""" app/routers/chat.py — RAG Endpunkt (WP-06 Hybrid Router + WP-07 Interview Mode) Version: 2.4.0 (Interview Support) Features: - Hybrid Intent Router (Keyword + LLM) - Strategic Retrieval (Late Binding via Config) - Interview Loop (Schema-driven Data Collection) - Context Enrichment (Payload/Source Fallback) - Data Flywheel (Feedback Logging Integration) """ 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 from app.services.feedback_service import log_search 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", {})) # --- Helper: Target Type Detection (WP-07) --- def _detect_target_type(message: str, configured_schemas: Dict[str, Any]) -> str: """ Versucht zu erraten, welchen Notiz-Typ der User erstellen will. Nutzt Keywords und Mappings. """ message_lower = message.lower() # 1. Direkter Match mit Schema-Keys (z.B. "projekt", "entscheidung") # Ignoriere 'default' hier for type_key in configured_schemas.keys(): if type_key == "default": continue if type_key in message_lower: return type_key # 2. Synonym-Mapping (Deutsch -> Schema Key) # Dies verbessert die UX, falls User deutsche Begriffe nutzen synonyms = { "projekt": "project", "vorhaben": "project", "entscheidung": "decision", "beschluss": "decision", "ziel": "goal", "erfahrung": "experience", "lektion": "experience", "wert": "value", "prinzip": "principle", "grundsatz": "principle", "notiz": "default", "idee": "default" } for term, schema_key in synonyms.items(): if term in message_lower: # Prüfen, ob der gemappte Key auch konfiguriert ist if schema_key in configured_schemas: return schema_key return "default" # --- 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" # [FIX] Robustes Auslesen des Typs (Payload > Source > Unknown) payload = hit.payload or {} note_type = payload.get("type") or source.get("type", "unknown") note_type = str(note_type).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) -> tuple[str, str]: """ Hybrid Router v3: Gibt Tuple zurück: (Intent, Source) """ 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: return best_intent, "Keyword (Fast Path)" # 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...") raw_response = await llm.generate_raw_response(prompt) # Parsing logic llm_output_upper = raw_response.upper() found_intents = [] for strat_key in strategies.keys(): if strat_key in llm_output_upper: found_intents.append(strat_key) if len(found_intents) == 1: return found_intents[0], "LLM Router (Slow Path)" elif len(found_intents) > 1: return found_intents[0], f"LLM Ambiguous {found_intents}" else: return "FACT", "LLM Fallback (No Match)" return "FACT", "Default (No Match)" @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 (mit Source) intent, intent_source = await _classify_intent(request.message, llm) logger.info(f"[{query_id}] Final Intent: {intent} via {intent_source}") # Strategy Load strategy = get_decision_strategy(intent) prompt_key = strategy.get("prompt_template", "rag_template") # --- SPLIT LOGIC: INTERVIEW vs. RAG --- sources_hits = [] final_prompt = "" if intent == "INTERVIEW": # --- WP-07: INTERVIEW MODE --- # Kein Retrieval. Wir nutzen den Dialog-Kontext. # 1. Schema Loading (Late Binding) schemas = strategy.get("schemas", {}) target_type = _detect_target_type(request.message, schemas) active_schema = schemas.get(target_type, schemas.get("default")) logger.info(f"[{query_id}] Starting Interview for Type: {target_type}") # Robustes Schema-Parsing (Dict vs List) if isinstance(active_schema, dict): fields_list = active_schema.get("fields", []) hint_str = active_schema.get("hint", "") else: fields_list = active_schema # Fallback falls nur Liste definiert hint_str = "" fields_str = "\n- " + "\n- ".join(fields_list) # 2. Context Logic # Hinweis: In einer Stateless-API ist {context_str} idealerweise die History. # Da ChatRequest (noch) kein History-Feld hat, nutzen wir einen Placeholder # oder verlassen uns darauf, dass der Client die History im Prompt mitschickt # (Streamlit Pattern: Appends history to prompt). # Wir labeln es hier explizit. context_str = "Bisheriger Verlauf (falls vorhanden): Siehe oben/unten." # 3. Prompt Assembly template = llm.prompts.get(prompt_key, "") final_prompt = template.replace("{context_str}", context_str) \ .replace("{query}", request.message) \ .replace("{target_type}", target_type) \ .replace("{schema_fields}", fields_str) \ .replace("{schema_hint}", hint_str) # Keine Hits im Interview sources_hits = [] else: # --- WP-06: STANDARD RAG MODE --- inject_types = strategy.get("inject_types", []) 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 (WP-06 Kernfeature) 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 Setup template = llm.prompts.get(prompt_key, "{context_str}\n\n{query}") if prepend_instr: context_str = f"{prepend_instr}\n\n{context_str}" final_prompt = template.replace("{context_str}", context_str).replace("{query}", request.message) sources_hits = hits # --- COMMON GENERATION --- system_prompt = llm.prompts.get("system_prompt", "") 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) # 6. Logging (Fire & Forget) try: log_search( query_id=query_id, query_text=request.message, results=sources_hits, mode="interview" if intent == "INTERVIEW" else "chat_rag", metadata={ "intent": intent, "intent_source": intent_source, "generated_answer": answer_text, "model": llm.settings.LLM_MODEL } ) except Exception as e: logger.error(f"Logging failed: {e}") # 7. Response return ChatResponse( query_id=query_id, answer=answer_text, sources=sources_hits, latency_ms=duration_ms, intent=intent, intent_source=intent_source ) except Exception as e: logger.error(f"Error in chat endpoint: {e}", exc_info=True) raise HTTPException(status_code=500, detail=str(e))