Unified configuration

.env.template

@@ -17,6 +17,10 @@
 #*******************************************************************#
 #**                         LLM MODELS                            **#
 #*******************************************************************#
+LLM_MODEL=vicuna-13b
+MODEL_SERVER=http://your_model_server_url
+LIMIT_MODEL_CONCURRENCY=5
+MAX_POSITION_EMBEDDINGS=4096
 
 ## SMART_LLM_MODEL - Smart language model (Default: vicuna-13b)
 ## FAST_LLM_MODEL - Fast language model (Default: chatglm-6b)
@@ -36,10 +40,10 @@
 #*******************************************************************#
 #**                  DATABASE SETTINGS                            **#
 #*******************************************************************#
-DB_SETTINGS_MYSQL_USER=root
+LOCAL_DB_USER=root
-DB_SETTINGS_MYSQL_PASSWORD=password
+LOCAL_DB_PASSWORD=password
-DB_SETTINGS_MYSQL_HOST=localhost
+LOCAL_DB_HOST=localhost
-DB_SETTINGS_MYSQL_PORT=3306
+LOCAL_DB_PORT=3306
 
 
 ### MILVUS

.gitignore

@@ -6,6 +6,7 @@ __pycache__/
 # C extensions
 *.so
 
+.env
 .idea
 .vscode
 .idea

README.en.md

@@ -179,7 +179,7 @@ Run gradio webui
 ```bash
 $ python pilot/server/webserver.py
 ```
-Notice:  the webserver need to connect llmserver,  so you need change the pilot/configs/model_config.py file. change the VICUNA_MODEL_SERVER = "http://127.0.0.1:8000" to your address.  It's very important.
+Notice:  the webserver need to connect llmserver,  so you need change the .env file. change the MODEL_SERVER = "http://127.0.0.1:8000" to your address.  It's very important.
 
 ## Usage Instructions
 We provide a user interface for Gradio, which allows you to use DB-GPT through our user interface. Additionally, we have prepared several reference articles (written in Chinese) that introduce the code and principles related to our project.

README.md

@@ -178,7 +178,7 @@ python llmserver.py
 ```bash
 $ python webserver.py 
 ```
-注意: 在启动Webserver之前, 需要修改pilot/configs/model_config.py 文件中的VICUNA_MODEL_SERVER = "http://127.0.0.1:8000", 将地址设置为你的服务器地址。
+注意: 在启动Webserver之前, 需要修改配置文件 .env文件中的MODEL_SERVER = "http://127.0.0.1:8000", 将地址设置为你的服务器地址。
 
 ## 使用说明
 

examples/embdserver.py

@@ -7,12 +7,15 @@ import time
 import uuid
 from urllib.parse import urljoin
 import gradio as gr
-from pilot.configs.model_config import *
+from pilot.configs.config import Config
 from pilot.conversation import conv_qa_prompt_template, conv_templates
 from langchain.prompts import PromptTemplate
 
+
 vicuna_stream_path = "generate_stream"
 
+CFG = Config()
+
 def generate(query):
 
     template_name = "conv_one_shot"
@@ -41,7 +44,7 @@ def generate(query):
     }
 
     response = requests.post(
-        url=urljoin(VICUNA_MODEL_SERVER, vicuna_stream_path), data=json.dumps(params)
+        url=urljoin(CFG.MODEL_SERVER, vicuna_stream_path), data=json.dumps(params)
     )
 
     skip_echo_len = len(params["prompt"]) + 1 - params["prompt"].count("</s>") * 3
@@ -54,7 +57,7 @@ def generate(query):
                 yield(output) 
  
 if __name__ == "__main__":
-    print(LLM_MODEL)
+    print(CFG.LLM_MODEL)
     with gr.Blocks() as demo:
         gr.Markdown("数据库SQL生成助手")
         with gr.Tab("SQL生成"):

pilot/configs/config.py

@@ -2,24 +2,23 @@
 # -*- coding: utf-8 -*-
 
 import os
+import nltk
 from typing import List
 
 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"""
 
-        # TODO change model_config there 
-
         self.debug_mode = False
         self.skip_reprompt = False
-
         self.temperature = float(os.getenv("TEMPERATURE", 0.7))
 
-        # TODO change model_config there
+
         self.execute_local_commands = (
             os.getenv("EXECUTE_LOCAL_COMMANDS", "False") == "True"
         )
@@ -46,17 +45,12 @@ class Config(metaclass=Singleton):
         self.milvus_collection = os.getenv("MILVUS_COLLECTION", "dbgpt")
         self.milvus_secure = os.getenv("MILVUS_SECURE") == "True"
 
+
         self.authorise_key = os.getenv("AUTHORISE_COMMAND_KEY", "y")
         self.exit_key = os.getenv("EXIT_KEY", "n")
-        self.image_provider = bool(os.getenv("IMAGE_PROVIDER", True))
+        self.image_provider = os.getenv("IMAGE_PROVIDER", True)
         self.image_size = int(os.getenv("IMAGE_SIZE", 256))
 
-        self.plugins_dir = os.getenv("PLUGINS_DIR", "../../plugins")
-        self.plugins: List[AutoGPTPluginTemplate] = []
-        self.plugins_openai = []
-
-        self.command_registry = []
-
         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))
@@ -68,6 +62,10 @@ class Config(metaclass=Singleton):
         )
         self.speak_mode = False
 
+
+        ### Related configuration of built-in commands
+        self.command_registry = []
+
         disabled_command_categories = os.getenv("DISABLED_COMMAND_CATEGORIES")
         if disabled_command_categories:
             self.disabled_command_categories = disabled_command_categories.split(",")
@@ -78,6 +76,12 @@ class Config(metaclass=Singleton):
             os.getenv("EXECUTE_LOCAL_COMMANDS", "False") == "True"
         )
 
+
+        ### The associated configuration parameters of the plug-in control the loading and use of the plug-in
+        self.plugins_dir = os.getenv("PLUGINS_DIR", "../../plugins")
+        self.plugins: List[AutoGPTPluginTemplate] = []
+        self.plugins_openai = []
+
         plugins_allowlist = os.getenv("ALLOWLISTED_PLUGINS")
         if plugins_allowlist:
             self.plugins_allowlist = plugins_allowlist.split(",")
@@ -90,6 +94,20 @@ class Config(metaclass=Singleton):
         else:
             self.plugins_denylist = []
 
+
+        ### Local database connection configuration
+        self.LOCAL_DB_HOST =  os.getenv("LOCAL_DB_HOST",  "127.0.0.1")
+        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")
+
+        ### LLM Model Service Configuration
+        self.LLM_MODEL = os.getenv("LLM_MODEL",  "vicuna-13b")
+        self.LIMIT_MODEL_CONCURRENCY = int(os.getenv("LIMIT_MODEL_CONCURRENCY",  5))
+        self.MAX_POSITION_EMBEDDINGS = int(os.getenv("MAX_POSITION_EMBEDDINGS",  4096))
+        self.MODEL_SERVER = os.getenv("MODEL_SERVER",  "http://121.41.167.183:8000")
+        self.ISLOAD_8BIT = os.getenv("ISLOAD_8BIT", "True") == "True"
+
     def set_debug_mode(self, value: bool) -> None:
         """Set the debug mode value"""
         self.debug_mode = value

pilot/configs/model_config.py

@@ -22,25 +22,18 @@ LLM_MODEL_CONFIG = {
     "vicuna-13b": os.path.join(MODEL_PATH, "vicuna-13b"),
     "sentence-transforms": os.path.join(MODEL_PATH, "all-MiniLM-L6-v2")
 }
-
-
-VECTOR_SEARCH_TOP_K = 3
-LLM_MODEL = "vicuna-13b"
-LIMIT_MODEL_CONCURRENCY = 5
-MAX_POSITION_EMBEDDINGS = 4096 
-VICUNA_MODEL_SERVER = "http://121.41.167.183:8000"
-
 # Load model config
 ISLOAD_8BIT = True
 ISDEBUG = False
 
 
-DB_SETTINGS = {
+VECTOR_SEARCH_TOP_K = 3
-    "user": "root",
+# LLM_MODEL = "vicuna-13b"
-    "password": "aa123456",
+# LIMIT_MODEL_CONCURRENCY = 5
-    "host": "127.0.0.1",
+# MAX_POSITION_EMBEDDINGS = 4096
-    "port": 3306
+# VICUNA_MODEL_SERVER = "http://121.41.167.183:8000"
-}
+
+
 
 VS_ROOT_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "vs_store")
 KNOWLEDGE_UPLOAD_ROOT_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "knowledge")

pilot/conversation.py

@@ -4,8 +4,16 @@
 import dataclasses
 from enum import auto, Enum
 from typing import List, Any
-from pilot.configs.model_config import DB_SETTINGS
+from pilot.configs.config import Config
 
+CFG = Config()
+
+DB_SETTINGS = {
+    "user": CFG.LOCAL_DB_USER,
+    "password":  CFG.LOCAL_DB_PASSWORD,
+    "host": CFG.LOCAL_DB_HOST,
+    "port": CFG.LOCAL_DB_PORT
+}
 
 class SeparatorStyle(Enum):
     SINGLE = auto()
@@ -91,7 +99,7 @@ class Conversation:
 def gen_sqlgen_conversation(dbname):
     from pilot.connections.mysql import MySQLOperator
     mo = MySQLOperator(
-        **DB_SETTINGS
+        **(DB_SETTINGS)
     )
 
     message = ""

pilot/model/vicuna_llm.py

@@ -8,8 +8,9 @@ from langchain.embeddings.base import Embeddings
 from pydantic import BaseModel
 from typing import Any, Mapping, Optional, List
 from langchain.llms.base import LLM
-from pilot.configs.model_config import *
+from pilot.configs.config import Config
 
+CFG = Config()
 class VicunaLLM(LLM):
 
     vicuna_generate_path = "generate_stream"
@@ -22,7 +23,7 @@ class VicunaLLM(LLM):
             "stop": stop
         }
         response = requests.post(
-            url=urljoin(VICUNA_MODEL_SERVER, self.vicuna_generate_path),
+            url=urljoin(CFG.MODEL_SERVER, self.vicuna_generate_path),
             data=json.dumps(params),
         )
 
@@ -51,7 +52,7 @@ class VicunaEmbeddingLLM(BaseModel, Embeddings):
         print("Sending prompt ", p)
 
         response = requests.post(
-            url=urljoin(VICUNA_MODEL_SERVER, self.vicuna_embedding_path),
+            url=urljoin(CFG.MODEL_SERVER, self.vicuna_embedding_path),
             json={
                 "prompt": p
             }

pilot/pturning/lora/finetune.py

@@ -17,14 +17,17 @@ from peft import (
 import torch
 from datasets import load_dataset
 import pandas as pd
+from pilot.configs.config import Config
 
 
-from pilot.configs.model_config import DATA_DIR, LLM_MODEL, LLM_MODEL_CONFIG
+from pilot.configs.model_config import DATA_DIR, LLM_MODEL_CONFIG
 device = "cuda" if torch.cuda.is_available() else "cpu"
 CUTOFF_LEN = 50
 
 df = pd.read_csv(os.path.join(DATA_DIR, "BTC_Tweets_Updated.csv"))
 
+CFG = Config()
+
 def sentiment_score_to_name(score: float):
     if score > 0:
         return "Positive"
@@ -49,7 +52,7 @@ with open(os.path.join(DATA_DIR, "alpaca-bitcoin-sentiment-dataset.json"), "w")
 data = load_dataset("json", data_files=os.path.join(DATA_DIR, "alpaca-bitcoin-sentiment-dataset.json"))
 print(data["train"])
 
-BASE_MODEL = LLM_MODEL_CONFIG[LLM_MODEL]
+BASE_MODEL = LLM_MODEL_CONFIG[CFG.LLM_MODEL]
 model = LlamaForCausalLM.from_pretrained(
     BASE_MODEL,
     torch_dtype=torch.float16,

pilot/server/llmserver.py

@@ -13,8 +13,11 @@ from pilot.model.inference import generate_output, get_embeddings
 
 from pilot.model.loader import ModelLoader
 from pilot.configs.model_config import *
+from pilot.configs.config import  Config
 
-model_path = LLM_MODEL_CONFIG[LLM_MODEL] 
+
+CFG = Config()
+model_path = LLM_MODEL_CONFIG[CFG.LLM_MODEL]
 
 
 global_counter = 0
@@ -60,7 +63,7 @@ def generate_stream_gate(params):
             tokenizer,
             params,
             DEVICE,
-            MAX_POSITION_EMBEDDINGS,
+            CFG.MAX_POSITION_EMBEDDINGS,
         ):
             print("output: ", output)
             ret = {
@@ -84,7 +87,7 @@ async def api_generate_stream(request: Request):
     print(model, tokenizer, params, DEVICE) 
 
     if model_semaphore is None:
-        model_semaphore = asyncio.Semaphore(LIMIT_MODEL_CONCURRENCY)
+        model_semaphore = asyncio.Semaphore(CFG.LIMIT_MODEL_CONCURRENCY)
     await model_semaphore.acquire() 
 
     generator = generate_stream_gate(params)

pilot/server/webserver.py

@@ -14,13 +14,13 @@ from urllib.parse import urljoin
 
 from langchain import PromptTemplate
 
-from pilot.configs.model_config import DB_SETTINGS, KNOWLEDGE_UPLOAD_ROOT_PATH, LLM_MODEL_CONFIG
+from pilot.configs.model_config import KNOWLEDGE_UPLOAD_ROOT_PATH, LLM_MODEL_CONFIG
 from pilot.server.vectordb_qa import KnownLedgeBaseQA
 from pilot.connections.mysql import MySQLOperator
 from pilot.source_embedding.knowledge_embedding import KnowledgeEmbedding
 from pilot.vector_store.extract_tovec import get_vector_storelist, load_knownledge_from_doc, knownledge_tovec_st
 
-from pilot.configs.model_config import LOGDIR, VICUNA_MODEL_SERVER, LLM_MODEL, DATASETS_DIR
+from pilot.configs.model_config import LOGDIR,  DATASETS_DIR
 
 from pilot.plugins import scan_plugins
 from pilot.configs.config import Config
@@ -67,7 +67,15 @@ priority = {
     "vicuna-13b": "aaa"
 }
 
+# 加载插件
+CFG= Config()
 
+DB_SETTINGS = {
+    "user": CFG.LOCAL_DB_USER,
+    "password":  CFG.LOCAL_DB_PASSWORD,
+    "host": CFG.LOCAL_DB_HOST,
+    "port": CFG.LOCAL_DB_PORT
+}
 def get_simlar(q):
     docsearch = knownledge_tovec_st(os.path.join(DATASETS_DIR, "plan.md"))
     docs = docsearch.similarity_search_with_score(q, k=1)
@@ -178,7 +186,7 @@ def http_bot(state, mode, sql_mode, db_selector, temperature, max_new_tokens, re
     print("是否是AUTO-GPT模式.", autogpt)
 
     start_tstamp = time.time()
-    model_name = LLM_MODEL
+    model_name = CFG.LLM_MODEL
 
     dbname = db_selector
     # TODO 这里的请求需要拼接现有知识库, 使得其根据现有知识库作答, 所以prompt需要继续优化
@@ -268,7 +276,7 @@ def http_bot(state, mode, sql_mode, db_selector, temperature, max_new_tokens, re
     logger.info(f"Requert: \n{payload}")
 
     if sql_mode == conversation_sql_mode["auto_execute_ai_response"]:
-        response = requests.post(urljoin(VICUNA_MODEL_SERVER, "generate"),
+        response = requests.post(urljoin(CFG.MODEL_SERVER, "generate"),
                                  headers=headers, json=payload, timeout=120)
 
         print(response.json())
@@ -316,7 +324,7 @@ def http_bot(state, mode, sql_mode, db_selector, temperature, max_new_tokens, re
 
         try:
             # Stream output
-            response = requests.post(urljoin(VICUNA_MODEL_SERVER, "generate_stream"),
+            response = requests.post(urljoin(CFG.MODEL_SERVER, "generate_stream"),
                                      headers=headers, json=payload, stream=True, timeout=20)
             for chunk in response.iter_lines(decode_unicode=False, delimiter=b"\0"):
                 if chunk:
@@ -595,9 +603,8 @@ if __name__ == "__main__":
 
     # dbs = get_database_list()
 
-    # 加载插件
+    # 配置初始化
     cfg = Config()
-
     cfg.set_plugins(scan_plugins(cfg, cfg.debug_mode))
 
     # 加载插件可执行命令

requirements.txt

@@ -57,6 +57,9 @@ pymdown-extensions
 mkdocs
 requests
 gTTS==2.3.1
+langchain
+nltk
+python-dotenv==1.0.0
 
 # Testing dependencies
 pytest