feat: auto table creation (lazy) for ragroleplay

- Refactor create_table() to use per-table schema functions
- Add _TABLE_SCHEMAS dict mapping table names to schema factories
- Add ensure_table() for lazy table creation on first access
- Tables auto-create if missing when any CRUD operation is called

lib/ragroleplay.py

@@ -1,10 +1,11 @@
 import requests, gc, lancedb, pyarrow, uuid
 from datetime import datetime
 
-def create_table(db_path, vector_size):
-    db = lancedb.connect(db_path)
 
-    schema_user = pyarrow.schema([
+# ─── Table Schemas ────────────────────────────────────────────────────────────
+
+def _schema_user(vector_size):
+    return pyarrow.schema([
         pyarrow.field('id',                 pyarrow.uuid(),                                metadata={'description': 'Unique identifier (UUID)'}),
         pyarrow.field('timestamp',          pyarrow.timestamp('ms'),                       metadata={'description': 'When record created'}),
         pyarrow.field('fullname',           pyarrow.string(),                              metadata={'description': 'Fullname'}),
@@ -16,9 +17,9 @@ def create_table(db_path, vector_size):
         pyarrow.field('xampp_username',     pyarrow.string(), nullable=True,               metadata={'description': 'XAMPP username'}),
         pyarrow.field('vector_fullname',    pyarrow.list_(pyarrow.float32(), vector_size), metadata={'description': 'Vector data of fullname'}),
     ])
-    db.create_table("knowledge_user", schema=schema_user)
+
-    
+def _schema_memories(vector_size):
-    schema_memories = pyarrow.schema([
+    return pyarrow.schema([
         pyarrow.field('id',                 pyarrow.uuid(),                                metadata={'description': 'Unique identifier (UUID)'}),
         pyarrow.field('timestamp',          pyarrow.timestamp('ms'),                       metadata={'description': 'When record created'}),
         pyarrow.field('character',          pyarrow.string(),                              metadata={'description': 'Active Character'}),
@@ -30,9 +31,9 @@ def create_table(db_path, vector_size):
         pyarrow.field('emotional',          pyarrow.string(),                              metadata={'description': 'Character emotional state'}),
         pyarrow.field('vector_context',     pyarrow.list_(pyarrow.float32(), vector_size), metadata={'description': 'Vector data of combined data'}),
     ])
-    db.create_table("knowledge_memories", schema=schema_memories)
+
-    
+def _schema_scenario(vector_size):
-    schema_scenario = pyarrow.schema([
+    return pyarrow.schema([
         pyarrow.field('id',                 pyarrow.uuid(),                                metadata={'description': 'Unique identifier (UUID)'}),
         pyarrow.field('timestamp',          pyarrow.timestamp('ms'),                       metadata={'description': 'When record created'}),
         pyarrow.field('character',          pyarrow.string(),                              metadata={'description': 'Active Character'}),
@@ -40,9 +41,9 @@ def create_table(db_path, vector_size):
         pyarrow.field('scenario',           pyarrow.string(),                              metadata={'description': 'Detailed scenario description'}),
         pyarrow.field('vector_context',     pyarrow.list_(pyarrow.float32(), vector_size), metadata={'description': 'Vector data of scenario'}),
     ])
-    db.create_table("knowledge_scenario", schema=schema_scenario)
+
-    
+def _schema_todo(vector_size):
-    schema_todo = pyarrow.schema([
+    return pyarrow.schema([
         pyarrow.field('id',                 pyarrow.uuid(),                                metadata={'description': 'Unique identifier (UUID)'}),
         pyarrow.field('timestamp',          pyarrow.timestamp('ms'),                       metadata={'description': 'When record created'}),
         pyarrow.field('keyword',            pyarrow.string(),                              metadata={'description': 'Keywords to trigger to-do'}),
@@ -50,9 +51,9 @@ def create_table(db_path, vector_size):
         pyarrow.field('do',                 pyarrow.string(),                              metadata={'description': 'Do'}),
         pyarrow.field('vector_context',     pyarrow.list_(pyarrow.float32(), vector_size), metadata={'description': 'Vector data of keyword+when'}),
     ])
-    db.create_table("knowledge_todo", schema=schema_todo)
+
-    
+def _schema_outfit_set(vector_size):
-    schema_outfit_set = pyarrow.schema([
+    return pyarrow.schema([
         pyarrow.field('id',                 pyarrow.uuid(),                                metadata={'description': 'Unique identifier (UUID)'}),
         pyarrow.field('timestamp',          pyarrow.timestamp('ms'),                       metadata={'description': 'When record created'}),
         pyarrow.field('keyword',            pyarrow.string(),                              metadata={'description': 'Keywords of outfit set'}),
@@ -60,9 +61,9 @@ def create_table(db_path, vector_size):
         pyarrow.field('outfit',             pyarrow.string(),                              metadata={'description': 'Outfit set description'}),
         pyarrow.field('vector_context',     pyarrow.list_(pyarrow.float32(), vector_size), metadata={'description': 'Vector data of keyword+when'}),
     ])
-    db.create_table("knowledge_outfit_set", schema=schema_outfit_set)
+
-    
+def _schema_world(vector_size):
-    schema_world = pyarrow.schema([
+    return pyarrow.schema([
         pyarrow.field('id',                 pyarrow.uuid(),                                metadata={'description': 'Unique identifier (UUID)'}),
         pyarrow.field('timestamp',          pyarrow.timestamp('ms'),                       metadata={'description': 'When record created'}),
         pyarrow.field('category',           pyarrow.string(),                              metadata={'description': 'Category of world'}),
@@ -70,9 +71,9 @@ def create_table(db_path, vector_size):
         pyarrow.field('description',        pyarrow.string(),                              metadata={'description': 'Detailed world description'}),
         pyarrow.field('vector_context',     pyarrow.list_(pyarrow.float32(), vector_size), metadata={'description': 'Vector data of combined content'}),
     ])
-    db.create_table("knowledge_world", schema=schema_world)
+
-    
+def _schema_object(vector_size):
-    schema_object = pyarrow.schema([
+    return pyarrow.schema([
         pyarrow.field('id',                 pyarrow.uuid(),                                metadata={'description': 'Unique identifier (UUID)'}),
         pyarrow.field('timestamp',          pyarrow.timestamp('ms'),                       metadata={'description': 'When record created'}),
         pyarrow.field('keyword',            pyarrow.string(),                              metadata={'description': 'Keywords of object'}),
@@ -84,11 +85,36 @@ def create_table(db_path, vector_size):
         pyarrow.field('usage',              pyarrow.string(),                              metadata={'description': 'Object usage'}),
         pyarrow.field('vector_context',     pyarrow.list_(pyarrow.float32(), vector_size), metadata={'description': 'Vector data of keyword+when'}),
     ])
-    db.create_table("knowledge_object", schema=schema_object)
 
-    # parameters: eat, drink, sleep
+
-    # Time: morning, afternoon, evening, night
+_TABLE_SCHEMAS = {
-    
+    'knowledge_user':        _schema_user,
+    'knowledge_memories':    _schema_memories,
+    'knowledge_scenario':    _schema_scenario,
+    'knowledge_todo':        _schema_todo,
+    'knowledge_outfit_set':  _schema_outfit_set,
+    'knowledge_world':       _schema_world,
+    'knowledge_object':      _schema_object,
+}
+
+
+# ─── Table Management ─────────────────────────────────────────────────────────
+
+def create_table(db_path, vector_size):
+    db = lancedb.connect(db_path)
+    for name, schema_fn in _TABLE_SCHEMAS.items():
+        db.create_table(name, schema=schema_fn(vector_size))
+    del db
+    gc.collect()
+
+
+def ensure_table(db_path, table_name, vector_size):
+    db = lancedb.connect(db_path)
+    existing = db.table_names()
+    if table_name not in existing:
+        schema_fn = _TABLE_SCHEMAS.get(table_name)
+        if schema_fn:
+            db.create_table(table_name, schema=schema_fn(vector_size))
     del db
     gc.collect()