345 lines
12 KiB
Python
345 lines
12 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: 2.4.0 (Interview Support)
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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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"""
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from fastapi import APIRouter, HTTPException, Depends
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from typing import List, Dict, Any
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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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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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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 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_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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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 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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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 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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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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"ziel": "goal",
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"erfahrung": "experience",
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"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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}
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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 "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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# [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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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 v3:
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Gibt Tuple zurück: (Intent, Source)
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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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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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if best_intent:
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return best_intent, "Keyword (Fast Path)"
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# 2. 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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# 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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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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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 (mit Source)
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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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# --- 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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# 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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logger.info(f"[{query_id}] Starting Interview for Type: {target_type}")
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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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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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template = llm.prompts.get(prompt_key, "")
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final_prompt = template.replace("{context_str}", context_str) \
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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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# 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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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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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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# 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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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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# 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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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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# --- COMMON 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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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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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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)
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except Exception as e:
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logger.error(f"Logging failed: {e}")
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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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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)) |