From fc645ea531ddc67959640b428007851d641c923e Mon Sep 17 00:00:00 2001
From: Panos Vagenas <35837085+vagenas@users.noreply.github.com>
Date: Thu, 19 Dec 2024 13:33:02 +0100
Subject: [PATCH 01/22] docs: document Haystack & Vectara support (#628)
Signed-off-by: Panos Vagenas <35837085+vagenas@users.noreply.github.com>
---
docs/examples/rag_haystack.ipynb | 2 +-
docs/integrations/haystack.md | 11 +++++++++++
docs/integrations/rhel_ai.md | 12 +++++-------
docs/integrations/vectara.md | 5 +++++
mkdocs.yml | 4 +++-
5 files changed, 25 insertions(+), 9 deletions(-)
create mode 100644 docs/integrations/haystack.md
create mode 100644 docs/integrations/vectara.md
diff --git a/docs/examples/rag_haystack.ipynb b/docs/examples/rag_haystack.ipynb
index 3e47eacb..e2dc380d 100644
--- a/docs/examples/rag_haystack.ipynb
+++ b/docs/examples/rag_haystack.ipynb
@@ -26,7 +26,7 @@
"metadata": {},
"source": [
"This example leverages the\n",
- "[Haystack Docling extension](https://github.com/DS4SD/docling-haystack), along with\n",
+ "[Haystack Docling extension](../../integrations/haystack/), along with\n",
"Milvus-based document store and retriever instances, as well as sentence-transformers\n",
"embeddings.\n",
"\n",
diff --git a/docs/integrations/haystack.md b/docs/integrations/haystack.md
new file mode 100644
index 00000000..08d051f0
--- /dev/null
+++ b/docs/integrations/haystack.md
@@ -0,0 +1,11 @@
+Docling is available as a converter in [Haystack](https://haystack.deepset.ai/):
+
+- ๐ [Docling Haystack integration docs](https://haystack.deepset.ai/integrations/docling)
+- ๐ป [Docling Haystack integration GitHub][github]
+- ๐ง๐ฝโ๐ณ [Docling Haystack integration example][example]
+- ๐ฆ [Docling Haystack integration PyPI][pypi]
+
+[github]: https://github.com/DS4SD/docling-haystack
+[docs]: https://haystack.deepset.ai/integrations/docling
+[pypi]: https://pypi.org/project/docling-haystack
+[example]: https://ds4sd.github.io/docling/examples/rag_haystack/
diff --git a/docs/integrations/rhel_ai.md b/docs/integrations/rhel_ai.md
index 0d603eb9..e7754459 100644
--- a/docs/integrations/rhel_ai.md
+++ b/docs/integrations/rhel_ai.md
@@ -1,10 +1,8 @@
-Docling is powering document processing in [Red Hat Enterprise Linux AI][home] (RHEL AI),
+Docling is powering document processing in [Red Hat Enterprise Linux AI (RHEL AI)](https://rhel.ai),
enabling users to unlock the knowledge hidden in documents and present it to
InstructLab's fine-tuning for aligning AI models to the user's specific data.
-More details can be found in this [blog post][blog].
-
-- ๐ [RHEL AI home][home]
-
-[home]: https://www.redhat.com/en/technologies/linux-platforms/enterprise-linux/ai
-[blog]: https://www.redhat.com/en/blog/docling-missing-document-processing-companion-generative-ai
+- ๐ฃ [RHEL AI 1.3 announcement](https://www.redhat.com/en/about/press-releases/red-hat-delivers-next-wave-gen-ai-innovation-new-red-hat-enterprise-linux-ai-capabilities)
+- โ๏ธ RHEL blog posts:
+ - [RHEL AI 1.3 Docling context aware chunking: What you need to know](https://www.redhat.com/en/blog/rhel-13-docling-context-aware-chunking-what-you-need-know)
+ - [Docling: The missing document processing companion for generative AI](https://www.redhat.com/en/blog/docling-missing-document-processing-companion-generative-ai)
diff --git a/docs/integrations/vectara.md b/docs/integrations/vectara.md
new file mode 100644
index 00000000..04bb47a7
--- /dev/null
+++ b/docs/integrations/vectara.md
@@ -0,0 +1,5 @@
+Docling is available as a document parser in [Vectara](https://www.vectara.com/).
+
+- ๐ป [Vectara GitHub org](https://github.com/vectara)
+ - [vectara-ingest GitHub repo](https://github.com/vectara/vectara-ingest)
+- ๐ [Vectara docs](https://docs.vectara.com/)
diff --git a/mkdocs.yml b/mkdocs.yml
index c0840855..b673ab41 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -89,13 +89,15 @@ nav:
- "Cloudera": integrations/cloudera.md
- "Data Prep Kit": integrations/data_prep_kit.md
- "DocETL": integrations/docetl.md
+ - "Haystack": integrations/haystack.md
- "๐ถ InstructLab": integrations/instructlab.md
- "Kotaemon": integrations/kotaemon.md
- "๐ฆ LlamaIndex": integrations/llamaindex.md
- "Prodigy": integrations/prodigy.md
- - "Red Hat Enterprise Linux AI": integrations/rhel_ai.md
+ - "RHEL AI": integrations/rhel_ai.md
- "spaCy": integrations/spacy.md
- "txtai": integrations/txtai.md
+ - "Vectara": integrations/vectara.md
# - "LangChain ๐ฆ๐": integrations/langchain.md
- Reference:
- Python API:
From 2b591f98726ed0d883236dd0550201b95203eebb Mon Sep 17 00:00:00 2001
From: m-newhauser <35735816+m-newhauser@users.noreply.github.com>
Date: Thu, 19 Dec 2024 14:57:40 -0600
Subject: [PATCH 02/22] docs: add Weaviate RAG recipe notebook (#451)
Signed-off-by: Panos Vagenas <35837085+vagenas@users.noreply.github.com>
Co-authored-by: Panos Vagenas <35837085+vagenas@users.noreply.github.com>
---
docs/examples/rag_weaviate.ipynb | 732 +++++++++++++++++++++++++++++++
mkdocs.yml | 1 +
2 files changed, 733 insertions(+)
create mode 100644 docs/examples/rag_weaviate.ipynb
diff --git a/docs/examples/rag_weaviate.ipynb b/docs/examples/rag_weaviate.ipynb
new file mode 100644
index 00000000..7f897d9e
--- /dev/null
+++ b/docs/examples/rag_weaviate.ipynb
@@ -0,0 +1,732 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Ag9kcX2B_atc"
+ },
+ "source": [
+ "# Performing RAG over PDFs with Weaviate and Docling\n",
+ "## A recipe ๐งโ๐ณ ๐ฅ ๐\n",
+ "[](https://colab.research.google.com/github/DS4SD/docling/blob/tree/main/docs/examples/rag_weaviate.ipynb)\n",
+ "\n",
+ "This is a code recipe that uses [Weaviate](https://weaviate.io/) to perform RAG over PDF documents parsed by [Docling](https://ds4sd.github.io/docling/).\n",
+ "\n",
+ "In this notebook, we accomplish the following:\n",
+ "* Parse the top machine learning papers on [arXiv](https://arxiv.org/) using Docling\n",
+ "* Perform hierarchical chunking of the documents using Docling\n",
+ "* Generate text embeddings with OpenAI\n",
+ "* Perform RAG using [Weaviate](https://weaviate.io/developers/weaviate/search/generative)\n",
+ "\n",
+ "To run this notebook, you'll need:\n",
+ "* An [OpenAI API key](https://platform.openai.com/docs/quickstart)\n",
+ "* Access to GPU/s\n",
+ "\n",
+ "Note: For best results, please use **GPU acceleration** to run this notebook. Here are two options for running this notebook:\n",
+ "1. **Locally on a MacBook with an Apple Silicon chip.** Converting all documents in the notebook takes ~2 minutes on a MacBook M2 due to Docling's usage of MPS accelerators.\n",
+ "2. **Run this notebook on Google Colab.** Converting all documents in the notebook takes ~8 mintutes on a Google Colab T4 GPU."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "4YgT7tpXCUl0"
+ },
+ "source": [
+ "### Install Docling and Weaviate client\n",
+ "\n",
+ "Note: If Colab prompts you to restart the session after running the cell below, click \"restart\" and proceed with running the rest of the notebook."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": true,
+ "id": "u076oUSF_YUG"
+ },
+ "outputs": [],
+ "source": [
+ "%%capture\n",
+ "%pip install docling~=\"2.7.0\"\n",
+ "%pip install -U weaviate-client~=\"4.9.4\"\n",
+ "%pip install rich\n",
+ "%pip install torch\n",
+ "\n",
+ "import warnings\n",
+ "\n",
+ "warnings.filterwarnings(\"ignore\")\n",
+ "\n",
+ "import logging\n",
+ "\n",
+ "# Suppress Weaviate client logs\n",
+ "logging.getLogger(\"weaviate\").setLevel(logging.ERROR)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2q2F9RUmR8Wj"
+ },
+ "source": [
+ "## ๐ฅ Part 1: Docling\n",
+ "\n",
+ "Part of what makes Docling so remarkable is the fact that it can run on commodity hardware. This means that this notebook can be run on a local machine with GPU acceleration. If you're using a MacBook with a silicon chip, Docling integrates seamlessly with Metal Performance Shaders (MPS). MPS provides out-of-the-box GPU acceleration for macOS, seamlessly integrating with PyTorch and TensorFlow, offering energy-efficient performance on Apple Silicon, and broad compatibility with all Metal-supported GPUs.\n",
+ "\n",
+ "The code below checks to see if a GPU is available, either via CUDA or MPS."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "MPS GPU is enabled.\n"
+ ]
+ }
+ ],
+ "source": [
+ "import torch\n",
+ "\n",
+ "# Check if GPU or MPS is available\n",
+ "if torch.cuda.is_available():\n",
+ " device = torch.device(\"cuda\")\n",
+ " print(f\"CUDA GPU is enabled: {torch.cuda.get_device_name(0)}\")\n",
+ "elif torch.backends.mps.is_available():\n",
+ " device = torch.device(\"mps\")\n",
+ " print(\"MPS GPU is enabled.\")\n",
+ "else:\n",
+ " raise EnvironmentError(\n",
+ " \"No GPU or MPS device found. Please check your environment and ensure GPU or MPS support is configured.\"\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "wHTsy4a8JFPl"
+ },
+ "source": [
+ "Here, we've collected 10 influential machine learning papers published as PDFs on arXiv. Because Docling does not yet have title extraction for PDFs, we manually add the titles in a corresponding list.\n",
+ "\n",
+ "Note: Converting all 10 papers should take around 8 minutes with a T4 GPU."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "id": "Vy5SMPiGDMy-"
+ },
+ "outputs": [],
+ "source": [
+ "# Influential machine learning papers\n",
+ "source_urls = [\n",
+ " \"https://arxiv.org/pdf/1706.03762\",\n",
+ " \"https://arxiv.org/pdf/1810.04805\",\n",
+ " \"https://arxiv.org/pdf/1406.2661\",\n",
+ " \"https://arxiv.org/pdf/1409.0473\",\n",
+ " \"https://arxiv.org/pdf/1412.6980\",\n",
+ " \"https://arxiv.org/pdf/1312.6114\",\n",
+ " \"https://arxiv.org/pdf/1312.5602\",\n",
+ " \"https://arxiv.org/pdf/1512.03385\",\n",
+ " \"https://arxiv.org/pdf/1409.3215\",\n",
+ " \"https://arxiv.org/pdf/1301.3781\",\n",
+ "]\n",
+ "\n",
+ "# And their corresponding titles (because Docling doesn't have title extraction yet!)\n",
+ "source_titles = [\n",
+ " \"Attention Is All You Need\",\n",
+ " \"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding\",\n",
+ " \"Generative Adversarial Nets\",\n",
+ " \"Neural Machine Translation by Jointly Learning to Align and Translate\",\n",
+ " \"Adam: A Method for Stochastic Optimization\",\n",
+ " \"Auto-Encoding Variational Bayes\",\n",
+ " \"Playing Atari with Deep Reinforcement Learning\",\n",
+ " \"Deep Residual Learning for Image Recognition\",\n",
+ " \"Sequence to Sequence Learning with Neural Networks\",\n",
+ " \"A Neural Probabilistic Language Model\",\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "5fi8wzHrCoLa"
+ },
+ "source": [
+ "### Convert PDFs to Docling documents\n",
+ "\n",
+ "Here we use Docling's `.convert_all()` to parse a batch of PDFs. The result is a list of Docling documents that we can use for text extraction.\n",
+ "\n",
+ "Note: Please ignore the `ERR#` message."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 67,
+ "referenced_widgets": [
+ "6d049f786a2f4ad7857a6cf2d95b5ba2",
+ "db2a7b9f549e4f0fb1ff3fce655d76a2",
+ "630967a2db4c4714b4c15d1358a0fcae",
+ "b3da9595ab7c4995a00e506e7b5202e3",
+ "243ecaf36ee24cafbd1c33d148f2ca78",
+ "5b7e22df1b464ca894126736e6f72207",
+ "02f6af5993bb4a6a9dbca77952f675d2",
+ "dea323b3de0e43118f338842c94ac065",
+ "bd198d2c0c4c4933a6e6544908d0d846",
+ "febd5c498e4f4f5dbde8dec3cd935502",
+ "ab4f282c0d37451092c60e6566e8e945"
+ ]
+ },
+ "id": "Sr44xGR1PNSc",
+ "outputId": "b5cca9ee-d7c0-4c8f-c18a-0ac4787984e9"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Fetching 9 files: 100%|โโโโโโโโโโ| 9/9 [00:00<00:00, 84072.91it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "ERR#: COULD NOT CONVERT TO RS THIS TABLE TO COMPUTE SPANS\n"
+ ]
+ }
+ ],
+ "source": [
+ "from docling.datamodel.document import ConversionResult\n",
+ "from docling.document_converter import DocumentConverter\n",
+ "\n",
+ "# Instantiate the doc converter\n",
+ "doc_converter = DocumentConverter()\n",
+ "\n",
+ "# Directly pass list of files or streams to `convert_all`\n",
+ "conv_results_iter = doc_converter.convert_all(source_urls) # previously `convert`\n",
+ "\n",
+ "# Iterate over the generator to get a list of Docling documents\n",
+ "docs = [result.document for result in conv_results_iter]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xHun_P-OCtKd"
+ },
+ "source": [
+ "### Post-process extracted document data\n",
+ "#### Perform hierarchical chunking on documents\n",
+ "\n",
+ "We use Docling's `HierarchicalChunker()` to perform hierarchy-aware chunking of our list of documents. This is meant to preserve some of the structure and relationships within the document, which enables more accurate and relevant retrieval in our RAG pipeline."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "id": "L17ju9xibuIo"
+ },
+ "outputs": [],
+ "source": [
+ "from docling_core.transforms.chunker import HierarchicalChunker\n",
+ "\n",
+ "# Initialize lists for text, and titles\n",
+ "texts, titles = [], []\n",
+ "\n",
+ "chunker = HierarchicalChunker()\n",
+ "\n",
+ "# Process each document in the list\n",
+ "for doc, title in zip(docs, source_titles): # Pair each document with its title\n",
+ " chunks = list(\n",
+ " chunker.chunk(doc)\n",
+ " ) # Perform hierarchical chunking and get text from chunks\n",
+ " for chunk in chunks:\n",
+ " texts.append(chunk.text)\n",
+ " titles.append(title)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "khbU9R1li2Kj"
+ },
+ "source": [
+ "Because we're splitting the documents into chunks, we'll concatenate the article title to the beginning of each chunk for additional context."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "id": "HNwYV9P57OwF"
+ },
+ "outputs": [],
+ "source": [
+ "# Concatenate title and text\n",
+ "for i in range(len(texts)):\n",
+ " texts[i] = f\"{titles[i]} {texts[i]}\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "uhLlCpQODaT3"
+ },
+ "source": [
+ "## ๐ Part 2: Weaviate\n",
+ "### Create and configure an embedded Weaviate collection"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ho7xYQTZK5Wk"
+ },
+ "source": [
+ "We'll be using the OpenAI API for both generating the text embeddings and for the generative model in our RAG pipeline. The code below dynamically fetches your API key based on whether you're running this notebook in Google Colab and running it as a regular Jupyter notebook. All you need to do is replace `openai_api_key_var` with the name of your environmental variable name or Colab secret name for the API key.\n",
+ "\n",
+ "If you're running this notebook in Google Colab, make sure you [add](https://medium.com/@parthdasawant/how-to-use-secrets-in-google-colab-450c38e3ec75) your API key as a secret."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "id": "PD53jOT4roj2"
+ },
+ "outputs": [],
+ "source": [
+ "# OpenAI API key variable name\n",
+ "openai_api_key_var = \"OPENAI_API_KEY\" # Replace with the name of your secret/env var\n",
+ "\n",
+ "# Fetch OpenAI API key\n",
+ "try:\n",
+ " # If running in Colab, fetch API key from Secrets\n",
+ " import google.colab\n",
+ " from google.colab import userdata\n",
+ "\n",
+ " openai_api_key = userdata.get(openai_api_key_var)\n",
+ " if not openai_api_key:\n",
+ " raise ValueError(f\"Secret '{openai_api_key_var}' not found in Colab secrets.\")\n",
+ "except ImportError:\n",
+ " # If not running in Colab, fetch API key from environment variable\n",
+ " import os\n",
+ "\n",
+ " openai_api_key = os.getenv(openai_api_key_var)\n",
+ " if not openai_api_key:\n",
+ " raise EnvironmentError(\n",
+ " f\"Environment variable '{openai_api_key_var}' is not set. \"\n",
+ " \"Please define it before running this script.\"\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "8G5jZSh6ti3e"
+ },
+ "source": [
+ "[Embedded Weaviate](https://weaviate.io/developers/weaviate/installation/embedded) allows you to spin up a Weaviate instance directly from your application code, without having to use a Docker container. If you're interested in other deployment methods, like using Docker-Compose or Kubernetes, check out this [page](https://weaviate.io/developers/weaviate/installation) in the Weaviate docs."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "hFUBEZiJUMic",
+ "outputId": "0b6534c9-66c9-4a47-9754-103bcc030019"
+ },
+ "outputs": [],
+ "source": [
+ "import weaviate\n",
+ "\n",
+ "# Connect to Weaviate embedded\n",
+ "client = weaviate.connect_to_embedded(headers={\"X-OpenAI-Api-Key\": openai_api_key})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "4nu9qM75hrsd"
+ },
+ "outputs": [],
+ "source": [
+ "import weaviate.classes.config as wc\n",
+ "from weaviate.classes.config import DataType, Property\n",
+ "\n",
+ "# Define the collection name\n",
+ "collection_name = \"docling\"\n",
+ "\n",
+ "# Delete the collection if it already exists\n",
+ "if client.collections.exists(collection_name):\n",
+ " client.collections.delete(collection_name)\n",
+ "\n",
+ "# Create the collection\n",
+ "collection = client.collections.create(\n",
+ " name=collection_name,\n",
+ " vectorizer_config=wc.Configure.Vectorizer.text2vec_openai(\n",
+ " model=\"text-embedding-3-large\", # Specify your embedding model here\n",
+ " ),\n",
+ " # Enable generative model from Cohere\n",
+ " generative_config=wc.Configure.Generative.openai(\n",
+ " model=\"gpt-4o\" # Specify your generative model for RAG here\n",
+ " ),\n",
+ " # Define properties of metadata\n",
+ " properties=[\n",
+ " wc.Property(name=\"text\", data_type=wc.DataType.TEXT),\n",
+ " wc.Property(name=\"title\", data_type=wc.DataType.TEXT, skip_vectorization=True),\n",
+ " ],\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "RgMcZDB9Dzfs"
+ },
+ "source": [
+ "### Wrangle data into an acceptable format for Weaviate\n",
+ "\n",
+ "Transform our data from lists to a list of dictionaries for insertion into our Weaviate collection."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "id": "kttDgwZEsIJQ"
+ },
+ "outputs": [],
+ "source": [
+ "# Initialize the data object\n",
+ "data = []\n",
+ "\n",
+ "# Create a dictionary for each row by iterating through the corresponding lists\n",
+ "for text, title in zip(texts, titles):\n",
+ " data_point = {\n",
+ " \"text\": text,\n",
+ " \"title\": title,\n",
+ " }\n",
+ " data.append(data_point)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "-4amqRaoD5g0"
+ },
+ "source": [
+ "### Insert data into Weaviate and generate embeddings\n",
+ "\n",
+ "Embeddings will be generated upon insertion to our Weaviate collection."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "g8VCYnhbaxcz",
+ "outputId": "cc900e56-9fb6-4d4e-ab18-ebd12b1f4201"
+ },
+ "outputs": [],
+ "source": [
+ "# Insert text chunks and metadata into vector DB collection\n",
+ "response = collection.data.insert_many(data)\n",
+ "\n",
+ "if response.has_errors:\n",
+ " print(response.errors)\n",
+ "else:\n",
+ " print(\"Insert complete.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "KI01PxjuD_XR"
+ },
+ "source": [
+ "### Query the data\n",
+ "\n",
+ "Here, we perform a simple similarity search to return the most similar embedded chunks to our search query."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "zbz6nWJc5CSj",
+ "outputId": "16aced21-4496-4c91-cc12-d5c9ac983351"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'text': 'BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding A distinctive feature of BERT is its unified architecture across different tasks. There is mini-', 'title': 'BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding'}\n",
+ "0.6578550338745117\n",
+ "{'text': 'BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding We introduce a new language representation model called BERT , which stands for B idirectional E ncoder R epresentations from T ransformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pretrain deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be finetuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial taskspecific architecture modifications.', 'title': 'BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding'}\n",
+ "0.6696287989616394\n"
+ ]
+ }
+ ],
+ "source": [
+ "from weaviate.classes.query import MetadataQuery\n",
+ "\n",
+ "response = collection.query.near_text(\n",
+ " query=\"bert\",\n",
+ " limit=2,\n",
+ " return_metadata=MetadataQuery(distance=True),\n",
+ " return_properties=[\"text\", \"title\"],\n",
+ ")\n",
+ "\n",
+ "for o in response.objects:\n",
+ " print(o.properties)\n",
+ " print(o.metadata.distance)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "elo32iMnEC18"
+ },
+ "source": [
+ "### Perform RAG on parsed articles\n",
+ "\n",
+ "Weaviate's `generate` module allows you to perform RAG over your embedded data without having to use a separate framework.\n",
+ "\n",
+ "We specify a prompt that includes the field we want to search through in the database (in this case it's `text`), a query that includes our search term, and the number of retrieved results to use in the generation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 233
+ },
+ "id": "7r2LMSX9bO4y",
+ "outputId": "84639adf-7783-4d43-94d9-711fb313a168"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ Prompt โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ\n",
+ "โ Explain how bert works, using only the retrieved context. โ\n",
+ "โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[1;31mโญโ\u001b[0m\u001b[1;31mโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ\u001b[0m\u001b[1;31m Prompt \u001b[0m\u001b[1;31mโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ\u001b[0m\u001b[1;31mโโฎ\u001b[0m\n",
+ "\u001b[1;31mโ\u001b[0m Explain how bert works, using only the retrieved context. \u001b[1;31mโ\u001b[0m\n",
+ "\u001b[1;31mโฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ Generated Content โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ\n",
+ "โ BERT, which stands for Bidirectional Encoder Representations from Transformers, is a language representation โ\n",
+ "โ model designed to pretrain deep bidirectional representations from unlabeled text. It conditions on both left โ\n",
+ "โ and right context in all layers, unlike traditional left-to-right or right-to-left language models. This โ\n",
+ "โ pre-training involves two unsupervised tasks. The pre-trained BERT model can then be fine-tuned with just one โ\n",
+ "โ additional output layer to create state-of-the-art models for various tasks, such as question answering and โ\n",
+ "โ language inference, without needing substantial task-specific architecture modifications. A distinctive feature โ\n",
+ "โ of BERT is its unified architecture across different tasks. โ\n",
+ "โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[1;32mโญโ\u001b[0m\u001b[1;32mโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ\u001b[0m\u001b[1;32m Generated Content \u001b[0m\u001b[1;32mโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ\u001b[0m\u001b[1;32mโโฎ\u001b[0m\n",
+ "\u001b[1;32mโ\u001b[0m BERT, which stands for Bidirectional Encoder Representations from Transformers, is a language representation \u001b[1;32mโ\u001b[0m\n",
+ "\u001b[1;32mโ\u001b[0m model designed to pretrain deep bidirectional representations from unlabeled text. It conditions on both left \u001b[1;32mโ\u001b[0m\n",
+ "\u001b[1;32mโ\u001b[0m and right context in all layers, unlike traditional left-to-right or right-to-left language models. This \u001b[1;32mโ\u001b[0m\n",
+ "\u001b[1;32mโ\u001b[0m pre-training involves two unsupervised tasks. The pre-trained BERT model can then be fine-tuned with just one \u001b[1;32mโ\u001b[0m\n",
+ "\u001b[1;32mโ\u001b[0m additional output layer to create state-of-the-art models for various tasks, such as question answering and \u001b[1;32mโ\u001b[0m\n",
+ "\u001b[1;32mโ\u001b[0m language inference, without needing substantial task-specific architecture modifications. A distinctive feature \u001b[1;32mโ\u001b[0m\n",
+ "\u001b[1;32mโ\u001b[0m of BERT is its unified architecture across different tasks. \u001b[1;32mโ\u001b[0m\n",
+ "\u001b[1;32mโฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from rich.console import Console\n",
+ "from rich.panel import Panel\n",
+ "\n",
+ "# Create a prompt where context from the Weaviate collection will be injected\n",
+ "prompt = \"Explain how {text} works, using only the retrieved context.\"\n",
+ "query = \"bert\"\n",
+ "\n",
+ "response = collection.generate.near_text(\n",
+ " query=query, limit=3, grouped_task=prompt, return_properties=[\"text\", \"title\"]\n",
+ ")\n",
+ "\n",
+ "# Prettify the output using Rich\n",
+ "console = Console()\n",
+ "\n",
+ "console.print(\n",
+ " Panel(f\"{prompt}\".replace(\"{text}\", query), title=\"Prompt\", border_style=\"bold red\")\n",
+ ")\n",
+ "console.print(\n",
+ " Panel(response.generated, title=\"Generated Content\", border_style=\"bold green\")\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 233
+ },
+ "id": "Dtju3oCiDOdD",
+ "outputId": "2f0f0cf8-0305-40cc-8409-07036c101938"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ Prompt โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ\n",
+ "โ Explain how a generative adversarial net works, using only the retrieved context. โ\n",
+ "โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[1;31mโญโ\u001b[0m\u001b[1;31mโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ\u001b[0m\u001b[1;31m Prompt \u001b[0m\u001b[1;31mโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ\u001b[0m\u001b[1;31mโโฎ\u001b[0m\n",
+ "\u001b[1;31mโ\u001b[0m Explain how a generative adversarial net works, using only the retrieved context. \u001b[1;31mโ\u001b[0m\n",
+ "\u001b[1;31mโฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ Generated Content โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ\n",
+ "โ Generative Adversarial Nets (GANs) operate within an adversarial framework where two models are trained โ\n",
+ "โ simultaneously: a generative model (G) and a discriminative model (D). The generative model aims to capture the โ\n",
+ "โ data distribution and generate samples that mimic real data, while the discriminative model's task is to โ\n",
+ "โ distinguish between samples from the real data and those generated by G. This setup is akin to a game where the โ\n",
+ "โ generative model acts like counterfeiters trying to produce indistinguishable fake currency, and the โ\n",
+ "โ discriminative model acts like the police trying to detect these counterfeits. โ\n",
+ "โ โ\n",
+ "โ The training process involves a minimax two-player game where G tries to maximize the probability of D making a โ\n",
+ "โ mistake, while D tries to minimize it. When both models are defined by multilayer perceptrons, they can be โ\n",
+ "โ trained using backpropagation without the need for Markov chains or approximate inference networks. The โ\n",
+ "โ ultimate goal is for G to perfectly replicate the training data distribution, making D's output equal to 1/2 โ\n",
+ "โ everywhere, indicating it cannot distinguish between real and generated data. This framework allows for โ\n",
+ "โ specific training algorithms and optimization techniques, such as backpropagation and dropout, to be โ\n",
+ "โ effectively utilized. โ\n",
+ "โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[1;32mโญโ\u001b[0m\u001b[1;32mโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ\u001b[0m\u001b[1;32m Generated Content \u001b[0m\u001b[1;32mโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ\u001b[0m\u001b[1;32mโโฎ\u001b[0m\n",
+ "\u001b[1;32mโ\u001b[0m Generative Adversarial Nets (GANs) operate within an adversarial framework where two models are trained \u001b[1;32mโ\u001b[0m\n",
+ "\u001b[1;32mโ\u001b[0m simultaneously: a generative model (G) and a discriminative model (D). The generative model aims to capture the \u001b[1;32mโ\u001b[0m\n",
+ "\u001b[1;32mโ\u001b[0m data distribution and generate samples that mimic real data, while the discriminative model's task is to \u001b[1;32mโ\u001b[0m\n",
+ "\u001b[1;32mโ\u001b[0m distinguish between samples from the real data and those generated by G. This setup is akin to a game where the \u001b[1;32mโ\u001b[0m\n",
+ "\u001b[1;32mโ\u001b[0m generative model acts like counterfeiters trying to produce indistinguishable fake currency, and the \u001b[1;32mโ\u001b[0m\n",
+ "\u001b[1;32mโ\u001b[0m discriminative model acts like the police trying to detect these counterfeits. \u001b[1;32mโ\u001b[0m\n",
+ "\u001b[1;32mโ\u001b[0m \u001b[1;32mโ\u001b[0m\n",
+ "\u001b[1;32mโ\u001b[0m The training process involves a minimax two-player game where G tries to maximize the probability of D making a \u001b[1;32mโ\u001b[0m\n",
+ "\u001b[1;32mโ\u001b[0m mistake, while D tries to minimize it. When both models are defined by multilayer perceptrons, they can be \u001b[1;32mโ\u001b[0m\n",
+ "\u001b[1;32mโ\u001b[0m trained using backpropagation without the need for Markov chains or approximate inference networks. The \u001b[1;32mโ\u001b[0m\n",
+ "\u001b[1;32mโ\u001b[0m ultimate goal is for G to perfectly replicate the training data distribution, making D's output equal to 1/2 \u001b[1;32mโ\u001b[0m\n",
+ "\u001b[1;32mโ\u001b[0m everywhere, indicating it cannot distinguish between real and generated data. This framework allows for \u001b[1;32mโ\u001b[0m\n",
+ "\u001b[1;32mโ\u001b[0m specific training algorithms and optimization techniques, such as backpropagation and dropout, to be \u001b[1;32mโ\u001b[0m\n",
+ "\u001b[1;32mโ\u001b[0m effectively utilized. \u001b[1;32mโ\u001b[0m\n",
+ "\u001b[1;32mโฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Create a prompt where context from the Weaviate collection will be injected\n",
+ "prompt = \"Explain how {text} works, using only the retrieved context.\"\n",
+ "query = \"a generative adversarial net\"\n",
+ "\n",
+ "response = collection.generate.near_text(\n",
+ " query=query, limit=3, grouped_task=prompt, return_properties=[\"text\", \"title\"]\n",
+ ")\n",
+ "\n",
+ "# Prettify the output using Rich\n",
+ "console = Console()\n",
+ "\n",
+ "console.print(\n",
+ " Panel(f\"{prompt}\".replace(\"{text}\", query), title=\"Prompt\", border_style=\"bold red\")\n",
+ ")\n",
+ "console.print(\n",
+ " Panel(response.generated, title=\"Generated Content\", border_style=\"bold green\")\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7tGz49nfUegG"
+ },
+ "source": [
+ "We can see that our RAG pipeline performs relatively well for simple queries, especially given the small size of the dataset. Scaling this method for converting a larger sample of PDFs would require more compute (GPUs) and a more advanced deployment of Weaviate (like Docker, Kubernetes, or Weaviate Cloud). For more information on available Weaviate configurations, check out the [documetation](https://weaviate.io/developers/weaviate/starter-guides/which-weaviate)."
+ ]
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "gpuType": "T4",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/mkdocs.yml b/mkdocs.yml
index b673ab41..0428693c 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -82,6 +82,7 @@ nav:
- "RAG with Haystack": examples/rag_haystack.ipynb
- "RAG with LlamaIndex ๐ฆ": examples/rag_llamaindex.ipynb
- "RAG with LangChain ๐ฆ๐": examples/rag_langchain.ipynb
+ - "RAG with Weaviate": examples/rag_weaviate.ipynb
- "Hybrid RAG with Qdrant": examples/hybrid_rag_qdrant.ipynb
- Integrations:
- Integrations: integrations/index.md
From 569038df4205703f87517ea58da7902d143e7699 Mon Sep 17 00:00:00 2001
From: JSIV <5049984+JSv4@users.noreply.github.com>
Date: Tue, 7 Jan 2025 04:14:42 -0500
Subject: [PATCH 03/22] docs: Add OpenContracts as an integration (#679)
* Add OpenContracts as an open source project
OpenContracts now offers Docling as a document ingestion and parsing pipeline
Signed-off-by: JSIV <5049984+JSv4@users.noreply.github.com>
* Update mkdocs.yml
Added OpenContracts to the nav configs
Signed-off-by: JSIV <5049984+JSv4@users.noreply.github.com>
---------
Signed-off-by: JSIV <5049984+JSv4@users.noreply.github.com>
---
docs/integrations/opencontracts.md | 10 ++++++++++
mkdocs.yml | 1 +
2 files changed, 11 insertions(+)
create mode 100644 docs/integrations/opencontracts.md
diff --git a/docs/integrations/opencontracts.md b/docs/integrations/opencontracts.md
new file mode 100644
index 00000000..df0e7c86
--- /dev/null
+++ b/docs/integrations/opencontracts.md
@@ -0,0 +1,10 @@
+Docling is available an ingestion engine for [OpenContracts](https://github.com/JSv4/OpenContracts), allowing you to use Docling's OCR engine(s), chunker(s), labels, etc. and load them into a platform supporting bulk data extraction, text annotating, and question-answering:
+
+- ๐ป [GitHub](https://github.com/JSv4/OpenContracts)
+- ๐ [Docs](https://jsv4.github.io/OpenContracts/)]
+
+ #### Docling in Action!
+
+
+
+
diff --git a/mkdocs.yml b/mkdocs.yml
index 0428693c..aa983701 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -94,6 +94,7 @@ nav:
- "๐ถ InstructLab": integrations/instructlab.md
- "Kotaemon": integrations/kotaemon.md
- "๐ฆ LlamaIndex": integrations/llamaindex.md
+ - "OpenContracts": integrations/opencontracts.md
- "Prodigy": integrations/prodigy.md
- "RHEL AI": integrations/rhel_ai.md
- "spaCy": integrations/spacy.md
From 0ee849e8bc8cf24d1c5597af3fe20a7fa19a29e0 Mon Sep 17 00:00:00 2001
From: Luke Harrison
Date: Tue, 7 Jan 2025 04:15:14 -0500
Subject: [PATCH 04/22] feat: added http header support for document converter
and cli (#642)
* added http header support for document converter and cli
Signed-off-by: Luke Harrison
* fixed formatting and typing issues
Signed-off-by: Luke Harrison
* use pydantic to parse dict
suggested by @dolfim-ibm
Co-authored-by: Michele Dolfi <97102151+dolfim-ibm@users.noreply.github.com>
Signed-off-by: Luke Harrison
---------
Signed-off-by: Luke Harrison
Signed-off-by: Luke Harrison
Co-authored-by: Michele Dolfi <97102151+dolfim-ibm@users.noreply.github.com>
---
docling/cli/main.py | 16 ++++++++++++++--
docling/datamodel/document.py | 7 ++++++-
docling/document_converter.py | 6 ++++--
3 files changed, 24 insertions(+), 5 deletions(-)
diff --git a/docling/cli/main.py b/docling/cli/main.py
index a83aecbf..e1ce289e 100644
--- a/docling/cli/main.py
+++ b/docling/cli/main.py
@@ -164,6 +164,11 @@ def convert(
to_formats: List[OutputFormat] = typer.Option(
None, "--to", help="Specify output formats. Defaults to Markdown."
),
+ headers: str = typer.Option(
+ None,
+ "--headers",
+ help="Specify http request headers used when fetching url input sources in the form of a JSON string",
+ ),
image_export_mode: Annotated[
ImageRefMode,
typer.Option(
@@ -279,12 +284,19 @@ def convert(
if from_formats is None:
from_formats = [e for e in InputFormat]
+ parsed_headers: Optional[Dict[str, str]] = None
+ if headers is not None:
+ headers_t = TypeAdapter(Dict[str, str])
+ parsed_headers = headers_t.validate_json(headers)
+
with tempfile.TemporaryDirectory() as tempdir:
input_doc_paths: List[Path] = []
for src in input_sources:
try:
# check if we can fetch some remote url
- source = resolve_source_to_path(source=src, workdir=Path(tempdir))
+ source = resolve_source_to_path(
+ source=src, headers=parsed_headers, workdir=Path(tempdir)
+ )
input_doc_paths.append(source)
except FileNotFoundError:
err_console.print(
@@ -390,7 +402,7 @@ def convert(
start_time = time.time()
conv_results = doc_converter.convert_all(
- input_doc_paths, raises_on_error=abort_on_error
+ input_doc_paths, headers=parsed_headers, raises_on_error=abort_on_error
)
output.mkdir(parents=True, exist_ok=True)
diff --git a/docling/datamodel/document.py b/docling/datamodel/document.py
index 4ed7d577..136428e8 100644
--- a/docling/datamodel/document.py
+++ b/docling/datamodel/document.py
@@ -227,13 +227,18 @@ class _DummyBackend(AbstractDocumentBackend):
class _DocumentConversionInput(BaseModel):
path_or_stream_iterator: Iterable[Union[Path, str, DocumentStream]]
+ headers: Optional[Dict[str, str]] = None
limits: Optional[DocumentLimits] = DocumentLimits()
def docs(
self, format_options: Dict[InputFormat, "FormatOption"]
) -> Iterable[InputDocument]:
for item in self.path_or_stream_iterator:
- obj = resolve_source_to_stream(item) if isinstance(item, str) else item
+ obj = (
+ resolve_source_to_stream(item, self.headers)
+ if isinstance(item, str)
+ else item
+ )
format = self._guess_format(obj)
backend: Type[AbstractDocumentBackend]
if format not in format_options.keys():
diff --git a/docling/document_converter.py b/docling/document_converter.py
index c9cbedd2..cb073949 100644
--- a/docling/document_converter.py
+++ b/docling/document_converter.py
@@ -176,6 +176,7 @@ class DocumentConverter:
def convert(
self,
source: Union[Path, str, DocumentStream], # TODO review naming
+ headers: Optional[Dict[str, str]] = None,
raises_on_error: bool = True,
max_num_pages: int = sys.maxsize,
max_file_size: int = sys.maxsize,
@@ -185,6 +186,7 @@ class DocumentConverter:
raises_on_error=raises_on_error,
max_num_pages=max_num_pages,
max_file_size=max_file_size,
+ headers=headers,
)
return next(all_res)
@@ -192,6 +194,7 @@ class DocumentConverter:
def convert_all(
self,
source: Iterable[Union[Path, str, DocumentStream]], # TODO review naming
+ headers: Optional[Dict[str, str]] = None,
raises_on_error: bool = True, # True: raises on first conversion error; False: does not raise on conv error
max_num_pages: int = sys.maxsize,
max_file_size: int = sys.maxsize,
@@ -201,8 +204,7 @@ class DocumentConverter:
max_file_size=max_file_size,
)
conv_input = _DocumentConversionInput(
- path_or_stream_iterator=source,
- limits=limits,
+ path_or_stream_iterator=source, limits=limits, headers=headers
)
conv_res_iter = self._convert(conv_input, raises_on_error=raises_on_error)
From d49650c54ffa60bc6d6106970e104071689bc7b0 Mon Sep 17 00:00:00 2001
From: Jinfeng Sun <86536994+Tendo33@users.noreply.github.com>
Date: Tue, 7 Jan 2025 20:58:10 +0800
Subject: [PATCH 05/22] fix(mspowerpoint): handle invalid images in PowerPoint
slides (#650)
- Add error handling for images that cannot be loaded by Pillow
- Improve resilience when encountering corrupted or unsupported image formats
- Maintain processing of other slide elements even if an image fails to load
Signed-off-by: Tendo33
---
docling/backend/mspowerpoint_backend.py | 24 ++++++++++++++----------
1 file changed, 14 insertions(+), 10 deletions(-)
diff --git a/docling/backend/mspowerpoint_backend.py b/docling/backend/mspowerpoint_backend.py
index f595e4bd..995969d4 100644
--- a/docling/backend/mspowerpoint_backend.py
+++ b/docling/backend/mspowerpoint_backend.py
@@ -16,7 +16,7 @@ from docling_core.types.doc import (
TableCell,
TableData,
)
-from PIL import Image
+from PIL import Image, UnidentifiedImageError
from pptx import Presentation
from pptx.enum.shapes import MSO_SHAPE_TYPE, PP_PLACEHOLDER
@@ -120,6 +120,7 @@ class MsPowerpointDocumentBackend(DeclarativeDocumentBackend, PaginatedDocumentB
bullet_type = "None"
list_text = ""
list_label = GroupLabel.LIST
+ doc_label = DocItemLabel.LIST_ITEM
prov = self.generate_prov(shape, slide_ind, shape.text.strip())
# Identify if shape contains lists
@@ -276,16 +277,19 @@ class MsPowerpointDocumentBackend(DeclarativeDocumentBackend, PaginatedDocumentB
im_dpi, _ = image.dpi
# Open it with PIL
- pil_image = Image.open(BytesIO(image_bytes))
+ try:
+ pil_image = Image.open(BytesIO(image_bytes))
- # shape has picture
- prov = self.generate_prov(shape, slide_ind, "")
- doc.add_picture(
- parent=parent_slide,
- image=ImageRef.from_pil(image=pil_image, dpi=im_dpi),
- caption=None,
- prov=prov,
- )
+ # shape has picture
+ prov = self.generate_prov(shape, slide_ind, "")
+ doc.add_picture(
+ parent=parent_slide,
+ image=ImageRef.from_pil(image=pil_image, dpi=im_dpi),
+ caption=None,
+ prov=prov,
+ )
+ except (UnidentifiedImageError, OSError) as e:
+ _log.warning(f"Warning: image cannot be loaded by Pillow: {e}")
return
def handle_tables(self, shape, parent_slide, slide_ind, doc):
From 2d24faecd96bfa656b2b8c80f25cdf251a50526a Mon Sep 17 00:00:00 2001
From: Panos Vagenas <35837085+vagenas@users.noreply.github.com>
Date: Tue, 7 Jan 2025 14:15:54 +0100
Subject: [PATCH 06/22] docs: add integrations, revamp docs (#693)
Signed-off-by: Panos Vagenas <35837085+vagenas@users.noreply.github.com>
---
docs/examples/hybrid_chunking.ipynb | 301 +++++++-----------
docs/examples/rag_haystack.ipynb | 13 +
docs/examples/rag_langchain.ipynb | 30 +-
docs/examples/rag_llamaindex.ipynb | 15 +-
docs/examples/rag_weaviate.ipynb | 28 +-
...ag_qdrant.ipynb => retrieval_qdrant.ipynb} | 211 ++++++------
docs/index.md | 9 +
docs/integrations/crewai.md | 10 +
docs/integrations/nvidia.md | 6 +
docs/integrations/opencontracts.md | 11 +-
mkdocs.yml | 51 +--
11 files changed, 355 insertions(+), 330 deletions(-)
rename docs/examples/{hybrid_rag_qdrant.ipynb => retrieval_qdrant.ipynb} (53%)
create mode 100644 docs/integrations/crewai.md
create mode 100644 docs/integrations/nvidia.md
diff --git a/docs/examples/hybrid_chunking.ipynb b/docs/examples/hybrid_chunking.ipynb
index 6f097a8f..6e3b930c 100644
--- a/docs/examples/hybrid_chunking.ipynb
+++ b/docs/examples/hybrid_chunking.ipynb
@@ -4,7 +4,30 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Hybrid Chunking"
+ "# Hybrid chunking"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Overview"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Hybrid chunking applies tokenization-aware refinements on top of document-based hierarchical chunking.\n",
+ "\n",
+ "For more details, see [here](../../concepts/chunking#hybrid-chunker)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Setup"
]
},
{
@@ -21,7 +44,7 @@
}
],
"source": [
- "%pip install -qU 'docling-core[chunking]' sentence-transformers transformers lancedb"
+ "%pip install -qU 'docling-core[chunking]' sentence-transformers transformers"
]
},
{
@@ -48,16 +71,12 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Chunking"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Notice how `tokenizer` and `embed_model` further below are single-sourced from `EMBED_MODEL_ID`.\n",
+ "## Chunking\n",
"\n",
- "This is important for making sure the chunker and the embedding model are using the same tokenizer."
+ "### Basic usage\n",
+ "\n",
+ "For a basic usage scenario, we can just instantiate a `HybridChunker`, which will use\n",
+ "the default parameters."
]
},
{
@@ -65,20 +84,102 @@
"execution_count": 3,
"metadata": {},
"outputs": [],
+ "source": [
+ "from docling.chunking import HybridChunker\n",
+ "\n",
+ "chunker = HybridChunker()\n",
+ "chunk_iter = chunker.chunk(dl_doc=doc)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Note that the text you would typically want to embed is the context-enriched one as\n",
+ "returned by the `serialize()` method:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "=== 0 ===\n",
+ "chunk.text:\n",
+ "'International Business Machines Corporation (using the trademark IBM), nicknamed Big Blue, is an American multinational technology company headquartered in Armonk, New York and present in over 175 countries.\\nIt is a publicly traded company and one of the 30 companies in the Dow Jones Industrial Averโฆ'\n",
+ "chunker.serialize(chunk):\n",
+ "'IBM\\nInternational Business Machines Corporation (using the trademark IBM), nicknamed Big Blue, is an American multinational technology company headquartered in Armonk, New York and present in over 175 countries.\\nIt is a publicly traded company and one of the 30 companies in the Dow Jones Industrial โฆ'\n",
+ "\n",
+ "=== 1 ===\n",
+ "chunk.text:\n",
+ "'IBM originated with several technological innovations developed and commercialized in the late 19th century. Julius E. Pitrap patented the computing scale in 1885;[17] Alexander Dey invented the dial recorder (1888);[18] Herman Hollerith patented the Electric Tabulating Machine (1889);[19] and Willaโฆ'\n",
+ "chunker.serialize(chunk):\n",
+ "'IBM\\n1910sโ1950s\\nIBM originated with several technological innovations developed and commercialized in the late 19th century. Julius E. Pitrap patented the computing scale in 1885;[17] Alexander Dey invented the dial recorder (1888);[18] Herman Hollerith patented the Electric Tabulating Machine (1889โฆ'\n",
+ "\n",
+ "=== 2 ===\n",
+ "chunk.text:\n",
+ "'Collectively, the companies manufactured a wide array of machinery for sale and lease, ranging from commercial scales and industrial time recorders, meat and cheese slicers, to tabulators and punched cards. Thomas J. Watson, Sr., fired from the National Cash Register Company by John Henry Patterson,โฆ'\n",
+ "chunker.serialize(chunk):\n",
+ "'IBM\\n1910sโ1950s\\nCollectively, the companies manufactured a wide array of machinery for sale and lease, ranging from commercial scales and industrial time recorders, meat and cheese slicers, to tabulators and punched cards. Thomas J. Watson, Sr., fired from the National Cash Register Company by John โฆ'\n",
+ "\n",
+ "=== 3 ===\n",
+ "chunk.text:\n",
+ "'In 1961, IBM developed the SABRE reservation system for American Airlines and introduced the highly successful Selectric typewriter.โฆ'\n",
+ "chunker.serialize(chunk):\n",
+ "'IBM\\n1960sโ1980s\\nIn 1961, IBM developed the SABRE reservation system for American Airlines and introduced the highly successful Selectric typewriter.โฆ'\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "for i, chunk in enumerate(chunk_iter):\n",
+ " print(f\"=== {i} ===\")\n",
+ " print(f\"chunk.text:\\n{repr(f'{chunk.text[:300]}โฆ')}\")\n",
+ "\n",
+ " enriched_text = chunker.serialize(chunk=chunk)\n",
+ " print(f\"chunker.serialize(chunk):\\n{repr(f'{enriched_text[:300]}โฆ')}\")\n",
+ "\n",
+ " print()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Advanced usage\n",
+ "\n",
+ "For more control on the chunking, we can parametrize through the `HybridChunker`\n",
+ "arguments illustrated below.\n",
+ "\n",
+ "Notice how `tokenizer` and `embed_model` further below are single-sourced from\n",
+ "`EMBED_MODEL_ID`.\n",
+ "This is important for making sure the chunker and the embedding model are using the same\n",
+ "tokenizer."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
"source": [
"from transformers import AutoTokenizer\n",
"\n",
"from docling.chunking import HybridChunker\n",
"\n",
"EMBED_MODEL_ID = \"sentence-transformers/all-MiniLM-L6-v2\"\n",
- "MAX_TOKENS = 64\n",
+ "MAX_TOKENS = 64 # set to a small number for illustrative purposes\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(EMBED_MODEL_ID)\n",
"\n",
"chunker = HybridChunker(\n",
- " tokenizer=tokenizer, # can also just pass model name instead of tokenizer instance\n",
+ " tokenizer=tokenizer, # instance or model name, defaults to \"sentence-transformers/all-MiniLM-L6-v2\"\n",
" max_tokens=MAX_TOKENS, # optional, by default derived from `tokenizer`\n",
- " # merge_peers=True, # optional, defaults to True\n",
+ " merge_peers=True, # optional, defaults to True\n",
")\n",
"chunk_iter = chunker.chunk(dl_doc=doc)\n",
"chunks = list(chunk_iter)"
@@ -88,7 +189,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Points to notice:\n",
+ "Points to notice looking at the output chunks below:\n",
"- Where possible, we fit the limit of 64 tokens for the metadata-enriched serialization form (see chunk 2)\n",
"- Where neeeded, we stop before the limit, e.g. see cases of 63 as it would otherwise run into a comma (see chunk 6)\n",
"- Where possible, we merge undersized peer chunks (see chunk 0)\n",
@@ -97,7 +198,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -245,174 +346,6 @@
"\n",
" print()"
]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Vector Retrieval"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
- "To disable this warning, you can either:\n",
- "\t- Avoid using `tokenizers` before the fork if possible\n",
- "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
- ]
- }
- ],
- "source": [
- "from sentence_transformers import SentenceTransformer\n",
- "\n",
- "embed_model = SentenceTransformer(EMBED_MODEL_ID)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " | \n",
- " vector | \n",
- " text | \n",
- " headings | \n",
- " captions | \n",
- " _distance | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " | 0 | \n",
- " [-0.1269039, -0.01948185, -0.07718097, -0.1116... | \n",
- " language, and the UPC barcode. The company has... | \n",
- " [IBM] | \n",
- " None | \n",
- " 1.164613 | \n",
- "
\n",
- " \n",
- " | 1 | \n",
- " [-0.10198064, 0.0055981805, -0.05095279, -0.13... | \n",
- " IBM originated with several technological inno... | \n",
- " [IBM, 1910sโ1950s] | \n",
- " None | \n",
- " 1.245144 | \n",
- "
\n",
- " \n",
- " | 2 | \n",
- " [-0.057121325, -0.034115084, -0.018113216, -0.... | \n",
- " As one of the world's oldest and largest techn... | \n",
- " [IBM] | \n",
- " None | \n",
- " 1.355586 | \n",
- "
\n",
- " \n",
- " | 3 | \n",
- " [-0.04429054, -0.058111433, -0.009330196, -0.0... | \n",
- " IBM is the largest industrial research organiz... | \n",
- " [IBM] | \n",
- " None | \n",
- " 1.398617 | \n",
- "
\n",
- " \n",
- " | 4 | \n",
- " [-0.11920792, 0.053496413, -0.042391937, -0.03... | \n",
- " Awards.[16] | \n",
- " [IBM] | \n",
- " None | \n",
- " 1.446295 | \n",
- "
\n",
- " \n",
- "
\n",
- "
![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\")
"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -15,7 +22,29 @@
"| --- | --- | --- |\n",
"| Embedding | Hugging Face / Sentence Transformers | ๐ป Local |\n",
"| Vector store | Milvus | ๐ป Local |\n",
- "| Gen AI | Hugging Face Inference API | ๐ Remote |"
+ "| Gen AI | Hugging Face Inference API | ๐ Remote | "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This example leverages the\n",
+ "[LangChain Docling integration](../../integrations/langchain/), along with a Milvus\n",
+ "vector store, as well as sentence-transformers embeddings.\n",
+ "\n",
+ "The presented `DoclingLoader` component enables you to:\n",
+ "- use various document types in your LLM applications with ease and speed, and\n",
+ "- leverage Docling's rich format for advanced, document-native grounding.\n",
+ "\n",
+ "`DoclingLoader` supports two different export modes:\n",
+ "- `ExportType.MARKDOWN`: if you want to capture each input document as a separate\n",
+ " LangChain document, or\n",
+ "- `ExportType.DOC_CHUNKS` (default): if you want to have each input document chunked and\n",
+ " to then capture each individual chunk as a separate LangChain document downstream.\n",
+ "\n",
+ "The example allows exploring both modes via parameter `EXPORT_TYPE`; depending on the\n",
+ "value set, the example pipeline is then set up accordingly."
]
},
{
@@ -25,6 +54,15 @@
"## Setup"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- ๐ For best conversion speed, use GPU acceleration whenever available; e.g. if running on Colab, use GPU-enabled runtime.\n",
+ "- Notebook uses HuggingFace's Inference API; for increased LLM quota, token can be provided via env var `HF_TOKEN`.\n",
+ "- Requirements can be installed as shown below (`--no-warn-conflicts` meant for Colab's pre-populated Python env; feel free to remove for stricter usage):"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
@@ -39,75 +77,105 @@
}
],
"source": [
- "# requirements for this example:\n",
- "%pip install -qq docling docling-core python-dotenv langchain-text-splitters langchain-huggingface langchain-milvus"
+ "%pip install -q --progress-bar off --no-warn-conflicts langchain-docling langchain-core langchain-huggingface langchain_milvus langchain python-dotenv"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "True"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"import os\n",
+ "from pathlib import Path\n",
+ "from tempfile import mkdtemp\n",
"\n",
"from dotenv import load_dotenv\n",
+ "from langchain_core.prompts import PromptTemplate\n",
+ "from langchain_docling.loader import ExportType\n",
"\n",
- "load_dotenv()"
+ "\n",
+ "def _get_env_from_colab_or_os(key):\n",
+ " try:\n",
+ " from google.colab import userdata\n",
+ "\n",
+ " try:\n",
+ " return userdata.get(key)\n",
+ " except userdata.SecretNotFoundError:\n",
+ " pass\n",
+ " except ImportError:\n",
+ " pass\n",
+ " return os.getenv(key)\n",
+ "\n",
+ "\n",
+ "load_dotenv()\n",
+ "\n",
+ "# https://github.com/huggingface/transformers/issues/5486:\n",
+ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n",
+ "\n",
+ "HF_TOKEN = _get_env_from_colab_or_os(\"HF_TOKEN\")\n",
+ "FILE_PATH = [\"https://arxiv.org/pdf/2408.09869\"] # Docling Technical Report\n",
+ "EMBED_MODEL_ID = \"sentence-transformers/all-MiniLM-L6-v2\"\n",
+ "GEN_MODEL_ID = \"mistralai/Mixtral-8x7B-Instruct-v0.1\"\n",
+ "EXPORT_TYPE = ExportType.DOC_CHUNKS\n",
+ "QUESTION = \"Which are the main AI models in Docling?\"\n",
+ "PROMPT = PromptTemplate.from_template(\n",
+ " \"Context information is below.\\n---------------------\\n{context}\\n---------------------\\nGiven the context information and not prior knowledge, answer the query.\\nQuery: {input}\\nAnswer:\\n\",\n",
+ ")\n",
+ "TOP_K = 3\n",
+ "MILVUS_URI = str(Path(mkdtemp()) / \"docling.db\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Loader and splitter"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Below we set up:\n",
- "- a `Loader` which will be used to create LangChain documents, and\n",
- "- a splitter, which will be used to split these documents"
+ "## Document loading\n",
+ "\n",
+ "Now we can instantiate our loader and load documents."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "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\n"
+ ]
+ }
+ ],
"source": [
- "from typing import Iterator\n",
+ "from langchain_docling import DoclingLoader\n",
"\n",
- "from langchain_core.document_loaders import BaseLoader\n",
- "from langchain_core.documents import Document as LCDocument\n",
+ "from docling.chunking import HybridChunker\n",
"\n",
- "from docling.document_converter import DocumentConverter\n",
+ "loader = DoclingLoader(\n",
+ " file_path=FILE_PATH,\n",
+ " export_type=EXPORT_TYPE,\n",
+ " chunker=HybridChunker(tokenizer=EMBED_MODEL_ID),\n",
+ ")\n",
"\n",
- "\n",
- "class DoclingPDFLoader(BaseLoader):\n",
- "\n",
- " def __init__(self, file_path: str | list[str]) -> None:\n",
- " self._file_paths = file_path if isinstance(file_path, list) else [file_path]\n",
- " self._converter = DocumentConverter()\n",
- "\n",
- " def lazy_load(self) -> Iterator[LCDocument]:\n",
- " for source in self._file_paths:\n",
- " dl_doc = self._converter.convert(source).document\n",
- " text = dl_doc.export_to_markdown()\n",
- " yield LCDocument(page_content=text)"
+ "docs = loader.load()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> Note: a message saying `\"Token indices sequence length is longer than the specified\n",
+ "maximum sequence length...\"` can be ignored in this case โ details\n",
+ "[here](https://github.com/DS4SD/docling-core/issues/119#issuecomment-2577418826)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Determining the splits:"
]
},
{
@@ -116,29 +184,57 @@
"metadata": {},
"outputs": [],
"source": [
- "FILE_PATH = \"https://raw.githubusercontent.com/DS4SD/docling/main/tests/data/2206.01062.pdf\" # DocLayNet paper"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [],
- "source": [
- "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
+ "if EXPORT_TYPE == ExportType.DOC_CHUNKS:\n",
+ " splits = docs\n",
+ "elif EXPORT_TYPE == ExportType.MARKDOWN:\n",
+ " from langchain_text_splitters import MarkdownHeaderTextSplitter\n",
"\n",
- "loader = DoclingPDFLoader(file_path=FILE_PATH)\n",
- "text_splitter = RecursiveCharacterTextSplitter(\n",
- " chunk_size=1000,\n",
- " chunk_overlap=200,\n",
- ")"
+ " splitter = MarkdownHeaderTextSplitter(\n",
+ " headers_to_split_on=[\n",
+ " (\"#\", \"Header_1\"),\n",
+ " (\"##\", \"Header_2\"),\n",
+ " (\"###\", \"Header_3\"),\n",
+ " ],\n",
+ " )\n",
+ " splits = [split for doc in docs for split in splitter.split_text(doc.page_content)]\n",
+ "else:\n",
+ " raise ValueError(f\"Unexpected export type: {EXPORT_TYPE}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "We now used the above-defined objects to get the document splits:"
+ "Inspecting some sample splits:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "- d.page_content='arXiv:2408.09869v5 [cs.CL] 9 Dec 2024'\n",
+ "- 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 Rยจuschlikon, Switzerland'\n",
+ "- 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.'\n",
+ "...\n"
+ ]
+ }
+ ],
+ "source": [
+ "for d in splits[:3]:\n",
+ " print(f\"- {d.page_content=}\")\n",
+ "print(\"...\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Ingestion"
]
},
{
@@ -147,93 +243,27 @@
"metadata": {},
"outputs": [],
"source": [
- "docs = loader.load()\n",
- "splits = text_splitter.split_documents(docs)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Embeddings"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [],
- "source": [
+ "import json\n",
+ "from pathlib import Path\n",
+ "from tempfile import mkdtemp\n",
+ "\n",
"from langchain_huggingface.embeddings import HuggingFaceEmbeddings\n",
- "\n",
- "HF_EMBED_MODEL_ID = \"BAAI/bge-small-en-v1.5\"\n",
- "embeddings = HuggingFaceEmbeddings(model_name=HF_EMBED_MODEL_ID)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Vector store"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
- "source": [
- "from tempfile import TemporaryDirectory\n",
- "\n",
"from langchain_milvus import Milvus\n",
"\n",
- "MILVUS_URI = os.environ.get(\n",
- " \"MILVUS_URI\", f\"{(tmp_dir := TemporaryDirectory()).name}/milvus_demo.db\"\n",
- ")\n",
+ "embedding = HuggingFaceEmbeddings(model_name=EMBED_MODEL_ID)\n",
"\n",
+ "\n",
+ "milvus_uri = str(Path(mkdtemp()) / \"docling.db\") # or set as needed\n",
"vectorstore = Milvus.from_documents(\n",
- " splits,\n",
- " embeddings,\n",
- " connection_args={\"uri\": MILVUS_URI},\n",
+ " documents=splits,\n",
+ " embedding=embedding,\n",
+ " collection_name=\"docling_demo\",\n",
+ " connection_args={\"uri\": milvus_uri},\n",
+ " index_params={\"index_type\": \"FLAT\"},\n",
" drop_old=True,\n",
")"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### LLM"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.\n",
- "Token is valid (permission: write).\n",
- "Your token has been saved to /Users/pva/.cache/huggingface/token\n",
- "Login successful\n"
- ]
- }
- ],
- "source": [
- "from langchain_huggingface import HuggingFaceEndpoint\n",
- "\n",
- "HF_API_KEY = os.environ.get(\"HF_API_KEY\")\n",
- "HF_LLM_MODEL_ID = \"mistralai/Mistral-7B-Instruct-v0.3\"\n",
- "\n",
- "llm = HuggingFaceEndpoint(\n",
- " repo_id=HF_LLM_MODEL_ID,\n",
- " huggingfacehub_api_token=HF_API_KEY,\n",
- ")"
- ]
- },
{
"cell_type": "markdown",
"metadata": {},
@@ -243,55 +273,89 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 7,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Note: Environment variable`HF_TOKEN` is set and is the current active token independently from the token you've just configured.\n"
+ ]
+ }
+ ],
"source": [
- "from typing import Iterable\n",
+ "from langchain.chains import create_retrieval_chain\n",
+ "from langchain.chains.combine_documents import create_stuff_documents_chain\n",
+ "from langchain_huggingface import HuggingFaceEndpoint\n",
"\n",
- "from langchain_core.documents import Document as LCDocument\n",
- "from langchain_core.output_parsers import StrOutputParser\n",
- "from langchain_core.prompts import PromptTemplate\n",
- "from langchain_core.runnables import RunnablePassthrough\n",
- "\n",
- "\n",
- "def format_docs(docs: Iterable[LCDocument]):\n",
- " return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
- "\n",
- "\n",
- "retriever = vectorstore.as_retriever()\n",
- "\n",
- "prompt = PromptTemplate.from_template(\n",
- " \"Context information is below.\\n---------------------\\n{context}\\n---------------------\\nGiven the context information and not prior knowledge, answer the query.\\nQuery: {question}\\nAnswer:\\n\"\n",
+ "retriever = vectorstore.as_retriever(search_kwargs={\"k\": TOP_K})\n",
+ "llm = HuggingFaceEndpoint(\n",
+ " repo_id=GEN_MODEL_ID,\n",
+ " huggingfacehub_api_token=HF_TOKEN,\n",
")\n",
"\n",
- "rag_chain = (\n",
- " {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
- " | prompt\n",
- " | llm\n",
- " | StrOutputParser()\n",
- ")"
+ "\n",
+ "def clip_text(text, threshold=100):\n",
+ " return f\"{text[:threshold]}...\" if len(text) > threshold else text"
]
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
- "data": {
- "text/plain": [
- "'- 80,863 pages were human annotated for DocLayNet.'"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Question:\n",
+ "Which are the main AI models in Docling?\n",
+ "\n",
+ "Answer:\n",
+ "Docling initially releases two AI models, a layout analysis model and TableFormer. The layout analysis model is an accurate object-detector for page elements, and TableFormer is a state-of-the-art tab...\n",
+ "\n",
+ "Source 1:\n",
+ " 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...\"\n",
+ " dl_meta: {'schema_name': 'docling_core.transforms.chunker.DocMeta', 'version': '1.0.0', 'doc_items': [{'self_ref': '#/texts/50', 'parent': {'$ref': '#/body'}, 'children': [], 'label': 'text', 'prov': [{'page_no': 3, 'bbox': {'l': 108.0, 't': 405.1419982910156, 'r': 504.00299072265625, 'b': 330.7799987792969, 'coord_origin': 'BOTTOMLEFT'}, 'charspan': [0, 608]}]}], 'headings': ['3.2 AI models'], 'origin': {'mimetype': 'application/pdf', 'binary_hash': 11465328351749295394, 'filename': '2408.09869v5.pdf'}}\n",
+ " source: https://arxiv.org/pdf/2408.09869\n",
+ "\n",
+ "Source 2:\n",
+ " text: \"3 Processing pipeline\\nDocling implements a linear pipeline of operations, which execute sequentially on each given document (see Fig. 1). Each document is first parsed by a PDF backend, which retrieves the programmatic text tokens, consisting of string content and its coordinates on the page, and also renders a bitmap image of each page to support ...\"\n",
+ " dl_meta: {'schema_name': 'docling_core.transforms.chunker.DocMeta', 'version': '1.0.0', 'doc_items': [{'self_ref': '#/texts/26', 'parent': {'$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'}}\n",
+ " source: https://arxiv.org/pdf/2408.09869\n",
+ "\n",
+ "Source 3:\n",
+ " 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 ...\"\n",
+ " 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'}}\n",
+ " source: https://arxiv.org/pdf/2408.09869\n"
+ ]
}
],
"source": [
- "rag_chain.invoke(\"How many pages were human annotated for DocLayNet?\")"
+ "question_answer_chain = create_stuff_documents_chain(llm, PROMPT)\n",
+ "rag_chain = create_retrieval_chain(retriever, question_answer_chain)\n",
+ "resp_dict = rag_chain.invoke({\"input\": QUESTION})\n",
+ "\n",
+ "clipped_answer = clip_text(resp_dict[\"answer\"], threshold=200)\n",
+ "print(f\"Question:\\n{resp_dict['input']}\\n\\nAnswer:\\n{clipped_answer}\")\n",
+ "for i, doc in enumerate(resp_dict[\"context\"]):\n",
+ " print()\n",
+ " print(f\"Source {i+1}:\")\n",
+ " print(f\" text: {json.dumps(clip_text(doc.page_content, threshold=350))}\")\n",
+ " for key in doc.metadata:\n",
+ " if key != \"pk\":\n",
+ " val = doc.metadata.get(key)\n",
+ " clipped_val = clip_text(val) if isinstance(val, str) else val\n",
+ " print(f\" {key}: {clipped_val}\")"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
@@ -310,7 +374,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.12.7"
+ "version": "3.12.8"
}
},
"nbformat": 4,
diff --git a/docs/index.md b/docs/index.md
index cb76c62f..c88ee7c6 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -21,7 +21,7 @@ Docling parses documents and exports them to the desired format with ease and sp
* ๐๏ธ Reads popular document formats (PDF, DOCX, PPTX, XLSX, Images, HTML, AsciiDoc & Markdown) and exports to HTML, Markdown and JSON (with embedded and referenced images)
* ๐ Advanced PDF document understanding incl. page layout, reading order & table structures
* ๐งฉ Unified, expressive [DoclingDocument](./concepts/docling_document.md) representation format
-* ๐ค Easy integration with ๐ฆ LlamaIndex & ๐ฆ๐ LangChain for powerful RAG / QA applications
+* ๐ค Plug-and-play [integrations](https://ds4sd.github.io/docling/integrations/) incl. LangChain, LlamaIndex, Crew AI & Haystack for agentic AI
* ๐ OCR support for scanned PDFs
* ๐ป Simple and convenient CLI
@@ -29,7 +29,6 @@ Docling parses documents and exports them to the desired format with ease and sp
* โพ๏ธ Equation & code extraction
* ๐ Metadata extraction, including title, authors, references & language
-* ๐ฆ๐ Native LangChain extension
## Get started
diff --git a/docs/integrations/haystack.md b/docs/integrations/haystack.md
index 08d051f0..77507e21 100644
--- a/docs/integrations/haystack.md
+++ b/docs/integrations/haystack.md
@@ -1,6 +1,6 @@
Docling is available as a converter in [Haystack](https://haystack.deepset.ai/):
-- ๐ [Docling Haystack integration docs](https://haystack.deepset.ai/integrations/docling)
+- ๐ [Docling Haystack integration docs][docs]
- ๐ป [Docling Haystack integration GitHub][github]
- ๐ง๐ฝโ๐ณ [Docling Haystack integration example][example]
- ๐ฆ [Docling Haystack integration PyPI][pypi]
@@ -8,4 +8,4 @@ Docling is available as a converter in [Haystack](https://haystack.deepset.ai/):
[github]: https://github.com/DS4SD/docling-haystack
[docs]: https://haystack.deepset.ai/integrations/docling
[pypi]: https://pypi.org/project/docling-haystack
-[example]: https://ds4sd.github.io/docling/examples/rag_haystack/
+[example]: ../examples/rag_haystack.ipynb
diff --git a/docs/integrations/langchain.md b/docs/integrations/langchain.md
new file mode 100644
index 00000000..baa2695e
--- /dev/null
+++ b/docs/integrations/langchain.md
@@ -0,0 +1,9 @@
+Docling is available as a [LangChain](https://www.langchain.com/) document loader:
+
+- ๐ป [LangChain Docling integration GitHub][github]
+- ๐ง๐ฝโ๐ณ [LangChain Docling integration example][example]
+- ๐ฆ [LangChain Docling integration PyPI][pypi]
+
+[github]: https://github.com/DS4SD/docling-langchain
+[example]: ../examples/rag_langchain.ipynb
+[pypi]: https://pypi.org/project/langchain-docling/
diff --git a/mkdocs.yml b/mkdocs.yml
index 01de6e7d..8d9f6591 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -91,7 +91,7 @@ nav:
- "Bee Agent Framework": integrations/bee.md
- "Crew AI": integrations/crewai.md
- "Haystack": integrations/haystack.md
- # - "LangChain": integrations/langchain.md
+ - "LangChain": integrations/langchain.md
- "LlamaIndex": integrations/llamaindex.md
- "txtai": integrations/txtai.md
- โญ๏ธ Featured:
From 9a6b5c8c8debc81e0ddcbe91df6afbbeb29e97e6 Mon Sep 17 00:00:00 2001
From: Panos Vagenas <35837085+vagenas@users.noreply.github.com>
Date: Thu, 9 Jan 2025 17:36:46 +0100
Subject: [PATCH 14/22] docs: add pointers to LangChain-side docs (#718)
Signed-off-by: Panos Vagenas <35837085+vagenas@users.noreply.github.com>
---
docs/integrations/langchain.md | 7 ++++++-
1 file changed, 6 insertions(+), 1 deletion(-)
diff --git a/docs/integrations/langchain.md b/docs/integrations/langchain.md
index baa2695e..bee3c0da 100644
--- a/docs/integrations/langchain.md
+++ b/docs/integrations/langchain.md
@@ -1,9 +1,14 @@
-Docling is available as a [LangChain](https://www.langchain.com/) document loader:
+Docling is available as an official [LangChain](https://python.langchain.com/) extension.
+To get started, check out the [step-by-step guide in LangChain][guide].
+
+- ๐ [LangChain Docling integration docs][docs]
- ๐ป [LangChain Docling integration GitHub][github]
- ๐ง๐ฝโ๐ณ [LangChain Docling integration example][example]
- ๐ฆ [LangChain Docling integration PyPI][pypi]
+[docs]: https://python.langchain.com/docs/integrations/providers/docling/
[github]: https://github.com/DS4SD/docling-langchain
+[guide]: https://python.langchain.com/docs/integrations/document_loaders/docling/
[example]: ../examples/rag_langchain.ipynb
[pypi]: https://pypi.org/project/langchain-docling/
From 5a060f237d1decd0ff9db9e73478978419315778 Mon Sep 17 00:00:00 2001
From: Christoph Auer <60343111+cau-git@users.noreply.github.com>
Date: Fri, 10 Jan 2025 10:38:49 +0100
Subject: [PATCH 15/22] fix: Improve OCR results, stricten criteria before
dropping bitmap areas (#719)
fix: Properly care for all bitmap elements in OCR
Signed-off-by: Christoph Auer
---
docling/backend/docling_parse_backend.py | 2 +-
docling/backend/docling_parse_v2_backend.py | 2 +-
docling/backend/pypdfium2_backend.py | 2 +-
docling/datamodel/pipeline_options.py | 2 +-
docling/models/base_ocr_model.py | 27 ++++++++++++---------
5 files changed, 19 insertions(+), 16 deletions(-)
diff --git a/docling/backend/docling_parse_backend.py b/docling/backend/docling_parse_backend.py
index bb1fe058..89b25ee1 100644
--- a/docling/backend/docling_parse_backend.py
+++ b/docling/backend/docling_parse_backend.py
@@ -132,7 +132,7 @@ class DoclingParsePageBackend(PdfPageBackend):
return cells
def get_bitmap_rects(self, scale: float = 1) -> Iterable[BoundingBox]:
- AREA_THRESHOLD = 32 * 32
+ AREA_THRESHOLD = 0 # 32 * 32
for i in range(len(self._dpage["images"])):
bitmap = self._dpage["images"][i]
diff --git a/docling/backend/docling_parse_v2_backend.py b/docling/backend/docling_parse_v2_backend.py
index 12d7df55..366fa6ac 100644
--- a/docling/backend/docling_parse_v2_backend.py
+++ b/docling/backend/docling_parse_v2_backend.py
@@ -140,7 +140,7 @@ class DoclingParseV2PageBackend(PdfPageBackend):
return cells
def get_bitmap_rects(self, scale: float = 1) -> Iterable[BoundingBox]:
- AREA_THRESHOLD = 32 * 32
+ AREA_THRESHOLD = 0 # 32 * 32
images = self._dpage["sanitized"]["images"]["data"]
images_header = self._dpage["sanitized"]["images"]["header"]
diff --git a/docling/backend/pypdfium2_backend.py b/docling/backend/pypdfium2_backend.py
index d24ba608..2566fc18 100644
--- a/docling/backend/pypdfium2_backend.py
+++ b/docling/backend/pypdfium2_backend.py
@@ -39,7 +39,7 @@ class PyPdfiumPageBackend(PdfPageBackend):
return self.valid
def get_bitmap_rects(self, scale: float = 1) -> Iterable[BoundingBox]:
- AREA_THRESHOLD = 32 * 32
+ AREA_THRESHOLD = 0 # 32 * 32
for obj in self._ppage.get_objects(filter=[pdfium_c.FPDF_PAGEOBJ_IMAGE]):
pos = obj.get_pos()
cropbox = BoundingBox.from_tuple(
diff --git a/docling/datamodel/pipeline_options.py b/docling/datamodel/pipeline_options.py
index fc78e27c..eeec6bab 100644
--- a/docling/datamodel/pipeline_options.py
+++ b/docling/datamodel/pipeline_options.py
@@ -139,7 +139,7 @@ class EasyOcrOptions(OcrOptions):
use_gpu: Optional[bool] = None
- confidence_threshold: float = 0.65
+ confidence_threshold: float = 0.5
model_storage_directory: Optional[str] = None
recog_network: Optional[str] = "standard"
diff --git a/docling/models/base_ocr_model.py b/docling/models/base_ocr_model.py
index 68553c48..9afb7dde 100644
--- a/docling/models/base_ocr_model.py
+++ b/docling/models/base_ocr_model.py
@@ -8,7 +8,7 @@ import numpy as np
from docling_core.types.doc import BoundingBox, CoordOrigin
from PIL import Image, ImageDraw
from rtree import index
-from scipy.ndimage import find_objects, label
+from scipy.ndimage import binary_dilation, find_objects, label
from docling.datamodel.base_models import Cell, OcrCell, Page
from docling.datamodel.document import ConversionResult
@@ -43,6 +43,12 @@ class BaseOcrModel(BasePageModel):
np_image = np.array(image)
+ # Dilate the image by 10 pixels to merge nearby bitmap rectangles
+ structure = np.ones(
+ (20, 20)
+ ) # Create a 20x20 structure element (10 pixels in all directions)
+ np_image = binary_dilation(np_image > 0, structure=structure)
+
# Find the connected components
labeled_image, num_features = label(
np_image > 0
@@ -72,7 +78,7 @@ class BaseOcrModel(BasePageModel):
bitmap_rects = []
coverage, ocr_rects = find_ocr_rects(page.size, bitmap_rects)
- # return full-page rectangle if sufficiently covered with bitmaps
+ # return full-page rectangle if page is dominantly covered with bitmaps
if self.options.force_full_page_ocr or coverage > max(
BITMAP_COVERAGE_TRESHOLD, self.options.bitmap_area_threshold
):
@@ -85,17 +91,11 @@ class BaseOcrModel(BasePageModel):
coord_origin=CoordOrigin.TOPLEFT,
)
]
- # return individual rectangles if the bitmap coverage is smaller
- else: # coverage <= BITMAP_COVERAGE_TRESHOLD:
-
- # skip OCR if the bitmap area on the page is smaller than the options threshold
- ocr_rects = [
- rect
- for rect in ocr_rects
- if rect.area() / (page.size.width * page.size.height)
- > self.options.bitmap_area_threshold
- ]
+ # return individual rectangles if the bitmap coverage is above the threshold
+ elif coverage > self.options.bitmap_area_threshold:
return ocr_rects
+ else: # overall coverage of bitmaps is too low, drop all bitmap rectangles.
+ return []
# Filters OCR cells by dropping any OCR cell that intersects with an existing programmatic cell.
def _filter_ocr_cells(self, ocr_cells, programmatic_cells):
@@ -162,6 +162,9 @@ class BaseOcrModel(BasePageModel):
x0 *= scale_x
x1 *= scale_x
+ if y1 <= y0:
+ y1, y0 = y0, y1
+
color = "gray"
if isinstance(tc, OcrCell):
color = "magenta"
From 1976584be15b6dea7451001adbfd8b9dc3422235 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]"
Date: Fri, 10 Jan 2025 10:29:32 +0000
Subject: [PATCH 16/22] chore: bump version to 2.15.1 [skip ci]
---
CHANGELOG.md | 12 ++++++++++++
pyproject.toml | 2 +-
2 files changed, 13 insertions(+), 1 deletion(-)
diff --git a/CHANGELOG.md b/CHANGELOG.md
index dee6e30c..21882618 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,3 +1,15 @@
+## [v2.15.1](https://github.com/DS4SD/docling/releases/tag/v2.15.1) - 2025-01-10
+
+### Fix
+
+* Improve OCR results, stricten criteria before dropping bitmap areas ([#719](https://github.com/DS4SD/docling/issues/719)) ([`5a060f2`](https://github.com/DS4SD/docling/commit/5a060f237d1decd0ff9db9e73478978419315778))
+* Allow earlier requests versions ([#716](https://github.com/DS4SD/docling/issues/716)) ([`e64b5a2`](https://github.com/DS4SD/docling/commit/e64b5a2f628acc340a6d94ee6f1ada2aa267cecc))
+
+### Documentation
+
+* Add pointers to LangChain-side docs ([#718](https://github.com/DS4SD/docling/issues/718)) ([`9a6b5c8`](https://github.com/DS4SD/docling/commit/9a6b5c8c8debc81e0ddcbe91df6afbbeb29e97e6))
+* Add LangChain docs ([#717](https://github.com/DS4SD/docling/issues/717)) ([`4fa8028`](https://github.com/DS4SD/docling/commit/4fa8028bd8120d7557e1d45ba31e200e130af698))
+
## [v2.15.0](https://github.com/DS4SD/docling/releases/tag/v2.15.0) - 2025-01-08
### Feature
diff --git a/pyproject.toml b/pyproject.toml
index 90f9e220..e84fa072 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
[tool.poetry]
name = "docling"
-version = "2.15.0" # DO NOT EDIT, updated automatically
+version = "2.15.1" # DO NOT EDIT, updated automatically
description = "SDK and CLI for parsing PDF, DOCX, HTML, and more, to a unified document representation for powering downstream workflows such as gen AI applications."
authors = ["Christoph Auer ", "Michele Dolfi ", "Maxim Lysak ", "Nikos Livathinos ", "Ahmed Nassar ", "Panos Vagenas ", "Peter Staar "]
license = "MIT"
From f7e1cbf629ae5f3e279296e72f656b7a453ab7a3 Mon Sep 17 00:00:00 2001
From: "Peter W. J. Staar" <91719829+PeterStaar-IBM@users.noreply.github.com>
Date: Wed, 15 Jan 2025 06:51:15 +0100
Subject: [PATCH 17/22] docs: Example to translate documents (#739)
* added example to translate documents
Signed-off-by: Peter Staar
* updated the mkdocs
Signed-off-by: Peter Staar
* fix PR hooks
Signed-off-by: Michele Dolfi
---------
Signed-off-by: Peter Staar
Signed-off-by: Michele Dolfi
Co-authored-by: Michele Dolfi
---
docs/examples/translate.py | 75 ++++++++++++++++++++++++++++++++++++++
mkdocs.yml | 1 +
2 files changed, 76 insertions(+)
create mode 100644 docs/examples/translate.py
diff --git a/docs/examples/translate.py b/docs/examples/translate.py
new file mode 100644
index 00000000..a36c6f71
--- /dev/null
+++ b/docs/examples/translate.py
@@ -0,0 +1,75 @@
+import logging
+import time
+from pathlib import Path
+
+from docling_core.types.doc import ImageRefMode, PictureItem, TableItem, TextItem
+
+from docling.datamodel.base_models import FigureElement, InputFormat, Table
+from docling.datamodel.pipeline_options import PdfPipelineOptions
+from docling.document_converter import DocumentConverter, PdfFormatOption
+
+_log = logging.getLogger(__name__)
+
+IMAGE_RESOLUTION_SCALE = 2.0
+
+
+# FIXME: put in your favorite translation code ....
+def translate(text: str, src: str = "en", dest: str = "de"):
+
+ _log.warning("!!! IMPLEMENT HERE YOUR FAVORITE TRANSLATION CODE!!!")
+ # from googletrans import Translator
+
+ # Initialize the translator
+ # translator = Translator()
+
+ # Translate text from English to German
+ # text = "Hello, how are you?"
+ # translated = translator.translate(text, src="en", dest="de")
+
+ return text
+
+
+def main():
+ logging.basicConfig(level=logging.INFO)
+
+ input_doc_path = Path("./tests/data/2206.01062.pdf")
+ output_dir = Path("scratch")
+
+ # Important: For operating with page images, we must keep them, otherwise the DocumentConverter
+ # will destroy them for cleaning up memory.
+ # This is done by setting PdfPipelineOptions.images_scale, which also defines the scale of images.
+ # scale=1 correspond of a standard 72 DPI image
+ # The PdfPipelineOptions.generate_* are the selectors for the document elements which will be enriched
+ # with the image field
+ pipeline_options = PdfPipelineOptions()
+ pipeline_options.images_scale = IMAGE_RESOLUTION_SCALE
+ pipeline_options.generate_page_images = True
+ pipeline_options.generate_picture_images = True
+
+ doc_converter = DocumentConverter(
+ format_options={
+ InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
+ }
+ )
+
+ start_time = time.time()
+
+ conv_res = doc_converter.convert(input_doc_path)
+ conv_doc = conv_res.document
+
+ # Save markdown with embedded pictures in original text
+ md_filename = output_dir / f"{doc_filename}-with-images-orig.md"
+ conv_doc.save_as_markdown(md_filename, image_mode=ImageRefMode.EMBEDDED)
+
+ for element, _level in conv_res.document.iterate_items():
+ if isinstance(element, TextItem):
+ element.orig = element.text
+ element.text = translate(text=element.text)
+
+ elif isinstance(element, TableItem):
+ for cell in element.data.table_cells:
+ cell.text = translate(text=element.text)
+
+ # Save markdown with embedded pictures in translated text
+ md_filename = output_dir / f"{doc_filename}-with-images-translated.md"
+ conv_doc.save_as_markdown(md_filename, image_mode=ImageRefMode.EMBEDDED)
diff --git a/mkdocs.yml b/mkdocs.yml
index 8d9f6591..8f8d86d9 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -76,6 +76,7 @@ nav:
- "Multimodal export": examples/export_multimodal.py
- "Force full page OCR": examples/full_page_ocr.py
- "Accelerator options": examples/run_with_accelerator.py
+ - "Simple translation": examples/translate.py
- โ๏ธ Chunking:
- "Hybrid chunking": examples/hybrid_chunking.ipynb
- ๐ฌ RAG / QA:
From 57fc28d3d8f0cc11ba86f654dfdd4f3a1ec9f002 Mon Sep 17 00:00:00 2001
From: Michele Dolfi <97102151+dolfim-ibm@users.noreply.github.com>
Date: Wed, 15 Jan 2025 09:52:38 +0100
Subject: [PATCH 18/22] refactor: allow the usage of backends in the enrich
models and generalize the interface (#742)
* fix get image with cropbox
Signed-off-by: Michele Dolfi
* allow the usage of backends in the enrich models and generalize the interface
Signed-off-by: Michele Dolfi
* move logic in BaseTextImageEnrichmentModel
Signed-off-by: Michele Dolfi
* renaming
Signed-off-by: Michele Dolfi
---------
Signed-off-by: Michele Dolfi
---
docling/backend/docling_parse_backend.py | 2 +-
docling/backend/docling_parse_v2_backend.py | 2 +-
docling/backend/pypdfium2_backend.py | 2 +-
docling/datamodel/base_models.py | 30 ++++++-
docling/models/base_model.py | 55 ++++++++++--
docling/models/page_assemble_model.py | 9 +-
docling/pipeline/base_pipeline.py | 51 ++++++++---
docling/pipeline/standard_pdf_pipeline.py | 7 +-
.../examples/develop_formula_understanding.py | 88 +++++++++++++++++++
9 files changed, 208 insertions(+), 38 deletions(-)
create mode 100644 docs/examples/develop_formula_understanding.py
diff --git a/docling/backend/docling_parse_backend.py b/docling/backend/docling_parse_backend.py
index 89b25ee1..6d22127b 100644
--- a/docling/backend/docling_parse_backend.py
+++ b/docling/backend/docling_parse_backend.py
@@ -163,7 +163,7 @@ class DoclingParsePageBackend(PdfPageBackend):
l=0, r=0, t=0, b=0, coord_origin=CoordOrigin.BOTTOMLEFT
)
else:
- padbox = cropbox.to_bottom_left_origin(page_size.height)
+ padbox = cropbox.to_bottom_left_origin(page_size.height).model_copy()
padbox.r = page_size.width - padbox.r
padbox.t = page_size.height - padbox.t
diff --git a/docling/backend/docling_parse_v2_backend.py b/docling/backend/docling_parse_v2_backend.py
index 366fa6ac..27a368f9 100644
--- a/docling/backend/docling_parse_v2_backend.py
+++ b/docling/backend/docling_parse_v2_backend.py
@@ -178,7 +178,7 @@ class DoclingParseV2PageBackend(PdfPageBackend):
l=0, r=0, t=0, b=0, coord_origin=CoordOrigin.BOTTOMLEFT
)
else:
- padbox = cropbox.to_bottom_left_origin(page_size.height)
+ padbox = cropbox.to_bottom_left_origin(page_size.height).model_copy()
padbox.r = page_size.width - padbox.r
padbox.t = page_size.height - padbox.t
diff --git a/docling/backend/pypdfium2_backend.py b/docling/backend/pypdfium2_backend.py
index 2566fc18..5b627da7 100644
--- a/docling/backend/pypdfium2_backend.py
+++ b/docling/backend/pypdfium2_backend.py
@@ -210,7 +210,7 @@ class PyPdfiumPageBackend(PdfPageBackend):
l=0, r=0, t=0, b=0, coord_origin=CoordOrigin.BOTTOMLEFT
)
else:
- padbox = cropbox.to_bottom_left_origin(page_size.height)
+ padbox = cropbox.to_bottom_left_origin(page_size.height).model_copy()
padbox.r = page_size.width - padbox.r
padbox.t = page_size.height - padbox.t
diff --git a/docling/datamodel/base_models.py b/docling/datamodel/base_models.py
index 5bd28ed6..99d30108 100644
--- a/docling/datamodel/base_models.py
+++ b/docling/datamodel/base_models.py
@@ -4,6 +4,7 @@ from typing import TYPE_CHECKING, Dict, List, Optional, Union
from docling_core.types.doc import (
BoundingBox,
DocItemLabel,
+ NodeItem,
PictureDataType,
Size,
TableCell,
@@ -201,6 +202,13 @@ class AssembledUnit(BaseModel):
headers: List[PageElement] = []
+class ItemAndImageEnrichmentElement(BaseModel):
+ model_config = ConfigDict(arbitrary_types_allowed=True)
+
+ item: NodeItem
+ image: Image
+
+
class Page(BaseModel):
model_config = ConfigDict(arbitrary_types_allowed=True)
@@ -219,12 +227,28 @@ class Page(BaseModel):
{}
) # Cache of images in different scales. By default it is cleared during assembling.
- def get_image(self, scale: float = 1.0) -> Optional[Image]:
+ def get_image(
+ self, scale: float = 1.0, cropbox: Optional[BoundingBox] = None
+ ) -> Optional[Image]:
if self._backend is None:
return self._image_cache.get(scale, None)
+
if not scale in self._image_cache:
- self._image_cache[scale] = self._backend.get_page_image(scale=scale)
- return self._image_cache[scale]
+ if cropbox is None:
+ self._image_cache[scale] = self._backend.get_page_image(scale=scale)
+ else:
+ return self._backend.get_page_image(scale=scale, cropbox=cropbox)
+
+ if cropbox is None:
+ return self._image_cache[scale]
+ else:
+ page_im = self._image_cache[scale]
+ assert self.size is not None
+ return page_im.crop(
+ cropbox.to_top_left_origin(page_height=self.size.height)
+ .scaled(scale=scale)
+ .as_tuple()
+ )
@property
def image(self) -> Optional[Image]:
diff --git a/docling/models/base_model.py b/docling/models/base_model.py
index 1147896c..5a98382a 100644
--- a/docling/models/base_model.py
+++ b/docling/models/base_model.py
@@ -1,9 +1,10 @@
from abc import ABC, abstractmethod
-from typing import Any, Iterable
+from typing import Any, Generic, Iterable, Optional
-from docling_core.types.doc import DoclingDocument, NodeItem
+from docling_core.types.doc import DoclingDocument, NodeItem, TextItem
+from typing_extensions import TypeVar
-from docling.datamodel.base_models import Page
+from docling.datamodel.base_models import ItemAndImageEnrichmentElement, Page
from docling.datamodel.document import ConversionResult
@@ -15,14 +16,54 @@ class BasePageModel(ABC):
pass
-class BaseEnrichmentModel(ABC):
+EnrichElementT = TypeVar("EnrichElementT", default=NodeItem)
+
+
+class GenericEnrichmentModel(ABC, Generic[EnrichElementT]):
@abstractmethod
def is_processable(self, doc: DoclingDocument, element: NodeItem) -> bool:
pass
@abstractmethod
- def __call__(
- self, doc: DoclingDocument, element_batch: Iterable[NodeItem]
- ) -> Iterable[Any]:
+ def prepare_element(
+ self, conv_res: ConversionResult, element: NodeItem
+ ) -> Optional[EnrichElementT]:
pass
+
+ @abstractmethod
+ def __call__(
+ self, doc: DoclingDocument, element_batch: Iterable[EnrichElementT]
+ ) -> Iterable[NodeItem]:
+ pass
+
+
+class BaseEnrichmentModel(GenericEnrichmentModel[NodeItem]):
+
+ def prepare_element(
+ self, conv_res: ConversionResult, element: NodeItem
+ ) -> Optional[NodeItem]:
+ if self.is_processable(doc=conv_res.document, element=element):
+ return element
+ return None
+
+
+class BaseItemAndImageEnrichmentModel(
+ GenericEnrichmentModel[ItemAndImageEnrichmentElement]
+):
+
+ images_scale: float
+
+ def prepare_element(
+ self, conv_res: ConversionResult, element: NodeItem
+ ) -> Optional[ItemAndImageEnrichmentElement]:
+ if not self.is_processable(doc=conv_res.document, element=element):
+ return None
+
+ assert isinstance(element, TextItem)
+ element_prov = element.prov[0]
+ page_ix = element_prov.page_no - 1
+ cropped_image = conv_res.pages[page_ix].get_image(
+ scale=self.images_scale, cropbox=element_prov.bbox
+ )
+ return ItemAndImageEnrichmentElement(item=element, image=cropped_image)
diff --git a/docling/models/page_assemble_model.py b/docling/models/page_assemble_model.py
index 3e202e20..6239dbfa 100644
--- a/docling/models/page_assemble_model.py
+++ b/docling/models/page_assemble_model.py
@@ -22,7 +22,7 @@ _log = logging.getLogger(__name__)
class PageAssembleOptions(BaseModel):
- keep_images: bool = False
+ pass
class PageAssembleModel(BasePageModel):
@@ -174,11 +174,4 @@ class PageAssembleModel(BasePageModel):
elements=elements, headers=headers, body=body
)
- # Remove page images (can be disabled)
- if not self.options.keep_images:
- page._image_cache = {}
-
- # Unload backend
- page._backend.unload()
-
yield page
diff --git a/docling/pipeline/base_pipeline.py b/docling/pipeline/base_pipeline.py
index c75faaec..034e6d42 100644
--- a/docling/pipeline/base_pipeline.py
+++ b/docling/pipeline/base_pipeline.py
@@ -28,6 +28,7 @@ _log = logging.getLogger(__name__)
class BasePipeline(ABC):
def __init__(self, pipeline_options: PipelineOptions):
self.pipeline_options = pipeline_options
+ self.keep_images = False
self.build_pipe: List[Callable] = []
self.enrichment_pipe: List[BaseEnrichmentModel] = []
@@ -40,7 +41,7 @@ class BasePipeline(ABC):
conv_res, "pipeline_total", scope=ProfilingScope.DOCUMENT
):
# These steps are building and assembling the structure of the
- # output DoclingDocument
+ # output DoclingDocument.
conv_res = self._build_document(conv_res)
conv_res = self._assemble_document(conv_res)
# From this stage, all operations should rely only on conv_res.output
@@ -50,6 +51,8 @@ class BasePipeline(ABC):
conv_res.status = ConversionStatus.FAILURE
if raises_on_error:
raise e
+ finally:
+ self._unload(conv_res)
return conv_res
@@ -62,21 +65,22 @@ class BasePipeline(ABC):
def _enrich_document(self, conv_res: ConversionResult) -> ConversionResult:
- def _filter_elements(
- doc: DoclingDocument, model: BaseEnrichmentModel
+ def _prepare_elements(
+ conv_res: ConversionResult, model: BaseEnrichmentModel
) -> Iterable[NodeItem]:
- for element, _level in doc.iterate_items():
- if model.is_processable(doc=doc, element=element):
- yield element
+ for doc_element, _level in conv_res.document.iterate_items():
+ prepared_element = model.prepare_element(
+ conv_res=conv_res, element=doc_element
+ )
+ if prepared_element is not None:
+ yield prepared_element
with TimeRecorder(conv_res, "doc_enrich", scope=ProfilingScope.DOCUMENT):
for model in self.enrichment_pipe:
for element_batch in chunkify(
- _filter_elements(conv_res.document, model),
+ _prepare_elements(conv_res, model),
settings.perf.elements_batch_size,
):
- # TODO: currently we assume the element itself is modified, because
- # we don't have an interface to save the element back to the document
for element in model(
doc=conv_res.document, element_batch=element_batch
): # Must exhaust!
@@ -88,6 +92,9 @@ class BasePipeline(ABC):
def _determine_status(self, conv_res: ConversionResult) -> ConversionStatus:
pass
+ def _unload(self, conv_res: ConversionResult):
+ pass
+
@classmethod
@abstractmethod
def get_default_options(cls) -> PipelineOptions:
@@ -107,6 +114,10 @@ class BasePipeline(ABC):
class PaginatedPipeline(BasePipeline): # TODO this is a bad name.
+ def __init__(self, pipeline_options: PipelineOptions):
+ super().__init__(pipeline_options)
+ self.keep_backend = False
+
def _apply_on_pages(
self, conv_res: ConversionResult, page_batch: Iterable[Page]
) -> Iterable[Page]:
@@ -148,7 +159,14 @@ class PaginatedPipeline(BasePipeline): # TODO this is a bad name.
pipeline_pages = self._apply_on_pages(conv_res, init_pages)
for p in pipeline_pages: # Must exhaust!
- pass
+
+ # Cleanup cached images
+ if not self.keep_images:
+ p._image_cache = {}
+
+ # Cleanup page backends
+ if not self.keep_backend and p._backend is not None:
+ p._backend.unload()
end_batch_time = time.monotonic()
total_elapsed_time += end_batch_time - start_batch_time
@@ -177,10 +195,15 @@ class PaginatedPipeline(BasePipeline): # TODO this is a bad name.
)
raise e
- finally:
- # Always unload the PDF backend, even in case of failure
- if conv_res.input._backend:
- conv_res.input._backend.unload()
+ return conv_res
+
+ def _unload(self, conv_res: ConversionResult) -> ConversionResult:
+ for page in conv_res.pages:
+ if page._backend is not None:
+ page._backend.unload()
+
+ if conv_res.input._backend:
+ conv_res.input._backend.unload()
return conv_res
diff --git a/docling/pipeline/standard_pdf_pipeline.py b/docling/pipeline/standard_pdf_pipeline.py
index 2f8c1421..758f4e94 100644
--- a/docling/pipeline/standard_pdf_pipeline.py
+++ b/docling/pipeline/standard_pdf_pipeline.py
@@ -1,7 +1,7 @@
import logging
import sys
from pathlib import Path
-from typing import Optional
+from typing import Iterable, Optional
from docling_core.types.doc import DocItem, ImageRef, PictureItem, TableItem
@@ -17,6 +17,7 @@ from docling.datamodel.pipeline_options import (
TesseractCliOcrOptions,
TesseractOcrOptions,
)
+from docling.models.base_model import BasePageModel
from docling.models.base_ocr_model import BaseOcrModel
from docling.models.ds_glm_model import GlmModel, GlmOptions
from docling.models.easyocr_model import EasyOcrModel
@@ -50,7 +51,7 @@ class StandardPdfPipeline(PaginatedPipeline):
else:
self.artifacts_path = Path(pipeline_options.artifacts_path)
- keep_images = (
+ self.keep_images = (
self.pipeline_options.generate_page_images
or self.pipeline_options.generate_picture_images
or self.pipeline_options.generate_table_images
@@ -87,7 +88,7 @@ class StandardPdfPipeline(PaginatedPipeline):
accelerator_options=pipeline_options.accelerator_options,
),
# Page assemble
- PageAssembleModel(options=PageAssembleOptions(keep_images=keep_images)),
+ PageAssembleModel(options=PageAssembleOptions()),
]
self.enrichment_pipe = [
diff --git a/docs/examples/develop_formula_understanding.py b/docs/examples/develop_formula_understanding.py
new file mode 100644
index 00000000..e2e508c3
--- /dev/null
+++ b/docs/examples/develop_formula_understanding.py
@@ -0,0 +1,88 @@
+import logging
+from pathlib import Path
+from typing import Iterable
+
+from docling_core.types.doc import DocItemLabel, DoclingDocument, NodeItem, TextItem
+
+from docling.datamodel.base_models import InputFormat, ItemAndImageEnrichmentElement
+from docling.datamodel.pipeline_options import PdfPipelineOptions
+from docling.document_converter import DocumentConverter, PdfFormatOption
+from docling.models.base_model import BaseItemAndImageEnrichmentModel
+from docling.pipeline.standard_pdf_pipeline import StandardPdfPipeline
+
+
+class ExampleFormulaUnderstandingPipelineOptions(PdfPipelineOptions):
+ do_formula_understanding: bool = True
+
+
+# A new enrichment model using both the document element and its image as input
+class ExampleFormulaUnderstandingEnrichmentModel(BaseItemAndImageEnrichmentModel):
+ images_scale = 2.6
+
+ def __init__(self, enabled: bool):
+ self.enabled = enabled
+
+ def is_processable(self, doc: DoclingDocument, element: NodeItem) -> bool:
+ return (
+ self.enabled
+ and isinstance(element, TextItem)
+ and element.label == DocItemLabel.FORMULA
+ )
+
+ def __call__(
+ self,
+ doc: DoclingDocument,
+ element_batch: Iterable[ItemAndImageEnrichmentElement],
+ ) -> Iterable[NodeItem]:
+ if not self.enabled:
+ return
+
+ for enrich_element in element_batch:
+ enrich_element.image.show()
+
+ yield enrich_element.item
+
+
+# How the pipeline can be extended.
+class ExampleFormulaUnderstandingPipeline(StandardPdfPipeline):
+
+ def __init__(self, pipeline_options: ExampleFormulaUnderstandingPipelineOptions):
+ super().__init__(pipeline_options)
+ self.pipeline_options: ExampleFormulaUnderstandingPipelineOptions
+
+ self.enrichment_pipe = [
+ ExampleFormulaUnderstandingEnrichmentModel(
+ enabled=self.pipeline_options.do_formula_understanding
+ )
+ ]
+
+ if self.pipeline_options.do_formula_understanding:
+ self.keep_backend = True
+
+ @classmethod
+ def get_default_options(cls) -> ExampleFormulaUnderstandingPipelineOptions:
+ return ExampleFormulaUnderstandingPipelineOptions()
+
+
+# Example main. In the final version, we simply have to set do_formula_understanding to true.
+def main():
+ logging.basicConfig(level=logging.INFO)
+
+ input_doc_path = Path("./tests/data/2203.01017v2.pdf")
+
+ pipeline_options = ExampleFormulaUnderstandingPipelineOptions()
+ pipeline_options.do_formula_understanding = True
+
+ doc_converter = DocumentConverter(
+ format_options={
+ InputFormat.PDF: PdfFormatOption(
+ pipeline_cls=ExampleFormulaUnderstandingPipeline,
+ pipeline_options=pipeline_options,
+ )
+ }
+ )
+ result = doc_converter.convert(input_doc_path)
+
+
+if __name__ == "__main__":
+ main()
From 768608351d40376c3504546f52e967195536b3d5 Mon Sep 17 00:00:00 2001
From: Iacopo Ghinassi <45108036+Ighina@users.noreply.github.com>
Date: Sun, 19 Jan 2025 15:55:26 +0000
Subject: [PATCH 19/22] docs: fix correct Accelerator pipeline options in
docs/examples/custom_convert.py (#733)
* Update custom_convert.py
Added the missing AcceleratorDevice and AcceleratorOptions functions in the imports and changed Device in the code to the correct AcceleratorDevice
Signed-off-by: Iacopo Ghinassi <45108036+Ighina@users.noreply.github.com>
* apply formatting
Signed-off-by: Michele Dolfi
---------
Signed-off-by: Iacopo Ghinassi <45108036+Ighina@users.noreply.github.com>
Signed-off-by: Michele Dolfi
Co-authored-by: Michele Dolfi
---
docs/examples/custom_convert.py | 8 ++++++--
1 file changed, 6 insertions(+), 2 deletions(-)
diff --git a/docs/examples/custom_convert.py b/docs/examples/custom_convert.py
index a7efa975..49ab739a 100644
--- a/docs/examples/custom_convert.py
+++ b/docs/examples/custom_convert.py
@@ -5,7 +5,11 @@ from pathlib import Path
from docling.backend.pypdfium2_backend import PyPdfiumDocumentBackend
from docling.datamodel.base_models import InputFormat
-from docling.datamodel.pipeline_options import PdfPipelineOptions
+from docling.datamodel.pipeline_options import (
+ AcceleratorDevice,
+ AcceleratorOptions,
+ PdfPipelineOptions,
+)
from docling.document_converter import DocumentConverter, PdfFormatOption
from docling.models.ocr_mac_model import OcrMacOptions
from docling.models.tesseract_ocr_cli_model import TesseractCliOcrOptions
@@ -76,7 +80,7 @@ def main():
pipeline_options.table_structure_options.do_cell_matching = True
pipeline_options.ocr_options.lang = ["es"]
pipeline_options.accelerator_options = AcceleratorOptions(
- num_threads=4, device=Device.AUTO
+ num_threads=4, device=AcceleratorDevice.AUTO
)
doc_converter = DocumentConverter(
From 670a08bdedda847ff3b6942bcaa1a2adef79afe2 Mon Sep 17 00:00:00 2001
From: Christoph Auer <60343111+cau-git@users.noreply.github.com>
Date: Mon, 20 Jan 2025 09:00:57 +0100
Subject: [PATCH 20/22] fix: Update docling-parse-v2 backend version with new
parsing fixes (#769)
* chore: Update lockfile with docling-parse git branch
Signed-off-by: Christoph Auer
* Update lockfile again
Signed-off-by: Christoph Auer
* Final docling-parse pinning
Signed-off-by: Christoph Auer
---------
Signed-off-by: Christoph Auer
---
poetry.lock | 1099 +++++++++++++++++++++++++++++-------------------
pyproject.toml | 2 +-
2 files changed, 675 insertions(+), 426 deletions(-)
diff --git a/poetry.lock b/poetry.lock
index 06cd0bba..64226c33 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -747,37 +747,37 @@ vision = ["Pillow (>=9.4.0)"]
[[package]]
name = "debugpy"
-version = "1.8.11"
+version = "1.8.12"
description = "An implementation of the Debug Adapter Protocol for Python"
optional = false
python-versions = ">=3.8"
files = [
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]
[[package]]
@@ -876,13 +876,13 @@ files = [
[[package]]
name = "docling-core"
-version = "2.13.1"
+version = "2.14.0"
description = "A python library to define and validate data types in Docling."
optional = false
python-versions = "<4.0,>=3.9"
files = [
- {file = "docling_core-2.13.1-py3-none-any.whl", hash = "sha256:5428c29efad7ae377e2d46df8fcd4185f9dbc7a6eebbea5cd0a3d6cf7162e26e"},
- {file = "docling_core-2.13.1.tar.gz", hash = "sha256:b29e61a1a74f4df070559acce334ba9417069c15bc99cb1f6d424b1e39e063f8"},
+ {file = "docling_core-2.14.0-py3-none-any.whl", hash = "sha256:05a7b89872260dcdba2b0fbcc3a4619aed4846f58f155d33a10b41b23eea5188"},
+ {file = "docling_core-2.14.0.tar.gz", hash = "sha256:0eb6a52e05f2a06e1777b0533d655a87b54a1a5d374b957beb244c8940aed7da"},
]
[package.dependencies]
@@ -903,67 +903,75 @@ chunking = ["semchunk (>=2.2.0,<3.0.0)", "transformers (>=4.34.0,<5.0.0)"]
[[package]]
name = "docling-ibm-models"
-version = "3.1.0"
+version = "3.1.2"
description = "This package contains the AI models used by the Docling PDF conversion package"
optional = false
python-versions = "<4.0,>=3.9"
files = [
- {file = "docling_ibm_models-3.1.0-py3-none-any.whl", hash = "sha256:a381a45dff16fdb2246b99c15a2e3d6ba880c573d48a1d6477d3ffb36bab807f"},
- {file = "docling_ibm_models-3.1.0.tar.gz", hash = "sha256:65d734ffa490edc4e2301d296b6e893afa536c63b7daae7bbda781bd15b3431e"},
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+ {file = "docling_ibm_models-3.1.2.tar.gz", hash = "sha256:68c8b8f1cb87a8d8c5c6d6fe2c86679d65a09d354ec413ed13e9493ee0cd3794"},
]
[package.dependencies]
huggingface_hub = ">=0.23,<1"
jsonlines = ">=3.1.0,<4.0.0"
-numpy = ">=1.24.4,<3.0.0"
+numpy = [
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description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
optional = false
python-versions = ">=3.9.0"
files = [
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@@ -6774,16 +7009,16 @@ tqdm = ">=4.27"
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audio = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
benchmark = ["optimum-benchmark (>=0.3.0)"]
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ftfy = ["ftfy"]
@@ -6804,17 +7039,17 @@ serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
sigopt = ["sigopt"]
sklearn = ["scikit-learn"]
speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
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tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
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timm = ["timm (<=1.0.11)"]
tokenizers = ["tokenizers (>=0.21,<0.22)"]
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torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
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video = ["av (==9.2.0)"]
vision = ["Pillow (>=10.0.1,<=15.0)"]
@@ -7067,13 +7302,13 @@ zstd = ["zstandard (>=0.18.0)"]
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name = "virtualenv"
-version = "20.28.1"
+version = "20.29.0"
description = "Virtual Python Environment builder"
optional = false
python-versions = ">=3.8"
files = [
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]
[package.dependencies]
@@ -7176,76 +7411,90 @@ files = [
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name = "wrapt"
-version = "1.17.0"
+version = "1.17.2"
description = "Module for decorators, wrappers and monkey patching."
optional = false
python-versions = ">=3.8"
files = [
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]
[[package]]
@@ -7514,4 +7763,4 @@ tesserocr = ["tesserocr"]
[metadata]
lock-version = "2.0"
python-versions = "^3.9"
-content-hash = "33da0f74647611b3d6160376dd172a22b7a9ca9f384c5c36c5dd863444f0dede"
+content-hash = "0c0bb9470144867d7f11dec7c3020ac3d4d9ef18b355c0852a9cb89b6270981d"
diff --git a/pyproject.toml b/pyproject.toml
index e84fa072..63f60160 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -29,7 +29,7 @@ docling-core = { version = "^2.13.1", extras = ["chunking"] }
pydantic = "^2.0.0"
docling-ibm-models = "^3.1.0"
deepsearch-glm = "^1.0.0"
-docling-parse = "^3.0.0"
+docling-parse = "^3.1.0"
filetype = "^1.2.0"
pypdfium2 = "^4.30.0"
pydantic-settings = "^2.3.0"
From e4c7210133ea9b5576096489c5e80b3c3aeee073 Mon Sep 17 00:00:00 2001
From: Selvam Palanimalai
Date: Mon, 20 Jan 2025 03:52:27 -0500
Subject: [PATCH 21/22] ci: added action to generate llms.txt (#701)
* ci: added action in docs.yml to generate llms.txt
Signed-off-by: Selvam Palanimalai
* ci: pinning llms-txt action version as per PR feedback
Signed-off-by: Selvam Palanimalai
---------
Signed-off-by: Selvam Palanimalai
---
.github/workflows/docs.yml | 7 +++++--
1 file changed, 5 insertions(+), 2 deletions(-)
diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
index 2733b522..0fc3ac7a 100644
--- a/.github/workflows/docs.yml
+++ b/.github/workflows/docs.yml
@@ -14,7 +14,10 @@ jobs:
- uses: ./.github/actions/setup-poetry
- name: Build docs
run: poetry run mkdocs build --verbose --clean
+ - name: Make docs LLM ready
+ if: inputs.deploy
+ uses: demodrive-ai/llms-txt-action@ad720693843126e6a73910a667d0eba37c1dea4b
- name: Build and push docs
if: inputs.deploy
- run: poetry run mkdocs gh-deploy --force
-
\ No newline at end of file
+ run: poetry run mkdocs gh-deploy --force --dirty
+
From c49b3526fb7b72e8007f785b1fcfdf58c2457756 Mon Sep 17 00:00:00 2001
From: Michele Dolfi <97102151+dolfim-ibm@users.noreply.github.com>
Date: Mon, 20 Jan 2025 09:52:59 +0100
Subject: [PATCH 22/22] docs: fix links between docs pages (#697)
Signed-off-by: Michele Dolfi
---
docs/concepts/chunking.md | 4 ++--
docs/v2.md | 6 +++---
2 files changed, 5 insertions(+), 5 deletions(-)
diff --git a/docs/concepts/chunking.md b/docs/concepts/chunking.md
index bed8bce3..94ae387e 100644
--- a/docs/concepts/chunking.md
+++ b/docs/concepts/chunking.md
@@ -54,12 +54,12 @@ tokens), &
chunks with same headings & captions) โ users can opt out of this step via param
`merge_peers` (by default `True`)
-๐ Example: see [here](../../examples/hybrid_chunking).
+๐ Example: see [here](../examples/hybrid_chunking.ipynb).
## Hierarchical Chunker
The `HierarchicalChunker` implementation uses the document structure information from
-the [`DoclingDocument`](../docling_document) to create one chunk for each individual
+the [`DoclingDocument`](./docling_document.md) to create one chunk for each individual
detected document element, by default only merging together list items (can be opted out
via param `merge_list_items`). It also takes care of attaching all relevant document
metadata, including headers and captions.
diff --git a/docs/v2.md b/docs/v2.md
index 5ccb0cdf..bf1de1c8 100644
--- a/docs/v2.md
+++ b/docs/v2.md
@@ -95,8 +95,8 @@ doc_converter = (
More options are shown in the following example units:
-- [run_with_formats.py](../examples/run_with_formats/)
-- [custom_convert.py](../examples/custom_convert/)
+- [run_with_formats.py](examples/run_with_formats.py)
+- [custom_convert.py](examples/custom_convert.py)
### Converting documents
@@ -226,4 +226,4 @@ leverages the new `DoclingDocument` and provides a new, richer chunk output form
- any applicable headings for context
- any applicable captions for context
-For an example, check out [Chunking usage](../usage/#chunking).
+For an example, check out [Chunking usage](usage.md#chunking).