### Summary
Adds a new document loader for processing e-publications. Works with
`unstructured>=0.5.4`. You need to have
[`pandoc`](https://pandoc.org/installing.html) installed for this loader
to work.
### Testing
```python
from langchain.document_loaders import UnstructuredEPubLoader
loader = UnstructuredEPubLoader("winter-sports.epub", mode="elements")
data = loader.load()
data[0]
```
Added support for document loaders for Azure Blob Storage using a
connection string. Fixes#1805
---------
Co-authored-by: Mick Vleeshouwer <mick@imick.nl>
Ran into a broken build if bs4 wasn't installed in the project.
Minor tweak to follow the other doc loaders optional package-loading
conventions.
Also updated html docs to include reference to this new html loader.
side note: Should there be 2 different html-to-text document loaders?
This new one only handles local files, while the existing unstructured
html loader handles HTML from local and remote. So it seems like the
improvement was adding the title to the metadata, which is useful but
could also be added to `html.py`
In https://github.com/hwchase17/langchain/issues/1716 , it was
identified that there were two .py files performing similar tasks. As a
resolution, one of the files has been removed, as its purpose had
already been fulfilled by the other file. Additionally, the init has
been updated accordingly.
Furthermore, the how_to_guides.rst file has been updated to include
links to documentation that was previously missing. This was deemed
necessary as the existing list on
https://langchain.readthedocs.io/en/latest/modules/document_loaders/how_to_guides.html
was incomplete, causing confusion for users who rely on the full list of
documentation on the left sidebar of the website.
### Summary
Allows users to pass in `**unstructured_kwargs` to Unstructured document
loaders. Implemented with the `strategy` kwargs in mind, but will pass
in other kwargs like `include_page_breaks` as well. The two currently
supported strategies are `"hi_res"`, which is more accurate but takes
longer, and `"fast"`, which processes faster but with lower accuracy.
The `"hi_res"` strategy is the default. For PDFs, if `detectron2` is not
available and the user selects `"hi_res"`, the loader will fallback to
using the `"fast"` strategy.
### Testing
#### Make sure the `strategy` kwarg works
Run the following in iPython to verify that the `"fast"` strategy is
indeed faster.
```python
from langchain.document_loaders import UnstructuredFileLoader
loader = UnstructuredFileLoader("layout-parser-paper-fast.pdf", strategy="fast", mode="elements")
%timeit loader.load()
loader = UnstructuredFileLoader("layout-parser-paper-fast.pdf", mode="elements")
%timeit loader.load()
```
On my system I get:
```python
In [3]: from langchain.document_loaders import UnstructuredFileLoader
In [4]: loader = UnstructuredFileLoader("layout-parser-paper-fast.pdf", strategy="fast", mode="elements")
In [5]: %timeit loader.load()
247 ms ± 369 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)
In [6]: loader = UnstructuredFileLoader("layout-parser-paper-fast.pdf", mode="elements")
In [7]: %timeit loader.load()
2.45 s ± 31 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```
#### Make sure older versions of `unstructured` still work
Run `pip install unstructured==0.5.3` and then verify the following runs
without error:
```python
from langchain.document_loaders import UnstructuredFileLoader
loader = UnstructuredFileLoader("layout-parser-paper-fast.pdf", mode="elements")
loader.load()
```
`OnlinePDFLoader` and `PagedPDFSplitter` lived separate from the rest of
the pdf loaders.
Because they're all similar, I propose moving all to `pdy.py` and the
same docs/examples page.
Additionally, `PagedPDFSplitter` naming doesn't match the pattern the
rest of the loaders follow, so I renamed to `PyPDFLoader` and had it
inherit from `BasePDFLoader` so it can now load from remote file
sources.
Simple CSV document loader which wraps `csv` reader, and preps the file
with a single `Document` per row.
The column header is prepended to each value for context which is useful
for context with embedding and semantic search
Different PDF libraries have different strengths and weaknesses. PyMuPDF
does a good job at extracting the most amount of content from the doc,
regardless of the source quality, extremely fast (especially compared to
Unstructured).
https://pymupdf.readthedocs.io/en/latest/index.html
iFixit is a wikipedia-like site that has a huge amount of open content
on how to fix things, questions/answers for common troubleshooting and
"things" related content that is more technical in nature. All content
is licensed under CC-BY-SA-NC 3.0
Adding docs from iFixit as context for user questions like "I dropped my
phone in water, what do I do?" or "My macbook pro is making a whining
noise, what's wrong with it?" can yield significantly better responses
than context free response from LLMs.
### Summary
Adds a document loader for image files such as `.jpg` and `.png` files.
### Testing
Run the following using the example document from the [`unstructured`
repo](https://github.com/Unstructured-IO/unstructured/tree/main/example-docs).
```python
from langchain.document_loaders.image import UnstructuredImageLoader
loader = UnstructuredImageLoader("layout-parser-paper-fast.jpg")
loader.load()
```
I've added a simple
[CoNLL-U](https://universaldependencies.org/format.html) document
loader. CoNLL-U is a common format for NLP tasks and is used, for
example, in the Universal Dependencies treebank corpora. The loader
reads a single file in standard CoNLL-U format and returns a document.
### Summary
Adds a document loader for MS Word Documents. Works with both `.docx`
and `.doc` files as longer as the user has installed
`unstructured>=0.4.11`.
### Testing
The follow workflow test the loader for both `.doc` and `.docx` files
using example docs from the `unstructured` repo.
#### `.docx`
```python
from langchain.document_loaders import UnstructuredWordDocumentLoader
filename = "../unstructured/example-docs/fake.docx"
loader = UnstructuredWordDocumentLoader(filename)
loader.load()
```
#### `.doc`
```python
from langchain.document_loaders import UnstructuredWordDocumentLoader
filename = "../unstructured/example-docs/fake.doc"
loader = UnstructuredWordDocumentLoader(filename)
loader.load()
```
`NotebookLoader.load()` loads the `.ipynb` notebook file into a
`Document` object.
**Parameters**:
* `include_outputs` (bool): whether to include cell outputs in the
resulting document (default is False).
* `max_output_length` (int): the maximum number of characters to include
from each cell output (default is 10).
* `remove_newline` (bool): whether to remove newline characters from the
cell sources and outputs (default is False).
* `traceback` (bool): whether to include full traceback (default is
False).
Added a GitBook document loader. It lets you both, (1) fetch text from
any single GitBook page, or (2) fetch all relative paths and return
their respective content in Documents.
I've modified the `scrape` method in the `WebBaseLoader` to accept
custom web paths if given, but happy to remove it and move that logic
into the `GitbookLoader` itself.
Co-authored-by: Andrew White <white.d.andrew@gmail.com>
Co-authored-by: Harrison Chase <harrisonchase@Harrisons-MBP.attlocal.net>
Co-authored-by: Peng Qu <82029664+pengqu123@users.noreply.github.com>
