PandaETL is an open-source, no-code ETL tool designed to extract and parse data from various document types including PDFs, emails, websites, audio files, and more. With an intuitive interface and powerful backend, PandaETL simplifies the process of data extraction and transformation, making it accessible to users without programming skills.

OpenAdapt is an open-source software adapter between Large Multimodal Models (LMMs) and traditional desktop and web Graphical User Interfaces (GUIs). It aims to automate repetitive GUI workflows by leveraging the power of LMMs. OpenAdapt records user input and screenshots, converts them into tokenized format, and generates synthetic input via transformer model completions. It also analyzes recordings to generate task trees and replay synthetic input to complete tasks. OpenAdapt is model agnostic and generates prompts automatically by learning from human demonstration, ensuring that agents are grounded in existing processes and mitigating hallucinations. It works with all types of desktop GUIs, including virtualized and web, and is open source under the MIT license.

AIRAVAT is a multifunctional Android Remote Access Tool (RAT) with a GUI-based Web Panel that does not require port forwarding. It allows users to access various features on the victim's device, such as reading files, downloading media, retrieving system information, managing applications, SMS, call logs, contacts, notifications, keylogging, admin permissions, phishing, audio recording, music playback, device control (vibration, torch light, wallpaper), executing shell commands, clipboard text retrieval, URL launching, and background operation. The tool requires a Firebase account and tools like ApkEasy Tool or ApkTool M for building. Users can set up Firebase, host the web panel, modify Instagram.apk for RAT functionality, and connect the victim's device to the web panel. The tool is intended for educational purposes only, and users are solely responsible for its use.

Paperless-AI is an automated document analyzer tool designed for Paperless-ngx users. It utilizes the OpenAI API and Ollama (Mistral, llama, phi 3, gemma 2) to automatically scan, analyze, and tag documents. The tool offers features such as automatic document scanning, AI-powered document analysis, automatic title and tag assignment, manual mode for analyzing documents, easy setup through a web interface, document processing dashboard, error handling, and Docker support. Users can configure the tool through a web interface and access a debug interface for monitoring and troubleshooting. Paperless-AI aims to streamline document organization and analysis processes for users with access to Paperless-ngx and AI capabilities.

AI Flow is an open-source, user-friendly UI application that empowers you to seamlessly connect multiple AI models together, specifically leveraging the capabilities of multiples AI APIs such as OpenAI, StabilityAI and Replicate. In a nutshell, AI Flow provides a visual platform for crafting and managing AI-driven workflows, thereby facilitating diverse and dynamic AI interactions.

UFO is a UI-focused dual-agent framework to fulfill user requests on Windows OS by seamlessly navigating and operating within individual or spanning multiple applications.

GraphRAG Local with Interactive UI is an adaptation of Microsoft's GraphRAG, tailored to support local models and featuring a comprehensive interactive user interface. It allows users to leverage local models for LLM and embeddings, visualize knowledge graphs in 2D or 3D, manage files, settings, and queries, and explore indexing outputs. The tool aims to be cost-effective by eliminating dependency on costly cloud-based models and offers flexible querying options for global, local, and direct chat queries.

DocsGPT is an open-source documentation assistant powered by GPT models. It simplifies the process of searching for information in project documentation by allowing developers to ask questions and receive accurate answers. With DocsGPT, users can say goodbye to manual searches and quickly find the information they need. The tool aims to revolutionize project documentation experiences and offers features like live previews, Discord community, guides, and contribution opportunities. It consists of a Flask app, Chrome extension, similarity search index creation script, and a frontend built with Vite and React. Users can quickly get started with DocsGPT by following the provided setup instructions and can contribute to its development by following the guidelines in the CONTRIBUTING.md file. The project follows a Code of Conduct to ensure a harassment-free community environment for all participants. DocsGPT is licensed under MIT and is built with LangChain.

Open Parse is a Python library for visually discerning document layouts and chunking them effectively. It is designed to fill the gap in open-source libraries for handling complex documents. Unlike text splitting, which converts a file to raw text and slices it up, Open Parse visually analyzes documents for superior LLM input. It also supports basic markdown for parsing headings, bold, and italics, and has high-precision table support, extracting tables into clean Markdown formats with accuracy that surpasses traditional tools. Open Parse is extensible, allowing users to easily implement their own post-processing steps. It is also intuitive, with great editor support and completion everywhere, making it easy to use and learn.

The llama-cpp-agent framework is a tool designed for easy interaction with Large Language Models (LLMs). Allowing users to chat with LLM models, execute structured function calls and get structured output (objects). It provides a simple yet robust interface and supports llama-cpp-python and OpenAI endpoints with GBNF grammar support (like the llama-cpp-python server) and the llama.cpp backend server. It works by generating a formal GGML-BNF grammar of the user defined structures and functions, which is then used by llama.cpp to generate text valid to that grammar. In contrast to most GBNF grammar generators it also supports nested objects, dictionaries, enums and lists of them.

AI File Organizer 2000 is an Obsidian Plugin that uses AI to transcribe audio, annotate images, and automatically organize files by moving them to the most likely folders. It supports text, audio, and images, with upcoming local-first LLM support. Users can simply place unorganized files into the 'Inbox' folder for automatic organization. The tool renames and moves files quickly, providing a seamless file organization experience. Self-hosting is also possible by running the server and enabling the 'Self-hosted' option in the plugin settings. Join the community Discord server for more information and use the provided iOS shortcut for easy access on mobile devices.

Auto-Analyst is an AI-driven data analytics agentic system designed to simplify and enhance the data science process. By integrating various specialized AI agents, this tool aims to make complex data analysis tasks more accessible and efficient for data analysts and scientists. Auto-Analyst provides a streamlined approach to data preprocessing, statistical analysis, machine learning, and visualization, all within an interactive Streamlit interface. It offers plug and play Streamlit UI, agents with data science speciality, complete automation, LLM agnostic operation, and is built using lightweight frameworks.

AgentScheduler is an Automatic/Vladmandic Stable Diffusion Web UI extension designed to enhance image generation workflows. It allows users to enqueue prompts, settings, and controlnets, manage queued tasks, prioritize, pause, resume, and delete tasks, view generation results, and more. The extension offers hidden features like queuing checkpoints, editing queued tasks, and custom checkpoint selection. Users can access the functionality through HTTP APIs and API callbacks. Troubleshooting steps are provided for common errors. The extension is compatible with latest versions of A1111 and Vladmandic. It is licensed under Apache License 2.0.

ZipNN is a lossless and near-lossless compression library optimized for numbers/tensors in the Foundation Models environment. It automatically prepares data for compression based on its type, allowing users to focus on core tasks without worrying about compression complexities. The library delivers effective compression techniques for different data types and structures, achieving high compression ratios and rates. ZipNN supports various compression methods like ZSTD, lz4, and snappy, and provides ready-made scripts for file compression/decompression. Users can also manually import the package to compress and decompress data. The library offers advanced configuration options for customization and validation tests for different input and compression types.

SillyTavern is a user interface you can install on your computer (and Android phones) that allows you to interact with text generation AIs and chat/roleplay with characters you or the community create. SillyTavern is a fork of TavernAI 1.2.8 which is under more active development and has added many major features. At this point, they can be thought of as completely independent programs.

Polymarket Agents is a developer framework and set of utilities for building AI agents to trade autonomously on Polymarket. It integrates with Polymarket API, provides AI agent utilities for prediction markets, supports local and remote RAG, sources data from various services, and offers comprehensive LLM tools for prompt engineering. The architecture features modular components like APIs and scripts for managing local environments, server set-up, and CLI for end-user commands.

PandaETL is an open-source, no-code ETL tool designed to extract and parse data from various document types including PDFs, emails, websites, audio files, and more. With an intuitive interface and powerful backend, PandaETL simplifies the process of data extraction and transformation, making it accessible to users without programming skills.

Skyvern automates browser-based workflows using LLMs and computer vision. It provides a simple API endpoint to fully automate manual workflows, replacing brittle or unreliable automation solutions. Traditional approaches to browser automations required writing custom scripts for websites, often relying on DOM parsing and XPath-based interactions which would break whenever the website layouts changed. Instead of only relying on code-defined XPath interactions, Skyvern adds computer vision and LLMs to the mix to parse items in the viewport in real-time, create a plan for interaction and interact with them. This approach gives us a few advantages: 1. Skyvern can operate on websites it’s never seen before, as it’s able to map visual elements to actions necessary to complete a workflow, without any customized code 2. Skyvern is resistant to website layout changes, as there are no pre-determined XPaths or other selectors our system is looking for while trying to navigate 3. Skyvern leverages LLMs to reason through interactions to ensure we can cover complex situations. Examples include: 1. If you wanted to get an auto insurance quote from Geico, the answer to a common question “Were you eligible to drive at 18?” could be inferred from the driver receiving their license at age 16 2. If you were doing competitor analysis, it’s understanding that an Arnold Palmer 22 oz can at 7/11 is almost definitely the same product as a 23 oz can at Gopuff (even though the sizes are slightly different, which could be a rounding error!) Want to see examples of Skyvern in action? Jump to #real-world-examples-of- skyvern

This repository contains Airbyte connectors used in Faros and Faros Community Edition platforms as well as Airbyte Connector Development Kit (CDK) for JavaScript/TypeScript.

Open Parse is a Python library for visually discerning document layouts and chunking them effectively. It is designed to fill the gap in open-source libraries for handling complex documents. Unlike text splitting, which converts a file to raw text and slices it up, Open Parse visually analyzes documents for superior LLM input. It also supports basic markdown for parsing headings, bold, and italics, and has high-precision table support, extracting tables into clean Markdown formats with accuracy that surpasses traditional tools. Open Parse is extensible, allowing users to easily implement their own post-processing steps. It is also intuitive, with great editor support and completion everywhere, making it easy to use and learn.

Unstract is a no-code platform that enables users to launch APIs and ETL pipelines to structure unstructured documents. With Unstract, users can go beyond co-pilots by enabling machine-to-machine automation. Unstract's Prompt Studio provides a simple, no-code approach to creating prompts for LLMs, vector databases, embedding models, and text extractors. Users can then configure Prompt Studio projects as API deployments or ETL pipelines to automate critical business processes that involve complex documents. Unstract supports a wide range of LLM providers, vector databases, embeddings, text extractors, ETL sources, and ETL destinations, providing users with the flexibility to choose the best tools for their needs.

Dot is a standalone, open-source application designed for seamless interaction with documents and files using local LLMs and Retrieval Augmented Generation (RAG). It is inspired by solutions like Nvidia's Chat with RTX, providing a user-friendly interface for those without a programming background. Pre-packaged with Mistral 7B, Dot ensures accessibility and simplicity right out of the box. Dot allows you to load multiple documents into an LLM and interact with them in a fully local environment. Supported document types include PDF, DOCX, PPTX, XLSX, and Markdown. Users can also engage with Big Dot for inquiries not directly related to their documents, similar to interacting with ChatGPT. Built with Electron JS, Dot encapsulates a comprehensive Python environment that includes all necessary libraries. The application leverages libraries such as FAISS for creating local vector stores, Langchain, llama.cpp & Huggingface for setting up conversation chains, and additional tools for document management and interaction.

Instructor is a Python library that makes it a breeze to work with structured outputs from large language models (LLMs). Built on top of Pydantic, it provides a simple, transparent, and user-friendly API to manage validation, retries, and streaming responses. Get ready to supercharge your LLM workflows!

Sparrow is an innovative open-source solution for efficient data extraction and processing from various documents and images. It seamlessly handles forms, invoices, receipts, and other unstructured data sources. Sparrow stands out with its modular architecture, offering independent services and pipelines all optimized for robust performance. One of the critical functionalities of Sparrow - pluggable architecture. You can easily integrate and run data extraction pipelines using tools and frameworks like LlamaIndex, Haystack, or Unstructured. Sparrow enables local LLM data extraction pipelines through Ollama or Apple MLX. With Sparrow solution you get API, which helps to process and transform your data into structured output, ready to be integrated with custom workflows. Sparrow Agents - with Sparrow you can build independent LLM agents, and use API to invoke them from your system. **List of available agents:** * **llamaindex** - RAG pipeline with LlamaIndex for PDF processing * **vllamaindex** - RAG pipeline with LLamaIndex multimodal for image processing * **vprocessor** - RAG pipeline with OCR and LlamaIndex for image processing * **haystack** - RAG pipeline with Haystack for PDF processing * **fcall** - Function call pipeline * **unstructured-light** - RAG pipeline with Unstructured and LangChain, supports PDF and image processing * **unstructured** - RAG pipeline with Weaviate vector DB query, Unstructured and LangChain, supports PDF and image processing * **instructor** - RAG pipeline with Unstructured and Instructor libraries, supports PDF and image processing. Works great for JSON response generation

Chaindesk is a no-code platform that allows users to easily set up a semantic search system for personal data without technical knowledge. It supports loading data from various sources such as raw text, web pages, files (Word, Excel, PowerPoint, PDF, Markdown, Plain Text), and upcoming support for web sites, Notion, and Airtable. The platform offers a user-friendly interface for managing datastores, querying data via a secure API endpoint, and auto-generating ChatGPT Plugins for each datastore. Chaindesk utilizes a Vector Database (Qdrant), Openai's text-embedding-ada-002 for embeddings, and has a chunk size of 1024 tokens. The technology stack includes Next.js, Joy UI, LangchainJS, PostgreSQL, Prisma, and Qdrant, inspired by the ChatGPT Retrieval Plugin.

OAD is a powerful open-source tool for analyzing and visualizing data. It provides a user-friendly interface for exploring datasets, generating insights, and creating interactive visualizations. With OAD, users can easily import data from various sources, clean and preprocess data, perform statistical analysis, and create customizable visualizations to communicate findings effectively. Whether you are a data scientist, analyst, or researcher, OAD can help you streamline your data analysis workflow and uncover valuable insights from your data.

Defog's SQLCoder is a family of state-of-the-art large language models (LLMs) designed for converting natural language questions into SQL queries. It outperforms popular open-source models like gpt-4 and gpt-4-turbo on SQL generation tasks. SQLCoder has been trained on more than 20,000 human-curated questions based on 10 different schemas, and the model weights are licensed under CC BY-SA 4.0. Users can interact with SQLCoder through the 'transformers' library and run queries using the 'sqlcoder launch' command in the terminal. The tool has been tested on NVIDIA GPUs with more than 16GB VRAM and Apple Silicon devices with some limitations. SQLCoder offers a demo on their website and supports quantized versions of the model for consumer GPUs with sufficient memory.

TableLLM is a large language model designed for efficient tabular data manipulation tasks in real office scenarios. It can generate code solutions or direct text answers for tasks like insert, delete, update, query, merge, and chart operations on tables embedded in spreadsheets or documents. The model has been fine-tuned based on CodeLlama-7B and 13B, offering two scales: TableLLM-7B and TableLLM-13B. Evaluation results show its performance on benchmarks like WikiSQL, Spider, and self-created table operation benchmark. Users can use TableLLM for code and text generation tasks on tabular data.

Synmetrix (prev. MLCraft) is an open source data engineering platform and semantic layer for centralized metrics management. It provides a complete framework for modeling, integrating, transforming, aggregating, and distributing metrics data at scale. Key features include data modeling and transformations, semantic layer for unified data model, scheduled reports and alerts, versioning, role-based access control, data exploration, caching, and collaboration on metrics modeling. Synmetrix leverages Cube (Cube.js) for flexible data models that consolidate metrics from various sources, enabling downstream distribution via a SQL API for integration into BI tools, reporting, dashboards, and data science. Use cases include data democratization, business intelligence, embedded analytics, and enhancing accuracy in data handling and queries. The tool speeds up data-driven workflows from metrics definition to consumption by combining data engineering best practices with self-service analytics capabilities.

The Data Scientist Roadmap2024 provides a comprehensive guide to mastering essential tools for data science success. It includes programming languages, machine learning libraries, cloud platforms, and concepts categorized by difficulty. The roadmap covers a wide range of topics from programming languages to machine learning techniques, data visualization tools, and DevOps/MLOps tools. It also includes web development frameworks and specific concepts like supervised and unsupervised learning, NLP, deep learning, reinforcement learning, and statistics. Additionally, it delves into DevOps tools like Airflow and MLFlow, data visualization tools like Tableau and Matplotlib, and other topics such as ETL processes, optimization algorithms, and financial modeling.

VMind is an open-source solution for intelligent visualization, providing an intelligent chart component based on LLM by VisActor. It allows users to create chart narrative works with natural language interaction, edit charts through dialogue, and export narratives as videos or GIFs. The tool is easy to use, scalable, supports various chart types, and offers one-click export functionality. Users can customize chart styles, specify themes, and aggregate data using LLM models. VMind aims to enhance efficiency in creating data visualization works through dialogue-based editing and natural language interaction.

Quadratic is a modern multiplayer spreadsheet application that integrates Python, AI, and SQL functionalities. It aims to streamline team collaboration and data analysis by enabling users to pull data from various sources and utilize popular data science tools. The application supports building dashboards, creating internal tools, mixing data from different sources, exploring data for insights, visualizing Python workflows, and facilitating collaboration between technical and non-technical team members. Quadratic is built with Rust + WASM + WebGL to ensure seamless performance in the browser, and it offers features like WebGL Grid, local file management, Python and Pandas support, Excel formula support, multiplayer capabilities, charts and graphs, and team support. The tool is currently in Beta with ongoing development for additional features like JS support, SQL database support, and AI auto-complete.