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aad7814 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 | """Per-tenant writing style profile from the reference corpus."""
from __future__ import annotations
import json
import logging
import threading
from dataclasses import asdict, dataclass, field
from pathlib import Path
from backend.config import settings
from backend.core.rag_store import TIER_REFERENCE, get_rag_store
from backend.llm import openai_client
from backend.utils import tenant_store
logger = logging.getLogger(__name__)
_lock = threading.RLock()
@dataclass
class StyleProfile:
tone: str = "formal"
formality_level: str = "professional"
avg_sentence_complexity: str = "moderate"
vocabulary_level: str = "technical"
common_phrases: list[str] = field(default_factory=lambda: [
"The property",
"It is noted that",
"In respect of",
"At the time of inspection",
"We would recommend",
])
structural_patterns: list[str] = field(default_factory=lambda: [
"Topic sentence followed by supporting detail and recommendation",
"Passive voice preferred for observations; active for recommendations",
])
writing_style_summary: str = (
"Formal UK RICS survey prose with measured technical vocabulary and professional hedging."
)
example_paragraphs: list[str] = field(default_factory=list)
def to_payload(self) -> dict:
return asdict(self)
DEFAULT_PROFILE = StyleProfile()
def _cache_path(tenant_id: str) -> Path:
return tenant_store.tenant_root(tenant_id) / "style_profile.json"
def _sample_reference_text(tenant_id: str, max_chars: int = 4800) -> str:
store = get_rag_store()
parts: list[str] = []
used = 0
for text in store.sample_chunk_texts(tenant_id, TIER_REFERENCE, limit=60):
if used + len(text) + 2 > max_chars:
break
parts.append(text)
used += len(text) + 2
return "\n\n".join(parts)
def _heuristic_profile(sample: str) -> StyleProfile:
if not sample.strip():
return DEFAULT_PROFILE
lower = sample.lower()
tone = "formal"
if "recommend" in lower or "advise" in lower:
tone = "semi-formal"
vocab = "technical"
if sample.count("£") + sample.count("mm") + sample.count("dpc") >= 3:
vocab = "specialist"
sentences = [s.strip() for s in sample.replace("\n", " ").split(".") if len(s.strip()) > 20]
avg_len = sum(len(s.split()) for s in sentences) / max(len(sentences), 1)
complexity = "moderate"
if avg_len > 22:
complexity = "complex"
elif avg_len < 14:
complexity = "simple"
examples = []
for block in sample.split("\n\n"):
words = block.split()
if 40 <= len(words) <= 120:
examples.append(block.strip())
if len(examples) >= 2:
break
return StyleProfile(
tone=tone,
avg_sentence_complexity=complexity,
vocabulary_level=vocab,
writing_style_summary=(
"Derived from uploaded past reports: measured UK surveyor voice with "
f"{complexity} sentences and {vocab} vocabulary."
),
example_paragraphs=examples,
)
def _llm_profile(sample: str) -> StyleProfile | None:
if not openai_client.is_available() or len(sample) < 200:
return None
prompt = f"""Analyse this UK RICS survey sample and return JSON with keys:
tone, formality_level, avg_sentence_complexity, vocabulary_level,
common_phrases (array), structural_patterns (array), writing_style_summary,
example_paragraphs (array of verbatim short extracts).
SAMPLE:
{sample[:4000]}"""
try:
raw = openai_client.chat_json(
[
{
"role": "system",
"content": "You analyse UK RICS report writing style. Output JSON only.",
},
{"role": "user", "content": prompt},
],
model=settings.mapping_model,
max_tokens=800,
)
return StyleProfile(
tone=str(raw.get("tone") or "formal"),
formality_level=str(raw.get("formality_level") or "professional"),
avg_sentence_complexity=str(raw.get("avg_sentence_complexity") or "moderate"),
vocabulary_level=str(raw.get("vocabulary_level") or "technical"),
common_phrases=list(raw.get("common_phrases") or DEFAULT_PROFILE.common_phrases),
structural_patterns=list(raw.get("structural_patterns") or DEFAULT_PROFILE.structural_patterns),
writing_style_summary=str(
raw.get("writing_style_summary") or DEFAULT_PROFILE.writing_style_summary
),
example_paragraphs=list(raw.get("example_paragraphs") or []),
)
except Exception as exc: # noqa: BLE001
logger.warning("LLM style profile failed (%s) — using heuristics", exc)
return None
def get_style_profile(tenant_id: str, *, force_refresh: bool = False) -> StyleProfile:
with _lock:
path = _cache_path(tenant_id)
if not force_refresh and path.is_file():
try:
data = json.loads(path.read_text(encoding="utf-8"))
return StyleProfile(**data)
except Exception: # noqa: BLE001
pass
sample = _sample_reference_text(tenant_id)
profile = _llm_profile(sample) or _heuristic_profile(sample)
path.write_text(json.dumps(profile.to_payload(), indent=2), encoding="utf-8")
return profile
def invalidate_style_profile(tenant_id: str) -> None:
path = _cache_path(tenant_id)
if path.is_file():
path.unlink(missing_ok=True)
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