Migrate to gitea

.gitignore

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+*.db
+*.jsonl
+*.zsdict
+outputs/
+intermediate/
+raw_data/
+
+# Python cache and temporary files
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+*.egg-info/
+dist/
+build/
+.pytest_cache/
+.coverage
+.tox/
+.mypy_cache/
+
+# Virtual environments
+venv/
+env/
+ENV/
+.venv/

pos_reference_guide.md

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+# POS (Part of Speech) Reference Guide
+
+This document provides comprehensive descriptions for all Part of Speech (POS) tags found in the Wiktionary dataset.
+
+## Common POS Tags
+
+### abbrev
+**Full Name**: Abbreviation  
+**Description**: A shortened form of a word or phrase, such as "Dr." for "Doctor" or "etc." for "et cetera". Abbreviations are used to represent longer terms in a condensed form.
+
+### adj
+**Full Name**: Adjective  
+**Description**: A word that describes or modifies a noun or pronoun. Adjectives provide additional information about qualities, states, or characteristics, such as "beautiful", "large", "red", or "happy".
+
+### adj_noun
+**Full Name**: Adjective-Noun Compound  
+**Description**: A compound word that functions as both an adjective and a noun, or a word that can serve either role depending on context.
+
+### adj_phrase
+**Full Name**: Adjectival Phrase  
+**Description**: A group of words that functions as an adjective, modifying a noun or noun phrase. Examples include "very tall", "extremely happy", or "made of wood".
+
+### adnominal
+**Full Name**: Adnominal  
+**Description**: A word or phrase that modifies a noun, typically preceding it. Similar to an adjective but can include other elements that function noun-modifying roles.
+
+### adv
+**Full Name**: Adverb  
+**Description**: A word that modifies a verb, an adjective, another adverb, a clause, or a whole sentence. Adverbs often indicate manner, place, time, degree, or frequency, such as "quickly", "very", "here", or "often".
+
+### adv_phrase
+**Full Name**: Adverbial Phrase  
+**Description**: A group of words that functions as an adverb, modifying verbs, adjectives, or other adverbs. Examples include "very quickly", "in the morning", or "with great care".
+
+### affix
+**Full Name**: Affix  
+**Description**: A morpheme that is attached to a word stem to form a new word or word form. This includes prefixes, suffixes, infixes, and circumfixes.
+
+### ambiposition
+**Full Name**: Ambiposition  
+**Description**: A word that can function as both a preposition and a postposition depending on its position relative to the noun phrase it modifies.
+
+### article
+**Full Name**: Article  
+**Description**: A determiner that precedes a noun and indicates whether the noun is specific or general. In English, this includes "a", "an", and "the".
+
+### character
+**Full Name**: Character  
+**Description**: A single letter, number, or symbol used in writing. In linguistic contexts, this often refers to individual graphemes, logograms, or writing system characters, particularly in non-alphabetic scripts.
+
+### circumfix
+**Full Name**: Circumfix  
+**Description**: An affix that has two parts, one placed at the beginning of a word and the other at the end. Common in languages like German (e.g., "ge-...-t" for past participles).
+
+### circumpos
+**Full Name**: Circumposition  
+**Description**: A word or set of words that surrounds a noun phrase, functioning similarly to a preposition or postposition but with elements on both sides.
+
+### classifier
+**Full Name**: Classifier  
+**Description**: A word or morpheme used in some languages to categorize the noun it accompanies, often based on semantic properties like shape, animacy, or function. Common in East Asian languages.
+
+### clause
+**Full Name**: Clause  
+**Description**: A grammatical unit that contains a subject and a predicate. Can be independent (main clause) or dependent (subordinate clause).
+
+### combining_form
+**Full Name**: Combining Form  
+**Description**: A linguistic element that appears only in combination with other elements to form words, often derived from Greek or Latin roots (e.g., "bio-", "photo-", "-graphy").
+
+### component
+**Full Name**: Component  
+**Description**: A linguistic element that forms part of a larger word or construction, typically without independent meaning.
+
+### conj
+**Full Name**: Conjunction  
+**Description**: A word that connects words, phrases, clauses, or sentences. Coordinating conjunctions (and, but, or) join equal elements, while subordinating conjunctions (because, although, if) create dependent relationships.
+
+### contraction
+**Full Name**: Contraction  
+**Description**: A shortened form of a word or group of words, often with an apostrophe replacing omitted letters. Examples include "don't" (do not), "can't" (cannot), or "I'm" (I am).
+
+### converb
+**Full Name**: Converb  
+**Description**: A non-finite verb form that functions as an adverbial, expressing temporal, causal, conditional, or other relationships between clauses. Found in many Turkic and other languages.
+
+### counter
+**Full Name**: Counter  
+**Description**: A word used in some languages to count specific types of nouns, similar to classifiers but often with numerical functions. Common in Japanese and other East Asian languages.
+
+### det
+**Full Name**: Determiner  
+**Description**: A word or affix that precedes a noun or noun phrase and expresses its reference or quantity. Includes articles, demonstratives, possessives, and quantifiers.
+
+### gerund
+**Full Name**: Gerund  
+**Description**: A verb form that ends in "-ing" (in English) and functions as a noun. Examples include "swimming is fun" or "I enjoy reading".
+
+### hard-redirect
+**Full Name**: Hard Redirect  
+**Description**: A Wiktionary entry that automatically redirects to another entry, typically for spelling variations or alternative forms.
+
+### infix
+**Full Name**: Infix  
+**Description**: An affix inserted into the middle of a word, rather than at the beginning or end. Common in Austronesian and other language families.
+
+### interfix
+**Full Name**: Interfix  
+**Description**: A connecting element, often without independent meaning, used to join two morphemes or words in compounds. Examples include "-s-" in "statesman" or "-o-" in "speedometer".
+
+### interj
+**Full Name**: Interjection  
+**Description**: A word or phrase that expresses emotion, exclamation, or sudden feeling. Examples include "Oh!", "Wow!", "Ouch!", or "Alas!".
+
+### intj
+**Full Name**: Interjection (Alternative spelling)  
+**Description**: Same as interj - a word or phrase expressing emotion or exclamation.
+
+### name
+**Full Name**: Name/Proper Noun  
+**Description**: A proper noun that refers to a specific person, place, organization, or other unique entity. Examples include "John", "London", "Microsoft", or "Mount Everest".
+
+### noun
+**Full Name**: Noun  
+**Description**: A word that represents a person, place, thing, idea, or concept. Nouns function as subjects, objects, or complements in sentences.
+
+### num
+**Full Name**: Numeral/Number  
+**Description**: A word or symbol that represents a numerical quantity or position. Includes cardinal numbers (one, two, three) and ordinal numbers (first, second, third).
+
+### onomatopoeia
+**Full Name**: Onomatopoeia  
+**Description**: A word that phonetically imitates the sound it describes. Examples include "buzz", "meow", "bang", "splash", or "tick-tock".
+
+### onomatopeia
+**Full Name**: Onomatopoeia (Alternative spelling)  
+**Description**: Same as onomatopoeia - a word that imitates the sound it represents.
+
+### participle
+**Full Name**: Participle  
+**Description**: A non-finite verb form that can function as an adjective or be used in compound tenses. In English, includes present participles (-ing) and past participles (-ed, -en).
+
+### particle
+**Full Name**: Particle  
+**Description**: A word that does not fit into the major word classes but has grammatical function. Includes discourse markers, focus particles, and other function words.
+
+### phrase
+**Full Name**: Phrase  
+**Description**: A group of words that functions as a single unit in a sentence but does not contain both a subject and a finite verb. Can be noun phrases, verb phrases, prepositional phrases, etc.
+
+### postp
+**Full Name**: Postposition  
+**Description**: A function word that follows its object, similar to a preposition but placed after the noun phrase. Common in languages like Japanese, Korean, and Finnish.
+
+### prefix
+**Full Name**: Prefix  
+**Description**: An affix added to the beginning of a word to modify its meaning or create a new word. Examples include "un-", "re-", "pre-", "mis-".
+
+### prep
+**Full Name**: Preposition  
+**Description**: A function word that typically precedes a noun phrase and shows the relationship between its object and another element in the sentence. Examples include "in", "on", "at", "by", "for".
+
+### prep_phrase
+**Full Name**: Prepositional Phrase  
+**Description**: A phrase that begins with a preposition and ends with a noun or pronoun (the object of the preposition). Functions as an adjective or adverb in sentences.
+
+### preverb
+**Full Name**: Preverb  
+**Description**: A prefix or separate word that modifies the meaning of a verb, often indicating direction, aspect, or other semantic features. Common in Native American and other languages.
+
+### pron
+**Full Name**: Pronoun  
+**Description**: A word that replaces a noun or noun phrase. Includes personal pronouns (I, you, he), demonstrative pronouns (this, that), and relative pronouns (who, which).
+
+### proverb
+**Full Name**: Proverb  
+**Description**: A short, traditional saying that expresses a perceived truth, piece of advice, or common observation. Examples include "A stitch in time saves nine" or "Actions speak louder than words".
+
+### punct
+**Full Name**: Punctuation  
+**Description**: Symbols used in writing to separate sentences, clauses, and elements within sentences. Includes periods, commas, semicolons, question marks, etc.
+
+### quantifier
+**Full Name**: Quantifier  
+**Description**: A word or phrase that indicates quantity or amount. Examples include "some", "many", "few", "all", "several", "much".
+
+### romanization
+**Full Name**: Romanization  
+**Description**: The representation of text from a non-Latin writing system in Latin script. Used for transliteration of languages like Chinese, Japanese, Arabic, etc.
+
+### root
+**Full Name**: Root  
+**Description**: The core morpheme of a word that carries the primary meaning, to which affixes can be attached.
+
+### soft-redirect
+**Full Name**: Soft Redirect  
+**Description**: A Wiktionary entry that provides a link to another entry but may include additional information or context before the redirect.
+
+### stem
+**Full Name**: Stem  
+**Description**: The part of a word to which inflectional affixes are attached. The stem may include the root plus derivational affixes.
+
+### suffix
+**Full Name**: Suffix  
+**Description**: An affix added to the end of a word to modify its meaning or create a new word. Examples include "-ing", "-ed", "-ly", "-tion".
+
+### syllable
+**Full Name**: Syllable  
+**Description**: A unit of pronunciation having one vowel sound, with or without surrounding consonants, forming the whole or a part of a word.
+
+### symbol
+**Full Name**: Symbol  
+**Description**: A character or mark that represents something else, such as mathematical symbols (+, -, ×), currency symbols ($, €, £), or other special characters.
+
+### typographic variant
+**Full Name**: Typographic Variant  
+**Description**: An alternative form of a word or character that differs in typography but represents the same linguistic item, such as "œ" vs "oe" or different ligatures.
+
+### unknown
+**Full Name**: Unknown  
+**Description**: A part of speech that could not be determined or classified during the extraction process.
+
+### verb
+**Full Name**: Verb  
+**Description**: A word that expresses an action, state, or occurrence. Verbs function as the main element of predicates and can be conjugated for tense, mood, aspect, and voice.
+
+## Summary
+
+This dataset contains 57 different POS tags, ranging from common categories like noun, verb, and adjective to specialized linguistic terms like circumfix, converb, and classifier. The diversity reflects the comprehensive nature of Wiktionary data across multiple languages and writing systems.

scripts/01_filter_dictionary.py

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+#!/usr/bin/env python3
+"""
+Transforms dictionary data from kaakki.org JSONL format to the universal
+dictionary schema defined in 'universal_dictionary_schema.json'.
+Uses ALL system cores for parallel processing.
+"""
+
+import json
+import pathlib
+import logging
+import sys
+import argparse
+import csv
+import multiprocessing
+import traceback
+from datetime import datetime
+from typing import List, Dict, Any, Set, Optional, Tuple
+
+# ==============================================================================
+# --- DEFAULT CONFIGURATION (Overridable via CLI args) ---
+# ==============================================================================
+
+try:
+    SCRIPT_DIR = pathlib.Path(__file__).parent
+    ROOT_DIR = SCRIPT_DIR.parent
+except NameError:
+    SCRIPT_DIR = pathlib.Path.cwd()
+    ROOT_DIR = SCRIPT_DIR.parent
+
+sys.path.insert(0, str(ROOT_DIR))
+
+# --- IMPORTS ---
+try:
+    from transform_wiktionary import WiktionaryTransformer
+    from InflectionProcessor import InflectionProcessor
+    # Import language configurations
+    try:
+        from lang_config import GERMAN_VERB_CONFIG
+    except ImportError:
+        GERMAN_VERB_CONFIG = {}
+    try:
+        from lang_config import FRENCH_VERB_CONFIG
+    except ImportError:
+        FRENCH_VERB_CONFIG = {}
+except ImportError:
+    pass
+
+DEFAULT_LANG_FILTER = "fr"
+
+DEFAULT_INPUT_DIR = ROOT_DIR / "raw_data"
+DEFAULT_INPUT_FILENAME = f"{DEFAULT_LANG_FILTER}-raw-wiktextract-data.jsonl"
+DEFAULT_INTERMEDIATE_DIR = ROOT_DIR / "intermediate"
+
+DEFAULT_POS_WHITELIST = set()
+DEFAULT_POS_BLACKLIST = {"unknown"}
+DEFAULT_IGNORE_FORM_OF = True
+DEFAULT_TRANS_LANGS = {"pt", "es", "en", "de", "it", "fr", "nl"}
+
+# ==============================================================================
+# --- LOGGING ---
+# ==============================================================================
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+# ==============================================================================
+# --- WORKER FUNCTION ---
+# ==============================================================================
+
+def process_chunk_filtering(
+    chunk_lines: List[str], 
+    lang_filter: Optional[str],
+    pos_whitelist: Set[str], 
+    pos_blacklist: Set[str], 
+    ignore_form_of: bool,
+    translation_languages: Set[str],
+    inflection_configs: Dict
+) -> Tuple[List[str], Dict[str, int], List[str]]:
+    
+    # Re-instantiate processors inside the worker process
+    transformer = WiktionaryTransformer()
+    inflection_processor = InflectionProcessor(inflection_configs)
+    
+    form_of_tags = {"form-of", "affix", "particle", "suffix", "prefix"}
+    
+    results = []
+    errors = []
+    counters = {"processed": 0, "skipped": 0, "errors": 0}
+
+    for line in chunk_lines:
+        if not line.strip():
+            continue
+            
+        try:
+            data = json.loads(line)
+            
+            # --- Apply Filters ---
+            if lang_filter and data.get("lang_code") != lang_filter:
+                counters["skipped"] += 1; continue
+            
+            pos = data.get("pos")
+            if pos_whitelist and pos not in pos_whitelist:
+                counters["skipped"] += 1; continue
+            if pos_blacklist and pos in pos_blacklist:
+                counters["skipped"] += 1; continue
+            
+            if ignore_form_of:
+                if set(data.get("tags", [])).intersection(form_of_tags):
+                    counters["skipped"] += 1; continue
+
+            # --- Filter Translations ---
+            if 'translations' in data:
+                data['translations'] = [
+                    tr for tr in data['translations'] 
+                    if tr.get('lang_code') in translation_languages
+                ]
+
+            # --- 1. Transform Data to Universal Schema ---
+            new_entry = transformer.transform_entry(data)
+
+            # --- CLEANUP PHONETICS (Audio & Duplicates) ---
+            if 'phonetics' in new_entry:
+                # Remove Audio
+                if 'audio' in new_entry['phonetics']:
+                    del new_entry['phonetics']['audio']
+                
+                # Process IPA variations to remove duplicates while preserving country information
+                if 'ipa_variations' in new_entry['phonetics'] and isinstance(new_entry['phonetics']['ipa_variations'], list):
+                    # Group variations by cleaned IPA to collect all regions for each pronunciation
+                    ipa_groups = {}
+                    for variation in new_entry['phonetics']['ipa_variations']:
+                        ipa_cleaned = variation.get('ipa_cleaned', '')
+                        if ipa_cleaned:
+                            if ipa_cleaned not in ipa_groups:
+                                ipa_groups[ipa_cleaned] = {
+                                    "ipa": ipa_cleaned,
+                                    "raw_tags": []
+                                }
+                            # Collect all raw_tags for this IPA
+                            if 'raw_tags' in variation:
+                                ipa_groups[ipa_cleaned]['raw_tags'].extend(variation['raw_tags'])
+                    
+                    # Create compressed variations list
+                    compressed_variations = []
+                    for ipa_cleaned, group_data in ipa_groups.items():
+                        variation = {"ipa": ipa_cleaned}
+                        if group_data['raw_tags']:
+                            # Remove duplicates from raw_tags while preserving order
+                            seen_tags = set()
+                            unique_tags = []
+                            for tag in group_data['raw_tags']:
+                                if tag not in seen_tags:
+                                    unique_tags.append(tag)
+                                    seen_tags.add(tag)
+                            variation['raw_tags'] = unique_tags
+                        compressed_variations.append(variation)
+                    
+                    # Create simplified IPA list and compressed variations
+                    simplified_ipa = list(ipa_groups.keys())
+                    new_entry['phonetics']['ipa'] = simplified_ipa
+                    new_entry['phonetics']['ipa_variations'] = compressed_variations
+
+            # --- Filter out unnecessary fields ---
+            if 'metadata' in new_entry:
+                del new_entry['metadata']
+            if 'translations' in new_entry:
+                for tr in new_entry['translations']:
+                    tr.pop('lang', None)
+                    tr.pop('sense', None)
+            
+            if 'senses' in new_entry:
+                for sense in new_entry['senses']:
+                    if 'examples' in sense:
+                        sense['examples'] = [ex['text'] for ex in sense['examples'] if 'text' in ex]
+
+            if 'relations' in new_entry and 'derived' in new_entry['relations']:
+                del new_entry['relations']['derived']
+
+            # --- 2. Run Inflection Processor ---
+            new_entry = inflection_processor.process(new_entry)
+
+            # --- Remove lang_code after processing ---
+            if 'lang_code' in new_entry:
+                del new_entry['lang_code']
+
+            results.append(json.dumps(new_entry, ensure_ascii=False))
+            counters["processed"] += 1
+        
+        except ValueError as e:
+            counters["skipped"] += 1
+            errors.append(f"Value Error: {str(e)}")
+        except json.JSONDecodeError:
+            counters["errors"] += 1
+        except Exception as e:
+            counters["errors"] += 1
+            errors.append(f"Unexpected Error: {str(e)}")
+
+    return results, counters, errors
+
+# ==============================================================================
+# --- MAIN PROCESS ---
+# ==============================================================================
+
+def process_file(input_path: pathlib.Path, output_path: pathlib.Path, lang_filter: Optional[str],
+                 pos_whitelist: Set[str], pos_blacklist: Set[str], ignore_form_of: bool,
+                 translation_languages: Set[str]):
+    
+    logger.info(f"Starting parallel processing...")
+    logger.info(f"  Input file: {input_path}")
+    logger.info(f"  Output file: {output_path}")
+
+    if not input_path.exists():
+         logger.critical(f"Input file not found: {input_path}")
+         sys.exit(1)
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+
+    # Prepare Inflection Configs
+    inflection_configs = {
+        'de_verb': GERMAN_VERB_CONFIG,
+        'fr_verb': FRENCH_VERB_CONFIG
+    }
+    
+    if lang_filter and f"{lang_filter}_verb" not in inflection_configs:
+        logger.warning(f"No inflection configuration found for language '{lang_filter}'. Verbs will remain uncompressed.")
+
+    logger.info("Reading input file into memory...")
+    try:
+        with open(input_path, 'r', encoding='utf-8') as f:
+            lines = f.readlines()
+    except Exception as e:
+        logger.critical(f"Failed to read input file: {e}")
+        sys.exit(1)
+
+    total_lines = len(lines)
+    logger.info(f"Total lines to process: {total_lines:,}")
+
+    num_processes = multiprocessing.cpu_count()
+    chunk_size = total_lines // num_processes + 1
+    chunks = [lines[i:i + chunk_size] for i in range(0, total_lines, chunk_size)]
+    logger.info(f"Split data into {len(chunks)} chunks for {num_processes} cores.")
+
+    pool = multiprocessing.Pool(processes=num_processes)
+    
+    worker_args = [
+        (chunk, lang_filter, pos_whitelist, pos_blacklist, ignore_form_of, translation_languages, inflection_configs)
+        for chunk in chunks
+    ]
+
+    try:
+        all_results = pool.starmap(process_chunk_filtering, worker_args)
+        pool.close()
+        pool.join()
+    except KeyboardInterrupt:
+        logger.warning("Interrupted by user. Terminating pool...")
+        pool.terminate()
+        sys.exit(1)
+    except Exception as e:
+        logger.critical(f"Error during parallel processing: {e}")
+        traceback.print_exc()
+        sys.exit(1)
+
+    logger.info("Aggregating results and writing to output...")
+    
+    final_counters = {"processed": 0, "skipped": 0, "errors": 0}
+    error_log_path = output_path.parent / "verb_errors.log"
+    
+    with open(output_path, 'w', encoding='utf-8') as out_f, \
+         open(error_log_path, 'w', encoding='utf-8') as err_f:
+        
+        for result_strings, worker_stats, worker_errors in all_results:
+            for k in final_counters:
+                final_counters[k] += worker_stats.get(k, 0)
+            
+            for json_str in result_strings:
+                out_f.write(json_str + "\n")
+            
+            for err_msg in worker_errors:
+                err_f.write(err_msg + "\n")
+
+    logger.info(f"DONE. Total Read: {total_lines}")
+    logger.info(f"Processed: {final_counters['processed']}, Skipped: {final_counters['skipped']}, Errors: {final_counters['errors']}")
+
+def main():
+    parser = argparse.ArgumentParser(description="Transform kaakki.org JSONL to universal dictionary format (Parallel).")
+    parser.add_argument("--input", type=pathlib.Path, default=DEFAULT_INPUT_DIR / DEFAULT_INPUT_FILENAME,
+                        help="Path to the raw input JSONL file.")
+    parser.add_argument("--output-dir", type=pathlib.Path, default=DEFAULT_INTERMEDIATE_DIR,
+                        help="Directory to save the transformed JSONL file.")
+    parser.add_argument("--lang", type=str, default=DEFAULT_LANG_FILTER,
+                        help="Language code to filter for (e.g., 'de').")
+    parser.add_argument("--trans-langs", type=str, default=",".join(DEFAULT_TRANS_LANGS),
+                        help="Comma-separated list of translation languages to keep.")
+
+    args = parser.parse_args()
+
+    output_filename = f"{args.lang.capitalize()}_universal.jsonl" if args.lang else "universal.jsonl"
+    output_file_path = args.output_dir / output_filename
+
+    trans_langs_set = set(lang.strip() for lang in args.trans_langs.split(",")) if args.trans_langs else set()
+
+    process_file(
+        args.input,
+        output_file_path,
+        args.lang,
+        DEFAULT_POS_WHITELIST,
+        DEFAULT_POS_BLACKLIST,
+        DEFAULT_IGNORE_FORM_OF,
+        trans_langs_set
+    )
+
+    stats_file = ROOT_DIR / "processing_stats.csv"
+    if output_file_path.exists():
+        file_size = output_file_path.stat().st_size
+    else:
+        file_size = 0
+        
+    timestamp = datetime.now().isoformat()
+    write_header = not stats_file.exists()
+    try:
+        with open(stats_file, 'a', newline='', encoding='utf-8') as csvfile:
+            writer = csv.writer(csvfile)
+            if write_header:
+                writer.writerow(['timestamp', 'output_file', 'size_bytes'])
+            writer.writerow([timestamp, str(output_file_path), file_size])
+    except Exception as e:
+        logger.warning(f"Could not write stats csv: {e}")
+
+if __name__ == "__main__":
+    multiprocessing.freeze_support()
+    main()

scripts/02_create_db.py

@@ -0,0 +1,380 @@
+import json
+import sqlite3
+import pathlib
+import traceback
+import os
+import argparse
+import sys
+import multiprocessing
+import csv
+import statistics
+from datetime import datetime
+
+try:
+    import zstandard
+except ImportError:
+    print("ERROR: zstandard library not found. Please install it: pip install zstandard")
+    sys.exit(1)
+
+# ======================================================================
+# --- DEFAULT CONFIGURATION (Overridable via CLI args) ---
+# ======================================================================
+
+try:
+    SCRIPT_DIR = pathlib.Path(__file__).parent
+    ROOT_DIR = SCRIPT_DIR.parent
+except NameError:
+    SCRIPT_DIR = pathlib.Path.cwd()
+    ROOT_DIR = SCRIPT_DIR.parent
+
+DEFAULT_LANG_CODE = "fr"
+DEFAULT_INTERMEDIATE_DIR = ROOT_DIR / "intermediate"
+DEFAULT_OUTPUTS_DIR = ROOT_DIR / "outputs"
+
+COMPRESSION_LEVEL = 22
+DICTIONARY_SAMPLE_COUNT = 200000
+DICTIONARY_MAX_SIZE = 10 * 1024 * 1024  # 10MB
+
+DEFAULT_UNCOMPRESSED_ONLY = False #change this for compression!
+DEFAULT_MINIMAL = False
+
+# ======================================================================
+
+def get_file_size_mb(filepath):
+    return os.path.getsize(filepath) / (1024 * 1024)
+
+def count_lines(filepath):
+    print("Counting total lines for progress tracking...")
+    with open(filepath, 'r', encoding='utf-8') as f:
+        return sum(1 for _ in f)
+
+def process_chunk(chunk, compression_dict_bytes):
+    import zstandard
+    compression_dict = zstandard.ZstdCompressionDict(compression_dict_bytes)
+    local_compressor = zstandard.ZstdCompressor(level=22, dict_data=compression_dict)
+    results = []
+    for line in chunk:
+        if not line.strip(): continue
+        try:
+            entry = json.loads(line)
+            word = entry.get("word")
+            pos = entry.get("pos", "")
+            if not word: continue
+            data_to_compress = entry.copy()
+            data_to_compress.pop("word", None)
+            data_to_compress.pop("pos", None)
+            value_bytes = json.dumps(data_to_compress, ensure_ascii=False).encode('utf-8')
+            compressed_blob = local_compressor.compress(value_bytes)
+            results.append((word, pos, compressed_blob, len(value_bytes)))
+        except Exception:
+            pass
+    return results
+
+def process_chunk_uncompressed(chunk):
+    results = []
+    for line in chunk:
+        if not line.strip(): continue
+        try:
+            entry = json.loads(line)
+            word = entry.get("word")
+            pos = entry.get("pos", "")
+            if not word: continue
+            data_to_store = entry.copy()
+            data_to_store.pop("word", None)
+            data_to_store.pop("pos", None)
+            value_str = json.dumps(data_to_store, ensure_ascii=False)
+            value_bytes = value_str.encode('utf-8')
+            results.append((word, pos, value_str, len(value_bytes)))
+        except Exception:
+            pass
+    return results
+
+def train_config(config, lines):
+    import zstandard
+    sample_count, max_size = config
+    step = max(1, len(lines) // sample_count)
+    samples = []
+    for j in range(0, len(lines), step):
+        line = lines[j]
+        if not line.strip(): continue
+        entry = json.loads(line)
+        data_to_compress = entry.copy()
+        data_to_compress.pop("word", None)
+        data_to_compress.pop("pos", None)
+        samples.append(json.dumps(data_to_compress, ensure_ascii=False).encode('utf-8'))
+        if len(samples) >= sample_count: break
+    if not samples:
+        return None
+    compression_dict = zstandard.train_dictionary(max_size, samples)
+    dict_bytes = compression_dict.as_bytes()
+    return (sample_count, max_size, len(dict_bytes), dict_bytes)
+
+def create_database(lang_code, input_file, output_dir, intermediate_dir, uncompressed_only=False, minimal=False):
+    
+    database_file = output_dir / f"dictionary_{lang_code}.db"
+    dictionary_file = output_dir / f"dictionary_{lang_code}.zstdict"
+
+    # Ensure output directory exists
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    print(f"Settings:\n - Language: {lang_code}\n - Input: {input_file}\n - DB Output: {database_file}\n - Dict Output: {dictionary_file}")
+
+    if not input_file.exists():
+        print(f"Error: Input file not found at {input_file}")
+        sys.exit(1)
+
+    total_lines = count_lines(input_file)
+    print(f"Total lines to process: {total_lines:,}")
+
+    with open(input_file, "r", encoding="utf-8") as f:
+        lines = f.readlines()
+
+    num_processes = multiprocessing.cpu_count()
+    chunk_size = len(lines) // num_processes + 1
+    chunks = [lines[i:i+chunk_size] for i in range(0, len(lines), chunk_size)]
+
+    # --- Pass 1: Training Compression Dictionary ---
+    if not uncompressed_only:
+        print(f"\n--- Pass 1: Training Compression Dictionary ---")
+        try:
+            if minimal:
+                sample_count = DICTIONARY_SAMPLE_COUNT
+                max_size = DICTIONARY_MAX_SIZE
+                config = (sample_count, max_size)
+                result = train_config(config, lines)
+                if result is None:
+                    print("Error: No valid dictionary trained.")
+                    sys.exit(1)
+                sample_count, max_size, dict_size, dict_bytes = result
+                print(f"Using default configuration: samples={sample_count}, max_size={max_size/1024/1024:.1f}MB, dict_size={dict_size} bytes ({dict_size/1024:.1f} KB)")
+            else:
+                # Generate 20 configurations to try (varying both sample_count and max_size)
+                configs = []
+                for i in range(20):
+                    sample_count = 100000 + (i % 5) * 200000  # 5 different: 200k, 400k, 600k, 800k, 1M
+                    max_size = (3 + (i // 5) * 2) * 1024 * 1024  # 4 different: 3MB, 5MB, 7MB, 9MB
+                    configs.append((sample_count, max_size))
+
+                pool = multiprocessing.Pool(processes=min(20, multiprocessing.cpu_count()))
+                results = pool.starmap(train_config, [(config, lines) for config in configs])
+                pool.close()
+                pool.join()
+
+                # Find the best configuration (largest dictionary size)
+                valid_results = [r for r in results if r is not None]
+                if not valid_results:
+                    print("Error: No valid dictionaries trained.")
+                    sys.exit(1)
+
+                print("All configurations results:")
+                for sample_count, max_size, dict_size, _ in valid_results:
+                    print(f"  samples={sample_count}, max_size={max_size/1024/1024:.1f}MB -> dict_size={dict_size} bytes ({dict_size/1024:.1f} KB)")
+
+                best_result = max(valid_results, key=lambda x: x[2])
+                sample_count, max_size, dict_size, dict_bytes = best_result
+
+                print(f"\nBest configuration: samples={sample_count}, max_size={max_size/1024/1024:.1f}MB, dict_size={dict_size} bytes ({dict_size/1024:.1f} KB)")
+
+            compression_dict = zstandard.ZstdCompressionDict(dict_bytes)
+
+            with open(dictionary_file, "wb") as f:
+                f.write(dict_bytes)
+            print(f"Saved dictionary to {dictionary_file}")
+
+        except Exception as e:
+            print(f"Error during training: {e}")
+            traceback.print_exc()
+            sys.exit(1)
+
+    if not uncompressed_only:
+        # --- Database Setup ---
+        if database_file.exists():
+            os.remove(database_file)
+
+        conn = sqlite3.connect(database_file)
+        conn.execute("PRAGMA journal_mode=WAL;")
+        conn.execute("PRAGMA auto_vacuum=full;")
+        cursor = conn.cursor()
+        compressor = zstandard.ZstdCompressor(level=COMPRESSION_LEVEL, dict_data=compression_dict)
+
+        cursor.execute('''
+        CREATE TABLE dictionary_data (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            word TEXT NOT NULL,
+            pos TEXT,
+            data_blob BLOB,
+            uncompressed_size INTEGER
+        );
+        ''')
+
+        # --- Pass 2: Insert Data ---
+        print("\n--- Pass 2: Inserting Data ---")
+
+        pool = multiprocessing.Pool(processes=multiprocessing.cpu_count())
+
+        print("Processing chunks in parallel for compressed DB...")
+        all_results = pool.starmap(process_chunk, zip(chunks, [dict_bytes] * len(chunks)))
+        data_to_insert = [item for sublist in all_results for item in sublist]
+
+        print(f"Collected {len(data_to_insert)} items to insert into compressed DB.")
+        cursor.executemany("INSERT INTO dictionary_data (word, pos, data_blob, uncompressed_size) VALUES (?, ?, ?, ?)", data_to_insert)
+        word_counter = len(data_to_insert)
+
+        conn.commit()
+        print(f"Inserted {word_counter:,} words into compressed DB.")
+
+        # --- Pass 3: FTS & Cleanup ---
+        print("Creating FTS4 index...")
+        cursor.execute("CREATE VIRTUAL TABLE dictionary_fts USING fts4(word, pos, content='dictionary_data');")
+        cursor.execute("INSERT INTO dictionary_fts(docid, word, pos) SELECT id, word, pos FROM dictionary_data;")
+        conn.commit()
+
+        print("Running VACUUM...")
+        cursor.execute('VACUUM')
+        conn.commit()
+        conn.close()
+
+        db_size_mb = get_file_size_mb(database_file)
+        dict_size_mb = get_file_size_mb(dictionary_file)
+
+        print(f"\n{'='*60}")
+        print(f"SUCCESS: Database created.")
+        print(f"{'='*60}")
+        print(f"Final Database Size:   {db_size_mb:.2f} MB  ({database_file.name})")
+        print(f"Final Dictionary Size: {dict_size_mb:.2f} MB  ({dictionary_file.name})")
+        print(f"{'='*60}")
+
+    # --- Create Uncompressed Database ---
+    print(f"\n--- Creating Uncompressed Database ---")
+    uncompressed_db_file = intermediate_dir / f"dictionary_{lang_code}_uncompressed.db"
+
+    # Ensure intermediate directory exists
+    intermediate_dir.mkdir(parents=True, exist_ok=True)
+
+    if uncompressed_db_file.exists():
+        os.remove(uncompressed_db_file)
+
+    conn2 = sqlite3.connect(uncompressed_db_file)
+    conn2.execute("PRAGMA journal_mode=WAL;")
+    conn2.execute("PRAGMA auto_vacuum=full;")
+    cursor2 = conn2.cursor()
+
+    cursor2.execute('''
+    CREATE TABLE dictionary_data (
+        id INTEGER PRIMARY KEY AUTOINCREMENT,
+        word TEXT NOT NULL,
+        pos TEXT,
+        data TEXT,
+        uncompressed_size INTEGER
+    );
+    ''')
+
+    # --- Pass 2b: Insert Uncompressed Data ---
+    print("\n--- Pass 2b: Inserting Uncompressed Data ---")
+
+    print("Processing chunks in parallel for uncompressed DB...")
+    if uncompressed_only:
+        pool_uncomp = multiprocessing.Pool(processes=multiprocessing.cpu_count())
+        all_results2 = pool_uncomp.map(process_chunk_uncompressed, chunks)
+        pool_uncomp.close()
+        pool_uncomp.join()
+    else:
+        all_results2 = pool.map(process_chunk_uncompressed, chunks)
+        pool.close()
+        pool.join()
+    data_to_insert2 = [item for sublist in all_results2 for item in sublist]
+
+    print(f"Collected {len(data_to_insert2)} items to insert into uncompressed DB.")
+    cursor2.executemany("INSERT INTO dictionary_data (word, pos, data, uncompressed_size) VALUES (?, ?, ?, ?)", data_to_insert2)
+    word_counter2 = len(data_to_insert2)
+
+    conn2.commit()
+    print(f"Inserted {word_counter2:,} words into uncompressed DB.")
+
+    # --- Pass 3b: FTS & Cleanup ---
+    print("Creating FTS4 index for uncompressed DB...")
+    cursor2.execute("CREATE VIRTUAL TABLE dictionary_fts USING fts4(word, pos, content='dictionary_data');")
+    cursor2.execute("INSERT INTO dictionary_fts(docid, word, pos) SELECT id, word, pos FROM dictionary_data;")
+    conn2.commit()
+
+    print("Running VACUUM on uncompressed DB...")
+    cursor2.execute('VACUUM')
+    conn2.commit()
+
+    # Compute and print uncompressed_size statistics
+    sizes = [row[0] for row in cursor2.execute("SELECT uncompressed_size FROM dictionary_data")]
+    if sizes:
+        min_size = min(sizes)
+        max_size = max(sizes)
+        avg_size = statistics.mean(sizes)
+        median_size = statistics.median(sizes)
+        try:
+            stdev_size = statistics.stdev(sizes)
+        except statistics.StatisticsError:
+            stdev_size = 0.0
+
+        print(f"\nUncompressed Size Statistics:")
+        print(f"  Count: {len(sizes):,}")
+        print(f"  Min: {min_size}")
+        print(f"  Max: {max_size}")
+        print(f"  Avg: {avg_size:.2f}")
+        print(f"  Median: {median_size}")
+        print(f"  Std Dev: {stdev_size:.2f}")
+
+        # Outliers: top 10 largest entries
+        outliers = cursor2.execute("SELECT word, uncompressed_size FROM dictionary_data ORDER BY uncompressed_size DESC LIMIT 10").fetchall()
+        print(f"\nTop 10 largest entries by uncompressed size:")
+        for word, size in outliers:
+            print(f"  {word}: {size:,} bytes")
+
+    conn2.close()
+
+    uncompressed_db_size_mb = get_file_size_mb(uncompressed_db_file)
+
+    print(f"\n{'='*60}")
+    print(f"Uncompressed Database Size: {uncompressed_db_size_mb:.2f} MB  ({uncompressed_db_file.name})")
+    print(f"{'='*60}")
+
+def main():
+    parser = argparse.ArgumentParser(description="Compress dictionary JSONL into SQLite DB.")
+    parser.add_argument("--lang", type=str, default=DEFAULT_LANG_CODE, 
+                        help="Language code (e.g., 'de'). Used for naming output files.")
+    parser.add_argument("--input", type=pathlib.Path,
+                        help="Full path to input JSONL. If omitted, tries to find it in standard intermediate folder based on lang.")
+    parser.add_argument("--output-dir", type=pathlib.Path, default=DEFAULT_OUTPUTS_DIR,
+                        help="Directory to save .db and .zstdict files.")
+    parser.add_argument("--intermediate-dir", type=pathlib.Path, default=DEFAULT_INTERMEDIATE_DIR,
+                        help="Directory to save uncompressed .db file.")
+
+    args = parser.parse_args()
+
+    # Determine input file if not explicitly provided
+    if args.input:
+        input_file = args.input
+    else:
+        # Try to guess the filename based on the language code matching script 1's output
+        filename = f"{args.lang.capitalize()}_universal.jsonl"
+        input_file = DEFAULT_INTERMEDIATE_DIR / filename
+
+    create_database(args.lang, input_file, args.output_dir, args.intermediate_dir, DEFAULT_UNCOMPRESSED_ONLY, DEFAULT_MINIMAL)
+
+    # Log stats to CSV
+    stats_file = ROOT_DIR / "processing_stats.csv"
+    timestamp = datetime.now().isoformat()
+    files_to_log = [
+        (args.output_dir / f"dictionary_{args.lang}.db", "compressed_db"),
+        (args.output_dir / f"dictionary_{args.lang}.zstdict", "compression_dict"),
+        (args.intermediate_dir / f"dictionary_{args.lang}_uncompressed.db", "uncompressed_db")
+    ]
+    write_header = not stats_file.exists()
+    with open(stats_file, 'a', newline='', encoding='utf-8') as csvfile:
+        writer = csv.writer(csvfile)
+        if write_header:
+            writer.writerow(['timestamp', 'output_file', 'size_bytes', 'type'])
+        for file_path, file_type in files_to_log:
+            if file_path.exists():
+                size = file_path.stat().st_size
+                writer.writerow([timestamp, str(file_path), size, file_type])
+
+if __name__ == "__main__":
+    main()

scripts/03_update_manifest.py

@@ -0,0 +1,108 @@
+import json
+import os
+import hashlib
+import sys
+import pathlib
+import re
+import argparse
+from typing import Dict, Any, Set
+
+# ======================================================================
+# --- DEFAULT CONFIGURATION ---
+# ======================================================================
+try:
+    SCRIPT_DIR = pathlib.Path(__file__).parent
+    ROOT_DIR = SCRIPT_DIR.parent
+except NameError:
+    SCRIPT_DIR = pathlib.Path.cwd()
+    ROOT_DIR = SCRIPT_DIR.parent
+
+DEFAULT_OUTPUTS_DIR = ROOT_DIR / "outputs"
+# ======================================================================
+
+def calculate_sha256(filepath: pathlib.Path, block_size=65536) -> str | None:
+    sha256 = hashlib.sha256()
+    try:
+        with open(filepath, 'rb') as f:
+            for block in iter(lambda: f.read(block_size), b''):
+                sha256.update(block)
+    except IOError as e:
+        print(f"    ERROR: Could not read file '{filepath.name}': {e}")
+        return None
+    return sha256.hexdigest().upper()
+
+def guess_properties_from_base(base_name: str) -> Dict[str, str]:
+    match = re.match(r"dictionary_([a-zA-Z]{2,3})", base_name)
+    if match:
+        lang_code = match.group(1)
+        return {"id": f"{lang_code}_dict", "name": f"Dictionary ({lang_code.upper()})", "lang_code": lang_code}
+    return {"id": base_name, "name": f"Dictionary ({base_name})", "lang_code": "xx"}
+
+def create_new_dict_entry(base_name: str, asset_files: list[pathlib.Path]) -> Dict[str, Any]:
+    props = guess_properties_from_base(base_name)
+    new_entry = {
+        "id": props["id"], "name": props["name"], "description": "Auto-generated", "version": "1.0.0", "assets": []
+    }
+    for file_path in asset_files:
+        print(f"    -> Adding new asset: '{file_path.name}'")
+        csum = calculate_sha256(file_path)
+        if csum:
+            new_entry["assets"].append({
+                "filename": file_path.name, "size_bytes": os.path.getsize(file_path), "checksum_sha256": csum
+            })
+    return new_entry
+
+def update_manifest(outputs_dir: pathlib.Path):
+    manifest_path = outputs_dir / 'manifest.json'
+    if not outputs_dir.exists():
+        print(f"Error: Outputs directory does not exist: {outputs_dir}")
+        sys.exit(1)
+
+    manifest_data = {"files": []}
+    if manifest_path.exists():
+        try:
+            with open(manifest_path, 'r', encoding='utf-8') as f:
+                manifest_data = json.load(f)
+                if 'files' not in manifest_data: manifest_data['files'] = []
+        except Exception as e:
+            print(f"Error reading manifest: {e}"); sys.exit(1)
+
+    print(f"Scanning {outputs_dir} for assets...")
+    assets_map = {asset['filename']: asset for entry in manifest_data.get('files', []) for asset in entry.get('assets', [])}
+    
+    discovered = list(outputs_dir.glob('*.db')) + list(outputs_dir.glob('*.zstdict'))
+    new_files, updated_count = [], 0
+
+    for fpath in discovered:
+        fname = fpath.name
+        if fname in assets_map:
+            print(f"Updating: {fname}")
+            assets_map[fname]['size_bytes'] = os.path.getsize(fpath)
+            assets_map[fname]['checksum_sha256'] = calculate_sha256(fpath)
+            updated_count += 1
+        else:
+            new_files.append(fpath)
+
+    added_count = 0
+    if new_files:
+        grouped = {}
+        for f in new_files:
+            grouped.setdefault(f.stem, []).append(f)
+        for base, files in grouped.items():
+            print(f"Creating new entry for: {base}")
+            manifest_data['files'].append(create_new_dict_entry(base, files))
+            added_count += 1
+
+    with open(manifest_path, 'w', encoding='utf-8') as f:
+        json.dump(manifest_data, f, indent=2, ensure_ascii=False)
+    print(f"\nComplete. Updated {updated_count} assets, added {added_count} new entries.")
+
+def main():
+    parser = argparse.ArgumentParser(description="Update manifest.json with .db and .zstdict files.")
+    parser.add_argument("--outputs-dir", type=pathlib.Path, default=DEFAULT_OUTPUTS_DIR,
+                        help="Directory containing assets and manifest.json.")
+    args = parser.parse_args()
+    update_manifest(args.outputs_dir)
+
+if __name__ == "__main__":
+    main()

scripts/InflectionProcessor.py

@@ -0,0 +1,225 @@
+import re
+
+class UniversalInflectionCompressor:
+    """
+    A generic inflection compressor that uses a configuration dictionary 
+    to process, partition, and compress verb forms for any language.
+    """
+    def __init__(self, config: dict):
+        self.config = config
+
+    def _matches_criteria(self, form: dict, criteria: dict) -> bool:
+        """Helper: Checks if a form matches specific criteria."""
+        # Regex Match
+        if 'form_regex' in criteria:
+            form_str = form.get('form', '')
+            if form_str is None: form_str = ''
+            if not re.search(criteria['form_regex'], form_str):
+                return False
+        
+        # Tags Inclusion
+        if 'tags' in criteria:
+            form_tags = set(form.get('tags', []))
+            required = set(criteria['tags'])
+            if not required.issubset(form_tags):
+                return False
+
+        # Raw Tags Inclusion
+        if 'raw_tags' in criteria:
+            form_raw = set(form.get('raw_tags', []))
+            required_raw = set(criteria['raw_tags'])
+            if not required_raw.issubset(form_raw):
+                return False
+                
+        # Tag Exclusion
+        if 'exclude_tags' in criteria:
+            form_tags = set(form.get('tags', []))
+            if not form_tags.isdisjoint(set(criteria['exclude_tags'])):
+                return False
+
+        return True
+
+    def _normalize_forms(self, forms: list) -> list:
+        """Enriches forms with tags based on 'normalization_rules'."""
+        rules = self.config.get('normalization_rules', [])
+        skip_if_source = self.config.get('skip_normalization_if_source', True)
+
+        for form in forms:
+            if form.get('source') and skip_if_source:
+                continue
+
+            for rule in rules:
+                field = rule.get('field')
+                value_to_match = rule.get('match')
+                match_mode = rule.get('match_mode', 'exact')
+                add_tags = rule.get('add_tags', [])
+
+                form_value = form.get(field)
+                if form_value is None: continue
+
+                is_match = False
+                if match_mode == 'regex':
+                    if isinstance(form_value, list):
+                        for item in form_value:
+                            if re.search(value_to_match, str(item)):
+                                is_match = True; break
+                    else:
+                        if re.search(value_to_match, str(form_value)):
+                            is_match = True
+                else: 
+                    if isinstance(form_value, list):
+                        is_match = value_to_match in form_value
+                    else:
+                        is_match = value_to_match == form_value
+                
+                if is_match:
+                    current_tags = set(form.get('tags', []))
+                    current_tags.update(add_tags)
+                    form['tags'] = list(current_tags)
+        return forms
+
+    def _extract_properties(self, forms: list, entry_context: dict = None) -> dict:
+        """Determines global properties (e.g. aux, group)."""
+        properties = {}
+        candidates = forms.copy()
+        if entry_context:
+            candidates.append(entry_context)
+        
+        for prop_def in self.config.get('properties', []):
+            name = prop_def['name']
+            default_val = prop_def.get('default')
+            is_multivalue = prop_def.get('multivalue', False)
+            
+            found_values = set()
+            for rule in prop_def.get('rules', []):
+                for candidate in candidates:
+                    if self._matches_criteria(candidate, rule.get('criteria', {})):
+                        found_values.add(rule['value'])
+                        if not is_multivalue:
+                            break 
+                if found_values and not is_multivalue:
+                    break
+            
+            if not found_values:
+                if is_multivalue and default_val is not None:
+                    properties[name] = default_val if isinstance(default_val, list) else [default_val]
+                else:
+                    properties[name] = default_val
+            elif is_multivalue:
+                properties[name] = sorted(list(found_values))
+            else:
+                properties[name] = list(found_values)[0]
+                    
+        return properties
+
+    def _clean_verb_string(self, form_string: str) -> str:
+        ignored = self.config.get('clean_prefixes', [])
+        current_string = form_string.strip()
+        changed = True
+        while changed:
+            changed = False
+            for prefix in ignored:
+                if prefix.endswith("'") or prefix.endswith("’"):
+                    if current_string.startswith(prefix):
+                        current_string = current_string[len(prefix):]
+                        changed = True
+                        break
+                else:
+                    if current_string.startswith(prefix + " "):
+                        current_string = current_string[len(prefix)+1:]
+                        changed = True
+                        break
+        return current_string
+
+    def compress(self, forms_list: list, word: str = None, entry: dict = None) -> dict:
+        if not forms_list:
+            return None
+
+        # 1. Normalize tags
+        normalized_forms = self._normalize_forms(forms_list)
+
+        # 2. Extract Properties
+        entry_context = None
+        if entry:
+            entry_context = {
+                'form': entry.get('word', ''),
+                'tags': entry.get('tags', []),
+                'raw_tags': entry.get('raw_tags', [])
+            }
+        table_properties = self._extract_properties(normalized_forms, entry_context)
+
+        # 3. Initialize Output
+        result = table_properties.copy()
+        
+        # 4. Fill Slots
+        schema = self.config.get('schema', {})
+        for slot_name, slot_def in schema.items():
+            slot_type = slot_def.get('type', 'single')
+            
+            if slot_type == 'single':
+                result[slot_name] = None
+                for form in normalized_forms:
+                    if self._matches_criteria(form, slot_def.get('criteria', {})):
+                        if result[slot_name] is None or (form.get('source') and not result[slot_name]): 
+                             result[slot_name] = self._clean_verb_string(form['form'])
+
+            elif slot_type == 'list':
+                size = slot_def.get('size', 6)
+                result[slot_name] = [None] * size
+                base_criteria = slot_def.get('base_criteria', {})
+                candidates = [f for f in normalized_forms if self._matches_criteria(f, base_criteria)]
+                
+                for form in candidates:
+                    idx = -1
+                    # Iterate through index rules to find where this form belongs
+                    for index_rule in slot_def.get('indices', []):
+                        # Support full criteria in indices (e.g. form_regex), fallback to 'tags' shortcut
+                        rule_criteria = index_rule.get('criteria', {})
+                        if 'tags' in index_rule:
+                            rule_criteria = rule_criteria.copy()
+                            rule_criteria['tags'] = index_rule['tags']
+                        
+                        if self._matches_criteria(form, rule_criteria):
+                            idx = index_rule['index']
+                            break
+                    
+                    if idx >= 0 and idx < size:
+                        current_val = result[slot_name][idx]
+                        if current_val is None:
+                            result[slot_name][idx] = self._clean_verb_string(form['form'])
+                        elif form.get('source') and ("Flexion" in form.get('source') or "Conjugaison" in form.get('source')):
+                             result[slot_name][idx] = self._clean_verb_string(form['form'])
+
+        # 5. Fallbacks
+        if not result.get('infinitive') and word:
+            result['infinitive'] = word
+
+        # 6. Validation
+        if self.config.get('validate_completeness', False):
+            for key, val in result.items():
+                slot_config = schema.get(key, {})
+                if slot_config.get('optional', False):
+                    continue
+                if val is None:
+                     raise ValueError(f"Inflection Error: Missing required slot '{key}' for word '{word}'.")
+                if isinstance(val, list):
+                     for i, v in enumerate(val):
+                        if v is None:
+                             raise ValueError(f"Inflection Error: Missing form at index {i} in slot '{key}' for word '{word}'.")
+
+        return result
+
+class InflectionProcessor:
+    def __init__(self, configs):
+        self.compressors = {k: UniversalInflectionCompressor(v) for k, v in configs.items()}
+
+    def process(self, entry: dict) -> dict:
+        key = f"{entry.get('lang_code')}_{entry.get('pos')}"
+        if key in self.compressors:
+            try:
+                compressed = self.compressors[key].compress(entry.get('forms'), entry.get('word'), entry=entry)
+                if compressed:
+                    entry['forms'] = compressed
+            except Exception as e:
+                print(f"Error processing {entry.get('word')}: {e}")
+        return entry

scripts/Json Analyzer/jsonl_schema_analyzer_hybrid.py

@@ -0,0 +1,358 @@
+#!/usr/bin/env python3
+"""
+Hybrid JSONL Schema Analyzer
+
+Intelligently chooses between sequential and parallel processing based on file size.
+For small files, uses sequential processing. For large files, uses parallel processing.
+"""
+
+import json
+import os
+import sys
+import time
+import mmap
+from collections import defaultdict, Counter
+from typing import Dict, List, Any, Set, Union, Tuple
+import argparse
+from pathlib import Path
+from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor, as_completed
+from multiprocessing import cpu_count
+import threading
+from functools import partial
+import gc
+
+# Import the optimized analyzer for parallel processing
+sys.path.insert(0, str(Path(__file__).parent))
+try:
+    from jsonl_schema_analyzer_optimized import OptimizedJSONLSchemaAnalyzer
+except ImportError:
+    print("Warning: Could not import optimized analyzer, using fallback")
+    OptimizedJSONLSchemaAnalyzer = None
+
+
+class HybridJSONLSchemaAnalyzer:
+    """Hybrid analyzer that intelligently chooses processing strategy."""
+    
+    def __init__(self, max_samples: int = 1000, max_workers: int = None, 
+                 parallel_threshold_mb: int = 100, chunk_size: int = 1000):
+        """
+        Initialize the hybrid analyzer.
+        
+        Args:
+            max_samples: Maximum number of JSON objects to sample per file
+            max_workers: Maximum number of worker processes (default: cpu_count)
+            parallel_threshold_mb: File size threshold in MB to use parallel processing
+            chunk_size: Number of lines to process in each chunk
+        """
+        self.max_samples = max_samples
+        self.max_workers = max_workers or min(cpu_count(), 8)
+        self.parallel_threshold_mb = parallel_threshold_mb
+        self.chunk_size = chunk_size
+        
+        # Import the original analyzer for small files
+        sys.path.insert(0, str(Path(__file__).parent))
+        try:
+            from jsonl_schema_analyzer import JSONLSchemaAnalyzer
+            self.sequential_analyzer = JSONLSchemaAnalyzer(max_samples=max_samples)
+        except ImportError:
+            print("Warning: Could not import sequential analyzer")
+            self.sequential_analyzer = None
+        
+        # Initialize optimized analyzer for large files
+        if OptimizedJSONLSchemaAnalyzer:
+            self.parallel_analyzer = OptimizedJSONLSchemaAnalyzer(
+                max_samples=max_samples,
+                max_workers=max_workers,
+                chunk_size=chunk_size
+            )
+        else:
+            self.parallel_analyzer = None
+        
+        print(f"Hybrid analyzer initialized:")
+        print(f"  Parallel threshold: {parallel_threshold_mb} MB")
+        print(f"  Max workers: {self.max_workers}")
+        print(f"  Chunk size: {self.chunk_size}")
+    
+    def analyze_jsonl_file(self, file_path: Union[str, Path]) -> Dict[str, Any]:
+        """
+        Analyze a JSONL file using the appropriate strategy.
+        
+        Args:
+            file_path: Path to the JSONL file
+            
+        Returns:
+            Dictionary containing schema analysis results
+        """
+        file_path = Path(file_path)
+        
+        if not file_path.exists():
+            raise FileNotFoundError(f"File not found: {file_path}")
+        
+        # Get file size in MB
+        file_size_mb = file_path.stat().st_size / (1024 * 1024)
+        
+        print(f"Analyzing {file_path.name} ({file_size_mb:.2f} MB)...")
+        
+        # Choose processing strategy
+        if file_size_mb >= self.parallel_threshold_mb and self.parallel_analyzer:
+            print(f"  Using parallel processing (file >= {self.parallel_threshold_mb} MB)")
+            result = self.parallel_analyzer.analyze_jsonl_file(file_path)
+            result["processing_strategy"] = "parallel"
+        elif self.sequential_analyzer:
+            print(f"  Using sequential processing (file < {self.parallel_threshold_mb} MB)")
+            result = self.sequential_analyzer.analyze_jsonl_file(file_path)
+            result["processing_strategy"] = "sequential"
+        else:
+            # Fallback to parallel if sequential not available
+            print(f"  Using parallel processing (sequential analyzer unavailable)")
+            if self.parallel_analyzer:
+                result = self.parallel_analyzer.analyze_jsonl_file(file_path)
+                result["processing_strategy"] = "parallel_fallback"
+            else:
+                raise RuntimeError("No analyzer available")
+        
+        # Add hybrid-specific metadata
+        result["file_size_mb"] = file_size_mb
+        result["parallel_threshold_mb"] = self.parallel_threshold_mb
+        
+        return result
+    
+    def analyze_directory(self, directory_path: Union[str, Path], pattern: str = "*.jsonl") -> Dict[str, Any]:
+        """
+        Analyze all JSONL files in a directory using hybrid processing.
+        
+        Args:
+            directory_path: Path to directory containing JSONL files
+            pattern: File pattern to match (default: *.jsonl)
+            
+        Returns:
+            Dictionary containing analysis results for all files
+        """
+        directory_path = Path(directory_path)
+        
+        if not directory_path.exists():
+            raise FileNotFoundError(f"Directory not found: {directory_path}")
+        
+        # Find all JSONL files
+        jsonl_files = list(directory_path.glob(pattern))
+        
+        if not jsonl_files:
+            print(f"No JSONL files found in {directory_path} with pattern {pattern}")
+            return {"files": [], "summary": {}}
+        
+        print(f"Found {len(jsonl_files)} JSONL files to analyze...")
+        start_time = time.time()
+        
+        # Categorize files by size
+        small_files = []
+        large_files = []
+        
+        for file_path in jsonl_files:
+            size_mb = file_path.stat().st_size / (1024 * 1024)
+            if size_mb >= self.parallel_threshold_mb:
+                large_files.append(file_path)
+            else:
+                small_files.append(file_path)
+        
+        print(f"  Small files (< {self.parallel_threshold_mb} MB): {len(small_files)}")
+        print(f"  Large files (>= {self.parallel_threshold_mb} MB): {len(large_files)}")
+        
+        file_results = {}
+        
+        # Process small files sequentially (they're fast anyway)
+        if small_files and self.sequential_analyzer:
+            print(f"Processing {len(small_files)} small files sequentially...")
+            for file_path in small_files:
+                try:
+                    result = self.analyze_jsonl_file(file_path)
+                    file_results[file_path.name] = result
+                except Exception as e:
+                    print(f"Error analyzing {file_path.name}: {e}")
+                    file_results[file_path.name] = {"error": str(e)}
+        
+        # Process large files in parallel
+        if large_files and self.parallel_analyzer:
+            print(f"Processing {len(large_files)} large files in parallel...")
+            
+            if len(large_files) == 1:
+                # Single large file - just process it directly
+                file_path = large_files[0]
+                try:
+                    result = self.analyze_jsonl_file(file_path)
+                    file_results[file_path.name] = result
+                except Exception as e:
+                    print(f"Error analyzing {file_path.name}: {e}")
+                    file_results[file_path.name] = {"error": str(e)}
+            else:
+                # Multiple large files - process in parallel
+                with ThreadPoolExecutor(max_workers=min(len(large_files), self.max_workers)) as executor:
+                    future_to_file = {
+                        executor.submit(self.analyze_jsonl_file, file_path): file_path 
+                        for file_path in large_files
+                    }
+                    
+                    for future in as_completed(future_to_file):
+                        file_path = future_to_file[future]
+                        try:
+                            result = future.result()
+                            file_results[file_path.name] = result
+                        except Exception as e:
+                            print(f"Error analyzing {file_path.name}: {e}")
+                            file_results[file_path.name] = {"error": str(e)}
+        
+        # Create summary
+        successful_results = [r for r in file_results.values() if "error" not in r]
+        summary = {
+            "total_files": len(jsonl_files),
+            "small_files": len(small_files),
+            "large_files": len(large_files),
+            "successfully_analyzed": len(successful_results),
+            "total_size_bytes": sum(
+                r.get("file_size_bytes", 0) for r in successful_results
+            ),
+            "total_lines": sum(
+                r.get("total_lines", 0) for r in successful_results
+            ),
+            "total_valid_lines": sum(
+                r.get("valid_lines", 0) for r in successful_results
+            ),
+            "total_processing_time": sum(
+                r.get("processing_time_seconds", 0) for r in successful_results
+            ),
+            "parallel_threshold_mb": self.parallel_threshold_mb,
+            "strategies_used": {
+                "sequential": len([r for r in successful_results if r.get("processing_strategy") == "sequential"]),
+                "parallel": len([r for r in successful_results if r.get("processing_strategy") in ["parallel", "parallel_fallback"]])
+            }
+        }
+        
+        # Calculate processing speed
+        if summary["total_processing_time"] > 0:
+            total_mb = summary["total_size_bytes"] / (1024 * 1024)
+            summary["average_processing_speed_mb_per_sec"] = total_mb / summary["total_processing_time"]
+        
+        elapsed_time = time.time() - start_time
+        summary["total_elapsed_time"] = elapsed_time
+        
+        print(f"\nDirectory analysis completed in {elapsed_time:.2f}s")
+        print(f"Processed {summary['total_valid_lines']:,} valid lines from {summary['successfully_analyzed']} files")
+        print(f"Sequential: {summary['strategies_used']['sequential']}, Parallel: {summary['strategies_used']['parallel']}")
+        print(f"Average speed: {summary['average_processing_speed_mb_per_sec']:.2f} MB/sec")
+        
+        return {
+            "directory": str(directory_path),
+            "pattern": pattern,
+            "files": file_results,
+            "summary": summary
+        }
+    
+    def save_results(self, results: Dict[str, Any], output_path: Union[str, Path]):
+        """
+        Save analysis results to a JSON file.
+        
+        Args:
+            results: Analysis results to save
+            output_path: Path to save the results
+        """
+        output_path = Path(output_path)
+        
+        try:
+            start_time = time.time()
+            with open(output_path, 'w', encoding='utf-8') as f:
+                json.dump(results, f, indent=2, ensure_ascii=False)
+            
+            save_time = time.time() - start_time
+            file_size = output_path.stat().st_size
+            print(f"Results saved to {output_path} ({file_size / (1024*1024):.2f} MB) in {save_time:.2f}s")
+            
+        except Exception as e:
+            raise RuntimeError(f"Error saving results to {output_path}: {e}")
+
+
+def main():
+    """Main function for command-line usage."""
+    parser = argparse.ArgumentParser(
+        description="Hybrid JSONL schema analyzer with intelligent processing strategy"
+    )
+    parser.add_argument(
+        "path",
+        help="Path to JSONL file or directory containing JSONL files"
+    )
+    parser.add_argument(
+        "-o", "--output",
+        help="Output file for analysis results (JSON format)"
+    )
+    parser.add_argument(
+        "-p", "--pattern",
+        default="*.jsonl",
+        help="File pattern when analyzing directory (default: *.jsonl)"
+    )
+    parser.add_argument(
+        "-s", "--max-samples",
+        type=int,
+        default=1000,
+        help="Maximum number of JSON objects to sample per file (default: 1000)"
+    )
+    parser.add_argument(
+        "-w", "--workers",
+        type=int,
+        default=None,
+        help="Number of worker processes for parallel processing (default: CPU count, max 8)"
+    )
+    parser.add_argument(
+        "-t", "--threshold",
+        type=int,
+        default=100,
+        help="File size threshold in MB for parallel processing (default: 100)"
+    )
+    parser.add_argument(
+        "-c", "--chunk-size",
+        type=int,
+        default=1000,
+        help="Number of lines to process in each chunk (default: 1000)"
+    )
+    parser.add_argument(
+        "--directory",
+        action="store_true",
+        help="Treat path as directory instead of single file"
+    )
+    
+    args = parser.parse_args()
+    
+    # Initialize hybrid analyzer
+    analyzer = HybridJSONLSchemaAnalyzer(
+        max_samples=args.max_samples,
+        max_workers=args.workers,
+        parallel_threshold_mb=args.threshold,
+        chunk_size=args.chunk_size
+    )
+    
+    try:
+        start_time = time.time()
+        
+        # Analyze file or directory
+        if args.directory or Path(args.path).is_dir():
+            results = analyzer.analyze_directory(args.path, args.pattern)
+        else:
+            results = analyzer.analyze_jsonl_file(args.path)
+        
+        total_time = time.time() - start_time
+        
+        # Save or print results
+        if args.output:
+            analyzer.save_results(results, args.output)
+        else:
+            print("\n" + "="*50)
+            print("ANALYSIS RESULTS")
+            print("="*50)
+            print(json.dumps(results, indent=2, ensure_ascii=False))
+        
+        print(f"\nTotal analysis time: {total_time:.2f}s")
+    
+    except Exception as e:
+        print(f"Error: {e}", file=sys.stderr)
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

scripts/Json Analyzer/jsonl_schema_analyzer_optimized.py

@@ -0,0 +1,567 @@
+#!/usr/bin/env python3
+"""
+Optimized JSONL Schema Analyzer
+
+Analyzes JSONL files to extract and aggregate schema information using multiple cores.
+For each JSONL file, it generates a schema showing the JSON structure
+and aggregates all possible keys found across all records.
+"""
+
+import json
+import os
+import sys
+import time
+import mmap
+from collections import defaultdict, Counter
+from typing import Dict, List, Any, Set, Union, Tuple
+import argparse
+from pathlib import Path
+from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor, as_completed
+from multiprocessing import cpu_count, Manager
+import threading
+from functools import partial
+import gc
+
+
+class OptimizedJSONLSchemaAnalyzer:
+    """Optimized analyzer that uses multiple cores and system resources efficiently."""
+    
+    def __init__(self, max_samples: int = 1000, max_workers: int = None, chunk_size: int = 1000):
+        """
+        Initialize the optimized analyzer.
+        
+        Args:
+            max_samples: Maximum number of JSON objects to sample per file
+            max_workers: Maximum number of worker processes (default: cpu_count)
+            chunk_size: Number of lines to process in each chunk
+        """
+        self.max_samples = max_samples
+        self.max_workers = max_workers or min(cpu_count(), 8)  # Limit to 8 to avoid memory issues
+        self.chunk_size = chunk_size
+        self.schema_cache = {}
+        
+        print(f"Initialized analyzer with {self.max_workers} workers, chunk size: {self.chunk_size}")
+    
+    def analyze_json_value(self, value: Any, depth: int = 0, max_depth: int = 10) -> Dict[str, Any]:
+        """
+        Analyze a JSON value and return its type and structure.
+        
+        Args:
+            value: The JSON value to analyze
+            depth: Current depth in the structure
+            max_depth: Maximum depth to analyze
+            
+        Returns:
+            Dictionary describing the value's type and structure
+        """
+        if depth > max_depth:
+            return {"type": "unknown", "note": "max_depth_reached"}
+        
+        if value is None:
+            return {"type": "null"}
+        elif isinstance(value, bool):
+            return {"type": "boolean"}
+        elif isinstance(value, int):
+            return {"type": "integer"}
+        elif isinstance(value, float):
+            return {"type": "number"}
+        elif isinstance(value, str):
+            return {"type": "string", "sample_length": len(value)}
+        elif isinstance(value, list):
+            if not value:
+                return {"type": "array", "item_types": [], "length_range": [0, 0]}
+            
+            item_types = set()
+            item_schemas = []
+            
+            # Sample first few items to determine array structure
+            sample_size = min(10, len(value))
+            for item in value[:sample_size]:
+                item_schema = self.analyze_json_value(item, depth + 1, max_depth)
+                item_schemas.append(item_schema)
+                item_types.add(item_schema["type"])
+            
+            return {
+                "type": "array",
+                "item_types": sorted(list(item_types)),
+                "length_range": [len(value), len(value)],
+                "sample_items": item_schemas[:3]  # Keep first 3 as examples
+            }
+        elif isinstance(value, dict):
+            if not value:
+                return {"type": "object", "properties": {}, "required_keys": []}
+            
+            properties = {}
+            for key, val in value.items():
+                properties[key] = self.analyze_json_value(val, depth + 1, max_depth)
+            
+            return {
+                "type": "object",
+                "properties": properties,
+                "required_keys": list(value.keys())
+            }
+        else:
+            return {"type": "unknown", "note": f"unexpected_type: {type(value)}"}
+    
+    def merge_schemas(self, schema1: Dict[str, Any], schema2: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Merge two schemas, combining their information.
+        
+        Args:
+            schema1: First schema
+            schema2: Second schema
+            
+        Returns:
+            Merged schema
+        """
+        if schema1["type"] != schema2["type"]:
+            # Different types, create a union
+            return {
+                "type": "union",
+                "possible_types": sorted(set([schema1["type"], schema2["type"]])),
+                "schemas": [schema1, schema2]
+            }
+        
+        merged = {"type": schema1["type"]}
+        
+        if schema1["type"] == "array":
+            # Merge array item types
+            item_types = set(schema1.get("item_types", []))
+            item_types.update(schema2.get("item_types", []))
+            merged["item_types"] = sorted(list(item_types))
+            
+            # Merge length ranges
+            len1 = schema1.get("length_range", [0, 0])
+            len2 = schema2.get("length_range", [0, 0])
+            merged["length_range"] = [min(len1[0], len2[0]), max(len1[1], len2[1])]
+            
+            # Merge sample items if available
+            if "sample_items" in schema1 or "sample_items" in schema2:
+                merged["sample_items"] = (
+                    schema1.get("sample_items", []) + 
+                    schema2.get("sample_items", [])
+                )[:5]  # Keep max 5 samples
+        
+        elif schema1["type"] == "object":
+            # Merge object properties
+            properties = {}
+            all_keys = set()
+            
+            # Copy properties from first schema
+            for key, val in schema1.get("properties", {}).items():
+                properties[key] = val
+                all_keys.add(key)
+            
+            # Merge properties from second schema
+            for key, val in schema2.get("properties", {}).items():
+                if key in properties:
+                    properties[key] = self.merge_schemas(properties[key], val)
+                else:
+                    properties[key] = val
+                all_keys.add(key)
+            
+            merged["properties"] = properties
+            merged["required_keys"] = sorted(list(all_keys))
+        
+        # Copy other fields
+        for key in schema1:
+            if key not in merged and key != "type":
+                merged[key] = schema1[key]
+        
+        return merged
+    
+    def _extract_all_keys(self, obj: Any, prefix: str = "") -> List[str]:
+        """
+        Recursively extract all keys from a JSON object.
+        
+        Args:
+            obj: JSON object to analyze
+            prefix: Prefix for nested keys
+            
+        Returns:
+            List of all keys found
+        """
+        keys = []
+        
+        if isinstance(obj, dict):
+            for key, value in obj.items():
+                full_key = f"{prefix}.{key}" if prefix else key
+                keys.append(full_key)
+                keys.extend(self._extract_all_keys(value, full_key))
+        
+        elif isinstance(obj, list):
+            for i, item in enumerate(obj):
+                keys.extend(self._extract_all_keys(item, f"{prefix}[{i}]" if prefix else f"[{i}]"))
+        
+        return keys
+    
+    def _process_chunk(self, chunk_data: List[str]) -> Tuple[Counter, List[Dict], int, int]:
+        """
+        Process a chunk of JSONL lines.
+        
+        Args:
+            chunk_data: List of JSONL lines to process
+            
+        Returns:
+            Tuple of (keys_counter, sample_objects, valid_count, error_count)
+        """
+        all_keys = Counter()
+        sample_objects = []
+        valid_count = 0
+        error_count = 0
+        
+        for line in chunk_data:
+            line = line.strip()
+            if not line:
+                continue
+            
+            try:
+                obj = json.loads(line)
+                valid_count += 1
+                
+                # Collect all keys from this object
+                keys = self._extract_all_keys(obj)
+                all_keys.update(keys)
+                
+                # Keep sample objects for schema analysis
+                if len(sample_objects) < self.max_samples:
+                    sample_objects.append(obj)
+            
+            except json.JSONDecodeError:
+                error_count += 1
+        
+        return all_keys, sample_objects, valid_count, error_count
+    
+    def _read_file_chunks(self, file_path: Path) -> List[List[str]]:
+        """
+        Read a JSONL file in chunks for parallel processing.
+        
+        Args:
+            file_path: Path to the JSONL file
+            
+        Returns:
+            List of chunks, each containing lines to process
+        """
+        chunks = []
+        current_chunk = []
+        
+        try:
+            with open(file_path, 'r', encoding='utf-8') as f:
+                for line in f:
+                    current_chunk.append(line)
+                    
+                    if len(current_chunk) >= self.chunk_size:
+                        chunks.append(current_chunk)
+                        current_chunk = []
+                
+                # Add remaining lines
+                if current_chunk:
+                    chunks.append(current_chunk)
+        
+        except Exception as e:
+            raise RuntimeError(f"Error reading file {file_path}: {e}")
+        
+        return chunks
+    
+    def analyze_jsonl_file(self, file_path: Union[str, Path]) -> Dict[str, Any]:
+        """
+        Analyze a JSONL file and return schema information using parallel processing.
+        
+        Args:
+            file_path: Path to the JSONL file
+            
+        Returns:
+            Dictionary containing schema analysis results
+        """
+        file_path = Path(file_path)
+        
+        if not file_path.exists():
+            raise FileNotFoundError(f"File not found: {file_path}")
+        
+        start_time = time.time()
+        file_size = file_path.stat().st_size
+        print(f"Analyzing {file_path.name} ({file_size / (1024*1024*1024):.2f} GB)...")
+        
+        # Statistics
+        total_lines = 0
+        valid_lines = 0
+        error_lines = 0
+        all_keys = Counter()
+        merged_schema = None
+        sample_objects = []
+        
+        # Read file in chunks and process in parallel
+        chunks = self._read_file_chunks(file_path)
+        
+        if len(chunks) == 1 or self.max_workers == 1:
+            # Process sequentially for small files or single worker
+            for chunk in chunks:
+                chunk_keys, chunk_samples, chunk_valid, chunk_errors = self._process_chunk(chunk)
+                all_keys.update(chunk_keys)
+                sample_objects.extend(chunk_samples)
+                valid_lines += chunk_valid
+                error_lines += chunk_errors
+                total_lines += len(chunk)
+        else:
+            # Process chunks in parallel
+            with ProcessPoolExecutor(max_workers=self.max_workers) as executor:
+                # Submit all chunks for processing
+                future_to_chunk = {
+                    executor.submit(self._process_chunk, chunk): chunk 
+                    for chunk in chunks
+                }
+                
+                # Collect results as they complete
+                for future in as_completed(future_to_chunk):
+                    chunk_keys, chunk_samples, chunk_valid, chunk_errors = future.result()
+                    all_keys.update(chunk_keys)
+                    sample_objects.extend(chunk_samples)
+                    valid_lines += chunk_valid
+                    error_lines += chunk_errors
+                    total_lines += len(future_to_chunk[future])
+                    
+                    # Limit sample objects
+                    if len(sample_objects) >= self.max_samples:
+                        sample_objects = sample_objects[:self.max_samples]
+        
+        # Analyze schema from sample objects
+        if sample_objects:
+            for obj in sample_objects:
+                obj_schema = self.analyze_json_value(obj)
+                
+                if merged_schema is None:
+                    merged_schema = obj_schema
+                else:
+                    merged_schema = self.merge_schemas(merged_schema, obj_schema)
+        
+        # Prepare results
+        elapsed_time = time.time() - start_time
+        results = {
+            "file_path": str(file_path),
+            "file_size_bytes": file_size,
+            "total_lines": total_lines,
+            "valid_lines": valid_lines,
+            "error_lines": error_lines,
+            "sample_count": len(sample_objects),
+            "all_keys": dict(all_keys.most_common()),
+            "unique_key_count": len(all_keys),
+            "schema": merged_schema,
+            "analysis_timestamp": time.time(),
+            "processing_time_seconds": elapsed_time,
+            "workers_used": self.max_workers,
+            "chunks_processed": len(chunks)
+        }
+        
+        print(f"  Completed in {elapsed_time:.2f}s - {valid_lines:,} valid lines, {error_lines:,} errors")
+        
+        # Clean up memory
+        gc.collect()
+        
+        return results
+    
+    def analyze_directory(self, directory_path: Union[str, Path], pattern: str = "*.jsonl") -> Dict[str, Any]:
+        """
+        Analyze all JSONL files in a directory using parallel processing.
+        
+        Args:
+            directory_path: Path to directory containing JSONL files
+            pattern: File pattern to match (default: *.jsonl)
+            
+        Returns:
+            Dictionary containing analysis results for all files
+        """
+        directory_path = Path(directory_path)
+        
+        if not directory_path.exists():
+            raise FileNotFoundError(f"Directory not found: {directory_path}")
+        
+        # Find all JSONL files
+        jsonl_files = list(directory_path.glob(pattern))
+        
+        if not jsonl_files:
+            print(f"No JSONL files found in {directory_path} with pattern {pattern}")
+            return {"files": [], "summary": {}}
+        
+        print(f"Found {len(jsonl_files)} JSONL files to analyze using {self.max_workers} workers...")
+        start_time = time.time()
+        
+        # Sort files by size (largest first) for better load balancing
+        jsonl_files.sort(key=lambda f: f.stat().st_size, reverse=True)
+        
+        # Analyze files in parallel
+        file_results = {}
+        
+        if len(jsonl_files) == 1 or self.max_workers == 1:
+            # Process sequentially for single file
+            for file_path in jsonl_files:
+                try:
+                    file_results[file_path.name] = self.analyze_jsonl_file(file_path)
+                except Exception as e:
+                    print(f"Error analyzing {file_path.name}: {e}")
+                    file_results[file_path.name] = {"error": str(e)}
+        else:
+            # Process files in parallel
+            with ThreadPoolExecutor(max_workers=self.max_workers) as executor:
+                # Submit all files for analysis
+                future_to_file = {
+                    executor.submit(self.analyze_jsonl_file, file_path): file_path 
+                    for file_path in jsonl_files
+                }
+                
+                # Collect results as they complete
+                for future in as_completed(future_to_file):
+                    file_path = future_to_file[future]
+                    try:
+                        result = future.result()
+                        file_results[file_path.name] = result
+                    except Exception as e:
+                        print(f"Error analyzing {file_path.name}: {e}")
+                        file_results[file_path.name] = {"error": str(e)}
+        
+        # Create summary
+        successful_results = [r for r in file_results.values() if "error" not in r]
+        summary = {
+            "total_files": len(jsonl_files),
+            "successfully_analyzed": len(successful_results),
+            "total_size_bytes": sum(
+                r.get("file_size_bytes", 0) for r in successful_results
+            ),
+            "total_lines": sum(
+                r.get("total_lines", 0) for r in successful_results
+            ),
+            "total_valid_lines": sum(
+                r.get("valid_lines", 0) for r in successful_results
+            ),
+            "total_processing_time": sum(
+                r.get("processing_time_seconds", 0) for r in successful_results
+            ),
+            "average_processing_speed_mb_per_sec": 0
+        }
+        
+        # Calculate processing speed
+        if summary["total_processing_time"] > 0:
+            total_mb = summary["total_size_bytes"] / (1024 * 1024)
+            summary["average_processing_speed_mb_per_sec"] = total_mb / summary["total_processing_time"]
+        
+        elapsed_time = time.time() - start_time
+        summary["total_elapsed_time"] = elapsed_time
+        
+        print(f"\nDirectory analysis completed in {elapsed_time:.2f}s")
+        print(f"Processed {summary['total_valid_lines']:,} valid lines from {summary['successfully_analyzed']} files")
+        print(f"Average speed: {summary['average_processing_speed_mb_per_sec']:.2f} MB/sec")
+        
+        return {
+            "directory": str(directory_path),
+            "pattern": pattern,
+            "files": file_results,
+            "summary": summary
+        }
+    
+    def save_results(self, results: Dict[str, Any], output_path: Union[str, Path]):
+        """
+        Save analysis results to a JSON file.
+        
+        Args:
+            results: Analysis results to save
+            output_path: Path to save the results
+        """
+        output_path = Path(output_path)
+        
+        try:
+            start_time = time.time()
+            with open(output_path, 'w', encoding='utf-8') as f:
+                json.dump(results, f, indent=2, ensure_ascii=False)
+            
+            save_time = time.time() - start_time
+            file_size = output_path.stat().st_size
+            print(f"Results saved to {output_path} ({file_size / (1024*1024):.2f} MB) in {save_time:.2f}s")
+            
+        except Exception as e:
+            raise RuntimeError(f"Error saving results to {output_path}: {e}")
+
+
+def main():
+    """Main function for command-line usage."""
+    parser = argparse.ArgumentParser(
+        description="Optimized JSONL schema analyzer using multiple cores"
+    )
+    parser.add_argument(
+        "path",
+        help="Path to JSONL file or directory containing JSONL files"
+    )
+    parser.add_argument(
+        "-o", "--output",
+        help="Output file for analysis results (JSON format)"
+    )
+    parser.add_argument(
+        "-p", "--pattern",
+        default="*.jsonl",
+        help="File pattern when analyzing directory (default: *.jsonl)"
+    )
+    parser.add_argument(
+        "-s", "--max-samples",
+        type=int,
+        default=1000,
+        help="Maximum number of JSON objects to sample per file (default: 1000)"
+    )
+    parser.add_argument(
+        "-w", "--workers",
+        type=int,
+        default=None,
+        help="Number of worker processes (default: CPU count, max 8)"
+    )
+    parser.add_argument(
+        "-c", "--chunk-size",
+        type=int,
+        default=1000,
+        help="Number of lines to process in each chunk (default: 1000)"
+    )
+    parser.add_argument(
+        "--directory",
+        action="store_true",
+        help="Treat path as directory instead of single file"
+    )
+    parser.add_argument(
+        "--profile",
+        action="store_true",
+        help="Enable performance profiling"
+    )
+    
+    args = parser.parse_args()
+    
+    # Initialize analyzer
+    analyzer = OptimizedJSONLSchemaAnalyzer(
+        max_samples=args.max_samples,
+        max_workers=args.workers,
+        chunk_size=args.chunk_size
+    )
+    
+    try:
+        start_time = time.time()
+        
+        # Analyze file or directory
+        if args.directory or Path(args.path).is_dir():
+            results = analyzer.analyze_directory(args.path, args.pattern)
+        else:
+            results = analyzer.analyze_jsonl_file(args.path)
+        
+        total_time = time.time() - start_time
+        
+        # Save or print results
+        if args.output:
+            analyzer.save_results(results, args.output)
+        else:
+            print("\n" + "="*50)
+            print("ANALYSIS RESULTS")
+            print("="*50)
+            print(json.dumps(results, indent=2, ensure_ascii=False))
+        
+        print(f"\nTotal analysis time: {total_time:.2f}s")
+    
+    except Exception as e:
+        print(f"Error: {e}", file=sys.stderr)
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

scripts/Json Analyzer/run_schema_analysis.py

@@ -0,0 +1,212 @@
+#!/usr/bin/env python3
+"""
+Run JSONL Schema Analysis with Default Configuration
+
+This script runs the JSONL schema analyzer using predefined constants,
+so you don't need to pass any command line arguments.
+"""
+
+import sys
+from pathlib import Path
+
+# Get the root directory (assuming this script is in the scripts folder)
+ROOT_DIR = Path(__file__).parent.parent.parent
+
+# Configuration constants
+DEFAULT_INPUT_DIR = ROOT_DIR / "raw_data"
+DEFAULT_OUTPUT_DIR = ROOT_DIR / "intermediate"
+DEFAULT_LANG_FILTER = "fr"
+DEFAULT_INPUT_FILENAME = f"{DEFAULT_LANG_FILTER}-raw-wiktextract-data.jsonl"
+DEFAULT_INPUT_FILE = DEFAULT_INPUT_DIR / DEFAULT_INPUT_FILENAME
+
+# Analyzer configuration
+DEFAULT_MAX_SAMPLES = 1000
+DEFAULT_MAX_WORKERS = None  # Will use CPU count
+DEFAULT_PARALLEL_THRESHOLD_MB = 100
+DEFAULT_CHUNK_SIZE = 1000
+
+# Output configuration
+DEFAULT_OUTPUT_FILENAME = f"{DEFAULT_LANG_FILTER}_schema_analysis.json"
+DEFAULT_OUTPUT_FILE = DEFAULT_OUTPUT_DIR / DEFAULT_OUTPUT_FILENAME
+
+def main():
+    """Run the schema analysis with default configuration."""
+    
+    print("=" * 60)
+    print("JSONL Schema Analysis - Default Configuration")
+    print("=" * 60)
+    
+    # Display configuration
+    print(f"Root directory: {ROOT_DIR}")
+    print(f"Input directory: {DEFAULT_INPUT_DIR}")
+    print(f"Input file: {DEFAULT_INPUT_FILENAME}")
+    print(f"Output directory: {DEFAULT_OUTPUT_DIR}")
+    print(f"Output file: {DEFAULT_OUTPUT_FILENAME}")
+    print(f"Language filter: {DEFAULT_LANG_FILTER}")
+    print(f"Max samples: {DEFAULT_MAX_SAMPLES:,}")
+    print(f"Parallel threshold: {DEFAULT_PARALLEL_THRESHOLD_MB} MB")
+    print(f"Chunk size: {DEFAULT_CHUNK_SIZE}")
+    print(f"Max workers: {DEFAULT_MAX_WORKERS or 'Auto (CPU count)'}")
+    print()
+    
+    # Check if input file exists
+    if not DEFAULT_INPUT_FILE.exists():
+        print(f"❌ Input file not found: {DEFAULT_INPUT_FILE}")
+        print()
+        print("Available files in raw_data directory:")
+        
+        # List available JSONL files
+        if DEFAULT_INPUT_DIR.exists():
+            jsonl_files = list(DEFAULT_INPUT_DIR.glob("*.jsonl"))
+            if jsonl_files:
+                for i, file in enumerate(sorted(jsonl_files), 1):
+                    size_mb = file.stat().st_size / (1024 * 1024)
+                    print(f"  {i:2d}. {file.name} ({size_mb:.1f} MB)")
+            else:
+                print("  No JSONL files found.")
+        else:
+            print("  raw_data directory not found.")
+        
+        print()
+        print("To analyze a different file, modify the constants in this script:")
+        print(f"  - DEFAULT_LANG_FILTER (currently: '{DEFAULT_LANG_FILTER}')")
+        print(f"  - DEFAULT_INPUT_FILENAME (currently: '{DEFAULT_INPUT_FILENAME}')")
+        return False
+    
+    # Create output directory if it doesn't exist
+    DEFAULT_OUTPUT_DIR.mkdir(exist_ok=True)
+    
+    print(f"✅ Input file found: {DEFAULT_INPUT_FILE.stat().st_size / (1024*1024):.1f} MB")
+    print()
+    
+    try:
+        # Import the hybrid analyzer
+        sys.path.insert(0, str(Path(__file__).parent))
+        from jsonl_schema_analyzer_hybrid import HybridJSONLSchemaAnalyzer
+        
+        # Initialize analyzer with default configuration
+        analyzer = HybridJSONLSchemaAnalyzer(
+            max_samples=DEFAULT_MAX_SAMPLES,
+            max_workers=DEFAULT_MAX_WORKERS,
+            parallel_threshold_mb=DEFAULT_PARALLEL_THRESHOLD_MB,
+            chunk_size=DEFAULT_CHUNK_SIZE
+        )
+        
+        print("🚀 Starting analysis...")
+        print()
+        
+        # Run analysis
+        results = analyzer.analyze_jsonl_file(DEFAULT_INPUT_FILE)
+        
+        # Save results
+        analyzer.save_results(results, DEFAULT_OUTPUT_FILE)
+        
+        print()
+        print("=" * 60)
+        print("ANALYSIS COMPLETE")
+        print("=" * 60)
+        print(f"📊 Results saved to: {DEFAULT_OUTPUT_FILE}")
+        print(f"📈 Valid lines processed: {results.get('valid_lines', 0):,}")
+        print(f"🔑 Unique keys found: {results.get('unique_key_count', 0):,}")
+        print(f"⏱️  Processing time: {results.get('processing_time_seconds', 0):.2f} seconds")
+        print(f"📁 File size: {results.get('file_size_bytes', 0) / (1024*1024):.1f} MB")
+        
+        if results.get('processing_strategy'):
+            print(f"🔧 Strategy used: {results['processing_strategy']}")
+        
+        return True
+        
+    except ImportError as e:
+        print(f"❌ Error importing analyzer: {e}")
+        print("Make sure jsonl_schema_analyzer_hybrid.py is in the same directory.")
+        return False
+    except Exception as e:
+        print(f"❌ Error during analysis: {e}")
+        return False
+
+def run_directory_analysis():
+    """Run analysis on entire directory with default configuration."""
+    
+    print("=" * 60)
+    print("Directory JSONL Schema Analysis - Default Configuration")
+    print("=" * 60)
+    
+    # Display configuration
+    print(f"Input directory: {DEFAULT_INPUT_DIR}")
+    print(f"Output directory: {DEFAULT_OUTPUT_DIR}")
+    print(f"Pattern: *.jsonl")
+    print(f"Max samples: {DEFAULT_MAX_SAMPLES:,}")
+    print(f"Parallel threshold: {DEFAULT_PARALLEL_THRESHOLD_MB} MB")
+    print(f"Chunk size: {DEFAULT_CHUNK_SIZE}")
+    print()
+    
+    # Check if input directory exists
+    if not DEFAULT_INPUT_DIR.exists():
+        print(f"❌ Input directory not found: {DEFAULT_INPUT_DIR}")
+        return False
+    
+    # Create output directory if it doesn't exist
+    DEFAULT_OUTPUT_DIR.mkdir(exist_ok=True)
+    
+    try:
+        # Import the hybrid analyzer
+        sys.path.insert(0, str(Path(__file__).parent))
+        from jsonl_schema_analyzer_hybrid import HybridJSONLSchemaAnalyzer
+        
+        # Initialize analyzer with default configuration
+        analyzer = HybridJSONLSchemaAnalyzer(
+            max_samples=DEFAULT_MAX_SAMPLES,
+            max_workers=DEFAULT_MAX_WORKERS,
+            parallel_threshold_mb=DEFAULT_PARALLEL_THRESHOLD_MB,
+            chunk_size=DEFAULT_CHUNK_SIZE
+        )
+        
+        print("🚀 Starting directory analysis...")
+        print()
+        
+        # Run analysis
+        results = analyzer.analyze_directory(DEFAULT_INPUT_DIR, "*.jsonl")
+        
+        # Save results
+        output_file = DEFAULT_OUTPUT_DIR / "directory_schema_analysis.json"
+        analyzer.save_results(results, output_file)
+        
+        print()
+        print("=" * 60)
+        print("DIRECTORY ANALYSIS COMPLETE")
+        print("=" * 60)
+        print(f"📊 Results saved to: {output_file}")
+        
+        summary = results.get('summary', {})
+        print(f"📁 Files analyzed: {summary.get('successfully_analyzed', 0)}")
+        print(f"📈 Total valid lines: {summary.get('total_valid_lines', 0):,}")
+        print(f"⏱️  Total processing time: {summary.get('total_processing_time', 0):.2f} seconds")
+        print(f"📦 Total data: {summary.get('total_size_bytes', 0) / (1024*1024*1024):.2f} GB")
+        print(f"🚀 Average speed: {summary.get('average_processing_speed_mb_per_sec', 0):.2f} MB/sec")
+        
+        if summary.get('strategies_used'):
+            strategies = summary['strategies_used']
+            print(f"🔧 Sequential files: {strategies.get('sequential', 0)}")
+            print(f"🔧 Parallel files: {strategies.get('parallel', 0)}")
+        
+        return True
+        
+    except ImportError as e:
+        print(f"❌ Error importing analyzer: {e}")
+        print("Make sure jsonl_schema_analyzer_hybrid.py is in the same directory.")
+        return False
+    except Exception as e:
+        print(f"❌ Error during analysis: {e}")
+        return False
+
+if __name__ == "__main__":
+    # You can choose what to run by default:
+    
+    # Option 1: Analyze single file (based on DEFAULT_LANG_FILTER)
+    success = main()
+    
+    # Option 2: Analyze entire directory (comment out the line above and uncomment below)
+    # success = run_directory_analysis()
+    
+    if not success:
+        sys.exit(1)

scripts/collect_samples.py

@@ -0,0 +1,152 @@
+import json
+import pathlib
+import logging
+import sys
+import os
+
+# ==============================================================================
+# --- CONFIGURATION ---
+# ==============================================================================
+
+# --- Paths ---
+# Try to determine project root relative to this script location
+try:
+    SCRIPT_DIR = pathlib.Path(__file__).parent
+    ROOT_DIR = SCRIPT_DIR.parent
+except NameError:
+    SCRIPT_DIR = pathlib.Path.cwd()
+    ROOT_DIR = SCRIPT_DIR.parent
+
+# Input directory containing the source semua.org files
+RAW_DATA_DIR = ROOT_DIR / "raw_data"
+
+# The pattern to match source files
+FILE_PATTERN = "*raw-wiktextract-data.jsonl"
+
+# Output directory for the collected samples
+SAMPLES_DIR = ROOT_DIR / "samples"
+
+# Final output filename
+OUTPUT_FILENAME = "combined_samples.jsonl"
+
+# --- Sampling Options ---
+
+# How many matching entries to take from EACH source file.
+SAMPLES_PER_FILE = 2
+
+# Filter by Language Code.
+# Set to None to include all languages.
+# Example: "en", "de", "fr", "no"
+LANG_FILTER = set()
+# set()
+
+# Filter by Part of Speech.
+# Leave empty set() to include ALL parts of speech.
+# Example: {"noun", "verb", "adj"}
+POS_FILTER = {"verb"}
+
+# Filter to only include entries in their own language (lang_code matches file prefix)
+OWN_LANG_FILTER = True
+
+# ==============================================================================
+# --- END OF CONFIGURATION ---
+# ==============================================================================
+
+# Setup simple logging to console
+logging.basicConfig(level=logging.INFO, format='%(message)s')
+logger = logging.getLogger(__name__)
+
+def collect_samples():
+    # 1. Setup Paths and Directories
+    input_dir = pathlib.Path(RAW_DATA_DIR)
+    output_dir = pathlib.Path(SAMPLES_DIR)
+    output_file = output_dir / OUTPUT_FILENAME
+
+    if not input_dir.exists():
+        logger.error(f"ERROR: Raw data directory not found at: {input_dir}")
+        logger.error("Please ensure your configuration points to the correct folder.")
+        sys.exit(1)
+
+    # Create samples directory if it doesn't exist
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    # 2. Find all matching input files
+    source_files = list(input_dir.glob(FILE_PATTERN))
+    if not source_files:
+        logger.warning(f"No files matching '{FILE_PATTERN}' found in {input_dir}")
+        sys.exit(0)
+
+    logger.info(f"Found {len(source_files)} source files to sample from.")
+    logger.info(f"Target: {SAMPLES_PER_FILE} samples per file.")
+    logger.info(f"Language Filter: {LANG_FILTER if LANG_FILTER else 'ALL'}")
+    logger.info(f"POS Filter: {POS_FILTER if POS_FILTER else 'ALL'}")
+    logger.info(f"Own Language Filter: {'ENABLED' if OWN_LANG_FILTER else 'DISABLED'}")
+    logger.info("-" * 50)
+
+    total_collected = 0
+    
+    # Open the output file once and append samples from all inputs to it
+    try:
+        with open(output_file, 'w', encoding='utf-8') as out_f:
+            
+            for src_file in source_files:
+                logger.info(f"Scanning: {src_file.name}...")
+                lang_from_file = src_file.name[:2]
+                file_collected = 0
+                lines_read = 0
+                
+                try:
+                    with open(src_file, 'r', encoding='utf-8') as in_f:
+                        for line in in_f:
+                            lines_read += 1
+                            
+                            # Stop reading this file if we have enough samples
+                            if file_collected >= SAMPLES_PER_FILE:
+                                break
+
+                            if not line.strip():
+                                continue
+
+                            try:
+                                entry = json.loads(line)
+                                
+                                # --- Filtering Logic ---
+                                # 1. Language Filter
+                                if LANG_FILTER and entry.get('lang_code') != LANG_FILTER:
+                                    continue
+
+                                # 2. POS Filter
+                                if POS_FILTER and entry.get('pos') not in POS_FILTER:
+                                    continue
+
+                                # 3. Own Language Filter
+                                if OWN_LANG_FILTER and entry.get('lang_code') != lang_from_file:
+                                    continue
+
+                                # --- If it passed filters, save it ---
+                                # We write it exactly as it is in the source
+                                json.dump(entry, out_f, ensure_ascii=False)
+                                out_f.write('\n')
+                                file_collected += 1
+                                total_collected += 1
+
+                            except json.JSONDecodeError:
+                                # Ignore bad lines in source files during sampling
+                                continue
+                                
+                    logger.info(f"   -> Collected {file_collected} samples (scanned {lines_read} lines)")
+
+                except Exception as e:
+                    logger.error(f"   ERROR reading {src_file.name}: {e}")
+
+    except Exception as e:
+         logger.critical(f"FATAL ERROR writing output file: {e}")
+         sys.exit(1)
+
+    logger.info("-" * 50)
+    logger.info("SAMPLING COMPLETE")
+    logger.info(f"Total entries collected: {total_collected}")
+    logger.info(f"Output saved to: {output_file}")
+
+if __name__ == "__main__":
+    collect_samples()

scripts/count_pos_values.py

@@ -0,0 +1,142 @@
+#!/usr/bin/env python3
+"""
+Script to count all different "pos" values in JSONL files using parallel processing.
+Analyzes all JSONL files in the raw_data directory and displays frequency counts.
+"""
+
+import json
+import os
+import glob
+from collections import Counter
+from concurrent.futures import ProcessPoolExecutor, as_completed
+from multiprocessing import cpu_count
+import time
+from typing import Dict, List, Tuple
+
+
+def process_jsonl_file(file_path: str) -> Tuple[str, Counter]:
+    """
+    Process a single JSONL file and count POS values.
+    
+    Args:
+        file_path: Path to the JSONL file
+        
+    Returns:
+        Tuple of (filename, Counter of POS values)
+    """
+    pos_counter = Counter()
+    line_count = 0
+    
+    try:
+        with open(file_path, 'r', encoding='utf-8') as f:
+            for line_num, line in enumerate(f, 1):
+                line = line.strip()
+                if not line:
+                    continue
+                    
+                try:
+                    data = json.loads(line)
+                    if 'pos' in data and data['pos']:
+                        pos_counter[data['pos']] += 1
+                    line_count += 1
+                except json.JSONDecodeError as e:
+                    print(f"Warning: JSON decode error in {file_path} at line {line_num}: {e}")
+                    continue
+                    
+    except Exception as e:
+        print(f"Error processing file {file_path}: {e}")
+        return file_path, Counter()
+    
+    print(f"Processed {file_path}: {line_count} lines, {sum(pos_counter.values())} POS entries")
+    return file_path, pos_counter
+
+
+def main():
+    """Main function to process all JSONL files and display POS statistics."""
+    # Find all JSONL files in raw_data directory
+    raw_data_dir = "raw_data"
+    jsonl_files = glob.glob(os.path.join(raw_data_dir, "*.jsonl"))
+    
+    if not jsonl_files:
+        print(f"No JSONL files found in {raw_data_dir}")
+        return
+    
+    print(f"Found {len(jsonl_files)} JSONL files to process")
+    print(f"Using {cpu_count()} CPU cores for parallel processing")
+    print("-" * 60)
+    
+    # Process files in parallel
+    start_time = time.time()
+    all_pos_counts = Counter()
+    file_results = {}
+    
+    with ProcessPoolExecutor(max_workers=cpu_count()) as executor:
+        # Submit all files for processing
+        future_to_file = {
+            executor.submit(process_jsonl_file, file_path): file_path 
+            for file_path in jsonl_files
+        }
+        
+        # Collect results as they complete
+        for future in as_completed(future_to_file):
+            file_path = future_to_file[future]
+            try:
+                filename, pos_counter = future.result()
+                file_results[filename] = pos_counter
+                all_pos_counts.update(pos_counter)
+            except Exception as e:
+                print(f"Error processing {file_path}: {e}")
+    
+    end_time = time.time()
+    processing_time = end_time - start_time
+    
+    # Display results
+    print("\n" + "=" * 80)
+    print("POS VALUE COUNTS ACROSS ALL FILES")
+    print("=" * 80)
+    print(f"Total processing time: {processing_time:.2f} seconds")
+    print(f"Total POS entries found: {sum(all_pos_counts.values()):,}")
+    print(f"Unique POS values: {len(all_pos_counts)}")
+    print("\nTop 50 most common POS values:")
+    print("-" * 80)
+    
+    # Sort by frequency (descending)
+    sorted_pos = sorted(all_pos_counts.items(), key=lambda x: x[1], reverse=True)
+    
+    for pos, count in sorted_pos[:100]:
+        percentage = (count / sum(all_pos_counts.values())) * 100
+        print(f"{pos:<20} {count:>10,} ({percentage:5.2f}%)")
+    
+    if len(sorted_pos) > 100:
+        print(f"\n... and {len(sorted_pos) - 100} more POS values")
+    
+    # Show all unique POS values (alphabetical)
+    print("\n" + "=" * 80)
+    print("ALL UNIQUE POS VALUES (ALPHABETICAL)")
+    print("=" * 80)
+    
+    for pos, count in sorted(all_pos_counts.items(), key=lambda x: x[0].lower()):
+        print(f"{pos:<30} {count:>10,}")
+    
+    # Per-file breakdown
+    print("\n" + "=" * 80)
+    print("PER-FILE BREAKDOWN")
+    print("=" * 80)
+    
+    for filename, pos_counter in sorted(file_results.items()):
+        total_entries = sum(pos_counter.values())
+        if total_entries > 0:
+            print(f"\n{os.path.basename(filename)}:")
+            print(f"  Total entries: {total_entries:,}")
+            print(f"  Unique POS values: {len(pos_counter)}")
+            
+            # All POS values for this file (sorted by frequency)
+            all_pos = sorted(pos_counter.items(), key=lambda x: x[1], reverse=True)
+            for pos, count in all_pos:
+                print(f"    {pos:<15} {count:>8,}")
+    
+    print(f"\nProcessing completed in {processing_time:.2f} seconds")
+
+
+if __name__ == "__main__":
+    main()

scripts/lang_config.py

@@ -0,0 +1,401 @@
+GERMAN_VERB_CONFIG = {
+    "clean_prefixes": ["ich", "du", "er/sie/es", "wir", "ihr", "sie"],
+    "normalization_rules": [
+        {"field": "pronouns", "match": "ich", "add_tags": ["first-person", "singular", "indicative", "active"]},
+        {"field": "pronouns", "match": "du", "add_tags": ["second-person", "singular", "indicative", "active"]},
+        {"field": "pronouns", "match": "er", "add_tags": ["third-person", "singular", "indicative", "active"]},
+        {"field": "pronouns", "match": "sie", "add_tags": ["third-person", "singular", "indicative", "active"]},
+        {"field": "pronouns", "match": "es", "add_tags": ["third-person", "singular", "indicative", "active"]},
+        {"field": "pronouns", "match": "wir", "add_tags": ["first-person", "plural", "indicative", "active"]},
+        {"field": "pronouns", "match": "ihr", "add_tags": ["second-person", "plural", "indicative", "active"]}
+    ],
+    "properties": [
+        {
+            "name": "auxiliary",
+            "multivalue": True,  # <--- CRITICAL CHANGE HERE
+            "default": ["haben"], 
+            "rules": [
+                # Check for explicit raw tags
+                {"value": "sein", "criteria": {"raw_tags": ["Hilfsverb sein"]}},
+                {"value": "haben", "criteria": {"raw_tags": ["Hilfsverb haben"]}},
+                # Check for 'common forms' that imply the aux
+                {"value": "sein", "criteria": {"form_regex": "^sein$", "tags": ["auxiliary", "perfect"]}},
+                {"value": "haben", "criteria": {"form_regex": "^haben$", "tags": ["auxiliary", "perfect"]}}
+            ]
+        },
+        {
+            "name": "separability",
+            "default": "inseparable",
+            "rules": [
+                {"value": "separable", "criteria": {"tags": ["separable"]}},
+                {"value": "inseparable", "criteria": {"tags": ["inseparable"]}},
+                {"value": "separable", "criteria": {"tags": ["participle-2"], "form_regex": "^(?!ge).+ge.+$"}} 
+            ]
+        }
+    ],
+    "schema": {
+        "infinitive": {
+            "type": "single",
+            "criteria": {"tags": ["infinitive", "present"], "exclude_tags": ["extended", "passive", "reflexive", "zu"]} 
+        },
+        "participle_perfect": {
+            "type": "single",
+            "criteria": {"tags": ["participle-2", "perfect"], "exclude_tags": ["active", "passive", "auxiliary"]}
+        },
+        "imperative": {
+            "type": "list",
+            "size": 2,
+            "base_criteria": {"tags": ["imperative", "present", "active"]},
+            "indices": [
+                {"index": 0, "tags": ["singular", "second-person"]},
+                {"index": 1, "tags": ["plural", "second-person"]}
+            ]
+        },
+        "present": {
+            "type": "list",
+            "size": 6,
+            "base_criteria": {"tags": ["indicative", "present", "active"], "exclude_tags": ["passive"]},
+            "indices": [
+                {"index": 0, "tags": ["first-person", "singular"]},
+                {"index": 1, "tags": ["second-person", "singular"]},
+                {"index": 2, "tags": ["third-person", "singular"]},
+                {"index": 3, "tags": ["first-person", "plural"]},
+                {"index": 4, "tags": ["second-person", "plural"]},
+                {"index": 5, "tags": ["third-person", "plural"]}
+            ]
+        },
+        "past": {
+            "type": "list",
+            "size": 6,
+            "base_criteria": {"tags": ["indicative", "past", "active"], "exclude_tags": ["passive"]},
+            "indices": [
+                {"index": 0, "tags": ["first-person", "singular"]},
+                {"index": 1, "tags": ["second-person", "singular"]},
+                {"index": 2, "tags": ["third-person", "singular"]},
+                {"index": 3, "tags": ["first-person", "plural"]},
+                {"index": 4, "tags": ["second-person", "plural"]},
+                {"index": 5, "tags": ["third-person", "plural"]}
+            ]
+        },
+        "subjunctive_ii": {
+            "type": "list",
+            "size": 6,
+            "base_criteria": {"tags": ["subjunctive-ii", "past", "active"], "exclude_tags": ["passive"]},
+            "indices": [
+                {"index": 0, "tags": ["first-person", "singular"]},
+                {"index": 1, "tags": ["second-person", "singular"]},
+                {"index": 2, "tags": ["third-person", "singular"]},
+                {"index": 3, "tags": ["first-person", "plural"]},
+                {"index": 4, "tags": ["second-person", "plural"]},
+                {"index": 5, "tags": ["third-person", "plural"]}
+            ]
+        }
+    }
+}
+
+FRENCH_VERB_CONFIG = {
+    "skip_normalization_if_source": False,
+    
+    # CHANGED: Set to False to prevent crashes on idioms, rare words, and defective verbs
+    "validate_completeness": False, 
+
+    "clean_prefixes": [
+        "qu'", "qu’", "que", "j'", "j’", "je", "tu", 
+        "il/elle/on", "il", "elle", "on", "nous", "vous", "ils/elles", "ils", "elles"
+    ],
+    
+    "normalization_rules": [
+        # Pronoun matches
+        {"field": "form", "match": r"\bje\b", "match_mode": "regex", "add_tags": ["first-person", "singular"]},
+        {"field": "form", "match": r"\bj[’']", "match_mode": "regex", "add_tags": ["first-person", "singular"]},
+        {"field": "form", "match": r"\btu\b", "match_mode": "regex", "add_tags": ["second-person", "singular"]},
+        {"field": "form", "match": r"\b(il|elle|on|il/elle/on)\b", "match_mode": "regex", "add_tags": ["third-person", "singular"]},
+        {"field": "form", "match": r"\[il/ɛl/ɔ̃\]", "match_mode": "regex", "add_tags": ["third-person", "singular"]},
+        {"field": "form", "match": r"\bnous\b", "match_mode": "regex", "add_tags": ["first-person", "plural"]},
+        {"field": "form", "match": r"\bvous\b", "match_mode": "regex", "add_tags": ["second-person", "plural"]},
+        {"field": "form", "match": r"\b(ils|elles|ils/elles)\b", "match_mode": "regex", "add_tags": ["third-person", "plural"]},
+        {"field": "form", "match": r"\[il/ɛl\]", "match_mode": "regex", "add_tags": ["third-person", "plural"]},
+
+        # Suffix Heuristics
+        {"field": "form", "match": r"ons$", "match_mode": "regex", "add_tags": ["first-person", "plural"]},
+        {"field": "form", "match": r"ez$", "match_mode": "regex", "add_tags": ["second-person", "plural"]}
+    ],
+
+    "properties": [
+        {
+            "name": "auxiliary",
+            "multivalue": True,
+            "default": ["avoir"], 
+            "rules": [
+                {"value": "être", "criteria": {"raw_tags": ["auxiliary être"]}},
+                {"value": "avoir", "criteria": {"raw_tags": ["auxiliary avoir"]}},
+                {"value": "être", "criteria": {"tags": ["auxiliary-être"]}},
+                {"value": "avoir", "criteria": {"tags": ["auxiliary-avoir"]}}
+            ]
+        },
+        {
+            "name": "group",
+            "default": "unknown", 
+            "rules": [
+                {"value": "1st-group", "criteria": {"raw_tags": ["1ᵉʳ groupe"]}},
+                {"value": "2nd-group", "criteria": {"raw_tags": ["2ᵉ groupe"]}},
+                {"value": "3rd-group", "criteria": {"raw_tags": ["3ᵉ groupe"]}},
+                {"value": "1st-group", "criteria": {"form_regex": "er$"}},
+                {"value": "2nd-group", "criteria": {"form_regex": "ir$"}},
+                {"value": "3rd-group", "criteria": {"form_regex": "(re|oir)$"}}
+            ]
+        }
+    ],
+
+    "schema": {
+        "infinitive": {
+            "type": "single",
+            "criteria": {"tags": ["infinitive", "present"]}
+        },
+        "participle_present": {
+            "type": "single",
+            "optional": True,
+            "criteria": {"tags": ["participle", "present"]}
+        },
+        "participle_past": {
+            "type": "single",
+            "optional": True,
+            "criteria": {"tags": ["participle", "past"], "exclude_tags": ["multiword-construction"]} 
+        },
+        # All lists are now marked optional to handle defective verbs (like 'traire') and sparse data
+        "indicative_present": {
+            "type": "list", "size": 6, "optional": True,
+            "base_criteria": {"tags": ["indicative", "present"]},
+            "indices": [
+                {"index": 0, "tags": ["first-person", "singular"]},
+                {"index": 1, "tags": ["second-person", "singular"]},
+                {"index": 2, "tags": ["third-person", "singular"]},
+                {"index": 3, "tags": ["first-person", "plural"]},
+                {"index": 4, "tags": ["second-person", "plural"]},
+                {"index": 5, "tags": ["third-person", "plural"]}
+            ]
+        },
+        "indicative_imperfect": {
+            "type": "list", "size": 6, "optional": True,
+            "base_criteria": {"tags": ["indicative", "imperfect"]},
+            "indices": [
+                {"index": 0, "tags": ["first-person", "singular"]},
+                {"index": 1, "tags": ["second-person", "singular"]},
+                {"index": 2, "tags": ["third-person", "singular"]},
+                {"index": 3, "tags": ["first-person", "plural"]},
+                {"index": 4, "tags": ["second-person", "plural"]},
+                {"index": 5, "tags": ["third-person", "plural"]}
+            ]
+        },
+        "indicative_future": {
+            "type": "list", "size": 6, "optional": True,
+            "base_criteria": {"tags": ["indicative", "future"], "exclude_tags": ["perfect"]},
+            "indices": [
+                {"index": 0, "tags": ["first-person", "singular"]},
+                {"index": 1, "tags": ["second-person", "singular"]},
+                {"index": 2, "tags": ["third-person", "singular"]},
+                {"index": 3, "tags": ["first-person", "plural"]},
+                {"index": 4, "tags": ["second-person", "plural"]},
+                {"index": 5, "tags": ["third-person", "plural"]}
+            ]
+        },
+        "indicative_simple_past": { 
+            "type": "list", "size": 6, "optional": True, # Traire/clore do not have this
+            "base_criteria": {"tags": ["indicative", "past"], "exclude_tags": ["multiword-construction", "imperfect", "perfect", "anterior"]},
+            "indices": [
+                {"index": 0, "tags": ["first-person", "singular"]},
+                {"index": 1, "tags": ["second-person", "singular"]},
+                {"index": 2, "tags": ["third-person", "singular"]},
+                {"index": 3, "tags": ["first-person", "plural"]},
+                {"index": 4, "tags": ["second-person", "plural"]},
+                {"index": 5, "tags": ["third-person", "plural"]}
+            ]
+        },
+        "subjunctive_present": {
+            "type": "list", "size": 6, "optional": True,
+            "base_criteria": {"tags": ["subjunctive", "present"]},
+            "indices": [
+                {"index": 0, "tags": ["first-person", "singular"]},
+                {"index": 1, "tags": ["second-person", "singular"]},
+                {"index": 2, "tags": ["third-person", "singular"]},
+                {"index": 3, "tags": ["first-person", "plural"]},
+                {"index": 4, "tags": ["second-person", "plural"]},
+                {"index": 5, "tags": ["third-person", "plural"]}
+            ]
+        },
+        "conditional_present": {
+            "type": "list", "size": 6, "optional": True,
+            "base_criteria": {"tags": ["conditional", "present"]},
+            "indices": [
+                {"index": 0, "tags": ["first-person", "singular"]},
+                {"index": 1, "tags": ["second-person", "singular"]},
+                {"index": 2, "tags": ["third-person", "singular"]},
+                {"index": 3, "tags": ["first-person", "plural"]},
+                {"index": 4, "tags": ["second-person", "plural"]},
+                {"index": 5, "tags": ["third-person", "plural"]}
+            ]
+        },
+        "imperative": {
+            "type": "list", "size": 3, "optional": True,
+            "base_criteria": {"tags": ["imperative", "present"]},
+            "indices": [
+                {"index": 0, "tags": ["singular"]},
+                {"index": 1, "tags": ["plural", "first-person"]},
+                {"index": 2, "tags": ["plural", "second-person"]},
+                {"index": 1, "criteria": {"form_regex": r"ons$"}},
+                {"index": 2, "criteria": {"form_regex": r"ez$"}},
+                {"index": 0, "criteria": {"form_regex": r"[es]$"}} 
+            ]
+        }
+    }
+}
+
+OLD_FRENCH_VERB_CONFIG = {
+    "skip_normalization_if_source": False,
+    "validate_completeness": True, 
+
+    # --- 1. Normalization ---
+    "clean_prefixes": [
+        "qu'", "qu’", "que", "j'", "j’", "je", "tu", 
+        "il/elle/on", "il", "elle", "on", "nous", "vous", "ils/elles", "ils", "elles"
+    ],
+    
+    "normalization_rules": [
+        {"field": "form", "match": r"\bje\b", "match_mode": "regex", "add_tags": ["first-person", "singular"]},
+        {"field": "form", "match": r"\bj[’']", "match_mode": "regex", "add_tags": ["first-person", "singular"]},
+        {"field": "form", "match": r"\btu\b", "match_mode": "regex", "add_tags": ["second-person", "singular"]},
+        {"field": "form", "match": r"\b(il|elle|on|il/elle/on)\b", "match_mode": "regex", "add_tags": ["third-person", "singular"]},
+        {"field": "form", "match": r"\[il/ɛl/ɔ̃\]", "match_mode": "regex", "add_tags": ["third-person", "singular"]},
+        {"field": "form", "match": r"\bnous\b", "match_mode": "regex", "add_tags": ["first-person", "plural"]},
+        {"field": "form", "match": r"\bvous\b", "match_mode": "regex", "add_tags": ["second-person", "plural"]},
+        {"field": "form", "match": r"\b(ils|elles|ils/elles)\b", "match_mode": "regex", "add_tags": ["third-person", "plural"]},
+        {"field": "form", "match": r"\[il/ɛl\]", "match_mode": "regex", "add_tags": ["third-person", "plural"]},
+    ],
+
+    # --- 2. Properties ---
+    "properties": [
+        {
+            "name": "auxiliary",
+            "multivalue": True,
+            "default": ["avoir"], 
+            "rules": [
+                {"value": "être", "criteria": {"raw_tags": ["auxiliary être"]}},
+                {"value": "avoir", "criteria": {"raw_tags": ["auxiliary avoir"]}},
+                {"value": "être", "criteria": {"tags": ["auxiliary-être"]}},
+                {"value": "avoir", "criteria": {"tags": ["auxiliary-avoir"]}}
+            ]
+        },
+        {
+            "name": "group",
+            "default": "unknown", 
+            "rules": [
+                {"value": "1st-group", "criteria": {"raw_tags": ["1ᵉʳ groupe"]}},
+                {"value": "2nd-group", "criteria": {"raw_tags": ["2ᵉ groupe"]}},
+                {"value": "3rd-group", "criteria": {"raw_tags": ["3ᵉ groupe"]}},
+                {"value": "1st-group", "criteria": {"form_regex": "er$"}},
+                {"value": "2nd-group", "criteria": {"form_regex": "ir$"}},
+                {"value": "3rd-group", "criteria": {"form_regex": "(re|oir)$"}}
+            ]
+        }
+    ],
+
+    # --- 3. Schema ---
+    "schema": {
+        "infinitive": {
+            "type": "single",
+            "criteria": {"tags": ["infinitive", "present"]}
+        },
+        "participle_present": {
+            "type": "single",
+            "optional": True,  # <--- NEW: Allows missing participle
+            "criteria": {"tags": ["participle", "present"]}
+        },
+        "participle_past": {
+            "type": "single",
+            "optional": True, # <--- Often missing in defective verbs
+            "criteria": {"tags": ["participle", "past"], "exclude_tags": ["multiword-construction"]} 
+        },
+        "indicative_present": {
+            "type": "list", "size": 6,
+            "base_criteria": {"tags": ["indicative", "present"]},
+            "indices": [
+                {"index": 0, "tags": ["first-person", "singular"]},
+                {"index": 1, "tags": ["second-person", "singular"]},
+                {"index": 2, "tags": ["third-person", "singular"]},
+                {"index": 3, "tags": ["first-person", "plural"]},
+                {"index": 4, "tags": ["second-person", "plural"]},
+                {"index": 5, "tags": ["third-person", "plural"]}
+            ]
+        },
+        "indicative_imperfect": {
+            "type": "list", "size": 6,
+            "base_criteria": {"tags": ["indicative", "imperfect"]},
+            "indices": [
+                {"index": 0, "tags": ["first-person", "singular"]},
+                {"index": 1, "tags": ["second-person", "singular"]},
+                {"index": 2, "tags": ["third-person", "singular"]},
+                {"index": 3, "tags": ["first-person", "plural"]},
+                {"index": 4, "tags": ["second-person", "plural"]},
+                {"index": 5, "tags": ["third-person", "plural"]}
+            ]
+        },
+        "indicative_future": {
+            "type": "list", "size": 6,
+            "base_criteria": {"tags": ["indicative", "future"], "exclude_tags": ["perfect"]},
+            "indices": [
+                {"index": 0, "tags": ["first-person", "singular"]},
+                {"index": 1, "tags": ["second-person", "singular"]},
+                {"index": 2, "tags": ["third-person", "singular"]},
+                {"index": 3, "tags": ["first-person", "plural"]},
+                {"index": 4, "tags": ["second-person", "plural"]},
+                {"index": 5, "tags": ["third-person", "plural"]}
+            ]
+        },
+        "indicative_simple_past": { 
+            "type": "list", "size": 6,
+            "base_criteria": {"tags": ["indicative", "past"], "exclude_tags": ["multiword-construction", "imperfect", "perfect", "anterior"]},
+            "indices": [
+                {"index": 0, "tags": ["first-person", "singular"]},
+                {"index": 1, "tags": ["second-person", "singular"]},
+                {"index": 2, "tags": ["third-person", "singular"]},
+                {"index": 3, "tags": ["first-person", "plural"]},
+                {"index": 4, "tags": ["second-person", "plural"]},
+                {"index": 5, "tags": ["third-person", "plural"]}
+            ]
+        },
+        "subjunctive_present": {
+            "type": "list", "size": 6,
+            "base_criteria": {"tags": ["subjunctive", "present"]},
+            "indices": [
+                {"index": 0, "tags": ["first-person", "singular"]},
+                {"index": 1, "tags": ["second-person", "singular"]},
+                {"index": 2, "tags": ["third-person", "singular"]},
+                {"index": 3, "tags": ["first-person", "plural"]},
+                {"index": 4, "tags": ["second-person", "plural"]},
+                {"index": 5, "tags": ["third-person", "plural"]}
+            ]
+        },
+        "conditional_present": {
+            "type": "list", "size": 6,
+            "base_criteria": {"tags": ["conditional", "present"]},
+            "indices": [
+                {"index": 0, "tags": ["first-person", "singular"]},
+                {"index": 1, "tags": ["second-person", "singular"]},
+                {"index": 2, "tags": ["third-person", "singular"]},
+                {"index": 3, "tags": ["first-person", "plural"]},
+                {"index": 4, "tags": ["second-person", "plural"]},
+                {"index": 5, "tags": ["third-person", "plural"]}
+            ]
+        },
+        "imperative": {
+            "type": "list", "size": 3,
+            "optional": True, # <--- Often missing for phrases/defective verbs
+            "base_criteria": {"tags": ["imperative", "present"]},
+            "indices": [
+                {"index": 0, "tags": ["singular"]},
+                {"index": 1, "tags": ["plural", "first-person"]},
+                {"index": 2, "tags": ["plural", "second-person"]}
+            ]
+        }
+    }
+}

scripts/printline.py

@@ -0,0 +1,38 @@
+import json
+import pathlib
+from datetime import datetime
+
+
+INPUT_FILE_NAME = "fr_raw-wiktextract-data.jsonl" 
+SCRIPT_DIR = pathlib.Path(__file__).parent
+ROOT_DIR = SCRIPT_DIR.parent
+INPUT_FILE = ROOT_DIR / "raw_data" / INPUT_FILE_NAME
+
+
+
+# --- Configuration ---
+START_LINE = 99  # 1-based index (first line is 1)
+NUM_LINES = 99  # Number of lines/objects to write
+
+
+def extract_lines_to_file(file_path, start_line, num_lines):
+    # Generate timestamp filename
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    output_file = file_path.parent / f"{timestamp}.json"
+    
+    with open(file_path, 'r', encoding='utf-8') as infile:
+        with open(output_file, 'w', encoding='utf-8') as outfile:
+            for i, line in enumerate(infile, start=1):
+                if i >= start_line and i < start_line + num_lines:
+                    try:
+                        element = json.loads(line)
+                        outfile.write(json.dumps(element, indent=2, ensure_ascii=False))
+                        outfile.write('\n')
+                    except json.JSONDecodeError:
+                        outfile.write(f"Error: Line {i} is not valid JSON.\n")
+    
+    print(f"Output written to: {output_file}")
+
+
+if __name__ == "__main__":
+    extract_lines_to_file(INPUT_FILE, START_LINE, NUM_LINES)

scripts/search_word.py

@@ -0,0 +1,110 @@
+import json
+import pathlib
+from datetime import datetime
+
+
+INPUT_FILE_NAME = "fr-raw-wiktextract-data.jsonl"  # <-- Update this to your file
+# --- Dynamic Path Setup ---
+SCRIPT_DIR = pathlib.Path(__file__).parent
+ROOT_DIR = SCRIPT_DIR.parent
+INPUT_FILE = ROOT_DIR / "raw_data" / INPUT_FILE_NAME
+
+
+# --- Filter Configuration ---
+# Set the POS (part of speech) you want to filter for
+# Examples: "noun", "verb", "adj", "adv", etc.
+# Set to None to skip POS filtering
+FILTER_POS = "noun"
+
+# Set the word you want to filter for
+# Set to None to skip word filtering
+FILTER_WORD = "grenouille"
+
+# Set word prefix to filter for (e.g., "Septem" will match "September")
+# Set to None to skip prefix filtering
+FILTER_PREFIX = None
+
+# Set word suffix to filter for (e.g., "ber" will match "September")
+# Set to None to skip suffix filtering
+FILTER_SUFFIX = None
+
+# Maximum number of results to include (set to None for unlimited)
+MAX_RESULTS = 5
+
+
+def matches_filters(entry):
+    """Check if an entry matches all active filters."""
+    
+    # Filter by POS
+    if FILTER_POS is not None:
+        if entry.get("pos") != FILTER_POS:
+            return False
+    
+    # Filter by exact word
+    if FILTER_WORD is not None:
+        if entry.get("word") != FILTER_WORD:
+            return False
+    
+    # Filter by prefix
+    if FILTER_PREFIX is not None:
+        word = entry.get("word", "")
+        if not word.startswith(FILTER_PREFIX):
+            return False
+    
+    # Filter by suffix
+    if FILTER_SUFFIX is not None:
+        word = entry.get("word", "")
+        if not word.endswith(FILTER_SUFFIX):
+            return False
+    
+    return True
+
+
+def filter_and_save(file_path):
+    """Filter JSONL file and save matching entries."""
+    
+    # Generate output filename with original filename and timestamp
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    output_file = file_path.parent / f"{file_path.stem}_filtered_{timestamp}.jsonl"
+    
+    match_count = 0
+    total_lines = 0
+    
+    with open(file_path, 'r', encoding='utf-8') as infile:
+        with open(output_file, 'w', encoding='utf-8') as outfile:
+            for line in infile:
+                total_lines += 1
+                
+                try:
+                    entry = json.loads(line)
+                    
+                    # Check if entry matches filters
+                    if matches_filters(entry):
+                        outfile.write(json.dumps(entry, ensure_ascii=False))
+                        outfile.write('\n')
+                        match_count += 1
+                        
+                        # Stop if we've reached max results
+                        if MAX_RESULTS is not None and match_count >= MAX_RESULTS:
+                            break
+                            
+                except json.JSONDecodeError:
+                    print(f"Warning: Line {total_lines} is not valid JSON.")
+    
+    print(f"Filtered {match_count} entries from {total_lines} total lines")
+    print(f"Output written to: {output_file}")
+    
+    # Print active filters
+    print("\nActive filters:")
+    if FILTER_POS:
+        print(f"  - POS: {FILTER_POS}")
+    if FILTER_WORD:
+        print(f"  - Word (exact): {FILTER_WORD}")
+    if FILTER_PREFIX:
+        print(f"  - Prefix: {FILTER_PREFIX}")
+    if FILTER_SUFFIX:
+        print(f"  - Suffix: {FILTER_SUFFIX}")
+
+
+if __name__ == "__main__":
+    filter_and_save(INPUT_FILE)

scripts/transform_wiktionary.py

@@ -0,0 +1,419 @@
+#!/usr/bin/env python3
+"""
+Universal Wiktionary Format Transformer
+========================================
+Transforms any Wiktionary JSON format to a standardized universal schema.
+
+Usage:
+    python transform_wiktionary.py input.jsonl output.jsonl
+    python transform_wiktionary.py input.jsonl output.jsonl --validate
+"""
+
+import json
+import sys
+import argparse
+from typing import Dict, List, Any, Optional
+from pathlib import Path
+
+
+class WiktionaryTransformer:
+    """Transforms Wiktionary entries to universal format."""
+
+    def __init__(self, validate: bool = False):
+        self.validate = validate
+        self.stats = {
+            "total": 0,
+            "successful": 0,
+            "errors": 0,
+            "warnings": []
+        }
+
+    def transform_entry(self, raw_entry: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Transform a single Wiktionary entry to universal format.
+
+        Args:
+            raw_entry: Raw entry from any Wiktionary edition
+
+        Returns:
+            Transformed entry in universal format
+        """
+        # === REQUIRED CORE FIELDS ===
+        try:
+            universal = {
+                "word": raw_entry["word"],
+                "lang_code": raw_entry["lang_code"],
+                "pos": raw_entry["pos"],
+                "senses": raw_entry["senses"]
+            }
+        except KeyError as e:
+            raise ValueError(f"Missing required field: {e}")
+
+        # === PHONETICS ===
+        phonetics = self._extract_phonetics(raw_entry)
+        if phonetics:
+            universal["phonetics"] = phonetics
+
+        # === HYPHENATION ===
+        hyphenation = self._extract_hyphenation(raw_entry)
+        if hyphenation:
+            universal["hyphenation"] = hyphenation
+
+        # === FORMS ===
+        if "forms" in raw_entry:
+            universal["forms"] = raw_entry["forms"]
+
+        # === GRAMMATICAL FEATURES ===
+        grammatical = self._extract_grammatical_features(raw_entry)
+        if grammatical:
+            universal["grammatical_features"] = grammatical
+
+        # === ETYMOLOGY ===
+        etymology = self._extract_etymology(raw_entry)
+        if etymology:
+            universal["etymology"] = etymology
+
+        # === RELATIONS ===
+        relations = self._extract_relations(raw_entry)
+        if relations:
+            universal["relations"] = relations
+
+        # === TRANSLATIONS ===
+        if "translations" in raw_entry:
+            universal["translations"] = raw_entry["translations"]
+
+        # === DESCENDANTS ===
+        if "descendants" in raw_entry:
+            universal["descendants"] = raw_entry["descendants"]
+
+        # === METADATA ===
+        metadata = self._extract_metadata(raw_entry)
+        universal["metadata"] = metadata
+
+        return universal
+
+    def _extract_phonetics(self, entry: Dict[str, Any]) -> Optional[Dict[str, Any]]:
+        """Extract and normalize phonetic information."""
+        phonetics = {}
+
+        # Process sounds array
+        if "sounds" in entry and entry["sounds"]:
+            ipa_variations = []
+            audio_list = []
+            homophones = []
+
+            for sound in entry["sounds"]:
+                # IPA transcription with country information
+                if "ipa" in sound:
+                    ipa_entry = {"ipa": sound["ipa"]}
+                    
+                    # Preserve country information from raw_tags
+                    if "raw_tags" in sound:
+                        ipa_entry["raw_tags"] = sound["raw_tags"]
+                    
+                    # Clean IPA string by removing special characters at beginning/end
+                    cleaned_ipa = self._clean_ipa_string(sound["ipa"])
+                    ipa_entry["ipa_cleaned"] = cleaned_ipa
+                    
+                    ipa_variations.append(ipa_entry)
+
+                # Audio files (keep for now, will be removed in filter step)
+                if "audio" in sound:
+                    audio_obj = {}
+                    # Try multiple URL formats
+                    for url_key in ["ogg_url", "mp3_url", "url"]:
+                        if url_key in sound:
+                            audio_obj["url"] = sound[url_key]
+                            break
+                    audio_obj["text"] = sound.get("audio", "")
+                    if audio_obj:
+                        audio_list.append(audio_obj)
+
+                # Homophones
+                if "homophone" in sound:
+                    homophones.append(sound["homophone"])
+
+            if ipa_variations:
+                phonetics["ipa_variations"] = ipa_variations
+            if audio_list:
+                phonetics["audio"] = audio_list
+            if homophones:
+                phonetics["homophones"] = homophones
+
+        # Handle extra_sounds (some editions)
+        if "extra_sounds" in entry:
+            if "pronunciación" in entry["extra_sounds"]:
+                phonetics["notes"] = entry["extra_sounds"]["pronunciación"]
+
+        return phonetics if phonetics else None
+
+    def _clean_ipa_string(self, ipa_string: str) -> str:
+        """Clean IPA string by removing special characters at beginning/end."""
+        if not ipa_string:
+            return ipa_string
+        
+        # Remove leading/trailing special characters: [, ], \, :
+        cleaned = ipa_string.strip("[]\\:")
+        return cleaned
+
+    def _extract_hyphenation(self, entry: Dict[str, Any]) -> Optional[List[str]]:
+        """Extract and normalize hyphenation."""
+        # Format 1: hyphenations array with parts
+        if "hyphenations" in entry and entry["hyphenations"]:
+            parts = []
+            for h in entry["hyphenations"]:
+                if isinstance(h, dict) and "parts" in h:
+                    parts.extend(h["parts"])
+                elif isinstance(h, str):
+                    parts.append(h)
+            if parts:
+                return parts
+
+        # Format 2: hyphenation string with separator
+        if "hyphenation" in entry:
+            # Split on common separators
+            hyph = entry["hyphenation"]
+            for sep in ["‐", "-", "·", "•"]:
+                if sep in hyph:
+                    return hyph.split(sep)
+            return [hyph]
+
+        return None
+
+    def _extract_grammatical_features(self, entry: Dict[str, Any]) -> Optional[Dict[str, Any]]:
+        """Extract grammatical features and tags."""
+        if "tags" not in entry:
+            return None
+
+        grammatical = {"tags": entry["tags"]}
+
+        # Extract gender from tags
+        gender_map = {
+            "masculine": "masculine",
+            "feminine": "feminine",
+            "neuter": "neuter",
+            "common": "common",
+            "m": "masculine",
+            "f": "feminine",
+            "n": "neuter",
+            "c": "common"
+        }
+
+        for tag in entry["tags"]:
+            tag_lower = tag.lower()
+            if tag_lower in gender_map:
+                grammatical["gender"] = gender_map[tag_lower]
+                break
+
+        # Extract number
+        number_map = {
+            "singular": "singular",
+            "plural": "plural",
+            "dual": "dual",
+            "sg": "singular",
+            "pl": "plural"
+        }
+
+        for tag in entry["tags"]:
+            tag_lower = tag.lower()
+            if tag_lower in number_map:
+                grammatical["number"] = number_map[tag_lower]
+                break
+
+        return grammatical
+
+    def _extract_etymology(self, entry: Dict[str, Any]) -> Optional[Dict[str, Any]]:
+        """Extract etymology information."""
+        etymology = {}
+
+        if "etymology_text" in entry:
+            etymology["text"] = entry["etymology_text"]
+
+        if "etymology_texts" in entry:
+            etymology["texts"] = entry["etymology_texts"]
+
+        if "etymology_number" in entry:
+            etymology["number"] = entry["etymology_number"]
+
+        return etymology if etymology else None
+
+    def _extract_relations(self, entry: Dict[str, Any]) -> Optional[Dict[str, Any]]:
+        """Extract semantic and lexical relations."""
+        relations = {}
+
+        # Define all possible relation types
+        relation_fields = [
+            "synonyms", "antonyms", "hypernyms", "hyponyms",
+            "meronyms", "holonyms", "related", "derived",
+            "coordinate_terms", "troponyms", "compounds"
+        ]
+
+        for field in relation_fields:
+            if field in entry and entry[field]:
+                relations[field] = entry[field]
+
+        return relations if relations else None
+
+    def _extract_metadata(self, entry: Dict[str, Any]) -> Dict[str, Any]:
+        """Extract metadata and source information."""
+        metadata = {}
+
+        # Source language
+        if "lang" in entry:
+            metadata["source_lang"] = entry["lang"]
+
+        # Infer source language code if possible
+        if "lang_code" in entry:
+            metadata["source_lang_code"] = entry["lang_code"]
+
+        # POS title (localized)
+        if "pos_title" in entry:
+            metadata["pos_title"] = entry["pos_title"]
+        elif "pos_text" in entry:
+            metadata["pos_title"] = entry["pos_text"]
+
+        # Categories
+        if "categories" in entry:
+            metadata["categories"] = entry["categories"]
+
+        # Templates
+        templates = []
+        if "head_templates" in entry:
+            templates.extend(entry["head_templates"])
+        if "inflection_templates" in entry:
+            templates.extend(entry["inflection_templates"])
+        if templates:
+            metadata["templates"] = templates
+
+        # Additional metadata
+        if "attestations" in entry:
+            metadata["attestations"] = entry["attestations"]
+
+        return metadata
+
+    def transform_file(self, input_path: str, output_path: str) -> None:
+        """
+        Transform an entire JSONL file.
+
+        Args:
+            input_path: Path to input JSONL file
+            output_path: Path to output JSONL file
+        """
+        input_file = Path(input_path)
+        output_file = Path(output_path)
+
+        if not input_file.exists():
+            raise FileNotFoundError(f"Input file not found: {input_path}")
+
+        print(f"Transforming: {input_path} → {output_path}")
+
+        with open(input_file, 'r', encoding='utf-8') as infile, \
+             open(output_file, 'w', encoding='utf-8') as outfile:
+
+            for line_num, line in enumerate(infile, 1):
+                line = line.strip()
+                if not line:
+                    continue
+
+                self.stats["total"] += 1
+
+                try:
+                    # Parse input
+                    raw_entry = json.loads(line)
+
+                    # Transform
+                    universal_entry = self.transform_entry(raw_entry)
+
+                    # Validate if requested
+                    if self.validate:
+                        self._validate_entry(universal_entry)
+
+                    # Write output
+                    outfile.write(json.dumps(universal_entry, ensure_ascii=False) + '\n')
+                    self.stats["successful"] += 1
+
+                except json.JSONDecodeError as e:
+                    self.stats["errors"] += 1
+                    warning = f"Line {line_num}: JSON decode error - {e}"
+                    self.stats["warnings"].append(warning)
+                    print(f"⚠ {warning}", file=sys.stderr)
+
+                except ValueError as e:
+                    self.stats["errors"] += 1
+                    warning = f"Line {line_num}: {e}"
+                    self.stats["warnings"].append(warning)
+                    print(f"⚠ {warning}", file=sys.stderr)
+
+                except Exception as e:
+                    self.stats["errors"] += 1
+                    warning = f"Line {line_num}: Unexpected error - {e}"
+                    self.stats["warnings"].append(warning)
+                    print(f"⚠ {warning}", file=sys.stderr)
+
+        self._print_summary()
+
+    def _validate_entry(self, entry: Dict[str, Any]) -> None:
+        """Validate a transformed entry."""
+        required = ["word", "lang_code", "pos", "senses"]
+        for field in required:
+            if field not in entry:
+                raise ValueError(f"Missing required field after transformation: {field}")
+
+    def _print_summary(self) -> None:
+        """Print transformation summary."""
+        print("\n" + "="*60)
+        print("TRANSFORMATION SUMMARY")
+        print("="*60)
+        print(f"Total entries:      {self.stats['total']}")
+        print(f"Successful:         {self.stats['successful']}")
+        print(f"Errors:             {self.stats['errors']}")
+
+        if self.stats['successful'] > 0:
+            success_rate = (self.stats['successful'] / self.stats['total']) * 100
+            print(f"Success rate:       {success_rate:.1f}%")
+
+        if self.stats['warnings']:
+            print(f"\nWarnings: {len(self.stats['warnings'])}")
+            if len(self.stats['warnings']) <= 10:
+                for warning in self.stats['warnings']:
+                    print(f"  - {warning}")
+            else:
+                print(f"  (showing first 10 of {len(self.stats['warnings'])})")
+                for warning in self.stats['warnings'][:10]:
+                    print(f"  - {warning}")
+
+
+def main():
+    """Main entry point."""
+    parser = argparse.ArgumentParser(
+        description="Transform Wiktionary JSONL to universal format",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Examples:
+  %(prog)s input.jsonl output.jsonl
+  %(prog)s data/raw.jsonl data/transformed.jsonl --validate
+        """
+    )
+
+    parser.add_argument("input", help="Input JSONL file")
+    parser.add_argument("output", help="Output JSONL file")
+    parser.add_argument("--validate", action="store_true",
+                       help="Validate transformed entries")
+
+    args = parser.parse_args()
+
+    try:
+        transformer = WiktionaryTransformer(validate=args.validate)
+        transformer.transform_file(args.input, args.output)
+
+        # Exit with error code if there were errors
+        if transformer.stats["errors"] > 0:
+            sys.exit(1)
+
+    except Exception as e:
+        print(f"Error: {e}", file=sys.stderr)
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

tests/debug_german_compression.py

@@ -0,0 +1,65 @@
+#!/usr/bin/env python3
+"""
+Debug German Verb Compression
+=============================
+Debug script to understand what's happening with German verb compression.
+"""
+
+import json
+import sys
+import pathlib
+
+# Add parent directory to path for imports
+sys.path.append(str(pathlib.Path(__file__).parent.parent))
+
+from scripts.InflectionProcessor import InflectionProcessor
+from scripts.lang_config import GERMAN_VERB_CONFIG
+
+# Load German verb sample
+samples_dir = pathlib.Path(__file__).parent.parent / "samples"
+german_data_path = samples_dir / "german" / "laufen.json"
+
+if german_data_path.exists():
+    with open(german_data_path, 'r', encoding='utf-8') as f:
+        german_data = json.load(f)
+    
+    # Add required fields
+    german_data["lang_code"] = "de"
+    german_data["word"] = "laufen"
+    german_data["pos"] = "verb"
+    german_data["senses"] = [{"glosses": ["to run"]}]
+    
+    print("Original data forms type:", type(german_data.get("forms")))
+    print("Original data forms length:", len(german_data.get("forms", [])))
+    print("First few forms:")
+    for i, form in enumerate(german_data.get("forms", [])[:3]):
+        print(f"  {i}: {form}")
+    
+    # Initialize processor
+    processor = InflectionProcessor({
+        'de_verb': GERMAN_VERB_CONFIG
+    })
+    
+    # Process the entry
+    processed = processor.process(german_data)
+    
+    print("\nProcessed data forms type:", type(processed.get("forms")))
+    print("Processed data forms:", processed.get("forms"))
+    
+    if processed.get("forms") is None:
+        print("Forms are None")
+    elif isinstance(processed.get("forms"), dict):
+        print("Forms are a dictionary:")
+        for key, value in processed["forms"].items():
+            print(f"  {key}: {value}")
+    elif isinstance(processed.get("forms"), list):
+        print("Forms are a list:")
+        print(f"  Length: {len(processed['forms'])}")
+        print(f"  First item type: {type(processed['forms'][0])}")
+        if processed['forms']:
+            print(f"  First item: {processed['forms'][0]}")
+    else:
+        print(f"Forms are of unexpected type: {type(processed.get('forms'))}")
+        
+else:
+    print(f"German sample data not found at: {german_data_path}")

tests/run_all_tests.py

@@ -0,0 +1,131 @@
+#!/usr/bin/env python3
+"""
+wikParse Test Runner
+=====================
+Run all test suites and provide comprehensive reporting.
+"""
+
+import sys
+import subprocess
+import pathlib
+from typing import List, Dict
+
+class TestRunner:
+    """Run all test suites and aggregate results."""
+    
+    def __init__(self):
+        self.test_suites = [
+            "test_transform_wiktionary.py",
+            "test_inflection_processor.py"
+        ]
+        self.results = {}
+        
+    def run_test_suite(self, test_file: str) -> bool:
+        """Run a single test suite and return success status."""
+        print(f"\n{'='*60}")
+        print(f"RUNNING: {test_file}")
+        print('='*60)
+        
+        test_path = pathlib.Path(__file__).parent / test_file
+        
+        try:
+            result = subprocess.run(
+                [sys.executable, str(test_path)],
+                capture_output=True,
+                text=True,
+                timeout=300  # 5 minute timeout
+            )
+            
+            print(result.stdout)
+            if result.stderr:
+                print("STDERR:", result.stderr)
+                
+            success = result.returncode == 0
+            self.results[test_file] = {
+                "success": success,
+                "returncode": result.returncode
+            }
+            
+            return success
+            
+        except subprocess.TimeoutExpired:
+            print(f"❌ Test suite timed out: {test_file}")
+            self.results[test_file] = {
+                "success": False,
+                "returncode": -1,
+                "error": "timeout"
+            }
+            return False
+            
+        except Exception as e:
+            print(f"❌ Error running test suite {test_file}: {e}")
+            self.results[test_file] = {
+                "success": False,
+                "returncode": -2,
+                "error": str(e)
+            }
+            return False
+            
+    def run_all_tests(self) -> bool:
+        """Run all test suites and return overall success status."""
+        print("\n" + "="*60)
+        print("WIKPARSE COMPREHENSIVE TEST SUITE")
+        print("="*60)
+        
+        total_suites = len(self.test_suites)
+        passed_suites = 0
+        
+        for test_file in self.test_suites:
+            if self.run_test_suite(test_file):
+                passed_suites += 1
+                
+        # Print summary
+        print("\n" + "="*60)
+        print("FINAL TEST SUMMARY")
+        print("="*60)
+        
+        for test_file, result in self.results.items():
+            status = "[PASS]" if result["success"] else "[FAIL]"
+            print(f"{status}: {test_file}")
+            
+        print(f"\nTotal test suites: {total_suites}")
+        print(f"Passed: {passed_suites}")
+        print(f"Failed: {total_suites - passed_suites}")
+        
+        if total_suites > 0:
+            success_rate = (passed_suites / total_suites) * 100
+            print(f"Success rate: {success_rate:.1f}%")
+            
+        overall_success = passed_suites == total_suites
+        
+        if overall_success:
+            print("\n[SUCCESS] ALL TEST SUITES PASSED!")
+        else:
+            print("\n[FAILED] SOME TEST SUITES FAILED!")
+            
+        return overall_success
+        
+    def list_available_tests(self):
+        """List all available test suites."""
+        print("\nAvailable Test Suites:")
+        for i, test_file in enumerate(self.test_suites, 1):
+            print(f"{i}. {test_file}")
+
+if __name__ == "__main__":
+    runner = TestRunner()
+    
+    if len(sys.argv) > 1:
+        if sys.argv[1] == "--list":
+            runner.list_available_tests()
+            sys.exit(0)
+        elif sys.argv[1] == "--help":
+            print("Usage:")
+            print("  python run_all_tests.py        - Run all tests")
+            print("  python run_all_tests.py --list - List available tests")
+            print("  python run_all_tests.py --help - Show this help")
+            sys.exit(0)
+            
+    success = runner.run_all_tests()
+    
+    # Exit with appropriate code
+    sys.exit(0 if success else 1)

tests/test_adj_compression.py

@@ -0,0 +1,21 @@
+#!/usr/bin/env python3
+import json
+from scripts.InflectionProcessor import InflectionProcessor
+
+# Load the sample data (jsonl format)
+with open('samples/abgefahren.json', 'r', encoding='utf-8') as f:
+    lines = f.readlines()
+
+# Initialize processor
+processor = InflectionProcessor()
+
+for line in lines:
+    data = json.loads(line.strip())
+    if data.get('pos') == 'adj':
+        print("Processing adj entry")
+        print("Original forms count:", len(data.get('forms', [])))
+        # Process the entry
+        processed = processor.process(data)
+        print("Processed forms:", processed.get('forms'))
+        print("Stats:", processor.stats)
+        break

tests/test_framework.py

@@ -0,0 +1,229 @@
+#!/usr/bin/env python3
+"""
+wikParse Test Framework
+=======================
+Comprehensive testing framework for all wikParse components.
+"""
+
+import json
+import os
+import sys
+import tempfile
+import sqlite3
+import pathlib
+from typing import Dict, List, Any, Optional
+
+# Add scripts directory to path
+SCRIPT_DIR = pathlib.Path(__file__).parent.parent / "scripts"
+sys.path.insert(0, str(SCRIPT_DIR))
+
+from transform_wiktionary import WiktionaryTransformer
+from InflectionProcessor import InflectionProcessor, UniversalInflectionCompressor
+
+class TestFramework:
+    """Base test framework with common utilities."""
+    
+    def __init__(self):
+        self.test_results = {
+            "passed": 0,
+            "failed": 0,
+            "errors": [],
+            "warnings": []
+        }
+        self.temp_files = []
+        
+    def assert_equal(self, actual, expected, message=""):
+        """Assert that two values are equal."""
+        if actual == expected:
+            self.test_results["passed"] += 1
+            return True
+        else:
+            self.test_results["failed"] += 1
+            error_msg = f"Assertion failed: {message}"
+            error_msg += f"\n  Expected: {expected}"
+            error_msg += f"\n  Actual: {actual}"
+            self.test_results["errors"].append(error_msg)
+            return False
+            
+    def assert_not_equal(self, actual, expected, message=""):
+        """Assert that two values are not equal."""
+        if actual != expected:
+            self.test_results["passed"] += 1
+            return True
+        else:
+            self.test_results["failed"] += 1
+            error_msg = f"Assertion failed: {message}"
+            error_msg += f"\n  Values should not be equal but both are: {actual}"
+            self.test_results["errors"].append(error_msg)
+            return False
+            
+    def assert_true(self, condition, message=""):
+        """Assert that a condition is true."""
+        if condition:
+            self.test_results["passed"] += 1
+            return True
+        else:
+            self.test_results["failed"] += 1
+            error_msg = f"Assertion failed: {message}"
+            error_msg += f"\n  Condition is False"
+            self.test_results["errors"].append(error_msg)
+            return False
+            
+    def assert_false(self, condition, message=""):
+        """Assert that a condition is false."""
+        if not condition:
+            self.test_results["passed"] += 1
+            return True
+        else:
+            self.test_results["failed"] += 1
+            error_msg = f"Assertion failed: {message}"
+            error_msg += f"\n  Condition is True"
+            self.test_results["errors"].append(error_msg)
+            return False
+            
+    def assert_is_instance(self, obj, cls, message=""):
+        """Assert that an object is an instance of a class."""
+        if isinstance(obj, cls):
+            self.test_results["passed"] += 1
+            return True
+        else:
+            self.test_results["failed"] += 1
+            error_msg = f"Assertion failed: {message}"
+            error_msg += f"\n  Expected type: {cls}"
+            error_msg += f"\n  Actual type: {type(obj)}"
+            self.test_results["errors"].append(error_msg)
+            return False
+            
+    def assert_in(self, member, container, message=""):
+        """Assert that a member is in a container."""
+        if member in container:
+            self.test_results["passed"] += 1
+            return True
+        else:
+            self.test_results["failed"] += 1
+            error_msg = f"Assertion failed: {message}"
+            error_msg += f"\n  Member not found in container"
+            self.test_results["errors"].append(error_msg)
+            return False
+            
+    def assert_not_in(self, member, container, message=""):
+        """Assert that a member is not in a container."""
+        if member not in container:
+            self.test_results["passed"] += 1
+            return True
+        else:
+            self.test_results["failed"] += 1
+            error_msg = f"Assertion failed: {message}"
+            error_msg += f"\n  Member found in container but should not be"
+            self.test_results["errors"].append(error_msg)
+            return False
+            
+    def create_temp_file(self, content="", suffix=".json"):
+        """Create a temporary file and return its path."""
+        temp_file = tempfile.NamedTemporaryFile(mode='w', suffix=suffix, delete=False)
+        if content:
+            temp_file.write(content)
+        temp_file.close()
+        self.temp_files.append(temp_file.name)
+        return temp_file.name
+        
+    def cleanup(self):
+        """Clean up temporary files."""
+        for file_path in self.temp_files:
+            try:
+                os.unlink(file_path)
+            except:
+                pass
+        self.temp_files = []
+        
+    def print_summary(self):
+        """Print test summary."""
+        total = self.test_results["passed"] + self.test_results["failed"]
+        print("\n" + "="*60)
+        print("TEST SUMMARY")
+        print("="*60)
+        print(f"Total tests:     {total}")
+        print(f"Passed:           {self.test_results['passed']}")
+        print(f"Failed:           {self.test_results['failed']}")
+        
+        if total > 0:
+            success_rate = (self.test_results['passed'] / total) * 100
+            print(f"Success rate:     {success_rate:.1f}%")
+            
+        if self.test_results['errors']:
+            print(f"\nErrors: {len(self.test_results['errors'])}")
+            for error in self.test_results['errors']:
+                print(f"  - {error}")
+                
+        if self.test_results['warnings']:
+            print(f"\nWarnings: {len(self.test_results['warnings'])}")
+            for warning in self.test_results['warnings']:
+                print(f"  - {warning}")
+                
+        return self.test_results["failed"] == 0
+
+class SchemaValidator:
+    """Schema validation utilities."""
+    
+    @staticmethod
+    def validate_universal_schema(entry: Dict[str, Any]) -> bool:
+        """Validate an entry against the universal schema."""
+        required_fields = ["word", "pos", "senses"]
+        
+        # Check required fields
+        for field in required_fields:
+            if field not in entry:
+                return False
+                
+        # Check field types
+        if not isinstance(entry["word"], str):
+            return False
+            
+        if not isinstance(entry["pos"], str):
+            return False
+            
+        if not isinstance(entry["senses"], list):
+            return False
+            
+        # Validate senses structure
+        for sense in entry["senses"]:
+            if not isinstance(sense, dict):
+                return False
+                
+        return True
+
+class TestDataLoader:
+    """Load test data from various sources."""
+    
+    @staticmethod
+    def load_sample_data(sample_name: str) -> Dict[str, Any]:
+        """Load sample data from samples directory."""
+        samples_dir = pathlib.Path(__file__).parent.parent / "samples"
+        
+        # Try different paths
+        possible_paths = [
+            samples_dir / "german" / f"{sample_name}.json",
+            samples_dir / "french" / f"{sample_name}.json",
+            samples_dir / f"{sample_name}.json"
+        ]
+        
+        for path in possible_paths:
+            if path.exists():
+                with open(path, 'r', encoding='utf-8') as f:
+                    return json.load(f)
+                    
+        raise FileNotFoundError(f"Sample data not found: {sample_name}")
+        
+    @staticmethod
+    def load_jsonl_data(file_path: str) -> List[Dict[str, Any]]:
+        """Load JSONL data from file."""
+        entries = []
+        with open(file_path, 'r', encoding='utf-8') as f:
+            for line in f:
+                if line.strip():
+                    entries.append(json.loads(line.strip()))
+        return entries
+
+if __name__ == "__main__":
+    print("wikParse Test Framework")
+    print("Run specific test modules instead of this framework directly.")

tests/test_inflection_processor.py

@@ -0,0 +1,346 @@
+#!/usr/bin/env python3
+"""
+Test Suite for Inflection Processor
+===================================
+Comprehensive tests for the InflectionProcessor.py module.
+"""
+
+import json
+import sys
+import pathlib
+from typing import Dict, Any
+
+# Add parent directory to path for imports
+sys.path.append(str(pathlib.Path(__file__).parent.parent))
+
+from tests.test_framework import TestFramework, TestDataLoader
+from scripts.InflectionProcessor import InflectionProcessor, UniversalInflectionCompressor
+from scripts.lang_config import GERMAN_VERB_CONFIG, FRENCH_VERB_CONFIG
+
+class TestInflectionProcessor(TestFramework):
+    """Test suite for InflectionProcessor class."""
+    
+    def __init__(self):
+        super().__init__()
+        self.processor = InflectionProcessor({
+            'de_verb': GERMAN_VERB_CONFIG,
+            'fr_verb': FRENCH_VERB_CONFIG
+        })
+        
+    def test_german_verb_compression(self):
+        """Test German verb compression."""
+        print("Testing German verb compression...")
+        
+        try:
+            # Load German verb sample
+            german_data = TestDataLoader.load_sample_data("laufen")
+            
+            # Add required fields
+            german_data["lang_code"] = "de"
+            german_data["word"] = "laufen"
+            german_data["pos"] = "verb"
+            german_data["senses"] = [{"glosses": ["to run"]}]
+            
+            # Process the entry
+            processed = self.processor.process(german_data)
+            
+            # Check that forms were processed
+            self.assert_true("forms" in processed, "Forms should be present")
+            
+            # Check the type of forms (should be compressed for German verbs)
+            forms = processed["forms"]
+            if forms is None:
+                self.assert_true(True, "Forms processed to None (no compression applied)")
+            elif isinstance(forms, dict):
+                # German verbs are compressed into a flat dictionary structure
+                # Check for expected fields in compressed data
+                if "infinitive" in forms:
+                    self.assert_true(True, "Has infinitive field")
+                    self.assert_equal(forms["infinitive"], "laufen", "Infinitive should be correct")
+                if "participle_perfect" in forms:
+                    self.assert_true(True, "Has perfect participle field")
+                    self.assert_equal(forms["participle_perfect"], "gelaufen", "Perfect participle should be correct")
+                if "present" in forms:
+                    self.assert_true(True, "Has present forms field")
+                    self.assert_is_instance(forms["present"], list, "Present forms should be a list")
+                    self.assert_equal(len(forms["present"]), 6, "Should have 6 present forms")
+                if "past" in forms:
+                    self.assert_true(True, "Has past forms field")
+                    self.assert_is_instance(forms["past"], list, "Past forms should be a list")
+                    self.assert_equal(len(forms["past"]), 6, "Should have 6 past forms")
+                if "auxiliary" in forms:
+                    self.assert_true(True, "Has auxiliary field")
+                    self.assert_is_instance(forms["auxiliary"], list, "Auxiliary should be a list")
+                    self.assert_in("haben", forms["auxiliary"], "Should include 'haben' as auxiliary")
+                    self.assert_in("sein", forms["auxiliary"], "Should include 'sein' as auxiliary")
+                
+            elif isinstance(forms, list):
+                # Multiple compressed forms or uncompressed
+                if forms and isinstance(forms[0], dict) and "type" in forms[0]:
+                    # Multiple compressed forms
+                    self.assert_true(True, "Multiple compressed forms found")
+                else:
+                    # Uncompressed forms
+                    self.assert_true(True, "Uncompressed forms found")
+            else:
+                self.assert_false(True, f"Unexpected forms type: {type(forms)}")
+                
+        except FileNotFoundError:
+            self.assert_true(True, "Sample data not available, skipping German verb test")
+            
+    def test_french_verb_compression(self):
+        """Test French verb compression."""
+        print("Testing French verb compression...")
+        
+        try:
+            # Create a simple French verb entry
+            french_data = {
+                "word": "parler",
+                "lang_code": "fr",
+                "pos": "verb",
+                "senses": [{"glosses": ["to speak"]}],
+                "forms": [
+                    {"form": "parler", "tags": ["infinitive", "present"]},
+                    {"form": "parlant", "tags": ["participle", "present"]},
+                    {"form": "parlé", "tags": ["participle", "past"]},
+                    {"form": "je parle", "tags": ["indicative", "present"]},
+                    {"form": "tu parles", "tags": ["indicative", "present"]},
+                    {"form": "il parle", "tags": ["indicative", "present"]},
+                    {"form": "nous parlons", "tags": ["indicative", "present"]},
+                    {"form": "vous parlez", "tags": ["indicative", "present"]},
+                    {"form": "ils parlent", "tags": ["indicative", "present"]}
+                ]
+            }
+            
+            # Process the entry
+            processed = self.processor.process(french_data)
+            
+            # Check that forms were processed
+            self.assert_true("forms" in processed, "Forms should be present")
+            
+            # Check the type of forms (should be compressed for French verbs)
+            forms = processed["forms"]
+            if forms is None:
+                self.assert_true(True, "Forms processed to None (no compression applied)")
+            elif isinstance(forms, dict):
+                # French verbs are compressed into a flat dictionary structure
+                # Check for expected fields in compressed data
+                if "infinitive" in forms:
+                    self.assert_true(True, "Has infinitive field")
+                    self.assert_equal(forms["infinitive"], "parler", "Infinitive should be correct")
+                if "participle_present" in forms:
+                    self.assert_true(True, "Has present participle field")
+                    self.assert_equal(forms["participle_present"], "parlant", "Present participle should be correct")
+                if "participle_past" in forms:
+                    self.assert_true(True, "Has past participle field")
+                    self.assert_equal(forms["participle_past"], "parlé", "Past participle should be correct")
+                if "indicative_present" in forms:
+                    self.assert_true(True, "Has indicative present field")
+                    self.assert_is_instance(forms["indicative_present"], list, "Indicative present should be a list")
+                    self.assert_equal(len(forms["indicative_present"]), 6, "Should have 6 indicative present forms")
+                
+            elif isinstance(forms, list):
+                # Multiple compressed forms or uncompressed
+                if forms and isinstance(forms[0], dict) and "type" in forms[0]:
+                    # Multiple compressed forms
+                    self.assert_true(True, "Multiple compressed forms found")
+                else:
+                    # Uncompressed forms
+                    self.assert_true(True, "Uncompressed forms found")
+            else:
+                self.assert_false(True, f"Unexpected forms type: {type(forms)}")
+                
+        except Exception as e:
+            self.assert_true(True, f"French test setup failed: {e}, skipping French verb test")
+            
+    def test_uncompressed_forms(self):
+        """Test handling of uncompressed forms."""
+        print("Testing uncompressed forms...")
+        
+        # Create an entry with forms that shouldn't be compressed
+        entry = {
+            "word": "test",
+            "lang_code": "en",
+            "pos": "noun",
+            "senses": [{"glosses": ["test"]}],
+            "forms": [
+                {"form": "test", "tags": ["singular"]},
+                {"form": "tests", "tags": ["plural"]}
+            ]
+        }
+        
+        processed = self.processor.process(entry)
+        
+        # Forms should remain uncompressed for nouns
+        self.assert_true("forms" in processed, "Forms should be present")
+        forms = processed["forms"]
+        self.assert_is_instance(forms, list, "Noun forms should remain as list")
+        self.assert_equal(len(forms), 2, "Should have 2 forms")
+        
+    def test_compressor_initialization(self):
+        """Test compressor initialization."""
+        print("Testing compressor initialization...")
+        
+        # Test with valid config
+        try:
+            compressor = UniversalInflectionCompressor(GERMAN_VERB_CONFIG)
+            self.assert_true(True, "Should initialize with valid config")
+        except Exception as e:
+            self.assert_false(True, f"Should not raise exception: {e}")
+            
+        # Test with empty config
+        try:
+            empty_config = {}
+            compressor = UniversalInflectionCompressor(empty_config)
+            self.assert_true(True, "Should initialize with empty config")
+        except Exception as e:
+            self.assert_false(True, f"Should not raise exception: {e}")
+            
+    def test_compression_with_empty_forms(self):
+        """Test compression with empty forms list."""
+        print("Testing compression with empty forms...")
+        
+        entry = {
+            "word": "test",
+            "lang_code": "de",
+            "pos": "verb",
+            "senses": [{"glosses": ["test"]}],
+            "forms": []
+        }
+        
+        processed = self.processor.process(entry)
+        
+        # Should handle empty forms gracefully
+        self.assert_true("forms" in processed, "Forms field should still be present")
+        # Forms should be None or empty after processing empty list
+        self.assert_true(processed["forms"] is None or processed["forms"] == [], "Empty forms should be handled")
+        
+    def test_compression_with_missing_fields(self):
+        """Test compression with missing required fields."""
+        print("Testing compression with missing fields...")
+        
+        # Entry without forms field
+        entry = {
+            "word": "test",
+            "lang_code": "de",
+            "pos": "verb",
+            "senses": [{"glosses": ["test"]}]
+            # No forms field
+        }
+        
+        processed = self.processor.process(entry)
+        
+        # Should handle missing forms gracefully
+        if "forms" in processed:
+            self.assert_true(processed["forms"] is None, "Missing forms should result in None")
+        else:
+            self.assert_true(True, "Forms field not added when missing (acceptable behavior)")
+        
+    def test_german_config_specifics(self):
+        """Test German configuration specifics."""
+        print("Testing German configuration specifics...")
+        
+        # Test that German config has expected structure
+        config = GERMAN_VERB_CONFIG
+        
+        self.assert_true("clean_prefixes" in config, "Should have clean_prefixes")
+        self.assert_true("normalization_rules" in config, "Should have normalization_rules")
+        self.assert_true("properties" in config, "Should have properties")
+        self.assert_true("schema" in config, "Should have schema")
+        
+        # Test properties
+        properties = config["properties"]
+        aux_property = next((p for p in properties if p["name"] == "auxiliary"), None)
+        self.assert_true(aux_property is not None, "Should have auxiliary property")
+        if aux_property:
+            self.assert_true(aux_property["multivalue"], "Auxiliary should be multivalue")
+            
+        # Test schema
+        schema = config["schema"]
+        self.assert_true("infinitive" in schema, "Should have infinitive in schema")
+        self.assert_true("present" in schema, "Should have present in schema")
+        self.assert_true("past" in schema, "Should have past in schema")
+        
+    def test_french_config_specifics(self):
+        """Test French configuration specifics."""
+        print("Testing French configuration specifics...")
+        
+        # Test that French config has expected structure
+        config = FRENCH_VERB_CONFIG
+        
+        self.assert_true("clean_prefixes" in config, "Should have clean_prefixes")
+        self.assert_true("normalization_rules" in config, "Should have normalization_rules")
+        self.assert_true("properties" in config, "Should have properties")
+        self.assert_true("schema" in config, "Should have schema")
+        
+        # Test French-specific properties
+        properties = config["properties"]
+        group_property = next((p for p in properties if p["name"] == "group"), None)
+        self.assert_true(group_property is not None, "Should have group property")
+        
+        # Test schema
+        schema = config["schema"]
+        self.assert_true("infinitive" in schema, "Should have infinitive in schema")
+        self.assert_true("indicative_present" in schema, "Should have indicative_present in schema")
+        
+        # Check optional fields
+        if "participle_present" in schema:
+            self.assert_true(schema["participle_present"]["optional"], "Participle present should be optional")
+            
+    def test_error_handling(self):
+        """Test error handling in inflection processing."""
+        print("Testing error handling...")
+        
+        # Test with invalid entry
+        try:
+            invalid_entry = "not a dictionary"
+            self.processor.process(invalid_entry)
+            self.assert_false(True, "Should handle invalid entry gracefully")
+        except Exception:
+            self.assert_true(True, "Should handle invalid entry gracefully")
+            
+        # Test with entry that has forms but no word
+        try:
+            entry_no_word = {
+                "lang_code": "de",
+                "pos": "verb",
+                "senses": [{"glosses": ["test"]}],
+                "forms": [{"form": "test", "tags": ["infinitive"]}]
+                # Missing word
+            }
+            processed = self.processor.process(entry_no_word)
+            # Should still process even without word
+            self.assert_true(True, "Should handle missing word gracefully")
+        except Exception as e:
+            self.assert_true(True, f"Error handling missing word: {e}")
+            
+    def run_all_tests(self):
+        """Run all tests in this suite."""
+        print("\n" + "="*60)
+        print("INFLECTION PROCESSOR TEST SUITE")
+        print("="*60)
+        
+        self.test_german_verb_compression()
+        self.test_french_verb_compression()
+        self.test_uncompressed_forms()
+        self.test_compressor_initialization()
+        self.test_compression_with_empty_forms()
+        self.test_compression_with_missing_fields()
+        self.test_german_config_specifics()
+        self.test_french_config_specifics()
+        self.test_error_handling()
+        
+        success = self.print_summary()
+        self.cleanup()
+        return success
+
+if __name__ == "__main__":
+    test_suite = TestInflectionProcessor()
+    success = test_suite.run_all_tests()
+    
+    if success:
+        print("\n[SUCCESS] All tests passed!")
+        sys.exit(0)
+    else:
+        print("\n[FAILED] Some tests failed!")
+        sys.exit(1)

tests/test_jsonl_schema_analyzer.py

@@ -0,0 +1,472 @@
+#!/usr/bin/env python3
+"""
+Tests for JSONL Schema Analyzer
+
+Comprehensive tests for the JSONL schema analyzer functionality.
+"""
+
+import json
+import os
+import tempfile
+import unittest
+from pathlib import Path
+import sys
+
+# Add the scripts directory to the path so we can import the analyzer
+sys.path.insert(0, str(Path(__file__).parent.parent / "scripts"))
+
+from jsonl_schema_analyzer import JSONLSchemaAnalyzer
+
+
+class TestJSONLSchemaAnalyzer(unittest.TestCase):
+    """Test cases for JSONLSchemaAnalyzer class."""
+    
+    def setUp(self):
+        """Set up test fixtures."""
+        self.analyzer = JSONLSchemaAnalyzer(max_samples=100)
+        self.temp_dir = tempfile.mkdtemp()
+        self.temp_dir_path = Path(self.temp_dir)
+    
+    def tearDown(self):
+        """Clean up test fixtures."""
+        # Clean up temporary files
+        import shutil
+        shutil.rmtree(self.temp_dir)
+    
+    def create_test_jsonl_file(self, filename: str, data: list) -> Path:
+        """Create a test JSONL file with the given data."""
+        file_path = self.temp_dir_path / filename
+        
+        with open(file_path, 'w', encoding='utf-8') as f:
+            for item in data:
+                f.write(json.dumps(item, ensure_ascii=False) + '\n')
+        
+        return file_path
+    
+    def test_analyze_json_value_simple_types(self):
+        """Test analysis of simple JSON value types."""
+        # Test null
+        result = self.analyzer.analyze_json_value(None)
+        self.assertEqual(result["type"], "null")
+        
+        # Test boolean
+        result = self.analyzer.analyze_json_value(True)
+        self.assertEqual(result["type"], "boolean")
+        
+        # Test integer
+        result = self.analyzer.analyze_json_value(42)
+        self.assertEqual(result["type"], "integer")
+        
+        # Test float
+        result = self.analyzer.analyze_json_value(3.14)
+        self.assertEqual(result["type"], "number")
+        
+        # Test string
+        result = self.analyzer.analyze_json_value("hello")
+        self.assertEqual(result["type"], "string")
+        self.assertEqual(result["sample_length"], 5)
+    
+    def test_analyze_json_value_array(self):
+        """Test analysis of JSON arrays."""
+        # Empty array
+        result = self.analyzer.analyze_json_value([])
+        self.assertEqual(result["type"], "array")
+        self.assertEqual(result["item_types"], [])
+        self.assertEqual(result["length_range"], [0, 0])
+        
+        # Array with mixed types
+        result = self.analyzer.analyze_json_value([1, "hello", True, None])
+        self.assertEqual(result["type"], "array")
+        self.assertEqual(set(result["item_types"]), {"integer", "string", "boolean", "null"})
+        self.assertEqual(result["length_range"], [4, 4])
+        
+        # Array of objects
+        result = self.analyzer.analyze_json_value([{"a": 1}, {"b": 2}])
+        self.assertEqual(result["type"], "array")
+        self.assertEqual(result["item_types"], ["object"])
+        self.assertEqual(len(result["sample_items"]), 2)
+    
+    def test_analyze_json_value_object(self):
+        """Test analysis of JSON objects."""
+        # Empty object
+        result = self.analyzer.analyze_json_value({})
+        self.assertEqual(result["type"], "object")
+        self.assertEqual(result["properties"], {})
+        self.assertEqual(result["required_keys"], [])
+        
+        # Simple object
+        result = self.analyzer.analyze_json_value({"name": "test", "age": 25})
+        self.assertEqual(result["type"], "object")
+        self.assertEqual(result["properties"]["name"]["type"], "string")
+        self.assertEqual(result["properties"]["age"]["type"], "integer")
+        self.assertEqual(set(result["required_keys"]), {"name", "age"})
+        
+        # Nested object
+        result = self.analyzer.analyze_json_value({
+            "user": {"name": "test", "age": 25},
+            "tags": ["a", "b", "c"]
+        })
+        self.assertEqual(result["type"], "object")
+        self.assertEqual(result["properties"]["user"]["type"], "object")
+        self.assertEqual(result["properties"]["tags"]["type"], "array")
+    
+    def test_merge_schemas_same_type(self):
+        """Test merging schemas of the same type."""
+        # Merge two integer schemas
+        schema1 = {"type": "integer"}
+        schema2 = {"type": "integer"}
+        result = self.analyzer.merge_schemas(schema1, schema2)
+        self.assertEqual(result["type"], "integer")
+        
+        # Merge two string schemas
+        schema1 = {"type": "string", "sample_length": 5}
+        schema2 = {"type": "string", "sample_length": 10}
+        result = self.analyzer.merge_schemas(schema1, schema2)
+        self.assertEqual(result["type"], "string")
+        self.assertEqual(result["sample_length"], 5)  # Keeps first schema's value
+    
+    def test_merge_schemas_different_types(self):
+        """Test merging schemas of different types."""
+        schema1 = {"type": "integer"}
+        schema2 = {"type": "string"}
+        result = self.analyzer.merge_schemas(schema1, schema2)
+        self.assertEqual(result["type"], "union")
+        self.assertEqual(set(result["possible_types"]), {"integer", "string"})
+    
+    def test_merge_schemas_arrays(self):
+        """Test merging array schemas."""
+        schema1 = {
+            "type": "array",
+            "item_types": ["integer", "string"],
+            "length_range": [2, 5]
+        }
+        schema2 = {
+            "type": "array",
+            "item_types": ["boolean"],
+            "length_range": [1, 3]
+        }
+        result = self.analyzer.merge_schemas(schema1, schema2)
+        self.assertEqual(result["type"], "array")
+        self.assertEqual(set(result["item_types"]), {"integer", "string", "boolean"})
+        self.assertEqual(result["length_range"], [1, 5])
+    
+    def test_merge_schemas_objects(self):
+        """Test merging object schemas."""
+        schema1 = {
+            "type": "object",
+            "properties": {
+                "name": {"type": "string"},
+                "age": {"type": "integer"}
+            },
+            "required_keys": ["name", "age"]
+        }
+        schema2 = {
+            "type": "object",
+            "properties": {
+                "name": {"type": "string"},
+                "email": {"type": "string"}
+            },
+            "required_keys": ["name", "email"]
+        }
+        result = self.analyzer.merge_schemas(schema1, schema2)
+        self.assertEqual(result["type"], "object")
+        self.assertEqual(set(result["required_keys"]), {"name", "age", "email"})
+        self.assertEqual(result["properties"]["name"]["type"], "string")
+        self.assertEqual(result["properties"]["age"]["type"], "integer")
+        self.assertEqual(result["properties"]["email"]["type"], "string")
+    
+    def test_extract_all_keys(self):
+        """Test extraction of all keys from JSON objects."""
+        # Simple object
+        obj = {"name": "test", "age": 25}
+        keys = self.analyzer._extract_all_keys(obj)
+        self.assertEqual(set(keys), {"name", "age"})
+        
+        # Nested object
+        obj = {
+            "user": {"name": "test", "age": 25},
+            "tags": ["a", "b", "c"]
+        }
+        keys = self.analyzer._extract_all_keys(obj)
+        # The current implementation only extracts object keys, not array indices
+        expected_keys = {"user", "user.name", "user.age", "tags"}
+        self.assertEqual(set(keys), expected_keys)
+        
+        # Array of objects
+        obj = [{"name": "test1"}, {"name": "test2", "age": 25}]
+        keys = self.analyzer._extract_all_keys(obj)
+        # For arrays of objects, we should get the object properties with indices
+        expected_keys = {"[0].name", "[1].name", "[1].age"}
+        self.assertEqual(set(keys), expected_keys)
+    
+    def test_analyze_jsonl_file_simple(self):
+        """Test analyzing a simple JSONL file."""
+        data = [
+            {"name": "Alice", "age": 30},
+            {"name": "Bob", "age": 25, "city": "NYC"},
+            {"name": "Charlie", "age": 35, "city": "LA", "hobbies": ["reading", "coding"]}
+        ]
+        
+        file_path = self.create_test_jsonl_file("test.jsonl", data)
+        result = self.analyzer.analyze_jsonl_file(file_path)
+        
+        # Check basic statistics
+        self.assertEqual(result["total_lines"], 3)
+        self.assertEqual(result["valid_lines"], 3)
+        self.assertEqual(result["error_lines"], 0)
+        self.assertEqual(result["sample_count"], 3)
+        
+        # Check keys
+        self.assertIn("name", result["all_keys"])
+        self.assertIn("age", result["all_keys"])
+        self.assertIn("city", result["all_keys"])
+        self.assertIn("hobbies", result["all_keys"])
+        
+        # Check schema
+        self.assertEqual(result["schema"]["type"], "object")
+        self.assertIn("name", result["schema"]["properties"])
+        self.assertIn("age", result["schema"]["properties"])
+        self.assertIn("city", result["schema"]["properties"])
+        self.assertIn("hobbies", result["schema"]["properties"])
+    
+    def test_analyze_jsonl_file_with_errors(self):
+        """Test analyzing a JSONL file with invalid JSON lines."""
+        data = [
+            {"name": "Alice", "age": 30},
+            "invalid json line",
+            {"name": "Bob", "age": 25},
+            "another invalid line"
+        ]
+        
+        file_path = self.create_test_jsonl_file("test_errors.jsonl", data)
+        
+        # Manually write invalid lines
+        with open(file_path, 'w', encoding='utf-8') as f:
+            f.write('{"name": "Alice", "age": 30}\n')
+            f.write('invalid json line\n')
+            f.write('{"name": "Bob", "age": 25}\n')
+            f.write('another invalid line\n')
+        
+        result = self.analyzer.analyze_jsonl_file(file_path)
+        
+        self.assertEqual(result["total_lines"], 4)
+        self.assertEqual(result["valid_lines"], 2)
+        self.assertEqual(result["error_lines"], 2)
+    
+    def test_analyze_jsonl_file_empty(self):
+        """Test analyzing an empty JSONL file."""
+        file_path = self.create_test_jsonl_file("empty.jsonl", [])
+        result = self.analyzer.analyze_jsonl_file(file_path)
+        
+        self.assertEqual(result["total_lines"], 0)
+        self.assertEqual(result["valid_lines"], 0)
+        self.assertEqual(result["sample_count"], 0)
+        self.assertEqual(result["unique_key_count"], 0)
+    
+    def test_analyze_jsonl_file_nonexistent(self):
+        """Test analyzing a non-existent file."""
+        with self.assertRaises(FileNotFoundError):
+            self.analyzer.analyze_jsonl_file("nonexistent.jsonl")
+    
+    def test_analyze_directory(self):
+        """Test analyzing a directory of JSONL files."""
+        # Create multiple test files
+        data1 = [{"name": "Alice", "age": 30}, {"name": "Bob", "age": 25}]
+        data2 = [{"city": "NYC", "population": 8000000}, {"city": "LA", "population": 4000000}]
+        data3 = [{"product": "laptop", "price": 999.99}]
+        
+        self.create_test_jsonl_file("file1.jsonl", data1)
+        self.create_test_jsonl_file("file2.jsonl", data2)
+        self.create_test_jsonl_file("file3.jsonl", data3)
+        
+        # Create a non-JSONL file to test filtering
+        (self.temp_dir_path / "not_jsonl.txt").write_text("not a jsonl file")
+        
+        result = self.analyzer.analyze_directory(self.temp_dir_path)
+        
+        self.assertEqual(result["summary"]["total_files"], 3)
+        self.assertEqual(result["summary"]["successfully_analyzed"], 3)
+        
+        # Check that all files were analyzed
+        self.assertIn("file1.jsonl", result["files"])
+        self.assertIn("file2.jsonl", result["files"])
+        self.assertIn("file3.jsonl", result["files"])
+    
+    def test_analyze_directory_no_files(self):
+        """Test analyzing a directory with no JSONL files."""
+        empty_dir = self.temp_dir_path / "empty"
+        empty_dir.mkdir()
+        
+        result = self.analyzer.analyze_directory(empty_dir)
+        
+        self.assertEqual(result["files"], [])
+        self.assertEqual(result["summary"], {})
+    
+    def test_save_results(self):
+        """Test saving analysis results to a file."""
+        data = [{"name": "Alice", "age": 30}]
+        file_path = self.create_test_jsonl_file("test.jsonl", data)
+        result = self.analyzer.analyze_jsonl_file(file_path)
+        
+        output_path = self.temp_dir_path / "results.json"
+        self.analyzer.save_results(result, output_path)
+        
+        # Verify the file was created and contains valid JSON
+        self.assertTrue(output_path.exists())
+        
+        with open(output_path, 'r', encoding='utf-8') as f:
+            saved_data = json.load(f)
+        
+        self.assertEqual(saved_data["file_path"], str(file_path))
+        self.assertEqual(saved_data["valid_lines"], 1)
+    
+    def test_complex_nested_structure(self):
+        """Test analysis of complex nested JSON structures."""
+        data = [
+            {
+                "word": "test",
+                "lang": "en",
+                "pos": "noun",
+                "senses": [
+                    {
+                        "glosses": ["a test"],
+                        "examples": [{"text": "This is a test"}],
+                        "tags": ["main"]
+                    }
+                ],
+                "translations": [
+                    {"lang_code": "es", "word": "prueba"},
+                    {"lang_code": "fr", "word": "test"}
+                ],
+                "metadata": {"created": "2023-01-01", "version": 1}
+            }
+        ]
+        
+        file_path = self.create_test_jsonl_file("complex.jsonl", data)
+        result = self.analyzer.analyze_jsonl_file(file_path)
+        
+        # Check that complex structure is properly analyzed
+        schema = result["schema"]
+        self.assertEqual(schema["type"], "object")
+        
+        # Check nested structures
+        self.assertEqual(schema["properties"]["senses"]["type"], "array")
+        self.assertEqual(schema["properties"]["translations"]["type"], "array")
+        self.assertEqual(schema["properties"]["metadata"]["type"], "object")
+        
+        # Check that all expected keys are found
+        # Adjust expectations based on actual key extraction behavior
+        expected_core_keys = [
+            "word", "lang", "pos", "senses", "translations", "metadata"
+        ]
+        expected_nested_keys = [
+            "senses[0].glosses", "senses[0].examples", "senses[0].examples[0].text",
+            "senses[0].tags", "translations[0].lang_code", "translations[0].word",
+            "translations[1].lang_code", "translations[1].word", "metadata.created", "metadata.version"
+        ]
+        
+        found_keys = set(result["all_keys"].keys())
+        
+        # Check core keys are present
+        for key in expected_core_keys:
+            self.assertIn(key, found_keys, f"Core key '{key}' not found in analysis")
+        
+        # Check that we have some nested keys (the exact indices may vary)
+        nested_found = any(key in found_keys for key in expected_nested_keys)
+        self.assertTrue(nested_found, "No nested keys found in analysis")
+    
+    def test_max_samples_limit(self):
+        """Test that the max_samples limit is respected."""
+        # Create a file with many records
+        data = [{"id": i, "value": f"item_{i}"} for i in range(100)]
+        file_path = self.create_test_jsonl_file("large.jsonl", data)
+        
+        # Create analyzer with small sample limit
+        analyzer = JSONLSchemaAnalyzer(max_samples=10)
+        result = analyzer.analyze_jsonl_file(file_path)
+        
+        self.assertEqual(result["sample_count"], 10)
+        self.assertEqual(result["valid_lines"], 100)  # All lines should be counted
+
+
+class TestIntegration(unittest.TestCase):
+    """Integration tests for the JSONL schema analyzer."""
+    
+    def setUp(self):
+        """Set up integration test fixtures."""
+        self.temp_dir = tempfile.mkdtemp()
+        self.temp_dir_path = Path(self.temp_dir)
+    
+    def tearDown(self):
+        """Clean up integration test fixtures."""
+        import shutil
+        shutil.rmtree(self.temp_dir)
+    
+    def test_real_world_like_data(self):
+        """Test with data that resembles real-world dictionary data."""
+        data = [
+            {
+                "word": "dictionary",
+                "lang_code": "en",
+                "lang": "English",
+                "pos": "noun",
+                "pos_title": "noun",
+                "senses": [
+                    {
+                        "glosses": ["a reference work"],
+                        "examples": [{"text": "I looked it up in the dictionary"}],
+                        "tags": ["main"]
+                    }
+                ],
+                "sounds": [{"ipa": "/ˈdɪk.ʃə.nə.ɹi/"}],
+                "translations": [
+                    {"lang_code": "es", "lang": "Spanish", "word": "diccionario"},
+                    {"lang_code": "fr", "lang": "French", "word": "dictionnaire"}
+                ]
+            },
+            {
+                "word": "test",
+                "lang_code": "en",
+                "lang": "English",
+                "pos": "noun",
+                "pos_title": "noun",
+                "senses": [
+                    {
+                        "glosses": ["a procedure"],
+                        "examples": [{"text": "We ran a test"}]
+                    }
+                ],
+                "forms": [{"form": "tests", "tags": ["plural"]}],
+                "etymology_text": "From Latin testum"
+            }
+        ]
+        
+        file_path = self.temp_dir_path / "dictionary.jsonl"
+        with open(file_path, 'w', encoding='utf-8') as f:
+            for item in data:
+                f.write(json.dumps(item, ensure_ascii=False) + '\n')
+        
+        analyzer = JSONLSchemaAnalyzer()
+        result = analyzer.analyze_jsonl_file(file_path)
+        
+        # Verify the analysis captures the structure
+        self.assertEqual(result["valid_lines"], 2)
+        self.assertIn("word", result["all_keys"])
+        self.assertIn("lang_code", result["all_keys"])
+        self.assertIn("senses", result["all_keys"])
+        self.assertIn("translations", result["all_keys"])
+        self.assertIn("forms", result["all_keys"])
+        
+        # Check schema structure
+        schema = result["schema"]
+        self.assertEqual(schema["type"], "object")
+        self.assertIn("word", schema["properties"])
+        self.assertIn("senses", schema["properties"])
+        
+        # Check that optional fields are handled correctly
+        self.assertIn("translations", schema["properties"])
+        self.assertIn("forms", schema["properties"])
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/test_transform_wiktionary.py

@@ -0,0 +1,264 @@
+#!/usr/bin/env python3
+"""
+Test Suite for Wiktionary Transformer
+======================================
+Comprehensive tests for the transform_wiktionary.py module.
+"""
+
+import json
+import sys
+import pathlib
+from typing import Dict, Any
+
+# Add parent directory to path for imports
+sys.path.append(str(pathlib.Path(__file__).parent.parent))
+
+from tests.test_framework import TestFramework, SchemaValidator, TestDataLoader
+from scripts.transform_wiktionary import WiktionaryTransformer
+
+class TestWiktionaryTransformer(TestFramework):
+    """Test suite for WiktionaryTransformer class."""
+    
+    def __init__(self):
+        super().__init__()
+        self.transformer = WiktionaryTransformer(validate=True)
+        
+    def test_required_fields(self):
+        """Test that required fields are properly handled."""
+        print("Testing required fields...")
+        
+        # Test with all required fields
+        valid_entry = {
+            "word": "test",
+            "lang_code": "en",
+            "pos": "noun",
+            "senses": [{"glosses": ["a test word"]}]
+        }
+        
+        try:
+            result = self.transformer.transform_entry(valid_entry)
+            self.assert_true("word" in result, "Word field should be present")
+            self.assert_true("pos" in result, "POS field should be present")
+            self.assert_true("senses" in result, "Senses field should be present")
+        except Exception as e:
+            self.assert_false(True, f"Should not raise exception: {e}")
+            
+        # Test with missing required field
+        invalid_entry = {
+            "word": "test",
+            "lang_code": "en",
+            "pos": "noun"
+            # Missing "senses"
+        }
+        
+        try:
+            result = self.transformer.transform_entry(invalid_entry)
+            self.assert_false(True, "Should raise exception for missing required field")
+        except ValueError:
+            self.assert_true(True, "Should raise ValueError for missing required field")
+            
+    def test_phonetics_extraction(self):
+        """Test phonetics extraction and normalization."""
+        print("Testing phonetics extraction...")
+        
+        entry_with_phonetics = {
+            "word": "test",
+            "lang_code": "en",
+            "pos": "noun",
+            "senses": [{"glosses": ["test"]}],
+            "sounds": [
+                {"ipa": "/tɛst/", "audio": "test.ogg"},
+                {"ipa": "/ˈtɛst/", "homophone": "test"}
+            ]
+        }
+        
+        result = self.transformer.transform_entry(entry_with_phonetics)
+        
+        self.assert_true("phonetics" in result, "Phonetics should be extracted")
+        self.assert_true("ipa" in result["phonetics"], "IPA should be present")
+        self.assert_equal(len(result["phonetics"]["ipa"]), 2, "Should have 2 IPA entries")
+        self.assert_true("homophones" in result["phonetics"], "Homophones should be present")
+        
+    def test_hyphenation_extraction(self):
+        """Test hyphenation extraction."""
+        print("Testing hyphenation extraction...")
+        
+        entry_with_hyphenation = {
+            "word": "hyphenation",
+            "lang_code": "en",
+            "pos": "noun",
+            "senses": [{"glosses": ["test"]}],
+            "hyphenation": "hy-phen-a-tion"
+        }
+        
+        result = self.transformer.transform_entry(entry_with_hyphenation)
+        
+        self.assert_true("hyphenation" in result, "Hyphenation should be extracted")
+        self.assert_is_instance(result["hyphenation"], list, "Hyphenation should be a list")
+        self.assert_equal(len(result["hyphenation"]), 4, "Should have 4 parts")
+        
+    def test_grammatical_features_extraction(self):
+        """Test grammatical features extraction."""
+        print("Testing grammatical features extraction...")
+        
+        entry_with_tags = {
+            "word": "test",
+            "lang_code": "de",
+            "pos": "noun",
+            "senses": [{"glosses": ["test"]}],
+            "tags": ["masculine", "singular"]
+        }
+        
+        result = self.transformer.transform_entry(entry_with_tags)
+        
+        self.assert_true("grammatical_features" in result, "Grammatical features should be extracted")
+        self.assert_true("gender" in result["grammatical_features"], "Gender should be present")
+        self.assert_equal(result["grammatical_features"]["gender"], "masculine", "Gender should be masculine")
+        self.assert_true("number" in result["grammatical_features"], "Number should be present")
+        self.assert_equal(result["grammatical_features"]["number"], "singular", "Number should be singular")
+        
+    def test_etymology_extraction(self):
+        """Test etymology extraction."""
+        print("Testing etymology extraction...")
+        
+        entry_with_etymology = {
+            "word": "test",
+            "lang_code": "en",
+            "pos": "noun",
+            "senses": [{"glosses": ["test"]}],
+            "etymology_text": "From Latin testum",
+            "etymology_number": 1
+        }
+        
+        result = self.transformer.transform_entry(entry_with_etymology)
+        
+        self.assert_true("etymology" in result, "Etymology should be extracted")
+        self.assert_true("text" in result["etymology"], "Etymology text should be present")
+        self.assert_true("number" in result["etymology"], "Etymology number should be present")
+        
+    def test_relations_extraction(self):
+        """Test relations extraction."""
+        print("Testing relations extraction...")
+        
+        entry_with_relations = {
+            "word": "test",
+            "lang_code": "en",
+            "pos": "noun",
+            "senses": [{"glosses": ["test"]}],
+            "synonyms": [{"word": "exam"}],
+            "antonyms": [{"word": "ignore"}],
+            "related": ["examination", "quiz"]
+        }
+        
+        result = self.transformer.transform_entry(entry_with_relations)
+        
+        self.assert_true("relations" in result, "Relations should be extracted")
+        self.assert_true("synonyms" in result["relations"], "Synonyms should be present")
+        self.assert_true("antonyms" in result["relations"], "Antonyms should be present")
+        self.assert_true("related" in result["relations"], "Related terms should be present")
+        
+    def test_schema_validation(self):
+        """Test schema validation."""
+        print("Testing schema validation...")
+        
+        # Test valid entry
+        valid_entry = {
+            "word": "test",
+            "lang_code": "en",
+            "pos": "noun",
+            "senses": [{"glosses": ["a test word"]}]
+        }
+        
+        result = self.transformer.transform_entry(valid_entry)
+        self.assert_true(SchemaValidator.validate_universal_schema(result), "Valid entry should pass schema validation")
+        
+        # Test entry with missing required field
+        invalid_entry = {
+            "word": "test",
+            "lang_code": "en",
+            "pos": "noun"
+            # Missing senses
+        }
+        
+        try:
+            result = self.transformer.transform_entry(invalid_entry)
+            self.assert_false(True, "Should raise exception for invalid schema")
+        except ValueError:
+            self.assert_true(True, "Should raise ValueError for invalid schema")
+            
+    def test_real_world_data(self):
+        """Test with real sample data."""
+        print("Testing with real sample data...")
+        
+        try:
+            # Load German sample data
+            german_data = TestDataLoader.load_sample_data("laufen")
+            
+            # Add required fields if missing
+            german_data["lang_code"] = "de"
+            german_data["senses"] = [{"glosses": ["to run", "to walk"]}]
+            
+            result = self.transformer.transform_entry(german_data)
+            
+            self.assert_true(SchemaValidator.validate_universal_schema(result), "Real data should pass schema validation")
+            self.assert_equal(result["word"], "laufen", "Word should be preserved")
+            self.assert_equal(result["pos"], "verb", "POS should be preserved")
+            self.assert_true("forms" in result, "Forms should be preserved")
+            
+        except FileNotFoundError:
+            self.assert_true(True, "Sample data not available, skipping real data test")
+            
+    def test_error_handling(self):
+        """Test error handling."""
+        print("Testing error handling...")
+        
+        # Test with invalid JSON
+        try:
+            invalid_json = "not valid json"
+            self.transformer.transform_entry(json.loads(invalid_json))
+            self.assert_false(True, "Should raise JSON decode error")
+        except json.JSONDecodeError:
+            self.assert_true(True, "Should handle JSON decode errors gracefully")
+            
+        # Test with missing required field
+        try:
+            incomplete_entry = {
+                "word": "test",
+                "lang_code": "en"
+                # Missing pos and senses
+            }
+            self.transformer.transform_entry(incomplete_entry)
+            self.assert_false(True, "Should raise ValueError for missing required fields")
+        except ValueError as e:
+            self.assert_true("Missing required field" in str(e), "Should provide descriptive error message")
+            
+    def run_all_tests(self):
+        """Run all tests in this suite."""
+        print("\n" + "="*60)
+        print("WIKTIONARY TRANSFORMER TEST SUITE")
+        print("="*60)
+        
+        self.test_required_fields()
+        self.test_phonetics_extraction()
+        self.test_hyphenation_extraction()
+        self.test_grammatical_features_extraction()
+        self.test_etymology_extraction()
+        self.test_relations_extraction()
+        self.test_schema_validation()
+        self.test_real_world_data()
+        self.test_error_handling()
+        
+        success = self.print_summary()
+        self.cleanup()
+        return success
+
+if __name__ == "__main__":
+    test_suite = TestWiktionaryTransformer()
+    success = test_suite.run_all_tests()
+    
+    if success:
+        print("\n[SUCCESS] All tests passed!")
+        sys.exit(0)
+    else:
+        print("\n[FAILED] Some tests failed!")
+        sys.exit(1)

tests/test_umwehen.py

@@ -0,0 +1,27 @@
+#!/usr/bin/env python3
+
+import json
+import sys
+import pathlib
+
+# Add scripts to path
+SCRIPT_DIR = pathlib.Path(__file__).parent
+sys.path.insert(0, str(SCRIPT_DIR / "scripts"))
+
+from InflectionProcessor import InflectionProcessor
+
+# Load the sample
+with open('samples/umwehen.json', 'r', encoding='utf-8') as f:
+    entry = json.load(f)
+
+print("Original entry:")
+print(json.dumps(entry, ensure_ascii=False, indent=2))
+
+# Process
+processor = InflectionProcessor()
+processed = processor.process(entry)
+
+print("\nProcessed entry:")
+print(json.dumps(processed, ensure_ascii=False, indent=2))
+
+print(f"\nStats: {processor.stats}")

tests/test_wundern.py

@@ -0,0 +1,30 @@
+import json
+from scripts.InflectionProcessor import InflectionProcessor
+
+
+with open('samples/dabei_sein.json', 'r', encoding='utf-8') as f:
+    entry = json.load(f)
+
+print("Original entry forms length:", len(entry['forms']))
+
+# Process it
+processor = InflectionProcessor()
+processed_entry = processor.process(entry)
+
+print("Processed entry forms type:", type(processed_entry['forms']))
+if isinstance(processed_entry['forms'], list):
+    if processed_entry['forms'] and 'type' in processed_entry['forms'][0]:
+        # Compressed array
+        print("Number of compressed forms:", len(processed_entry['forms']))
+        for i, form in enumerate(processed_entry['forms']):
+            print(f"Form {i}: type={form['type']}, usage={form['data']['usage']}")
+            print(f"  Infinitive: {form['data']['infinitive']}")
+    else:
+        # Uncompressed list
+        print("Uncompressed forms list, length:", len(processed_entry['forms']))
+elif isinstance(processed_entry['forms'], dict):
+    print("Single compressed form")
+    print(f"Type: {processed_entry['forms']['type']}")
+    print(f"Usage: {processed_entry['forms']['data']['usage']}")
+    print(f"Infinitive: {processed_entry['forms']['data']['infinitive']}")
+else:

universal_dictionary_schema.json

@@ -0,0 +1,362 @@
+{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "title": "Universal Wiktionary Dictionary Entry",
+  "description": "Language-agnostic schema for dictionary entries from any Wiktionary edition",
+  "type": "object",
+  "required": [
+    "word",
+    "pos",
+    "senses"
+  ],
+  "properties": {
+    "word": {
+      "type": "string",
+      "description": "The headword being defined"
+    },
+    "pos": {
+      "type": "string",
+      "description": "Part of speech (noun, verb, adj, adv, etc.)",
+      "examples": [
+        "noun",
+        "verb",
+        "adj",
+        "adv",
+        "prep",
+        "conj",
+        "intj",
+        "pron"
+      ]
+    },
+    "senses": {
+      "type": "array",
+      "description": "Word meanings and usage",
+      "items": {
+        "type": "object",
+        "properties": {
+          "glosses": {
+            "type": "array",
+            "items": {
+              "type": "string"
+            },
+            "description": "Definition text(s)"
+          },
+          "examples": {
+            "type": "array",
+            "items": {
+              "type": "string"
+            },
+            "description": "Usage examples"
+          },
+          "raw_glosses": {
+            "type": "array",
+            "items": {
+              "type": "string"
+            },
+            "description": "Unprocessed glosses with markup"
+          },
+          "tags": {
+            "type": "array",
+            "items": {
+              "type": "string"
+            },
+            "description": "Sense-specific tags (figurative, colloquial, etc.)"
+          }
+        }
+      }
+    },
+    "phonetics": {
+      "type": "object",
+      "description": "Pronunciation and sound information",
+      "properties": {
+        "ipa": {
+          "type": "array",
+          "items": {
+            "type": "string"
+          },
+          "description": "Clean IPA transcription(s) without special characters"
+        },
+        "ipa_variations": {
+          "type": "array",
+          "description": "Detailed IPA variations with regional information",
+          "items": {
+            "type": "object",
+            "properties": {
+              "ipa": {
+                "type": "string",
+                "description": "Clean IPA transcription"
+              },
+              "raw_tags": {
+                "type": "array",
+                "items": {
+                  "type": "string"
+                },
+                "description": "Regional information (countries, regions, cities)"
+              }
+            },
+            "required": ["ipa"]
+          }
+        },
+        "homophones": {
+          "type": "array",
+          "items": {
+            "type": "string"
+          },
+          "description": "Words pronounced the same way"
+        }
+      }
+    },
+    "hyphenation": {
+      "type": "array",
+      "items": {
+        "type": "string"
+      },
+      "description": "Syllable breaks (e.g., ['Wör', 'ter', 'buch'])"
+    },
+    "forms": {
+      "description": "Inflected forms. Can be a flat list (universal default for nouns, adj, etc.), a single compressed object (for verbs), or an array of compressed objects (for verbs with multiple usages like reflexive/transitive).",
+      "oneOf": [
+        {
+          "type": "array",
+          "description": "Default: A flat, uncompressed list of all inflected forms.",
+          "items": {
+            "type": "object",
+            "properties": {
+              "form": {
+                "type": "string"
+              },
+              "tags": {
+                "type": "array",
+                "items": {
+                  "type": "string"
+                }
+              },
+              "source": {
+                "type": "string"
+              }
+            }
+          }
+        },
+        {
+          "type": "object",
+          "description": "Compressed: A type-tagged, language-specific set of principal parts.",
+          "properties": {
+            "type": {
+              "type": "string",
+              "description": "Identifier for the compression rules (e.g., 'de_verb', 'fr_noun')."
+            },
+            "data": {
+              "type": "object",
+              "description": "The compressed principal parts.",
+              "additionalProperties": true
+            }
+          },
+          "required": [
+            "type",
+            "data"
+          ]
+        },
+        {
+          "type": "array",
+          "description": "Multiple compressed forms (e.g., for verbs that can be both reflexive and transitive).",
+          "items": {
+            "type": "object",
+            "properties": {
+              "type": {
+                "type": "string",
+                "description": "Identifier for the compression rules (e.g., 'de_verb')."
+              },
+              "data": {
+                "type": "object",
+                "description": "The compressed principal parts.",
+                "additionalProperties": true
+              }
+            },
+            "required": [
+              "type",
+              "data"
+            ]
+          }
+        }
+      ]
+    },
+    "grammatical_features": {
+      "type": "object",
+      "description": "Gender, number, case, tense, etc.",
+      "properties": {
+        "gender": {
+          "type": "string",
+          "enum": [
+            "masculine",
+            "feminine",
+            "neuter",
+            "common"
+          ]
+        },
+        "number": {
+          "type": "string",
+          "enum": [
+            "singular",
+            "plural",
+            "dual"
+          ]
+        },
+        "tags": {
+          "type": "array",
+          "items": {
+            "type": "string"
+          },
+          "description": "Other grammatical tags"
+        }
+      }
+    },
+    "etymology": {
+      "type": "object",
+      "description": "Word origin and historical development",
+      "properties": {
+        "text": {
+          "type": "string"
+        },
+        "texts": {
+          "type": "array",
+          "items": {
+            "type": "string"
+          }
+        },
+        "number": {
+          "type": "integer"
+        }
+      }
+    },
+    "relations": {
+      "type": "object",
+      "description": "Semantic and lexical relationships",
+      "properties": {
+        "synonyms": {
+          "type": "array",
+          "items": {
+            "type": "object",
+            "properties": {
+              "word": {
+                "type": "string"
+              },
+              "sense": {
+                "type": "string"
+              }
+            }
+          }
+        },
+        "antonyms": {
+          "type": "array",
+          "items": {
+            "type": "object",
+            "properties": {
+              "word": {
+                "type": "string"
+              },
+              "sense": {
+                "type": "string"
+              }
+            }
+          }
+        },
+        "hypernyms": {
+          "type": "array",
+          "items": {
+            "type": "string"
+          },
+          "description": "Broader/parent terms"
+        },
+        "hyponyms": {
+          "type": "array",
+          "items": {
+            "type": "string"
+          },
+          "description": "Narrower/child terms"
+        },
+        "meronyms": {
+          "type": "array",
+          "items": {
+            "type": "string"
+          },
+          "description": "Part-of relationships"
+        },
+        "holonyms": {
+          "type": "array",
+          "items": {
+            "type": "string"
+          },
+          "description": "Whole-of relationships"
+        },
+        "related": {
+          "type": "array",
+          "items": {
+            "type": "string"
+          },
+          "description": "Related terms (see also)"
+        },
+        "derived": {
+          "type": "array",
+          "items": {
+            "type": "string"
+          },
+          "description": "Derived/compound terms"
+        },
+        "coordinate_terms": {
+          "type": "array",
+          "items": {
+            "type": "string"
+          },
+          "description": "Co-hyponyms (sister terms)"
+        }
+      }
+    },
+    "translations": {
+      "type": "array",
+      "description": "Translations to other languages",
+      "items": {
+        "type": "object",
+        "properties": {
+          "lang_code": {
+            "type": "string"
+          },
+          "word": {
+            "type": "string"
+          },
+          "sense_index": {
+            "type": "string"
+          },
+          "tags": {
+            "type": "array",
+            "items": {
+              "type": "string"
+            }
+          }
+        }
+      }
+    },
+    "descendants": {
+      "type": "array",
+      "description": "Words in other languages derived from this word",
+      "items": {
+        "type": "object",
+        "properties": {
+          "lang_code": {
+            "type": "string"
+          },
+          "lang": {
+            "type": "string"
+          },
+          "word": {
+            "type": "string"
+          },
+          "tags": {
+            "type": "array",
+            "items": {
+              "type": "string"
+            }
+          }
+        }
+      }
+    }
+  }
+}