feat(frontend): 添加数据集页面并重构主页面布局

- 新增 dataset_page 模块，实现数据集页面的基本布局
- 重构 main.py 中的页面加载方式，使用列表收集所有页面
- 更新主页面布局，将聊天页面作为第一个选项卡
- 调整设置页面的加载方式，直接使用函数调用

.gitignore

@@ -11,6 +11,7 @@ env/
 # IDE
 .vscode/
 .idea/
+.roo
 
 # Environment files
 .env

LICENSE

@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2022 C-a-r-r-y
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.

README.md

@@ -1,15 +0,0 @@
-# 基于文档驱动的自适应编码大模型微调框架
-## 简介
-本人的毕业设计,这个是mvp分支（MVP 是指最小可行产品Minimum Viable Product），其他功能在master分支中
-### 项目概述
-
-* 通过深度解析私有库的文档以及其他资源，生成指令型语料，据此对大语言模型进行针对私有库的微调。
-
-### 项目技术（预计）
-
-* 使用unsloth框架在GPU上实现大语言模型的qlora微调
-* 使用langchain框架编写工作流实现批量生成微调语料
-* 使用tinydb和sqlite实现数据的持久化
-* 使用gradio框架实现前端展示
-
-**施工中......**

config/llm.py

@@ -1,15 +0,0 @@
-import os
-from dotenv import load_dotenv
-from typing import Dict, Any
-
-def load_config() -> Dict[str, Any]:
-    """从.env文件加载配置"""
-    load_dotenv()
-    
-    return {
-        "openai": {
-            "api_key": os.getenv("OPENAI_API_KEY"),
-            "base_url": os.getenv("OPENAI_BASE_URL"),
-            "model_id": os.getenv("OPENAI_MODEL_ID")
-        }
-    }

dataset_generator.py

@@ -1,94 +0,0 @@
-import os
-import json
-from tools.parse_markdown import parse_markdown, MarkdownNode
-from tools.openai_api import generate_json_via_llm
-from prompt.base import create_dataset
-from config.llm import load_config
-from tqdm import tqdm
-
-def process_markdown_file(file_path):
-    with open(file_path, 'r', encoding='utf-8') as f:
-        content = f.read()
-    
-    root = parse_markdown(content)
-    results = []
-    
-    def traverse(node, parent_titles):
-        current_titles = parent_titles.copy()
-        current_titles.append(node.title)
-        
-        if not node.children:  # 叶子节点
-            if node.content:
-                full_text = ' -> '.join(current_titles) + '\n' + node.content
-                results.append(full_text)
-        else:
-            for child in node.children:
-                traverse(child, current_titles)
-    
-    traverse(root, [])
-    return results
-
-def find_markdown_files(directory):
-    markdown_files = []
-    for root, dirs, files in os.walk(directory):
-        for file in files:
-            if file.endswith('.md'):
-                markdown_files.append(os.path.join(root, file))
-    return markdown_files
-
-def process_all_markdown(doc_dir):
-    all_results = []
-    
-    markdown_files = find_markdown_files(doc_dir)
-    for file_path in markdown_files:
-        results = process_markdown_file(file_path)
-        all_results.extend(results)
-    
-    return all_results
-
-def save_dataset(dataset, output_dir):
-    os.makedirs(output_dir, exist_ok=True)
-    output_path = os.path.join(output_dir, 'dataset.json')
-    with open(output_path, 'w', encoding='utf-8') as f:
-        json.dump(dataset, f, ensure_ascii=False, indent=2)
-
-if __name__ == "__main__":
-    # 解析markdown文档
-    results = process_all_markdown('workdir/my_docs')
-    
-    # 加载LLM配置
-    config = load_config()
-    
-    dataset = []
-    # 使用tqdm包装外部循环以显示进度条
-    for content in tqdm(results, desc="生成数据集进度", unit="文档"):
-        for _ in range(3):
-            prompt = create_dataset.create(
-                "LLaMA-Factory",  # 项目名
-                content,  # 文档内容
-                """{
-            "dataset":[
-                {
-                    "question":"",
-                    "answer":""
-                }
-            ]
-        }"""
-            )
-            
-            # 调用LLM生成JSON
-            try:
-                result = generate_json_via_llm(
-                    prompt=prompt,
-                    base_url=config["openai"]["base_url"],
-                    api_key=config["openai"]["api_key"],
-                    model_id=config["openai"]["model_id"]
-                )
-                print(json.loads(result)["dataset"])
-                dataset.extend(json.loads(result)["dataset"])
-            except Exception as e:
-                print(f"生成数据集时出错: {e}")
-
-    # 保存数据集
-    save_dataset(dataset, 'workdir/dataset2')
-    print(f"数据集已生成，共{len(dataset)}条数据")

db/__init__.py

@@ -0,0 +1,3 @@
+from .init_db import get_engine, initialize_db
+
+__all__ = ['get_engine', 'initialize_db']

db/init_db.py

@@ -0,0 +1,79 @@
+from sqlmodel import SQLModel, create_engine, Session
+from sqlmodel import select
+from typing import Optional
+import os
+from pathlib import Path
+import sys
+from dotenv import load_dotenv
+from sqlalchemy.engine import Engine
+
+# 将项目根目录添加到系统路径中，以便能够导入项目中的其他模块
+sys.path.append(str(Path(__file__).resolve().parent.parent))
+from schema.dataset_generation import APIProvider
+
+# 全局变量，用于存储数据库引擎实例
+_engine: Optional[Engine] = None
+
+def get_engine(workdir: str) -> Engine:
+    """
+    获取数据库引擎实例。如果引擎尚未创建，则创建一个新的引擎并返回。
+
+    Args:
+        workdir (str): 工作目录路径，用于确定数据库文件的存储位置。
+
+    Returns:
+        Engine: SQLAlchemy 数据库引擎实例。
+    """
+    global _engine
+    if not _engine:
+        # 创建数据库目录（如果不存在）
+        db_dir = os.path.join(workdir, "db")
+        os.makedirs(db_dir, exist_ok=True)
+        # 定义数据库文件路径
+        db_path = os.path.join(db_dir, "db.sqlite")
+        # 创建数据库URL
+        db_url = f"sqlite:///{db_path}"
+        # 创建数据库引擎
+        _engine = create_engine(db_url)
+    return _engine
+
+def initialize_db(engine: Engine) -> None:
+    """
+    初始化数据库，创建所有表结构，并插入初始数据（如果不存在）。
+
+    Args:
+        engine (Engine): SQLAlchemy 数据库引擎实例。
+    """
+    # 创建所有表结构
+    SQLModel.metadata.create_all(engine)
+    # 加载环境变量
+    load_dotenv()
+    # 从环境变量中获取API相关配置
+    api_key = os.getenv("API_KEY")
+    base_url = os.getenv("BASE_URL")
+    model_id = os.getenv("MODEL_ID")
+    
+    # 如果所有必要的环境变量都存在，则插入初始数据
+    if api_key and base_url and model_id:
+        with Session(engine) as session:
+            # 查询是否已存在APIProvider记录
+            statement = select(APIProvider).limit(1)
+            existing_provider = session.exec(statement).first()
+            
+            # 如果不存在，则插入新的APIProvider记录
+            if not existing_provider:
+                provider = APIProvider(
+                    base_url=base_url,
+                    model_id=model_id,
+                    api_key=api_key
+                )
+                session.add(provider)
+                session.commit()
+
+if __name__ == "__main__":
+    # 定义工作目录路径
+    workdir = os.path.join(os.path.dirname(__file__), "..", "workdir")
+    # 获取数据库引擎
+    engine = get_engine(workdir)
+    # 初始化数据库
+    initialize_db(engine)

frontend/__init__.py

@@ -0,0 +1,4 @@
+from .chat_page import chat_page
+from .setting_page import setting_page
+from .train_page import train_page
+from .dataset_page import dataset_page

frontend/chat_page.py

@@ -0,0 +1,9 @@
+import gradio as gr
+
+def chat_page():
+    with gr.Blocks() as demo:
+        gr.Markdown("## 聊天")
+        with gr.Row():
+            with gr.Column():
+                pass
+    return demo

frontend/dataset_page.py

@@ -0,0 +1,9 @@
+import gradio as gr
+
+def dataset_page():
+    with gr.Blocks() as demo:
+        gr.Markdown("## 数据集")
+        with gr.Row():
+            with gr.Column():
+                pass
+    return demo

frontend/setting_page.py

@@ -0,0 +1,35 @@
+import gradio as gr
+from typing import List, Dict
+from sqlmodel import Session, select
+from db import get_engine
+from schema import APIProvider
+import os
+
+# 获取数据库引擎
+engine = get_engine(os.path.join(os.path.dirname(__file__), "..", "workdir"))
+
+def setting_page():
+    def get_providers() -> List[List[str]]:
+        with Session(engine) as session:
+            providers = session.exec(select(APIProvider)).all()
+            return [
+                [p.id, p.model_id, p.base_url, p.api_key or ""]
+                for p in providers
+            ]
+
+    with gr.Blocks() as demo:
+        gr.Markdown("## API Provider 管理")
+        
+        with gr.Row():
+            # API Provider列表
+            with gr.Column(scale=2):
+                provider_table = gr.DataFrame(
+                    headers=["id" , "model id", "URL", "API Key"],
+                    datatype=["number","str", "str", "str"],
+                    interactive=True,
+                    value=get_providers(),
+                    wrap=True,
+                    col_count=(4, "fixed")
+                )
+        
+    return demo

frontend/train_page.py

@@ -0,0 +1,9 @@
+import gradio as gr
+
+def train_page():
+    with gr.Blocks() as demo:
+        gr.Markdown("## 微调")
+        with gr.Row():
+            with gr.Column():
+                pass
+    return demo

main.py

@@ -0,0 +1,27 @@
+import gradio as gr
+from frontend.setting_page import setting_page
+from frontend import chat_page,setting_page,train_page,dataset_page
+from db import initialize_db as init_db,get_engine
+
+if __name__ == "__main__":
+    init_db(get_engine("workdir"))
+
+    pages = []
+    pages.append(setting_page())
+    pages.append(chat_page())
+    pages.append(train_page())
+    pages.append(dataset_page())
+
+    with gr.Blocks() as app:
+        gr.Markdown("# 基于文档驱动的自适应编码大模型微调框架")
+        with gr.Tabs():
+            with gr.TabItem("聊天"):
+                chat_page()
+            with gr.TabItem("微调"):
+                train_page()
+            with gr.TabItem("数据集"):
+                dataset_page()
+            with gr.TabItem("设置"):
+                setting_page()
+    
+    app.launch()

prompt/base.py

@@ -1,25 +0,0 @@
-class create_dataset:
-    """用于生成微调数据集模板的类"""
-    
-    template = """
-项目名为:{}
-请依据以下该项目官方文档的部分内容，创造合适的对话数据集用于微调一个了解该项目的小模型的语料，要求兼顾文档中间尽可能多的信息点，使用中文
-文档节选：{}
-按照如下json格式返回：{}
-"""
-
-    @staticmethod
-    def create(*args: any) -> str:
-        """根据提供的任意数量参数生成数据集模板
-        
-        Args:
-            *args: 任意数量的参数，将按顺序填充到模板中
-            
-        Returns:
-            格式化后的模板字符串
-        """
-        return create_dataset.template.format(*args)
-    
-
-if __name__=="__main__":
-    print(create_dataset.create("a", "b", "c"))

requirements.txt

@@ -1,2 +1,4 @@
 openai>=1.0.0
-python-dotenv>=1.0.0
+python-dotenv>=1.0.0
+pydantic>=2.0.0
+gradio>=3.0.0

schema/__init__.py

@@ -0,0 +1,4 @@
+from .dataset import *
+from .dataset_generation import APIProvider, LLMResponse, LLMRequest
+from .md_doc import MarkdownNode
+from .prompt import promptTempleta

schema/dataset.py

@@ -1,9 +0,0 @@
-from pydantic import BaseModel, RootModel
-from typing import List
-
-class QAPair(BaseModel):
-    question: str
-    response: str
-
-class QAArray(RootModel):
-    root: List[QAPair]

schema/dataset_generation.py

@@ -0,0 +1,51 @@
+from datetime import datetime, timezone
+from typing import Optional
+from sqlmodel import SQLModel, Relationship, Field
+
+class APIProvider(SQLModel, table=True):
+    id: Optional[int] = Field(default=None, primary_key=True)
+    base_url: str = Field(..., description="API的基础URL")
+    model_id: str = Field(..., description="API使用的模型ID")
+    api_key: Optional[str] = Field(default=None, description="用于身份验证的API密钥")
+    created_at: datetime = Field(
+        default_factory=lambda: datetime.now(timezone.utc),
+        description="记录创建时间"
+    )
+
+class LLMResponse(SQLModel):
+    timestamp: datetime  = Field(
+        default_factory=lambda: datetime.now(timezone.utc),
+        description="响应的时间戳"
+    )
+    response_id: str = Field(..., description="响应的唯一ID")
+    tokens_usage: dict = Field(default_factory=lambda: {
+        "prompt_tokens": 0,
+        "completion_tokens": 0,
+        "prompt_cache_hit_tokens": None,
+        "prompt_cache_miss_tokens": None
+    }, description="token使用信息")
+    response_content: dict = Field(default_factory=dict, description="API响应的内容")
+    total_duration: float = Field(default=0.0, description="请求的总时长，单位为秒")
+    llm_parameters: dict = Field(default_factory=lambda: {
+        "temperature": None,
+        "max_tokens": None,
+        "top_p": None,
+        "frequency_penalty": None,
+        "presence_penalty": None,
+        "seed": None
+    }, description="API的生成参数")
+
+class LLMRequest(SQLModel):
+    prompt: str = Field(..., description="发送给API的提示词") 
+    provider_id: int = Field(foreign_key="apiprovider.id")
+    provider: APIProvider = Relationship()
+    format: Optional[str] = Field(default=None, description="API响应的格式")
+    response: list[LLMResponse] = Field(default_factory=list, description="API响应列表")
+    error: Optional[list[str]] = Field(default=None, description="API请求过程中发生的错误信息")
+    total_duration: float = Field(default=0.0, description="请求的总时长，单位为秒")
+    total_tokens_usage: dict = Field(default_factory=lambda: {
+        "prompt_tokens": 0,
+        "completion_tokens": 0,
+        "prompt_cache_hit_tokens": None,
+        "prompt_cache_miss_tokens": None
+    }, description="token使用信息")

schema/md_doc.py

@@ -0,0 +1,13 @@
+from pydantic import BaseModel, Field
+from typing import List, Optional
+
+class MarkdownNode(BaseModel):
+    level: int = Field(default=0, description="节点层级")
+    title: str = Field(default="Root", description="节点标题")
+    content: Optional[str] = Field(default=None, description="节点内容")
+    children: List['MarkdownNode'] = Field(default_factory=list, description="子节点列表")
+
+    class Config:
+        arbitrary_types_allowed = True
+
+MarkdownNode.model_rebuild()

tools/openai_api.py

@@ -1,69 +0,0 @@
-import json
-from openai import OpenAI
-
-def generate_json_via_llm(
-    prompt: str,
-    base_url: str,
-    api_key: str,
-    model_id: str
-) -> str:
-    client = OpenAI(
-        api_key=api_key,
-        base_url=base_url
-    )
-    
-    try:
-
-        
-        response = client.chat.completions.create(
-            model=model_id,
-            messages=[
-                {
-                    "role": "user",
-                    "content": prompt
-                }
-            ],
-            response_format={
-                'type': 'json_object'
-            }
-        )
-        
-        return response.choices[0].message.content
-    except Exception as e:
-        raise RuntimeError(f"API请求失败: {e}")
-
-
-if __name__ == "__main__":
-    import sys
-    import os
-    sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
-    from config.llm import load_config
-    # 将项目根目录添加到 sys.path 中
-
-    # 示例用法
-    try:
-        config = load_config()
-        print(config)
-        result = generate_json_via_llm(
-            prompt="""测试，随便生成点什么，返回json格式的字符串,格式如下
-{
-    "dataset":[
-        {
-            "question":"",
-            "answer":""
-        },
-        {
-            "question":"",
-            "answer":""
-        }
-        ......
-    ]
-}
-            """,
-            base_url=config["openai"]["base_url"],
-            api_key=config["openai"]["api_key"],
-            model_id=config["openai"]["model_id"],
-        )
-        print(result)
-    except Exception as e:
-        print(f"错误: {e}")

tools/parse_markdown.py

@@ -1,28 +1,24 @@
 import re
+import sys
+from pathlib import Path
 
-class MarkdownNode:
+# 添加项目根目录到sys.path
-    def __init__(self, level=0, title="Root"):
+sys.path.append(str(Path(__file__).resolve().parent.parent))
-        self.level = level
+from schema import MarkdownNode
-        self.title = title
-        self.content = ""  # 使用字符串存储合并后的内容
-        self.children = []
 
-    def __repr__(self):
+def add_child(parent, child):
-        return f"({self.level}) {self.title}"
+    parent.children.append(child)
 
-    def add_child(self, child):
+def print_tree(node, indent=0):
-        self.children.append(child)
+    prefix = "│  " * (indent - 1) + "└─ " if indent > 0 else ""
-
+    print(f"{prefix}{node.title}")
-    def print_tree(self, indent=0):
+    if node.content:
-        prefix = "│  " * (indent - 1) + "└─ " if indent > 0 else ""
+        content_prefix = "│  " * indent + "├─ [内容]"
-        print(f"{prefix}{self.title}")
+        print(content_prefix)
-        if self.content:
+        for line in node.content.split('\n'):
-            content_prefix = "│  " * indent + "├─ [内容]"
+            print("│  " * indent + "│  " + line)
-            print(content_prefix)
+    for child in node.children:
-            for line in self.content.split('\n'):
+        print_tree(child, indent + 1)
-                print("│  " * indent + "│  " + line)
-        for child in self.children:
-            child.print_tree(indent + 1)
 
 def parse_markdown(markdown):
     lines = markdown.split('\n')
@@ -51,10 +47,10 @@ def parse_markdown(markdown):
         if match:
             level = len(match.group(1))
             title = match.group(2)
-            node = MarkdownNode(level, title)
+            node = MarkdownNode(level=level, title=title, content="", children=[])
             while stack[-1].level >= level:
                 stack.pop()
-            stack[-1].add_child(node)
+            add_child(stack[-1], node)
             stack.append(node)
         else:
             if stack[-1].content:
@@ -65,9 +61,9 @@ def parse_markdown(markdown):
 
 if __name__=="__main__":
     # 从文件读取 Markdown 内容
-    with open("example.md", "r", encoding="utf-8") as f:
+    with open("workdir/example.md", "r", encoding="utf-8") as f:
         markdown = f.read()
 
     # 解析 Markdown 并打印树结构
     root = parse_markdown(markdown)
-    root.print_tree()
+    print_tree(root)

trainer.py

@@ -1,70 +0,0 @@
-from unsloth import FastLanguageModel
-import torch
-
-# 基础配置参数
-max_seq_length = 4096 # 最大序列长度
-dtype = None # 自动检测数据类型
-load_in_4bit = True # 使用4位量化以减少内存使用
-
-# 加载预训练模型和分词器
-model, tokenizer = FastLanguageModel.from_pretrained(
-    model_name = "workdir\model\Qwen2.5-3B-Instruct-bnb-4bit", # 选择Qwen2.5 32B指令模型
-    max_seq_length = max_seq_length,
-    dtype = dtype,
-    load_in_4bit = load_in_4bit,
-)
-
-model = FastLanguageModel.get_peft_model(
-    model,
-    r = 64, # LoRA秩,控制可训练参数数量
-    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
-                      "gate_proj", "up_proj", "down_proj",], # 需要训练的目标模块
-    lora_alpha = 64, # LoRA缩放因子
-    lora_dropout = 0, # LoRA dropout率
-    bias = "none", # 是否训练偏置项
-    use_gradient_checkpointing = "unsloth", # 使用梯度检查点节省显存
-    random_state = 114514, # 随机数种子
-    use_rslora = False, # 是否使用稳定版LoRA
-    loftq_config = None, # LoftQ配置
-)
-
-from unsloth.chat_templates import get_chat_template
-# 配置分词器使用qwen-2.5对话模板
-tokenizer = get_chat_template(
-    tokenizer,
-    chat_template="qwen-2.5",
-)
-
-def formatting_prompts_func(examples):
-    """格式化对话数据的函数
-    Args:
-        examples: 包含对话列表的字典
-    Returns:
-        包含格式化文本的字典
-    """
-    questions = examples["question"]
-    answer = examples["answer"]
-    
-    # 将Question和Response组合成对话形式
-    convos = [
-        [{"role": "user", "content": q}, {"role": "assistant", "content": r}]
-        for q, r in zip(questions, answer)
-    ]
-    
-    # 使用tokenizer.apply_chat_template格式化对话
-    texts = [
-        tokenizer.apply_chat_template(convo, tokenize=False, add_generation_prompt=False)
-        for convo in convos
-    ]
-    
-    return {"text": texts}
-
-from unsloth.chat_templates import standardize_sharegpt
-
-# 加载数据集
-from datasets import load_dataset
-dataset = load_dataset("json", data_files="workdir\dataset\dataset.json")
-dataset = dataset.map(formatting_prompts_func, batched = True)
-
-print(dataset[5])
-print(dataset[5]["text"])