""" app/routers/chat.py — RAG Endpunkt (WP-06 Hybrid Router v2) Update: Robusteres LLM-Parsing für Small Language Models (SLMs). """ from fastapi import APIRouter, HTTPException, Depends from typing import List, Dict, Any import time import uuid import logging import yaml from pathlib import Path from app.config import get_settings from app.models.dto import ChatRequest, ChatResponse, QueryRequest, QueryHit from app.services.llm_service import LLMService from app.core.retriever import Retriever router = APIRouter() logger = logging.getLogger(__name__) # --- Helper: Config Loader --- _DECISION_CONFIG_CACHE = None def _load_decision_config() -> Dict[str, Any]: settings = get_settings() path = Path(settings.DECISION_CONFIG_PATH) default_config = { "strategies": { "FACT": {"trigger_keywords": []} } } if not path.exists(): logger.warning(f"Decision config not found at {path}, using defaults.") return default_config try: with open(path, "r", encoding="utf-8") as f: return yaml.safe_load(f) except Exception as e: logger.error(f"Failed to load decision config: {e}") return default_config def get_full_config() -> Dict[str, Any]: global _DECISION_CONFIG_CACHE if _DECISION_CONFIG_CACHE is None: _DECISION_CONFIG_CACHE = _load_decision_config() return _DECISION_CONFIG_CACHE def get_decision_strategy(intent: str) -> Dict[str, Any]: config = get_full_config() strategies = config.get("strategies", {}) return strategies.get(intent, strategies.get("FACT", {})) # --- Dependencies --- def get_llm_service(): return LLMService() def get_retriever(): return Retriever() # --- Logic --- def _build_enriched_context(hits: List[QueryHit]) -> str: context_parts = [] for i, hit in enumerate(hits, 1): source = hit.source or {} content = ( source.get("text") or source.get("content") or source.get("page_content") or source.get("chunk_text") or "[Kein Text]" ) title = hit.note_id or "Unbekannt" note_type = source.get("type", "unknown").upper() entry = ( f"### QUELLE {i}: {title}\n" f"TYP: [{note_type}] (Score: {hit.total_score:.2f})\n" f"INHALT:\n{content}\n" ) context_parts.append(entry) return "\n\n".join(context_parts) async def _classify_intent(query: str, llm: LLMService) -> str: """ Hybrid Router v2: 1. Keyword Check (Best/Longest Match) -> FAST 2. LLM Fallback (Robust Parsing) -> SMART """ config = get_full_config() strategies = config.get("strategies", {}) settings = config.get("settings", {}) query_lower = query.lower() best_intent = None max_match_length = 0 # 1. FAST PATH: Keywords for intent_name, strategy in strategies.items(): if intent_name == "FACT": continue keywords = strategy.get("trigger_keywords", []) for k in keywords: if k.lower() in query_lower: if len(k) > max_match_length: max_match_length = len(k) best_intent = intent_name if best_intent: logger.info(f"Intent detected via KEYWORD: {best_intent}") return best_intent # 2. SLOW PATH: LLM Router if settings.get("llm_fallback_enabled", False): router_prompt_template = settings.get("llm_router_prompt", "") if router_prompt_template: prompt = router_prompt_template.replace("{query}", query) logger.info("Keywords failed. Asking LLM for Intent...") # Kurzer Raw Call raw_response = await llm.generate_raw_response(prompt) # --- Robust Parsing für SLMs --- # Wir suchen nach den bekannten Strategie-Namen im Output llm_output_upper = raw_response.upper() logger.info(f"LLM Router Raw Output: '{raw_response}'") # Debugging found_intents = [] for strat_key in strategies.keys(): # Wir prüfen, ob der Strategie-Name (z.B. "EMPATHY") im Text vorkommt if strat_key in llm_output_upper: found_intents.append(strat_key) # Entscheidung final_intent = "FACT" if len(found_intents) == 1: # Eindeutiger Treffer final_intent = found_intents[0] logger.info(f"Intent detected via LLM (Parsed): {final_intent}") return final_intent elif len(found_intents) > 1: # Mehrere Treffer (z.B. "Es ist FACT oder DECISION") -> Nimm den ersten oder Fallback logger.warning(f"LLM returned multiple intents {found_intents}. Using first match: {found_intents[0]}") return found_intents[0] else: logger.warning(f"LLM did not return a valid strategy name. Falling back to FACT.") return "FACT" @router.post("/", response_model=ChatResponse) async def chat_endpoint( request: ChatRequest, llm: LLMService = Depends(get_llm_service), retriever: Retriever = Depends(get_retriever) ): start_time = time.time() query_id = str(uuid.uuid4()) logger.info(f"Chat request [{query_id}]: {request.message[:50]}...") try: # 1. Intent Detection intent = await _classify_intent(request.message, llm) logger.info(f"[{query_id}] Final Intent: {intent}") # Strategy Load strategy = get_decision_strategy(intent) inject_types = strategy.get("inject_types", []) prompt_key = strategy.get("prompt_template", "rag_template") prepend_instr = strategy.get("prepend_instruction", "") # 2. Primary Retrieval query_req = QueryRequest( query=request.message, mode="hybrid", top_k=request.top_k, explain=request.explain ) retrieve_result = await retriever.search(query_req) hits = retrieve_result.results # 3. Strategic Retrieval if inject_types: logger.info(f"[{query_id}] Executing Strategic Retrieval for types: {inject_types}...") strategy_req = QueryRequest( query=request.message, mode="hybrid", top_k=3, filters={"type": inject_types}, explain=False ) strategy_result = await retriever.search(strategy_req) existing_ids = {h.node_id for h in hits} for strat_hit in strategy_result.results: if strat_hit.node_id not in existing_ids: hits.append(strat_hit) # 4. Context Building if not hits: context_str = "Keine relevanten Notizen gefunden." else: context_str = _build_enriched_context(hits) # 5. Generation template = llm.prompts.get(prompt_key, "{context_str}\n\n{query}") system_prompt = llm.prompts.get("system_prompt", "") if prepend_instr: context_str = f"{prepend_instr}\n\n{context_str}" final_prompt = template.replace("{context_str}", context_str).replace("{query}", request.message) logger.info(f"[{query_id}] Sending to LLM (Intent: {intent}, Template: {prompt_key})...") # System-Prompt separat übergeben answer_text = await llm.generate_raw_response(prompt=final_prompt, system=system_prompt) duration_ms = int((time.time() - start_time) * 1000) return ChatResponse( query_id=query_id, answer=answer_text, sources=hits, latency_ms=duration_ms, intent=intent ) except Exception as e: logger.error(f"Error in chat endpoint: {e}", exc_info=True) raise HTTPException(status_code=500, detail=str(e))