refactor(knowledge): 重构文件处理逻辑并提取到独立模块
将文件处理和URL处理逻辑从KnowledgeBase类提取到独立的indexing模块 更新模型配置和README，添加新的API Key获取链接 优化日志输出格式并添加新的文件预处理脚本
This commit is contained in:
Wenjie Zhang
parent
24f4ffa73e
commit
b4439996fa
@ -53,6 +53,7 @@ https://github.com/user-attachments/assets/15f7f315-003d-4e41-a260-739c2529f824
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SILICONFLOW_API_KEY=sk-270ea********8bfa97.e3XOMd****Q1Sk
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```
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> 💡 [免费获取 SiliconFlow API Key](https://cloud.siliconflow.cn/i/Eo5yTHGJ)（注册即送 14 元额度）
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> 💡 [免费获取 智谱 API Key](https://www.bigmodel.cn/invite?icode=6BruxYJDdROL5pQX%2FOeYvA%3D%3D)（注册即送 2000 万Tokens）
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3. **启动服务**
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```bash
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@ -21,3 +21,4 @@
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- [ ] 添加用户日志与用户反馈模块，可以在 AgentView 中查看信息（🌟🌟）
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- [ ] 对话页面支持文档/图片临时上传（🌟🌟🌟🌟）
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- [ ] 在Prompt中添加对于参考文献的要求，同时要求使用链接的形式，在聊天页面点击该链接后，可以选择跳转或者弹出信息预览框（最好可以提供源文件下载的功能），这里需要将DatabaseInfoView 里面的那个文件详情的弹窗给组件化。但是同时也要能够配置，选择显示源文件还是chunk的结果，如果可以的话，最好是在一个文件里面显示所有的chunk，而不是每个chunk都单独显示。不过似乎有点难度，毕竟是有 overlap 的存在，还是分开吧。对于URL链接的这种，可以选择直接跳转就可以。
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- [ ] 各种结果的可视化：知识库检索页面，工具调用检索页面，知识图谱检索页面
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74
scripts/preprocessors/split_data_to_subfiles.py
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74
scripts/preprocessors/split_data_to_subfiles.py
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@ -0,0 +1,74 @@
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import typer
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import pandas as pd
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import json
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import random
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import re
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from pathlib import Path
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app = typer.Typer()
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def sanitize_filename(name: str) -> str:
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return re.sub(r'[\\/*?:"<>|]', "_", str(name).strip())
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def random_suffix() -> str:
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return f"_{random.randint(10000000, 99999999)}"
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def read_table(file_path: Path) -> pd.DataFrame:
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suffix = file_path.suffix.lower()
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if suffix in ['.xlsx', '.xls']:
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return pd.read_excel(file_path)
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elif suffix == '.csv':
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return pd.read_csv(file_path)
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elif suffix == '.json':
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with open(file_path, encoding='utf-8') as f:
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data = json.load(f)
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if isinstance(data, list) and len(data) > 1 and isinstance(data[0], dict):
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return pd.DataFrame(data)
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else:
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raise ValueError("JSON 文件格式不符合要求：应为元素个数 > 1 的数组，每个元素是对象。")
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else:
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raise ValueError(f"不支持的文件格式：{suffix}")
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def export_txts(df: pd.DataFrame, output_dir: Path, title_field: str = "标题"):
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output_dir.mkdir(parents=True, exist_ok=True)
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df.columns = [c.strip() for c in df.columns]
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if title_field not in df.columns:
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title_field = df.columns[0] # fallback
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for idx, row in df.iterrows():
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title = str(row.get(title_field, "")).strip()
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if not title:
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title = f"{str(row[df.columns[0]])}{random_suffix()}"
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else:
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title = sanitize_filename(title)
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filename = f"{title}.txt"
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file_path = output_dir / filename
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# 构造内容：字段: 值，每行一个
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content = "\n".join(f"{col}: {row[col]}" for col in df.columns)
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with open(file_path, "w", encoding="utf-8") as f:
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f.write(content)
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typer.echo(f"✅ 成功导出 {len(df)} 个文件到目录：{output_dir}")
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@app.command()
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def convert(
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input_file: Path = typer.Argument(..., help="输入文件（.xlsx/.xls/.csv/.json）"),
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out_dir: Path = typer.Option("output", help="输出目录"),
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title_field: str = typer.Option("标题", help="标题字段名（用于文件名）")
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):
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"""
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将结构化数据文件（Excel/CSV/JSON）转换为多个 .txt 文件。
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"""
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try:
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df = read_table(input_file)
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export_txts(df, out_dir, title_field)
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except Exception as e:
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typer.echo(f"❌ 错误：{e}", err=True)
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raise typer.Exit(code=1)
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if __name__ == "__main__":
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app()
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@ -3,8 +3,9 @@ import asyncio
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import traceback
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from fastapi import APIRouter, File, UploadFile, HTTPException, Depends, Body, Form, Query
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from src.utils import logger, hashstr
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from src import executor, config, knowledge_base
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from src.utils import logger, hashstr
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from src.knowledge.indexing import process_file_to_markdown
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from server.utils.auth_middleware import get_admin_user
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from server.models.user_model import User
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@ -373,6 +374,20 @@ async def upload_file(
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return {"message": "File successfully uploaded", "file_path": file_path, "db_id": db_id}
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@knowledge.post("/files/markdown")
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async def mark_it_down(
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file: UploadFile = File(...),
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current_user: User = Depends(get_admin_user)
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):
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"""调用 src.knowledge.indexing 下面的 process_file_to_markdown 解析为 markdown，参数是文件，需要管理员权限"""
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try:
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content = await file.read()
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markdown_content = await process_file_to_markdown(content)
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return {"markdown_content": markdown_content, "message": "success"}
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except Exception as e:
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logger.error(f"文件解析失败 {e}, {traceback.format_exc()}")
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return {"message": f"文件解析失败 {e}", "markdown_content": ""}
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# =============================================================================
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# === 知识库类型分组 ===
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# =============================================================================
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@ -25,7 +25,7 @@ class ChatbotConfiguration(Configuration):
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)
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model: str = field(
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default="zhipu/glm-4-plus",
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default="siliconflow/Qwen/Qwen3-235B-A22B-Instruct-2507",
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metadata={
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"name": "智能体模型",
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"options": [],
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@ -11,6 +11,7 @@ from chromadb.config import Settings
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from chromadb.api.types import EmbeddingFunction, Documents, Embeddings
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from chromadb.utils.embedding_functions import OpenAIEmbeddingFunction
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from src.knowledge.indexing import process_url_to_markdown, process_file_to_markdown
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from src.knowledge.knowledge_base import KnowledgeBase
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from src.knowledge.kb_utils import split_text_into_chunks, split_text_into_qa_chunks, prepare_item_metadata, get_embedding_config
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from src.utils import logger, hashstr
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@ -188,9 +189,9 @@ class ChromaKB(KnowledgeBase):
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try:
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# 根据内容类型处理内容
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if content_type == "file":
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markdown_content = await self._process_file_to_markdown(item, params=params)
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markdown_content = await process_file_to_markdown(item, params=params)
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else: # URL
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markdown_content = await self._process_url_to_markdown(item, params=params)
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markdown_content = await process_url_to_markdown(item, params=params)
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# 分割文本成块
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chunks = self._split_text_into_chunks(markdown_content, file_id, filename, params)
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@ -216,3 +216,120 @@ async def parse_pdf_async(file, params=None):
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async def parse_image_async(file, params=None):
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return await asyncio.to_thread(parse_image, file, params=params)
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async def process_file_to_markdown(file_path: str, params: dict | None = None) -> str:
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"""
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将不同类型的文件转换为markdown格式
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Args:
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file_path: 文件路径
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params: 处理参数
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Returns:
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markdown格式内容
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"""
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file_path_obj = Path(file_path)
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file_ext = file_path_obj.suffix.lower()
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if file_ext == '.pdf':
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# 使用 OCR 处理 PDF
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text = await parse_pdf_async(str(file_path_obj), params=params)
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return f"# {file_path_obj.name}\n\n{text}"
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elif file_ext in ['.txt', '.md']:
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# 直接读取文本文件
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with open(file_path_obj, encoding='utf-8') as f:
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content = f.read()
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return f"# {file_path_obj.name}\n\n{content}"
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elif file_ext in ['.doc', '.docx']:
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# 处理 Word 文档
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from docx import Document # type: ignore
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doc = Document(file_path_obj)
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text = '\n'.join([para.text for para in doc.paragraphs])
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return f"# {file_path_obj.name}\n\n{text}"
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elif file_ext in ['.jpg', '.jpeg', '.png', '.bmp', '.tiff', '.tif']:
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# 使用 OCR 处理图片
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text = await parse_image_async(str(file_path_obj), params=params)
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return f"# {file_path_obj.name}\n\n{text}"
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elif file_ext in ['.html', '.htm']:
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# 使用 BeautifulSoup 处理 HTML 文件
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from markdownify import markdownify as md
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with open(file_path_obj, encoding='utf-8') as f:
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content = f.read()
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text = md(content, heading_style="ATX")
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return f"# {file_path_obj.name}\n\n{text}"
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elif file_ext == '.csv':
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# 处理 CSV 文件
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import pandas as pd
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df = pd.read_csv(file_path_obj)
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# 将每一行数据与表头组合成独立的表格
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markdown_content = f"# {file_path_obj.name}\n\n"
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for index, row in df.iterrows():
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# 创建包含表头和当前行的小表格
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row_df = pd.DataFrame([row], columns=df.columns)
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markdown_table = row_df.to_markdown(index=False)
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markdown_content += f"{markdown_table}\n\n"
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return markdown_content.strip()
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elif file_ext in ['.xls', '.xlsx']:
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# 处理 Excel 文件
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import pandas as pd
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# 读取所有工作表
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excel_file = pd.ExcelFile(file_path_obj)
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markdown_content = f"# {file_path_obj.name}\n\n"
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for sheet_name in excel_file.sheet_names:
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df = pd.read_excel(file_path_obj, sheet_name=sheet_name)
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markdown_content += f"## {sheet_name}\n\n"
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# 将每一行数据与表头组合成独立的表格
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for index, row in df.iterrows():
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# 创建包含表头和当前行的小表格
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row_df = pd.DataFrame([row], columns=df.columns)
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markdown_table = row_df.to_markdown(index=False)
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markdown_content += f"{markdown_table}\n\n"
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return markdown_content.strip()
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elif file_ext == '.json':
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# 处理 JSON 文件
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import json
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with open(file_path_obj, encoding='utf-8') as f:
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data = json.load(f)
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# 将 JSON 数据格式化为 markdown 代码块
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json_str = json.dumps(data, ensure_ascii=False, indent=2)
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return f"# {file_path_obj.name}\n\n```json\n{json_str}\n```"
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else:
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# 尝试作为文本文件读取
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raise ValueError(f"Unsupported file type: {file_ext}")
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async def process_url_to_markdown(url: str, params: dict | None = None) -> str:
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"""
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将URL转换为markdown格式
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Args:
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url: URL地址
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params: 处理参数
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Returns:
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markdown格式内容
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"""
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import requests
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from bs4 import BeautifulSoup
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try:
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response = requests.get(url, timeout=30)
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soup = BeautifulSoup(response.content, 'html.parser')
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text_content = soup.get_text()
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return f"# {url}\n\n{text_content}"
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except Exception as e:
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logger.error(f"Failed to process URL {url}: {e}")
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return f"# {url}\n\nFailed to process URL: {e}"
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@ -377,122 +377,3 @@ class KnowledgeBase(ABC):
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json.dump(data, f, ensure_ascii=False, indent=2)
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except Exception as e:
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logger.error(f"Failed to save {self.kb_type} metadata: {e}")
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async def _process_file_to_markdown(self, file_path: str, params: dict | None = None) -> str:
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"""
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将不同类型的文件转换为markdown格式
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Args:
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file_path: 文件路径
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params: 处理参数
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Returns:
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markdown格式内容
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"""
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file_path_obj = Path(file_path)
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file_ext = file_path_obj.suffix.lower()
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if file_ext == '.pdf':
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# 使用 OCR 处理 PDF
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from src.knowledge.indexing import parse_pdf_async
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text = await parse_pdf_async(str(file_path_obj), params=params)
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return f"# {file_path_obj.name}\n\n{text}"
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elif file_ext in ['.txt', '.md']:
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# 直接读取文本文件
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with open(file_path_obj, encoding='utf-8') as f:
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content = f.read()
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return f"# {file_path_obj.name}\n\n{content}"
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elif file_ext in ['.doc', '.docx']:
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# 处理 Word 文档
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from docx import Document # type: ignore
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doc = Document(file_path_obj)
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text = '\n'.join([para.text for para in doc.paragraphs])
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return f"# {file_path_obj.name}\n\n{text}"
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elif file_ext in ['.jpg', '.jpeg', '.png', '.bmp', '.tiff', '.tif']:
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# 使用 OCR 处理图片
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from src.knowledge.indexing import parse_image_async
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text = await parse_image_async(str(file_path_obj), params=params)
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return f"# {file_path_obj.name}\n\n{text}"
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elif file_ext in ['.html', '.htm']:
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# 使用 BeautifulSoup 处理 HTML 文件
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from markdownify import markdownify as md
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with open(file_path_obj, encoding='utf-8') as f:
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content = f.read()
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text = md(content, heading_style="ATX")
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return f"# {file_path_obj.name}\n\n{text}"
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elif file_ext == '.csv':
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# 处理 CSV 文件
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import pandas as pd
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df = pd.read_csv(file_path_obj)
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# 将每一行数据与表头组合成独立的表格
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markdown_content = f"# {file_path_obj.name}\n\n"
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for index, row in df.iterrows():
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# 创建包含表头和当前行的小表格
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row_df = pd.DataFrame([row], columns=df.columns)
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markdown_table = row_df.to_markdown(index=False)
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markdown_content += f"{markdown_table}\n\n"
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return markdown_content.strip()
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elif file_ext in ['.xls', '.xlsx']:
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# 处理 Excel 文件
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import pandas as pd
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# 读取所有工作表
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excel_file = pd.ExcelFile(file_path_obj)
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markdown_content = f"# {file_path_obj.name}\n\n"
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for sheet_name in excel_file.sheet_names:
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df = pd.read_excel(file_path_obj, sheet_name=sheet_name)
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markdown_content += f"## {sheet_name}\n\n"
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# 将每一行数据与表头组合成独立的表格
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for index, row in df.iterrows():
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# 创建包含表头和当前行的小表格
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row_df = pd.DataFrame([row], columns=df.columns)
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markdown_table = row_df.to_markdown(index=False)
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markdown_content += f"{markdown_table}\n\n"
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return markdown_content.strip()
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elif file_ext == '.json':
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# 处理 JSON 文件
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import json
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with open(file_path_obj, encoding='utf-8') as f:
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data = json.load(f)
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# 将 JSON 数据格式化为 markdown 代码块
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json_str = json.dumps(data, ensure_ascii=False, indent=2)
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return f"# {file_path_obj.name}\n\n```json\n{json_str}\n```"
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else:
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# 尝试作为文本文件读取
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raise ValueError(f"Unsupported file type: {file_ext}")
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async def _process_url_to_markdown(self, url: str,
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params: dict | None = None) -> str:
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"""
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将URL转换为markdown格式
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Args:
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url: URL地址
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params: 处理参数
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Returns:
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markdown格式内容
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"""
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import requests
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from bs4 import BeautifulSoup
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try:
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response = requests.get(url, timeout=30)
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soup = BeautifulSoup(response.content, 'html.parser')
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text_content = soup.get_text()
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return f"# {url}\n\n{text_content}"
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to process URL {url}: {e}")
|
||||
return f"# {url}\n\nFailed to process URL: {e}"
|
||||
|
||||
@ -10,6 +10,7 @@ from lightrag.utils import EmbeddingFunc, setup_logger
|
||||
from lightrag.kg.shared_storage import initialize_pipeline_status
|
||||
|
||||
from src.knowledge.knowledge_base import KnowledgeBase
|
||||
from src.knowledge.indexing import process_url_to_markdown, process_file_to_markdown
|
||||
from src.knowledge.kb_utils import prepare_item_metadata, get_embedding_config
|
||||
from src import config
|
||||
from src.utils import logger, hashstr, get_docker_safe_url
|
||||
@ -166,11 +167,11 @@ class LightRagKB(KnowledgeBase):
|
||||
try:
|
||||
# 根据内容类型处理内容
|
||||
if content_type == "file":
|
||||
markdown_content = await self._process_file_to_markdown(item, params=params)
|
||||
markdown_content = await process_file_to_markdown(item, params=params)
|
||||
markdown_content_lines = markdown_content[:100].replace('\n', ' ')
|
||||
logger.info(f"Markdown content: {markdown_content_lines}...")
|
||||
else: # URL
|
||||
markdown_content = await self._process_url_to_markdown(item, params=params)
|
||||
markdown_content = await process_url_to_markdown(item, params=params)
|
||||
|
||||
# 使用 LightRAG 插入内容
|
||||
await rag.ainsert(
|
||||
|
||||
@ -16,6 +16,7 @@ from pymilvus import (
|
||||
from src import config
|
||||
from src.models.embedding import OtherEmbedding
|
||||
from src.knowledge.knowledge_base import KnowledgeBase
|
||||
from src.knowledge.indexing import process_url_to_markdown, process_file_to_markdown
|
||||
from src.knowledge.kb_utils import split_text_into_chunks, split_text_into_qa_chunks, prepare_item_metadata, get_embedding_config
|
||||
from src.utils import logger, hashstr
|
||||
|
||||
@ -258,9 +259,9 @@ class MilvusKB(KnowledgeBase):
|
||||
|
||||
try:
|
||||
if content_type == "file":
|
||||
markdown_content = await self._process_file_to_markdown(item, params=params)
|
||||
markdown_content = await process_file_to_markdown(item, params=params)
|
||||
else:
|
||||
markdown_content = await self._process_url_to_markdown(item, params=params)
|
||||
markdown_content = await process_url_to_markdown(item, params=params)
|
||||
|
||||
chunks = self._split_text_into_chunks(markdown_content, file_id, filename, params)
|
||||
logger.info(f"Split {filename} into {len(chunks)} chunks")
|
||||
|
||||
@ -48,7 +48,7 @@ class BaseEmbeddingModel:
|
||||
return await asyncio.to_thread(self.batch_encode, messages, batch_size)
|
||||
|
||||
def batch_encode(self, messages, batch_size=40):
|
||||
logger.info(f"Batch encoding {len(messages)} messages")
|
||||
# logger.info(f"Batch encoding {len(messages)} messages")
|
||||
data = []
|
||||
|
||||
if len(messages) > batch_size:
|
||||
@ -61,7 +61,7 @@ class BaseEmbeddingModel:
|
||||
|
||||
for i in range(0, len(messages), batch_size):
|
||||
group_msg = messages[i:i+batch_size]
|
||||
logger.info(f"Encoding {i} to {i+batch_size} with {len(messages)} messages")
|
||||
logger.info(f"Encoding [{i}/{len(messages)}] messages (bsz={batch_size})")
|
||||
response = self.encode(group_msg)
|
||||
# logger.debug(f"Response: {len(response)=}, {len(group_msg)=}, {len(response[0])=}")
|
||||
data.extend(response)
|
||||
|
||||
@ -11,14 +11,13 @@ MODEL_NAMES:
|
||||
name: OpenAI
|
||||
url: https://platform.openai.com/docs/models
|
||||
base_url: https://api.openai.com/v1
|
||||
default: gpt-3.5-turbo
|
||||
default: gpt-4o-mini
|
||||
env:
|
||||
- OPENAI_API_KEY
|
||||
models:
|
||||
- gpt-4
|
||||
- gpt-4o
|
||||
- gpt-4o-mini
|
||||
- gpt-3.5-turbo
|
||||
|
||||
deepseek:
|
||||
name: DeepSeek
|
||||
@ -35,15 +34,13 @@ MODEL_NAMES:
|
||||
name: 智谱AI (Zhipu)
|
||||
url: https://open.bigmodel.cn/dev/api
|
||||
base_url: https://open.bigmodel.cn/api/paas/v4/
|
||||
default: glm-4-flash
|
||||
default: glm-4.5-flash
|
||||
env:
|
||||
- ZHIPUAI_API_KEY
|
||||
models:
|
||||
- glm-4
|
||||
- glm-4-plus
|
||||
- glm-4-air
|
||||
- glm-4-flash
|
||||
- glm-z1-air
|
||||
- glm-4.5
|
||||
- glm-4.5-air
|
||||
- glm-4.5-flash
|
||||
|
||||
siliconflow:
|
||||
name: SiliconFlow
|
||||
@ -55,8 +52,10 @@ MODEL_NAMES:
|
||||
models:
|
||||
- Pro/deepseek-ai/DeepSeek-R1
|
||||
- Pro/deepseek-ai/DeepSeek-V3
|
||||
- Qwen/QwQ-32B
|
||||
- Qwen/Qwen3-8B
|
||||
- Qwen/Qwen3-235B-A22B-Thinking-2507
|
||||
- Qwen/Qwen3-235B-A22B-Instruct-2507
|
||||
- moonshotai/Kimi-K2-Instruct
|
||||
- zai-org/GLM-4.5
|
||||
|
||||
together.ai:
|
||||
name: Together.ai
|
||||
@ -66,34 +65,33 @@ MODEL_NAMES:
|
||||
env:
|
||||
- TOGETHER_API_KEY
|
||||
models:
|
||||
- meta-llama/Llama-3.3-70B-Instruct-Turbo
|
||||
- meta-llama/Llama-3.3-70B-Instruct-Turbo-Free
|
||||
- deepseek-ai/DeepSeek-R1-Distill-Llama-70B-free
|
||||
- Qwen/QwQ-32B
|
||||
|
||||
dashscope:
|
||||
name: 阿里百炼 (DashScope)
|
||||
url: https://bailian.console.aliyun.com/?switchAgent=10226727&productCode=p_efm#/model-market
|
||||
base_url: https://dashscope.aliyuncs.com/compatible-mode/v1
|
||||
default: qwen3-235b-a22b
|
||||
default: qwen-max-latest
|
||||
env:
|
||||
- DASHSCOPE_API_KEY
|
||||
models:
|
||||
- qwen-max-latest
|
||||
- qwen3-235b-a22b
|
||||
- qwen3-32b
|
||||
- qwen-plus-latest
|
||||
- qwen-turbo-latest
|
||||
- qwen3-235b-a22b-thinking-2507
|
||||
- qwen3-235b-a22b-instruct-2507
|
||||
|
||||
ark:
|
||||
name: 豆包（Ark）
|
||||
url: https://console.volcengine.com/ark/region:ark+cn-beijing/model
|
||||
base_url: https://ark.cn-beijing.volces.com/api/v3
|
||||
default: doubao-1-5-pro-32k-250115
|
||||
default: doubao-seed-1-6-250615
|
||||
env:
|
||||
- ARK_API_KEY
|
||||
models:
|
||||
- doubao-1-5-pro-32k-250115
|
||||
- doubao-1-5-lite-32k-250115
|
||||
- deepseek-r1-250120
|
||||
- doubao-seed-1-6-250615
|
||||
- doubao-seed-1-6-thinking-250715
|
||||
- doubao-seed-1-6-flash-250715
|
||||
|
||||
openrouter:
|
||||
name: OpenRouter
|
||||
@ -104,14 +102,9 @@ MODEL_NAMES:
|
||||
- OPENROUTER_API_KEY
|
||||
models:
|
||||
- openai/gpt-4o
|
||||
- openai/gpt-4o-mini
|
||||
- google/gemini-2.5-pro-exp-03-25:free
|
||||
- x-ai/grok-3-beta
|
||||
- meta-llama/llama-4-maverick
|
||||
- meta-llama/llama-4-maverick:free
|
||||
- anthropic/claude-3.7-sonnet
|
||||
- anthropic/claude-3.7-sonnet:thinking
|
||||
|
||||
- x-ai/grok-4
|
||||
- google/gemini-2.5-pro
|
||||
- anthropic/claude-sonnet-4
|
||||
|
||||
EMBED_MODEL_INFO:
|
||||
siliconflow/BAAI/bge-m3:
|
||||
|
||||
Loading…
Reference in New Issue
Block a user