mirror of
https://github.com/csunny/DB-GPT.git
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277 lines
12 KiB
Python
277 lines
12 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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from __future__ import annotations
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import os
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from typing import TYPE_CHECKING, List, Optional
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from dbgpt.util.singleton import Singleton
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if TYPE_CHECKING:
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from auto_gpt_plugin_template import AutoGPTPluginTemplate
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from dbgpt.component import SystemApp
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class Config(metaclass=Singleton):
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"""Configuration class to store the state of bools for different scripts access"""
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def __init__(self) -> None:
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"""Initialize the Config class"""
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self.NEW_SERVER_MODE = False
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self.SERVER_LIGHT_MODE = False
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# Gradio language version: en, zh
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self.LANGUAGE = os.getenv("LANGUAGE", "en")
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self.WEB_SERVER_PORT = int(os.getenv("WEB_SERVER_PORT", 7860))
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self.debug_mode = False
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self.skip_reprompt = False
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self.temperature = float(os.getenv("TEMPERATURE", 0.7))
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# self.NUM_GPUS = int(os.getenv("NUM_GPUS", 1))
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self.execute_local_commands = (
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os.getenv("EXECUTE_LOCAL_COMMANDS", "False").lower() == "true"
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)
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# User agent header to use when making HTTP requests
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# Some websites might just completely deny request with an error code if
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# no user agent was found.
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self.user_agent = os.getenv(
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"USER_AGENT",
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"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_4) AppleWebKit/537.36"
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" (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36",
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)
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# This is a proxy server, just for test_py. we will remove this later.
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self.proxy_api_key = os.getenv("PROXY_API_KEY")
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self.bard_proxy_api_key = os.getenv("BARD_PROXY_API_KEY")
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# In order to be compatible with the new and old model parameter design
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if self.bard_proxy_api_key:
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os.environ["bard_proxyllm_proxy_api_key"] = self.bard_proxy_api_key
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# tongyi
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self.tongyi_proxy_api_key = os.getenv("TONGYI_PROXY_API_KEY")
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if self.tongyi_proxy_api_key:
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os.environ["tongyi_proxyllm_proxy_api_key"] = self.tongyi_proxy_api_key
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# zhipu
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self.zhipu_proxy_api_key = os.getenv("ZHIPU_PROXY_API_KEY")
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if self.zhipu_proxy_api_key:
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os.environ["zhipu_proxyllm_proxy_api_key"] = self.zhipu_proxy_api_key
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os.environ["zhipu_proxyllm_proxyllm_backend"] = os.getenv(
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"ZHIPU_MODEL_VERSION"
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)
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# wenxin
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self.wenxin_proxy_api_key = os.getenv("WEN_XIN_API_KEY")
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self.wenxin_proxy_api_secret = os.getenv("WEN_XIN_API_SECRET")
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self.wenxin_model_version = os.getenv("WEN_XIN_MODEL_VERSION")
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if self.wenxin_proxy_api_key and self.wenxin_proxy_api_secret:
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os.environ["wenxin_proxyllm_proxy_api_key"] = self.wenxin_proxy_api_key
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os.environ[
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"wenxin_proxyllm_proxy_api_secret"
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] = self.wenxin_proxy_api_secret
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os.environ["wenxin_proxyllm_proxyllm_backend"] = self.wenxin_model_version
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# xunfei spark
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self.spark_api_version = os.getenv("XUNFEI_SPARK_API_VERSION")
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self.spark_proxy_api_key = os.getenv("XUNFEI_SPARK_API_KEY")
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self.spark_proxy_api_secret = os.getenv("XUNFEI_SPARK_API_SECRET")
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self.spark_proxy_api_appid = os.getenv("XUNFEI_SPARK_APPID")
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if self.spark_proxy_api_key and self.spark_proxy_api_secret:
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os.environ["spark_proxyllm_proxy_api_key"] = self.spark_proxy_api_key
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os.environ["spark_proxyllm_proxy_api_secret"] = self.spark_proxy_api_secret
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os.environ["spark_proxyllm_proxyllm_backend"] = self.spark_api_version
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os.environ["spark_proxyllm_proxy_api_app_id"] = self.spark_proxy_api_appid
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# baichuan proxy
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self.bc_proxy_api_key = os.getenv("BAICHUAN_PROXY_API_KEY")
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self.bc_model_name = os.getenv("BAICHUN_MODEL_NAME", "Baichuan2-Turbo-192k")
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if self.bc_proxy_api_key and self.bc_model_name:
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os.environ["bc_proxyllm_proxy_api_key"] = self.bc_proxy_api_key
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os.environ["bc_proxyllm_proxyllm_backend"] = self.bc_model_name
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# gemini proxy
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self.gemini_proxy_api_key = os.getenv("GEMINI_PROXY_API_KEY")
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if self.gemini_proxy_api_key:
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os.environ["gemini_proxyllm_proxy_api_key"] = self.gemini_proxy_api_key
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os.environ["gemini_proxyllm_proxyllm_backend"] = os.getenv(
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"GEMINI_MODEL_VERSION", "gemini-pro"
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)
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self.proxy_server_url = os.getenv("PROXY_SERVER_URL")
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self.elevenlabs_api_key = os.getenv("ELEVENLABS_API_KEY")
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self.elevenlabs_voice_1_id = os.getenv("ELEVENLABS_VOICE_1_ID")
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self.elevenlabs_voice_2_id = os.getenv("ELEVENLABS_VOICE_2_ID")
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self.use_mac_os_tts = False
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self.use_mac_os_tts = os.getenv("USE_MAC_OS_TTS")
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self.authorise_key = os.getenv("AUTHORISE_COMMAND_KEY", "y")
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self.exit_key = os.getenv("EXIT_KEY", "n")
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self.image_provider = os.getenv("IMAGE_PROVIDER", True)
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self.image_size = int(os.getenv("IMAGE_SIZE", 256))
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self.huggingface_api_token = os.getenv("HUGGINGFACE_API_TOKEN")
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self.image_provider = os.getenv("IMAGE_PROVIDER")
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self.image_size = int(os.getenv("IMAGE_SIZE", 256))
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self.huggingface_image_model = os.getenv(
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"HUGGINGFACE_IMAGE_MODEL", "CompVis/stable-diffusion-v1-4"
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)
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self.huggingface_audio_to_text_model = os.getenv(
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"HUGGINGFACE_AUDIO_TO_TEXT_MODEL"
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)
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self.speak_mode = False
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from dbgpt.core._private.prompt_registry import PromptTemplateRegistry
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self.prompt_template_registry = PromptTemplateRegistry()
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### Related configuration of built-in commands
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self.command_registry = []
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### Relate configuration of display commands
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self.command_dispaly = []
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disabled_command_categories = os.getenv("DISABLED_COMMAND_CATEGORIES")
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if disabled_command_categories:
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self.disabled_command_categories = disabled_command_categories.split(",")
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else:
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self.disabled_command_categories = []
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self.execute_local_commands = (
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os.getenv("EXECUTE_LOCAL_COMMANDS", "False").lower() == "true"
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)
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### message stor file
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self.message_dir = os.getenv("MESSAGE_HISTORY_DIR", "../../message")
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### The associated configuration parameters of the plug-in control the loading and use of the plug-in
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self.plugins: List["AutoGPTPluginTemplate"] = []
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self.plugins_openai = []
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self.plugins_auto_load = os.getenv("AUTO_LOAD_PLUGIN", "True").lower() == "true"
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self.plugins_git_branch = os.getenv("PLUGINS_GIT_BRANCH", "plugin_dashboard")
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plugins_allowlist = os.getenv("ALLOWLISTED_PLUGINS")
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if plugins_allowlist:
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self.plugins_allowlist = plugins_allowlist.split(",")
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else:
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self.plugins_allowlist = []
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plugins_denylist = os.getenv("DENYLISTED_PLUGINS")
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if plugins_denylist:
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self.plugins_denylist = plugins_denylist.split(",")
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else:
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self.plugins_denylist = []
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### Native SQL Execution Capability Control Configuration
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self.NATIVE_SQL_CAN_RUN_DDL = (
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os.getenv("NATIVE_SQL_CAN_RUN_DDL", "True").lower() == "true"
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)
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self.NATIVE_SQL_CAN_RUN_WRITE = (
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os.getenv("NATIVE_SQL_CAN_RUN_WRITE", "True").lower() == "true"
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)
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self.LOCAL_DB_MANAGE = None
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###dbgpt meta info database connection configuration
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self.LOCAL_DB_HOST = os.getenv("LOCAL_DB_HOST")
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self.LOCAL_DB_PATH = os.getenv("LOCAL_DB_PATH", "data/default_sqlite.db")
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self.LOCAL_DB_TYPE = os.getenv("LOCAL_DB_TYPE", "sqlite")
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if self.LOCAL_DB_HOST is None and self.LOCAL_DB_PATH == "":
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self.LOCAL_DB_HOST = "127.0.0.1"
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self.LOCAL_DB_NAME = os.getenv("LOCAL_DB_NAME", "dbgpt")
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self.LOCAL_DB_PORT = int(os.getenv("LOCAL_DB_PORT", 3306))
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self.LOCAL_DB_USER = os.getenv("LOCAL_DB_USER", "root")
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self.LOCAL_DB_PASSWORD = os.getenv("LOCAL_DB_PASSWORD", "aa123456")
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self.LOCAL_DB_POOL_SIZE = int(os.getenv("LOCAL_DB_POOL_SIZE", 10))
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self.LOCAL_DB_POOL_OVERFLOW = int(os.getenv("LOCAL_DB_POOL_OVERFLOW", 20))
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self.CHAT_HISTORY_STORE_TYPE = os.getenv("CHAT_HISTORY_STORE_TYPE", "db")
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### LLM Model Service Configuration
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self.LLM_MODEL = os.getenv("LLM_MODEL", "vicuna-13b-v1.5")
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self.LLM_MODEL_PATH = os.getenv("LLM_MODEL_PATH")
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### Proxy llm backend, this configuration is only valid when "LLM_MODEL=proxyllm"
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### When we use the rest API provided by deployment frameworks like fastchat as a proxyllm, "PROXYLLM_BACKEND" is the model they actually deploy.
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### We need to use "PROXYLLM_BACKEND" to load the prompt of the corresponding scene.
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self.PROXYLLM_BACKEND = None
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if self.LLM_MODEL == "proxyllm":
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self.PROXYLLM_BACKEND = os.getenv("PROXYLLM_BACKEND")
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self.LIMIT_MODEL_CONCURRENCY = int(os.getenv("LIMIT_MODEL_CONCURRENCY", 5))
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self.MAX_POSITION_EMBEDDINGS = int(os.getenv("MAX_POSITION_EMBEDDINGS", 4096))
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self.MODEL_PORT = os.getenv("MODEL_PORT", 8000)
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self.MODEL_SERVER = os.getenv(
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"MODEL_SERVER", "http://127.0.0.1" + ":" + str(self.MODEL_PORT)
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)
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### Vector Store Configuration
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self.VECTOR_STORE_TYPE = os.getenv("VECTOR_STORE_TYPE", "Chroma")
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self.MILVUS_URL = os.getenv("MILVUS_URL", "127.0.0.1")
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self.MILVUS_PORT = os.getenv("MILVUS_PORT", "19530")
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self.MILVUS_USERNAME = os.getenv("MILVUS_USERNAME", None)
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self.MILVUS_PASSWORD = os.getenv("MILVUS_PASSWORD", None)
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# QLoRA
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self.QLoRA = os.getenv("QUANTIZE_QLORA", "True")
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self.IS_LOAD_8BIT = os.getenv("QUANTIZE_8bit", "True").lower() == "true"
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self.IS_LOAD_4BIT = os.getenv("QUANTIZE_4bit", "False").lower() == "true"
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if self.IS_LOAD_8BIT and self.IS_LOAD_4BIT:
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self.IS_LOAD_8BIT = False
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# In order to be compatible with the new and old model parameter design
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os.environ["load_8bit"] = str(self.IS_LOAD_8BIT)
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os.environ["load_4bit"] = str(self.IS_LOAD_4BIT)
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### EMBEDDING Configuration
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self.EMBEDDING_MODEL = os.getenv("EMBEDDING_MODEL", "text2vec")
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self.KNOWLEDGE_CHUNK_SIZE = int(os.getenv("KNOWLEDGE_CHUNK_SIZE", 100))
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self.KNOWLEDGE_CHUNK_OVERLAP = int(os.getenv("KNOWLEDGE_CHUNK_OVERLAP", 50))
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self.KNOWLEDGE_SEARCH_TOP_SIZE = int(os.getenv("KNOWLEDGE_SEARCH_TOP_SIZE", 5))
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# default recall similarity score, between 0 and 1
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self.KNOWLEDGE_SEARCH_RECALL_SCORE = float(
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os.getenv("KNOWLEDGE_SEARCH_RECALL_SCORE", 0.3)
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)
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self.KNOWLEDGE_SEARCH_MAX_TOKEN = int(
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os.getenv("KNOWLEDGE_SEARCH_MAX_TOKEN", 2000)
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)
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# Whether to enable Chat Knowledge Search Rewrite Mode
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self.KNOWLEDGE_SEARCH_REWRITE = (
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os.getenv("KNOWLEDGE_SEARCH_REWRITE", "False").lower() == "true"
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)
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# Control whether to display the source document of knowledge on the front end.
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self.KNOWLEDGE_CHAT_SHOW_RELATIONS = (
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os.getenv("KNOWLEDGE_CHAT_SHOW_RELATIONS", "False").lower() == "true"
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)
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### SUMMARY_CONFIG Configuration
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self.SUMMARY_CONFIG = os.getenv("SUMMARY_CONFIG", "FAST")
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self.MAX_GPU_MEMORY = os.getenv("MAX_GPU_MEMORY", None)
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### Log level
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self.DBGPT_LOG_LEVEL = os.getenv("DBGPT_LOG_LEVEL", "INFO")
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self.SYSTEM_APP: Optional["SystemApp"] = None
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### Temporary configuration
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self.USE_FASTCHAT: bool = os.getenv("USE_FASTCHAT", "True").lower() == "true"
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self.MODEL_CACHE_ENABLE: bool = (
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os.getenv("MODEL_CACHE_ENABLE", "True").lower() == "true"
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)
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self.MODEL_CACHE_STORAGE_TYPE: str = os.getenv(
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"MODEL_CACHE_STORAGE_TYPE", "disk"
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)
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self.MODEL_CACHE_MAX_MEMORY_MB: int = int(
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os.getenv("MODEL_CACHE_MAX_MEMORY_MB", 256)
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)
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self.MODEL_CACHE_STORAGE_DISK_DIR: str = os.getenv(
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"MODEL_CACHE_STORAGE_DISK_DIR"
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)
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