feat(dataset): 添加数据集生成功能

- 新增数据集生成页面和相关逻辑
- 实现数据集名称重复性检查
- 添加数据集对象创建和保存功能
- 优化文档处理和提示模板应用
- 增加错误处理和数据解析

db/__init__.py

@@ -1,11 +1,12 @@
 from .init_db import load_sqlite_engine, initialize_sqlite_db
 from .prompt_store import get_prompt_tinydb, initialize_prompt_store
-from .dataset_store import get_all_dataset
+from .dataset_store import get_all_dataset, save_dataset
 
 __all__ = [
     "load_sqlite_engine",
     "initialize_sqlite_db",
     "get_prompt_tinydb",
     "initialize_prompt_store",
-    "get_all_dataset"
+    "get_all_dataset",
+    "save_dataset"
 ]

db/dataset_store.py

@@ -8,7 +8,7 @@ from tinydb.storages import MemoryStorage
 
 # 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 import dataset, dataset_item, Q_A
+from schema.dataset import Dataset, DatasetItem, Q_A
 
 def get_all_dataset(workdir: str) -> TinyDB:
     """
@@ -39,6 +39,30 @@ def get_all_dataset(workdir: str) -> TinyDB:
     return db
 
 
+def save_dataset(db: TinyDB, workdir: str, name: str = None) -> None:
+    """
+    将TinyDB中的数据集保存为单独的json文件
+    
+    Args:
+        db (TinyDB): 包含数据集对象的TinyDB实例
+        workdir (str): 工作目录路径
+        name (str, optional): 要保存的数据集名称，None表示保存所有
+    """
+    dataset_dir = os.path.join(workdir, "dataset")
+    os.makedirs(dataset_dir, exist_ok=True)
+    
+    datasets = db.all() if name is None else db.search(Query().name == name)
+    
+    for dataset in datasets:
+        try:
+            filename = f"{dataset.get(dataset['name'])}.json"
+            filepath = os.path.join(dataset_dir, filename)
+            
+            with open(filepath, "w", encoding="utf-8") as f:
+                json.dump(dataset, f, ensure_ascii=False, indent=2)
+        except Exception as e:
+            print(f"Error saving dataset {dataset.get('id', 'unknown')}: {str(e)}")
+
 if __name__ == "__main__":
     # 定义工作目录路径
     workdir = os.path.join(os.path.dirname(__file__), "..", "workdir")
@@ -47,4 +71,11 @@ if __name__ == "__main__":
     # 打印结果
     print(f"Found {len(datasets)} datasets:")
     for ds in datasets.all():
-        print(f"- {ds['name']} (ID: {ds['id']})")
+        print(f"- {ds['name']} (ID: {ds['id']})")
+    
+    # 询问要保存的数据集名称
+    name = input("输入要保存的数据集名称(直接回车保存所有): ").strip() or None
+    
+    # 保存数据集到文件
+    save_dataset(datasets, workdir, name)
+    print(f"Datasets {'all' if name is None else name} saved to json files")

frontend/dataset_generate_page.py

@@ -1,13 +1,18 @@
 import gradio as gr
 import sys
+import json
+from tinydb import Query
 from pathlib import Path
 from langchain.prompts import PromptTemplate
 from sqlmodel import Session, select
+from schema import Dataset, DatasetItem, Q_A
+from db.dataset_store import get_all_dataset
 
 sys.path.append(str(Path(__file__).resolve().parent.parent))
-from schema import APIProvider
+from schema import APIProvider, LLMRequest, LLMResponse, DatasetItem
-from tools import call_openai_api
+from db import save_dataset
-from global_var import get_docs, get_prompt_store, get_sql_engine
+from tools import call_openai_api, process_markdown_file, generate_json_example
+from global_var import get_docs, get_prompt_store, get_sql_engine, get_datasets, get_workdir
 
 def dataset_generate_page():
     with gr.Blocks() as demo:
@@ -16,13 +21,6 @@ def dataset_generate_page():
             with gr.Column(scale=1):
                 docs_list = [str(doc.name) for doc in get_docs()]
                 initial_doc = docs_list[0] if docs_list else None
-                doc_dropdown = gr.Dropdown(
-                    choices=docs_list,
-                    value=initial_doc,
-                    label="选择文档",
-                    interactive=True
-                )
-                doc_choice = gr.State(value=initial_doc)
                 prompts = get_prompt_store().all()
                 prompt_list = [f"{p['id']} {p['name']}" for p in prompts]
                 initial_prompt = prompt_list[0] if prompt_list else None
@@ -37,14 +35,6 @@ def dataset_generate_page():
                     input_variables = prompt_template.input_variables
                     input_variables.remove("document_slice")
                     initial_dataframe_value = [[var, ""] for var in input_variables]
-
-                prompt_dropdown = gr.Dropdown(
-                    choices=prompt_list,
-                    value=initial_prompt,
-                    label="选择模板",
-                    interactive=True
-                )
-                prompt_choice = gr.State(value=initial_prompt)
                 # 从数据库获取API Provider列表
                 with Session(get_sql_engine()) as session:
                     providers = session.exec(select(APIProvider)).all()
@@ -57,8 +47,18 @@ def dataset_generate_page():
                     label="选择API",
                     interactive=True
                 )
-                api_choice = gr.State(value=initial_api)
+                doc_dropdown = gr.Dropdown(
-
+                    choices=docs_list,
+                    value=initial_doc,
+                    label="选择文档",
+                    interactive=True
+                )
+                prompt_dropdown = gr.Dropdown(
+                    choices=prompt_list,
+                    value=initial_prompt,
+                    label="选择模板",
+                    interactive=True
+                )
                 rounds_input = gr.Number(
                     value=1,
                     label="生成轮次",
@@ -67,10 +67,25 @@ def dataset_generate_page():
                     step=1,
                     interactive=True
                 )
+                concurrency_input = gr.Number(
+                    value=1,
+                    label="并发数",
+                    minimum=1,
+                    maximum=10,
+                    step=1,
+                    interactive=True,
+                    visible=False
+                )
+                dataset_name_input = gr.Textbox(
+                    label="数据集名称",
+                    placeholder="输入数据集保存名称",
+                    interactive=True
+                )
+                prompt_choice = gr.State(value=initial_prompt)
                 generate_button = gr.Button("生成数据集",variant="primary")
-
+                doc_choice = gr.State(value=initial_doc)
                 output_text = gr.Textbox(label="生成结果", interactive=False)
-
+                api_choice = gr.State(value=initial_api)
             with gr.Column(scale=2):
                 variables_dataframe = gr.Dataframe(
                     headers=["变量名", "变量值"],
@@ -79,8 +94,6 @@ def dataset_generate_page():
                     label="变量列表",
                     value=initial_dataframe_value  # 设置初始化数据
                 )
-
-
         def on_doc_change(selected_doc):
             return selected_doc
         
@@ -100,8 +113,14 @@ def dataset_generate_page():
             dataframe_value = [[var, ""] for var in input_variables]
             return selected_prompt, dataframe_value
 
-        def on_generate_click(doc_state, prompt_state, api_state, variables_dataframe, rounds, progress=gr.Progress()):
+        def on_generate_click(doc_state, prompt_state, api_state, variables_dataframe, rounds, concurrency, dataset_name, progress=gr.Progress()):
-            doc = [i for i in get_docs() if i.name == doc_state][0].markdown_files
+            dataset_db = get_datasets()
+            if not dataset_db.search(Query().name == dataset_name):
+                raise gr.Error("数据集名称已存在")
+            
+            doc = [i for i in get_docs() if i.name == doc_state][0]
+            doc_files = doc.markdown_files
+            document_slice_list = [process_markdown_file(doc) for doc in doc_files]
             prompt = [i for i in get_prompt_store().all() if i["id"] == int(prompt_state.split(" ")[0])][0]
             prompt = PromptTemplate.from_template(prompt["content"])
             with Session(get_sql_engine()) as session:
@@ -114,23 +133,42 @@ def dataset_generate_page():
                 if var_name:
                     variables_dict[var_name] = var_value
 
-            # 注入除document_slice以外的所有参数
             prompt = prompt.partial(**variables_dict)
+            
 
-            print(doc)
-            print(prompt.format(document_slice="test"))
-            print(variables_dict)
 
-            import time
+            dataset = Dataset(
-            total_steps = rounds
+                name=dataset_name,
-            for i in range(total_steps):
+                model_id=[api_provider.model_id],
-                # 模拟每个步骤的工作负载
+                source_doc=doc,
-                time.sleep(0.5)
+                dataset_items=[]
+            )
+            
+            for document_slice in document_slice_list:
+                request = LLMRequest(api_provider=api_provider,
+                                      prompt=prompt.format(document_slice=document_slice),
+                                      format=generate_json_example(DatasetItem))
+                call_openai_api(request, rounds)
 
-                current_progress = (i + 1) / total_steps
+                for resp in request.response:
-                progress(current_progress, desc=f"处理步骤 {i + 1}/{total_steps}")
+                    try:
+                        content = json.loads(resp.content)
+                        dataset_item = DatasetItem(
+                            message=[Q_A(
+                                question=content.get("question", ""),
+                                answer=content.get("answer", "")
+                            )]
+                        )
+                        dataset.dataset_items.append(dataset_item)
+                    except json.JSONDecodeError as e:
+                        print(f"Failed to parse response: {e}")
 
-            return "all done"
+            # 保存数据集到TinyDB
+            dataset_db.insert(dataset.model_dump())
+
+            save_dataset(dataset_db,get_workdir(),dataset_name)
+            
+            return f"数据集 {dataset_name} 生成完成，共 {len(dataset.dataset_items)} 条数据"
 
         doc_dropdown.change(on_doc_change, inputs=doc_dropdown, outputs=doc_choice)
         prompt_dropdown.change(on_prompt_change, inputs=prompt_dropdown, outputs=[prompt_choice, variables_dataframe])
@@ -138,7 +176,7 @@ def dataset_generate_page():
 
         generate_button.click(
             on_generate_click,
-            inputs=[doc_choice, prompt_choice, api_choice, variables_dataframe, rounds_input],
+            inputs=[doc_choice, prompt_choice, api_choice, variables_dataframe, rounds_input, concurrency_input, dataset_name_input],
             outputs=output_text
         )
 

frontend/train_page.py

@@ -74,7 +74,7 @@ def train_page():
 
             # 动态生成 TensorBoard iframe
             tensorboard_url = f"http://localhost:{tensorboard_port}"
-            tensorboard_iframe_value = f'<iframe src="{tensorboard_url}" width="100%" height="500px"></iframe>'
+            tensorboard_iframe_value = f'<iframe src="{tensorboard_url}" width="100%" height="1000px"></iframe>'
             yield "训练开始...", tensorboard_iframe_value  # 返回两个值，分别对应 textbox 和 html
 
             try:

global_var.py

@@ -37,10 +37,6 @@ def get_docs():
 def get_datasets():
     return _datasets
 
-def set_datasets(new_datasets):
-    global _datasets
-    _datasets = new_datasets
-
 def get_model():
     return _model
 

schema/dataset.py

@@ -2,7 +2,7 @@ from typing import Optional
 from pydantic import BaseModel, Field
 from datetime import datetime, timezone
 
-class doc(BaseModel):
+class Doc(BaseModel):
     id: Optional[int] = Field(default=None, description="文档ID")
     name: str = Field(default="", description="文档名称")
     path: str = Field(default="", description="文档路径")
@@ -13,18 +13,18 @@ class Q_A(BaseModel):
     question: str = Field(default="", min_length=1,description="问题")
     answer: str = Field(default="", min_length=1, description="答案")
 
-class dataset_item(BaseModel):
+class DatasetItem(BaseModel):
     id: Optional[int] = Field(default=None, description="数据集项ID")
     message: list[Q_A] = Field(description="数据集项内容")
 
-class dataset(BaseModel):
+class Dataset(BaseModel):
     id: Optional[int] = Field(default=None, description="数据集ID")
     name: str = Field(default="", description="数据集名称")
     model_id: Optional[list[str]] = Field(default=None, description="数据集使用的模型ID")
-    source_doc: Optional[doc] = Field(default=None, description="数据集来源文档")
+    source_doc: Optional[Doc] = Field(default=None, description="数据集来源文档")
     description: Optional[str] = Field(default="", description="数据集描述")
     created_at: datetime = Field(
         default_factory=lambda: datetime.now(timezone.utc),
         description="记录创建时间"
     )
-    dataset_items: list[dataset_item] = Field(default_factory=list, description="数据集项列表")
+    dataset_items: list[DatasetItem] = Field(default_factory=list, description="数据集项列表")

schema/dataset_generation.py

@@ -33,7 +33,7 @@ class LLMResponse(SQLModel):
     )
     response_id: str = Field(..., description="响应的唯一ID")
     tokens_usage: TokensUsage = Field(default_factory=TokensUsage, description="token使用信息")
-    response_content: dict = Field(default_factory=dict, description="API响应的内容")
+    content: str = 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: Optional[LLMParameters] = Field(default=None, description="LLM参数")
 

tools/__init__.py

@@ -1,5 +1,5 @@
 from .parse_markdown import *
 from .document import *
-from .json_example import generate_example_json
+from .json_example import generate_json_example
 from .port import *
 from .reasoning import call_openai_api

tools/convert_json_to_dataset.py

@@ -1,19 +1,19 @@
 from typing import List
-from schema.dataset import dataset, dataset_item, Q_A
+from schema.dataset import Dataset, DatasetItem, Q_A
 import json
 
-def convert_json_to_dataset(json_data: List[dict]) -> dataset:
+def convert_json_to_dataset(json_data: List[dict]) -> Dataset:
     # 将JSON数据转换为dataset格式
     dataset_items = []
     item_id = 1  # 自增ID计数器
     for item in json_data:
         qa = Q_A(question=item["question"], answer=item["answer"])
-        dataset_item_obj = dataset_item(id=item_id, message=[qa])
+        dataset_item_obj = DatasetItem(id=item_id, message=[qa])
         dataset_items.append(dataset_item_obj)
         item_id += 1  # ID自增
     
     # 创建dataset对象
-    result_dataset = dataset(
+    result_dataset = Dataset(
         name="Converted Dataset",
         model_id=None,
         description="Dataset converted from JSON",

tools/document.py

@@ -4,7 +4,7 @@ from pathlib import Path
 
 # 添加项目根目录到sys.path
 sys.path.append(str(Path(__file__).resolve().parent.parent))
-from schema import doc
+from schema import Doc
 
 def scan_docs_directory(workdir: str):
     docs_dir = os.path.join(workdir, "docs")
@@ -21,7 +21,7 @@ def scan_docs_directory(workdir: str):
                 for file in files:
                     if file.endswith(".md"):
                         markdown_files.append(os.path.join(root, file))
-            to_return.append(doc(name=doc_name, path=doc_path, markdown_files=markdown_files))
+            to_return.append(Doc(name=doc_name, path=doc_path, markdown_files=markdown_files))
                     
     return to_return
 

tools/json_example.py

@@ -3,7 +3,7 @@ from typing import Any, Dict, List, Optional, Union, get_args, get_origin
 import json
 from datetime import datetime, date
 
-def generate_example_json(model: type[BaseModel], include_optional: bool = False,list_length = 2) -> str:
+def generate_json_example(model: type[BaseModel], include_optional: bool = False,list_length = 2) -> str:
     """
     根据 Pydantic V2 模型生成示例 JSON 数据结构。
     """
@@ -37,14 +37,14 @@ def generate_example_json(model: type[BaseModel], include_optional: bool = False
         elif field_type is date:
             return date.today().isoformat()
         elif isinstance(field_type, type) and issubclass(field_type, BaseModel):
-            return json.loads(generate_example_json(field_type, include_optional))
+            return json.loads(generate_json_example(field_type, include_optional))
         else:
             # 处理直接类型注解（非泛型）
             if field_type is type(None):
                 return None
             try:
                 if issubclass(field_type, BaseModel):
-                    return json.loads(generate_example_json(field_type, include_optional))
+                    return json.loads(generate_json_example(field_type, include_optional))
             except TypeError:
                 pass
             return "unknown"
@@ -61,7 +61,7 @@ if __name__ == "__main__":
     from pathlib import Path
     # 添加项目根目录到sys.path
     sys.path.append(str(Path(__file__).resolve().parent.parent))
-    from schema import dataset
+    from schema import Dataset
 
     print("示例 JSON:")
-    print(generate_example_json(dataset))
+    print(generate_json_example(Dataset))

tools/reasoning.py

@@ -68,7 +68,7 @@ async def call_openai_api(llm_request: LLMRequest, rounds: int = 1, llm_paramete
             llm_request.response.append(LLMResponse(
                 response_id=response.id,
                 tokens_usage=tokens_usage,
-                response_content={"content": response.choices[0].message.content},
+                content = response.choices[0].message.content,
                 total_duration=duration,
                 llm_parameters=llm_parameters
             ))
@@ -79,7 +79,7 @@ async def call_openai_api(llm_request: LLMRequest, rounds: int = 1, llm_paramete
             
             llm_request.response.append(LLMResponse(
                 response_id=f"error-round-{i+1}",
-                response_content={"error": str(e)},
+                content={"error": str(e)},
                 total_duration=duration
             ))
             if llm_request.error is None:
@@ -93,10 +93,10 @@ async def call_openai_api(llm_request: LLMRequest, rounds: int = 1, llm_paramete
     return llm_request
 
 if __name__ == "__main__":
-    from json_example import generate_example_json
+    from json_example import generate_json_example
     from sqlmodel import Session, select
     from global_var import get_sql_engine, init_global_var
-    from schema import dataset_item
+    from schema import DatasetItem
 
     init_global_var("workdir")
     api_state = "1 deepseek-chat"
@@ -105,7 +105,7 @@ if __name__ == "__main__":
     llm_request = LLMRequest(
         prompt="测试，随便说点什么",
         api_provider=api_provider,
-        format=generate_example_json(dataset_item)
+        format=generate_json_example(DatasetItem)
     )
 
     # # 单次调用示例
@@ -120,4 +120,4 @@ if __name__ == "__main__":
     print(f"\n3次调用结果 - 总耗时: {result.total_duration:.2f}s")
     print(f"总token使用: prompt={result.total_tokens_usage.prompt_tokens}, completion={result.total_tokens_usage.completion_tokens}")
     for i, resp in enumerate(result.response, 1):
-        print(f"响应{i}: {resp.response_content}")
+        print(f"响应{i}: {resp.content}")