present\n *\n * @param prev - Previous partial state from the stream (or `{}` for first chunk)\n * @param next - Current partial state from the stream\n * @param schema - Table schema definition for type-aware completeness checks\n * @returns Array of newly completed or updated fields ready for `adapter.setField()`\n */\nexport function diffPartialState(\n prev: Record,\n next: Record,\n schema: TableSchemaDefinition,\n): CompletedField[] {\n const completed: CompletedField[] = [];\n\n for (const [key, builder] of Object.entries(schema)) {\n const config = builder._config;\n if (!config.filter) continue;\n\n const nextVal = next[key];\n const prevVal = prev[key];\n\n // Skip if the field hasn't appeared yet\n if (nextVal === undefined || nextVal === null) continue;\n\n const filterType = config.filter.type;\n\n switch (filterType) {\n case \"input\": {\n // Complete when non-null. Emit only if changed.\n if (nextVal !== prevVal) {\n completed.push({ key, value: nextVal });\n }\n break;\n }\n\n case \"checkbox\": {\n // Array — complete on first value, update as more arrive\n if (!Array.isArray(nextVal)) break;\n if (nextVal.length === 0) break;\n\n const prevArr = Array.isArray(prevVal) ? prevVal : [];\n if (\n nextVal.length !== prevArr.length ||\n nextVal.some((v, i) => v !== prevArr[i])\n ) {\n completed.push({ key, value: nextVal });\n }\n break;\n }\n\n case \"slider\": {\n // Tuple[2] — wait for both values\n if (!Array.isArray(nextVal)) break;\n if (nextVal.length < 2) break;\n if (typeof nextVal[0] !== \"number\" || typeof nextVal[1] !== \"number\")\n break;\n\n const prevArr = Array.isArray(prevVal) ? prevVal : [];\n if (nextVal[0] !== prevArr[0] || nextVal[1] !== prevArr[1]) {\n completed.push({ key, value: nextVal });\n }\n break;\n }\n\n case \"timerange\": {\n // Tuple[2] — wait for both date strings\n if (!Array.isArray(nextVal)) break;\n if (nextVal.length < 2) break;\n if (typeof nextVal[0] !== \"string\" || typeof nextVal[1] !== \"string\")\n break;\n\n const prevArr = Array.isArray(prevVal) ? prevVal : [];\n if (nextVal[0] !== prevArr[0] || nextVal[1] !== prevArr[1]) {\n completed.push({ key, value: nextVal });\n }\n break;\n }\n }\n }\n\n return completed;\n}\n", "type": "registry:lib" }, { "path": "src/lib/ai/parse-response.ts", "content": "import type { TableSchemaDefinition } from \"@/lib/table-schema\";\n\n/**\n * Validates and coerces a complete LLM response into clean filter state.\n *\n * - Strips fields not in the schema or not filterable\n * - Converts ISO date strings to Date objects for timerange fields\n * - Validates checkbox values against allowed options\n * - Clamps slider values to min/max bounds\n * - Returns `null` if the response is empty (no valid filters)\n */\nexport function parseAIResponse(\n schema: TableSchemaDefinition,\n response: Record,\n): Record | null {\n const result: Record = {};\n let hasFields = false;\n\n for (const [key, builder] of Object.entries(schema)) {\n const config = builder._config;\n if (!config.filter) continue;\n\n const value = response[key];\n if (value === undefined || value === null) continue;\n\n const filterType = config.filter.type;\n\n switch (filterType) {\n case \"input\": {\n if (config.kind === \"number\") {\n if (typeof value === \"number\") {\n result[key] = value;\n hasFields = true;\n }\n } else {\n if (typeof value === \"string\" && value.length > 0) {\n result[key] = value;\n hasFields = true;\n }\n }\n break;\n }\n\n case \"checkbox\": {\n if (!Array.isArray(value)) break;\n if (value.length === 0) break;\n\n const options = config.filter.options;\n if (options && options.length > 0) {\n // Build a map from string representation to original typed value\n const optionMap = new Map(\n options.map((o) => [String(o.value), o.value]),\n );\n // Match AI strings against allowed values, coerce back to original type\n const valid = value\n .map(String)\n .filter((v) => optionMap.has(v))\n .map((v) => optionMap.get(v)!);\n if (valid.length > 0) {\n result[key] = valid;\n hasFields = true;\n }\n } else {\n result[key] = value;\n hasFields = true;\n }\n break;\n }\n\n case \"slider\": {\n if (!Array.isArray(value)) break;\n if (value.length < 2) break;\n\n let [min, max] = value as [number, number];\n if (typeof min !== \"number\" || typeof max !== \"number\") break;\n\n // Clamp to bounds\n if (config.filter.min !== undefined) {\n min = Math.max(min, config.filter.min);\n }\n if (config.filter.max !== undefined) {\n max = Math.min(max, config.filter.max);\n }\n\n if (min > max) break;\n\n result[key] = [min, max];\n hasFields = true;\n break;\n }\n\n case \"timerange\": {\n if (!Array.isArray(value)) break;\n if (value.length < 2) break;\n\n const [startStr, endStr] = value as [string, string];\n if (typeof startStr !== \"string\" || typeof endStr !== \"string\") break;\n\n const start = new Date(startStr);\n const end = new Date(endStr);\n\n if (isNaN(start.getTime()) || isNaN(end.getTime())) break;\n if (start > end) break;\n\n result[key] = [start, end];\n hasFields = true;\n break;\n }\n }\n }\n\n return hasFields ? result : null;\n}\n", "type": "registry:lib" }, { "path": "src/lib/ai/detect.ts", "content": "import type { TableSchemaDefinition } from \"@/lib/table-schema\";\n\n/**\n * Determines if the user's input is a structured `key:value` filter query\n * or natural language that should be sent to an AI endpoint.\n *\n * Returns `true` if the input matches the structured query format (i.e.,\n * contains a known field name followed by `:`). Returns `false` for natural\n * language input.\n *\n * @param input - The raw user input from the command palette\n * @param schema - Table schema definition to derive known field names\n */\nexport function isStructuredQuery(\n input: string,\n schema: TableSchemaDefinition,\n): boolean {\n const trimmed = input.trim();\n if (trimmed.length === 0) return false;\n\n // Collect known filterable field keys\n const fieldKeys = new Set();\n for (const [key, builder] of Object.entries(schema)) {\n const config = builder._config;\n if (config.filter && !config.filter.commandDisabled) {\n fieldKeys.add(key);\n }\n }\n\n // Check if any token in the input starts with `knownField:`\n // This handles both `regions:ams,gru` and `regions:ams latency:100-500`\n const tokens = trimmed.split(/\\s+/);\n for (const token of tokens) {\n const colonIndex = token.indexOf(\":\");\n if (colonIndex > 0) {\n const field = token.substring(0, colonIndex);\n if (fieldKeys.has(field)) {\n return true;\n }\n }\n }\n\n return false;\n}\n", "type": "registry:lib" }, { "path": "src/lib/ai/create-ai-filter-handler.ts", "content": "import type { TableSchemaDefinition } from \"@/lib/table-schema\";\nimport { streamObject } from \"ai\";\nimport type { LanguageModel } from \"ai\";\nimport { generateAIOutputSchema } from \"./output-schema\";\nimport { generateAIPrompt } from \"./prompt\";\n\nexport type AIFilterHandlerOptions = {\n model: LanguageModel;\n schema: TableSchemaDefinition;\n};\n\n/**\n * Creates a POST handler that streams AI-inferred filter state from a natural language query.\n *\n * The consumer provides a model and table schema. The handler generates the system prompt\n * and Zod output schema automatically, then streams the structured object response.\n *\n * The static schema description is cached via Anthropic's prompt caching\n * (`cache_control: { type: \"ephemeral\" }`). Only the dynamic timestamp and\n * user query are sent fresh on each request.\n *\n * @example\n * ```ts\n * import { createAnthropic } from \"@ai-sdk/anthropic\";\n * import { createAIFilterHandler } from \"@/lib/ai/create-ai-filter-handler\";\n * import { tableSchema } from \"../table-schema\";\n *\n * const anthropic = createAnthropic({\n * baseURL: \"https://ai-gateway.vercel.sh/v1\",\n * apiKey: process.env.AI_GATEWAY_API_KEY,\n * });\n *\n * export const POST = createAIFilterHandler({\n * model: anthropic(\"anthropic/claude-opus-4.5\"),\n * schema: tableSchema.definition,\n * });\n * ```\n */\nexport function createAIFilterHandler({\n model,\n schema,\n}: AIFilterHandlerOptions) {\n // Static prompt (cacheable) — generated once, same for every request\n const staticPrompt = generateAIPrompt(schema);\n const outputSchema = generateAIOutputSchema(schema);\n\n return async function POST(req: Request) {\n const body = await req.json();\n const query = typeof body?.query === \"string\" ? body.query.trim() : \"\";\n\n if (!query || query.length > 500) {\n return Response.json(\n { error: \"Invalid query: must be a non-empty string (max 500 chars)\" },\n { status: 400 },\n );\n }\n\n const now = new Date().toISOString();\n\n try {\n const result = streamObject({\n model,\n schema: outputSchema,\n messages: [\n {\n role: \"system\",\n content: staticPrompt,\n providerOptions: {\n anthropic: { cacheControl: { type: \"ephemeral\" } },\n },\n },\n {\n role: \"user\",\n content: `Current date/time: ${now}\\n\\nQuery: ${query}`,\n },\n ],\n });\n\n return result.toTextStreamResponse();\n } catch (error) {\n const message =\n error instanceof Error ? error.message : \"AI filter inference failed\";\n const status =\n error && typeof error === \"object\" && \"statusCode\" in error\n ? (error.statusCode as number)\n : 500;\n\n return Response.json({ error: message }, { status });\n }\n };\n}\n", "type": "registry:lib" } ], "tailwind": { "config": { "theme": { "keyframes": { "shimmer": { "0%": { "backgroundPosition": "200% 50%" }, "100%": { "backgroundPosition": "-200% 50%" } } } } } }, "type": "registry:block" }