mirror of
https://github.com/csunny/DB-GPT.git
synced 2025-07-26 21:37:40 +00:00
471689ba20
Co-authored-by: Fangyin Cheng <staneyffer@gmail.com> Co-authored-by: 谨欣 <echo.cmy@antgroup.com> Co-authored-by: 严志勇 <yanzhiyong@tiansuixiansheng.com> Co-authored-by: yanzhiyong <932374019@qq.com>
342 lines
15 KiB
Python
342 lines
15 KiB
Python
#!/usr/bin/env python3
|
|
# -*- coding: utf-8 -*-
|
|
from __future__ import annotations
|
|
|
|
import os
|
|
from typing import TYPE_CHECKING, Optional
|
|
|
|
from dbgpt.util.singleton import Singleton
|
|
|
|
if TYPE_CHECKING:
|
|
from dbgpt.component import SystemApp
|
|
from dbgpt.datasource.manages import ConnectorManager
|
|
|
|
|
|
class Config(metaclass=Singleton):
|
|
"""Configuration class to store the state of bools for different scripts access"""
|
|
|
|
def __init__(self) -> None:
|
|
"""Initialize the Config class"""
|
|
|
|
self.NEW_SERVER_MODE = False
|
|
self.SERVER_LIGHT_MODE = False
|
|
|
|
# Gradio language version: en, zh
|
|
self.LANGUAGE = os.getenv("LANGUAGE", "en")
|
|
self.DBGPT_WEBSERVER_PORT = int(os.getenv("DBGPT_WEBSERVER_PORT", 5670))
|
|
|
|
self.debug_mode = False
|
|
self.skip_reprompt = False
|
|
self.temperature = float(os.getenv("TEMPERATURE", 0.7))
|
|
|
|
# self.NUM_GPUS = int(os.getenv("NUM_GPUS", 1))
|
|
|
|
self.execute_local_commands = (
|
|
os.getenv("EXECUTE_LOCAL_COMMANDS", "False").lower() == "true"
|
|
)
|
|
# User agent header to use when making HTTP requests
|
|
# Some websites might just completely deny request with an error code if
|
|
# no user agent was found.
|
|
self.user_agent = os.getenv(
|
|
"USER_AGENT",
|
|
"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_4) AppleWebKit/537.36"
|
|
" (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36",
|
|
)
|
|
|
|
# This is a proxy server, just for test_py. we will remove this later.
|
|
self.proxy_api_key = os.getenv("PROXY_API_KEY")
|
|
self.bard_proxy_api_key = os.getenv("BARD_PROXY_API_KEY")
|
|
|
|
# In order to be compatible with the new and old model parameter design
|
|
if self.bard_proxy_api_key:
|
|
os.environ["bard_proxyllm_proxy_api_key"] = self.bard_proxy_api_key
|
|
|
|
# tongyi
|
|
self.tongyi_proxy_api_key = os.getenv("TONGYI_PROXY_API_KEY")
|
|
if self.tongyi_proxy_api_key:
|
|
os.environ["tongyi_proxyllm_proxy_api_key"] = self.tongyi_proxy_api_key
|
|
|
|
# zhipu
|
|
self.zhipu_proxy_api_key = os.getenv("ZHIPU_PROXY_API_KEY")
|
|
if self.zhipu_proxy_api_key:
|
|
os.environ["zhipu_proxyllm_proxy_api_key"] = self.zhipu_proxy_api_key
|
|
os.environ["zhipu_proxyllm_proxyllm_backend"] = os.getenv(
|
|
"ZHIPU_MODEL_VERSION", ""
|
|
)
|
|
|
|
# wenxin
|
|
self.wenxin_proxy_api_key = os.getenv("WEN_XIN_API_KEY")
|
|
self.wenxin_proxy_api_secret = os.getenv("WEN_XIN_API_SECRET")
|
|
self.wenxin_model_version = os.getenv("WEN_XIN_MODEL_VERSION")
|
|
if self.wenxin_proxy_api_key and self.wenxin_proxy_api_secret:
|
|
os.environ["wenxin_proxyllm_proxy_api_key"] = self.wenxin_proxy_api_key
|
|
os.environ[
|
|
"wenxin_proxyllm_proxy_api_secret"
|
|
] = self.wenxin_proxy_api_secret
|
|
os.environ["wenxin_proxyllm_proxyllm_backend"] = (
|
|
self.wenxin_model_version or ""
|
|
)
|
|
|
|
# xunfei spark
|
|
self.spark_api_version = os.getenv("XUNFEI_SPARK_API_VERSION")
|
|
self.spark_proxy_api_key = os.getenv("XUNFEI_SPARK_API_KEY")
|
|
self.spark_proxy_api_secret = os.getenv("XUNFEI_SPARK_API_SECRET")
|
|
self.spark_proxy_api_appid = os.getenv("XUNFEI_SPARK_APPID")
|
|
if self.spark_proxy_api_key and self.spark_proxy_api_secret:
|
|
os.environ["spark_proxyllm_proxy_api_key"] = self.spark_proxy_api_key
|
|
os.environ["spark_proxyllm_proxy_api_secret"] = self.spark_proxy_api_secret
|
|
os.environ["spark_proxyllm_proxyllm_backend"] = self.spark_api_version or ""
|
|
os.environ["spark_proxyllm_proxy_api_app_id"] = (
|
|
self.spark_proxy_api_appid or ""
|
|
)
|
|
|
|
# baichuan proxy
|
|
self.bc_proxy_api_key = os.getenv("BAICHUAN_PROXY_API_KEY")
|
|
self.bc_model_name = os.getenv("BAICHUN_MODEL_NAME", "Baichuan2-Turbo-192k")
|
|
if self.bc_proxy_api_key and self.bc_model_name:
|
|
os.environ["bc_proxyllm_proxy_api_key"] = self.bc_proxy_api_key
|
|
os.environ["bc_proxyllm_proxyllm_backend"] = self.bc_model_name
|
|
|
|
# gemini proxy
|
|
self.gemini_proxy_api_key = os.getenv("GEMINI_PROXY_API_KEY")
|
|
if self.gemini_proxy_api_key:
|
|
os.environ["gemini_proxyllm_proxy_api_key"] = self.gemini_proxy_api_key
|
|
os.environ["gemini_proxyllm_proxyllm_backend"] = os.getenv(
|
|
"GEMINI_MODEL_VERSION", "gemini-pro"
|
|
)
|
|
|
|
# Yi proxy
|
|
self.yi_proxy_api_key = os.getenv("YI_API_KEY")
|
|
if self.yi_proxy_api_key:
|
|
os.environ["yi_proxyllm_proxy_api_key"] = self.yi_proxy_api_key
|
|
os.environ["yi_proxyllm_proxyllm_backend"] = os.getenv(
|
|
"YI_MODEL_VERSION", "yi-34b-chat-0205"
|
|
)
|
|
os.environ["yi_proxyllm_proxy_api_base"] = os.getenv(
|
|
"YI_API_BASE", "https://api.lingyiwanwu.com/v1"
|
|
)
|
|
# Moonshot proxy
|
|
self.moonshot_proxy_api_key = os.getenv("MOONSHOT_API_KEY")
|
|
if self.moonshot_proxy_api_key:
|
|
os.environ["moonshot_proxyllm_proxy_api_key"] = self.moonshot_proxy_api_key
|
|
os.environ["moonshot_proxyllm_proxyllm_backend"] = os.getenv(
|
|
"MOONSHOT_MODEL_VERSION", "moonshot-v1-8k"
|
|
)
|
|
os.environ["moonshot_proxyllm_api_base"] = os.getenv(
|
|
"MOONSHOT_API_BASE", "https://api.moonshot.cn/v1"
|
|
)
|
|
# Deepseek proxy
|
|
self.deepseek_proxy_api_key = os.getenv("DEEPSEEK_API_KEY")
|
|
if self.deepseek_proxy_api_key:
|
|
os.environ["deepseek_proxyllm_proxy_api_key"] = self.deepseek_proxy_api_key
|
|
os.environ["deepseek_proxyllm_proxyllm_backend"] = os.getenv(
|
|
"DEEPSEEK_MODEL_VERSION", "deepseek-chat"
|
|
)
|
|
os.environ["deepseek_proxyllm_api_base"] = os.getenv(
|
|
"DEEPSEEK_API_BASE", "https://api.deepseek.com/v1"
|
|
)
|
|
|
|
self.proxy_server_url = os.getenv("PROXY_SERVER_URL")
|
|
|
|
self.elevenlabs_api_key = os.getenv("ELEVENLABS_API_KEY")
|
|
self.elevenlabs_voice_1_id = os.getenv("ELEVENLABS_VOICE_1_ID")
|
|
self.elevenlabs_voice_2_id = os.getenv("ELEVENLABS_VOICE_2_ID")
|
|
|
|
self.use_mac_os_tts = os.getenv("USE_MAC_OS_TTS", "False") == "True"
|
|
|
|
self.authorise_key = os.getenv("AUTHORISE_COMMAND_KEY", "y")
|
|
self.exit_key = os.getenv("EXIT_KEY", "n")
|
|
self.image_size = int(os.getenv("IMAGE_SIZE", 256))
|
|
|
|
self.huggingface_api_token = os.getenv("HUGGINGFACE_API_TOKEN")
|
|
self.image_provider = os.getenv("IMAGE_PROVIDER")
|
|
self.image_size = int(os.getenv("IMAGE_SIZE", 256))
|
|
self.huggingface_image_model = os.getenv(
|
|
"HUGGINGFACE_IMAGE_MODEL", "CompVis/stable-diffusion-v1-4"
|
|
)
|
|
self.huggingface_audio_to_text_model = os.getenv(
|
|
"HUGGINGFACE_AUDIO_TO_TEXT_MODEL"
|
|
)
|
|
self.speak_mode = False
|
|
|
|
from dbgpt.core._private.prompt_registry import PromptTemplateRegistry
|
|
|
|
self.prompt_template_registry = PromptTemplateRegistry()
|
|
|
|
self.execute_local_commands = (
|
|
os.getenv("EXECUTE_LOCAL_COMMANDS", "False").lower() == "true"
|
|
)
|
|
# message stor file
|
|
self.message_dir = os.getenv("MESSAGE_HISTORY_DIR", "../../message")
|
|
|
|
# Native SQL Execution Capability Control Configuration
|
|
self.NATIVE_SQL_CAN_RUN_DDL = (
|
|
os.getenv("NATIVE_SQL_CAN_RUN_DDL", "True").lower() == "true"
|
|
)
|
|
self.NATIVE_SQL_CAN_RUN_WRITE = (
|
|
os.getenv("NATIVE_SQL_CAN_RUN_WRITE", "True").lower() == "true"
|
|
)
|
|
|
|
# dbgpt meta info database connection configuration
|
|
self.LOCAL_DB_HOST = os.getenv("LOCAL_DB_HOST")
|
|
self.LOCAL_DB_PATH = os.getenv("LOCAL_DB_PATH", "data/default_sqlite.db")
|
|
self.LOCAL_DB_TYPE = os.getenv("LOCAL_DB_TYPE", "sqlite")
|
|
if self.LOCAL_DB_HOST is None and self.LOCAL_DB_PATH == "":
|
|
self.LOCAL_DB_HOST = "127.0.0.1"
|
|
|
|
self.LOCAL_DB_NAME = os.getenv("LOCAL_DB_NAME", "dbgpt")
|
|
self.LOCAL_DB_PORT = int(os.getenv("LOCAL_DB_PORT", 3306))
|
|
self.LOCAL_DB_USER = os.getenv("LOCAL_DB_USER", "root")
|
|
self.LOCAL_DB_PASSWORD = os.getenv("LOCAL_DB_PASSWORD", "aa123456")
|
|
self.LOCAL_DB_POOL_SIZE = int(os.getenv("LOCAL_DB_POOL_SIZE", 10))
|
|
self.LOCAL_DB_POOL_OVERFLOW = int(os.getenv("LOCAL_DB_POOL_OVERFLOW", 20))
|
|
|
|
self.CHAT_HISTORY_STORE_TYPE = os.getenv("CHAT_HISTORY_STORE_TYPE", "db")
|
|
|
|
# LLM Model Service Configuration
|
|
self.LLM_MODEL = os.getenv("LLM_MODEL", "glm-4-9b-chat")
|
|
self.LLM_MODEL_PATH = os.getenv("LLM_MODEL_PATH")
|
|
|
|
# Proxy llm backend, this configuration is only valid when "LLM_MODEL=proxyllm"
|
|
# When we use the rest API provided by deployment frameworks like fastchat as a proxyllm, "PROXYLLM_BACKEND" is the model they actually deploy.
|
|
# We need to use "PROXYLLM_BACKEND" to load the prompt of the corresponding scene.
|
|
self.PROXYLLM_BACKEND = None
|
|
if self.LLM_MODEL == "proxyllm":
|
|
self.PROXYLLM_BACKEND = os.getenv("PROXYLLM_BACKEND")
|
|
|
|
self.LIMIT_MODEL_CONCURRENCY = int(os.getenv("LIMIT_MODEL_CONCURRENCY", 5))
|
|
self.MAX_POSITION_EMBEDDINGS = int(os.getenv("MAX_POSITION_EMBEDDINGS", 4096))
|
|
self.MODEL_PORT = os.getenv("MODEL_PORT", 8000)
|
|
self.MODEL_SERVER = os.getenv(
|
|
"MODEL_SERVER", "http://127.0.0.1" + ":" + str(self.MODEL_PORT)
|
|
)
|
|
|
|
# Vector Store Configuration
|
|
self.VECTOR_STORE_TYPE = os.getenv("VECTOR_STORE_TYPE", "Chroma")
|
|
self.MILVUS_URL = os.getenv("MILVUS_URL", "127.0.0.1")
|
|
self.MILVUS_PORT = os.getenv("MILVUS_PORT", "19530")
|
|
self.MILVUS_USERNAME = os.getenv("MILVUS_USERNAME", None)
|
|
self.MILVUS_PASSWORD = os.getenv("MILVUS_PASSWORD", None)
|
|
# Elasticsearch Vector Configuration
|
|
self.ELASTICSEARCH_URL = os.getenv("ELASTICSEARCH_URL", "127.0.0.1")
|
|
self.ELASTICSEARCH_PORT = os.getenv("ELASTICSEARCH_PORT", "9200")
|
|
self.ELASTICSEARCH_USERNAME = os.getenv("ELASTICSEARCH_USERNAME", None)
|
|
self.ELASTICSEARCH_PASSWORD = os.getenv("ELASTICSEARCH_PASSWORD", None)
|
|
|
|
# OceanBase Configuration
|
|
self.OB_HOST = os.getenv("OB_HOST", "127.0.0.1")
|
|
self.OB_PORT = int(os.getenv("OB_PORT", "2881"))
|
|
self.OB_USER = os.getenv("OB_USER", "root")
|
|
self.OB_PASSWORD = os.getenv("OB_PASSWORD", "")
|
|
self.OB_DATABASE = os.getenv("OB_DATABASE", "test")
|
|
self.OB_SQL_DBG_LOG_PATH = os.getenv("OB_SQL_DBG_LOG_PATH", "")
|
|
self.OB_ENABLE_NORMALIZE_VECTOR = bool(
|
|
os.getenv("OB_ENABLE_NORMALIZE_VECTOR", "")
|
|
)
|
|
self.OB_ENABLE_INDEX = bool(os.getenv("OB_ENABLE_INDEX", ""))
|
|
|
|
# QLoRA
|
|
self.QLoRA = os.getenv("QUANTIZE_QLORA", "True")
|
|
self.IS_LOAD_8BIT = os.getenv("QUANTIZE_8bit", "True").lower() == "true"
|
|
self.IS_LOAD_4BIT = os.getenv("QUANTIZE_4bit", "False").lower() == "true"
|
|
if self.IS_LOAD_8BIT and self.IS_LOAD_4BIT:
|
|
self.IS_LOAD_8BIT = False
|
|
# In order to be compatible with the new and old model parameter design
|
|
os.environ["load_8bit"] = str(self.IS_LOAD_8BIT)
|
|
os.environ["load_4bit"] = str(self.IS_LOAD_4BIT)
|
|
|
|
# EMBEDDING Configuration
|
|
self.EMBEDDING_MODEL = os.getenv("EMBEDDING_MODEL", "text2vec")
|
|
# Rerank model configuration
|
|
self.RERANK_MODEL = os.getenv("RERANK_MODEL")
|
|
self.RERANK_MODEL_PATH = os.getenv("RERANK_MODEL_PATH")
|
|
self.RERANK_TOP_K = int(os.getenv("RERANK_TOP_K", 3))
|
|
self.KNOWLEDGE_CHUNK_SIZE = int(os.getenv("KNOWLEDGE_CHUNK_SIZE", 100))
|
|
self.KNOWLEDGE_CHUNK_OVERLAP = int(os.getenv("KNOWLEDGE_CHUNK_OVERLAP", 50))
|
|
self.KNOWLEDGE_SEARCH_TOP_SIZE = int(os.getenv("KNOWLEDGE_SEARCH_TOP_SIZE", 5))
|
|
self.KNOWLEDGE_GRAPH_SEARCH_TOP_SIZE = int(
|
|
os.getenv("KNOWLEDGE_GRAPH_SEARCH_TOP_SIZE", 50)
|
|
)
|
|
self.KNOWLEDGE_MAX_CHUNKS_ONCE_LOAD = int(
|
|
os.getenv("KNOWLEDGE_MAX_CHUNKS_ONCE_LOAD", 10)
|
|
)
|
|
# default recall similarity score, between 0 and 1
|
|
self.KNOWLEDGE_SEARCH_RECALL_SCORE = float(
|
|
os.getenv("KNOWLEDGE_SEARCH_RECALL_SCORE", 0.3)
|
|
)
|
|
self.KNOWLEDGE_SEARCH_MAX_TOKEN = int(
|
|
os.getenv("KNOWLEDGE_SEARCH_MAX_TOKEN", 2000)
|
|
)
|
|
# Whether to enable Chat Knowledge Search Rewrite Mode
|
|
self.KNOWLEDGE_SEARCH_REWRITE = (
|
|
os.getenv("KNOWLEDGE_SEARCH_REWRITE", "False").lower() == "true"
|
|
)
|
|
# Control whether to display the source document of knowledge on the front end.
|
|
self.KNOWLEDGE_CHAT_SHOW_RELATIONS = (
|
|
os.getenv("KNOWLEDGE_CHAT_SHOW_RELATIONS", "False").lower() == "true"
|
|
)
|
|
|
|
# SUMMARY_CONFIG Configuration
|
|
self.SUMMARY_CONFIG = os.getenv("SUMMARY_CONFIG", "FAST")
|
|
|
|
self.MAX_GPU_MEMORY = os.getenv("MAX_GPU_MEMORY", None)
|
|
|
|
# Log level
|
|
self.DBGPT_LOG_LEVEL = os.getenv("DBGPT_LOG_LEVEL", "INFO")
|
|
|
|
self.SYSTEM_APP: Optional["SystemApp"] = None
|
|
|
|
# Temporary configuration
|
|
self.USE_FASTCHAT: bool = os.getenv("USE_FASTCHAT", "True").lower() == "true"
|
|
|
|
self.MODEL_CACHE_ENABLE: bool = (
|
|
os.getenv("MODEL_CACHE_ENABLE", "True").lower() == "true"
|
|
)
|
|
self.MODEL_CACHE_STORAGE_TYPE: str = os.getenv(
|
|
"MODEL_CACHE_STORAGE_TYPE", "disk"
|
|
)
|
|
self.MODEL_CACHE_MAX_MEMORY_MB: int = int(
|
|
os.getenv("MODEL_CACHE_MAX_MEMORY_MB", 256)
|
|
)
|
|
self.MODEL_CACHE_STORAGE_DISK_DIR: Optional[str] = os.getenv(
|
|
"MODEL_CACHE_STORAGE_DISK_DIR"
|
|
)
|
|
# global dbgpt api key
|
|
self.API_KEYS = os.getenv("API_KEYS", None)
|
|
self.ENCRYPT_KEY = os.getenv("ENCRYPT_KEY", "your_secret_key")
|
|
|
|
# Non-streaming scene retries
|
|
self.DBGPT_APP_SCENE_NON_STREAMING_RETRIES_BASE = int(
|
|
os.getenv("DBGPT_APP_SCENE_NON_STREAMING_RETRIES_BASE", 1)
|
|
)
|
|
# Non-streaming scene parallelism
|
|
self.DBGPT_APP_SCENE_NON_STREAMING_PARALLELISM_BASE = int(
|
|
os.getenv("DBGPT_APP_SCENE_NON_STREAMING_PARALLELISM_BASE", 1)
|
|
)
|
|
# experimental financial report model configuration
|
|
self.FIN_REPORT_MODEL = os.getenv("FIN_REPORT_MODEL", None)
|
|
# Whether to enable the new web UI, enabled by default
|
|
|
|
self.USE_NEW_WEB_UI: bool = (
|
|
os.getenv("USE_NEW_WEB_UI", "True").lower() == "true"
|
|
)
|
|
|
|
# file server configuration
|
|
# The host of the current file server, if None, get the host automatically
|
|
self.FILE_SERVER_HOST = os.getenv("FILE_SERVER_HOST")
|
|
self.FILE_SERVER_LOCAL_STORAGE_PATH = os.getenv(
|
|
"FILE_SERVER_LOCAL_STORAGE_PATH"
|
|
)
|
|
# multi-instance flag
|
|
self.WEBSERVER_MULTI_INSTANCE = (
|
|
os.getenv("MULTI_INSTANCE", "False").lower() == "true"
|
|
)
|
|
|
|
@property
|
|
def local_db_manager(self) -> "ConnectorManager":
|
|
from dbgpt.datasource.manages import ConnectorManager
|
|
|
|
if not self.SYSTEM_APP:
|
|
raise ValueError("SYSTEM_APP is not set")
|
|
return ConnectorManager.get_instance(self.SYSTEM_APP)
|