korrektur zu besseren Analyse
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@ -1,5 +1,6 @@
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"""
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app/routers/chat.py — RAG Endpunkt (WP-06 Hybrid Router)
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Version: 0.2.1 (Fix: System Prompt Separation)
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"""
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from fastapi import APIRouter, HTTPException, Depends
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@ -23,7 +24,6 @@ logger = logging.getLogger(__name__)
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_DECISION_CONFIG_CACHE = None
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def _load_decision_config() -> Dict[str, Any]:
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"""Lädt die Decision-Engine Konfiguration (Late Binding)."""
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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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@ -88,11 +88,6 @@ def _build_enriched_context(hits: List[QueryHit]) -> str:
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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:
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1. Keyword Check (Best/Longest Match) -> FAST
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2. LLM Fallback (wenn in config aktiv) -> 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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@ -101,7 +96,7 @@ async def _classify_intent(query: str, llm: LLMService) -> str:
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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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# 1. FAST PATH
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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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@ -115,23 +110,21 @@ async def _classify_intent(query: str, llm: LLMService) -> str:
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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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# 2. SLOW PATH
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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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# Router braucht keinen System-Prompt, nur den Classifier-Prompt
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llm_decision = await llm.generate_raw_response(prompt)
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# Cleaning
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llm_decision = llm_decision.strip().upper()
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if ":" in llm_decision:
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llm_decision = llm_decision.split(":")[-1].strip()
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# Validierung: Nur bekannte Intents zulassen
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# Entferne Satzzeichen
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# Satzzeichen entfernen für sauberen Match
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llm_decision = ''.join(filter(str.isalnum, llm_decision))
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if llm_decision in strategies:
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@ -185,7 +178,6 @@ async def chat_endpoint(
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)
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strategy_result = await retriever.search(strategy_req)
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# Merge
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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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@ -198,27 +190,18 @@ async def chat_endpoint(
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context_str = _build_enriched_context(hits)
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# 5. Generation
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# Wir laden das Template aus dem Service (da dort die prompts.yaml geladen ist)
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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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# Manuelles Bauen des finalen Prompts für volle Kontrolle
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final_prompt = template.replace("{context_str}", context_str).replace("{query}", request.message)
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# Aufruf via Raw Response (da wir den Prompt schon fertig haben)
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# Wir müssen den System-Prompt manuell mitgeben?
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# generate_raw_response in llm_service unterstützt aktuell kein 'system'.
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# -> Wir erweitern generate_raw_response oder nutzen einen Hack: System + Prompt.
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# SAUBERER WEG: Wir bauen den Payload für Ollama hier manuell zusammen und rufen eine generische Methode.
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# Da LLMService.generate_raw_response keine System-Msg nimmt, packen wir sie davor.
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full_text_prompt = f"{system_prompt}\n\n{final_prompt}"
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logger.info(f"[{query_id}] Sending to LLM (Intent: {intent}, Template: {prompt_key})...")
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answer_text = await llm.generate_raw_response(full_text_prompt)
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# FIX: 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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@ -1,6 +1,6 @@
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"""
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app/services/llm_service.py — LLM Client (Ollama)
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Version: 0.2.0 (WP-06 Hybrid Router Support)
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Version: 0.2.1 (Fix: System Prompt Handling for Phi-3)
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"""
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import httpx
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@ -17,7 +17,6 @@ class LLMService:
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self.settings = get_settings()
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self.prompts = self._load_prompts()
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# Timeout aus Config nutzen (Default 120s)
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self.client = httpx.AsyncClient(
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base_url=self.settings.OLLAMA_URL,
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timeout=self.settings.LLM_TIMEOUT
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@ -34,21 +33,27 @@ class LLMService:
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logger.error(f"Failed to load prompts: {e}")
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return {}
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async def generate_raw_response(self, prompt: str) -> str:
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async def generate_raw_response(self, prompt: str, system: str = None) -> str:
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"""
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NEU: Führt einen direkten LLM Call ohne RAG-Template aus.
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Wird vom Router für die Antwortgenerierung genutzt.
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Führt einen LLM Call aus.
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Unterstützt nun explizite System-Prompts für sauberes Templating.
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"""
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payload = {
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"model": self.settings.LLM_MODEL,
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"prompt": prompt,
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"stream": False,
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"options": {
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"temperature": 0.0,
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"num_ctx": 512
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# Temperature etwas höher für Empathie, niedriger für Code?
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# Wir lassen es auf Standard, oder steuern es später via Config.
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"temperature": 0.7,
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"num_ctx": 2048
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}
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}
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# WICHTIG: System-Prompt separat übergeben, damit Ollama formatiert
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if system:
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payload["system"] = system
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try:
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response = await self.client.post("/api/generate", json=payload)
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if response.status_code != 200:
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@ -63,14 +68,13 @@ class LLMService:
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return "Interner LLM Fehler."
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async def generate_rag_response(self, query: str, context_str: str) -> str:
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"""Legacy Support / Fallback"""
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"""Legacy Support"""
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system_prompt = self.prompts.get("system_prompt", "")
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rag_template = self.prompts.get("rag_template", "{context_str}\n\n{query}")
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final_prompt = rag_template.format(context_str=context_str, query=query)
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# Wir nutzen intern nun auch raw_response, um Code zu sparen
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full_prompt = f"{system_prompt}\n\n{final_prompt}"
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return await self.generate_raw_response(full_prompt)
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# Leite an die neue Methode weiter
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return await self.generate_raw_response(final_prompt, system=system_prompt)
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async def close(self):
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await self.client.aclose()
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