Docling 集成

与Docling文档加载器通过LangChain Python集成

Docling 解析 PDF、DOCX、PPTX、HTML 等格式为包含文档布局、表格等丰富统一表示，使其适用于检索增强生成（RAG）等生成式 AI 工作流。

该集成通过 DoclingLoader 文档加载器提供 Docling 的功能。

概述

集成详情

类别	包名	本地支持	可序列化	JS 支持
langchain_docling.loader	langchain-docling	✅	❌	❌

加载器特性

来源	文档懒加载	原生异步支持
DoclingLoader	✅	❌

DoclingLoader 组件支持以下功能：

• 灵活使用多种文档类型构建 LLM 应用
• 利用 Docling 的丰富格式实现文档原生锚定

DoclingLoader 支持两种导出模式：

• ExportType.DOC_CHUNKS（默认）：将输入文档切分为块，每个块作为独立 LangChain 文档
• ExportType.MARKDOWN：将整个输入文档作为独立 LangChain 文档

通过参数 EXPORT_TYPE 可切换模式，示例流程将根据参数值自动配置。

配置

pip install -qU langchain-docling

建议使用 GPU 加速提升转换速度（例如 Colab 环境启用 GPU 运行时）

初始化

基础初始化示例如下：

from langchain_docling.loader import DoclingLoader

FILE_PATH = "https://arxiv.org/pdf/2408.09869"

loader = DoclingLoader(file_path=FILE_PATH)

高级用法可通过 DoclingLoader 配置以下参数：

• file_path：源（单个字符串或迭代器）
• converter（可选）：指定 Docling 转换器实例
• convert_kwargs（可选）：转换执行参数
• export_type（可选）：导出模式（ExportType.DOC_CHUNKS 默认或 ExportType.MARKDOWN）
• md_export_kwargs（可选）：Markdown 导出参数
• chunker（可选）：文档切分器实例
• meta_extractor（可选）：元数据提取器

加载

docs = loader.load()

Token indices sequence length is longer than the specified maximum sequence length for this model (1041 > 512). Running this sequence through the model will result in indexing errors

“Token 序列长度超过指定最大值” 的提示可忽略（详见 docling-core GitHub issue）。

示例文档预览：

for d in docs[:3]:
    print(f"- {d.page_content=}")

- d.page_content='arXiv:2408.09869v5  [cs.CL]  9 Dec 2024'
- d.page_content='Docling Technical Report\nVersion 1.0\nChristoph Auer Maksym Lysak Ahmed Nassar Michele Dolfi Nikolaos Livathinos Panos Vagenas Cesar Berrospi Ramis Matteo Omenetti Fabian Lindlbauer Kasper Dinkla Lokesh Mishra Yusik Kim Shubham Gupta Rafael Teixeira de Lima Valery Weber Lucas Morin Ingmar Meijer Viktor Kuropiatnyk Peter W. J. Staar\nAI4K Group, IBM Research R¨uschlikon, Switzerland'
- d.page_content='Abstract\nThis technical report introduces Docling , an easy to use, self-contained, MITlicensed open-source package for PDF document conversion. It is powered by state-of-the-art specialized AI models for layout analysis (DocLayNet) and table structure recognition (TableFormer), and runs efficiently on commodity hardware in a small resource budget. The code interface allows for easy extensibility and addition of new features and models.'

懒加载

支持按需加载文档：

doc_iter = loader.lazy_load()
for doc in doc_iter:
    pass  # you can operate on `doc` here

全流程示例

import os

# https://github.com/huggingface/transformers/issues/5486:
os.environ["TOKENIZERS_PARALLELISM"] = "false"

• 使用 HuggingFace 推理 API（通过环境变量 HF_TOKEN 提供 Token 可提升额度）
• 依赖安装方式（--no-warn-conflicts 适用于 Colab 预置环境，按需可删除）

pip install -q --progress-bar off --no-warn-conflicts langchain-core langchain-huggingface langchain-milvus langchain python-dotenv

定义管道参数：

from pathlib import Path
from tempfile import mkdtemp

from dotenv import load_dotenv
from langchain_core.prompts import PromptTemplate
from langchain_docling.loader import ExportType


def _get_env_from_colab_or_os(key):
    try:
        from google.colab import userdata

        try:
            return userdata.get(key)
        except userdata.SecretNotFoundError:
            pass
    except ImportError:
        pass
    return os.getenv(key)


load_dotenv()

HF_TOKEN = _get_env_from_colab_or_os("HF_TOKEN")
FILE_PATH = ["https://arxiv.org/pdf/2408.09869"]  # Docling Technical Report
EMBED_MODEL_ID = "sentence-transformers/all-MiniLM-L6-v2"
GEN_MODEL_ID = "mistralai/Mixtral-8x7B-Instruct-v0.1"
EXPORT_TYPE = ExportType.DOC_CHUNKS
QUESTION = "Which are the main AI models in Docling?"
PROMPT = PromptTemplate.from_template(
    "Context information is below.\n---------------------\n{context}\n---------------------\nGiven the context information and not prior knowledge, answer the query.\nQuery: {input}\nAnswer:\n",
)
TOP_K = 3
MILVUS_URI = str(Path(mkdtemp()) / "docling.db")

from docling.chunking import HybridChunker
from langchain_docling import DoclingLoader

loader = DoclingLoader(
    file_path=FILE_PATH,
    export_type=EXPORT_TYPE,
    chunker=HybridChunker(tokenizer=EMBED_MODEL_ID),
)

docs = loader.load()

Token indices sequence length is longer than the specified maximum sequence length for this model (1041 > 512). Running this sequence through the model will result in indexing errors

确定分割方式：

if EXPORT_TYPE == ExportType.DOC_CHUNKS:
    splits = docs
elif EXPORT_TYPE == ExportType.MARKDOWN:
    from langchain_text_splitters import MarkdownHeaderTextSplitter

    splitter = MarkdownHeaderTextSplitter(
        headers_to_split_on=[
            ("#", "Header_1"),
            ("##", "Header_2"),
            ("###", "Header_3"),
        ],
    )
    splits = [split for doc in docs for split in splitter.split_text(doc.page_content)]
else:
    raise ValueError(f"Unexpected export type: {EXPORT_TYPE}")

示例分割结果：

for d in splits[:3]:
    print(f"- {d.page_content=}")
print("...")

- d.page_content='arXiv:2408.09869v5  [cs.CL]  9 Dec 2024'
- d.page_content='Docling Technical Report\nVersion 1.0\nChristoph Auer Maksym Lysak Ahmed Nassar Michele Dolfi Nikolaos Livathinos Panos Vagenas Cesar Berrospi Ramis Matteo Omenetti Fabian Lindlbauer Kasper Dinkla Lokesh Mishra Yusik Kim Shubham Gupta Rafael Teixeira de Lima Valery Weber Lucas Morin Ingmar Meijer Viktor Kuropiatnyk Peter W. J. Staar\nAI4K Group, IBM Research R¨uschlikon, Switzerland'
- d.page_content='Abstract\nThis technical report introduces Docling , an easy to use, self-contained, MITlicensed open-source package for PDF document conversion. It is powered by state-of-the-art specialized AI models for layout analysis (DocLayNet) and table structure recognition (TableFormer), and runs efficiently on commodity hardware in a small resource budget. The code interface allows for easy extensibility and addition of new features and models.'
...

数据摄入

import json
from pathlib import Path
from tempfile import mkdtemp

from langchain_huggingface.embeddings import HuggingFaceEmbeddings
from langchain_milvus import Milvus

embedding = HuggingFaceEmbeddings(model_name=EMBED_MODEL_ID)

milvus_uri = str(Path(mkdtemp()) / "docling.db")  # or set as needed
vectorstore = Milvus.from_documents(
    documents=splits,
    embedding=embedding,
    collection_name="docling_demo",
    connection_args={"uri": milvus_uri},
    index_params={"index_type": "FLAT"},
    drop_old=True,
)

检索增强生成

from langchain_classic.chains import create_retrieval_chain
from langchain_classic.chains.combine_documents import create_stuff_documents_chain
from langchain_huggingface import HuggingFaceEndpoint

retriever = vectorstore.as_retriever(search_kwargs={"k": TOP_K})
llm = HuggingFaceEndpoint(
    repo_id=GEN_MODEL_ID,
    huggingfacehub_api_token=HF_TOKEN,
    task="text-generation",
)

def clip_text(text, threshold=100):
    return f"{text[:threshold]}..." if len(text) > threshold else text

question_answer_chain = create_stuff_documents_chain(llm, PROMPT)
rag_chain = create_retrieval_chain(retriever, question_answer_chain)
resp_dict = rag_chain.invoke({"input": QUESTION})

clipped_answer = clip_text(resp_dict["answer"], threshold=350)
print(f"Question:\n{resp_dict['input']}\n\nAnswer:\n{clipped_answer}")
for i, doc in enumerate(resp_dict["context"]):
    print()
    print(f"Source {i + 1}:")
    print(f"  text: {json.dumps(clip_text(doc.page_content, threshold=350))}")
    for key in doc.metadata:
        if key != "pk":
            val = doc.metadata.get(key)
            clipped_val = clip_text(val) if isinstance(val, str) else val
            print(f"  {key}: {clipped_val}")

Question:
Which are the main AI models in Docling?

Answer:
The main AI models in Docling are a layout analysis model, which is an accurate object-detector for page elements, and TableFormer, a state-of-the-art table structure recognition model.

Source 1:
  text: "3.2 AI models\nAs part of Docling, we initially release two highly capable AI models to the open-source community, which have been developed and published recently by our team. The first model is a layout analysis model, an accurate object-detector for page elements [13]. The second model is TableFormer [12, 9], a state-of-the-art table structure re..."
  dl_meta: {'schema_name': 'docling_core.transforms.chunker.DocMeta', 'version': '1.0.0', 'doc_items': [{'self_ref': '#/texts/50', 'parent': {'$ref': '#/body'}, 'children': [], 'label': 'text', 'prov': [{'page_no': 3, 'bbox': {'l': 108.0, 't': 405.1419982910156, 'r': 504.00299072265625, 'b': 330.7799987792969, 'coord_origin': 'BOTTOMLEFT'}, 'charspan': [0, 608]}]}], 'headings': ['3.2 AI models'], 'origin': {'mimetype': 'application/pdf', 'binary_hash': 11465328351749295394, 'filename': '2408.09869v5.pdf'}}
  source: https://arxiv.org/pdf/2408.09869

Source 2:
  text: "3 Processing pipeline\nDocling implements a linear pipeline of operations, which execute sequentially on each given document (see Fig. 1). Each document is first parsed by a PDF backend, which retrieves the programmatic text tokens, consisting of string content and its coordinates on the page, and also renders a bitmap image of each page to support ..."
  dl_meta: {'schema_name': 'docling_core.transforms.chunker.DocMeta', 'version': '1.0.0', 'doc_items': [{'self_ref': '#/texts/26', 'parent': {'$ref': '#/body'}, 'children': [], 'label': 'text', 'prov': [{'page_no': 2, 'bbox': {'l': 108.0, 't': 273.01800537109375, 'r': 504.00299072265625, 'b': 176.83799743652344, 'coord_origin': 'BOTTOMLEFT'}, 'charspan': [0, 796]}]}], 'headings': ['3 Processing pipeline'], 'origin': {'mimetype': 'application/pdf', 'binary_hash': 11465328351749295394, 'filename': '2408.09869v5.pdf'}}
  source: https://arxiv.org/pdf/2408.09869

Source 3:
  text: "6 Future work and contributions\nDocling is designed to allow easy extension of the model library and pipelines. In the future, we plan to extend Docling with several more models, such as a figure-classifier model, an equationrecognition model, a code-recognition model and more. This will help improve the quality of conversion for specific types of ..."
  dl_meta: {'schema_name': 'docling_core.transforms.chunker.DocMeta', 'version': '1.0.0', 'doc_items': [{'self_ref': '#/texts/76', 'parent': {'$ref': '#/body'}, 'children': [], 'label': 'text', 'prov': [{'page_no': 5, 'bbox': {'l': 108.0, 't': 322.468994140625, 'r': 504.00299072265625, 'b': 259.0169982910156, 'coord_origin': 'BOTTOMLEFT'}, 'charspan': [0, 543]}]}, {'self_ref': '#/texts/77', 'parent': {'$ref': '#/body'}, 'children': [], 'label': 'text', 'prov': [{'page_no': 5, 'bbox': {'l': 108.0, 't': 251.6540069580078, 'r': 504.00299072265625, 'b': 198.99200439453125, 'coord_origin': 'BOTTOMLEFT'}, 'charspan': [0, 402]}]}], 'headings': ['6 Future work and contributions'], 'origin': {'mimetype': 'application/pdf', 'binary_hash': 11465328351749295394, 'filename': '2408.09869v5.pdf'}}
  source: https://arxiv.org/pdf/2408.09869

注意：源数据包含丰富锚定信息，包括章节（section）、页码和精确边界框。

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API 参考

• LangChain Docling 集成 GitHub
• Docling GitHub
• Docling 文档

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官方原文：src/oss/python/integrations/document_loaders/docling.mdx · 非官方翻译，译文由 AI 生成，以英文原文为准。