feat: 更新 Docker 配置和添加 MinerU OCR 服务

- 修改了 docker-compose.yml，更新了 API 和 Web 服务的镜像名称，并添加了 MinerU OCR 服务。
- 在 pyproject.toml 中添加了 Ruff 代码检查工具的配置。
- 更新了 API 和 Web Dockerfile，添加了代理环境变量。
- 新增了用于拉取 Docker 镜像的脚本。
- 在文档中添加了关于如何使用 MinerU OCR 的说明。
- 优化了代码结构和日志记录，提升了可读性和维护性。

docker-compose.yml

@@ -3,7 +3,7 @@ services:
     build:
       context: .
       dockerfile: docker/api.Dockerfile
-    image: api:0.1.0
+    image: yuxi-api:0.1.0
     container_name: api-dev
     working_dir: /app
     volumes:
@@ -16,7 +16,7 @@ services:
         reservations:
           devices:
             - driver: nvidia
-              count: 1
+              device_ids: ['2']
               capabilities: [gpu]
     ports:
       - "5050:5050"
@@ -29,6 +29,7 @@ services:
       - NEO4J_USERNAME=${NEO4J_USERNAME:-neo4j}
       - NEO4J_PASSWORD=${NEO4J_PASSWORD:-0123456789}
       - MILVUS_URI=http://milvus:19530
+      - MINERU_OCR_URI=http://mineru-api:5051
       - MODEL_DIR=/models
       - RUNNING_IN_DOCKER=true
     command: uv run uvicorn server.main:app --host 0.0.0.0 --port 5050 --reload
@@ -39,7 +40,7 @@ services:
       context: .
       dockerfile: docker/web.Dockerfile
       target: development
-    image: web:0.1.0
+    image: yuxi-web:0.1.0
     container_name: web-dev
     volumes:
       - ./web:/app
@@ -58,7 +59,7 @@ services:
 
   graph:
     image: neo4j:5.26
-    container_name: graph-dev
+    container_name: graph
     ports:
       - "7474:7474"
       - "7687:7687"
@@ -96,7 +97,7 @@ services:
     restart: unless-stopped
 
   minio:
-    container_name: milvus-minio-dev
+    container_name: milvus-minio
     image: minio/minio:RELEASE.2023-03-20T20-16-18Z
     environment:
       MINIO_ACCESS_KEY: ${MINIO_ACCESS_KEY:-minioadmin}
@@ -116,7 +117,7 @@ services:
 
   milvus:
     image: milvusdb/milvus:v2.5.6
-    container_name: milvus-standalone-dev
+    container_name: milvus
     command: ["milvus", "run", "standalone"]
     security_opt:
       - seccomp:unconfined
@@ -143,6 +144,26 @@ services:
       - app-network
     restart: unless-stopped
 
+  mineru-api:
+    build:
+      context: scripts/mineru-api
+      dockerfile: Dockerfile
+    image: mineru-api:latest
+    container_name: mineru-api
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              device_ids: ['2']
+              capabilities: [gpu]
+    ports:
+      - "5051:5051"
+    networks:
+      - app-network
+    restart: unless-stopped
+    command: python -m uvicorn app:app --host 0.0.0.0 --port 5051 --app-dir /app
+
 networks:
   app-network:
     driver: bridge

docker/api.Dockerfile

@@ -5,9 +5,13 @@ COPY --from=ghcr.io/astral-sh/uv:0.7.2 /uv /uvx /bin/
 # 设置工作目录
 WORKDIR /app
 
-# 设置时区为 UTC+8
+# 环境变量设置
-ENV TZ=Asia/Shanghai
+ARG http_proxy
-ENV UV_LINK_MODE=copy
+ARG https_proxy
+ENV http_proxy=$http_proxy \
+    https_proxy=$https_proxy \
+    TZ=Asia/Shanghai \
+    UV_LINK_MODE=copy
 
 RUN ln -snf /usr/share/zoneinfo/$TZ /etc/localtime && echo $TZ > /etc/timezone
 

docker/pull_image.sh

@@ -0,0 +1,38 @@
+#!/bin/bash
+
+if [ $# -ne 1 ]; then
+    echo "Usage: $0 <image:tag>"
+    exit 1
+fi
+
+set -e  # 当命令失败时，立即退出脚本
+
+IMAGE_TAG=$1
+
+# Count the number of slashes to determine the image format
+SLASH_COUNT=$(echo $IMAGE_TAG | tr -cd '/' | wc -c)
+
+# Set mirror URL based on image format
+if [ $SLASH_COUNT -eq 0 ]; then
+    # No prefix (e.g., python:3.12-slim)
+    MIRROR_URL="m.daocloud.io/docker.io/library"
+elif [ $SLASH_COUNT -eq 1 ]; then
+    # One prefix (e.g., milvusdb/milvus:latest)
+    MIRROR_URL="m.daocloud.io/docker.io"
+else
+    # Two or more prefixes (e.g., quay.io/coreos/etcd:v3.5.5)
+    MIRROR_URL="m.daocloud.io"
+fi
+
+# Pull image from mirror
+docker pull $MIRROR_URL/$IMAGE_TAG
+
+# Tag image with original name
+docker tag $MIRROR_URL/$IMAGE_TAG $IMAGE_TAG
+
+# Remove mirror image
+docker rmi $MIRROR_URL/$IMAGE_TAG
+
+docker images
+
+echo "Process completed successfully!"

docker/web.Dockerfile

@@ -2,6 +2,13 @@
 FROM node:latest AS development
 WORKDIR /app
 
+
+ARG http_proxy
+ARG https_proxy
+ENV http_proxy=$http_proxy \
+    https_proxy=$https_proxy \
+    TZ=Asia/Shanghai
+
 # 复制 package.json 和 package-lock.json（如果存在）
 COPY ./web/package*.json ./
 

docs/how-to.md

@@ -1,3 +1,7 @@
+### 如何优雅的拉取镜像？
+
+使用 `bash docker/pull_image.sh python:3.12` 就可以。
+
 ### 如何配置本地大语言模型？
 
 支持添加以 OpenAI 兼容模式运行的本地模型，可在 Web 设置中直接添加（适用于 vllm 和 Ollama 等）。
@@ -69,3 +73,41 @@ docker compose up --build -d
     name: nomic-embed-text
     dimension: 768
 ```
+
+### 如何配置 MinerU 抽取数据
+
+在 PDF 数据处理中，可以选择配置 [MinerU](https://github.com/opendatalab/MinerU) 来实现更快速、更准确的 PDF 识别效果。
+
+```yml
+  mineru-api:
+    build:
+      context: scripts/mineru-api
+      dockerfile: Dockerfile
+    image: mineru-api:latest
+    container_name: mineru-api
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities: [gpu]
+    ports:
+      - "5051:5051"
+    networks:
+      - app-network
+    restart: unless-stopped
+    command: python -m uvicorn app:app --host 0.0.0.0 --port 5051 --app-dir /app
+```
+
+如果要添加代理的话，可以添加 build args
+
+```yml
+  mineru-api:
+    build:
+      context: scripts/mineru-api
+      dockerfile: Dockerfile
+      args:
+        http_proxy: http://宿主机IP:7890
+        https_proxy: http://宿主机IP:7890
+```

pyproject.toml

@@ -39,3 +39,12 @@ dependencies = [
     "uvicorn[standard]>=0.34.2",
     "zhipuai>=2.1.5.20250421",
 ]
+[tool.ruff]
+line-length = 140  # 代码最大行宽
+select = [         # 选择的规则
+    "F",
+    "E",
+    "W",
+    "UP",
+]
+ignore = ["F401"]  # 忽略的规则

scripts/mineru-api/Dockerfile

@@ -0,0 +1,63 @@
+# Use the official Ubuntu base image
+FROM ubuntu:22.04
+
+# Set environment variables to non-interactive to avoid prompts during installation
+# 环境变量设置
+ARG http_proxy
+ARG https_proxy
+ENV http_proxy=$http_proxy \
+    https_proxy=$https_proxy \
+    DEBIAN_FRONTEND=noninteractive
+
+# Update the package list and install necessary packages
+RUN apt-get update && \
+    apt-get install -y \
+        software-properties-common && \
+    add-apt-repository ppa:deadsnakes/ppa && \
+    apt-get update && \
+    apt-get install -y \
+        python3.10 \
+        python3.10-venv \
+        python3.10-distutils \
+        python3-pip \
+        wget \
+        git \
+        libgl1 \
+        libreoffice \
+        fonts-noto-cjk \
+        fonts-wqy-zenhei \
+        fonts-wqy-microhei \
+        ttf-mscorefonts-installer \
+        fontconfig \
+        libglib2.0-0 \
+        libxrender1 \
+        libsm6 \
+        libxext6 \
+        poppler-utils \
+        && rm -rf /var/lib/apt/lists/*
+
+# Set Python 3.10 as the default python3
+RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.10 1
+
+# Create a virtual environment for MinerU
+RUN python3 -m venv /opt/mineru_venv
+
+# Copy the configuration file template and install magic-pdf latest
+RUN /bin/bash -c "source /opt/mineru_venv/bin/activate && \
+    pip3 install --upgrade pip -i https://mirrors.aliyun.com/pypi/simple && \
+    pip3 install -U magic-pdf[full] -i https://mirrors.aliyun.com/pypi/simple"
+
+# Download models and update the configuration file
+COPY magic-pdf.json /root/magic-pdf.json
+RUN /bin/bash -c "pip3 install modelscope -i https://mirrors.aliyun.com/pypi/simple && \
+    wget https://gcore.jsdelivr.net/gh/opendatalab/MinerU@master/scripts/download_models.py -O download_models.py && \
+    python3 download_models.py && \
+    sed -i 's|cpu|cuda|g' /root/magic-pdf.json"
+
+COPY app.py /app/app.py
+RUN /bin/bash -c "source /opt/mineru_venv/bin/activate && \
+    pip3 install fastapi uvicorn python-multipart loguru -i https://mirrors.aliyun.com/pypi/simple"
+
+
+# Set the entry point to activate the virtual environment and run the command line tool
+ENTRYPOINT ["/bin/bash", "-c", "source /opt/mineru_venv/bin/activate && exec \"$@\"", "--"]

scripts/mineru-api/app.py

@@ -0,0 +1,303 @@
+import json
+import os
+import tempfile
+from base64 import b64encode
+from glob import glob
+from io import StringIO
+from typing import Tuple, Union
+
+import magic_pdf.model as model_config
+import uvicorn
+from fastapi import FastAPI, Form, UploadFile
+from fastapi.responses import JSONResponse
+from loguru import logger
+from magic_pdf.config.enums import SupportedPdfParseMethod
+from magic_pdf.data.data_reader_writer import DataWriter, FileBasedDataWriter
+from magic_pdf.data.data_reader_writer.s3 import S3DataReader, S3DataWriter
+from magic_pdf.data.dataset import ImageDataset, PymuDocDataset
+from magic_pdf.data.read_api import read_local_images, read_local_office
+from magic_pdf.libs.config_reader import get_bucket_name, get_s3_config
+from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
+from magic_pdf.operators.models import InferenceResult
+from magic_pdf.operators.pipes import PipeResult
+
+model_config.__use_inside_model__ = True
+
+app = FastAPI()
+
+pdf_extensions = [".pdf"]
+office_extensions = [".ppt", ".pptx", ".doc", ".docx"]
+image_extensions = [".png", ".jpg", ".jpeg"]
+
+class MemoryDataWriter(DataWriter):
+    def __init__(self):
+        self.buffer = StringIO()
+
+    def write(self, path: str, data: bytes) -> None:
+        if isinstance(data, str):
+            self.buffer.write(data)
+        else:
+            self.buffer.write(data.decode("utf-8"))
+
+    def write_string(self, path: str, data: str) -> None:
+        self.buffer.write(data)
+
+    def get_value(self) -> str:
+        return self.buffer.getvalue()
+
+    def close(self):
+        self.buffer.close()
+
+
+def init_writers(
+    file_path: str = None,
+    file: UploadFile = None,
+    output_path: str = None,
+    output_image_path: str = None,
+) -> Tuple[
+    Union[S3DataWriter, FileBasedDataWriter],
+    Union[S3DataWriter, FileBasedDataWriter],
+    bytes,
+]:
+    """
+    Initialize writers based on path type
+
+    Args:
+        file_path: file path (local path or S3 path)
+        file: Uploaded file object
+        output_path: Output directory path
+        output_image_path: Image output directory path
+
+    Returns:
+        Tuple[writer, image_writer, file_bytes]: Returns initialized writer tuple and file content
+    """
+    file_extension:str = None
+    if file_path:
+        is_s3_path = file_path.startswith("s3://")
+        if is_s3_path:
+            bucket = get_bucket_name(file_path)
+            ak, sk, endpoint = get_s3_config(bucket)
+
+            writer = S3DataWriter(
+                output_path, bucket=bucket, ak=ak, sk=sk, endpoint_url=endpoint
+            )
+            image_writer = S3DataWriter(
+                output_image_path, bucket=bucket, ak=ak, sk=sk, endpoint_url=endpoint
+            )
+            # 临时创建reader读取文件内容
+            temp_reader = S3DataReader(
+                "", bucket=bucket, ak=ak, sk=sk, endpoint_url=endpoint
+            )
+            file_bytes = temp_reader.read(file_path)
+            file_extension = os.path.splitext(file_path)[1]
+        else:
+            writer = FileBasedDataWriter(output_path)
+            image_writer = FileBasedDataWriter(output_image_path)
+            os.makedirs(output_image_path, exist_ok=True)
+            with open(file_path, "rb") as f:
+                file_bytes = f.read()
+            file_extension = os.path.splitext(file_path)[1]
+    else:
+        # 处理上传的文件
+        file_bytes = file.file.read()
+        file_extension = os.path.splitext(file.filename)[1]
+
+        writer = FileBasedDataWriter(output_path)
+        image_writer = FileBasedDataWriter(output_image_path)
+        os.makedirs(output_image_path, exist_ok=True)
+
+    return writer, image_writer, file_bytes, file_extension
+
+
+def process_file(
+    file_bytes: bytes,
+    file_extension: str,
+    parse_method: str,
+    image_writer: Union[S3DataWriter, FileBasedDataWriter],
+) -> Tuple[InferenceResult, PipeResult]:
+    """
+    Process PDF file content
+
+    Args:
+        file_bytes: Binary content of file
+        file_extension: file extension
+        parse_method: Parse method ('ocr', 'txt', 'auto')
+        image_writer: Image writer
+
+    Returns:
+        Tuple[InferenceResult, PipeResult]: Returns inference result and pipeline result
+    """
+
+    ds: Union[PymuDocDataset, ImageDataset] = None
+    if file_extension in pdf_extensions:
+        ds = PymuDocDataset(file_bytes)
+    elif file_extension in office_extensions:
+        # 需要使用office解析
+        temp_dir = tempfile.mkdtemp()
+        with open(os.path.join(temp_dir, f"temp_file.{file_extension}"), "wb") as f:
+            f.write(file_bytes)
+        ds = read_local_office(temp_dir)[0]
+    elif file_extension in image_extensions:
+        # 需要使用ocr解析
+        temp_dir = tempfile.mkdtemp()
+        with open(os.path.join(temp_dir, f"temp_file.{file_extension}"), "wb") as f:
+            f.write(file_bytes)
+        ds = read_local_images(temp_dir)[0]
+    infer_result: InferenceResult = None
+    pipe_result: PipeResult = None
+
+    if parse_method == "ocr":
+        infer_result = ds.apply(doc_analyze, ocr=True)
+        pipe_result = infer_result.pipe_ocr_mode(image_writer)
+    elif parse_method == "txt":
+        infer_result = ds.apply(doc_analyze, ocr=False)
+        pipe_result = infer_result.pipe_txt_mode(image_writer)
+    else:  # auto
+        if ds.classify() == SupportedPdfParseMethod.OCR:
+            infer_result = ds.apply(doc_analyze, ocr=True)
+            pipe_result = infer_result.pipe_ocr_mode(image_writer)
+        else:
+            infer_result = ds.apply(doc_analyze, ocr=False)
+            pipe_result = infer_result.pipe_txt_mode(image_writer)
+
+    return infer_result, pipe_result
+
+
+def encode_image(image_path: str) -> str:
+    """Encode image using base64"""
+    with open(image_path, "rb") as f:
+        return b64encode(f.read()).decode()
+
+
+@app.post(
+    "/file_parse",
+    tags=["projects"],
+    summary="Parse files (supports local files and S3)",
+)
+async def file_parse(
+    file: UploadFile = None,
+    file_path: str = Form(None),
+    parse_method: str = Form("auto"),
+    is_json_md_dump: bool = Form(False),
+    output_dir: str = Form("output"),
+    return_layout: bool = Form(False),
+    return_info: bool = Form(False),
+    return_content_list: bool = Form(False),
+    return_images: bool = Form(False),
+):
+    """
+    Execute the process of converting PDF to JSON and MD, outputting MD and JSON files
+    to the specified directory.
+
+    Args:
+        file: The PDF file to be parsed. Must not be specified together with
+            `file_path`
+        file_path: The path to the PDF file to be parsed. Must not be specified together
+            with `file`
+        parse_method: Parsing method, can be auto, ocr, or txt. Default is auto. If
+            results are not satisfactory, try ocr
+        is_json_md_dump: Whether to write parsed data to .json and .md files. Default
+            to False. Different stages of data will be written to different .json files
+            (3 in total), md content will be saved to .md file
+        output_dir: Output directory for results. A folder named after the PDF file
+            will be created to store all results
+        return_layout: Whether to return parsed PDF layout. Default to False
+        return_info: Whether to return parsed PDF info. Default to False
+        return_content_list: Whether to return parsed PDF content list. Default to False
+    """
+    try:
+        if (file is None and file_path is None) or (
+            file is not None and file_path is not None
+        ):
+            return JSONResponse(
+                content={"error": "Must provide either file or file_path"},
+                status_code=400,
+            )
+
+        # Get PDF filename
+        file_name = os.path.basename(file_path if file_path else file.filename).split(
+            "."
+        )[0]
+        output_path = f"{output_dir}/{file_name}"
+        output_image_path = f"{output_path}/images"
+
+        # Initialize readers/writers and get PDF content
+        writer, image_writer, file_bytes, file_extension = init_writers(
+            file_path=file_path,
+            file=file,
+            output_path=output_path,
+            output_image_path=output_image_path,
+        )
+
+        # Process PDF
+        infer_result, pipe_result = process_file(file_bytes, file_extension, parse_method, image_writer)
+
+        # Use MemoryDataWriter to get results
+        content_list_writer = MemoryDataWriter()
+        md_content_writer = MemoryDataWriter()
+        middle_json_writer = MemoryDataWriter()
+
+        # Use PipeResult's dump method to get data
+        pipe_result.dump_content_list(content_list_writer, "", "images")
+        pipe_result.dump_md(md_content_writer, "", "images")
+        pipe_result.dump_middle_json(middle_json_writer, "")
+
+        # Get content
+        content_list = json.loads(content_list_writer.get_value())
+        md_content = md_content_writer.get_value()
+        middle_json = json.loads(middle_json_writer.get_value())
+        model_json = infer_result.get_infer_res()
+
+        # If results need to be saved
+        if is_json_md_dump:
+            writer.write_string(
+                f"{file_name}_content_list.json", content_list_writer.get_value()
+            )
+            writer.write_string(f"{file_name}.md", md_content)
+            writer.write_string(
+                f"{file_name}_middle.json", middle_json_writer.get_value()
+            )
+            writer.write_string(
+                f"{file_name}_model.json",
+                json.dumps(model_json, indent=4, ensure_ascii=False),
+            )
+            # Save visualization results
+            pipe_result.draw_layout(os.path.join(output_path, f"{file_name}_layout.pdf"))
+            pipe_result.draw_span(os.path.join(output_path, f"{file_name}_spans.pdf"))
+            pipe_result.draw_line_sort(
+                os.path.join(output_path, f"{file_name}_line_sort.pdf")
+            )
+            infer_result.draw_model(os.path.join(output_path, f"{file_name}_model.pdf"))
+
+        # Build return data
+        data = {}
+        if return_layout:
+            data["layout"] = model_json
+        if return_info:
+            data["info"] = middle_json
+        if return_content_list:
+            data["content_list"] = content_list
+        if return_images:
+            image_paths = glob(f"{output_image_path}/*.jpg")
+            data["images"] = {
+                os.path.basename(
+                    image_path
+                ): f"data:image/jpeg;base64,{encode_image(image_path)}"
+                for image_path in image_paths
+            }
+        data["md_content"] = md_content  # md_content is always returned
+
+        # Clean up memory writers
+        content_list_writer.close()
+        md_content_writer.close()
+        middle_json_writer.close()
+
+        return JSONResponse(data, status_code=200)
+
+    except Exception as e:
+        logger.exception(e)
+        return JSONResponse(content={"error": str(e)}, status_code=500)
+
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8888)

scripts/mineru-api/magic-pdf.json

@@ -0,0 +1,44 @@
+{
+    "bucket_info":{
+        "bucket-name-1":["ak", "sk", "endpoint"],
+        "bucket-name-2":["ak", "sk", "endpoint"]
+    },
+    "models-dir":"/opt/models",
+    "layoutreader-model-dir":"/opt/layoutreader",
+    "device-mode":"cuda",
+    "layout-config": {
+        "model": "doclayout_yolo"
+    },
+    "formula-config": {
+        "mfd_model": "yolo_v8_mfd",
+        "mfr_model": "unimernet_small",
+        "enable": true
+    },
+    "table-config": {
+        "model": "rapid_table",
+        "sub_model": "slanet_plus",
+        "enable": true,
+        "max_time": 400
+    },
+    "llm-aided-config": {
+        "formula_aided": {
+            "api_key": "your_api_key",
+            "base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
+            "model": "qwen2.5-7b-instruct",
+            "enable": false
+        },
+        "text_aided": {
+            "api_key": "your_api_key",
+            "base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
+            "model": "qwen2.5-7b-instruct",
+            "enable": false
+        },
+        "title_aided": {
+            "api_key": "your_api_key",
+            "base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
+            "model": "qwen2.5-32b-instruct",
+            "enable": false
+        }
+    },
+    "config_version": "1.2.0"
+}

server/models/__init__.py

@@ -1,8 +1,3 @@
 from sqlalchemy.ext.declarative import declarative_base
 
 Base = declarative_base()
-
-# 导入所有模型文件，确保它们被注册到Base.metadata
-from . import user_model
-from . import kb_models
-from . import thread_model

server/models/kb_models.py

@@ -1,4 +1,4 @@
-from sqlalchemy import Column, Integer, String, DateTime, JSON, Float, ForeignKey, Text
+from sqlalchemy import Column, Integer, String, DateTime, JSON, ForeignKey, Text
 from sqlalchemy.orm import relationship
 from sqlalchemy.sql import func
 import time

server/routers/data_router.py

@@ -54,7 +54,7 @@ async def query_test(query: str = Body(...), meta: dict = Body(...), current_use
 
 @data.post("/file-to-chunk")
 async def file_to_chunk(files: List[str] = Body(...), params: dict = Body(...), current_user: User = Depends(get_admin_user)):
-    logger.debug(f"File to chunk: {files}")
+    logger.debug(f"File to chunk: {files} {params=}")
     result = await knowledge_base.file_to_chunk(files, params=params)
     return result
 

server/utils/auth_utils.py

@@ -2,7 +2,7 @@ import hashlib
 import os
 import jwt
 from datetime import datetime, timedelta
-from typing import Optional, Dict, Any
+from typing import Any
 
 # JWT配置
 JWT_SECRET_KEY = os.environ.get("JWT_SECRET_KEY", "yuxi_know_secure_key")
@@ -38,7 +38,7 @@ class AuthUtils:
         return hashed == check_hash
 
     @staticmethod
-    def create_access_token(data: Dict[str, Any], expires_delta: Optional[timedelta] = None) -> str:
+    def create_access_token(data: dict[str, Any], expires_delta: timedelta | None = None) -> str:
         """创建JWT访问令牌"""
         to_encode = data.copy()
 
@@ -55,7 +55,7 @@ class AuthUtils:
         return encoded_jwt
 
     @staticmethod
-    def decode_token(token: str) -> Optional[Dict[str, Any]]:
+    def decode_token(token: str) -> dict[str, Any] | None:
         """解码验证JWT令牌"""
         try:
             payload = jwt.decode(token, JWT_SECRET_KEY, algorithms=[JWT_ALGORITHM])
@@ -64,7 +64,7 @@ class AuthUtils:
             return None
 
     @staticmethod
-    def verify_access_token(token: str) -> Dict[str, Any]:
+    def verify_access_token(token: str) -> dict[str, Any]:
         """验证访问令牌，如果无效则抛出异常"""
         try:
             payload = jwt.decode(token, JWT_SECRET_KEY, algorithms=[JWT_ALGORITHM])

src/agents/tools_factory.py

@@ -1,14 +1,14 @@
 import json
 import re
-import os
+from collections.abc import Callable
-from typing import Any, Callable, Optional, Type, Union, Annotated
+from typing import Annotated, Any
 
-
-from pydantic import BaseModel, Field
-from langchain_core.tools import tool, BaseTool, StructuredTool
 from langchain_community.tools.tavily_search import TavilySearchResults
+from langchain_core.tools import BaseTool, StructuredTool, tool
+from pydantic import BaseModel, Field
+
+from src import config, graph_base, knowledge_base
 
-from src import graph_base, knowledge_base, config
 
 # refs https://github.com/chatchat-space/LangGraph-Chatchat chatchat-server/chatchat/server/agent/tools_factory/tools_registry.py
 def regist_tool(
@@ -16,9 +16,9 @@ def regist_tool(
     title: str = "",
     description: str = "",
     return_direct: bool = False,
-    args_schema: Optional[Type[BaseModel]] = None,
+    args_schema: type[BaseModel] | None = None,
     infer_schema: bool = True,
-) -> Union[Callable, BaseTool]:
+) -> Callable | BaseTool:
     """
     wrapper of langchain tool decorator
     add tool to registry automatically
@@ -66,7 +66,12 @@ def regist_tool(
 
 
 class KnowledgeRetrieverModel(BaseModel):
-    query: str = Field(description="查询的关键词，查询的时候，应该尽量以可能帮助回答这个问题的关键词进行查询，不要直接使用用户的原始输入去查询。")
+    query: str = Field(
+        description=(
+            "查询的关键词，查询的时候，应该尽量以可能帮助回答这个问题的关键词进行查询，"
+            "不要直接使用用户的原始输入去查询。"
+        )
+    )
 
 
 

src/agents/utils.py

@@ -40,12 +40,12 @@ async def agent_cli(agent: BaseAgent, config: RunnableConfig = None):
                 content = msg.content or msg.tool_calls
 
                 if not content:
-                    if stream_flag == True:
+                    if stream_flag:
                         print()
                         stream_flag = False
                     continue
 
-                if stream_flag == False and content:
+                if not stream_flag and content:
                     print(f"AI: {content}", end="", flush=True)
                     stream_flag = True
                     continue

src/config/__init__.py

@@ -127,7 +127,7 @@ class Config(SimpleConfig):
 
         if self.model_dir:
             if os.path.exists(self.model_dir):
-                logger.info(f"MODEL_DIR （{self.model_dir}） 下面的文件夹: {os.listdir(self.model_dir)}")
+                logger.debug(f"MODEL_DIR （{self.model_dir}） 下面的文件夹: {os.listdir(self.model_dir)}")
             else:
                 logger.warning(f"提醒：MODEL_DIR （{self.model_dir}） 不存在，如果未配置，请忽略，如果配置了，请检查是否配置正确，比如 docker-compose 文件中的映射")
 

src/core/indexing.py

@@ -1,99 +1,140 @@
 import os
 import asyncio
 from pathlib import Path
-from llama_index.core import Document
+from langchain.schema.document import Document
-from llama_index.core.node_parser import SimpleFileNodeParser
+from langchain.text_splitter import RecursiveCharacterTextSplitter
-from llama_index.core.node_parser import SentenceSplitter
+from langchain_community.document_loaders import (
-from llama_index.readers.file import FlatReader, DocxReader
+    TextLoader,
+    PyPDFLoader,
+    Docx2txtLoader,
+    UnstructuredMarkdownLoader,
+    UnstructuredHTMLLoader,
+    CSVLoader,
+    JSONLoader
+)
 
 from src.utils import hashstr, logger
 
 
-def chunk(text_or_path, params=None):
+def chunk_with_parser(file_path, params=None):
     """
-    将文本或文件切分成固定大小的块
+    使用文件解析器将文件切分成固定大小的块
 
     Args:
-        text_or_path: 文本或文件路径
+        file_path: 文件路径
         params: 参数
-            chunk_size: 块大小
-            chunk_overlap: 块重叠大小
-            use_parser: 是否使用文件解析器
-    Returns:
-        nodes: 节点列表
     """
     params = params or {}
     chunk_size = int(params.get("chunk_size", 500))
     chunk_overlap = int(params.get("chunk_overlap", 100))
-    splitter = SentenceSplitter(
-        chunk_size=chunk_size,
-        chunk_overlap=chunk_overlap,
-    )
 
-    # 如果文件存在，并且是当前目录下的文件，则使用文件解析器
+    file_type = Path(file_path).suffix.lower()
-    if os.path.isfile(text_or_path) and os.path.exists(text_or_path) and os.path.abspath(text_or_path).startswith(os.getcwd()):
-        parser = SimpleFileNodeParser()
-        file_type = Path(text_or_path).suffix.lower()
-        if file_type in [".txt", ".json", ".md"]:
-            docs = FlatReader().load_data(Path(text_or_path))
-        elif file_type in [".docx"]:
-            docs = DocxReader().load_data(Path(text_or_path))
-        else:
-            raise ValueError(f"Unsupported file type `{file_type}`")
 
-        if params.get("use_parser"):
+    # 选择合适的加载器
-            nodes = parser.get_nodes_from_documents(docs)
+    if file_type in ['.txt']:
-        else:
+        loader = TextLoader(file_path)
-            nodes = splitter.get_nodes_from_documents(docs)
+
+    elif file_type in ['.md']:
+        loader = UnstructuredMarkdownLoader(file_path)
+
+    elif file_type in ['.docx', '.doc']:
+        loader = Docx2txtLoader(file_path)
+
+    elif file_type in ['.html', '.htm']:
+        loader = UnstructuredHTMLLoader(file_path)
+
+    elif file_type in ['.json']:
+        loader = JSONLoader(file_path, jq_schema=".")
+
+    elif file_type in ['.csv']:
+        loader = CSVLoader(file_path)
 
     else:
-        docs = [Document(id_=hashstr(text_or_path), text=text_or_path)]
+        raise ValueError(f"不支持的文件类型: {file_type}")
-        nodes = splitter.get_nodes_from_documents(docs)
+
+    # 加载文档
+    docs = loader.load()
+
+    # 创建文本分割器
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=chunk_size,
+        chunk_overlap=chunk_overlap,
+        separators=["\n\n", "\n", ".", " ", ""],
+    )
+
+    # 分割文档
+    nodes = text_splitter.split_documents(docs)
+
+    # 添加序号信息到metadata
+    for i, node in enumerate(nodes):
+        if node.metadata is None:
+            node.metadata = {}
+        node.metadata["chunk_idx"] = i
 
     return nodes
 
+def chunk_text(text, params=None):
+    """
+    将文本切分成固定大小的块
+    """
+    params = params or {}
+    chunk_size = int(params.get("chunk_size", 500))
+    chunk_overlap = int(params.get("chunk_overlap", 100))
 
+    # 创建文本分割器
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=chunk_size,
+        chunk_overlap=chunk_overlap,
+        separators=["\n\n", "\n", ".", " ", ""]
+    )
 
-def pdfreader(file_path):
+    # 分割文档
+    nodes = text_splitter.split_text(text)
+
+    # 添加序号信息到metadata
+    nodes = [{"text": node, "metadata": {"chunk_idx": i}} for i, node in enumerate(nodes)]
+    return nodes
+
+def chunk(text_or_path, params=None):
+    raise NotImplementedError("chunk is deprecated, use chunk_with_parser or chunk_text instead")
+
+def pdfreader(file_path, params=None):
     """读取PDF文件并返回text文本"""
-    assert os.path.exists(file_path), "File not found"
+    assert file_path.exists(), "File not found"
-    assert file_path.endswith(".pdf"), "File format not supported"
+    assert file_path.suffix.lower() == ".pdf", "File format not supported"
 
-    from llama_index.readers.file import PDFReader
+    # 使用LangChain的PDF加载器
-    doc = PDFReader().load_data(file=Path(file_path))
+    loader = PyPDFLoader(str(file_path))
+    docs = loader.load()
 
     # 简单的拼接起来之后返回纯文本
-    text = "\n\n".join([d.get_content() for d in doc])
+    text = "\n\n".join([d.page_content for d in docs])
     return text
 
 def plainreader(file_path):
     """读取普通文本文件并返回text文本"""
     assert os.path.exists(file_path), "File not found"
 
-    with open(file_path, "r") as f:
+    # 使用LangChain的文本加载器
-        text = f.read()
+    loader = TextLoader(str(file_path))
+    docs = loader.load()
+    text = "\n\n".join([d.page_content for d in docs])
     return text
 
-def read_text(file, params=None):
+def parse_pdf(file, params=None):
-    support_format = [".pdf", ".txt", ".md"]
+    params = params or {}
-    assert os.path.exists(file), "File not found"
+    opt_ocr = params.get("enable_ocr", "disable")
-    logger.info(f"Try to read file {file}")
 
-    if not os.path.isfile(file):
+    if opt_ocr == "onnx_rapid_ocr":
-        logger.error(f"Directory not supported now!")
-        raise NotImplementedError("Directory not supported now!")
-
-    if file.endswith(".pdf"):
         from src.plugins import ocr
         return ocr.process_pdf(file)
 
-    elif file.endswith(".txt") or file.endswith(".md"):
+    elif opt_ocr == "mineru_ocr":
-        return plainreader(file)
+        from src.plugins import ocr
+        return ocr.process_pdf_mineru(file)
 
     else:
-        logger.error(f"File format not supported, only support {support_format}")
+        return pdfreader(file, params=params)
-        raise Exception(f"File format not supported, only support {support_format}")
 
-
+async def parse_pdf_async(file, params=None):
-async def read_text_async(file):
+    return await asyncio.to_thread(parse_pdf, file, params=params)
-    return await asyncio.to_thread(read_text, file)

src/core/knowledgebase.py

@@ -4,12 +4,14 @@ import time
 import traceback
 import shutil
 from sqlalchemy.orm import joinedload
+from pathlib import Path
+import asyncio
 
 from pymilvus import MilvusClient, MilvusException
 
 from src import config
 from src.utils import logger, hashstr
-from src.core.indexing import chunk, read_text_async
+from src.core.indexing import chunk_with_parser, chunk_text, parse_pdf_async
 from server.db_manager import db_manager
 from server.models.kb_models import KnowledgeDatabase, KnowledgeFile, KnowledgeNode
 from src.utils.db_migration import migrate_knowledge_db
@@ -367,7 +369,7 @@ class KnowledgeBase:
         for line in lines:
             line.pop("vector")
 
-        lines.sort(key=lambda x: x.get("start_char_idx") or 0)
+        lines.sort(key=lambda x: x.get("start_char_idx") or x.get("metadata", {}).get("chunk_idx", 0))
         return {"lines": lines}
 
     def get_kb_by_id(self, db_id):
@@ -387,25 +389,35 @@ class KnowledgeBase:
         """
         file_infos = {}
         for file in files:
-            file_id = "file_" + hashstr(file + str(time.time()))
+            file_path = Path(file)
-
+            file_id = "file_" + hashstr(str(file_path) + str(time.time()))
-            file_type = file.split(".")[-1].lower()
+            file_type = file_path.suffix.lower().replace(".", "")
-
+            file_info_dict = {
-            if file_type == "pdf":
-                texts = await read_text_async(file)
-                nodes = chunk(texts, params=params)
-            else:
-                nodes = chunk(file, params=params)
-
-            file_infos[file_id] = {
                 "file_id": file_id,
-                "filename": os.path.basename(file),
+                "filename": file_path.name,
-                "path": file,
+                "path": str(file_path),
                 "type": file_type,
                 "status": "waiting",
                 "created_at": time.time(),
-                "nodes": [node.dict() for node in nodes]
+                "nodes": []
             }
+            logger.debug(f"{file_info_dict=}")
+            try:
+                if file_type == "pdf":
+                    texts = await parse_pdf_async(file_path, params=params)
+                    nodes = chunk_text(texts, params=params)
+                else:
+                    nodes = chunk_with_parser(file_path, params=params)
+
+                processed_nodes = [parse_node_data(node) for node in nodes]
+
+                file_info_dict["nodes"] = processed_nodes
+            except Exception as e:
+                logger.error(f"处理文件 {file_path} 时出错: {e}")
+                file_info_dict["status"] = "failed"
+                file_info_dict["error"] = str(e)
+
+            file_infos[file_id] = file_info_dict
 
         return file_infos
 
@@ -426,56 +438,32 @@ class KnowledgeBase:
 
         file_infos = {}
 
-        # 使用UnstructuredURLLoader加载URL内容
+        for url in urls:
-        # loader = UnstructuredURLLoader(urls=urls, continue_on_failure=True)
-
-        for url_idx, url in enumerate(urls):
             file_id = "url_" + hashstr(url + str(time.time()))
-
+            file_info_dict = {
+                "file_id": file_id,
+                "filename": gen_filename_from_url(url),
+                "path": url,
+                "type": "url",
+                "status": "waiting",
+                "created_at": time.time(),
+                "nodes": []
+            }
             try:
                 # 加载单个URL内容
                 single_loader = UnstructuredURLLoader(urls=[url], continue_on_failure=False)
                 documents = await single_loader.aload()
 
-                # 将文档内容合并
+                # 合并文档内容
                 text_content = "\n\n".join([doc.page_content for doc in documents])
+                file_info_dict["nodes"] = chunk_text(text_content, params=params)
 
-                # 对内容进行分块
-                nodes = chunk(text_content, params=params)
-
-                # 从URL中提取域名作为文件名
-                from urllib.parse import urlparse, unquote
-                parsed_url = urlparse(unquote(url))
-                domain = parsed_url.netloc
-                path = parsed_url.path
-                filename = f"{domain}{path}"
-                if filename.endswith('/'):
-                    filename = filename[:-1]
-                if len(filename) > 100:
-                    filename = filename[:97] + "..."
-                filename = filename.replace('/', '_')
-
-                file_infos[file_id] = {
-                    "file_id": file_id,
-                    "filename": filename,
-                    "path": url,
-                    "type": "url",
-                    "status": "waiting",
-                    "created_at": time.time(),
-                    "nodes": [node.dict() for node in nodes]
-                }
             except Exception as e:
                 logger.error(f"处理URL {url} 时出错: {e}")
-                file_infos[file_id] = {
+                file_info_dict["status"] = "failed"
-                    "file_id": file_id,
+                file_info_dict["error"] = str(e)
-                    "filename": url[:100] + "..." if len(url) > 100 else url,
+
-                    "path": url,
+            file_infos[file_id] = file_info_dict
-                    "type": "url",
-                    "status": "failed",
-                    "created_at": time.time(),
-                    "error": str(e),
-                    "nodes": []
-                }
 
         return file_infos
 
@@ -765,3 +753,30 @@ class KnowledgeBase:
         updated_db = self.update_database_record(db_id, name, description)
         return updated_db
 
+
+def parse_node_data(node):
+    node_data = node.model_dump() if hasattr(node, "model_dump") else node
+    node_text = node_data.get("page_content", node_data.get("text", ""))
+    node_dict = {
+        "text": node_text,
+        "hash": hashstr(node_text, with_salt=True),
+        "start_char_idx": node_data.get("start_char_idx", None),
+        "end_char_idx": node_data.get("end_char_idx", None),
+        "metadata": node_data.get("metadata", {}),
+    }
+    return node_dict
+
+
+def gen_filename_from_url(url):
+    # 从URL中提取域名作为文件名
+    from urllib.parse import urlparse, unquote
+    parsed_url = urlparse(unquote(url))
+    domain = parsed_url.netloc
+    path = parsed_url.path
+    filename = f"{domain}{path}"
+    if filename.endswith('/'):
+        filename = filename[:-1]
+    if len(filename) > 100:
+        filename = filename[:97] + "..."
+    filename = filename.replace('/', '_')
+    return filename

src/models/embedding.py

@@ -82,8 +82,8 @@ class LocalEmbeddingModel(BaseEmbeddingModel):
             else:
                 logger.warning(f"Local model `{info['name']}` not found in `{self.model}`, using `{info['name']}`")
 
-        logger.info(f"Loading local model `{info['name']}` from `{self.model}` with device `{config.device}`，"
+        logger.info(f"Loading local model `{info['name']}` from `{self.model}` with device `{config.device}`")
-                    f"如果没配置任何路径的话，正常情况下会自动从 Huggingface 下载模型，如果遇到下载失败，可以尝试使用 HF_MIRROR 环境变量；"
+        logger.debug("如果没配置任何路径的话，正常情况下会自动从 Huggingface 下载模型，如果遇到下载失败，可以尝试使用 HF_MIRROR 环境变量；"
                     f"如果还是不行，建议手动下载到某个文件夹，比如  {os.getenv('MODEL_DIR', '/models')}/BAAI/bge-m3 目录下；")
 
         self.model = HuggingFaceEmbeddings(

src/plugins/_ocr.py

@@ -123,17 +123,23 @@ class OCRPlugin:
             raise FileNotFoundError(f"PDF file not found: {pdf_path}")
 
         try:
-            # 检查是否为文本PDF
+            # 检查是否为文本PDF，可能会出现错误，比如每一页都有可读取的水印文字，但是内容本身是扫描件，需要使用OCR处理
-            if is_text_pdf(pdf_path):
+            # if is_text_pdf(pdf_path):
-                logger.info(f"PDF file is text, use llama_index.readers.file to read")
+            #     from src.core.indexing import pdfreader
-                return pdfreader(pdf_path)
+            #     logger.info("PDF file is text, use llama_index.readers.file to read")
+            #     return pdfreader(pdf_path)
 
-            # 将PDF转换为图像
+            images = []
-            filename = os.path.basename(pdf_path).split('.')[0]
-            output_dir = os.path.join('saves', 'data', 'pdf2txt', filename)
-            os.makedirs(output_dir, exist_ok=True)
 
-            images = self.convert_imgs(pdf_path, output_dir)
+            pdfDoc = fitz.open(pdf_path)
+            totalPage = pdfDoc.page_count
+            for pg in tqdm(range(totalPage), desc='to images', ncols=100):
+                page = pdfDoc[pg]
+                rotate, zoom_x, zoom_y = 0, 2, 2
+                mat = fitz.Matrix(zoom_x, zoom_y).prerotate(rotate)
+                pix = page.get_pixmap(matrix=mat, alpha=False)
+                img_pil = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
+                images.append(img_pil)
 
             # 处理每个图像并合并文本
             all_text = []
@@ -147,46 +153,58 @@ class OCRPlugin:
             logger.error(f"PDF processing error: {str(e)}")
             return ""
 
+    def process_pdf_mineru(self, pdf_path):
+        """
+        使用Mineru OCR处理PDF文件
+        :param pdf_path: PDF文件路径
+        :return: 提取的文本
+        """
+        mineru_ocr_uri = os.getenv("MINERU_OCR_URI", "http://localhost:5051")
+        import requests
+        import json
 
-    def convert_imgs(self, pdf_path, output_dir):
+        # 读取PDF文件
-        imgs = []
+        with open(pdf_path, 'rb') as f:
-        img_dir = os.path.join(output_dir, 'imgs')
+            files = {'file': f}
-        if not os.path.exists(img_dir):
+            data = {
-            os.makedirs(img_dir)
+                'parse_method': 'ocr',  # 使用OCR模式
-            pdfDoc = fitz.open(pdf_path)
+                'is_json_md_dump': False,  # 不需要保存中间文件
-            totalPage = pdfDoc.page_count
+                'return_layout': False,  # 不需要返回布局信息
-            for pg in tqdm(range(totalPage), desc='to imgs', ncols=100):
+                'return_info': False,  # 不需要返回额外信息
-                page = pdfDoc[pg]
+                'return_content_list': False,  # 不需要返回内容列表
-                rotate = int(0)
+                'return_images': False,  # 不需要返回图片
-                zoom_x = 2
+            }
-                zoom_y = 2
-                mat = fitz.Matrix(zoom_x, zoom_y).prerotate(rotate)
-                pix = page.get_pixmap(matrix=mat, alpha=False)
-                img_filename = os.path.join(img_dir, f'images_{pg+1}.png')
-                pix.save(img_filename)  # os.sep
-                imgs.append(img_filename)
-        else:
-            img_names = sorted(os.listdir(img_dir))
-            imgs = [os.path.join(img_dir, img_name) for img_name in img_names]
 
-        return imgs
+            try:
+                # 发送POST请求到Mineru OCR服务
+                response = requests.post(
+                    f"{mineru_ocr_uri}/file_parse",
+                    files=files,
+                    data=data
+                )
+                response.raise_for_status()  # 检查响应状态
+
+                # 解析响应
+                result = response.json()
+                if 'md_content' in result:
+                    return result['md_content']
+                else:
+                    logger.error("Mineru OCR response does not contain md_content")
+                    return ""
+
+            except requests.exceptions.RequestException as e:
+                logger.error(f"Mineru OCR request failed: {str(e)}")
+                return ""
+            except json.JSONDecodeError as e:
+                logger.error(f"Failed to parse Mineru OCR response: {str(e)}")
+                return ""
+            except Exception as e:
+                logger.error(f"Unexpected error in Mineru OCR processing: {str(e)}")
+                return ""
 
 def get_state(task_id):
     return GOLBAL_STATE.get(task_id, {})
 
-
-def pdfreader(file_path):
-    """读取PDF文件并返回text文本"""
-    assert os.path.exists(file_path), "File not found"
-    assert file_path.endswith(".pdf"), "File format not supported"
-
-    from llama_index.readers.file import PDFReader
-    doc = PDFReader().load_data(file=Path(file_path))
-
-    # 简单的拼接起来之后返回纯文本
-    text = "\n\n".join([d.get_content() for d in doc])
-    return text
-
 def plainreader(file_path):
     """读取普通文本文件并返回text文本"""
     assert os.path.exists(file_path), "File not found"

src/utils/web_search.py

@@ -1,5 +1,4 @@
 import os
-from typing import List, Dict
 from tavily import TavilyClient
 from src.utils.logging_config import logger
 
@@ -11,7 +10,7 @@ class WebSearcher:
         self.client = TavilyClient(api_key)
         logger.info("WebSearcher initialized with Tavily client")
 
-    def search(self, query: str, max_results: int = 1) -> List[Dict]:
+    def search(self, query: str, max_results: int = 1) -> list[dict]:
         """
         使用 Tavily 搜索相关内容
 
@@ -45,7 +44,7 @@ class WebSearcher:
             logger.error(f"Error during web search: {str(e)}")
             return []
 
-    def format_search_results(self, results: List[Dict]) -> str:
+    def format_search_results(self, results: list[dict]) -> str:
         """
         将搜索结果格式化为文本
 

web/src/assets/base.css

@@ -34,6 +34,7 @@
 
   --main-color: #1c6586;
   --main-color-dark: #004d5c;
+  --main-color-text: #104461;
   --main-light-1: #0076AB;
   --main-light-2: #DAEAED;
   --main-light-3: #EDF0F1;

web/src/components/ChatComponent.vue

@@ -93,7 +93,7 @@
               v-if="configStore.config.enable_web_search"
               @click="meta.use_web=!meta.use_web"
             >
-              <CompassOutlined style="margin-right: 3px;"/>
+              <Compass style="margin-right: 3px;" size="14"/>
               联网搜索
             </div>
             <div
@@ -101,7 +101,7 @@
               v-if="configStore.config.enable_knowledge_graph"
               @click="meta.use_graph=!meta.use_graph"
             >
-              <DeploymentUnitOutlined style="margin-right: 3px;"/>
+              <Waypoints style="margin-right: 3px;" size="14"/>
               知识图谱
             </div>
             <a-dropdown
@@ -109,7 +109,7 @@
               :class="{'opt-item': true, 'active': meta.selectedKB !== null}"
             >
               <a class="ant-dropdown-link" @click.prevent>
-                <BookOutlined style="margin-right: 3px;"/>
+                <BookCheck style="margin-right: 3px;" size="14"/>
                 <span class="text">{{ meta.selectedKB === null ? '不使用知识库' : opts.databases[meta.selectedKB]?.name }}</span>
               </a>
               <template #overlay>
@@ -147,7 +147,7 @@ import {
   PlusCircleOutlined,
   DeploymentUnitOutlined,
 } from '@ant-design/icons-vue'
-import { Ellipsis, PanelLeftOpen, MessageSquarePlus } from 'lucide-vue-next'
+import { Ellipsis, PanelLeftOpen, MessageSquarePlus, Compass, Waypoints, BookCheck } from 'lucide-vue-next'
 import { onClickOutside } from '@vueuse/core'
 import { useConfigStore } from '@/stores/config'
 import { useUserStore } from '@/stores/user'
@@ -972,6 +972,13 @@ const findLastIndex = (array, predicate) => {
       max-width: 1200px;
     }
 
+    .opt-item {
+      display: flex;
+      justify-content: center;
+      align-items: center;
+      gap: 4px;
+    }
+
     .note {
       width: 100%;
       font-size: small;

web/src/views/DataBaseInfoView.vue

@@ -115,9 +115,9 @@
                     <a-input-number v-model:value="chunkParams.chunk_overlap" :min="0" :max="1000" />
                     <p class="param-description">相邻文本片段间的重叠字符数</p>
                   </a-form-item>
-                  <a-form-item label="使用文件节点解析器" name="use_parser">
+                  <a-form-item label="使用OCR" name="enable_ocr">
-                    <a-switch v-model:checked="chunkParams.use_parser" />
+                    <a-select v-model:value="chunkParams.enable_ocr" :options="enable_ocr_options" />
-                    <p class="param-description">启用特定文件格式的智能分析</p>
+                    <p class="param-description">启用OCR功能，支持PDF文件的文本提取</p>
                   </a-form-item>
                 </a-form>
               </div>
@@ -389,6 +389,12 @@ const meta = reactive({
   sortBy: 'rerank_score',
 });
 
+const enable_ocr_options = ref([
+  { value: 'disable', payload: { title: '不启用' } },
+  { value: 'onnx_rapid_ocr', payload: { title: 'ONNX with RapidOCR' } },
+  { value: 'mineru_ocr', payload: { title: 'MinerU OCR' } },
+])
+
 const use_rewrite_queryOptions = ref([
   { value: 'off', payload: { title: 'off', subTitle: '不启用' } },
   { value: 'on', payload: { title: 'on', subTitle: '启用重写' } },
@@ -635,7 +641,7 @@ const deleteFile = (fileId) => {
 const chunkParams = ref({
   chunk_size: 1000,
   chunk_overlap: 200,
-  use_parser: false,
+  enable_ocr: 'disable',
 })
 
 const chunkResults = ref([]);