fix confilict

.env.template

@@ -0,0 +1,72 @@
+#*******************************************************************#
+#**             DB-GPT  - GENERAL SETTINGS                        **#  
+#*******************************************************************#
+## DISABLED_COMMAND_CATEGORIES - The list of categories of commands that are disabled. Each of the below are an option:
+## pilot.commands.query_execute
+
+## For example, to disable coding related features, uncomment the next line
+# DISABLED_COMMAND_CATEGORIES=   
+
+
+#*******************************************************************#
+#***                       LLM PROVIDER                          ***#
+#*******************************************************************#
+
+# TEMPERATURE=0
+
+#*******************************************************************#
+#**                         LLM MODELS                            **#
+#*******************************************************************#
+
+## SMART_LLM_MODEL - Smart language model (Default: vicuna-13b)
+## FAST_LLM_MODEL - Fast language model (Default: chatglm-6b)
+# SMART_LLM_MODEL=vicuna-13b
+# FAST_LLM_MODEL=chatglm-6b
+
+
+### EMBEDDINGS
+## EMBEDDING_MODEL       - Model to use for creating embeddings
+## EMBEDDING_TOKENIZER   - Tokenizer to use for chunking large inputs
+## EMBEDDING_TOKEN_LIMIT - Chunk size limit for large inputs
+# EMBEDDING_MODEL=all-MiniLM-L6-v2
+# EMBEDDING_TOKENIZER=all-MiniLM-L6-v2
+# EMBEDDING_TOKEN_LIMIT=8191
+
+
+#*******************************************************************#
+#**                  DATABASE SETTINGS                            **#
+#*******************************************************************#
+DB_SETTINGS_MYSQL_USER=root
+DB_SETTINGS_MYSQL_PASSWORD=password
+DB_SETTINGS_MYSQL_HOST=localhost
+DB_SETTINGS_MYSQL_PORT=3306
+
+
+### MILVUS
+## MILVUS_ADDR - Milvus remote address (e.g. localhost:19530)
+## MILVUS_USERNAME - username for your Milvus database
+## MILVUS_PASSWORD - password for your Milvus database
+## MILVUS_SECURE - True to enable TLS. (Default: False)
+##   Setting MILVUS_ADDR to a `https://` URL will override this setting.
+## MILVUS_COLLECTION - Milvus collection, change it if you want to start a new memory and retain the old memory.
+# MILVUS_ADDR=localhost:19530
+# MILVUS_USERNAME=
+# MILVUS_PASSWORD=
+# MILVUS_SECURE=
+# MILVUS_COLLECTION=dbgpt
+
+#*******************************************************************#
+#**                  ALLOWLISTED PLUGINS                          **#
+#*******************************************************************#
+
+#ALLOWLISTED_PLUGINS - Sets the listed plugins that are allowed (Example: plugin1,plugin2,plugin3)
+#DENYLISTED_PLUGINS - Sets the listed plugins that are not allowed (Example: plugin1,plugin2,plugin3)
+ALLOWLISTED_PLUGINS=
+DENYLISTED_PLUGINS=
+
+
+#*******************************************************************#
+#**                 CHAT PLUGIN SETTINGS                          **#
+#*******************************************************************#
+# CHAT_MESSAGES_ENABLED - Enable chat messages (Default: False)
+# CHAT_MESSAGES_ENABLED=False

.github/workflows/pylint.yml

@@ -0,0 +1,23 @@
+name: Pylint
+
+on: [push]
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.8", "3.9", "3.10"]
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v3
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install pylint
+    - name: Analysing the code with pylint
+      run: |
+        pylint $(git ls-files '*.py')

.github/workflows/python-publish.yml

@@ -0,0 +1,39 @@
+# This workflow will upload a Python Package using Twine when a release is created
+# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python#publishing-to-package-registries
+
+# This workflow uses actions that are not certified by GitHub.
+# They are provided by a third-party and are governed by
+# separate terms of service, privacy policy, and support
+# documentation.
+
+name: Upload Python Package
+
+on:
+  release:
+    types: [published]
+
+permissions:
+  contents: read
+
+jobs:
+  deploy:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python
+      uses: actions/setup-python@v3
+      with:
+        python-version: '3.x'
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install build
+    - name: Build package
+      run: python -m build
+    - name: Publish package
+      uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29
+      with:
+        user: __token__
+        password: ${{ secrets.PYPI_API_TOKEN }}

README.md

@@ -44,7 +44,7 @@ The Generated SQL is runable.
 1. First you need to install python requirements.
 ```
 python>=3.9
-pip install -r requirements
+pip install -r requirements.txt
 ```
 or if you use conda envirenment, you can use this command
 ```
@@ -63,7 +63,7 @@ The password just for test, you can change this if necessary
 # Install
 1. 基础模型下载
 关于基础模型, 可以根据[vicuna](https://github.com/lm-sys/FastChat/blob/main/README.md#model-weights)合成教程进行合成。 
-如果此步有困难的同学，也可以直接使用[Hugging Face](https://huggingface.co/)上的模型进行替代。 替代模型: [vicuna-13b](https://huggingface.co/Tribbiani/vicuna-13b)
+如果此步有困难的同学，也可以直接使用[Hugging Face](https://huggingface.co/)上的模型进行替代. [替代模型](https://huggingface.co/Tribbiani/vicuna-7b)
 
 2. Run model server
 ```
@@ -86,4 +86,4 @@ python webserver.py
 # Contribute
 [Contribute](https://github.com/csunny/DB-GPT/blob/main/CONTRIBUTING)
 # Licence
-[MIT](https://github.com/csunny/DB-GPT/blob/main/LICENSE)
+[MIT](https://github.com/csunny/DB-GPT/blob/main/LICENSE)

environment.yml

@@ -7,11 +7,9 @@ dependencies:
   - python=3.9
   - cudatoolkit
   - pip
-  - pytorch=1.12.1
   - pytorch-mutex=1.0=cuda
-  - torchaudio=0.12.1
-  - torchvision=0.13.1
   - pip:
+    - pytorch
     - accelerate==0.16.0
     - aiohttp==3.8.4
     - aiosignal==1.3.1
@@ -57,11 +55,12 @@ dependencies:
     - sentence-transformers
     - umap-learn
     - notebook
-    - gradio==3.24.1
+    - gradio==3.23
     - gradio-client==0.0.8
     - wandb
-    - fschat=0.1.10
-    - llama-index=0.5.27
+    - llama-index==0.5.27
     - pymysql
     - unstructured==0.6.3
     - pytesseract==0.3.10
+    - markdown2
+    - chromadb 

examples/gradio_test.py

@@ -0,0 +1,19 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+
+import gradio as gr
+
+def change_tab():
+    return gr.Tabs.update(selected=1)
+
+with gr.Blocks() as demo:
+    with gr.Tabs() as tabs:
+        with gr.TabItem("Train", id=0):
+            t = gr.Textbox()
+        with gr.TabItem("Inference", id=1):
+            i = gr.Image()
+    
+    btn = gr.Button()
+    btn.click(change_tab, None, tabs)
+
+demo.launch()

pilot/commands/command.py

@@ -0,0 +1,36 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import functools
+import importlib
+import inspect
+from typing import Any, Callable, Optional
+
+class Command:
+    """A class representing a command.
+
+    Attributes:
+        name (str): The name of the command.
+        description (str): A brief description of what the command does.
+        signature (str): The signature of the function that the command executes. Default to None.
+    """
+
+    def __init__(self,
+                 name: str,
+                 description: str,
+                 method: Callable[..., Any],
+                 signature: str = "",
+                 enabled: bool = True,
+                 disabled_reason: Optional[str] = None,
+    ) -> None:
+        self.name = name
+        self.description = description
+        self.method = method
+        self.signature = signature if signature else str(inspect.signature(self.method))
+        self.enabled = enabled
+        self.disabled_reason = disabled_reason
+
+    def __call__(self, *args: Any, **kwds: Any) -> Any:
+        if not self.enabled:
+            return f"Command '{self.name}' is disabled: {self.disabled_reason}"
+        return self.method(*args, **kwds)

pilot/configs/config.py

@@ -12,26 +12,55 @@ class Config(metaclass=Singleton):
     def __init__(self) -> None:
         """Initialize the Config class"""
 
-        # TODO change model_config there
+        # TODO change model_config there 
+
         self.debug_mode = False
+        self.skip_reprompt = False
+        
+        self.plugins_dir = os.getenv("PLUGINS_DIR", "plugins")
+        self.plugins = List[AutoGPTPluginTemplate] = []
+        self.temperature = float(os.getenv("TEMPERATURE", 0.7))
+
+        # TODO change model_config there
         self.execute_local_commands = (
             os.getenv("EXECUTE_LOCAL_COMMANDS", "False") == "True"
         )
-        self.temperature = float(os.getenv("TEMPERATURE", "0.7"))
+        # 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",
+        )
+        
+        # milvus or zilliz cloud configuration
+        self.milvus_addr = os.getenv("MILVUS_ADDR", "localhost:19530")
+        self.milvus_username = os.getenv("MILVUS_USERNAME")
+        self.milvus_password = os.getenv("MILVUS_PASSWORD")
+        self.milvus_collection = os.getenv("MILVUS_COLLECTION", "dbgpt")
+        self.milvus_secure = os.getenv("MILVUS_SECURE") == "True"
 
-        self.plugins_dir = os.getenv("PLUGINS_DIR", 'plugins')
-        self.plugins:List[AutoGPTPluginTemplate] = []
+        plugins_allowlist = os.getenv("ALLOWLISTED_PLUGINS")
+        if plugins_allowlist:
+            self.plugins_allowlist = plugins_allowlist.split(",")
+        else:
+            self.plugins_allowlist = [] 
 
+        plugins_denylist = os.getenv("DENYLISTED_PLUGINS") 
+        if plugins_denylist:
+            self.plugins_denylist = []
+    
     def set_debug_mode(self, value: bool) -> None:
-        """Set the debug mode value."""
+        """Set the debug mode value"""
         self.debug_mode = value
 
-    def set_plugins(self,value: bool) -> None:
-        """Set the plugins value."""
+    def set_plugins(self, value: list) -> None:
+        """Set the plugins value. """
         self.plugins = value
 
-    def set_temperature(self, value: int) -> None:
-        """ Set the temperature value."""
+    def set_templature(self, value: int) -> None:
+        """Set the temperature value."""
         self.temperature = value
 
-  
+    

pilot/configs/model_config.py

@@ -5,6 +5,7 @@ import torch
 import os
 import nltk
 
+
 ROOT_PATH = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 MODEL_PATH = os.path.join(ROOT_PATH, "models")
 PILOT_PATH = os.path.join(ROOT_PATH, "pilot")
@@ -26,9 +27,8 @@ LLM_MODEL_CONFIG = {
 VECTOR_SEARCH_TOP_K = 3
 LLM_MODEL = "vicuna-13b"
 LIMIT_MODEL_CONCURRENCY = 5
-MAX_POSITION_EMBEDDINGS = 2048
-VICUNA_MODEL_SERVER = "http://192.168.31.114:8000"
-
+MAX_POSITION_EMBEDDINGS = 4096 
+VICUNA_MODEL_SERVER = "http://47.97.125.199:8000"
 
 # Load model config
 ISLOAD_8BIT = True
@@ -37,7 +37,7 @@ ISDEBUG = False
 
 DB_SETTINGS = {
     "user": "root",
-    "password": "aa123456",
+    "password": "aa12345678",
     "host": "localhost",
     "port": 3306
 }

pilot/conversation.py

@@ -10,6 +10,8 @@ class SeparatorStyle(Enum):
     
     SINGLE = auto()
     TWO = auto()
+    THREE = auto()
+    FOUR = auto()
 
 @dataclasses.dataclass
 class Conversation:
@@ -146,10 +148,103 @@ conv_vicuna_v1 = Conversation(
     sep2="</s>",
 )
 
+auto_dbgpt_one_shot = Conversation(
+    system="You are DB-GPT, an AI designed to answer questions about HackerNews by query `hackerbews` database in MySQL. "
+    "Your decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.",
+    roles=("USER", "ASSISTANT"),
+    messages=(
+        (
+          "USER", 
+          """ Answer how many users does hackernews have by query mysql database
+            Constraints:
+            1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.
+            2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.
+            3. No user assistance
+            4. Exclusively use the commands listed in double quotes e.g. "command name"
 
-conv_qa_prompt_template = """ 基于以下已知的信息, 专业、详细的回答用户的问题。
-            如果无法从提供的恶内容中获取答案, 请说: "知识库中提供的内容不足以回答此问题", 但是你可以给出一些与问题相关答案的建议:   
-            
+            Commands:
+            1. analyze_code: Analyze Code, args: "code": "<full_code_string>"
+            2. execute_python_file: Execute Python File, args: "filename": "<filename>"
+            3. append_to_file: Append to file, args: "filename": "<filename>", "text": "<text>"
+            4. delete_file: Delete file, args: "filename": "<filename>"
+            5. list_files: List Files in Directory, args: "directory": "<directory>"
+            6. read_file: Read file, args: "filename": "<filename>"
+            7. write_to_file: Write to file, args: "filename": "<filename>", "text": "<text>"
+            8. tidb_sql_executor: "Execute SQL in TiDB Database.", args: "sql": "<sql>"
+
+            Resources:
+            1. Internet access for searches and information gathering.
+            2. Long Term memory management.
+            3. vicuna powered Agents for delegation of simple tasks.
+            4. File output.
+
+            Performance Evaluation:
+            1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.
+            2. Constructively self-criticize your big-picture behavior constantly.
+            3. Reflect on past decisions and strategies to refine your approach.
+            4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.
+            5. Write all code to a file.
+
+            You should only respond in JSON format as described below
+            Response Format: 
+            {
+                "thoughts": {
+                    "text": "thought",
+                    "reasoning": "reasoning",
+                    "plan": "- short bulleted\n- list that conveys\n- long-term plan",
+                    "criticism": "constructive self-criticism",
+                    "speak": "thoughts summary to say to user"
+                },
+                "command": {
+                    "name": "command name",
+                    "args": {
+                        "arg name": "value"
+                    }
+                }
+            } 
+            Ensure the response can be parsed by Python json.loads
+          """
+        ),
+        (
+            "ASSISTANT",
+            """
+            {
+                "thoughts": {
+                    "text": "thought",
+                    "reasoning": "reasoning",
+                    "plan": "- short bulleted\n- list that conveys\n- long-term plan",
+                    "criticism": "constructive self-criticism",
+                    "speak": "thoughts summary to say to user"
+                },
+                "command": {
+                    "name": "command name",
+                    "args": {
+                        "arg name": "value"
+                    }
+                }
+            } 
+            """
+        )
+    ),
+    offset=0,
+    sep_style=SeparatorStyle.THREE,
+    sep=" ",
+    sep2="</s>",
+)
+
+auto_dbgpt_without_shot = Conversation(
+    system="You are DB-GPT, an AI designed to answer questions about HackerNews by query `hackerbews` database in MySQL. "
+    "Your decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.",
+    roles=("USER", "ASSISTANT"),
+    messages=(),
+    offset=0,
+    sep_style=SeparatorStyle.FOUR,
+    sep=" ",
+    sep2="</s>",
+)
+
+conv_qa_prompt_template = """ 基于以下已知的信息, 专业、详细的回答用户的问题,
+            如果无法从提供的恶内容中获取答案, 请说: "知识库中提供的内容不足以回答此问题", 但是你可以给出一些与问题相关答案的建议。 
             已知内容: 
             {context}
             问题:

pilot/model/compression.py

@@ -0,0 +1,121 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+import dataclasses
+
+import torch
+from torch import Tensor
+import torch.nn as nn
+from torch.nn import functional as F
+
+
+@dataclasses.dataclass
+class CompressionConfig:
+    """Group-wise quantization."""
+    num_bits: int
+    group_size: int
+    group_dim: int
+    symmetric: bool
+    enabled: bool = True
+
+
+default_compression_config = CompressionConfig(
+    num_bits=8, group_size=256, group_dim=1, symmetric=True, enabled=True)
+
+
+class CLinear(nn.Module):
+    """Compressed Linear Layer."""
+
+    def __init__(self, weight, bias, device):
+        super().__init__()
+
+        self.weight = compress(weight.data.to(device), default_compression_config)
+        self.bias = bias
+
+    def forward(self, input: Tensor) -> Tensor:
+        weight = decompress(self.weight, default_compression_config)
+        return F.linear(input, weight, self.bias)
+
+
+def compress_module(module, target_device):
+    for attr_str in dir(module):
+        target_attr = getattr(module, attr_str)
+        if type(target_attr) == torch.nn.Linear:
+            setattr(module, attr_str,
+                CLinear(target_attr.weight, target_attr.bias, target_device))
+    for name, child in module.named_children():
+        compress_module(child, target_device)
+
+
+def compress(tensor, config):
+    """Simulate group-wise quantization."""
+    if not config.enabled:
+        return tensor
+
+    group_size, num_bits, group_dim, symmetric = (
+        config.group_size, config.num_bits, config.group_dim, config.symmetric)
+    assert num_bits <= 8
+
+    original_shape = tensor.shape
+    num_groups = (original_shape[group_dim] + group_size - 1) // group_size
+    new_shape = (original_shape[:group_dim] + (num_groups, group_size) +
+                 original_shape[group_dim+1:])
+
+    # Pad
+    pad_len = (group_size - original_shape[group_dim] % group_size) % group_size
+    if pad_len != 0:
+        pad_shape = original_shape[:group_dim] + (pad_len,) + original_shape[group_dim+1:]
+        tensor = torch.cat([
+            tensor,
+            torch.zeros(pad_shape, dtype=tensor.dtype, device=tensor.device)],
+            dim=group_dim)
+    data = tensor.view(new_shape)
+
+    # Quantize
+    if symmetric:
+        B = 2 ** (num_bits - 1) - 1
+        scale = B / torch.max(data.abs(), dim=group_dim + 1, keepdim=True)[0]
+        data = data * scale
+        data = data.clamp_(-B, B).round_().to(torch.int8)
+        return data, scale, original_shape
+    else:
+        B = 2 ** num_bits - 1
+        mn = torch.min(data, dim=group_dim + 1, keepdim=True)[0]
+        mx = torch.max(data, dim=group_dim + 1, keepdim=True)[0]
+
+        scale = B / (mx - mn)
+        data = data - mn
+        data.mul_(scale)
+
+        data = data.clamp_(0, B).round_().to(torch.uint8)
+        return data, mn, scale, original_shape
+
+
+def decompress(packed_data, config):
+    """Simulate group-wise dequantization."""
+    if not config.enabled:
+        return packed_data
+
+    group_size, num_bits, group_dim, symmetric = (
+        config.group_size, config.num_bits, config.group_dim, config.symmetric)
+
+    # Dequantize
+    if symmetric:
+        data, scale, original_shape = packed_data
+        data = data / scale
+    else:
+        data, mn, scale, original_shape = packed_data
+        data = data / scale
+        data.add_(mn)
+
+    # Unpad
+    pad_len = (group_size - original_shape[group_dim] % group_size) % group_size
+    if pad_len:
+        padded_original_shape = (
+            original_shape[:group_dim] +
+            (original_shape[group_dim] + pad_len,) +
+            original_shape[group_dim+1:])
+        data = data.reshape(padded_original_shape)
+        indices = [slice(0, x) for x in original_shape]
+        return data[indices].contiguous()
+    else:
+        return data.view(original_shape)

pilot/model/inference.py

@@ -5,13 +5,13 @@ import torch
 
 @torch.inference_mode()
 def generate_stream(model, tokenizer, params, device,
-                    context_len=2048, stream_interval=2):
+                    context_len=4096, stream_interval=2):
 
     """Fork from fastchat: https://github.com/lm-sys/FastChat/blob/main/fastchat/serve/inference.py """
     prompt = params["prompt"]
     l_prompt = len(prompt)
     temperature = float(params.get("temperature", 1.0))
-    max_new_tokens = int(params.get("max_new_tokens", 256))
+    max_new_tokens = int(params.get("max_new_tokens", 2048))
     stop_str = params.get("stop", None)
 
     input_ids = tokenizer(prompt).input_ids

pilot/model/llm/base.py

@@ -0,0 +1,34 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+from dataclasses import dataclass, field
+from typing import List, TypedDict
+
+
+class Message(TypedDict):
+    """Vicuna Message object containing a role and the message content """
+
+    role: str
+    content: str
+
+
+@dataclass
+class ModelInfo:
+    """Struct for model information.
+
+    Would be lovely to eventually get this directly from APIs
+    """
+    name: str
+    max_tokens: int
+
+@dataclass
+class LLMResponse:
+    """Standard response struct for a response from a LLM model."""
+    model_info = ModelInfo
+
+
+@dataclass
+class ChatModelResponse(LLMResponse):
+    """Standard response struct for a response from an LLM model."""
+
+    content: str = None

pilot/model/llm/llm_utils.py

@@ -0,0 +1,108 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import abc
+import time
+import functools
+from typing import List, Optional
+from pilot.model.llm.base import Message
+from pilot.conversation import conv_templates, Conversation, conv_one_shot, auto_dbgpt_one_shot 
+from pilot.configs.config import Config
+
+
+# TODO Rewrite this
+def retry_stream_api(
+    num_retries: int = 10,
+    backoff_base: float = 2.0,
+    warn_user: bool = True
+): 
+    """Retry an Vicuna Server call.
+
+        Args: 
+            num_retries int: Number of retries. Defaults to 10.
+            backoff_base float: Base for exponential backoff. Defaults to 2.
+            warn_user bool: Whether to warn the user. Defaults to True.
+    """
+    retry_limit_msg = f"Error: Reached rate limit, passing..."
+    backoff_msg = (f"Error: API Bad gateway. Waiting {{backoff}} seconds...")
+
+    def _wrapper(func):
+        @functools.wraps(func)
+        def _wrapped(*args, **kwargs):
+            user_warned = not warn_user
+            num_attempts = num_retries + 1 # +1 for the first attempt
+            for attempt in range(1, num_attempts + 1):
+                try:
+                    return func(*args, **kwargs)
+                except Exception as e:
+                    if (e.http_status != 502) or (attempt == num_attempts):
+                        raise
+
+                backoff = backoff_base ** (attempt + 2)
+                time.sleep(backoff) 
+        return _wrapped
+    return _wrapper
+
+# Overly simple abstraction util we create something better
+# simple retry mechanism when getting a rate error or a bad gateway
+def create_chat_competion(
+    conv: Conversation,
+    model: Optional[str] = None,
+    temperature: float = None,
+    max_new_tokens: Optional[int] = None,
+) -> str:
+    """Create a chat completion using the Vicuna-13b
+    
+        Args:
+            messages(List[Message]): The messages to send to the chat completion
+            model (str, optional): The model to use. Default to None.
+            temperature (float, optional): The temperature to use. Defaults to 0.7.
+            max_tokens (int, optional): The max tokens to use. Defaults to None.
+
+        Returns:
+            str: The response from the chat completion
+    """
+    cfg = Config()
+    if temperature is None:
+        temperature = cfg.temperature
+
+    # TODO request vicuna model get response
+    # convert vicuna message to chat completion.
+    for plugin in cfg.plugins:
+        if plugin.can_handle_chat_completion():
+            pass
+
+
+class ChatIO(abc.ABC):
+    @abc.abstractmethod
+    def prompt_for_input(self, role: str) -> str:
+        """Prompt for input from a role."""
+    
+    @abc.abstractmethod
+    def prompt_for_output(self, role: str) -> str:
+        """Prompt for output from a role."""
+
+    @abc.abstractmethod
+    def stream_output(self, output_stream, skip_echo_len: int):
+        """Stream output."""
+
+
+class SimpleChatIO(ChatIO):
+    def prompt_for_input(self, role: str) -> str:
+        return input(f"{role}: ")
+
+    def prompt_for_output(self, role: str) -> str:
+        print(f"{role}: ", end="", flush=True)
+
+    def stream_output(self, output_stream, skip_echo_len: int):
+        pre = 0
+        for outputs in output_stream:
+            outputs = outputs[skip_echo_len:].strip()
+            now = len(outputs) - 1
+            if now > pre:
+                print(" ".join(outputs[pre:now]), end=" ", flush=True)
+                pre = now
+
+        print(" ".join(outputs[pre:]), flush=True)
+        return " ".join(outputs)
+

pilot/model/loader.py

@@ -2,15 +2,17 @@
 # -*- coding: utf-8 -*-
 
 import torch
+from pilot.singleton import Singleton
+
 from transformers import (
     AutoTokenizer,
     AutoModelForCausalLM,
     AutoModel
 )
 
-from fastchat.serve.compression import compress_module
+from pilot.model.compression import compress_module 
 
-class ModelLoader:
+class ModelLoader(metaclass=Singleton):
     """Model loader is a class for model load
     
       Args: model_path

pilot/server/vicuna_server.py

@@ -10,8 +10,6 @@ from fastapi.responses import StreamingResponse
 from pilot.model.inference import generate_stream
 from pydantic import BaseModel
 from pilot.model.inference import generate_output, get_embeddings
-from fastchat.serve.inference import load_model
-
 
 from pilot.model.loader import ModelLoader
 from pilot.configs.model_config import *

pilot/server/webserver.py

@@ -24,11 +24,9 @@ from pilot.conversation import (
     SeparatorStyle
 )
 
-from fastchat.utils import (
+from pilot.utils import (
     build_logger,
     server_error_msg,
-    violates_moderation,
-    moderation_msg
 )
 
 from pilot.server.gradio_css import code_highlight_css
@@ -45,6 +43,7 @@ enable_moderation = False
 models = []
 dbs = []
 vs_list = ["新建知识库"] + get_vector_storelist()
+autogpt = False
 
 priority = {
     "vicuna-13b": "aaa"
@@ -58,8 +57,6 @@ def get_simlar(q):
     contents = [dc.page_content for dc, _ in docs]
     return "\n".join(contents)
     
-    
-
 def gen_sqlgen_conversation(dbname):
     mo = MySQLOperator(
         **DB_SETTINGS
@@ -118,6 +115,8 @@ def regenerate(state, request: gr.Request):
     return (state, state.to_gradio_chatbot(), "") + (disable_btn,) * 5
 
 def clear_history(request: gr.Request):
+
+    
     logger.info(f"clear_history. ip: {request.client.host}")
     state = None
     return (state, [], "") + (disable_btn,) * 5
@@ -128,14 +127,9 @@ def add_text(state, text, request: gr.Request):
     if len(text) <= 0:
         state.skip_next = True
         return (state, state.to_gradio_chatbot(), "") + (no_change_btn,) * 5
-    if args.moderate:
-        flagged = violates_moderation(text)
-        if flagged:
-            state.skip_next = True
-            return (state, state.to_gradio_chatbot(), moderation_msg) + (
-                no_change_btn,) * 5
 
-    text = text[:1536]  # Hard cut-off
+    """ Default support 4000 tokens, if tokens too lang, we will cut off  """
+    text = text[:4000]  
     state.append_message(state.roles[0], text)
     state.append_message(state.roles[1], None)
     state.skip_next = False
@@ -152,6 +146,8 @@ def post_process_code(code):
     return code
 
 def http_bot(state, mode, db_selector, temperature, max_new_tokens, request: gr.Request):
+
+    print("是否是AUTO-GPT模式.", autogpt)
     start_tstamp = time.time()
     model_name = LLM_MODEL
 
@@ -162,7 +158,8 @@ def http_bot(state, mode, db_selector, temperature, max_new_tokens, request: gr.
         yield (state, state.to_gradio_chatbot()) + (no_change_btn,) * 5
         return
 
-   
+
+    # TODO when tab mode is AUTO_GPT, Prompt need to rebuild.
     if len(state.messages) == state.offset + 2:
         # 第一轮对话需要加入提示Prompt 
         
@@ -251,29 +248,28 @@ def http_bot(state, mode, db_selector, temperature, max_new_tokens, request: gr.
 block_css = (
     code_highlight_css
     + """
-pre {
-    white-space: pre-wrap;       /* Since CSS 2.1 */
-    white-space: -moz-pre-wrap;  /* Mozilla, since 1999 */
-    white-space: -pre-wrap;      /* Opera 4-6 */
-    white-space: -o-pre-wrap;    /* Opera 7 */
-    word-wrap: break-word;       /* Internet Explorer 5.5+ */
-}
-#notice_markdown th {
-    display: none;
-}
-    """
+        pre {
+            white-space: pre-wrap;       /* Since CSS 2.1 */
+            white-space: -moz-pre-wrap;  /* Mozilla, since 1999 */
+            white-space: -pre-wrap;      /* Opera 4-6 */
+            white-space: -o-pre-wrap;    /* Opera 7 */
+            word-wrap: break-word;       /* Internet Explorer 5.5+ */
+        }
+        #notice_markdown th {
+            display: none;
+        }
+            """
 )
 
-def change_tab(tab):
-    pass
-
 def change_mode(mode):
     if mode in ["默认知识库对话", "LLM原生对话"]:
         return gr.update(visible=False)
     else:
         return gr.update(visible=True)
 
-
+def change_tab():
+    autogpt = True 
+ 
 def build_single_model_ui():
    
     notice_markdown = """
@@ -301,16 +297,17 @@ def build_single_model_ui():
 
         max_output_tokens = gr.Slider(
             minimum=0,
-            maximum=1024,
-            value=512,
+            maximum=4096,
+            value=2048,
             step=64,
             interactive=True,
             label="最大输出Token数",
         )
-    tabs = gr.Tabs() 
+    tabs= gr.Tabs()
     with tabs:
-        with gr.TabItem("SQL生成与诊断", elem_id="SQL"):
-        # TODO A selector to choose database
+        tab_sql = gr.TabItem("SQL生成与诊断", elem_id="SQL")
+        with tab_sql:
+            # TODO A selector to choose database
             with gr.Row(elem_id="db_selector"):
                 db_selector = gr.Dropdown(
                     label="请选择数据库",
@@ -318,9 +315,12 @@ def build_single_model_ui():
                     value=dbs[0] if len(models) > 0 else "",
                     interactive=True,
                     show_label=True).style(container=False) 
+        tab_auto = gr.TabItem("AUTO-GPT", elem_id="auto")
+        with tab_auto:
+            gr.Markdown("自动执行模式下, DB-GPT可以具备执行SQL、从网络读取知识自动化存储学习的能力")
 
-        with gr.TabItem("知识问答", elem_id="QA"):
-            
+        tab_qa = gr.TabItem("知识问答", elem_id="QA")
+        with tab_qa:
             mode = gr.Radio(["LLM原生对话", "默认知识库对话", "新增知识库对话"], show_label=False, value="LLM原生对话")
             vs_setting = gr.Accordion("配置知识库", open=False)
             mode.change(fn=change_mode, inputs=mode, outputs=vs_setting)
@@ -360,9 +360,7 @@ def build_single_model_ui():
         regenerate_btn = gr.Button(value="重新生成", interactive=False)
         clear_btn = gr.Button(value="清理", interactive=False)
 
-
     gr.Markdown(learn_more_markdown)
-
     btn_list = [regenerate_btn, clear_btn]
     regenerate_btn.click(regenerate, state, [state, chatbot, textbox] + btn_list).then(
         http_bot,
@@ -434,9 +432,7 @@ if __name__ == "__main__":
         "--model-list-mode", type=str, default="once", choices=["once", "reload"]
     )
     parser.add_argument("--share", default=False, action="store_true")
-    parser.add_argument(
-        "--moderate", action="store_true", help="Enable content moderation"
-    )
+
     args = parser.parse_args()
     logger.info(f"args: {args}")
 

pilot/utils.py

@@ -3,6 +3,20 @@
 
 import torch
 
+import datetime
+import logging
+import logging.handlers
+import os
+import sys
+
+import requests
+
+from pilot.configs.model_config import LOGDIR
+
+server_error_msg = "**NETWORK ERROR DUE TO HIGH TRAFFIC. PLEASE REGENERATE OR REFRESH THIS PAGE.**"
+
+handler = None
+
 def get_gpu_memory(max_gpus=None):
     gpu_memory = []
     num_gpus = (
@@ -20,3 +34,98 @@ def get_gpu_memory(max_gpus=None):
             available_memory = total_memory - allocated_memory
             gpu_memory.append(available_memory)
         return gpu_memory
+
+
+
+def build_logger(logger_name, logger_filename):
+    global handler
+
+    formatter = logging.Formatter(
+        fmt="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
+        datefmt="%Y-%m-%d %H:%M:%S",
+    )
+
+    # Set the format of root handlers
+    if not logging.getLogger().handlers:
+        logging.basicConfig(level=logging.INFO, encoding='utf-8')
+    logging.getLogger().handlers[0].setFormatter(formatter)
+
+    # Redirect stdout and stderr to loggers
+    stdout_logger = logging.getLogger("stdout")
+    stdout_logger.setLevel(logging.INFO)
+    sl = StreamToLogger(stdout_logger, logging.INFO)
+    sys.stdout = sl
+
+    stderr_logger = logging.getLogger("stderr")
+    stderr_logger.setLevel(logging.ERROR)
+    sl = StreamToLogger(stderr_logger, logging.ERROR)
+    sys.stderr = sl
+
+    # Get logger
+    logger = logging.getLogger(logger_name)
+    logger.setLevel(logging.INFO)
+
+    # Add a file handler for all loggers
+    if handler is None:
+        os.makedirs(LOGDIR, exist_ok=True)
+        filename = os.path.join(LOGDIR, logger_filename)
+        handler = logging.handlers.TimedRotatingFileHandler(
+            filename, when='D', utc=True)
+        handler.setFormatter(formatter)
+
+        for name, item in logging.root.manager.loggerDict.items():
+            if isinstance(item, logging.Logger):
+                item.addHandler(handler)
+
+    return logger
+
+
+class StreamToLogger(object):
+    """
+    Fake file-like stream object that redirects writes to a logger instance.
+    """
+    def __init__(self, logger, log_level=logging.INFO):
+        self.terminal = sys.stdout
+        self.logger = logger
+        self.log_level = log_level
+        self.linebuf = ''
+
+    def __getattr__(self, attr):
+        return getattr(self.terminal, attr)
+
+    def write(self, buf):
+        temp_linebuf = self.linebuf + buf
+        self.linebuf = ''
+        for line in temp_linebuf.splitlines(True):
+            # From the io.TextIOWrapper docs:
+            #   On output, if newline is None, any '\n' characters written
+            #   are translated to the system default line separator.
+            # By default sys.stdout.write() expects '\n' newlines and then
+            # translates them so this is still cross platform.
+            if line[-1] == '\n':
+                encoded_message = line.encode('utf-8', 'ignore').decode('utf-8')
+                self.logger.log(self.log_level, encoded_message.rstrip())
+            else:
+                self.linebuf += line
+
+    def flush(self):
+        if self.linebuf != '':
+            encoded_message = self.linebuf.encode('utf-8', 'ignore').decode('utf-8')
+            self.logger.log(self.log_level, encoded_message.rstrip())
+        self.linebuf = ''
+
+
+def disable_torch_init():
+    """
+    Disable the redundant torch default initialization to accelerate model creation.
+    """
+    import torch
+    setattr(torch.nn.Linear, "reset_parameters", lambda self: None)
+    setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None)
+
+
+def pretty_print_semaphore(semaphore):
+    if semaphore is None:
+        return "None"
+    return f"Semaphore(value={semaphore._value}, locked={semaphore.locked()})"
+

requirements.txt

@@ -1,7 +1,5 @@
 accelerate==0.16.0
 torch==2.0.0
-torchvision==0.13.1
-torchaudio==0.12.1
 accelerate==0.16.0
 aiohttp==3.8.4
 aiosignal==1.3.1
@@ -47,11 +45,12 @@ pycocoevalcap
 sentence-transformers
 umap-learn
 notebook
-gradio==3.24.1
+gradio==3.23
 gradio-client==0.0.8
 wandb
-fschat=0.1.10
-llama-index=0.5.27
+llama-index==0.5.27
 pymysql
 unstructured==0.6.3
-pytesseract==0.3.10
+pytesseract==0.3.10
+chromadb
+markdown2