WP25b #21
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@ -3,18 +3,20 @@ FILE: app/core/retrieval/decision_engine.py
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DESCRIPTION: Der Agentic Orchestrator für MindNet (WP-25b Edition).
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Realisiert Multi-Stream Retrieval, Intent-basiertes Routing
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und die neue Lazy-Prompt Orchestrierung (Module A & B).
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VERSION: 1.3.1 (WP-25b: Robust Intent Cleaning & Lazy Loading)
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VERSION: 1.3.2 (WP-25b: Full Robustness Recovery & Regex Parsing)
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STATUS: Active
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FIX:
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- WP-25b: Robuste Bereinigung von Intent-Strings (Fix: CODING[/S] -> CODING).
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- WP-25b: Umstellung auf Lazy-Loading (Übergabe von prompt_key + variables).
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- WP-25b: ULTRA-Robustes Intent-Parsing via Regex (Fix: 'CODING[/S]' -> 'CODING').
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- WP-25b: Wiederherstellung der prepend_instruction Logik via variables.
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- WP-25a: Voller Erhalt der Profil-Kaskade via LLMService v3.5.5.
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- WP-25: Beibehaltung von Stream-Tracing, Edge-Boosts und Pre-Initialization.
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- RECOVERY: Wiederherstellung der lokalen Sicherheits-Gates aus v1.2.1.
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"""
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import asyncio
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import logging
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import yaml
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import os
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import re # Neu für robustes Intent-Parsing
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from typing import List, Dict, Any, Optional
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# Core & Service Imports
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@ -41,7 +43,9 @@ class DecisionEngine:
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return {"strategies": {}}
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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) or {}
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config = yaml.safe_load(f) or {}
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logger.info(f"⚙️ Decision Engine Config loaded (v{config.get('version', 'unknown')})")
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return config
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except Exception as e:
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logger.error(f"❌ Failed to load decision_engine.yaml: {e}")
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return {"strategies": {}}
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@ -76,13 +80,13 @@ class DecisionEngine:
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return await self._generate_final_answer(strategy_key, strategy, query, stream_results)
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async def _determine_strategy(self, query: str) -> str:
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"""WP-25b: Nutzt den LLM-Router via Lazy-Loading und bereinigt Modell-Artefakte."""
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"""WP-25b: Nutzt den LLM-Router via Lazy-Loading und bereinigt Modell-Artefakte via Regex."""
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settings_cfg = self.config.get("settings", {})
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prompt_key = settings_cfg.get("router_prompt_key", "intent_router_v1")
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router_profile = settings_cfg.get("router_profile")
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try:
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# WP-25b: Delegation an LLMService ohne manuelle Vor-Formatierung.
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# Delegation an LLMService ohne manuelle Vor-Formatierung
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response = await self.llm_service.generate_raw_response(
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prompt_key=prompt_key,
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variables={"query": query},
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@ -91,28 +95,29 @@ class DecisionEngine:
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profile_name=router_profile
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)
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# WP-25b FIX: Bereinigung von Stop-Markern wie [/S] oder </s>
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raw_intent = str(response).replace("[/S]", "").replace("</s>", "").strip().upper()
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# --- ULTRA-ROBUST PARSING (Fix für 'CODING[/S]') ---
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# 1. Alles in Großbuchstaben umwandeln
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raw_text = str(response).upper()
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# Robustheit: Nur das erste Wort nehmen, falls das Modell zu viel plaudert
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intent = raw_intent.split()[0] if raw_intent else "FACT_WHAT"
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# 2. Regex: Suche das erste Wort, das nur aus A-Z und Unterstrichen besteht
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# Dies ignoriert [/S], </s>, Newlines oder Plaudereien des Modells
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match = re.search(r'\b(FACT_WHEN|FACT_WHAT|DECISION|EMPATHY|CODING|INTERVIEW)\b', raw_text)
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# Validierung gegen bekannte Strategien aus der decision_engine.yaml
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known_strategies = self.config.get("strategies", {}).keys()
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if intent not in known_strategies:
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logger.warning(f"⚠️ Unmapped intent '{intent}' from router. Falling back.")
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return "FACT_WHAT"
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return intent
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if match:
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intent = match.group(1)
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logger.info(f"🎯 [ROUTING] Parsed Intent: '{intent}' from raw response: '{response.strip()}'")
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return intent
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# Fallback, falls Regex nicht greift
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logger.warning(f"⚠️ Unmapped intent '{response.strip()}' from router. Falling back to FACT_WHAT.")
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return "FACT_WHAT"
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except Exception as e:
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logger.error(f"Strategy Routing failed: {e}")
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return "FACT_WHAT"
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async def _execute_parallel_streams(self, strategy: Dict, query: str) -> Dict[str, str]:
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"""
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Führt Such-Streams aus und komprimiert überlange Ergebnisse (Pre-Synthesis).
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WP-25b: Unterstützt Lazy-Compression über Experten-Profile.
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"""
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"""Führt Such-Streams aus und komprimiert überlange Ergebnisse (Pre-Synthesis)."""
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stream_keys = strategy.get("use_streams", [])
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library = self.config.get("streams_library", {})
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@ -130,15 +135,13 @@ class DecisionEngine:
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# Phase 2: Formatierung und optionale Kompression
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final_stream_tasks = []
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for name, res in zip(active_streams, retrieval_results):
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if isinstance(res, Exception):
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logger.error(f"Stream '{name}' failed during retrieval: {res}")
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async def _err(): return "[Fehler beim Abruf dieses Wissens-Streams]"
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async def _err(): return f"[Fehler im Wissens-Stream {name}]"
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final_stream_tasks.append(_err())
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continue
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# Formatierung der Hits in Text
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formatted_context = self._format_stream_context(res)
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# WP-25a: Kompressions-Check (Inhaltsverdichtung)
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@ -160,9 +163,8 @@ class DecisionEngine:
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return dict(zip(active_streams, final_contents))
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async def _compress_stream_content(self, stream_name: str, content: str, query: str, profile: Optional[str]) -> str:
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"""WP-25b Module A: Inhaltsverdichtung via Lazy-Loading 'compression_template'."""
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"""WP-25b: Inhaltsverdichtung via Lazy-Loading 'compression_template'."""
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try:
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# WP-25b: Delegation der Inhaltsverdichtung an den LLMService.
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summary = await self.llm_service.generate_raw_response(
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prompt_key="compression_template",
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variables={
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@ -180,7 +182,7 @@ class DecisionEngine:
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return content
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async def _run_single_stream(self, name: str, cfg: Dict, query: str) -> QueryResponse:
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"""Spezialisierte Graph-Suche mit Stream-Tracing und Edge-Boosts (WP-25)."""
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"""Spezialisierte Graph-Suche mit Stream-Tracing und Edge-Boosts."""
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transformed_query = cfg.get("query_template", "{query}").format(query=query)
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request = QueryRequest(
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@ -188,7 +190,7 @@ class DecisionEngine:
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top_k=cfg.get("top_k", 5),
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filters={"type": cfg.get("filter_types", [])},
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expand={"depth": 1},
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boost_edges=cfg.get("edge_boosts", {}), # WP-25a Erhalt
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boost_edges=cfg.get("edge_boosts", {}), # Erhalt der Gewichtung
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explain=True
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)
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@ -200,7 +202,7 @@ class DecisionEngine:
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def _format_stream_context(self, response: QueryResponse) -> str:
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"""Wandelt QueryHits in einen formatierten Kontext-String um."""
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if not response.results:
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return "Keine spezifischen Informationen in diesem Stream gefunden."
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return "Keine spezifischen Informationen gefunden."
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lines = []
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for i, hit in enumerate(response.results, 1):
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source = hit.source.get("path", "Unbekannt")
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@ -215,9 +217,8 @@ class DecisionEngine:
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query: str,
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stream_results: Dict[str, str]
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) -> str:
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"""WP-25b: Finale Synthese via Lazy-Prompt Orchestrierung."""
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"""WP-25b: Finale Synthese via Lazy-Prompt mit Robustheit aus v1.2.1."""
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profile = strategy.get("llm_profile")
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# Nutzt den Key aus der YAML oder 'fact_synthesis_v1' als sicheren Default
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template_key = strategy.get("prompt_template", "fact_synthesis_v1")
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system_prompt = self.llm_service.get_prompt("system_prompt")
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@ -227,19 +228,34 @@ class DecisionEngine:
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template_vars.update(stream_results)
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template_vars["query"] = query
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# WP-25a Erhalt: Optionale Prepend-Anweisung
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template_vars["prepend_instruction"] = strategy.get("prepend_instruction", "")
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# WP-25b: Delegation der Synthese an den LLMService.
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# Formatierung erfolgt erst nach Profil-Auflösung (Gemini vs. Llama vs. Qwen).
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# WP-25a Erhalt: Prepend Instructions aus der strategy_config
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prepend = strategy.get("prepend_instruction", "")
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template_vars["prepend_instruction"] = prepend
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try:
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return await self.llm_service.generate_raw_response(
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# WP-25b: Delegation der Synthese an den LLMService
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response = await self.llm_service.generate_raw_response(
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prompt_key=template_key,
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variables=template_vars,
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system=system_prompt,
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profile_name=profile,
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priority="realtime"
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)
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# WP-25a RECOVERY: Falls dieprepend_instruction nicht im Template-Key
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# der prompts.yaml enthalten ist (WP-25b Lazy Loading), fügen wir sie
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# hier manuell an den Anfang, um die Logik aus v1.2.1 zu bewahren.
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if prepend and prepend not in response[:len(prepend)+50]:
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logger.info("ℹ️ Adding prepend_instruction manually (not found in response).")
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response = f"{prepend}\n\n{response}"
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return response
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except Exception as e:
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logger.error(f"Final Synthesis failed: {e}")
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return "Ich konnte keine Antwort generieren."
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# ROBUST FALLBACK (v1.2.1 Gate): Versuche eine minimale Antwort zu generieren
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fallback_context = "\n\n".join([v for v in stream_results.values() if len(v) > 20])
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return await self.llm_service.generate_raw_response(
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prompt=f"Beantworte: {query}\n\nKontext:\n{fallback_context}",
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system=system_prompt, priority="realtime", profile_name=profile
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)
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