222 lines
7.3 KiB
Python
222 lines
7.3 KiB
Python
"""
|
|
app/routers/chat.py — RAG Endpunkt (WP-06 Hybrid Router v3)
|
|
Update: Transparenz über Intent-Source (Keyword vs. LLM).
|
|
"""
|
|
|
|
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) -> 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)
|
|
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,
|
|
intent_source=intent_source # NEU: Source durchreichen
|
|
)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error in chat endpoint: {e}", exc_info=True)
|
|
raise HTTPException(status_code=500, detail=str(e)) |