docs(mvp): 添加项目基础文档

- 新增 LICENSE 文件，定义项目开源许可证
- 新增 README.md 文件，介绍项目的基本信息和预期技术栈

LICENSE

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+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

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+# 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

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+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

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+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")

prompt/base.py

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+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"))

schema/dataset.py

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

tools/openai_api.py

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+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}")

trainer.py

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+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"])