108 lines
3.2 KiB
Python
108 lines
3.2 KiB
Python
"""
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app/routers/chat.py — RAG Endpunkt (WP-05)
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Version:
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0.1.1 (Debug-Logging enabled)
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"""
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from fastapi import APIRouter, HTTPException, Depends
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from typing import List
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import time
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import uuid
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import logging
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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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# Annahme: Der Retriever aus WP-04 liegt hier.
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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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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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def _build_context_from_hits(hits: List[QueryHit]) -> str:
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"""
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Formatiert die Suchtreffer zu einem String für den Prompt.
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"""
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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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# Wir probieren alle möglichen Felder, in denen Text stecken könnte
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content = (
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source.get("text") or
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source.get("content") or
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source.get("page_content") or
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source.get("chunk_text") or # Oft verwendet
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"[[LEERER INHALT - PAYLOAD PRÜFEN]]"
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)
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title = hit.note_id or "Unknown Note"
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# Debug Log für jeden Hit
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logger.info(f"Building Context [{i}]: ID={hit.node_id} Content-Length={len(str(content))}")
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entry = (
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f"SOURCE [{i}]: {title} (Score: {hit.total_score:.2f})\n"
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f"CONTENT: {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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@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}")
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try:
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# 1. 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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# 2. Kontext bauen
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if not hits:
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logger.info(f"[{query_id}] No hits found for context.")
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context_str = "Keine relevanten Notizen gefunden."
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else:
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context_str = _build_context_from_hits(hits)
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# WICHTIG: Wir loggen den ersten Teil des Kontextes, um zu sehen, was das LLM bekommt
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logger.info(f"--- LLM CONTEXT PREVIEW ---\n{context_str[:500]}\n--- END PREVIEW ---")
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# 3. LLM Generation
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answer_text = await llm.generate_rag_response(
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query=request.message,
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context_str=context_str
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)
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# 4. Response
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duration_ms = int((time.time() - start_time) * 1000)
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return ChatResponse(
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query_id=retrieve_result.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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)
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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)) |