235 lines
7.9 KiB
Python
235 lines
7.9 KiB
Python
"""
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app/routers/chat.py — RAG Endpunkt (WP-06 Hybrid Router v2)
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Update: Robusteres LLM-Parsing für Small Language Models (SLMs).
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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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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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# --- 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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note_type = source.get("type", "unknown").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) -> str:
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"""
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Hybrid Router v2:
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1. Keyword Check (Best/Longest Match) -> FAST
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2. LLM Fallback (Robust Parsing) -> SMART
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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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logger.info(f"Intent detected via KEYWORD: {best_intent}")
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return best_intent
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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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# Kurzer Raw Call
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raw_response = await llm.generate_raw_response(prompt)
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# --- Robust Parsing für SLMs ---
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# Wir suchen nach den bekannten Strategie-Namen im Output
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llm_output_upper = raw_response.upper()
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logger.info(f"LLM Router Raw Output: '{raw_response}'") # Debugging
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found_intents = []
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for strat_key in strategies.keys():
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# Wir prüfen, ob der Strategie-Name (z.B. "EMPATHY") im Text vorkommt
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if strat_key in llm_output_upper:
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found_intents.append(strat_key)
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# Entscheidung
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final_intent = "FACT"
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if len(found_intents) == 1:
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# Eindeutiger Treffer
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final_intent = found_intents[0]
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logger.info(f"Intent detected via LLM (Parsed): {final_intent}")
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return final_intent
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elif len(found_intents) > 1:
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# Mehrere Treffer (z.B. "Es ist FACT oder DECISION") -> Nimm den ersten oder Fallback
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logger.warning(f"LLM returned multiple intents {found_intents}. Using first match: {found_intents[0]}")
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return found_intents[0]
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else:
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logger.warning(f"LLM did not return a valid strategy name. Falling back to FACT.")
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return "FACT"
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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 = await _classify_intent(request.message, llm)
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logger.info(f"[{query_id}] Final Intent: {intent}")
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# Strategy Load
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strategy = get_decision_strategy(intent)
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inject_types = strategy.get("inject_types", [])
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prompt_key = strategy.get("prompt_template", "rag_template")
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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
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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
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template = llm.prompts.get(prompt_key, "{context_str}\n\n{query}")
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system_prompt = llm.prompts.get("system_prompt", "")
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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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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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return ChatResponse(
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query_id=query_id,
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answer=answer_text,
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sources=hits,
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latency_ms=duration_ms,
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intent=intent
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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)) |