Docitup
This package provides various document loaders that utilize different methods for processing and chunking documents. It is designed to facilitate the loading of documents in various formats into a structured format suitable for using them with langchain vector databases
Overview
The package includes the following loaders:
- PyMUPdf4LLMLoader: Loads and splits documents from files using the
pymupdf4llmlibrary. - MarkitdownLoader: Loads documents using the
MarkItDownlibrary. - LlamaparseLoader: Loads documents using the
LlamaParselibrary and processes different file types. - DoclingPDFLoader: Converts documents to text and splits them accordingly.
Installation
To install this package, simply run:
pip install docitup
Usage
PyMUPdf4LLMLoader
from docitup import PyMUPdf4LLMLoader
loader = PyMUPdf4LLMLoader(file_path='path/to/your/file.pdf')
documents = loader.load()
MarkitdownLoader
from docitup import MarkitDownLoader
loader = MarkitdownLoader(file_path='path/to/your/file.md')
documents = loader.load()
LlamaparseLoader
from docitup import LlamaparseLoader
from llama_parse.utils import ResultType
loader = LlamaparseLoader(file_path='path/to/your/directory', result_type=ResultType.MD, api_key='your_api_key')
documents = loader.load()
DoclingPDFLoader
from docitup import DoclingLoader
loader = DoclingLoader(file_path='path/to/your/file.pdf')
documents = loader.load()
FitzPyMUPDFLoader
from docitup import FitzPyMUPDFLoader
loader = FitzPyMUPDFLoader(file_path='path/to/your/file.pdf')
documents = loader.load()
PyPdfLoader
from docitup import PyPdfLoader
loader = PyPdfLoader(file_path='path/to/your/file.pdf')
documents = loader.load()
PyPdf2Loader
from docitup import PyPdfLoader2
loader = PyPdf2Loader(file_path='path/to/your/file.pdf')
documents = loader.load()
Configuration Options
Each loader can be configured with the following optional parameters:
splitter_type: The type of text splitter to use ("recursive" or other).
chunk_size: The size of each chunk (default is 1000).
chunk_overlap: The number of overlapping characters between chunks (default is 100).
Example Usage with all parameters
from docitup import LlamaparseLoader
# Initialize the loader
loader = LlamaparseLoader(
file_path="example.pdf",
api_key="your_api_key",
splitter_type="recursive",
chunk_size=500,
chunk_overlap=50,
extra_metadata={"category": "example"}
)
# Load documents lazily
for document in loader.load():
print("Text Chunk:", document.text)
print("Metadata:", document.metadata)
Contributing
Contributions are welcome! Please feel free to submit issues or pull requests for improvements or bug fixes.
License
This project is licensed under the MIT License. See the LICENSE file for more information.
Acknowledgements
This package is made possible by the following libraries:
Metadata
Release files for docitup 0.1.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| docitup-0.1.4.tar.gz | 4.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| docitup-0.1.4-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 15.0 kB
Release files / docitup-0.1.4.tar.gz
| Download URL | docitup-0.1.4.tar.gz |
|---|---|
| Size | 4.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/6.0.1 CPython/3.12.0
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Release files / docitup-0.1.4-py3-none-any.whl
| Download URL | docitup-0.1.4-py3-none-any.whl |
|---|---|
| Size | 10.3 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
227c48caca30501414060be5fc5dd79369339bf27ae31931376024df566aafa8
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.0.1 CPython/3.12.0
|