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' icon: package canonical: "https://html.spec.whatwg.org/" references: null notes: > New in v0.5.1. HTML is NOT well-formed XML: HTML5 allows unclosed tags (
, ), unquoted attributes, optional closing. Transform strips all HTML tags to extract plain text content. Detection relies on recognizing common HTML tag names to distinguish from XML (which requires well-formedness). Common in CMS exports (Shopify, HubSpot), email templates, and web scraping datasets. container.object.yaml: title: "YAML Document" description: > YAML (YAML Ain't Markup Language) document stored as VARCHAR. Human-readable data serialization format with nested indentation. RECURSIVE INFERENCE: When classified as container.object.yaml, FineType will: 1. Parse YAML text 2. Classify each value (may be nested) 3. Return as struct (complex nested structures may fail) designation: universal locales: [UNIVERSAL] broad_type: VARCHAR frictionless: type: string format_string: null transform: "CAST({col} AS VARCHAR)" transform_ext: null decompose: null validation: type: string pattern: "^[a-zA-Z_][a-zA-Z0-9_]*:\\s*.*" tier: [VARCHAR, object] aliases: [yaml] samples: - "name: John\nage: 30\nactive: true" - "user:\n id: 123\n email: john@example.com" icon: package canonical: "https://yaml.org/spec/1.2.2/" references: null notes: > v1 migration: New for v2. YAML is common in configuration files but less common in analytics data. DuckDB does not have native YAML support; parsing requires external crates and is slower than JSON. container.object.csv: title: "CSV Record" description: > Single CSV record or row stored as a delimited string. Format: value1,value2,value3 (may include quoted values). RECURSIVE INFERENCE: When classified as container.object.csv, FineType requires a schema (column names and types). With schema, parses and types each value. Without schema, returns unparsed CSV string. NOTE: CSV classification is rare for individual records. More common to classify at the column level (entire CSV file as one string). designation: universal locales: [UNIVERSAL] broad_type: VARCHAR frictionless: type: string format_string: null transform: "CAST({col} AS VARCHAR)" transform_ext: null decompose: null validation: type: string pattern: "^[^,]+(,[^,]+)*$" tier: [VARCHAR, object] aliases: [csv_row, delimited_record] samples: - "John,30,true" - '"Smith, John",30,Engineer' icon: package canonical: "https://www.rfc-editor.org/rfc/rfc4180" references: null notes: > v1 migration: New for v2. CSV is typically a multi-row format, but may appear as individual records in some contexts. Parsing requires explicit schema. container.object.s_expression: title: "S-Expression" description: > Balanced nested parenthetical serialization — the S-expression family: Lisp/Scheme source, linguistic constituency/dependency parse trees (Penn Treebank, OntoNotes: "(ROOT (S (NP (NN cat)) (VP (VBZ sits))))"), and code ASTs ("(program (call (id print) (string hi)))"). A nested structure of the form (head child child ...), distinguished from a comma/whitespace list by its balanced, recursively-nested parentheses. This is NOT container.array.comma_separated: parse trees carry Penn comma-tokens "(, ,)" that fool a delimiter detector into reading a CSV array, but the value is a single nested tree, not a delimited list. designation: universal locales: [UNIVERSAL] broad_type: VARCHAR frictionless: type: string format_string: null transform: "CAST({col} AS VARCHAR)" transform_ext: null decompose: null validation: type: string pattern: "^\\s*\$.*\$\\s*$" minLength: 5 tier: [VARCHAR, object] generate: on_request aliases: [sexp, s_expr, parse_tree, lisp] samples: - "(ROOT (S (NP (NN cat)) (VP (VBZ sits))))" - "(program (call (id print) (string hello)))" - "(+ (* 2 3) (- 4 1))" - "(a (b c) (d (e f)))" - "(S (INTJ (UH Uh)) (, ,) (NP-SBJ (PRP I)) (VP (VBP think)))" icon: package references: null notes: > New 2026-07-05 (company-reference audit; author-approved as a general S-expression type over a narrow parse_tree leaf). Mined from 1,292 corpus columns — `trees`/`parse_tree`/`ast` — every one mislabelled container.array.comma_separated by the delimiter detector. The 244-dim model cannot predict this leaf; it is recovered at profile time by the deterministic `s_expression_recovery` Sharpen guard when >=90% of a column's values pass the balanced-nested-paren structural check (finetype_core::structure::is_s_expression). Value-based (0048), no header gate — the structural signature is self-precise (corpus over-recovery: zero). NO retrain (0096; value-determinable → Sharpen). Generated on request only: the model does not predict this leaf, a rule recovers it, so its synthetic values exist for the training set's label coverage and not for the default output. # ═════════════════════════════════════════════════════════════════════ # CATEGORY: array # # Simple and complex array/list formats. # ═════════════════════════════════════════════════════════════════════ container.array.comma_separated: title: "Comma-Separated Values (Simple)" description: > Simple list of comma-separated values (not JSON array, plain CSV). Example: "apple,banana,cherry" RECURSIVE INFERENCE: Samples elements and infers common type, returns LIST. If all elements parse as integers, returns LIST. If mixed types, returns LIST. designation: universal locales: [UNIVERSAL] broad_type: VARCHAR frictionless: type: list format_string: null transform: "CAST({col} AS VARCHAR)" transform_ext: null decompose: "STRING_SPLIT({col}, ',')" validation: type: string pattern: "^[^,]+(,[^,]+)*$" tier: [VARCHAR, array] aliases: [csv_simple, comma_list] samples: - "apple,banana,cherry" - "1,2,3,4,5" - "red,green,blue" icon: list references: null notes: > v1 migration: New for v2. Simple comma-separated list (not JSON). Decompose splits on comma. Recursive inference samples to determine element type. container.array.pipe_separated: title: "Pipe-Separated Values" description: > List separated by pipe character: value1|value2|value3. Similar to CSV but uses pipe as delimiter. RECURSIVE INFERENCE: Samples elements and infers common type, returns LIST. designation: universal locales: [UNIVERSAL] broad_type: VARCHAR frictionless: type: string format_string: null transform: "CAST({col} AS VARCHAR)" transform_ext: null decompose: "STRING_SPLIT({col}, '|')" validation: type: string pattern: "^[^|]+(\\|[^|]+)*$" tier: [VARCHAR, array] aliases: [pipe_list] samples: - "apple|banana|cherry" - "1|2|3|4|5" icon: list references: null notes: > v1 migration: New for v2. Pipe-separated format common in legacy databases and log files. Decompose splits on pipe. container.array.semicolon_separated: title: "Semicolon-Separated Values" description: > List separated by semicolon: value1;value2;value3. Common in European CSV exports (where comma is decimal separator). RECURSIVE INFERENCE: Samples elements and infers common type, returns LIST. designation: universal locales: [UNIVERSAL] broad_type: VARCHAR frictionless: type: string format_string: null transform: "CAST({col} AS VARCHAR)" transform_ext: null decompose: "STRING_SPLIT({col}, ';')" validation: type: string pattern: "^[^;]+(;[^;]+)*$" tier: [VARCHAR, array] aliases: [semicolon_list] samples: - "apple;banana;cherry" - "1,5;2,3;4,7" icon: list references: null notes: > v1 migration: New for v2. Semicolon separator common in Europe (RFC 4180 variant with comma as decimal). Decompose splits on semicolon. container.array.whitespace_separated: title: "Whitespace-Separated Values" description: > List separated by spaces or tabs: value1 value2 value3. Example: "one two three" or "1 2 3 4 5". RECURSIVE INFERENCE: Samples elements and infers common type, returns LIST. designation: universal locales: [UNIVERSAL] broad_type: VARCHAR frictionless: type: string format_string: null transform: "CAST({col} AS VARCHAR)" transform_ext: null decompose: "STRING_SPLIT(TRIM({col}), '\\s+')" validation: type: string pattern: "^[^\\s]+(\\s+[^\\s]+)*$" tier: [VARCHAR, array] aliases: [space_separated, space_list] samples: - "apple banana cherry" - "1 2 3 4 5" - "red\tgreen\tblue" icon: list references: null notes: > v1 migration: New for v2. Whitespace separator common in plain-text logs and Unix tools. Regex split on whitespace. # ═════════════════════════════════════════════════════════════════════ # CATEGORY: key_value # # Key-value pair formats (simple string representation). # ═════════════════════════════════════════════════════════════════════ container.key_value.query_string: title: "Query String Parameters" description: > URL query string format: key1=value1&key2=value2. Commonly found in URLs and HTTP POST bodies. designation: universal locales: [UNIVERSAL] broad_type: VARCHAR frictionless: type: string format_string: null transform: "CAST({col} AS VARCHAR)" transform_ext: null decompose: null validation: type: string pattern: "^[^=&]+=([^&]*)(&[^=&]+=[^&]*)*$" tier: [VARCHAR, key_value] aliases: null samples: - "name=John&age=30&city=NewYork" - "q=python&page=1" - "user_id=123&token=abc123xyz" icon: key references: null notes: > v1 migration: New for v2. URL query string format. Values are typically URL-encoded. Recursive inference parses key-value pairs and types each value independently. # ═════════════════════════════════════════════════════════════════════ # Recursive inference notes: # ───────────────────────────────────────────────────────────────────── # # Recursive Inference Requirements: # - All container types MUST implement field/element-level classification # - Heterogeneous arrays default to LIST or LIST # - Nested objects/arrays should be classified recursively # - Deep nesting (>3 levels) may be truncated or marked as broad_object # ═════════════════════════════════════════════════════════════════════