rag: LLM-Query-Rewrite (Stufe B, gpt-4o-mini) als Default-Modus
Query wird vor ES-Suche durch gpt-4o-mini in 4 DE/EN-Varianten umformuliert (Synonyme, Fachbegriffe, Zahl-/Kosten-/Summenbegriffe). Dann Multi-Query-Merge durch _merge_hits_from_queries. Fallback auf Single-Query bei API-Fehler oder fehlendem Key. 1h-Cache, 8s-Timeout. Loest ua. Kosten-/Preis-Fragen, die zuvor am Standardpfad gescheitert sind (Beispiel: "was haben die wohnungen in kambodscha gekostet" findet jetzt G2010B und D1603 in einem Rutsch).
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1 changed files with 132 additions and 6 deletions
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@ -6,7 +6,9 @@ RAGFlow bleibt Ingestion; Suche geht direkt an ES (Issue #51).
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import base64
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import json
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import logging
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import os
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import re
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import time
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import urllib.error
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import urllib.request
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@ -466,6 +468,103 @@ def _expand_multilingual(q: str) -> list:
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return variants[:8]
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# --- /Multi-Query-Erweiterung ---------------------------------------------
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# --- LLM Query Rewrite (Stufe B) ---
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_LLM_REWRITE_CACHE: dict = {}
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_LLM_REWRITE_TTL = 3600
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_LLM_REWRITE_MAX = 6
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_LLM_REWRITE_TIMEOUT = 8
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_LLM_REWRITE_MODEL = "gpt-4o-mini"
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_LLM_REWRITE_PROMPT = (
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"Du bist ein Query-Rewriter fuer eine deutschsprachige Dokumenten-Suche "
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"(Vertraege, Versicherungen, Rechnungen, Bescheide, Kaufvertraege, Kontoauszuege). "
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"Formuliere die Nutzer-Frage in {n} unterschiedliche kompakte Such-Queries um. "
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"Nutze Synonyme, Fachbegriffe, Zahl-/Kosten-/Summenbegriffe und sowohl deutsche "
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"als auch englische Termini. Jede Query: 2-6 Woerter, Keywords statt Saetze, "
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"keine Fragezeichen. Antworte NUR als JSON-Liste von Strings, nichts sonst."
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)
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def _openai_key() -> str:
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try:
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from core import config as _cfg # type: ignore
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v = (_cfg.parse_config().raw.get("OPENAI_API_KEY") or "").strip()
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if v:
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return v
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except Exception:
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pass
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return (os.environ.get("OPENAI_API_KEY") or "").strip()
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def _llm_query_rewrite(query: str, n: int = 4) -> list:
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"""gpt-4o-mini -> bis zu n DE/EN-Reformulierungen. Cache + Fehler-Fallback."""
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q = (query or "").strip()
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if not q or n <= 0:
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return []
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cache_key = f"{n}::{q.lower()}"
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now = time.time()
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entry = _LLM_REWRITE_CACHE.get(cache_key)
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if entry and (now - entry[0]) < _LLM_REWRITE_TTL:
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return list(entry[1])
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key = _openai_key()
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if not key:
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return []
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body = json.dumps({
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"model": _LLM_REWRITE_MODEL,
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"messages": [
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{"role": "system", "content": _LLM_REWRITE_PROMPT.format(n=n)},
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{"role": "user", "content": q},
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],
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"temperature": 0.3,
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"max_tokens": 220,
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}).encode()
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req = urllib.request.Request(
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"https://api.openai.com/v1/chat/completions",
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data=body,
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method="POST",
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headers={
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"Content-Type": "application/json",
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"Authorization": f"Bearer {key}",
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},
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)
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try:
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with urllib.request.urlopen(req, timeout=_LLM_REWRITE_TIMEOUT) as resp:
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data = json.load(resp)
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except Exception as e:
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log.warning("llm rewrite failed: %s", e)
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return []
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try:
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txt = (data["choices"][0]["message"]["content"] or "").strip()
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except Exception:
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return []
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m = re.search(r"\[[\s\S]*\]", txt)
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raw = m.group(0) if m else txt
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variants: list = []
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try:
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arr = json.loads(raw)
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if isinstance(arr, list):
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for x in arr:
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if isinstance(x, str):
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s = x.strip()
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while s and s[-1] in ".?!;,":
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s = s[:-1]
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s = s.strip()
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if s and s.lower() != q.lower() and s not in variants:
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variants.append(s)
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except Exception:
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for line in txt.splitlines():
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s = re.sub(r"^[\s\-\*\d\.\)]+", "", line).strip()
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while s and s[-1] in ".?!;,":
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s = s[:-1]
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s = s.strip()
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if s and s.lower() != q.lower() and s not in variants:
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variants.append(s)
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variants = variants[:n]
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_LLM_REWRITE_CACHE[cache_key] = (now, variants)
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log.info("llm rewrite %r -> %s variants", q[:60], len(variants))
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return variants
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# --- /LLM Query Rewrite ---
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def handle_rag_search(query: str, top_k: int = 8, **kw):
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if not query or not query.strip():
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return "rag_search: query fehlt."
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@ -514,12 +613,39 @@ def handle_rag_search(query: str, top_k: int = 8, **kw):
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)
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snip_len = 500
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else:
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data = _es_hybrid_search(qstrip, es_size)
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if "_error" in data:
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return f"Fehler bei der Dokumentensuche: {data['_error']}"
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hits = (data.get("hits") or {}).get("hits") or []
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header = f"**Dokumente fuer '{qstrip}' (bis {top_k}):**\n"
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snip_len = 650
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# Modus 3 (2026-04-16): LLM-Query-Rewrite via gpt-4o-mini.
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# Fallback auf Single-Query bei API-Fehler / fehlendem Key.
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rewrites = _llm_query_rewrite(qstrip, n=4)
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if rewrites:
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subqs = [qstrip] + [r for r in rewrites if r.lower() != qstrip.lower()]
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subqs = subqs[:_LLM_REWRITE_MAX]
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pool_cap = max(top_k * 5, 80)
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hits, err = _merge_hits_from_queries(
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subqs,
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es_size,
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pool_cap=pool_cap,
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full_path_dedup=False,
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)
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if hits:
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header = (
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f"**LLM-Rewrite ({len(subqs)} Varianten, gpt-4o-mini) '{qstrip}' — "
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f"{len(hits)} Kandidaten, zeige bis {top_k}:**\n"
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)
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snip_len = 600
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else:
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data = _es_hybrid_search(qstrip, es_size)
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if "_error" in data:
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return f"Fehler bei der Dokumentensuche: {data['_error']}"
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hits = (data.get("hits") or {}).get("hits") or []
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header = f"**Dokumente fuer '{qstrip}' (bis {top_k}):**\n"
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snip_len = 650
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else:
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data = _es_hybrid_search(qstrip, es_size)
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if "_error" in data:
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return f"Fehler bei der Dokumentensuche: {data['_error']}"
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hits = (data.get("hits") or {}).get("hits") or []
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header = f"**Dokumente fuer '{qstrip}' (bis {top_k}):**\n"
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snip_len = 650
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if not hits:
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return f"Keine Ergebnisse fuer '{qstrip}' in der Wissensbasis gefunden."
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