353 lines
13 KiB
Python
353 lines
13 KiB
Python
"""
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app/routers/chat.py — RAG Endpunkt (WP-06 Hybrid Router + WP-07 Interview Mode)
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Version: 2.4.1 (Fix: Type-based Intent Detection)
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Features:
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- Hybrid Intent Router (Keyword + LLM)
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- Strategic Retrieval (Late Binding via Config)
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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, 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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from app.models.dto import ChatRequest, ChatResponse, QueryRequest, QueryHit
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from app.services.llm_service import LLMService
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from app.core.retriever import Retriever
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from app.services.feedback_service import log_search
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router = APIRouter()
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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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path = Path(settings.DECISION_CONFIG_PATH)
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default_config = {
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"strategies": {
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"FACT": {"trigger_keywords": []}
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}
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}
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if not path.exists():
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logger.warning(f"Decision config not found at {path}, using defaults.")
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return default_config
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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)
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except Exception as e:
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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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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 aus types.yaml UND Mappings.
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"""
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message_lower = message.lower()
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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": continue
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if type_key in message_lower:
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return type_key
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# 3. Synonym-Mapping (Legacy Fallback)
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synonyms = {
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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", "lektion": "experience",
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"wert": "value",
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"prinzip": "principle",
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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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return schema_key
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return "default"
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# --- Dependencies ---
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def get_llm_service():
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return LLMService()
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def get_retriever():
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return Retriever()
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# --- Logic ---
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def _build_enriched_context(hits: List[QueryHit]) -> str:
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context_parts = []
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for i, hit in enumerate(hits, 1):
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source = hit.source or {}
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content = (
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source.get("text") or source.get("content") or
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source.get("page_content") or source.get("chunk_text") or
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"[Kein Text]"
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)
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title = hit.note_id or "Unbekannt"
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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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entry = (
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f"### QUELLE {i}: {title}\n"
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f"TYP: [{note_type}] (Score: {hit.total_score:.2f})\n"
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f"INHALT:\n{content}\n"
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)
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context_parts.append(entry)
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return "\n\n".join(context_parts)
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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 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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# 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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return intent_name, "Keyword (Strategy)"
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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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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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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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# 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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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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@router.post("/", response_model=ChatResponse)
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async def chat_endpoint(
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request: ChatRequest,
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llm: LLMService = Depends(get_llm_service),
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retriever: Retriever = Depends(get_retriever)
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):
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start_time = time.time()
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query_id = str(uuid.uuid4())
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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
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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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# Strategy Load
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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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sources_hits = []
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final_prompt = ""
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if intent == "INTERVIEW":
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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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# 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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# 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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# 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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# Prompt Assembly
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template = llm.prompts.get(prompt_key, "")
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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}", "")
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sources_hits = []
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else:
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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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query_req = QueryRequest(
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query=request.message,
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mode="hybrid",
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top_k=request.top_k,
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explain=request.explain
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)
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retrieve_result = await retriever.search(query_req)
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hits = retrieve_result.results
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if 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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top_k=3,
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filters={"type": inject_types},
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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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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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template = llm.prompts.get(prompt_key, "{context_str}\n\n{query}")
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if prepend_instr:
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context_str = f"{prepend_instr}\n\n{context_str}"
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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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# --- GENERATION ---
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system_prompt = llm.prompts.get("system_prompt", "")
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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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# 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={"intent": intent, "source": intent_source}
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)
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except: pass
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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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sources=sources_hits,
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latency_ms=duration_ms,
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intent=intent,
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intent_source=intent_source
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)
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except Exception as e:
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logger.error(f"Error in chat endpoint: {e}", exc_info=True)
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raise HTTPException(status_code=500, detail=str(e)) |