Docling 将 PDF、DOCX、PPTX、HTML 和其他格式解析为包含文档布局、表格等的统一丰富表示，使其可以用于 RAG 等生成式 AI 工作流程。

此集成通过以下方式提供 Docling 的功能： DoclingLoader 文档加载器。

概述

集成详情

加载器功能

所呈现的 DoclingLoader 组件使您能够：

• - 轻松快速地在 LLM 应用程序中使用各种文档类型，并
• - 利用 Docling 的丰富格式进行高级的原生文档锚定。

DoclingLoader 支持两种不同的导出模式：

- ExportType.DOC_CHUNKS （默认）：如果您希望将每个输入文档分块，然后 将每个单独的块作为下游的单独 LangChain Document 捕获，或 - ExportType.MARKDOWN：如果您希望将每个输入文档作为单独的 LangChain Document 捕获

该示例允许通过参数探索两种模式 EXPORT_TYPE；根据设置的 值，示例管道将相应地进行设置。

设置

pip install -qU langchain-docling

> 为获得最佳转换速度，请尽可能使用 GPU 加速；例如，如果在 Colab 上运行，请使用 GPU 运行时。

初始化

基本初始化如下所示：

from langchain_docling.loader import DoclingLoader

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

loader = DoclingLoader(file_path=FILE_PATH)

关于高级用法， DoclingLoader 具有以下参数：

- file_path：源为单个 str（URL 或本地文件）或其可迭代对象 - converter （可选）：要使用的任何特定 Docling 转换器实例 - convert_kwargs （可选）：转换执行的任何特定 kwargs - export_type （可选）：要使用的导出模式： ExportType.DOC_CHUNKS （默认）或 ExportType.MARKDOWN - md_export_kwargs （可选）：Markdown 导出的任何特定 kwargs（用于 Markdown 模式） - chunker （可选）：要使用的任何特定 Docling 分块器实例（用于文档块 模式） - meta_extractor （可选）：要使用的任何特定元数据提取器

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

> 注意：可以忽略一条消息，显示 "令牌索引序列长度大于指定的 最大序列长度..."——更多详情请参阅此 docling-core GitHub 问题.

检查一些示例文档：

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.'

惰性加载

文档也可以惰性方式加载：

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

端到端示例

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

> - 以下示例管道使用 HuggingFace 的推理 API；如需增加 LLM 配额，可通过环境变量提供 token HF_TOKEN. > - 此管道的依赖项可按如下方式安装（--no-warn-conflicts 适用于 Colab 预装的 Python 环境；如需更严格的使用，可自行删除）：

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")

现在我们可以实例化加载器并加载文档：

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：

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}")

检查一些样本分割：

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.'
...

摄取

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,
)

RAG

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

请注意，源包含丰富的定位信息，包括段落 标题（即章节）、页码和精确边界框。

API 参考

• - LangChain Docling 集成 GitHub
• - Docling GitHub
• - Docling 文档