chore: Add pylint for DB-GPT core lib (#1076)

dbgpt/app/base.py

@@ -192,7 +192,8 @@ def _create_mysql_database(db_name: str, db_url: str, try_to_create_db: bool = F
                 with engine_no_db.connect() as conn:
                     conn.execute(
                         DDL(
-                            f"CREATE DATABASE {db_name} CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_ci"
+                            f"CREATE DATABASE {db_name} CHARACTER SET utf8mb4 COLLATE "
+                            f"utf8mb4_unicode_ci"
                         )
                     )
                     logger.info(f"Database {db_name} successfully created")
@@ -218,26 +219,31 @@ class WebServerParameters(BaseParameters):
     controller_addr: Optional[str] = field(
         default=None,
         metadata={
-            "help": "The Model controller address to connect. If None, read model controller address from environment key `MODEL_SERVER`."
+            "help": "The Model controller address to connect. If None, read model "
+            "controller address from environment key `MODEL_SERVER`."
         },
     )
     model_name: str = field(
         default=None,
         metadata={
-            "help": "The default model name to use. If None, read model name from environment key `LLM_MODEL`.",
+            "help": "The default model name to use. If None, read model name from "
+            "environment key `LLM_MODEL`.",
             "tags": "fixed",
         },
     )
     share: Optional[bool] = field(
         default=False,
         metadata={
-            "help": "Whether to create a publicly shareable link for the interface. Creates an SSH tunnel to make your UI accessible from anywhere. "
+            "help": "Whether to create a publicly shareable link for the interface. "
+            "Creates an SSH tunnel to make your UI accessible from anywhere. "
         },
     )
     remote_embedding: Optional[bool] = field(
         default=False,
         metadata={
-            "help": "Whether to enable remote embedding models. If it is True, you need to start a embedding model through `dbgpt start worker --worker_type text2vec --model_name xxx --model_path xxx`"
+            "help": "Whether to enable remote embedding models. If it is True, you need"
+            " to start a embedding model through `dbgpt start worker --worker_type "
+            "text2vec --model_name xxx --model_path xxx`"
         },
     )
     log_level: Optional[str] = field(
@@ -286,3 +292,10 @@ class WebServerParameters(BaseParameters):
             "help": "The directories to search awel files, split by `,`",
         },
     )
+    default_thread_pool_size: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "The default thread pool size, If None, "
+            "use default config of python thread pool",
+        },
+    )

dbgpt/app/component_configs.py

@@ -25,7 +25,9 @@ def initialize_components(
     from dbgpt.model.cluster.controller.controller import controller
 
     # Register global default executor factory first
-    system_app.register(DefaultExecutorFactory)
+    system_app.register(
+        DefaultExecutorFactory, max_workers=param.default_thread_pool_size
+    )
     system_app.register_instance(controller)
 
     from dbgpt.serve.agent.hub.controller import module_agent

dbgpt/app/dbgpt_server.py

@@ -3,8 +3,6 @@ import os
 import sys
 from typing import List
 
-ROOT_PATH = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-sys.path.append(ROOT_PATH)
 from fastapi import FastAPI
 from fastapi.exceptions import RequestValidationError
 from fastapi.middleware.cors import CORSMiddleware
@@ -41,6 +39,10 @@ from dbgpt.util.utils import (
     setup_logging,
 )
 
+ROOT_PATH = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+sys.path.append(ROOT_PATH)
+
+
 static_file_path = os.path.join(ROOT_PATH, "dbgpt", "app/static")
 
 CFG = Config()

dbgpt/app/knowledge/_cli/knowledge_client.py

@@ -5,6 +5,7 @@ from concurrent.futures import ThreadPoolExecutor, as_completed
 from urllib.parse import urljoin
 
 import requests
+from prettytable import PrettyTable
 
 from dbgpt.app.knowledge.request.request import (
     ChunkQueryRequest,
@@ -193,9 +194,6 @@ def knowledge_init(
             return
 
 
-from prettytable import PrettyTable
-
-
 class _KnowledgeVisualizer:
     def __init__(self, api_address: str, out_format: str):
         self.client = KnowledgeApiClient(api_address)

dbgpt/app/llmserver.py

@@ -4,13 +4,14 @@
 import os
 import sys
 
-ROOT_PATH = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-sys.path.append(ROOT_PATH)
-
 from dbgpt._private.config import Config
 from dbgpt.configs.model_config import EMBEDDING_MODEL_CONFIG, LLM_MODEL_CONFIG
 from dbgpt.model.cluster import run_worker_manager
 
+ROOT_PATH = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+sys.path.append(ROOT_PATH)
+
+
 CFG = Config()
 
 model_path = LLM_MODEL_CONFIG.get(CFG.LLM_MODEL)

dbgpt/app/scene/base_chat.py

@@ -313,8 +313,9 @@ class BaseChat(ABC):
             )
             ### store current conversation
             span.end(metadata={"error": str(e)})
-        # self.memory.append(self.current_message)
-        self.current_message.end_current_round()
+        await blocking_func_to_async(
+            self._executor, self.current_message.end_current_round
+        )
 
     async def nostream_call(self):
         payload = await self._build_model_request()
@@ -381,8 +382,9 @@ class BaseChat(ABC):
             )
             span.end(metadata={"error": str(e)})
         ### store dialogue
-        # self.memory.append(self.current_message)
-        self.current_message.end_current_round()
+        await blocking_func_to_async(
+            self._executor, self.current_message.end_current_round
+        )
         return self.current_ai_response()
 
     async def get_llm_response(self):