feat(automod): store semantic cache embeddings in Qdrant

New qdrantClient.ts (zero-dep fetch REST): ensure collection with cosine
distance (auto-recreate on vector-size change), upsert point w/ verdict
payload, search w/ expires_at filter + score threshold.

textCacheStore: when QDRANT_URL set, embeddings are upserted to Qdrant
(primary) and searched there first; Postgres embedding column remains as
legacy fallback for pre-Qdrant rows. Config: QDRANT_URL/COLLECTION/API_KEY.
QDRANT_URL already in repo .env; added to VPS env + GATEWAY_ENV secret.

.env.example

@@ -90,6 +90,9 @@ AI_LLM_MODEL=text                       # LLM text model name (default: text)
 # AI_LLM_VISION_MODEL=                   # Vision model for image analysis (falls back to AI_LLM_MODEL)
 # AI_LLM_EMBEDDING_MODEL=                # Embedding model for semantic moderation cache (optional; enables near-duplicate text reuse to save LLM calls)
 # AI_LLM_EMBEDDING_MIN_SIMILARITY=0.97   # Min cosine similarity to reuse a cached verdict (default: 0.97)
+# QDRANT_URL=http://100.121.180.82:6333/ # Qdrant vector store for embeddings (semantic cache); when set, vectors are stored/searched in Qdrant instead of Postgres
+# QDRANT_COLLECTION=gmw_text_moderation  # Qdrant collection name (default: gmw_text_moderation)
+# QDRANT_API_KEY=                        # Qdrant API key (optional)
 AI_LLM_MAX_CONCURRENT=5                 # Max concurrent LLM API calls (default: 5)
 AI_LLM_IMAGE_MAX_DIMENSION=1024         # Max image dimension in pixels before resize (default: 1024)
 AI_LLM_TEXT_BATCH_SIZE=20               # Max messages per text-only moderation batch (default: 20)