import json import re from Levenshtein import distance as levenshtein # ---- CONFIG ---- MAX_USER_LEN = 900 LEVENSHTEIN_THRESH = 19 MIN_TURN_LEN = 5 DEFAULT_ANSWER = "Informação não encontrada no contexto fornecido." MARKDOWN_THRESH = 3 # for rule 5 def clean_text(text: str, counters=None) -> str: """Clean user text and track modifications for rule 6.""" # Rule: remove ', conforme descrito no texto.' def remove_conforme_texto(m): if counters is not None: counters["turns_modified"] += 1 return "." text = re.sub(r", conforme descrito no texto\.", remove_conforme_texto, text) # Rule 6: remove 'Conforme o contexto,' at the beginning def remove_conforme(m): if counters is not None: counters["rule6_conforme_contexto"] += 1 counters["turns_modified"] += 1 return m.group(1).upper() text = re.sub( r"^\s*Conforme o contexto,\s*([a-zA-Z])", remove_conforme, text ) return text def is_default_answer(msg: str) -> bool: return msg.strip() == DEFAULT_ANSWER def count_markdown(text: str) -> int: """Count occurrences of specific markdown symbols.""" return text.count("\n") + text.count("|") # ---- MAIN CLEANING FUNCTION ---- def clean_conversations(conversations, verbose=False): cleaned = [] counters = { "rule1_default_answer": 0, "rule2_long_user": 0, "rule3_similar_to_assistant": 0, "rule4_similar_to_user": 0, "rule5_markdown_count": 0, "rule6_conforme_contexto": 0, "rule7_too_short_turn": 0, "empty_turns": 0, "conversations_dropped": 0, "conversations_kept": 0, "turns_removed": 0, "turns_modified": 0 } empty_turns_info = [] for conv in conversations: seq_id = conv["seq_id"] convo = conv["conversation"] new_convo = [] skip_conversation = False user_prompts = [] last_assistant = None i = 0 while i < len(convo): turn_user = convo[i] if i < len(convo) and convo[i]["role"] == "user" else None turn_assistant = convo[i + 1] if i + 1 < len(convo) and convo[i + 1]["role"] == "assistant" else None if ( turn_user is None or turn_assistant is None or turn_user.get("content") is None or turn_assistant.get("content") is None ): counters["empty_turns"] += 1 counters["turns_removed"] += 1 empty_turns_info.append({ "seq_id": seq_id, "conversation": convo }) if verbose: print(f" → Dropping turn {i//2+1} in {seq_id}: empty user or assistant message") i += 2 continue user_msg = clean_text(turn_user["content"], counters=counters) assistant_msg = turn_assistant["content"] # --- Rule 7: turn too short (user or assistant) --- if len(user_msg.strip()) < MIN_TURN_LEN or len(assistant_msg.strip()) < MIN_TURN_LEN: counters["rule7_too_short_turn"] += 1 counters["turns_removed"] += 1 if verbose: print( f" → Dropping turn {i//2+1} in {seq_id}: " f"user({len(user_msg.strip())}) or assistant({len(assistant_msg.strip())}) < {MIN_TURN_LEN} chars" ) i += 2 continue # --- Rule 1: default assistant answer --- if is_default_answer(assistant_msg): counters["rule1_default_answer"] += 1 if not new_convo: skip_conversation = True if verbose: print(f" → Dropping entire conversation {seq_id}: first assistant default") break else: counters["turns_removed"] += 1 if verbose: print(f" → Dropping turn {i//2+1} in {seq_id}: assistant default answer") i += 2 continue # --- Rule 2: user too long (not first user) --- if len(user_msg) > MAX_USER_LEN and new_convo: counters["rule2_long_user"] += 1 counters["turns_removed"] += 1 if verbose: print( f" → Dropping turn {i//2+1} in {seq_id}: " f"user message too long ({len(user_msg)} chars)" ) i += 2 continue # --- Rule 3: user similar to previous assistant --- if last_assistant and levenshtein(user_msg, last_assistant) <= LEVENSHTEIN_THRESH: counters["rule3_similar_to_assistant"] += 1 counters["turns_removed"] += 1 if verbose: print(f" → Dropping turn {i//2+1} in {seq_id}: user similar to last assistant") i += 2 continue # --- Rule 4: user similar to previous users --- if any(levenshtein(user_msg, prev) <= LEVENSHTEIN_THRESH for prev in user_prompts): counters["rule4_similar_to_user"] += 1 counters["turns_removed"] += 1 if verbose: print( f" → Dropping turn {i//2+1} in {seq_id}: " f"user duplicate or similar to previous user" ) i += 2 continue # --- Rule 5: too many markdown symbols (skip for first turn) --- if new_convo and count_markdown(user_msg) >= MARKDOWN_THRESH: counters["rule5_markdown_count"] += 1 counters["turns_removed"] += 1 if verbose: print( f" → Dropping turn {i//2+1} in {seq_id}: " f"user has {count_markdown(user_msg)} markdown symbols" ) i += 2 continue # Passed all filters, keep turn new_convo.append({"role": "user", "content": user_msg}) new_convo.append({"role": "assistant", "content": assistant_msg}) user_prompts.append(user_msg) last_assistant = assistant_msg i += 2 if not skip_conversation and new_convo: cleaned.append({ "seq_id": seq_id, "conversation": new_convo, "question_style": conv.get("question_style"), "context_id": conv.get("context_id") }) counters["conversations_kept"] += 1 else: if skip_conversation: counters["conversations_dropped"] += 1 return cleaned, counters # ---- MAIN EXECUTION ---- if __name__ == "__main__": verbose = False input_file = "magpie_conversations_cemig_v1_objetiva.jsonl" output_file = "./cemig_cleaned/magpie_conversations_cemig_v1_objetiva.jsonl" with open(input_file, "r", encoding="utf-8") as f: data = [] for lineno, line in enumerate(f, start=1): if not line.strip(): continue try: obj = json.loads(line) data.append(obj) except json.JSONDecodeError as e: print(f"JSON error at file line {lineno}, char {e.pos}: {e}") print("Problematic line:") print(line) print("Attempting automatic fix by splitting '}{'") parts = line.replace("}{", "}\n{").splitlines() fixed_objs = 0 for part in parts: try: obj = json.loads(part) data.append(obj) fixed_objs += 1 except json.JSONDecodeError as e2: print(f"Still failed to parse part: {e2}") print(part) print(f"Fixed {fixed_objs} objects from problematic line.") total_turns = sum(len(conv["conversation"]) // 2 for conv in data) cleaned, counters = clean_conversations(data, verbose=verbose) kept_turns = sum(len(conv["conversation"]) // 2 for conv in cleaned) percentage_kept = (kept_turns / total_turns * 100) if total_turns > 0 else 0 with open(output_file, "w", encoding="utf-8") as f: for conv in cleaned: f.write(json.dumps(conv, ensure_ascii=False) + "\n") print("\nFinal results") print(f"Rule 1 (default answer) was triggered {counters['rule1_default_answer']} times") print(f"Rule 2 (long user message) was triggered {counters['rule2_long_user']} times") print(f"Rule 3 (user similar to assistant) was triggered {counters['rule3_similar_to_assistant']} times") print(f"Rule 4 (user similar to another user) was triggered {counters['rule4_similar_to_user']} times") print(f"Rule 5 (user contains too many markdown symbols) was triggered {counters['rule5_markdown_count']} times") print(f"Rule 6 (Conforme o contexto, removed) was triggered {counters['rule6_conforme_contexto']} times") print(f"Rule 7 (turn too short) was triggered {counters['rule7_too_short_turn']} times") print(f"Empty turns removed: {counters['empty_turns']}") print(f"Turns removed: {counters['turns_removed']}") print(f"Turns modified: {counters['turns_modified']}") print(f"Total turns in original file: {total_turns}") print(f"Turns kept after cleaning: {kept_turns} ({percentage_kept:.2f}%)") print(f"Conversations kept: {counters['conversations_kept']}") print(f"Conversations dropped: {counters['conversations_dropped']}")