223 lines
7 KiB
Python
223 lines
7 KiB
Python
# Copyright (c) 2025 Patrick Motsch
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# All rights reserved.
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"""
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CommCoach Context Retrieval.
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Intent detection, retrieval strategies, and context assembly for intelligent session continuity.
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"""
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import re
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import logging
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from datetime import datetime
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from typing import Optional, Dict, Any, List, Tuple
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from enum import Enum
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logger = logging.getLogger(__name__)
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# Retrieval config
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PREVIOUS_SESSION_SUMMARIES_COUNT = 5
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ROLLING_OVERVIEW_SESSION_THRESHOLD = 10
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ROLLING_OVERVIEW_EVERY_N_SESSIONS = 10
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TOPIC_SEARCH_MAX_RESULTS = 5
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class RetrievalIntent(str, Enum):
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NORMAL = "normal"
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SUMMARIZE_ALL = "summarize_all"
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RECALL_SESSION = "recall_session"
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RECALL_TOPIC = "recall_topic"
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def detectIntent(userMessage: str) -> RetrievalIntent:
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"""
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Lightweight intent detection from user message.
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Uses keyword/regex heuristics.
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"""
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text = (userMessage or "").strip().lower()
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if len(text) < 3:
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return RetrievalIntent.NORMAL
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summarizePatterns = [
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r"\b(fasse|zusammenfass|zusammenfassung|ueberblick|gesamte?r?\s*chat|alles\s+zusammen)\b",
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r"\b(summarize|summary\s+of\s+all|complete\s+summary)\b",
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r"zusammenfassung\s+(des\s+)?gesamten",
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r"gesamten\s+chat",
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]
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for p in summarizePatterns:
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if re.search(p, text, re.IGNORECASE):
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return RetrievalIntent.SUMMARIZE_ALL
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datePatterns = [
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r"\b(session|gespraech|besprechung)\s+(vom|am|vom)\s*(\d{1,2})\.(\d{1,2})\.(\d{2,4})",
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r"\b(am|vom)\s*(\d{1,2})\.(\d{1,2})\.(\d{2,4})",
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r"\b(letzte\s+woche|voriger\s+monat|gestern)\b",
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r"\b(session|gespraech)\s+(vom|from)\s+(\d{4}-\d{2}-\d{2})",
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]
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for p in datePatterns:
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if re.search(p, text, re.IGNORECASE):
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return RetrievalIntent.RECALL_SESSION
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recallTopicPatterns = [
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r"\b(erinnerst\s+du\s+dich|damals\s+als|thema\s+.*\s+von|ueber\s+was\s+haben\s+wir)\b",
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r"\b(was\s+war\s+.*\s+nochmal|thema\s+.*\s+besprochen)\b",
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r"\b(recall|remember|vor\s+\d+\s+sessions?)\b",
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]
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for p in recallTopicPatterns:
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if re.search(p, text, re.IGNORECASE):
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return RetrievalIntent.RECALL_TOPIC
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return RetrievalIntent.NORMAL
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def _parseDateFromMessage(text: str) -> Optional[datetime]:
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"""Extract date from user message. Returns date or None."""
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text = text.strip()
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patterns = [
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(r"(\d{1,2})\.(\d{1,2})\.(\d{2,4})", lambda m: (int(m[1]), int(m[2]), int(m[3]))),
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(r"(\d{4})-(\d{2})-(\d{2})", lambda m: (int(m[3]), int(m[2]), int(m[1]))),
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]
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for pattern, extractor in patterns:
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match = re.search(pattern, text)
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if match:
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try:
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day, month, year = extractor(match)
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if year < 100:
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year += 2000
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return datetime(year, month, day)
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except (ValueError, IndexError):
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pass
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return None
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def findSessionByDate(
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sessions: List[Dict[str, Any]],
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targetDate: Optional[datetime],
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) -> Optional[Dict[str, Any]]:
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"""
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Find session closest to targetDate.
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sessions: list of session dicts with startedAt/endedAt.
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"""
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if not targetDate or not sessions:
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return None
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targetDateOnly = targetDate.date()
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bestMatch = None
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bestDiff = None
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for s in sessions:
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if s.get("status") != "completed":
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continue
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startedAt = s.get("startedAt") or s.get("endedAt") or s.get("createdAt")
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if not startedAt:
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continue
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try:
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dt = datetime.fromisoformat(startedAt.replace("Z", "+00:00"))
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sessionDate = dt.date()
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diff = abs((sessionDate - targetDateOnly).days)
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if bestDiff is None or diff < bestDiff:
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bestDiff = diff
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bestMatch = s
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except Exception:
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continue
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return bestMatch
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def searchSessionsByTopic(
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sessions: List[Dict[str, Any]],
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query: str,
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maxResults: int = TOPIC_SEARCH_MAX_RESULTS,
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) -> List[Dict[str, Any]]:
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"""
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Topic search over sessions.
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Phase 5: Keyword-based (keyTopics + summary).
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Phase 7: Falls back to embedding search when available; for now uses keyword only.
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"""
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if not query or not sessions:
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return []
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queryWords = set(re.findall(r"\w+", query.lower()))
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if not queryWords:
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return []
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scored = []
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for s in sessions:
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if s.get("status") != "completed":
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continue
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score = 0
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summary = (s.get("summary") or "").lower()
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keyTopicsRaw = s.get("keyTopics")
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keyTopics = []
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if keyTopicsRaw:
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try:
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import json
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parsed = json.loads(keyTopicsRaw) if isinstance(keyTopicsRaw, str) else keyTopicsRaw
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keyTopics = [t.lower() if isinstance(t, str) else str(t).lower() for t in parsed] if isinstance(parsed, list) else []
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except Exception:
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pass
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for word in queryWords:
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if len(word) < 3:
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continue
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if word in summary:
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score += 1
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for topic in keyTopics:
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if word in topic:
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score += 2
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if score > 0:
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scored.append((score, s))
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scored.sort(key=lambda x: -x[0])
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return [s for _, s in scored[:maxResults]]
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def searchSessionsByTopicRag(
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sessions: List[Dict[str, Any]],
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query: str,
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maxResults: int = TOPIC_SEARCH_MAX_RESULTS,
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embeddingProvider: Optional[Any] = None,
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) -> List[Dict[str, Any]]:
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"""
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Phase 7 RAG: Semantic search via embeddings.
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When embeddingProvider is None, falls back to keyword search.
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Future: Pass embeddingProvider that has embed(text) -> vector and similarity search.
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"""
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if embeddingProvider is None:
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return searchSessionsByTopic(sessions, query, maxResults)
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# TODO: When embedding API exists: embed query, embed session summaries, cosine similarity
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return searchSessionsByTopic(sessions, query, maxResults)
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def buildSessionSummariesForPrompt(
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sessions: List[Dict[str, Any]],
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excludeSessionId: Optional[str] = None,
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limit: int = PREVIOUS_SESSION_SUMMARIES_COUNT,
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) -> List[Dict[str, Any]]:
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"""
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Build list of session summaries with date for prompt.
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Each item: {summary, date, sessionId, keyTopics}.
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"""
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completed = [
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s for s in sessions
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if s.get("status") == "completed"
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and s.get("summary")
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and s.get("id") != excludeSessionId
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]
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completed.sort(key=lambda x: x.get("startedAt") or x.get("createdAt") or "", reverse=True)
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result = []
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for s in completed[:limit]:
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startedAt = s.get("startedAt") or s.get("createdAt") or ""
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dateStr = ""
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if startedAt:
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try:
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dt = datetime.fromisoformat(startedAt.replace("Z", "+00:00"))
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dateStr = dt.strftime("%d.%m.%Y")
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except Exception:
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pass
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result.append({
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"summary": s.get("summary", ""),
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"date": dateStr,
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"sessionId": s.get("id"),
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"keyTopics": s.get("keyTopics"),
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})
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return result
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