Marly is a tool that allows users to search for and extract context-specific data from various types of documents such as PDFs, Word files, Powerpoints, and websites. It provides the ability to extract data in structured formats like JSON or Markdown, making it easy to integrate into workflows. Marly supports multi-schema and multi-document extraction, offers built-in caching for rapid repeat extractions, and ensures no vendor lock-in by allowing flexibility in choosing model providers.

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.

Giskard is an open-source Python library that automatically detects performance, bias & security issues in AI applications. The library covers LLM-based applications such as RAG agents, all the way to traditional ML models for tabular data.

PremSQL is an open-source library designed to help developers create secure, fully local Text-to-SQL solutions using small language models. It provides essential tools for building and deploying end-to-end Text-to-SQL pipelines with customizable components, ideal for secure, autonomous AI-powered data analysis. The library offers features like Local-First approach, Customizable Datasets, Robust Executors and Evaluators, Advanced Generators, Error Handling and Self-Correction, Fine-Tuning Support, and End-to-End Pipelines. Users can fine-tune models, generate SQL queries from natural language inputs, handle errors, and evaluate model performance against predefined metrics. PremSQL is extendible for customization and private data usage.

Raga LLM Hub is a comprehensive evaluation toolkit for Language and Learning Models (LLMs) with over 100 meticulously designed metrics. It allows developers and organizations to evaluate and compare LLMs effectively, establishing guardrails for LLMs and Retrieval Augmented Generation (RAG) applications. The platform assesses aspects like Relevance & Understanding, Content Quality, Hallucination, Safety & Bias, Context Relevance, Guardrails, and Vulnerability scanning, along with Metric-Based Tests for quantitative analysis. It helps teams identify and fix issues throughout the LLM lifecycle, revolutionizing reliability and trustworthiness.

Superlinked is a compute framework for information retrieval and feature engineering systems, focusing on converting complex data into vector embeddings for RAG, Search, RecSys, and Analytics stack integration. It enables custom model performance in machine learning with pre-trained model convenience. The tool allows users to build multimodal vectors, define weights at query time, and avoid postprocessing & rerank requirements. Users can explore the computational model through simple scripts and python notebooks, with a future release planned for production usage with built-in data infra and vector database integrations.

Reflection-Tuning is a project focused on improving the quality of instruction-tuning data through a reflection-based method. It introduces Selective Reflection-Tuning, where the student model can decide whether to accept the improvements made by the teacher model. The project aims to generate high-quality instruction-response pairs by defining specific criteria for the oracle model to follow and respond to. It also evaluates the efficacy and relevance of instruction-response pairs using the r-IFD metric. The project provides code for reflection and selection processes, along with data and model weights for both V1 and V2 methods.

tinyllm is a lightweight framework designed for developing, debugging, and monitoring LLM and Agent powered applications at scale. It aims to simplify code while enabling users to create complex agents or LLM workflows in production. The core classes, Function and FunctionStream, standardize and control LLM, ToolStore, and relevant calls for scalable production use. It offers structured handling of function execution, including input/output validation, error handling, evaluation, and more, all while maintaining code readability. Users can create chains with prompts, LLM models, and evaluators in a single file without the need for extensive class definitions or spaghetti code. Additionally, tinyllm integrates with various libraries like Langfuse and provides tools for prompt engineering, observability, logging, and finite state machine design.

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.

BasicLingua is a Python library that provides functionalities for linguistic tasks such as tokenization, stemming, lemmatization, and many others. It is based on the Gemini Language Model, which has demonstrated promising results in dealing with text data. BasicLingua can be used as an API or through a web demo. It is available under the MIT license and can be used in various projects.

ChatDev is a virtual software company powered by intelligent agents like CEO, CPO, CTO, programmer, reviewer, tester, and art designer. These agents collaborate to revolutionize the digital world through programming. The platform offers an easy-to-use, highly customizable, and extendable framework based on large language models, ideal for studying collective intelligence. ChatDev introduces innovative methods like Iterative Experience Refinement and Experiential Co-Learning to enhance software development efficiency. It supports features like incremental development, Docker integration, Git mode, and Human-Agent-Interaction mode. Users can customize ChatChain, Phase, and Role settings, and share their software creations easily. The project is open-source under the Apache 2.0 License and utilizes data licensed under CC BY-NC 4.0.

Zshot is a highly customizable framework for performing Zero and Few shot named entity and relationships recognition. It can be used for mentions extraction, wikification, zero and few shot named entity recognition, zero and few shot named relationship recognition, and visualization of zero-shot NER and RE extraction. The framework consists of two main components: the mentions extractor and the linker. There are multiple mentions extractors and linkers available, each serving a specific purpose. Zshot also includes a relations extractor and a knowledge extractor for extracting relations among entities and performing entity classification. The tool requires Python 3.6+ and dependencies like spacy, torch, transformers, evaluate, and datasets for evaluation over datasets like OntoNotes. Optional dependencies include flair and blink for additional functionalities. Zshot provides examples, tutorials, and evaluation methods to assess the performance of the components.

LLM-Zero-to-Hundred is a repository showcasing various applications of LLM chatbots and providing insights into training and fine-tuning Language Models. It includes projects like WebGPT, RAG-GPT, WebRAGQuery, LLM Full Finetuning, RAG-Master LLamaindex vs Langchain, open-source-RAG-GEMMA, and HUMAIN: Advanced Multimodal, Multitask Chatbot. The projects cover features like ChatGPT-like interaction, RAG capabilities, image generation and understanding, DuckDuckGo integration, summarization, text and voice interaction, and memory access. Tutorials include LLM Function Calling and Visualizing Text Vectorization. The projects have a general structure with folders for README, HELPER, .env, configs, data, src, images, and utils.

LoRAX is a framework that allows users to serve thousands of fine-tuned models on a single GPU, dramatically reducing the cost of serving without compromising on throughput or latency. It features dynamic adapter loading, heterogeneous continuous batching, adapter exchange scheduling, optimized inference, and is ready for production with prebuilt Docker images, Helm charts for Kubernetes, Prometheus metrics, and distributed tracing with Open Telemetry. LoRAX supports a number of Large Language Models as the base model including Llama, Mistral, and Qwen, and any of the linear layers in the model can be adapted via LoRA and loaded in LoRAX.

The `sec-parser` project simplifies extracting meaningful information from SEC EDGAR HTML documents by organizing them into semantic elements and a tree structure. It helps in parsing SEC filings for financial and regulatory analysis, analytics and data science, AI and machine learning, causal AI, and large language models. The tool is especially beneficial for AI, ML, and LLM applications by streamlining data pre-processing and feature extraction.

Vertex AI is a platform for end-to-end model development. It consist of core components that make the processes of MLOps possible for design patterns of all types.

Marly is a tool that allows users to search for and extract context-specific data from various types of documents such as PDFs, Word files, Powerpoints, and websites. It provides the ability to extract data in structured formats like JSON or Markdown, making it easy to integrate into workflows. Marly supports multi-schema and multi-document extraction, offers built-in caching for rapid repeat extractions, and ensures no vendor lock-in by allowing flexibility in choosing model providers.

Podwise is an AI knowledge management app designed specifically for podcast listeners. With the Podwise platform, you only need to follow your favorite podcasts, such as "Hardcore Hackers". When a program is released, Podwise will use AI to transcribe, extract, summarize, and analyze the podcast content, helping you to break down the hard-core podcast knowledge. At the same time, it is connected to platforms such as Notion, Obsidian, Logseq, and Readwise, embedded in your knowledge management workflow, and integrated with content from other channels including news, newsletters, and blogs, helping you to improve your second brain 🧠.

Extractor is an AI-powered data extraction library for Laravel that leverages OpenAI's capabilities to effortlessly extract structured data from various sources, including images, PDFs, and emails. It features a convenient wrapper around OpenAI Chat and Completion endpoints, supports multiple input formats, includes a flexible Field Extractor for arbitrary data extraction, and integrates with Textract for OCR functionality. Extractor utilizes JSON Mode from the latest GPT-3.5 and GPT-4 models, providing accurate and efficient data extraction.

ScrapeGraphAI is a Python library that uses Large Language Models (LLMs) and direct graph logic to create web scraping pipelines for websites, documents, and XML files. It allows users to extract specific information from web pages by providing a prompt describing the desired data. ScrapeGraphAI supports various LLMs, including Ollama, OpenAI, Gemini, and Docker, enabling users to choose the most suitable model for their needs. The library provides a user-friendly interface through its `SmartScraper` class, which simplifies the process of building and executing scraping pipelines. ScrapeGraphAI is open-source and available on GitHub, with extensive documentation and examples to guide users. It is particularly useful for researchers and data scientists who need to extract structured data from web pages for analysis and exploration.

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.

Auto-News is an automatic news aggregator tool that utilizes Large Language Models (LLM) to pull information from various sources such as Tweets, RSS feeds, YouTube videos, web articles, Reddit, and journal notes. The tool aims to help users efficiently read and filter content based on personal interests, providing a unified reading experience and organizing information effectively. It features feed aggregation with summarization, transcript generation for videos and articles, noise reduction, task organization, and deep dive topic exploration. The tool supports multiple LLM backends, offers weekly top-k aggregations, and can be deployed on Linux/MacOS using docker-compose or Kubernetes.

SemanticFinder is a frontend-only live semantic search tool that calculates embeddings and cosine similarity client-side using transformers.js and SOTA embedding models from Huggingface. It allows users to search through large texts like books with pre-indexed examples, customize search parameters, and offers data privacy by keeping input text in the browser. The tool can be used for basic search tasks, analyzing texts for recurring themes, and has potential integrations with various applications like wikis, chat apps, and personal history search. It also provides options for building browser extensions and future ideas for further enhancements and integrations.

1filellm is a command-line data aggregation tool designed for LLM ingestion. It aggregates and preprocesses data from various sources into a single text file, facilitating the creation of information-dense prompts for large language models. The tool supports automatic source type detection, handling of multiple file formats, web crawling functionality, integration with Sci-Hub for research paper downloads, text preprocessing, and token count reporting. Users can input local files, directories, GitHub repositories, pull requests, issues, ArXiv papers, YouTube transcripts, web pages, Sci-Hub papers via DOI or PMID. The tool provides uncompressed and compressed text outputs, with the uncompressed text automatically copied to the clipboard for easy pasting into LLMs.

Agently Daily News Collector is an open-source project showcasing a workflow powered by the Agent ly AI application development framework. It allows users to generate news collections on various topics by inputting the field topic. The AI agents automatically perform the necessary tasks to generate a high-quality news collection saved in a markdown file. Users can edit settings in the YAML file, install Python and required packages, input their topic idea, and wait for the news collection to be generated. The process involves tasks like outlining, searching, summarizing, and preparing column data. The project dependencies include Agently AI Development Framework, duckduckgo-search, BeautifulSoup4, and PyYAM.