Files
aramesh-backend/chatbot/embeddings.py
T
MMD ce0735d775 feat(stack): full overhaul — hardening, ops tooling, monitoring, analytics, chatbot
Security: deep /api/health (DB+MinIO probes, 503 on failure) + /api/live;
rate-limited public forms with honeypots; real client IP behind Traefik
(--proxy-headers); TipTap URL sanitization on write (nh3); non-root
containers + slim .dockerignore; prod MinIO console bound to 127.0.0.1;
HSTS/nosniff on TLS routers; JWT 8h; no default credentials in code.

Reliability: Alembic migrations (create_all baseline); graceful shutdown
(exec uvicorn); hardened backups (checksums, sanity check, freshness gauge,
optional rclone off-site) + restore.sh + hourly db-backup sidecar (ops
profile); stack.sh ops tooling (deploy/logs/health/backup/restore/monitoring);
.gitattributes LF for shell scripts.

Orchestration: json-file log rotation, pinned images, per-service memory
limits, edge/internal networks, Traefik ping/accesslog/metrics + flood
limiter, conditional minio-setup, pgvector db image, one-shot db-init
(umami + aramesh_chatbot databases).

Chatbot: unified from site-chatbot branch (RAG + pgvector, SSE, ChatWidget).

Monitoring: Prometheus/Alertmanager/Loki/Promtail/Grafana + cAdvisor,
node/postgres/blackbox exporters, provisioned dashboards and 10 alert
rules; alerts delivered to Bale (tapi.bale.ai — Telegram-compatible).

Analytics: Umami on shared Postgres, build-time tracker injection, custom
events (contact/newsletter/phone/chat/lang) via frontend/src/analytics.ts.

Hygiene: CSV export for contact messages and subscribers; upload image
optimization (Pillow); CI script + GitHub-compatible workflow; audit.sh;
gen-env.sh for strong credentials.
2026-09-19 00:24:57 +03:30

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Python

"""مدل امبدینگ — با لود تنبل تا اولین استفاده (برای تست‌های سبک، torch لود نمی‌شود)."""
import os
from functools import lru_cache
from .config import settings
@lru_cache(maxsize=1)
def get_embeddings():
from langchain_huggingface import HuggingFaceEmbeddings
os.makedirs(settings.hf_cache_dir, exist_ok=True)
return HuggingFaceEmbeddings(
model_name=settings.embedding_model,
model_kwargs={"device": "cpu"},
encode_kwargs={"normalize_embeddings": False},
cache_folder=settings.hf_cache_dir,
)