neue architekturaufteilung für chat in WP11 gebaut
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@ -1,6 +1,6 @@
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"""
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app/routers/chat.py — RAG Endpunkt (WP-06 Hybrid Router + WP-07 Interview Mode)
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Version: 2.4.0 (Interview Support)
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Version: 2.4.1 (Fix: Type-based Intent Detection)
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Features:
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- Hybrid Intent Router (Keyword + LLM)
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@ -8,14 +8,16 @@ Features:
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- Interview Loop (Schema-driven Data Collection)
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- Context Enrichment (Payload/Source Fallback)
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- Data Flywheel (Feedback Logging Integration)
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- NEU: Lädt detection_keywords aus types.yaml für präzise Erkennung.
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"""
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from fastapi import APIRouter, HTTPException, Depends
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from typing import List, Dict, Any
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from typing import List, Dict, Any, Optional
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import time
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import uuid
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import logging
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import yaml
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import os
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from pathlib import Path
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from app.config import get_settings
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@ -30,6 +32,7 @@ logger = logging.getLogger(__name__)
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# --- Helper: Config Loader ---
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_DECISION_CONFIG_CACHE = None
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_TYPES_CONFIG_CACHE = None
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def _load_decision_config() -> Dict[str, Any]:
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settings = get_settings()
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@ -51,15 +54,32 @@ def _load_decision_config() -> Dict[str, Any]:
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logger.error(f"Failed to load decision config: {e}")
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return default_config
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def _load_types_config() -> Dict[str, Any]:
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"""Lädt die types.yaml für Keyword-Erkennung."""
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path = os.getenv("MINDNET_TYPES_FILE", "config/types.yaml")
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try:
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with open(path, "r", encoding="utf-8") as f:
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return yaml.safe_load(f) or {}
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except Exception:
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return {}
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def get_full_config() -> Dict[str, Any]:
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global _DECISION_CONFIG_CACHE
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if _DECISION_CONFIG_CACHE is None:
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_DECISION_CONFIG_CACHE = _load_decision_config()
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return _DECISION_CONFIG_CACHE
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def get_types_config() -> Dict[str, Any]:
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global _TYPES_CONFIG_CACHE
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if _TYPES_CONFIG_CACHE is None:
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_TYPES_CONFIG_CACHE = _load_types_config()
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return _TYPES_CONFIG_CACHE
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def get_decision_strategy(intent: str) -> Dict[str, Any]:
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config = get_full_config()
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strategies = config.get("strategies", {})
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# Fallback: Wenn Intent INTERVIEW ist, aber nicht konfiguriert, nehme FACT
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# (Aber INTERVIEW sollte in decision_engine.yaml stehen!)
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return strategies.get(intent, strategies.get("FACT", {}))
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# --- Helper: Target Type Detection (WP-07) ---
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@ -67,40 +87,40 @@ def get_decision_strategy(intent: str) -> Dict[str, Any]:
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def _detect_target_type(message: str, configured_schemas: Dict[str, Any]) -> str:
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"""
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Versucht zu erraten, welchen Notiz-Typ der User erstellen will.
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Nutzt Keywords und Mappings.
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Nutzt Keywords aus types.yaml UND Mappings.
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"""
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message_lower = message.lower()
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# 1. Direkter Match mit Schema-Keys (z.B. "projekt", "entscheidung")
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# Ignoriere 'default' hier
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# 1. Check types.yaml detection_keywords (Priority!)
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types_cfg = get_types_config()
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types_def = types_cfg.get("types", {})
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for type_name, type_data in types_def.items():
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keywords = type_data.get("detection_keywords", [])
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for kw in keywords:
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if kw.lower() in message_lower:
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return type_name
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# 2. Direkter Match mit Schema-Keys
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for type_key in configured_schemas.keys():
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if type_key == "default":
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continue
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if type_key == "default": continue
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if type_key in message_lower:
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return type_key
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# 2. Synonym-Mapping (Deutsch -> Schema Key)
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# Dies verbessert die UX, falls User deutsche Begriffe nutzen
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# 3. Synonym-Mapping (Legacy Fallback)
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synonyms = {
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"projekt": "project",
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"vorhaben": "project",
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"entscheidung": "decision",
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"beschluss": "decision",
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"projekt": "project", "vorhaben": "project",
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"entscheidung": "decision", "beschluss": "decision",
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"ziel": "goal",
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"erfahrung": "experience",
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"lektion": "experience",
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"erfahrung": "experience", "lektion": "experience",
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"wert": "value",
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"prinzip": "principle",
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"grundsatz": "principle",
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"notiz": "default",
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"idee": "default"
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"notiz": "default", "idee": "default"
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}
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for term, schema_key in synonyms.items():
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if term in message_lower:
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# Prüfen, ob der gemappte Key auch konfiguriert ist
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if schema_key in configured_schemas:
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return schema_key
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return schema_key
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return "default"
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@ -126,7 +146,6 @@ def _build_enriched_context(hits: List[QueryHit]) -> str:
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)
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title = hit.note_id or "Unbekannt"
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# [FIX] Robustes Auslesen des Typs (Payload > Source > Unknown)
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payload = hit.payload or {}
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note_type = payload.get("type") or source.get("type", "unknown")
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note_type = str(note_type).upper()
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@ -142,52 +161,58 @@ def _build_enriched_context(hits: List[QueryHit]) -> str:
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async def _classify_intent(query: str, llm: LLMService) -> tuple[str, str]:
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"""
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Hybrid Router v3:
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Gibt Tuple zurück: (Intent, Source)
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Hybrid Router v4:
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1. Decision Keywords (Strategie)
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2. Type Keywords (Interview Trigger)
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3. LLM (Fallback)
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"""
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config = get_full_config()
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strategies = config.get("strategies", {})
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settings = config.get("settings", {})
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query_lower = query.lower()
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best_intent = None
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max_match_length = 0
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# 1. FAST PATH: Keywords
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# 1. FAST PATH A: Strategie Keywords (z.B. "Soll ich...")
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for intent_name, strategy in strategies.items():
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if intent_name == "FACT": continue
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keywords = strategy.get("trigger_keywords", [])
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for k in keywords:
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if k.lower() in query_lower:
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if len(k) > max_match_length:
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max_match_length = len(k)
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best_intent = intent_name
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return intent_name, "Keyword (Strategy)"
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if best_intent:
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return best_intent, "Keyword (Fast Path)"
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# 2. FAST PATH B: Type Keywords (z.B. "Projekt", "passiert") -> INTERVIEW
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# Wir prüfen, ob ein Typ erkannt wird. Wenn ja -> Interview.
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# Wir laden Schemas nicht hier, sondern nutzen types.yaml global
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types_cfg = get_types_config()
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types_def = types_cfg.get("types", {})
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# 2. SLOW PATH: LLM Router
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for type_name, type_data in types_def.items():
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keywords = type_data.get("detection_keywords", [])
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for kw in keywords:
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if kw.lower() in query_lower:
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return "INTERVIEW", f"Keyword (Type: {type_name})"
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# 3. SLOW PATH: LLM Router
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if settings.get("llm_fallback_enabled", False):
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router_prompt_template = settings.get("llm_router_prompt", "")
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if router_prompt_template:
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prompt = router_prompt_template.replace("{query}", query)
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logger.info("Keywords failed. Asking LLM for Intent...")
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raw_response = await llm.generate_raw_response(prompt)
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try:
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raw_response = await llm.generate_raw_response(prompt)
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llm_output_upper = raw_response.upper()
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# Parsing logic
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llm_output_upper = raw_response.upper()
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found_intents = []
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for strat_key in strategies.keys():
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if strat_key in llm_output_upper:
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found_intents.append(strat_key)
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# Zuerst INTERVIEW prüfen (LLMs erkennen oft "Create" Intention)
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if "INTERVIEW" in llm_output_upper or "CREATE" in llm_output_upper:
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return "INTERVIEW", "LLM Router"
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if len(found_intents) == 1:
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return found_intents[0], "LLM Router (Slow Path)"
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elif len(found_intents) > 1:
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return found_intents[0], f"LLM Ambiguous {found_intents}"
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else:
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return "FACT", "LLM Fallback (No Match)"
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for strat_key in strategies.keys():
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if strat_key in llm_output_upper:
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return strat_key, "LLM Router"
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except Exception as e:
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logger.error(f"Router LLM failed: {e}")
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return "FACT", "Default (No Match)"
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@ -202,7 +227,7 @@ async def chat_endpoint(
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logger.info(f"Chat request [{query_id}]: {request.message[:50]}...")
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try:
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# 1. Intent Detection (mit Source)
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# 1. Intent Detection
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intent, intent_source = await _classify_intent(request.message, llm)
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logger.info(f"[{query_id}] Final Intent: {intent} via {intent_source}")
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@ -210,57 +235,55 @@ async def chat_endpoint(
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strategy = get_decision_strategy(intent)
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prompt_key = strategy.get("prompt_template", "rag_template")
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# --- SPLIT LOGIC: INTERVIEW vs. RAG ---
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sources_hits = []
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final_prompt = ""
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if intent == "INTERVIEW":
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# --- WP-07: INTERVIEW MODE ---
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# Kein Retrieval. Wir nutzen den Dialog-Kontext.
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# --- INTERVIEW MODE ---
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# Wir müssen jetzt herausfinden, WELCHES Schema wir nutzen.
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# Dazu schauen wir wieder in die types.yaml (via _detect_target_type)
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# 1. Schema Loading (Late Binding)
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schemas = strategy.get("schemas", {})
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target_type = _detect_target_type(request.message, schemas)
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active_schema = schemas.get(target_type, schemas.get("default"))
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# Schemas aus decision_engine.yaml laden (falls dort overrides sind)
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# oder generisch aus types.yaml bauen (besser!)
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logger.info(f"[{query_id}] Starting Interview for Type: {target_type}")
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# Strategie: Wir nutzen _detect_target_type, das jetzt types.yaml kennt.
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target_type = _detect_target_type(request.message, strategy.get("schemas", {}))
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# Robustes Schema-Parsing (Dict vs List)
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if isinstance(active_schema, dict):
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fields_list = active_schema.get("fields", [])
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hint_str = active_schema.get("hint", "")
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else:
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fields_list = active_schema # Fallback falls nur Liste definiert
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hint_str = ""
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# Schema laden (aus types.yaml bevorzugt)
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types_cfg = get_types_config()
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type_def = types_cfg.get("types", {}).get(target_type, {})
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# Hole Schema-Felder aus types.yaml (schema: [...])
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fields_list = type_def.get("schema", [])
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# Fallback auf decision_engine.yaml, falls in types.yaml nichts steht
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if not fields_list:
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configured_schemas = strategy.get("schemas", {})
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fallback_schema = configured_schemas.get(target_type, configured_schemas.get("default"))
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if isinstance(fallback_schema, dict):
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fields_list = fallback_schema.get("fields", [])
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else:
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fields_list = fallback_schema or []
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logger.info(f"[{query_id}] Interview Type: {target_type}. Fields: {len(fields_list)}")
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fields_str = "\n- " + "\n- ".join(fields_list)
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# 2. Context Logic
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# Hinweis: In einer Stateless-API ist {context_str} idealerweise die History.
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# Da ChatRequest (noch) kein History-Feld hat, nutzen wir einen Placeholder
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# oder verlassen uns darauf, dass der Client die History im Prompt mitschickt
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# (Streamlit Pattern: Appends history to prompt).
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# Wir labeln es hier explizit.
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context_str = "Bisheriger Verlauf (falls vorhanden): Siehe oben/unten."
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# 3. Prompt Assembly
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# Prompt Assembly
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template = llm.prompts.get(prompt_key, "")
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final_prompt = template.replace("{context_str}", context_str) \
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final_prompt = template.replace("{context_str}", "Dialogverlauf...") \
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.replace("{query}", request.message) \
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.replace("{target_type}", target_type) \
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.replace("{schema_fields}", fields_str) \
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.replace("{schema_hint}", hint_str)
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.replace("{schema_hint}", "")
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# Keine Hits im Interview
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sources_hits = []
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else:
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# --- WP-06: STANDARD RAG MODE ---
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# --- RAG MODE ---
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inject_types = strategy.get("inject_types", [])
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prepend_instr = strategy.get("prepend_instruction", "")
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# 2. Primary Retrieval
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query_req = QueryRequest(
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query=request.message,
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mode="hybrid",
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@ -270,9 +293,7 @@ async def chat_endpoint(
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retrieve_result = await retriever.search(query_req)
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hits = retrieve_result.results
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# 3. Strategic Retrieval (WP-06 Kernfeature)
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if inject_types:
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logger.info(f"[{query_id}] Executing Strategic Retrieval for types: {inject_types}...")
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strategy_req = QueryRequest(
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query=request.message,
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mode="hybrid",
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@ -281,19 +302,16 @@ async def chat_endpoint(
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explain=False
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)
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strategy_result = await retriever.search(strategy_req)
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existing_ids = {h.node_id for h in hits}
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for strat_hit in strategy_result.results:
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if strat_hit.node_id not in existing_ids:
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hits.append(strat_hit)
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# 4. Context Building
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if not hits:
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context_str = "Keine relevanten Notizen gefunden."
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else:
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context_str = _build_enriched_context(hits)
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# 5. Generation Setup
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template = llm.prompts.get(prompt_key, "{context_str}\n\n{query}")
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if prepend_instr:
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@ -302,35 +320,25 @@ async def chat_endpoint(
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final_prompt = template.replace("{context_str}", context_str).replace("{query}", request.message)
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sources_hits = hits
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# --- COMMON GENERATION ---
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# --- GENERATION ---
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system_prompt = llm.prompts.get("system_prompt", "")
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logger.info(f"[{query_id}] Sending to LLM (Intent: {intent}, Template: {prompt_key})...")
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# System-Prompt separat übergeben
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# Hier nutzen wir das erhöhte Timeout aus dem LLMService Update
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answer_text = await llm.generate_raw_response(prompt=final_prompt, system=system_prompt)
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duration_ms = int((time.time() - start_time) * 1000)
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# 6. Logging (Fire & Forget)
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# Logging
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try:
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log_search(
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query_id=query_id,
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query_text=request.message,
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results=sources_hits,
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mode="interview" if intent == "INTERVIEW" else "chat_rag",
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metadata={
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"intent": intent,
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"intent_source": intent_source,
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"generated_answer": answer_text,
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"model": llm.settings.LLM_MODEL
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}
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metadata={"intent": intent, "source": intent_source}
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)
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except Exception as e:
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logger.error(f"Logging failed: {e}")
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except: pass
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# 7. Response
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return ChatResponse(
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query_id=query_id,
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answer=answer_text,
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@ -1,32 +1,31 @@
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# config/decision_engine.yaml
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# Steuerung der Decision Engine (WP-06 + WP-07)
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# Hybrid-Modus: Keywords (Fast) + LLM Router (Smart Fallback)
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version: 1.3
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# Steuerung der Decision Engine (Intent Recognition)
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# Version: 2.4.0 (Clean Architecture: Generic Intents only)
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version: 1.4
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settings:
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llm_fallback_enabled: true
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# Few-Shot Prompting für bessere SLM-Performance
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# Erweitert um INTERVIEW Beispiele
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# Few-Shot Prompting für den LLM-Router (Slow Path)
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llm_router_prompt: |
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Du bist ein Klassifikator. Analysiere die Nachricht und wähle die passende Strategie.
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Antworte NUR mit dem Namen der Strategie.
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STRATEGIEN:
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- INTERVIEW: User will Wissen strukturieren, Notizen anlegen, Projekte starten ("Neu", "Festhalten").
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- INTERVIEW: User will Wissen erfassen, Notizen anlegen oder Dinge festhalten.
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- DECISION: Rat, Strategie, Vor/Nachteile, "Soll ich".
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- EMPATHY: Gefühle, Frust, Freude, Probleme, "Alles ist sinnlos", "Ich bin traurig".
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- CODING: Code, Syntax, Programmierung, Python.
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- EMPATHY: Gefühle, Frust, Freude, Probleme.
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- CODING: Code, Syntax, Programmierung.
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- FACT: Wissen, Fakten, Definitionen.
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BEISPIELE:
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User: "Wie funktioniert Qdrant?" -> FACT
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User: "Soll ich Qdrant nutzen?" -> DECISION
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User: "Ich möchte ein neues Projekt anlegen" -> INTERVIEW
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User: "Lass uns eine Entscheidung festhalten" -> INTERVIEW
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User: "Ich möchte etwas notieren" -> INTERVIEW
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User: "Lass uns das festhalten" -> INTERVIEW
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User: "Schreibe ein Python Script" -> CODING
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User: "Alles ist grau und sinnlos" -> EMPATHY
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User: "Mir geht es heute gut" -> EMPATHY
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NACHRICHT: "{query}"
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||||
|
||||
|
|
@ -51,11 +50,9 @@ strategies:
|
|||
- "empfehlung"
|
||||
- "strategie"
|
||||
- "entscheidung"
|
||||
- "wert"
|
||||
- "prinzip"
|
||||
- "vor- und nachteile"
|
||||
- "abwägung"
|
||||
inject_types: ["value", "principle", "goal"]
|
||||
- "vergleich"
|
||||
inject_types: ["value", "principle", "goal", "risk"]
|
||||
prompt_template: "decision_template"
|
||||
prepend_instruction: |
|
||||
!!! ENTSCHEIDUNGS-MODUS !!!
|
||||
|
|
@ -72,6 +69,7 @@ strategies:
|
|||
- "angst"
|
||||
- "nervt"
|
||||
- "überfordert"
|
||||
- "müde"
|
||||
inject_types: ["experience", "belief", "profile"]
|
||||
prompt_template: "empathy_template"
|
||||
prepend_instruction: null
|
||||
|
|
@ -88,56 +86,37 @@ strategies:
|
|||
- "syntax"
|
||||
- "json"
|
||||
- "yaml"
|
||||
- "bash"
|
||||
inject_types: ["snippet", "reference", "source"]
|
||||
prompt_template: "technical_template"
|
||||
prepend_instruction: null
|
||||
|
||||
# 5. Interview / Datenerfassung (WP-07)
|
||||
# 5. Interview / Datenerfassung
|
||||
# HINWEIS: Spezifische Typen (Projekt, Ziel etc.) werden automatisch
|
||||
# über die types.yaml erkannt. Hier stehen nur generische Trigger.
|
||||
INTERVIEW:
|
||||
description: "Der User möchte strukturiertes Wissen erfassen (Projekt, Notiz, Idee)."
|
||||
description: "Der User möchte Wissen erfassen."
|
||||
trigger_keywords:
|
||||
- "neue notiz"
|
||||
- "neues projekt"
|
||||
- "neue entscheidung"
|
||||
- "neues ziel"
|
||||
- "etwas notieren"
|
||||
- "festhalten"
|
||||
- "entwurf erstellen"
|
||||
- "interview"
|
||||
- "erstellen"
|
||||
- "dokumentieren"
|
||||
- "anlegen"
|
||||
- "interview"
|
||||
- "erfassen"
|
||||
- "idee speichern"
|
||||
inject_types: [] # Keine RAG-Suche, reiner Kontext-Dialog
|
||||
- "draft"
|
||||
inject_types: []
|
||||
prompt_template: "interview_template"
|
||||
prepend_instruction: null
|
||||
|
||||
# LATE BINDING SCHEMAS:
|
||||
# Definition der Pflichtfelder pro Typ (korrespondiert mit types.yaml)
|
||||
# Wenn ein Typ hier fehlt, wird 'default' genutzt.
|
||||
# Schemas: Hier nur der Fallback.
|
||||
# Spezifische Schemas (Project, Experience) kommen jetzt aus types.yaml!
|
||||
schemas:
|
||||
default:
|
||||
fields: ["Titel", "Thema/Inhalt", "Tags"]
|
||||
fields:
|
||||
- "Titel"
|
||||
- "Thema/Inhalt"
|
||||
- "Tags"
|
||||
hint: "Halte es einfach und übersichtlich."
|
||||
|
||||
project:
|
||||
fields: ["Titel", "Zielsetzung (Goal)", "Status (draft/active)", "Wichtige Stakeholder", "Nächste Schritte"]
|
||||
hint: "Achte darauf, Abhängigkeiten zu anderen Projekten mit [[rel:depends_on]] zu erfragen."
|
||||
|
||||
decision:
|
||||
fields: ["Titel", "Kontext (Warum entscheiden wir?)", "Getroffene Entscheidung", "Betrachtete Alternativen", "Status (proposed/final)"]
|
||||
hint: "Wichtig: Frage explizit nach den Gründen gegen die Alternativen."
|
||||
|
||||
goal:
|
||||
fields: ["Titel", "Zeitrahmen (Deadline)", "Messkriterien (KPIs)", "Verbundene Werte"]
|
||||
hint: "Ziele sollten SMART formuliert sein."
|
||||
|
||||
experience:
|
||||
fields: ["Titel", "Situation (Kontext)", "Erkenntnis (Learning)", "Emotionale Keywords (für Empathie-Suche)"]
|
||||
hint: "Fokussiere dich auf die persönliche Lektion."
|
||||
|
||||
value:
|
||||
fields: ["Titel (Name des Werts)", "Definition (Was bedeutet das für uns?)", "Anti-Beispiel (Was ist es nicht?)"]
|
||||
hint: "Werte dienen als Entscheidungsgrundlage."
|
||||
|
||||
principle:
|
||||
fields: ["Titel", "Handlungsanweisung", "Begründung"]
|
||||
hint: "Prinzipien sind härter als Werte."
|
||||
|
|
@ -99,44 +99,37 @@ technical_template: |
|
|||
# ---------------------------------------------------------
|
||||
interview_template: |
|
||||
TASK:
|
||||
Erstelle einen Markdown-Entwurf für eine Notiz vom Typ '{target_type}'.
|
||||
Du bist ein professioneller Ghostwriter. Verwandle den "USER INPUT" in eine strukturierte Notiz vom Typ '{target_type}'.
|
||||
|
||||
SCHEMA (Inhaltliche Pflichtfelder für den Body):
|
||||
STRUKTUR (Nutze EXAKT diese Überschriften):
|
||||
{schema_fields}
|
||||
|
||||
USER INPUT:
|
||||
"{query}"
|
||||
|
||||
ANWEISUNG:
|
||||
1. Extrahiere Informationen aus dem Input.
|
||||
2. Generiere validen Markdown.
|
||||
ANWEISUNG ZUM INHALT:
|
||||
1. Analysiere den Input genau.
|
||||
2. Schreibe die Inhalte unter die passenden Überschriften aus der STRUKTUR-Liste oben.
|
||||
3. STIL: Schreibe flüssig, professionell und in der Ich-Perspektive. Korrigiere Grammatikfehler, aber behalte den persönlichen Ton bei.
|
||||
4. Wenn Informationen für einen Abschnitt fehlen, schreibe nur: "[TODO: Ergänzen]". Erfinde nichts dazu.
|
||||
|
||||
OUTPUT REGELN (STRIKT BEACHTEN):
|
||||
A. FRONTMATTER (YAML):
|
||||
- 'type': Muss '{target_type}' sein (oder 'experience', 'project' etc.). NIEMALS 'draft'.
|
||||
- 'status': Muss IMMER 'draft' sein.
|
||||
- 'tags': Eine JSON-Liste von Strings OHNE Hashtags. Beispiel: ['Recycling', 'Konflikt']. NICHT: [#Recycling].
|
||||
- Keine Sätze im YAML, nur Daten.
|
||||
|
||||
B. BODY (Markdown):
|
||||
- Nutze für jedes Schema-Feld eine Markdown-Überschrift (## Feldname).
|
||||
- Schreibe den Inhalt DARUNTER.
|
||||
|
||||
HINWEIS ZUM TYP:
|
||||
{schema_hint}
|
||||
|
||||
OUTPUT FORMAT BEISPIEL:
|
||||
```markdown
|
||||
OUTPUT FORMAT (YAML + MARKDOWN):
|
||||
---
|
||||
type: {target_type}
|
||||
status: draft
|
||||
title: ...
|
||||
tags: ["Tag1", "Tag2"]
|
||||
title: (Erstelle einen treffenden, kurzen Titel für den Inhalt)
|
||||
tags: [Tag1, Tag2]
|
||||
---
|
||||
# Titel der Notiz
|
||||
|
||||
## Erstes Schema Feld
|
||||
Der Inhalt hier...
|
||||
# (Wiederhole den Titel hier)
|
||||
|
||||
## (Erster Begriff aus STRUKTUR)
|
||||
(Text...)
|
||||
|
||||
## (Zweiter Begriff aus STRUKTUR)
|
||||
(Text...)
|
||||
|
||||
(usw.)
|
||||
|
||||
|
||||
# ---------------------------------------------------------
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
version: 1.6 # Balance zwischen Speed, Kontext und Smartness
|
||||
version: 2.4.0 # Optimized for Async Intelligence & Hybrid Router
|
||||
|
||||
# ==============================================================================
|
||||
# 1. CHUNKING PROFILES
|
||||
|
|
@ -6,42 +6,40 @@ version: 1.6 # Balance zwischen Speed, Kontext und Smartness
|
|||
|
||||
chunking_profiles:
|
||||
|
||||
# A. SHORT & FAST (Für atomare Schnipsel)
|
||||
# Einsatz: Glossar, Tasks, Risiken
|
||||
# Vorteil: Präzise Treffer für kurze Infos.
|
||||
# A. SHORT & FAST
|
||||
# Für Glossar, Tasks, Risiken. Kleine Schnipsel.
|
||||
sliding_short:
|
||||
strategy: sliding_window
|
||||
enable_smart_edge_allocation: false # AUS (Speed)
|
||||
enable_smart_edge_allocation: false
|
||||
target: 200
|
||||
max: 350
|
||||
overlap: [30, 50]
|
||||
|
||||
# B. STANDARD & FAST (Der neue "Mittelweg")
|
||||
# Einsatz: Quellen, Journal, Daily Logs
|
||||
# Vorteil: Viel Kontext für RAG, aber rasendschneller Import ohne LLM.
|
||||
# B. STANDARD & FAST
|
||||
# Der "Traktor": Robust für Quellen, Journal, Daily Logs.
|
||||
sliding_standard:
|
||||
strategy: sliding_window
|
||||
enable_smart_edge_allocation: false # AUS (Speed)
|
||||
target: 450 # Größerer Kontext!
|
||||
enable_smart_edge_allocation: false
|
||||
target: 450
|
||||
max: 650
|
||||
overlap: [50, 100]
|
||||
|
||||
# C. SMART FLOW (Premium Chunking)
|
||||
# Einsatz: Konzepte, Projekte, Erfahrungen
|
||||
# Vorteil: LLM prüft Inhalt und verlinkt präzise. Kostet Zeit.
|
||||
# C. SMART FLOW (Performance-Safe Mode)
|
||||
# Für Konzepte, Projekte, Erfahrungen.
|
||||
# HINWEIS: 'enable_smart_edge_allocation' ist vorerst FALSE, um Ollama
|
||||
# bei der Generierung nicht zu überlasten. Später wieder aktivieren.
|
||||
sliding_smart_edges:
|
||||
strategy: sliding_window
|
||||
enable_smart_edge_allocation: true # AN (Intelligenz)
|
||||
enable_smart_edge_allocation: false
|
||||
target: 400
|
||||
max: 600
|
||||
overlap: [50, 80]
|
||||
|
||||
# D. SMART STRUCTURE
|
||||
# Einsatz: Profile, Werte, Prinzipien
|
||||
# Vorteil: Respektiert die Markdown-Struktur (H2).
|
||||
# Für Profile, Werte, Prinzipien. Trennt hart an Überschriften (H2).
|
||||
structured_smart_edges:
|
||||
strategy: by_heading
|
||||
enable_smart_edge_allocation: true # AN (Intelligenz)
|
||||
enable_smart_edge_allocation: false
|
||||
split_level: 2
|
||||
max: 600
|
||||
target: 400
|
||||
|
|
@ -52,7 +50,7 @@ chunking_profiles:
|
|||
# ==============================================================================
|
||||
defaults:
|
||||
retriever_weight: 1.0
|
||||
chunking_profile: sliding_standard # Fallback auf Standard (sicher & performant)
|
||||
chunking_profile: sliding_standard
|
||||
edge_defaults: []
|
||||
|
||||
# ==============================================================================
|
||||
|
|
@ -61,53 +59,110 @@ defaults:
|
|||
|
||||
types:
|
||||
|
||||
# --- MASSENDATEN (Speed + Kontext) ---
|
||||
# --- KERNTYPEN (Hoch priorisiert & Smart) ---
|
||||
|
||||
source:
|
||||
chunking_profile: sliding_standard # JETZT: Mehr Kontext (450 Token), trotzdem schnell
|
||||
retriever_weight: 0.50
|
||||
edge_defaults: []
|
||||
experience:
|
||||
chunking_profile: sliding_smart_edges
|
||||
retriever_weight: 0.90
|
||||
edge_defaults: ["derived_from", "references"]
|
||||
# Hybrid Classifier: Wenn diese Worte fallen, ist es eine Experience
|
||||
detection_keywords:
|
||||
- "passiert"
|
||||
- "erlebt"
|
||||
- "gefühl"
|
||||
- "situation"
|
||||
- "stolz"
|
||||
- "geärgert"
|
||||
- "reaktion"
|
||||
- "moment"
|
||||
- "konflikt"
|
||||
# Ghostwriter Schema: Sprechende Anweisungen für besseren Textfluss
|
||||
schema:
|
||||
- "Metadaten (Autor, URL, Datum)"
|
||||
- "Zusammenfassung"
|
||||
- "Originaltext / Ausschnitte"
|
||||
- "Situation (Was ist passiert?)"
|
||||
- "Meine Reaktion (Was habe ich getan?)"
|
||||
- "Ergebnis & Auswirkung"
|
||||
- "Reflexion & Learning (Was lerne ich daraus?)"
|
||||
|
||||
journal:
|
||||
chunking_profile: sliding_standard # JETZT: Mehr Kontext für Tagebucheinträge
|
||||
retriever_weight: 0.80
|
||||
edge_defaults: ["references", "related_to"]
|
||||
project:
|
||||
chunking_profile: sliding_smart_edges
|
||||
retriever_weight: 0.97
|
||||
edge_defaults: ["references", "depends_on"]
|
||||
detection_keywords:
|
||||
- "projekt"
|
||||
- "vorhaben"
|
||||
- "ziel ist"
|
||||
- "meilenstein"
|
||||
- "planen"
|
||||
- "starten"
|
||||
- "mission"
|
||||
schema:
|
||||
- "Tages-Log"
|
||||
- "Erkenntnisse"
|
||||
- "Entscheidungen"
|
||||
- "Mission & Zielsetzung"
|
||||
- "Aktueller Status & Blockaden"
|
||||
- "Nächste konkrete Schritte"
|
||||
- "Stakeholder & Ressourcen"
|
||||
|
||||
# --- ATOMARE DATEN (Speed + Präzision) ---
|
||||
decision:
|
||||
chunking_profile: structured_smart_edges
|
||||
retriever_weight: 1.00 # MAX: Entscheidungen sind Gesetz
|
||||
edge_defaults: ["caused_by", "references"]
|
||||
detection_keywords:
|
||||
- "entschieden"
|
||||
- "wahl"
|
||||
- "optionen"
|
||||
- "alternativen"
|
||||
- "beschluss"
|
||||
- "adr"
|
||||
schema:
|
||||
- "Kontext & Problemstellung"
|
||||
- "Betrachtete Optionen (Alternativen)"
|
||||
- "Die Entscheidung"
|
||||
- "Begründung (Warum diese Wahl?)"
|
||||
|
||||
task:
|
||||
chunking_profile: sliding_short # Kurz halten
|
||||
retriever_weight: 0.80
|
||||
edge_defaults: ["depends_on", "part_of"]
|
||||
schema: ["Aufgabe", "Kontext", "DoD"]
|
||||
# --- PERSÖNLICHKEIT & IDENTITÄT ---
|
||||
|
||||
glossary:
|
||||
chunking_profile: sliding_short # Kurz halten
|
||||
retriever_weight: 0.40
|
||||
value:
|
||||
chunking_profile: structured_smart_edges
|
||||
retriever_weight: 1.00
|
||||
edge_defaults: ["related_to"]
|
||||
schema: ["Begriff", "Definition"]
|
||||
detection_keywords: ["wert", "wichtig ist", "moral", "ethik"]
|
||||
schema: ["Definition", "Warum mir das wichtig ist", "Leitsätze für den Alltag"]
|
||||
|
||||
risk:
|
||||
chunking_profile: sliding_short
|
||||
retriever_weight: 0.85
|
||||
edge_defaults: ["related_to", "blocks"]
|
||||
schema: ["Beschreibung", "Mitigation"]
|
||||
principle:
|
||||
chunking_profile: structured_smart_edges
|
||||
retriever_weight: 0.95
|
||||
edge_defaults: ["derived_from", "references"]
|
||||
detection_keywords: ["prinzip", "regel", "grundsatz", "leitlinie"]
|
||||
schema: ["Das Prinzip", "Anwendung & Beispiele"]
|
||||
|
||||
belief:
|
||||
chunking_profile: sliding_short
|
||||
retriever_weight: 0.90
|
||||
edge_defaults: ["related_to"]
|
||||
schema: ["Glaubenssatz", "Reflexion"]
|
||||
detection_keywords: ["glaube", "überzeugung", "denke dass", "meinung"]
|
||||
schema: ["Der Glaubenssatz", "Ursprung & Reflexion"]
|
||||
|
||||
# --- KERN-WISSEN (Smart Edges / LLM Active) ---
|
||||
profile:
|
||||
chunking_profile: structured_smart_edges
|
||||
retriever_weight: 0.70
|
||||
edge_defaults: ["references", "related_to"]
|
||||
schema: ["Rolle / Identität", "Fakten & Daten", "Historie"]
|
||||
|
||||
# --- STRATEGIE & RISIKO ---
|
||||
|
||||
goal:
|
||||
chunking_profile: sliding_smart_edges
|
||||
retriever_weight: 0.95
|
||||
edge_defaults: ["depends_on", "related_to"]
|
||||
schema: ["Zielzustand", "Zeitrahmen & KPIs", "Motivation"]
|
||||
|
||||
risk:
|
||||
chunking_profile: sliding_short
|
||||
retriever_weight: 0.85
|
||||
edge_defaults: ["related_to", "blocks"]
|
||||
detection_keywords: ["risiko", "gefahr", "bedrohung", "problem", "angst"]
|
||||
schema: ["Beschreibung des Risikos", "Mögliche Auswirkungen", "Gegenmaßnahmen"]
|
||||
|
||||
# --- BASIS & WISSEN ---
|
||||
|
||||
concept:
|
||||
chunking_profile: sliding_smart_edges
|
||||
|
|
@ -115,46 +170,32 @@ types:
|
|||
edge_defaults: ["references", "related_to"]
|
||||
schema:
|
||||
- "Definition"
|
||||
- "Kontext"
|
||||
- "Kontext & Hintergrund"
|
||||
- "Verwandte Konzepte"
|
||||
|
||||
project:
|
||||
chunking_profile: sliding_smart_edges
|
||||
retriever_weight: 0.97
|
||||
edge_defaults: ["references", "depends_on"]
|
||||
schema:
|
||||
- "Mission"
|
||||
- "Status"
|
||||
- "Next Actions"
|
||||
task:
|
||||
chunking_profile: sliding_short
|
||||
retriever_weight: 0.80
|
||||
edge_defaults: ["depends_on", "part_of"]
|
||||
schema: ["Aufgabe", "Kontext", "Definition of Done"]
|
||||
|
||||
experience:
|
||||
chunking_profile: sliding_smart_edges
|
||||
retriever_weight: 0.90
|
||||
edge_defaults: ["derived_from", "references"]
|
||||
schema: ["Situation", "Aktion", "Ergebnis", "Learning"]
|
||||
|
||||
# --- STRUKTUR-DATEN (Smart Structure / LLM Active) ---
|
||||
|
||||
profile:
|
||||
chunking_profile: structured_smart_edges
|
||||
retriever_weight: 0.70
|
||||
journal:
|
||||
chunking_profile: sliding_standard
|
||||
retriever_weight: 0.80
|
||||
edge_defaults: ["references", "related_to"]
|
||||
schema: ["Rolle", "Fakten", "Historie"]
|
||||
schema: ["Log-Eintrag", "Gedanken & Erkenntnisse"]
|
||||
|
||||
value:
|
||||
chunking_profile: structured_smart_edges
|
||||
retriever_weight: 1.00
|
||||
source:
|
||||
chunking_profile: sliding_standard
|
||||
retriever_weight: 0.50
|
||||
edge_defaults: []
|
||||
schema:
|
||||
- "Metadaten (Autor, URL, Datum)"
|
||||
- "Kernaussage / Zusammenfassung"
|
||||
- "Zitate & Notizen"
|
||||
|
||||
glossary:
|
||||
chunking_profile: sliding_short
|
||||
retriever_weight: 0.40
|
||||
edge_defaults: ["related_to"]
|
||||
schema: ["Definition", "Motivation", "Leitsätze"]
|
||||
|
||||
principle:
|
||||
chunking_profile: structured_smart_edges
|
||||
retriever_weight: 0.95
|
||||
edge_defaults: ["derived_from", "references"]
|
||||
schema: ["Prinzip", "Anwendung"]
|
||||
|
||||
decision:
|
||||
chunking_profile: structured_smart_edges
|
||||
retriever_weight: 1.00
|
||||
edge_defaults: ["caused_by", "references"]
|
||||
schema: ["Problem", "Optionen", "Entscheidung", "Warum"]
|
||||
schema: ["Begriff", "Definition"]
|
||||
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