cr

Per discussion on Discord. This adds a PDF reader that uses `PyPDF` - a
simple PDF reader. It also tracks page numbers in a per split metadata.
Here's an example:

```python
from langchain.document_loaders import PagedPDFSplitter
from langchain.vectorstores import FAISS
from langchain.embeddings.openai import OpenAIEmbeddings

loader = PagedPDFSplitter(chunk_size=250)
splits, metadatas = loader.load_and_split("examples/example_data/layout-parser-paper.pdf")

faiss_index = FAISS.from_texts(splits, OpenAIEmbeddings(), metadatas=metadatas)
docs = faiss_index.similarity_search("How will the community be engaged?", k=2)
for doc in docs:
    print(doc.metadata["pages"] + ":", doc.page_content)
```

## TODO

- [x] Learn where to add `pypdf` as dependency for building docs
- [x] Add unit test?

---------

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>

docs/modules/document_loaders/examples/paged_pdf_splitter.ipynb

@@ -0,0 +1,161 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Paged PDF Splitter\n",
+    "\n",
+    "This notebook shows how to load a PDF file with the `PagedPDFSplitter`, which \n",
+    "uses the [pypdf](https://github.com/mstamy2/PyPDF2) library to read a \n",
+    "PDF file. **Note this reads & splits.** \n",
+    "\n",
+    "## Compared with other PDF Reader\n",
+    "Compared with the `unstructured` PDF reader, this one is local\n",
+    "and does not require an model - it just extracts text. This means it will\n",
+    "not work for scanned documents or PDFs containing images of text."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import PagedPDFSplitter\n",
+    "\n",
+    "loader = PagedPDFSplitter(\"example_data/layout-parser-paper.pdf\")\n",
+    "pages = loader.load_and_split()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using with document retrieval\n",
+    "\n",
+    "An advantage of this approach is that documents can be retrieved with page numbers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "9: 10 Z. Shen et al.\n",
+      "Fig. 4: Illustration of (a) the original historical Japanese document with layout\n",
+      "detection results and (b) a recreated version of the document image that achieves\n",
+      "much better character recognition recall. The reorganization algorithm rearranges\n",
+      "the tokens based on the their detected bounding boxes given a maximum allowed\n",
+      "height.\n",
+      "4LayoutParser Community Platform\n",
+      "Another focus of LayoutParser is promoting the reusability of layout detection\n",
+      "models and full digitization pipelines. Similar to many existing deep learning\n",
+      "libraries, LayoutParser comes with a community model hub for distributing\n",
+      "layout models. End-users can upload their self-trained models to the model hub,\n",
+      "and these models can be loaded into a similar interface as the currently available\n",
+      "LayoutParser pre-trained models. For example, the model trained on the News\n",
+      "Navigator dataset [17] has been incorporated in the model hub.\n",
+      "Beyond DL models, LayoutParser also promotes the sharing of entire doc-\n",
+      "ument digitization pipelines. For example, sometimes the pipeline requires the\n",
+      "combination of multiple DL models to achieve better accuracy. Currently, pipelines\n",
+      "are mainly described in academic papers and implementations are often not pub-\n",
+      "licly available. To this end, the LayoutParser community platform also enables\n",
+      "the sharing of layout pipelines to promote the discussion and reuse of techniques.\n",
+      "For each shared pipeline, it has a dedicated project page, with links to the source\n",
+      "code, documentation, and an outline of the approaches. A discussion panel is\n",
+      "provided for exchanging ideas. Combined with the core LayoutParser library,\n",
+      "users can easily build reusable components based on the shared pipelines and\n",
+      "apply them to solve their unique problems.\n",
+      "5 Use Cases\n",
+      "The core objective of LayoutParser is to make it easier to create both large-scale\n",
+      "and light-weight document digitization pipelines. Large-scale document processing\n",
+      "3: 4 Z. Shen et al.\n",
+      "Efficient Data AnnotationC u s t o m i z e d  M o d e l  T r a i n i n gModel Cust omizationDI A Model HubDI A Pipeline SharingCommunity PlatformLa y out Detection ModelsDocument Images \n",
+      "T h e  C o r e  L a y o u t P a r s e r  L i b r a r yOCR ModuleSt or age & VisualizationLa y out Data Structur e\n",
+      "Fig. 1: The overall architecture of LayoutParser . For an input document image,\n",
+      "the core LayoutParser library provides a set of o\u000b",
+      "-the-shelf tools for layout\n",
+      "detection, OCR, visualization, and storage, backed by a carefully designed layout\n",
+      "data structure. LayoutParser also supports high level customization via e\u000ecient\n",
+      "layout annotation and model training functions. These improve model accuracy\n",
+      "on the target samples. The community platform enables the easy sharing of DIA\n",
+      "models and whole digitization pipelines to promote reusability and reproducibility.\n",
+      "A collection of detailed documentation, tutorials and exemplar projects make\n",
+      "LayoutParser easy to learn and use.\n",
+      "AllenNLP [ 8] and transformers [ 34] have provided the community with complete\n",
+      "DL-based support for developing and deploying models for general computer\n",
+      "vision and natural language processing problems. LayoutParser , on the other\n",
+      "hand, specializes speci\f",
+      "cally in DIA tasks. LayoutParser is also equipped with a\n",
+      "community platform inspired by established model hubs such as Torch Hub [23]\n",
+      "andTensorFlow Hub [1]. It enables the sharing of pretrained models as well as\n",
+      "full document processing pipelines that are unique to DIA tasks.\n",
+      "There have been a variety of document data collections to facilitate the\n",
+      "development of DL models. Some examples include PRImA [ 3](magazine layouts),\n",
+      "PubLayNet [ 38](academic paper layouts), Table Bank [ 18](tables in academic\n",
+      "papers), Newspaper Navigator Dataset [ 16,17](newspaper \f",
+      "gure layouts) and\n",
+      "HJDataset [31](historical Japanese document layouts). A spectrum of models\n",
+      "trained on these datasets are currently available in the LayoutParser model zoo\n",
+      "to support di\u000b",
+      "erent use cases.\n",
+      "3 The Core LayoutParser Library\n",
+      "At the core of LayoutParser is an o\u000b",
+      "-the-shelf toolkit that streamlines DL-\n",
+      "based document image analysis. Five components support a simple interface\n",
+      "with comprehensive functionalities: 1) The layout detection models enable using\n",
+      "pre-trained or self-trained DL models for layout detection with just four lines\n",
+      "of code. 2) The detected layout information is stored in carefully engineered\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.vectorstores import FAISS\n",
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "\n",
+    "faiss_index = FAISS.from_documents(pages, OpenAIEmbeddings())\n",
+    "docs = faiss_index.similarity_search(\"How will the community be engaged?\", k=2)\n",
+    "for doc in docs:\n",
+    "    print(str(doc.metadata[\"page\"]) + \":\", doc.page_content)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "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": "3.9.1"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "5c7b89af1651d0b8571dde13640ecdccf7d5a6204171d6ab33e7c296e100e08a"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/modules/document_loaders/how_to_guides.rst

@@ -15,6 +15,8 @@ There are a lot of different document loaders that LangChain supports. Below are
 
 `PDF <./examples/pdf.html>`_: A walkthrough of how to load data from a PDF file.
 
+`Paged PDF <./examples/paged_pdf_splitter.html>`_: A walkthrough of how to load data from a PDF file with a text reader while tracking page numbers.
+
 `PowerPoint <./examples/powerpoint.html>`_: A walkthrough of how to load data from a powerpoint file.
 
 `Email <./examples/email.html>`_: A walkthrough of how to load data from an email (`.eml`) file.

langchain/document_loaders/__init__.py

@@ -13,6 +13,7 @@ from langchain.document_loaders.html import UnstructuredHTMLLoader
 from langchain.document_loaders.imsdb import IMSDbLoader
 from langchain.document_loaders.notion import NotionDirectoryLoader
 from langchain.document_loaders.obsidian import ObsidianLoader
+from langchain.document_loaders.paged_pdf import PagedPDFSplitter
 from langchain.document_loaders.pdf import UnstructuredPDFLoader
 from langchain.document_loaders.powerpoint import UnstructuredPowerPointLoader
 from langchain.document_loaders.readthedocs import ReadTheDocsLoader
@@ -46,4 +47,5 @@ __all__ = [
     "AZLyricsLoader",
     "CollegeConfidentialLoader",
     "GutenbergLoader",
+    "PagedPDFSplitter",
 ]

langchain/document_loaders/paged_pdf.py

@@ -0,0 +1,36 @@
+"""Loads a PDF with pypdf and chunks at character level."""
+from typing import List
+
+from langchain.docstore.document import Document
+from langchain.document_loaders.base import BaseLoader
+
+
+class PagedPDFSplitter(BaseLoader):
+    """Loads a PDF with pypdf and chunks at character level.
+
+    Loader also stores page numbers in metadatas.
+    """
+
+    def __init__(self, file_path: str):
+        """Initialize with file path."""
+        try:
+            import pypdf  # noqa:F401
+        except ImportError:
+            raise ValueError(
+                "pypdf package not found, please install it with " "`pip install pypdf`"
+            )
+        self._file_path = file_path
+
+    def load(self) -> List[Document]:
+        """Load given path as pages."""
+        import pypdf
+
+        pdf_file_obj = open(self._file_path, "rb")
+        pdf_reader = pypdf.PdfReader(pdf_file_obj)
+        docs = []
+        for i, page in enumerate(pdf_reader.pages):
+            text = page.extract_text()
+            metadata = {"source": self._file_path, "page": i}
+            docs.append(Document(page_content=text, metadata=metadata))
+        pdf_file_obj.close()
+        return docs

poetry.lock

@@ -384,14 +384,14 @@ files = [
 
 [[package]]
 name = "beautifulsoup4"
-version = "4.11.1"
+version = "4.11.2"
 description = "Screen-scraping library"
 category = "main"
 optional = false
 python-versions = ">=3.6.0"
 files = [
-    {file = "beautifulsoup4-4.11.1-py3-none-any.whl", hash = "sha256:58d5c3d29f5a36ffeb94f02f0d786cd53014cf9b3b3951d42e0080d8a9498d30"},
-    {file = "beautifulsoup4-4.11.1.tar.gz", hash = "sha256:ad9aa55b65ef2808eb405f46cf74df7fcb7044d5cbc26487f96eb2ef2e436693"},
+    {file = "beautifulsoup4-4.11.2-py3-none-any.whl", hash = "sha256:0e79446b10b3ecb499c1556f7e228a53e64a2bfcebd455f370d8927cb5b59e39"},
+    {file = "beautifulsoup4-4.11.2.tar.gz", hash = "sha256:bc4bdda6717de5a2987436fb8d72f45dc90dd856bdfd512a1314ce90349a0106"},
 ]
 
 [package.dependencies]
@@ -403,32 +403,46 @@ lxml = ["lxml"]
 
 [[package]]
 name = "black"
-version = "22.12.0"
+version = "23.1.0"
 description = "The uncompromising code formatter."
-category = "dev"
+category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "black-23.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2a951cc83ab535d248c89f300eccbd625e80ab880fbcfb5ac8afb5f01a258ac9"},
+    {file = "black-23.1.0-cp39-cp39-win_amd64.whl", hash = "sha256:0680d4380db3719ebcfb2613f34e86c8e6d15ffeabcf8ec59355c5e7b85bb555"},
+    {file = "black-23.1.0-py3-none-any.whl", hash = "sha256:7a0f701d314cfa0896b9001df70a530eb2472babb76086344e688829efd97d32"},
+    {file = "black-23.1.0.tar.gz", hash = "sha256:b0bd97bea8903f5a2ba7219257a44e3f1f9d00073d6cc1add68f0beec69692ac"},
 ]
 
 [package.dependencies]
 click = ">=8.0.0"
 mypy-extensions = ">=0.4.3"
+packaging = ">=22.0"
 pathspec = ">=0.9.0"
 platformdirs = ">=2"
-tomli = {version = ">=1.1.0", markers = "python_full_version < \"3.11.0a7\""}
+tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
 typing-extensions = {version = ">=3.10.0.0", markers = "python_version < \"3.10\""}
 
 [package.extras]
@@ -787,47 +801,49 @@ toml = ["tomli"]
 
 [[package]]
 name = "cryptography"
-version = "39.0.0"
+version = "39.0.1"
 description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
 category = "main"
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "cryptography-39.0.0-cp36-abi3-macosx_10_12_universal2.whl", hash = "sha256:c52a1a6f81e738d07f43dab57831c29e57d21c81a942f4602fac7ee21b27f288"},
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 docstest = ["pyenchant (>=1.6.11)", "sphinxcontrib-spelling (>=4.0.1)", "twine (>=1.12.0)"]
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 ssh = ["bcrypt (>=3.1.5)"]
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+test-randomorder = ["pytest-randomly"]
+tox = ["tox"]
 
 [[package]]
 name = "cymem"
@@ -1969,22 +1985,23 @@ files = [
 
 [[package]]
 name = "ipykernel"
-version = "6.20.2"
+version = "6.21.1"
 description = "IPython Kernel for Jupyter"
 category = "dev"
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 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
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 comm = ">=0.1.1"
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 ipython = ">=7.23.1"
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 matplotlib-inline = ">=0.1"
 nest-asyncio = "*"
 packaging = "*"
@@ -2265,14 +2282,14 @@ testing = ["coverage", "ipykernel", "jupytext", "matplotlib", "nbdime", "nbforma
 
 [[package]]
 name = "jupyter-client"
-version = "8.0.1"
+version = "8.0.2"
 description = "Jupyter protocol implementation and client libraries"
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
@@ -2289,36 +2306,39 @@ test = ["codecov", "coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-co
 
 [[package]]
 name = "jupyter-console"
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+version = "6.5.0"
 description = "Jupyter terminal console"
 category = "dev"
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 [package.dependencies]
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 ipython = "*"
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-prompt-toolkit = ">=2.0.0,<3.0.0 || >3.0.0,<3.0.1 || >3.0.1,<3.1.0"
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+prompt-toolkit = ">=3.0.30"
 pygments = "*"
+pyzmq = ">=17"
+traitlets = ">=5.4"
 
 [package.extras]
-test = ["pexpect"]
+test = ["pexpect", "pytest"]
 
 [[package]]
 name = "jupyter-core"
-version = "5.1.5"
+version = "5.2.0"
 description = "Jupyter core package. A base package on which Jupyter projects rely."
 category = "dev"
 optional = false
 python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
@@ -2357,18 +2377,18 @@ test = ["click", "coverage", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=
 
 [[package]]
 name = "jupyter-server"
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+version = "2.2.1"
 description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications."
 category = "dev"
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 python-versions = ">=3.8"
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 [package.dependencies]
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+anyio = ">=3.1.0"
 argon2-cffi = "*"
 jinja2 = "*"
 jupyter-client = ">=7.4.4"
@@ -2845,14 +2865,14 @@ files = [
 
 [[package]]
 name = "mistune"
-version = "2.0.4"
+version = "2.0.5"
 description = "A sane Markdown parser with useful plugins and renderers"
 category = "dev"
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 python-versions = "*"
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 [[package]]
@@ -3042,14 +3062,14 @@ reports = ["lxml"]
 
 [[package]]
 name = "mypy-extensions"
-version = "0.4.3"
-description = "Experimental type system extensions for programs checked with the mypy typechecker."
+version = "1.0.0"
+description = "Type system extensions for programs checked with the mypy type checker."
 category = "main"
 optional = false
-python-versions = "*"
+python-versions = ">=3.5"
 files = [
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 ]
 
 [[package]]
@@ -3355,40 +3375,40 @@ test = ["pytest", "pytest-console-scripts", "pytest-tornasync"]
 
 [[package]]
 name = "numpy"
-version = "1.24.1"
+version = "1.24.2"
 description = "Fundamental package for array computing in Python"
 category = "main"
 optional = false
 python-versions = ">=3.8"
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 [[package]]
@@ -3558,7 +3578,7 @@ testing = ["docopt", "pytest (<6.0.0)"]
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 version = "0.11.0"
 description = "Utility library for gitignore style pattern matching of file paths."
-category = "dev"
+category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -3746,19 +3766,19 @@ files = [
 
 [[package]]
 name = "platformdirs"
-version = "2.6.2"
+version = "3.0.0"
 description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
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 python-versions = ">=3.7"
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 [[package]]
 name = "playwright"
@@ -4205,6 +4225,28 @@ files = [
 [package.extras]
 diagrams = ["jinja2", "railroad-diagrams"]
 
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+name = "pypdf"
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+docs = ["myst_parser", "sphinx", "sphinx_rtd_theme"]
+full = ["Pillow", "PyCryptodome"]
+image = ["Pillow"]
+
 [[package]]
 name = "pyrsistent"
 version = "0.19.3"
@@ -4635,14 +4677,14 @@ test = ["pytest (>=6,!=7.0.0,!=7.0.1)", "pytest-cov (>=3.0.0)", "pytest-qt"]
 
 [[package]]
 name = "redis"
-version = "4.4.2"
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 description = "Python client for Redis database and key-value store"
 category = "main"
 optional = true
 python-versions = ">=3.7"
 files = [
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 [package.dependencies]
@@ -5043,14 +5085,14 @@ files = [
 
 [[package]]
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+version = "67.2.0"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
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 python-versions = ">=3.7"
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 [package.extras]
@@ -5341,19 +5383,20 @@ themes = ["myst-parser (>=0.12.9,<0.13.0)", "pydata-sphinx-theme (>=0.4.0,<0.5.0
 
 [[package]]
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-version = "1.1.1"
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 description = "Read the Docs theme for Sphinx"
 category = "dev"
 optional = false
-python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7"
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 [package.dependencies]
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-sphinx = ">=1.6,<6"
+docutils = "<0.19"
+sphinx = ">=1.6,<7"
+sphinxcontrib-jquery = {version = ">=2.0.0,<3.0.0 || >3.0.0", markers = "python_version > \"3\""}
 
 [package.extras]
 dev = ["bump2version", "sphinxcontrib-httpdomain", "transifex-client", "wheel"]
@@ -5407,20 +5450,35 @@ test = ["pytest"]
 
 [[package]]
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+version = "2.0.1"
 description = "sphinxcontrib-htmlhelp is a sphinx extension which renders HTML help files"
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-python-versions = ">=3.6"
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 ]
 
 [package.extras]
 lint = ["docutils-stubs", "flake8", "mypy"]
 test = ["html5lib", "pytest"]
 
+[[package]]
+name = "sphinxcontrib-jquery"
+version = "2.0.0"
+description = "Extension to include jQuery on newer Sphinx releases"
+category = "dev"
+optional = false
+python-versions = ">=2.7"
+files = [
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+
+[package.dependencies]
+setuptools = "*"
+
 [[package]]
 name = "sphinxcontrib-jsmath"
 version = "1.0.1"
@@ -5632,14 +5690,14 @@ widechars = ["wcwidth"]
 
 [[package]]
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 description = "Retry code until it succeeds"
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 python-versions = ">=3.6"
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 ]
 
 [package.extras]
@@ -6001,33 +6059,33 @@ files = [
 
 [[package]]
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+version = "0.2.0"
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 python-versions = ">=3.8"
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+    {file = "tiktoken-0.2.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:8f1a7c6bec42a2fb5309a161d1b891fe5e181d4b620a962923a925f45fe25697"},
+    {file = "tiktoken-0.2.0-cp39-cp39-win_amd64.whl", hash = "sha256:3349fd809d17b722814a6a700e4bc0125527f39057b57a02ed42f53bb4e6e2f5"},
+    {file = "tiktoken-0.2.0.tar.gz", hash = "sha256:df41a3d478499757b5b32eae5e97657cf159d8d9e6764049dd7c3abb49e1b40f"},
 ]
 
 [package.dependencies]
@@ -6125,7 +6183,7 @@ files = [
 name = "tomli"
 version = "2.0.1"
 description = "A lil' TOML parser"
-category = "dev"
+category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -6257,14 +6315,14 @@ telegram = ["requests"]
 
 [[package]]
 name = "traitlets"
-version = "5.8.1"
+version = "5.9.0"
 description = "Traitlets Python configuration system"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "traitlets-5.8.1-py3-none-any.whl", hash = "sha256:a1ca5df6414f8b5760f7c5f256e326ee21b581742114545b462b35ffe3f04861"},
-    {file = "traitlets-5.8.1.tar.gz", hash = "sha256:32500888f5ff7bbf3b9267ea31748fa657aaf34d56d85e60f91dda7dc7f5785b"},
+    {file = "traitlets-5.9.0-py3-none-any.whl", hash = "sha256:9e6ec080259b9a5940c797d58b613b5e31441c2257b87c2e795c5228ae80d2d8"},
+    {file = "traitlets-5.9.0.tar.gz", hash = "sha256:f6cde21a9c68cf756af02035f72d5a723bf607e862e7be33ece505abf4a3bad9"},
 ]
 
 [package.extras]
@@ -6273,14 +6331,14 @@ test = ["argcomplete (>=2.0)", "pre-commit", "pytest", "pytest-mock"]
 
 [[package]]
 name = "transformers"
-version = "4.26.0"
+version = "4.26.1"
 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
 category = "main"
 optional = true
 python-versions = ">=3.7.0"
 files = [
-    {file = "transformers-4.26.0-py3-none-any.whl", hash = "sha256:6a902eee6098d9a737faadf185b8df5a169acc695ebbde5a81b90528f43e665f"},
-    {file = "transformers-4.26.0.tar.gz", hash = "sha256:d7859bd83829a3682ca632197ee5c72556e1063d199ab84eec35c4f23b3d73a3"},
+    {file = "transformers-4.26.1-py3-none-any.whl", hash = "sha256:dae2fa15290c1f526e1b629b0e235eea5e4c04078fcaf1f197a70d51b4f65df2"},
+    {file = "transformers-4.26.1.tar.gz", hash = "sha256:32dc474157367f8e551f470af0136a1ddafc9e18476400c3869f1ef4f0c12042"},
 ]
 
 [package.dependencies]
@@ -6376,26 +6434,26 @@ cryptography = ">=35.0.0"
 
 [[package]]
 name = "types-pyyaml"
-version = "6.0.12.3"
+version = "6.0.12.5"
 description = "Typing stubs for PyYAML"
 category = "dev"
 optional = false
 python-versions = "*"
 files = [
-    {file = "types-PyYAML-6.0.12.3.tar.gz", hash = "sha256:17ce17b3ead8f06e416a3b1d5b8ddc6cb82a422bb200254dd8b469434b045ffc"},
-    {file = "types_PyYAML-6.0.12.3-py3-none-any.whl", hash = "sha256:879700e9f215afb20ab5f849590418ab500989f83a57e635689e1d50ccc63f0c"},
+    {file = "types-PyYAML-6.0.12.5.tar.gz", hash = "sha256:3b61b7a8111ce368eb366e4a13f3e94e568bc2ed6227e01520a50ee07993bf38"},
+    {file = "types_PyYAML-6.0.12.5-py3-none-any.whl", hash = "sha256:dcaf87b65b839e7b641721346ef8b12a87f94071e15205a64ac93ca0e0afc77a"},
 ]
 
 [[package]]
 name = "types-redis"
-version = "4.4.0.4"
+version = "4.4.0.6"
 description = "Typing stubs for redis"
 category = "dev"
 optional = false
 python-versions = "*"
 files = [
-    {file = "types-redis-4.4.0.4.tar.gz", hash = "sha256:b70829ca3401d3153d628e28d860070eff1b36b2fa3e5af3e583c1d167383cab"},
-    {file = "types_redis-4.4.0.4-py3-none-any.whl", hash = "sha256:802e893ad3f88e03d3a2feb0d23a715d60b0bb330bc598a52f1de237fc2547a5"},
+    {file = "types-redis-4.4.0.6.tar.gz", hash = "sha256:57f8b3706afe47ef36496d70a97a3783560e6cb19e157be12985dbb31de1d853"},
+    {file = "types_redis-4.4.0.6-py3-none-any.whl", hash = "sha256:8b40d6bf3a54352d4cb2aa7d01294c572a39d40a9d289b96bdf490b51d3a42d2"},
 ]
 
 [package.dependencies]
@@ -6404,14 +6462,14 @@ types-pyOpenSSL = "*"
 
 [[package]]
 name = "types-requests"
-version = "2.28.11.8"
+version = "2.28.11.12"
 description = "Typing stubs for requests"
 category = "dev"
 optional = false
 python-versions = "*"
 files = [
-    {file = "types-requests-2.28.11.8.tar.gz", hash = "sha256:e67424525f84adfbeab7268a159d3c633862dafae15c5b19547ce1b55954f0a3"},
-    {file = "types_requests-2.28.11.8-py3-none-any.whl", hash = "sha256:61960554baca0008ae7e2db2bd3b322ca9a144d3e80ce270f5fb640817e40994"},
+    {file = "types-requests-2.28.11.12.tar.gz", hash = "sha256:fd530aab3fc4f05ee36406af168f0836e6f00f1ee51a0b96b7311f82cb675230"},
+    {file = "types_requests-2.28.11.12-py3-none-any.whl", hash = "sha256:dbc2933635860e553ffc59f5e264264981358baffe6342b925e3eb8261f866ee"},
 ]
 
 [package.dependencies]
@@ -6419,26 +6477,26 @@ types-urllib3 = "<1.27"
 
 [[package]]
 name = "types-toml"
-version = "0.10.8.1"
+version = "0.10.8.3"
 description = "Typing stubs for toml"
 category = "dev"
 optional = false
 python-versions = "*"
 files = [
-    {file = "types-toml-0.10.8.1.tar.gz", hash = "sha256:171bdb3163d79a520560f24ba916a9fc9bff81659c5448a9fea89240923722be"},
-    {file = "types_toml-0.10.8.1-py3-none-any.whl", hash = "sha256:b7b5c4977f96ab7b5ac06d8a6590d17c0bf252a96efc03b109c2711fb3e0eafd"},
+    {file = "types-toml-0.10.8.3.tar.gz", hash = "sha256:f37244eff4cd7eace9cb70d0bac54d3eba77973aa4ef26c271ac3d1c6503a48e"},
+    {file = "types_toml-0.10.8.3-py3-none-any.whl", hash = "sha256:a2286a053aea6ab6ff814659272b1d4a05d86a1dd52b807a87b23511993b46c5"},
 ]
 
 [[package]]
 name = "types-urllib3"
-version = "1.26.25.4"
+version = "1.26.25.5"
 description = "Typing stubs for urllib3"
 category = "dev"
 optional = false
 python-versions = "*"
 files = [
-    {file = "types-urllib3-1.26.25.4.tar.gz", hash = "sha256:eec5556428eec862b1ac578fb69aab3877995a99ffec9e5a12cf7fbd0cc9daee"},
-    {file = "types_urllib3-1.26.25.4-py3-none-any.whl", hash = "sha256:ed6b9e8a8be488796f72306889a06a3fc3cb1aa99af02ab8afb50144d7317e49"},
+    {file = "types-urllib3-1.26.25.5.tar.gz", hash = "sha256:5630e578246d170d91ebe3901788cd28d53c4e044dc2e2488e3b0d55fb6895d8"},
+    {file = "types_urllib3-1.26.25.5-py3-none-any.whl", hash = "sha256:e8f25c8bb85cde658c72ee931e56e7abd28803c26032441eea9ff4a4df2b0c31"},
 ]
 
 [[package]]
@@ -6642,14 +6700,14 @@ files = [
 
 [[package]]
 name = "websocket-client"
-version = "1.5.0"
+version = "1.5.1"
 description = "WebSocket client for Python with low level API options"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "websocket-client-1.5.0.tar.gz", hash = "sha256:561ca949e5bbb5d33409a37235db55c279235c78ee407802f1d2314fff8a8536"},
-    {file = "websocket_client-1.5.0-py3-none-any.whl", hash = "sha256:fb5d81b95d350f3a54838ebcb4c68a5353bbd1412ae8f068b1e5280faeb13074"},
+    {file = "websocket-client-1.5.1.tar.gz", hash = "sha256:3f09e6d8230892547132177f575a4e3e73cfdf06526e20cc02aa1c3b47184d40"},
+    {file = "websocket_client-1.5.1-py3-none-any.whl", hash = "sha256:cdf5877568b7e83aa7cf2244ab56a3213de587bbe0ce9d8b9600fc77b455d89e"},
 ]
 
 [package.extras]
@@ -6929,14 +6987,14 @@ multidict = ">=4.0"
 
 [[package]]
 name = "zipp"
-version = "3.12.0"
+version = "3.13.0"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "zipp-3.12.0-py3-none-any.whl", hash = "sha256:9eb0a4c5feab9b08871db0d672745b53450d7f26992fd1e4653aa43345e97b86"},
-    {file = "zipp-3.12.0.tar.gz", hash = "sha256:73efd63936398aac78fd92b6f4865190119d6c91b531532e798977ea8dd402eb"},
+    {file = "zipp-3.13.0-py3-none-any.whl", hash = "sha256:e8b2a36ea17df80ffe9e2c4fda3f693c3dad6df1697d3cd3af232db680950b0b"},
+    {file = "zipp-3.13.0.tar.gz", hash = "sha256:23f70e964bc11a34cef175bc90ba2914e1e4545ea1e3e2f67c079671883f9cb6"},
 ]
 
 [package.extras]
@@ -6944,10 +7002,10 @@ docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker
 testing = ["flake8 (<5)", "func-timeout", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"]
 
 [extras]
-all = ["cohere", "openai", "nlpcloud", "huggingface_hub", "manifest-ml", "elasticsearch", "google-search-results", "faiss-cpu", "sentence-transformers", "transformers", "spacy", "nltk", "wikipedia", "beautifulsoup4", "tiktoken", "torch", "jinja2", "pinecone-client", "weaviate-client", "redis", "google-api-python-client", "wolframalpha", "qdrant-client", "tensorflow-text"]
+all = ["cohere", "openai", "nlpcloud", "huggingface_hub", "manifest-ml", "elasticsearch", "google-search-results", "faiss-cpu", "sentence-transformers", "transformers", "spacy", "nltk", "wikipedia", "beautifulsoup4", "tiktoken", "torch", "jinja2", "pinecone-client", "weaviate-client", "redis", "google-api-python-client", "wolframalpha", "qdrant-client", "tensorflow-text", "pypdf"]
 llms = ["cohere", "openai", "nlpcloud", "huggingface_hub", "manifest-ml", "torch", "transformers"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<4.0"
-content-hash = "b190d518d7a99484ccb0aaf0ed43dfaf9c8cc74481b00da9d4fadd9d02c0dda2"
+content-hash = "55ff8e2f70840a299ca72a27468cf18ec732514bdc2aa2ed9e8faf9bc5caa71f"

pyproject.toml

@@ -44,6 +44,8 @@ huggingface_hub = {version = "^0", optional = true}
 google-search-results = {version = "^2", optional = true}
 sentence-transformers = {version = "^2", optional = true}
 aiohttp = "^3.8.3"
+pypdf = {version = "^3.4.0", optional = true}
+black = "^23.1.0"
 
 
 [tool.poetry.group.docs.dependencies]
@@ -72,7 +74,6 @@ pytest-asyncio = "^0.20.3"
 
 [tool.poetry.group.lint.dependencies]
 flake8-docstrings = "^1.6.0"
-black = "^22.10.0"
 isort = "^5.10.1"
 flake8 = "^6.0.0"
 types-toml = "^0.10.8.1"
@@ -92,7 +93,7 @@ playwright = "^1.28.0"
 
 [tool.poetry.extras]
 llms = ["cohere", "openai", "nlpcloud", "huggingface_hub", "manifest-ml", "torch", "transformers"]
-all = ["cohere", "openai", "nlpcloud", "huggingface_hub", "manifest-ml", "elasticsearch", "google-search-results", "faiss-cpu", "sentence-transformers", "transformers", "spacy", "nltk", "wikipedia", "beautifulsoup4", "tiktoken", "torch", "jinja2", "pinecone-client", "weaviate-client", "redis", "google-api-python-client", "wolframalpha", "qdrant-client", "tensorflow-text"]
+all = ["cohere", "openai", "nlpcloud", "huggingface_hub", "manifest-ml", "elasticsearch", "google-search-results", "faiss-cpu", "sentence_transformers", "transformers", "spacy", "nltk", "wikipedia", "beautifulsoup4", "tiktoken", "torch", "jinja2", "pinecone-client", "weaviate-client", "redis", "google-api-python-client", "wolframalpha", "qdrant-client", "tensorflow-text", "pypdf"]
 
 [tool.isort]
 profile = "black"

tests/integration_tests/test_pdf_pagesplitter.py

@@ -0,0 +1,19 @@
+"""Test splitting with page numbers included."""
+import os
+
+from langchain.document_loaders import PagedPDFSplitter
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.vectorstores import FAISS
+
+
+def test_pdf_pagesplitter() -> None:
+    """Test splitting with page numbers included."""
+    script_dir = os.path.dirname(__file__)
+    loader = PagedPDFSplitter(os.path.join(script_dir, "examples/hello.pdf"))
+    docs = loader.load()
+    assert "page" in docs[0].metadata
+    assert "source" in docs[0].metadata
+
+    faiss_index = FAISS.from_documents(docs, OpenAIEmbeddings())
+    docs = faiss_index.similarity_search("Complete this sentence: Hello", k=1)
+    assert "Hello world" in docs[0].page_content