fix yesterdays schedule blocking todays

core/adaptive_meds.py

@@ -468,6 +468,8 @@ def create_daily_schedule(user_uuid: str, med_id: str, base_times: List[str], re
     # Check recent med logs to skip doses already taken/skipped.
     # Handles cross-midnight: if adaptive offset shifts 23:00 → 00:42 today,
     # but the user already took the 23:00 dose last night, don't schedule it.
+    # Yesterday's logs only suppress if the scheduled_time is late-night
+    # (21:00+), since only those could plausibly cross midnight with an offset.
     user_tz = tz_for_user(user_uuid)
     yesterday = today - timedelta(days=1)
     recent_logs = postgres.select("med_logs", {"medication_id": med_id, "user_uuid": user_uuid})
@@ -481,11 +483,15 @@ def create_daily_schedule(user_uuid: str, med_id: str, base_times: List[str], re
         if created_at.tzinfo is None:
             created_at = created_at.replace(tzinfo=timezone.utc)
         log_date = created_at.astimezone(user_tz).date()
-        if log_date not in (today, yesterday):
+        if log_date == today:
-            continue
+            log_sched = _normalize_time(log.get("scheduled_time"))
-        log_sched = _normalize_time(log.get("scheduled_time"))
+            if log_sched:
-        if log_sched:
+                taken_base_times.add(log_sched)
-            taken_base_times.add(log_sched)
+        elif log_date == yesterday:
+            # Only suppress cross-midnight doses (late-night times like 21:00+)
+            log_sched = _normalize_time(log.get("scheduled_time"))
+            if log_sched and log_sched >= "21:00":
+                taken_base_times.add(log_sched)
 
     # Create schedule records for each time
     for base_time, (adjusted_time, offset) in zip(base_times, adjusted_times):

regenerate_embeddings.py

@@ -0,0 +1,56 @@
+#!/usr/bin/env python3
+"""Regenerate DBT embeddings with qwen/qwen3-embedding-8b model (384 dimensions)"""
+
+import json
+import os
+from openai import OpenAI
+import time
+
+# Load config
+with open("config.json", "r") as f:
+    config = json.load(f)
+
+# Initialize OpenAI client with OpenRouter
+client = OpenAI(
+    base_url="https://openrouter.ai/api/v1",
+    api_key=config["openrouter_api_key"],
+)
+
+# Load text data
+with open("bot/data/dbt_knowledge.text.json", "r") as f:
+    text_data = json.load(f)
+
+print(f"Regenerating embeddings for {len(text_data)} chunks...")
+
+# Generate embeddings
+embeddings_data = []
+for i, item in enumerate(text_data):
+    try:
+        response = client.embeddings.create(
+            model="qwen/qwen3-embedding-8b",
+            input=item["text"]
+        )
+        embedding = response.data[0].embedding
+        
+        embeddings_data.append({
+            "id": item["id"],
+            "source": item["source"],
+            "text": item["text"],
+            "embedding": embedding
+        })
+        
+        if (i + 1) % 10 == 0:
+            print(f"Processed {i + 1}/{len(text_data)} chunks...")
+            
+        # Small delay to avoid rate limits
+        time.sleep(0.1)
+        
+    except Exception as e:
+        print(f"Error processing item {i}: {e}")
+        continue
+
+# Save new embeddings
+with open("bot/data/dbt_knowledge.embeddings.json", "w") as f:
+    json.dump(embeddings_data, f)
+
+print(f"\nDone! Generated {len(embeddings_data)} embeddings with {len(embeddings_data[0]['embedding'])} dimensions")