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>
