DoclingLoader 文档加载器提供 Docling 的功能。
概述
集成详情
| 类 | 包 | 本地 | 可序列化 | JS 支持 |
|---|---|---|---|---|
| langchain_docling.loader | langchain-docling | ✅ | ❌ | ❌ |
加载器特性
| 来源 | 文档延迟加载 | 原生异步支持 |
|---|---|---|
DoclingLoader | ✅ | ❌ |
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、文档原生的 grounding。
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，或者ExportType.MARKDOWN：如果您希望将每个输入文档作为单独的 LangChain Document 捕获
EXPORT_TYPE 探索两种模式；根据设置的值，示例管道将相应设置。
设置
pip install -qU langchain-docling
为获得最佳转换速度，请在可用时使用 GPU 加速；例如，如果在 Colab 上运行，请使用支持 GPU 的运行时。
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)
DoclingLoader 具有以下参数：
file_path：作为单个字符串（URL 或本地文件）或其可迭代对象的来源converter（可选）：要使用的任何特定 Docling 转换器实例convert_kwargs（可选）：用于转换执行的任何特定 kwargsexport_type（可选）：要使用的导出模式：ExportType.DOC_CHUNKS（默认）或ExportType.MARKDOWNmd_export_kwargs（可选）：任何特定的 Markdown 导出 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 doc-chunk 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：在这种情况下，可以忽略显示 "Token indices sequence length is longer than the specified maximum sequence length..." 的消息——更多详情请参阅此 docling-core GitHub issue。
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.'
延迟加载
文档也可以以延迟方式加载：doc_iter = loader.lazy_load()
for doc in doc_iter:
pass # you can operate on `doc` here
端到端示例
import os
# https://github.com/huggingface/transformers/issues/5486:
os.environ["TOKENIZERS_PARALLELISM"] = "false"
- 以下示例管道使用 HuggingFace 的 Inference API；要增加 LLM 配额，可以通过环境变量
HF_TOKEN提供令牌。- 此管道的依赖项可以如下所示安装（
--no-warn-conflicts适用于 Colab 预填充的 Python 环境；对于更严格的使用，可以随意移除）：
pip install -q --progress-bar off --no-warn-conflicts langchain-core langchain-huggingface langchain-milvus langchain python-dotenv
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
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}")
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,
)
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
API 参考
连接这些文档 到 Claude、VSCode 等，通过 MCP 获取实时答案。