A basic document parsing and loading utility - Loaders

Overview

The docp-* project suite is designed as a comprehensive (doc)ument (p)arsing library. Built in CPython, it consolidates the capabilities of various lower-level libraries, offering a unified solution for parsing binary document structures.

The suite is extended by several sister projects, each providing unique functionality:

Project	Description
docp-core	Centralized core objects, functionality and settings.
docp-parsers	Parse binary documents (e.g. PDF, PPTX, etc.) into Python objects.
docp-loaders	Load a parsed document's embeddings into a Chroma vector database, for RAG-enabled LLM use.
docp-docling	Convert a PDF into Markdown format via wrappers to the docling libraries.
docp-dbi	Interfaces to document databases such as ChromaDB, and Neo4j (coming soon).

The Toolset (Loaders)

As of this release, loaders for the following binary document types are supported:

• PDF
• MS PowerPoint (PPTX)
• (more coming soon)

Quickstart

Installation

To install docp-loaders, first activate your target virtual environment, then use pip:

pip install docp-loaders

For older releases, visit PyPI or the GitHub Releases page.

Example Usage

For convenience, here are a couple examples for how to parse and load the supported document types into a ChromaDB vector database.

Parse and load a single PDF file into a Chroma database collection:

>>> from docp_loaders import ChromaPDFLoader

>>> l = ChromaPDFLoader(path='/path/to/chroma',
                        collection='spam')
>>> l.load(path='/path/to/directory/myfile.pdf')

Parse and load a directory of PDF files into a Chroma database collection:

>>> from docp_loaders import ChromaPDFLoader

>>> l = ChromaPDFLoader(path='/path/to/chroma',
                        collection='spam')
>>> l.load(path='/path/to/directory', ext='pdf')

Parse and load a single PDF file into a Chroma database collection, offline using a local embedding model:

>>> from docp_loaders import ChromaPDFLoader

>>> l = ChromaPDFLoader(path='/path/to/chroma',
                        collection='spam',
                        offline=True,
                        embedding_model_path='/path/to/embedding-model-repo')
>>> l.load(path='/path/to/directory/myfile.pdf')

Parse and load a single PPTX file into a Chroma database collection:

>>> from docp_loaders import ChromaPPTXLoader

>>> l = ChromaPPTXLoader(path='/path/to/chroma',
                         collection='spam',
                         split_text=False)
>>> l.load(path='/path/to/directory/myfile.pptx')

Parse and load a directory of PPTX files into a Chroma database collection:

>>> from docp_loaders import ChromaPPTXLoader

>>> l = ChromaPPTXLoader(path='/path/to/chroma',
                         collection='spam',
                         split_text=False)
>>> l.load(path='/path/to/directory', ext='pptx')

Using the Library

The documentation suite provides detailed explanations and usage examples for each importable module. For in-depth documentation, code examples, and source links, refer to the Library API page.

A search field is available in the left navigation bar to help you quickly locate specific modules or methods.

Troubleshooting

No troubleshooting guidance is available at this time.

For questions not covered here, or to report bugs, issues, or suggestions, please open an issue on GitHub.