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from datetime import datetime, timedelta, UTC |
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from difflib import SequenceMatcher |
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from typing import Optional, List, Dict |
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STAGNATION_WINDOW = 5 |
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STAGNATION_THRESHOLD = 0.95 |
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def get_last_responses(db, limit=STAGNATION_WINDOW) -> List[str]: |
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records = db.get_llm_recent_responses(limit=limit) |
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return [r["content"] if isinstance(r, dict) else r for r in records] |
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def is_stagnant(responses: List[str]) -> bool: |
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if len(responses) < 2: |
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return False |
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similarities = [] |
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for i in range(len(responses) - 1): |
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sim = string_similarity(responses[i], responses[i + 1]) |
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similarities.append(sim) |
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avg_sim = sum(similarities) / len(similarities) |
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return avg_sim >= STAGNATION_THRESHOLD |
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def string_similarity(a: str, b: str) -> float: |
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return SequenceMatcher(None, a.strip(), b.strip()).ratio() |
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def get_last_stagnation_info(db) -> Optional[Dict]: |
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responses = get_last_responses(db) |
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if is_stagnant(responses): |
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return { |
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"detected": True, |
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"average_similarity": round(sum( |
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string_similarity(responses[i], responses[i+1]) |
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for i in range(len(responses)-1) |
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) / (len(responses)-1), 4), |
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"last_messages": responses |
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} |
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return {"detected": False} |
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def add_to_llm_memory(db, title: str, content: str, tags: str = "meta"): |
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db.insert("llm_memory", { |
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"title": title, |
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"content": content, |
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"tags": tags, |
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"created_at": datetime.now(UTC).isoformat(), |
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"updated_at": datetime.now(UTC).isoformat() |
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}) |
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def record_reflection_if_stagnant(db): |
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stagnation = get_last_stagnation_info(db) |
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if stagnation.get("detected"): |
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content = f"Обнаружена стагнация размышлений. Последние сообщения были очень похожи.\n\n" |
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content += "---\n" + "\n---\n".join(stagnation["last_messages"]) |
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add_to_llm_memory(db, "Стагнация размышлений", content, tags="meta,warning") |
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return True |
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return False |
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