- ppt2desc: Convert PowerPoint files into semantically rich text using vision language models
- Project Overview
- How It Works
- Usage
- Contributing and Future Work
- Licensing and Acknowledgments
- lucide-icons/lucide: Beautiful & consistent icon toolkit made by the community. Open-source project and a fork of Feather Icons.
- zauberzeug/nicegui: Create web-based user interfaces with Python. The nice way.
- Zackriya-Solutions/meeting-minutes: A free and open source, self hosted Ai based live meeting note taker and minutes summary generator that can completely run in your Local device (Mac OS and windows OS Support added. Working on adding linux support soon) https://meetily.zackriya.com/
- GitHub - pyper-dev/pyper: Concurrent Python made simple
- GitHub - zilliztech/deep-searcher: Open Source Deep Research Alternative to Reason and Search on Private Data. Written in Python.
- GitHub - imanoop7/Ollama-OCR
- text-extract-api: Document (PDF, Word, PPTX ...) extraction and parse API using state of the art modern OCRs + Ollama supported models. Anonymize documents. Remove PII. Convert any document or picture to structured JSON or Markdown
- superfile: Pretty fancy and modern terminal file manager
- ArchiveBox: 🗃 Open source self-hosted web archiving. Takes URLs
- Key Features of ArchiveBox
- Contributing to ArchiveBox
- Building and Releasing
- Getting Help
- monolith: ⬛️ CLI tool and library for saving complete web pages as a single HTML file
- OCRmyPDF: OCRmyPDF adds an OCR text layer to scanned PDF files, allowing them to be searched
- docling: Get your documents ready for gen AI
- crawl4ai: 🚀🤖 Crawl4AI: Open-source LLM Friendly Web Crawler & Scraper. Don't be shy, join here: https:
- Key Features of Crawl4AI
- Mission and Impact
- Community and Development
- Legal and Citation
- jan: Jan is an open source alternative to ChatGPT that runs 100% offline on your computer
ppt2desc: Convert PowerPoint files into semantically rich text using vision language models
The text you've provided appears to be the README file from a GitHub repository for a project named "Convert PowerPoint files into semantically rich text using vision language models." Here's a breakdown of what the project does and how it works based on the information given:
Project Overview
This project aims to convert PowerPoint (.pptx) files into semantically rich text using Vision Language Models (VLLMs). The goal is to provide detailed descriptions of the content in each slide, including text, images, diagrams, and other elements.
How It Works
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LibreOffice Conversion: The project utilizes LibreOffice to convert PowerPoint (.pptx) files into a format that can be processed by the vision language models. This step is crucial for handling the complex structures and layouts found in presentation slides.
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Vision Language Models (VLLMs): After conversion, the project employs VLLMs to analyze the content of each slide. These models are capable of understanding both textual and visual elements within an image or document, generating detailed descriptions of what they "see."
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JSON Output: The output from the VLLM analysis is structured in JSON format. Each JSON file contains information about the presentation deck, including the model used for analysis, and a detailed description of each slide's content.
Usage
To use this project, one would need to:
- Clone the repository.
- Install any required dependencies (likely specified in a
requirements.txtfile). - Prepare their PowerPoint files for conversion.
- Run the script with appropriate arguments, such as specifying the input directory for PowerPoint files, output directory for JSON files, and options for which VLLM to use.
The project supports various command-line arguments for customization, including:
--input_dir: Directory containing PowerPoint files to convert.--output_dir: Directory where JSON output files will be saved.--libreoffice_pathor--libreoffice_url: Path to the local LibreOffice installation or URL of a LibreOffice service (if using Docker).--client: Specifies which VLLM client to use (e.g., Vertex AI, Azure OpenAI Foundry, AWS Bedrock).--model: Name of the specific model to use for analysis.--instructions: Custom instructions that can be passed to the VLLM for tailored output.
Contributing and Future Work
The project is open to contributions. There are several areas marked for future development or improvement, including better support for AWS Llama Vision, dynamic font understanding, and unified SDK experiences for different providers like Google's GenAI.
Licensing and Acknowledgments
The project is licensed under the MIT License. It acknowledges and thanks LibreOffice for PPT/PPTX conversion capabilities and PyMuPDF for PDF processing.
Overall, this project offers a novel approach to extracting meaningful information from PowerPoint presentations using advanced AI models, potentially useful in education, research, accessibility, and content creation contexts.
lucide-icons/lucide: Beautiful & consistent icon toolkit made by the community. Open-source project and a fork of Feather Icons.
This website is about Lucide, an open-source icon library that provides over 1000 vector icons for use in digital and non-digital projects. It's a community-driven project with various packages available for easy integration into different frameworks and libraries (e.g., React, Vue, Angular). The icons are free to use for both personal and commercial purposes under the ISC License. This resource could be useful for designers and developers looking for a consistent and high-quality icon set for their projects.
zauberzeug/nicegui: Create web-based user interfaces with Python. The nice way.
https://github.com/zauberzeug/nicegui
This website is about NiceGUI, a Python-based UI framework for creating web-based user interfaces. It allows you to create buttons, dialogs, plots, and more, and is useful for micro web apps, dashboards, robotics projects, and smart home solutions. You can use it to build interactive web applications with a simple and intuitive API, and it has features like automatic reloading, data binding, and customization options.
Zackriya-Solutions/meeting-minutes: A free and open source, self hosted Ai based live meeting note taker and minutes summary generator that can completely run in your Local device (Mac OS and windows OS Support added. Working on adding linux support soon) https://meetily.zackriya.com/
https://github.com/Zackriya-Solutions/meeting-minutes
The text you've provided appears to be a README file from a GitHub repository for an open-source project named "meeting-minutes." This project is designed to create a self-hosted, AI-based live meeting note taker and minutes summary generator. The tool can run locally on your device (currently supporting Mac OS and Windows, with Linux support planned) and utilizes Whisper for transcription and various other technologies like Rust, Python, and TypeScript for its backend and frontend components.
Here's a breakdown of the key points from the provided text:
- Project Overview:
- The project is an open-source, self-hosted AI solution for taking live meeting notes and generating summaries.
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It can run on local devices (Mac OS and Windows are supported, with Linux support in development).
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Technologies Used:
- Whisper.cpp for local transcription.
- Rust and Python for the backend.
- TypeScript for parts of the frontend.
- FastAPI for creating the API layer.
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SQLite for process tracking and metadata storage.
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Setup Instructions:
- Users can download packaged versions from the releases page or build it themselves by cloning the repository and following setup instructions provided in the README.
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Setup involves installing dependencies, setting up environment variables (for API keys), and building certain components like Whisper.cpp.
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Development Guidelines:
-
The project encourages contributors to follow specific guidelines such as maintaining the project structure, writing tests for new features, documenting API changes, using type hints in Python, and adhering to ESLint configuration for JavaScript/TypeScript.
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Contributing and License:
- The project is open to contributions via pull requests.
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It's licensed under the MIT License, allowing users to freely use and modify the software for their purposes.
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Future Plans:
-
There are plans to introduce a subscription option that would allow users to access the service without having to run the backend on their own server, facilitating better scalability and 24/7 operation.
-
Languages and Topics:
- The project involves several programming languages (C++, TypeScript, Rust, Python) and topics related to AI, cross-platform development, live transcription, meeting notes, etc.
In summary, "meeting-minutes" is an innovative open-source project aiming to provide a privacy-focused solution for automatic meeting note-taking and summarization, leveraging the power of local processing and AI technologies.
GitHub - pyper-dev/pyper: Concurrent Python made simple
https://github.com/pyper-dev/pyper
The website is about Pyper, a Python framework for concurrent and parallel data processing. It provides an intuitive API for building data pipelines, allowing you to write clean, reusable code using functional programming patterns. Pyper simplifies the execution of tasks, hiding underlying complexities such as thread management and resource cleanup.
You can use Pyper for:
- Building ETL systems
- Creating data microservices
- Data collection and processing
Pyper's key features include lazy execution, safety, and efficiency, making it a useful tool for developers working with large datasets or complex data pipelines.
GitHub - zilliztech/deep-searcher: Open Source Deep Research Alternative to Reason and Search on Private Data. Written in Python.
https://github.com/zilliztech/deep-searcher
This is a README file for the DeepSearcher project, an open-source deep research alternative to reason and search on private data. Here's a summary of the content:
Overview
DeepSearcher is a Python-based project that allows users to search and reason on private data using various large language models (LLMs) and vector databases.
Modules
The project consists of several modules, including:
- Embedding Models: Supports open-source embedding models like OpenAI, VoyageAI, and Amazon Bedrock.
- LLM Support: Supports LLMs like OpenAI, DeepSeek, XAI Grok, Anthropic Claude, and others.
- Document Loader: Supports local file loading (PDF, txt, md) and web crawling using FireCrawl, Jina Reader, and Crawl4AI.
- Vector Database Support: Supports Milvus and Zilliz Cloud (fully managed Milvus).
Deployment
To deploy DeepSearcher, users need to configure the modules by modifying the config.yaml file and setting up environment variables for API keys. The project can be run as a FastAPI service on localhost:8000.
Q&A
The README includes answers to common questions, such as:
- Why parsing LLM output format fails?
- How to resolve connection issues with Hugging Face?
- Why DeepSearcher doesn't run in Jupyter notebook?
Evaluation
The project includes an evaluation directory for more details on performance and testing.
Future Plans
The developers plan to enhance web crawling functionality, support more vector databases, add large models, and provide a RESTful API interface.
Contributions
The project welcomes contributions and encourages users to star and fork the repository to help build a more powerful DeepSearcher.
GitHub - imanoop7/Ollama-OCR
https://github.com/imanoop7/Ollama-OCR
This website is about Ollama-OCR, a powerful Optical Character Recognition (OCR) package that extracts text from images and PDFs using state-of-the-art vision language models. It's available as a Python package and a Streamlit web application, offering features like batch processing, custom prompts, and multiple output formats. You can use it to automate text extraction from documents, making it useful for tasks like data entry, document analysis, and information retrieval.
text-extract-api: Document (PDF, Word, PPTX ...) extraction and parse API using state of the art modern OCRs + Ollama supported models. Anonymize documents. Remove PII. Convert any document or picture to structured JSON or Markdown
https://github.com/CatchTheTornado/text-extract-api
This is a documentation for a document extraction and parsing API called Doctractor. Here's a summary of the key points:
Overview
Doctractor is an API that uses state-of-the-art OCRs (Optical Character Recognition) and Ollama supported models to extract and parse documents, including PDFs, Word documents, and images. It can anonymize documents, remove PII (Personally Identifiable Information), and convert them into structured JSON or Markdown.
Features
- Document Extraction: Extract text and data from documents using OCRs.
- Anonymization: Remove PII from documents to protect sensitive information.
- Conversion: Convert documents into structured JSON or Markdown.
- Storage: Save extracted data using different storage strategies, including local file system, Google Drive, and Amazon S3.
API Endpoints
The API has several endpoints for different tasks:
- /storage/list: List files in a storage profile.
- /storage/load: Download a file from a storage profile.
- /storage/delete: Delete a file from a storage profile.
- /llm/pull: Pull an Ollama model.
- /llm/generate: Generate text using an Ollama model.
- /ocr/clear_cache: Clear the OCR cache.
Storage Profiles
Doctractor uses storage profiles to configure different storage strategies. The available storage profiles are:
- Local File System: Store files locally on the server.
- Google Drive: Store files in a Google Drive account using a service account.
- Amazon S3: Store files in an Amazon S3 bucket.
Requirements
To use Doctractor, you need to have the following:
- Python: Python 3.x installed on your system.
- OCR libraries: Tesseract-OCR and other OCR libraries installed.
- Ollama models: Ollama models downloaded and configured.
- Storage credentials: Credentials for your chosen storage profile (e.g., Google Drive service account, Amazon S3 access key).
License
Doctractor is licensed under the MIT License.
Contact
For questions or issues, contact the developers at info@catchthetornado.com.
superfile: Pretty fancy and modern terminal file manager
https://github.com/yorukot/superfile
The website is about Superfile, a modern terminal file manager. It's an open-source project that provides a user-friendly interface for managing files and directories in the terminal. The website offers installation instructions, tutorials, and documentation for customizing and extending Superfile with plugins and themes. It could be useful to you if you're looking for a more efficient and visually appealing way to manage your files in the terminal.
ArchiveBox: 🗃 Open source self-hosted web archiving. Takes URLs
https://github.com/ArchiveBox/ArchiveBox
ArchiveBox is an open-source tool for self-hosted web archiving, allowing users to save HTML, JavaScript, PDFs, media, and more from websites.
If you're looking for instructions or information on how to use ArchiveBox, contribute to it, or understand its features, I can help summarize the key points:
Key Features of ArchiveBox
- Web Archiving: Saves web pages in various formats.
- Multi-Extractor Support: Uses multiple extractors (like SingleFile, YT-DLP) to archive different types of content.
- Customizable: Allows for customization through configuration settings.
- Self-Hosted: Can be run on your own server or local machine.
Contributing to ArchiveBox
If you're interested in contributing a new extractor or feature:
1. Open an Issue: Discuss your proposal first, including any dependencies.
2. Implement the Extractor: Create a Python script in archivebox/extractors/.
3. Add Config Settings: For enabling/disabling the extractor and its dependencies.
4. Modify Templates (if necessary): To display the output of your extractor in the UI.
5. Submit a Pull Request: Include tests for your changes.
Building and Releasing
For developers or maintainers, there are scripts to build the documentation, pip package, and Docker image manually:
- ./bin/build.sh for building everything.
- Specific scripts for docs (build_docs.sh), pip package (build_pip.sh), and Docker image (build_docker.sh).
Getting Help
- Bug Tracker: GitHub Issues.
- Discussions: GitHub Discussions or community forums like Zulip.
If you have a specific question about using ArchiveBox, contributing to it, or need further clarification on any of the points mentioned, feel free to ask!
monolith: ⬛️ CLI tool and library for saving complete web pages as a single HTML file
https://github.com/Y2Z/monolith
It looks like you've copied the entire README file of the Monolith project on GitHub!
To summarize, Monolith is a command-line tool and library written in Rust that saves complete web pages as a single HTML file. It's designed to help with tasks such as:
- Saving web pages for offline access
- Archiving web content
- Avoiding link rot (broken links)
- Reducing procrastination by saving interesting pages for later
The project has the following features:
- Supports saving web pages with all assets (images, CSS, JavaScript, etc.)
- Allows customizing the output file name and format
- Supports authentication and proxies
- Can be used as a library or a command-line tool
- Has a simple and easy-to-use API
Monolith is open-source software released under the CC0-1.0 license, which means it's dedicated to the public domain.
If you're interested in using Monolith, you can install it from source or use pre-built binaries available on the project's GitHub page. There are also examples and documentation provided to help you get started.
Is there something specific you'd like to know about Monolith or how to use it?
OCRmyPDF: OCRmyPDF adds an OCR text layer to scanned PDF files, allowing them to be searched
It seems like you've provided the entire README file for the OCRmyPDF project on GitHub. I'll summarize the key points:
What is OCRmyPDF?
OCRmyPDF is a Python-based software that adds an Optical Character Recognition (OCR) text layer to scanned PDF files, making them searchable.
Key Features:
- Converts scanned PDFs to searchable PDFs
- Supports multiple languages using Tesseract OCR
- Can deskew (straighten) crooked pages
- Can convert images to single-page PDFs
Requirements:
- Python 3.x
- Ghostscript
- Tesseract OCR
Installation:
The installation instructions vary depending on your operating system. You can find the detailed installation steps in the documentation.
Documentation and Support:
The project has extensive documentation on Read the Docs, and you can report issues on the GitHub issues page.
License:
OCRmyPDF is licensed under the Mozilla Public License 2.0 (MPL-2.0).
If you have any specific questions about using OCRmyPDF or would like to know more about a particular feature, I'd be happy to help!
docling: Get your documents ready for gen AI
Docling is a document processing tool that simplifies parsing of diverse formats, including PDF, DOCX, XLSX, HTML, and images. Key features include:
- Advanced PDF understanding (page layout, reading order, table structure, etc.)
- Unified document representation format
- Export to various formats (Markdown, HTML, JSON)
- Local execution for sensitive data
- Integrations with AI frameworks (LangChain, LlamaIndex, Crew AI, Haystack)
- OCR support for scanned documents
- Support for Visual Language Models
It is an open-source tool under the MIT license, available for installation via pip, and works on macOS, Linux, and Windows environments.
crawl4ai: 🚀🤖 Crawl4AI: Open-source LLM Friendly Web Crawler & Scraper. Don't be shy, join here: https:
It appears you've provided an extensive overview of the Crawl4AI project, including its mission, features, licensing, and community engagement. To effectively engage with your request, I'll need to clarify what specific aspects of Crawl4AI or its documentation you would like me to focus on. However, based on the information given, here are some key points and potential discussions related to Crawl4AI:
Key Features of Crawl4AI
- Open-source LLM Friendly Web Crawler & Scraper: Designed to extract data from the web in a manner that is compatible with Large Language Models (LLMs), facilitating the use of real-world data for AI training.
- Data Extraction and Structuring: Enables users to transform unstructured digital footprints into structured, valuable assets.
- Ethical Data Sharing and Marketplace: Aims to create a fair data economy where data creators benefit directly from their contributions.
Mission and Impact
- Democratizing Data Access: By providing open-source tools for data extraction and structuring, Crawl4AI aims to make web data more accessible for research, development, and innovation.
- Empowering Data Creators: The project seeks to ensure that individuals and organizations can capitalize on their digital assets, promoting a shared economy based on ethical data practices.
Community and Development
- Open-source Community: Encourages collaboration and contributions from developers worldwide to improve the platform.
- Documentation and Support: Offers comprehensive documentation and support channels for users and contributors, including GitHub issues, Discord, and social media platforms.
Legal and Citation
- Licensing: Crawl4AI is licensed under Apache-2.0, which allows for free use, modification, and distribution.
- Citation: Provides guidelines on how to cite Crawl4AI in academic and professional works, ensuring proper attribution.
If you have specific questions about using Crawl4AI, contributing to the project, or understanding its features and mission in more detail, please let me know. I'm here to provide guidance based on the information available up to my last update in 2023.
jan: Jan is an open source alternative to ChatGPT that runs 100% offline on your computer
Jan is an open-source alternative to ChatGPT that runs 100% offline on your computer. Key features include:
- Model Library with popular LLMs like Llama, Gemma, Mistral, or Qwen
- Connection to Remote AI APIs like Groq and OpenRouter
- Local API Server with OpenAI-equivalent API
- Extensions for customizing Jan
Jan is free and open-source, under the AGPLv3 license, and supports Windows, MacOS, and Linux. It requires specific system requirements to enable GPU support.