adjust project content

pilot/agent/__init__.py

@@ -0,0 +1,2 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-

pilot/agent/agent.py

@@ -0,0 +1,7 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+
+
+class Agent:
+    """Agent class for interacting with DB-GPT """
+    pass

pilot/agent/agent_manager.py

@@ -0,0 +1,23 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+
+from pilot.singleton import Singleton
+
+class AgentManager(metaclass=Singleton):
+    """Agent manager for managing DB-GPT agents"""
+    def __init__(self) -> None:
+        
+        self.agents = {} #TODO need to define
+
+    def create_agent(self):
+        pass
+
+    def message_agent(self):
+        pass
+
+    def list_agents(self):
+        pass
+
+    def delete_agent(self):
+        pass
+

pilot/client/auto.py

@@ -1,3 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-

pilot/client/chart.py

@@ -1,2 +0,0 @@
-#/usr/bin/env python3
-# -*- coding: utf-8 -*-

pilot/configs/config.py

@@ -0,0 +1,12 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+from auto_gpt_plugin_template import AutoGPTPluginTemplate
+from pilot.singleton import Singleton
+
+class Config(metaclass=Singleton):
+    """Configuration class to store the state of bools for different scripts access"""
+    def __init__(self) -> None:
+        """Initialize the Config class"""
+        pass
+

pilot/model/loader.py

@@ -10,7 +10,12 @@ from transformers import (
 
 from fastchat.serve.compression import compress_module
 
-class ModerLoader:
+class ModelLoader:
+    """Model loader is a class for model load
+    
+      Args: model_path
+     
+    """
 
     kwargs = {}
 

pilot/server/vicuna_server.py

@@ -13,7 +13,7 @@ from pilot.model.inference import generate_output, get_embeddings
 from fastchat.serve.inference import load_model
 
 
-from pilot.model.loader import ModerLoader
+from pilot.model.loader import ModelLoader
 from pilot.configs.model_config import *
 
 model_path = LLM_MODEL_CONFIG[LLM_MODEL] 
@@ -22,7 +22,7 @@ model_path = LLM_MODEL_CONFIG[LLM_MODEL]
 global_counter = 0
 model_semaphore = None
 
-ml = ModerLoader(model_path=model_path)
+ml = ModelLoader(model_path=model_path)
 model, tokenizer = ml.loader(num_gpus=1, load_8bit=ISLOAD_8BIT, debug=ISDEBUG)
 #model, tokenizer = load_model(model_path=model_path, device=DEVICE, num_gpus=1, load_8bit=True, debug=False)
 

pilot/singleton.py

@@ -0,0 +1,21 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+"""The singleton metaclass for ensuring only one instance of a class."""
+import abc
+from typing import Any
+
+class Singleton(abc.ABCMeta, type):
+    """ Singleton metaclass for ensuring only one instance of a class"""
+
+    _instances = {}
+    def __call__(cls, *args: Any, **kwargs: Any) -> Any:
+        """Call method for the singleton metaclass"""
+        if cls not in cls._instances:
+            cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs)
+        return cls._instances[cls]
+
+
+class AbstractSingleton(abc.ABC, metaclass=Singleton):
+    """Abstract singleton class for ensuring only one instance of a class"""
+    pass

pilot/vector_store/file_loader.py

@@ -15,6 +15,20 @@ from pilot.configs.model_config import VECTORE_PATH, DATASETS_DIR, LLM_MODEL_CON
 
 class KnownLedge2Vector:
 
+    """KnownLedge2Vector class is order to load document to vector 
+    and persist to vector store.
+        
+        Args: 
+           - model_name
+
+        Usage:
+            k2v = KnownLedge2Vector()
+            persist_dir = os.path.join(VECTORE_PATH, ".vectordb") 
+            print(persist_dir)
+            for s, dc in k2v.query("what is oceanbase?"):
+                print(s, dc.page_content, dc.metadata)
+
+    """
     embeddings: object = None 
     model_name = LLM_MODEL_CONFIG["sentence-transforms"]
     top_k: int = VECTOR_SEARCH_TOP_K
@@ -81,11 +95,4 @@ class KnownLedge2Vector:
             dc, s = doc
             yield s, dc
 
-if __name__ == "__main__":
-    k2v = KnownLedge2Vector()
-
-    persist_dir = os.path.join(VECTORE_PATH, ".vectordb") 
-    print(persist_dir)
-    for s, dc in k2v.query("什么是OceanBase"):
-        print(s, dc.page_content, dc.metadata)