feat(blog): جدول — ستون‌ها قابل تغییر اندازه/افزودن و حذف؛ لینک کپشن تصویر؛ هدینگ‌های نسخه انگلیسی بزرگ‌تر
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aramesh-site deploy
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"""خواندن محتوای سایت از PostgreSQL و تبدیل آن به سندهای قابل ایندکس.
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هر سند یک dict با کلیدهای text / type / title / url است. بدنه مقاله‌ها و
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پروژه‌ها هم می‌تواند آرایه پاراگراف باشد و هم سند TipTap (JSON) — هر دو
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پشتیبانی می‌شوند. مقاله‌های پیش‌نویس ایندکس نمی‌شوند.
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"""
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import logging
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from typing import Any
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from .persian import normalize
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log = logging.getLogger(__name__)
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# URL پایه صفحات — چت‌بات مسیر نسبی می‌دهد؛ فرانت‌اند روی همان دامنه است
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_ARTICLE_URL = "/blog/{slug}"
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_PROJECT_URL = "/projects/{slug}"
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def _psycopg_dsn(database_url: str) -> str:
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"""DATABASE_URL فرمت SQLAlchemy (postgresql+psycopg://) را به DSN psycopg3 تبدیل می‌کند."""
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return database_url.replace("+psycopg", "").replace("+asyncpg", "")
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def _fetch_rows(dsn: str, sql: str) -> list[dict[str, Any]]:
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import psycopg
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from psycopg.rows import dict_row
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with psycopg.connect(dsn, row_factory=dict_row, connect_timeout=10) as conn:
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cur = conn.execute(sql)
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return cur.fetchall()
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def tiptap_blocks(node: Any) -> list[str]:
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"""متن بلوک‌به‌بلوک از سند TipTap (یا هر JSON تو در تو) بیرون می‌کشد."""
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if not isinstance(node, dict):
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return []
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ntype = node.get("type")
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if ntype in ("paragraph", "heading", "codeBlock"):
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text = _inline_text(node).strip()
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return [text] if text else []
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blocks: list[str] = []
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for child in node.get("content") or []:
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blocks.extend(tiptap_blocks(child))
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return blocks
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def _inline_text(node: dict) -> str:
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parts: list[str] = []
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for child in node.get("content") or []:
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ctype = child.get("type")
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if ctype == "text":
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parts.append(child.get("text", ""))
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elif ctype == "hardBreak":
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parts.append(" ")
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elif ctype == "image":
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alt = (child.get("attrs") or {}).get("alt") or ""
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if alt:
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parts.append(f"[تصویر: {alt}]")
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else:
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parts.append(_inline_text(child))
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return "".join(parts)
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def body_to_text(body: Any) -> str:
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"""بدنه مقاله/پروژه (آرایه پاراگراف یا سند TipTap) → متن یکپارچه."""
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if not body:
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return ""
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if isinstance(body, list):
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# فرمت قدیمی: آرایه رشته‌ای پاراگراف‌ها
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return "\n".join(str(p).strip() for p in body if str(p).strip())
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blocks = tiptap_blocks(body)
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return "\n".join(b for b in blocks if b)
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def _doc(text: str, dtype: str, title: str, url: str) -> dict | None:
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text = (text or "").strip()
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if not text:
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return None
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return {"text": text, "type": dtype, "title": title, "url": url}
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def _clean(value: Any) -> str:
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return normalize(str(value or "")).strip()
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def fetch_site_documents(database_url: str) -> list[dict]:
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"""همه محتوای عمومی سایت را به سند تبدیل می‌کند (بدون وابستگی به ORM بک‌اند)."""
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dsn = _psycopg_dsn(database_url)
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docs: list[dict] = []
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# --- برند و اطلاعات تماس (رکورد یگانه) ---
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brand_rows = _fetch_rows(dsn, "SELECT name, phone, email, address FROM brand LIMIT 1")
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if brand_rows:
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b = brand_rows[0]
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contact = "\n".join(
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filter(None, [
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f"نام مجموعه: {_clean(b['name'])}",
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f"تلفن تماس: {_clean(b['phone'])}",
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f"ایمیل: {_clean(b['email'])}",
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f"آدرس: {_clean(b['address'])}",
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])
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)
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d = _doc(contact, "contact", "اطلاعات تماس", "/contact")
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if d:
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docs.append(d)
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# --- دفاتر ---
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for row in _fetch_rows(dsn, "SELECT city, address, hours FROM offices ORDER BY position"):
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d = _doc(
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f"دفتر {_clean(row['city'])} — آدرس: {_clean(row['address'])} — ساعت کاری: {_clean(row['hours'])}",
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"office", f"دفتر {_clean(row['city'])}", "/contact",
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)
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if d:
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docs.append(d)
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# --- خدمات ---
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for row in _fetch_rows(dsn, 'SELECT title, "desc" FROM services ORDER BY position'):
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d = _doc(
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f"خدمت «{_clean(row['title'])}»: {_clean(row['desc'])}",
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"service", _clean(row["title"]), "/services",
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)
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if d:
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docs.append(d)
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# --- پرسش‌های متداول ---
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for row in _fetch_rows(dsn, "SELECT question, answer FROM faqs ORDER BY position"):
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d = _doc(
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f"پرسش: {_clean(row['question'])}\nپاسخ: {_clean(row['answer'])}",
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"faq", _clean(row["question"]), "/faq",
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)
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if d:
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docs.append(d)
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# --- پروژه‌ها ---
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for row in _fetch_rows(
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dsn,
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"SELECT slug, title, location, year, status, area, floors, units, client, summary, body FROM projects ORDER BY position",
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):
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meta = "\n".join(filter(None, [
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f"مکان: {_clean(row['location'])}" if row["location"] else "",
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f"سال: {_clean(row['year'])}" if row["year"] else "",
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f"وضعیت: {'در حال ساخت' if row['status'] == 'progress' else 'تکمیل‌شده'}",
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f"متراژ: {_clean(row['area'])}" if row["area"] else "",
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f"طبقات: {_clean(row['floors'])}" if row["floors"] else "",
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f"واحدها: {_clean(row['units'])}" if row["units"] else "",
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f"کارفرما: {_clean(row['client'])}" if row["client"] else "",
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]))
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text = "\n".join(filter(None, [meta, _clean(row["summary"]), body_to_text(row["body"])]))
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d = _doc(text, "project", _clean(row["title"]), _PROJECT_URL.format(slug=row["slug"]))
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if d:
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docs.append(d)
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# --- مقاله‌ها (فقط منتشرشده) ---
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for row in _fetch_rows(
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dsn,
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"SELECT slug, title, date, category, excerpt, body FROM articles WHERE status = 'published' ORDER BY position",
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):
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text = "\n".join(filter(None, [
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f"دسته‌بندی: {_clean(row['category'])}" if row["category"] else "",
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_clean(row["excerpt"]),
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body_to_text(row["body"]),
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]))
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d = _doc(text, "article", _clean(row["title"]), _ARTICLE_URL.format(slug=row["slug"]))
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if d:
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docs.append(d)
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# --- مدل‌های سرمایه‌گذاری ---
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for row in _fetch_rows(dsn, 'SELECT title, "desc", points FROM investment_models ORDER BY position'):
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points = "؛ ".join(_clean(p) for p in (row["points"] or []))
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d = _doc(
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"\n".join(filter(None, [
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f"«{_clean(row['title'])}»: {_clean(row['desc'])}",
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f"نکات: {points}" if points else "",
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])),
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"investment", _clean(row["title"]), "/investment",
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)
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if d:
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docs.append(d)
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# --- مراحل همکاری ---
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for row in _fetch_rows(dsn, 'SELECT step, title, "desc" FROM process_steps ORDER BY position'):
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d = _doc(
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f"مرحله {_clean(row['step'])} — {_clean(row['title'])}: {_clean(row['desc'])}",
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"process", _clean(row["title"]), "/investment",
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)
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if d:
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docs.append(d)
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# --- چرا ما / تیم / نظرسنجی / تایم‌لاین / داستان ما ---
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for row in _fetch_rows(dsn, 'SELECT title, "desc" FROM why_us ORDER BY position'):
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d = _doc(f"{_clean(row['title'])}: {_clean(row['desc'])}", "about", _clean(row["title"]), "/about")
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if d:
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docs.append(d)
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for row in _fetch_rows(dsn, "SELECT name, role, bio FROM team ORDER BY position"):
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d = _doc(
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f"{_clean(row['name'])} — {_clean(row['role'])}. {_clean(row['bio'])}",
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"team", _clean(row["name"]), "/about",
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)
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if d:
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docs.append(d)
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for row in _fetch_rows(dsn, "SELECT name, role, quote FROM testimonials ORDER BY position"):
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d = _doc(
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f"نظر {_clean(row['name'])} ({_clean(row['role'])}): {_clean(row['quote'])}",
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"testimonial", _clean(row["name"]), "/",
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)
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if d:
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docs.append(d)
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for row in _fetch_rows(dsn, 'SELECT year, title, "desc" FROM timeline ORDER BY position'):
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d = _doc(
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f"سال {_clean(row['year'])} — {_clean(row['title'])}: {_clean(row['desc'])}",
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"timeline", _clean(row["title"]), "/about",
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)
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if d:
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docs.append(d)
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story_rows = _fetch_rows(dsn, "SELECT title, paragraphs, quote FROM story LIMIT 1")
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if story_rows and story_rows[0]:
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s = story_rows[0]
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paragraphs = "\n".join(_clean(p) for p in (s["paragraphs"] or []) if _clean(p))
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text = "\n".join(filter(None, [_clean(s["title"]), paragraphs, _clean(s["quote"])]))
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d = _doc(text, "about", _clean(s["title"]) or "درباره ما", "/about")
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if d:
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docs.append(d)
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log.info("Fetched %d documents from site database", len(docs))
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return docs
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def chunk_documents(docs: list[dict], chunk_size: int = 700, overlap: int = 120) -> list[dict]:
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"""متن‌های بلند (مقاله/پروژه) را به قطعات هم‌پوشان تقسیم می‌کند.
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هر قطعه یک doc_id پایدار می‌گیرد («type:ترتیب در همان url:شماره قطعه») تا
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همگام‌سازی افزایشی pgvector بتواند فقط سطرهای تغییر یافته را امبد کند.
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"""
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from langchain_text_splitters import RecursiveCharacterTextSplitter
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from .vectorstore import chunk_content_hash
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splitter = RecursiveCharacterTextSplitter(
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chunk_size=chunk_size,
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chunk_overlap=overlap,
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separators=["\n", "۔", ".", "،", " ", ""],
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)
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# شماره ردیف هر سند در میان سندهای همtype و هم-url (برای FAQs که url یکسان دارند)
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occurrence: dict[str, int] = {}
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out: list[dict] = []
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for doc in docs:
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key = f"{doc['type']}|{doc['url']}"
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occ = occurrence.get(key, 0)
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occurrence[key] = occ + 1
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pieces = splitter.split_text(doc["text"]) if len(doc["text"]) > chunk_size * 1.3 else [doc["text"]]
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for i, piece in enumerate(pieces, 1):
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chunk = {
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"doc_id": f"{doc['type']}:{occ}:{i}",
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"text": piece if len(pieces) == 1 else f"[{doc['title']} — بخش {i} از {len(pieces)}]\n{piece}",
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"type": doc["type"],
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"title": doc["title"],
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"url": doc["url"],
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}
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chunk["content_hash"] = chunk_content_hash(chunk)
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out.append(chunk)
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log.info("Chunked %d documents into %d pieces", len(docs), len(out))
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return out
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def content_fingerprint(docs: list[dict]) -> str:
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"""هش کل محتوا — فقط برای لاگ؛ تشخیص تغییر ردیفی در vectorstore انجام می‌شود."""
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import hashlib
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import json
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payload = json.dumps(docs, ensure_ascii=False, sort_keys=True)
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return hashlib.sha256(payload.encode("utf-8")).hexdigest()[:16]
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# --- زمینه همیشه‌تازه سایت (بلوک کوتاه تماس/خدمات برای پرامپت) ---
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_site_context_cache: tuple[float, str] | None = None
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def fetch_site_context(database_url: str, ttl: int = 120) -> str:
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"""نام برند، تماس و فهرست خدمات — با کش کوتاه‌مدت تا هر پیام یک کوئری سبک بزند."""
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global _site_context_cache
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import time as _time
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now = _time.time()
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if _site_context_cache and now - _site_context_cache[0] < ttl:
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return _site_context_cache[1]
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try:
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dsn = _psycopg_dsn(database_url)
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brand = _fetch_rows(dsn, "SELECT name, phone, email, address FROM brand LIMIT 1")
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services = _fetch_rows(dsn, "SELECT title FROM services ORDER BY position")
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lines: list[str] = []
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if brand:
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b = brand[0]
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lines += [
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f"نام مجموعه: {_clean(b['name'])}",
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f"تلفن تماس: {_clean(b['phone'])}",
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f"ایمیل: {_clean(b['email'])}",
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f"آدرس: {_clean(b['address'])}",
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]
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if services:
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lines.append("خدمات سایت: " + "، ".join(_clean(s["title"]) for s in services))
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ctx = "\n".join(lines)
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_site_context_cache = (now, ctx)
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return ctx
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except Exception as e:
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log.warning("Site context fetch failed: %s", e)
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return _site_context_cache[1] if _site_context_cache else ""
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Reference in New Issue
Block a user