chore: 添加vLLM相关配置与脚本 (#30)
更新.gitignore文件以忽略vLLM模型目录，新增docker-compose.yml、download_model.py和model_test.py文件，用于配置和测试vLLM服务。删除不再使用的magic_pdf_parser.py文件。
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@ -46,4 +46,5 @@ nltk_data/
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web/public/logo_secret.svg
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web/public/logo_old.svg
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web/public/logo.svg
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web/src/locales/zh.ts
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web/src/locales/zh.ts
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vllm/models
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@ -1,141 +0,0 @@
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import os
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from io import BytesIO
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from magic_pdf.data.data_reader_writer import FileBasedDataWriter, FileBasedDataReader
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from magic_pdf.data.dataset import PymuDocDataset
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from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
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from magic_pdf.config.enums import SupportedPdfParseMethod
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def process_pdf_with_magic(file_content, callback=None):
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"""
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使用magic_pdf处理PDF文件
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Args:
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file_content: PDF文件内容
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callback: 回调函数，用于更新进度
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Returns:
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解析后的内容列表
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"""
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try:
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from magic_pdf.processor import PDFProcessor
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from magic_pdf.extractor import TextExtractor, ImageExtractor
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if callback:
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callback(0.1, "初始化Magic PDF解析器")
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# 创建临时文件
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temp_dir = os.path.join(os.getcwd(), "temp")
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os.makedirs(temp_dir, exist_ok=True)
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temp_pdf_path = os.path.join(temp_dir, "temp.pdf")
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with open(temp_pdf_path, "wb") as f:
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f.write(file_content)
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if callback:
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callback(0.2, "开始解析PDF")
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# 初始化处理器
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processor = PDFProcessor(temp_pdf_path)
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if callback:
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callback(0.3, "提取文本内容")
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# 提取文本
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text_extractor = TextExtractor(processor)
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text_content = text_extractor.extract()
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if callback:
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callback(0.5, "提取图片内容")
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# 提取图片
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image_extractor = ImageExtractor(processor)
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images = image_extractor.extract()
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if callback:
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callback(0.7, "组织解析结果")
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# 组织结果
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content_list = []
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# 添加文本内容
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for page_num, page_text in enumerate(text_content):
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content_list.append({
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"type": "text",
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"page": page_num + 1,
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"text": page_text
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})
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# 添加图片内容
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for i, img in enumerate(images):
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content_list.append({
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"type": "image",
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"page": img.get("page", i + 1),
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"image_path": img.get("path", ""),
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"caption": img.get("caption", "")
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})
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# 清理临时文件
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try:
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os.remove(temp_pdf_path)
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except:
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pass
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if callback:
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callback(1.0, "PDF解析完成")
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return content_list
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except ImportError:
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# 如果magic_pdf未安装，使用简单的文本提取
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if callback:
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callback(0.2, "Magic PDF未安装，使用备用方法")
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try:
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import PyPDF2
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if callback:
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callback(0.3, "使用PyPDF2提取文本")
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pdf_reader = PyPDF2.PdfReader(BytesIO(file_content))
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content_list = []
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for i, page in enumerate(pdf_reader.pages):
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if callback and i % 5 == 0:
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progress = 0.3 + (i / len(pdf_reader.pages)) * 0.6
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callback(progress, f"正在处理第 {i+1}/{len(pdf_reader.pages)} 页")
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text = page.extract_text()
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if text:
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content_list.append({
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"type": "text",
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"page": i + 1,
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"text": text
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})
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if callback:
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callback(0.9, "文本提取完成")
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return content_list
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except Exception as e:
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if callback:
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callback(0.5, f"PDF解析失败: {str(e)}")
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# 最简单的备用方案
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return [{
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"type": "text",
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"page": 1,
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"text": "无法解析PDF文件内容"
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}]
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except Exception as e:
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if callback:
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callback(0.5, f"PDF解析失败: {str(e)}")
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# 出错时返回空列表
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return [{
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"type": "text",
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"page": 1,
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"text": f"解析失败: {str(e)}"
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}]
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@ -0,0 +1,50 @@
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services:
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vllm-bge:
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image: vllm/vllm-openai:latest
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ipc: host
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volumes:
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- ./models/bge-m3:/models
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command: [
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"--model", "/models",
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"--served-model-name", "bge-m3",
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"--dtype", "float16",
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"--gpu-memory-utilization", "0.9",
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]
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ports:
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- "8000:8000"
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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capabilities: [gpu]
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networks:
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- ragflow
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vllm-deepseek:
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image: vllm/vllm-openai:latest
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ipc: host
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volumes:
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- ./models/DeepSeek-R1-1.5B:/models
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command: [
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"--model", "/models",
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"--served-model-name", "deepseek-r1",
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"--dtype", "float16",
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"--tensor-parallel-size", "1",
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"--max-model-len", "4096"
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]
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ports:
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- "8001:8000"
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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capabilities: [gpu]
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networks:
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- ragflow
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networks:
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ragflow:
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name: docker_ragflow
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driver: bridge
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@ -0,0 +1,34 @@
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import os
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from huggingface_hub import snapshot_download
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# 1. 设置镜像源（国内加速）
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# os.environ["HF_ENDPOINT"] = "https://mirrors.tuna.tsinghua.edu.cn/hugging-face/"
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# 2. 定义模型列表（名称 + 下载路径）
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models_to_download = [
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{
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"repo_id": "BAAI/bge-m3", # Embedding 模型
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"local_dir": os.path.expanduser("./models/bge-m3"),
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},
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{
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"repo_id": "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B", # LLM 模型
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"local_dir": os.path.expanduser("./models/DeepSeek-R1-1.5B"),
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}
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]
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# 3. 遍历下载所有模型
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for model in models_to_download:
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while True: # 断点续传重试机制
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try:
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print(f"开始下载模型: {model['repo_id']} 到目录: {model['local_dir']}")
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snapshot_download(
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repo_id=model["repo_id"],
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local_dir=model["local_dir"],
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resume_download=True, # 启用断点续传
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force_download=False, # 避免重复下载已有文件
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token=None, # 如需访问私有模型，替换为你的 token
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)
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print(f"模型 {model['repo_id']} 下载完成！")
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break
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except Exception as e:
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print(f"下载失败: {e}, 重试中...")
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@ -0,0 +1,47 @@
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import requests
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from openai import OpenAI
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# 测试 embedding 模型 (vllm-bge)
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def test_embedding(model, text):
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"""测试嵌入模型"""
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client = OpenAI(base_url="http://localhost:8000/v1", api_key="1")
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response = client.embeddings.create(
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model=model, # 使用支持嵌入的模型
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input=text # 需要嵌入的文本
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)
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# 打印嵌入响应内容
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print(f"Embedding response: {response}")
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if response and response.data:
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print(f"Embedding: {response.data[0].embedding}")
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else:
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print("Failed to get embedding.")
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# 测试文本生成模型 (vllm-deepseek)
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def test_chat(model, prompt):
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"""测试文本生成模型"""
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client = OpenAI(base_url="http://localhost:8001/v1", api_key="1")
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response = client.completions.create(
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model=model,
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prompt=prompt
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)
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# 打印生成的文本
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print(f"Chat response: {response.choices[0].text}")
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def main():
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# 测试文本生成模型 deepseek-r1
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prompt = "你好，今天的天气怎么样？"
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print("Testing vllm-deepseek model for chat...")
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test_chat("deepseek-r1", prompt)
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# 测试嵌入模型 bge-m3
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embedding_text = "我喜欢编程，尤其是做AI模型。"
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print("\nTesting vllm-bge model for embedding...")
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test_embedding("bge-m3", embedding_text)
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if __name__ == "__main__":
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main()
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