default-config.yaml

@@ -35,7 +35,7 @@ llm:
         - name     : "z-ai/glm-5"
 
 ragroleplay:
-  db_path      : "~/.config/hendrik/ragroleplay"
+  db_path      : "./.ragroleplay"
   vector_size  : 768 # used on table create only
   model_url    : "http://localhost:11434/api/embed"
   model_name   : "nomic-embed-text" # Need to download model from local ollama
@@ -64,4 +64,4 @@ delay: # Humanize Delay (anti-bot detection)
   typing_max    : 10.0 # max typing delay limit per second
 
 user:
-  id: # Fill your user UUID here
+  id: "YOUR_USER_ID_HERE"

interfaces/tui/agent.py

@@ -130,30 +130,6 @@ def _agent_loop(app):
         if response.warning:
             log(app, "system", f"  {response.warning}")
 
-        # Cek apakah response adalah error dari LLM client
-        content = response.content
-        is_error = isinstance(content, str) and (content.startswith("Error:") or content.startswith("HTTP Error:"))
-
-        if is_error:
-            # Hapus "Thinking..." log dan placeholder
-            for i in range(len(app.log) - 1, -1, -1):
-                if app.log[i].get('role') == 'system' and 'Thinking' in app.log[i].get('text', ''):
-                    app.log.pop(i)
-                    break
-            if placeholder_marker is not None:
-                for i in range(len(app.log) - 1, -1, -1):
-                    if app.log[i].get('role') == 'ai':
-                        text = app.log[i].get('text', '')
-                        if placeholder_marker in text or text == "" or text == "...":
-                            app.log.pop(i)
-                            break
-            log(app, "error", content)
-            _add_msg(app, "assistant", content)
-            log(app, "sep", "")
-            ntro.end(stamp)
-            app.agent_done.set()
-            return
-
         # Cek apakah ada tool_calls
         has_tool_calls = bool(response.tool_calls)
 

lib/ragbusiness.py

@@ -1,84 +0,0 @@
-import requests, gc, lancedb, pyarrow, uuid
-from datetime import datetime
-
-def _schema_domainlogic(vector_size):
-    return pyarrow.schema([
-        pyarrow.field('timestamp',          pyarrow.timestamp('ms'),                       metadata={'description': 'When record created'}),
-        pyarrow.field('content',            pyarrow.string(),                              metadata={'description': 'Content'}),
-        pyarrow.field('category',           pyarrow.string(),                              metadata={'description': 'Category. Comma separated.'}),
-        pyarrow.field('vector_context',     pyarrow.list_(pyarrow.float32(), vector_size), metadata={'description': 'Vector data of combined data'}),
-    ])
-
-_TABLE_SCHEMAS = {
-    'knowledge_domainlogic':  _schema_domainlogic,
-}
-
-def db_init(db_path, vector_size):
-    db        = lancedb.connect(db_path)
-    existing  = db.table_names()
-    for name, schema_fn in _TABLE_SCHEMAS.items():
-        if name not in existing:
-            db.create_table(name, schema=schema_fn(vector_size))
-            print(f"[ragroleplay] Created table: {name}", flush=True)
-    del db
-    gc.collect()
-    
-def table_ensure(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()
-
-def _uuid(val):
-    return uuid.UUID(val) if isinstance(val, str) else val
-
-def embed_text(url, model, text):
-    response  = requests.post(url=url, json={"model": model, "input": text} )
-    data      = response.json()
-    return data["embeddings"][0]
-
-def domainlogic_store(db_path, model_url, model_name, payload):
-    try:
-        db      = lancedb.connect(db_path)
-        table   = db.open_table("knowledge_domainlogic")
-        
-        vector  = embed_text(model_url, model_name, f'Category: {payload["category"]}. \n{payload["content"]}')
-        
-        record  = {
-            "timestamp"       : datetime.now(),
-            "content"         : payload["content"   ],
-            "category"        : payload["category"  ],
-            "vector_context"  : vector
-        }
-        
-        table.add([record])
-        del table
-        del db
-        gc.collect()
-        
-        return "success"
-    
-    except Exception as e:
-        return e
-
-def domainlogic_load(db_path, payload):
-    try:
-        db       = lancedb.connect(db_path)
-        table    = db.open_table("knowledge_domainlogic")
-        
-        results  = table.search().to_list()
-        
-        del table
-        del db
-        gc.collect()
-        
-        return results
-    
-    except Exception as e:
-        print(f"error: {e}")
-        return []
-

lib/ragroleplay.py

@@ -115,8 +115,11 @@ def ensure_table(db_path, table_name, vector_size): # table_ensure seharusnya
     del db
     gc.collect()
 
+
 def _uuid(val):
-    return uuid.UUID(val) if isinstance(val, str) else val
+    if isinstance(val, str):
+        return uuid.UUID(val)
+    return val
 
 def embed_text(url, model, text):
     response  = requests.post(url=url, json={"model": model, "input": text} )