Best AI tools for< Organize Data >

Frame is an Intelligent Company Workspace solution that serves as the control center for AI workforce, offering a suite of AI-powered tools and AI employees to scale businesses. It provides features like building custom AI employees, cross-app search, cross-app linking, Chrome extension, and real-time data awareness. Frame empowers users to manage product development, hire employees, manage sales pipelines, raise venture capital, collaborate remotely, centralize company knowledge, and augment teams with AI employees. The application is loved by users from forward-thinking companies and offers a delightful suite of native apps for teamwork, including note-taking, task management, whiteboard, and more. Frame is designed for speed and efficiency, enabling users to switch between apps seamlessly and maintain clean data through auto-enrichment.

DataBanc is an AI-powered platform that serves as a data bank, allowing users to retrieve, store, and utilize their personal data for personalized experiences. It empowers individuals to take control of their data, enabling them to access insights and recommendations tailored to their preferences. DataBanc aims to revolutionize the way people interact with their data, offering a secure and user-friendly solution for managing personal information in the digital age.

Quivr is an open-source chat-powered second brain application that transforms private and enterprise knowledge into a personal AI assistant. It continuously learns and improves at every interaction, offering AI-powered workplace search synced with user data. Quivr allows users to connect with their favorite tools, databases, and applications, and configure their 'second brain' to train on their company's unique context for improved search relevance and knowledge discovery.

Excel Bot is an AI assistant designed to help users work more efficiently with Excel and Google Sheets. It offers features such as converting formulas to text, saving time, and providing personalized assistance. Trusted by over 5000 happy customers, Excel Bot is a reliable tool for automating tasks and improving productivity in various business sectors. With Zigment AI technology at its core, Excel Bot ensures accuracy and effectiveness in handling data-related tasks.

MakeForms is a powerful and secure form builder that empowers teams to create advanced, visually stunning forms with top-notch security standards, now enhanced by AI capabilities. With MakeForms, you can create one-at-a-time, step forms, or all-at-once forms with ease using our user-friendly interface and intuitive design. You can also customize your forms with your own fonts and branding, and even publish them on your own domain, giving you complete control over your form's appearance and online presence. MakeForms also offers a variety of features to help you collect and organize your data, including a table view, summary view, and BI view. With MakeForms, you can be sure that your forms are secure and your data is protected.

EmailTree is an AI-powered email management platform that helps businesses automate their email responses, prioritize and organize tickets, and quickly integrate their data. With EmailTree, businesses can improve their customer experience, reduce their first response rate, and drive more revenue.

SheetGPT is an add-on for Google Sheets that allows users to integrate OpenAI's text and image generation capabilities into their spreadsheets. It is designed to be easy to use, with no API keys required, and offers a range of features including content creation, research and organization, summarization, and prototyping. SheetGPT is suitable for a variety of users, including content creators, digital marketing managers, researchers, and product managers.

myReach is an AI-powered knowledge management tool that helps you find new worth in your data. It uses AI to connect your knowledge and build a powerful network. You can use myReach to save everything in one place, automate with AI, chat with your knowledge, interconnect your data, and deploy everywhere. myReach is ISO/IEC 27001 certified and uses TLS 1.3 encryption and AES-256 bit encryption to protect your data.

CategorAIze.io is an AI-powered tool that helps users categorize data effortlessly using the latest AI technologies. Users can define custom categories, upload data items, and let the cutting-edge LLM AI automatically assign entries based on their content without the need for pretraining. The tool supports multi-level hierarchies, text and image-based categorization, and offers pay-as-you-go pricing options. Additionally, users can access the tool via browser, API, and plugins for a seamless experience.

txyz.ai is an AI-powered platform that aims to integrate all paths to knowledge. It leverages artificial intelligence algorithms to provide users with a comprehensive and efficient way to access and organize information. The platform offers a user-friendly interface that allows individuals to streamline their research process, gather insights, and make data-driven decisions. With txyz.ai, users can explore diverse sources of information, extract valuable insights, and stay updated on the latest trends in their field of interest.

Relativity is an AI-powered eDiscovery and legal search software solution that helps customers organize data, discover truth, and act on it. It offers a range of features such as proactive security, user experience enhancements, open platform for customization, legendary support, and AI-powered review tools. Relativity is trusted by thousands of organizations to handle sensitive data and streamline data discovery processes.

PoweredbyAI is a platform offering a variety of free AI tools for users to utilize. Users can access a range of AI-powered applications to assist with various tasks and projects. The platform aims to simplify the use of AI technology for individuals and businesses, providing easy access to tools that can enhance productivity and efficiency. With a user-friendly interface, PoweredbyAI caters to both beginners and advanced users looking to leverage AI capabilities in their work.

Vespa is a platform that performs operations such as selecting a subset of data in a large corpus, evaluating machine-learned models over the selected data, organizing and aggregating it, and returning it, typically in less than 100 milliseconds, all while the data corpus is continuously changing. It has been in development for many years and is used on a number of large internet services and apps which serve hundreds of thousands of queries from Vespa per second.

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.

ShapeLLM is the first 3D Multimodal Large Language Model designed for embodied interaction, exploring a universal 3D object understanding with 3D point clouds and languages. It supports single-view colored point cloud input and introduces a robust 3D QA benchmark, 3D MM-Vet, encompassing various variants. The model extends the powerful point encoder architecture, ReCon++, achieving state-of-the-art performance across a range of representation learning tasks. ShapeLLM can be used for tasks such as training, zero-shot understanding, visual grounding, few-shot learning, and zero-shot learning on 3D MM-Vet.

LoLLMs WebUI (Lord of Large Language Multimodal Systems: One tool to rule them all) is a user-friendly interface to access and utilize various LLM (Large Language Models) and other AI models for a wide range of tasks. With over 500 AI expert conditionings across diverse domains and more than 2500 fine tuned models over multiple domains, LoLLMs WebUI provides an immediate resource for any problem, from car repair to coding assistance, legal matters, medical diagnosis, entertainment, and more. The easy-to-use UI with light and dark mode options, integration with GitHub repository, support for different personalities, and features like thumb up/down rating, copy, edit, and remove messages, local database storage, search, export, and delete multiple discussions, make LoLLMs WebUI a powerful and versatile tool.

Agent Zero is a personal and organic AI framework designed to be dynamic, organically growing, and learning as you use it. It is fully transparent, readable, comprehensible, customizable, and interactive. The framework uses the computer as a tool to accomplish tasks, with no single-purpose tools pre-programmed. It emphasizes multi-agent cooperation, complete customization, and extensibility. Communication is key in this framework, allowing users to give proper system prompts and instructions to achieve desired outcomes. Agent Zero is capable of dangerous actions and should be run in an isolated environment. The framework is prompt-based, highly customizable, and requires a specific environment to run effectively.

AvaTaR is a novel and automatic framework that optimizes an LLM agent to effectively use provided tools and improve performance on a given task/domain. It designs a comparator module to provide insightful prompts to the LLM agent via reasoning between positive and negative examples from training data.

This repository contains the code that Defog uses for the evaluation of generated SQL. It's based off the schema from the Spider, but with a new set of hand-selected questions and queries grouped by query category. The testing procedure involves generating a SQL query, running both the 'gold' query and the generated query on their respective database to obtain dataframes with the results, comparing the dataframes using an 'exact' and a 'subset' match, logging these alongside other metrics of interest, and aggregating the results for reporting. The repository provides comprehensive instructions for installing dependencies, starting a Postgres instance, importing data into Postgres, importing data into Snowflake, using private data, implementing a query generator, and running the test with different runners.

SoM-LLaVA is a new data source and learning paradigm for Multimodal LLMs, empowering open-source Multimodal LLMs with Set-of-Mark prompting and improved visual reasoning ability. The repository provides a new dataset that is complementary to existing training sources, enhancing multimodal LLMs with Set-of-Mark prompting and improved general capacity. By adding 30k SoM data to the visual instruction tuning stage of LLaVA, the tool achieves 1% to 6% relative improvements on all benchmarks. Users can train SoM-LLaVA via command line and utilize the implementation to annotate COCO images with SoM. Additionally, the tool can be loaded in Huggingface for further usage.

LLMGA (Multimodal Large Language Model-based Generation Assistant) is a tool that leverages Large Language Models (LLMs) to assist users in image generation and editing. It provides detailed language generation prompts for precise control over Stable Diffusion (SD), resulting in more intricate and precise content in generated images. The tool curates a dataset for prompt refinement, similar image generation, inpainting & outpainting, and visual question answering. It offers a two-stage training scheme to optimize SD alignment and a reference-based restoration network to alleviate texture, brightness, and contrast disparities in image editing. LLMGA shows promising generative capabilities and enables wider applications in an interactive manner.

This repository contains the training code of MobileLLM, a language model optimized for on-device use cases with fewer than a billion parameters. It integrates SwiGLU activation function, deep and thin architectures, embedding sharing, and grouped-query attention to achieve high-quality LLMs. MobileLLM-125M/350M shows significant accuracy improvements over previous models on zero-shot commonsense reasoning tasks. The design philosophy scales effectively to larger models, with state-of-the-art results for MobileLLM-600M/1B/1.5B.

DataChain is an open-source Python library for processing and curating unstructured data at scale. It supports AI-driven data curation using local ML models and LLM APIs, handles large datasets, and is Python-friendly with Pydantic objects. It excels at optimizing batch operations and is designed for offline data processing, curation, and ETL. Typical use cases include Computer Vision data curation, LLM analytics, and validation.

LazyLLM is a low-code development tool for building complex AI applications with multiple agents. It assists developers in building AI applications at a low cost and continuously optimizing their performance. The tool provides a convenient workflow for application development and offers standard processes and tools for various stages of application development. Users can quickly prototype applications with LazyLLM, analyze bad cases with scenario task data, and iteratively optimize key components to enhance the overall application performance. LazyLLM aims to simplify the AI application development process and provide flexibility for both beginners and experts to create high-quality applications.

Taiyi (太一) is a bilingual large language model fine-tuned for diverse biomedical tasks. It aims to facilitate communication between healthcare professionals and patients, provide medical information, and assist in diagnosis, biomedical knowledge discovery, drug development, and personalized healthcare solutions. The model is based on the Qwen-7B-base model and has been fine-tuned using rich bilingual instruction data. It covers tasks such as question answering, biomedical dialogue, medical report generation, biomedical information extraction, machine translation, title generation, text classification, and text semantic similarity. The project also provides standardized data formats, model training details, model inference guidelines, and overall performance metrics across various BioNLP tasks.

Cognita is an open-source framework to organize your RAG codebase along with a frontend to play around with different RAG customizations. It provides a simple way to organize your codebase so that it becomes easy to test it locally while also being able to deploy it in a production ready environment. The key issues that arise while productionizing RAG system from a Jupyter Notebook are: 1. **Chunking and Embedding Job** : The chunking and embedding code usually needs to be abstracted out and deployed as a job. Sometimes the job will need to run on a schedule or be trigerred via an event to keep the data updated. 2. **Query Service** : The code that generates the answer from the query needs to be wrapped up in a api server like FastAPI and should be deployed as a service. This service should be able to handle multiple queries at the same time and also autoscale with higher traffic. 3. **LLM / Embedding Model Deployment** : Often times, if we are using open-source models, we load the model in the Jupyter notebook. This will need to be hosted as a separate service in production and model will need to be called as an API. 4. **Vector DB deployment** : Most testing happens on vector DBs in memory or on disk. However, in production, the DBs need to be deployed in a more scalable and reliable way. Cognita makes it really easy to customize and experiment everything about a RAG system and still be able to deploy it in a good way. It also ships with a UI that makes it easier to try out different RAG configurations and see the results in real time. You can use it locally or with/without using any Truefoundry components. However, using Truefoundry components makes it easier to test different models and deploy the system in a scalable way. Cognita allows you to host multiple RAG systems using one app. ### Advantages of using Cognita are: 1. A central reusable repository of parsers, loaders, embedders and retrievers. 2. Ability for non-technical users to play with UI - Upload documents and perform QnA using modules built by the development team. 3. Fully API driven - which allows integration with other systems. > If you use Cognita with Truefoundry AI Gateway, you can get logging, metrics and feedback mechanism for your user queries. ### Features: 1. Support for multiple document retrievers that use `Similarity Search`, `Query Decompostion`, `Document Reranking`, etc 2. Support for SOTA OpenSource embeddings and reranking from `mixedbread-ai` 3. Support for using LLMs using `Ollama` 4. Support for incremental indexing that ingests entire documents in batches (reduces compute burden), keeps track of already indexed documents and prevents re-indexing of those docs.

aiosql is a Python module that allows you to organize SQL statements in .sql files and load them into your Python application as methods to call. It supports various database drivers like SQLite, PostgreSQL, MySQL, MariaDB, and DuckDB. The project is an implementation of Kris Jenkins' yesql library to the Python ecosystem, allowing users to easily reuse SQL code in SQL GUIs or CLI tools. With aiosql, you can write, version control, comment, and run SQL code using files without losing the ability to use them as you would any other SQL file. It provides support for PEP 249 and asyncio based drivers, enabling users to execute parametric SQL queries from Python methods.

This repository focuses on organizing resources for building synthetic datasets using large language models. It covers important datasets, libraries, tools, tutorials, and papers related to synthetic data generation. The goal is to provide pragmatic and practical resources for individuals interested in creating synthetic datasets for machine learning applications.

This repository provides a systematic and comprehensive review of efficient LLMs research. We organize the literature in a taxonomy consisting of three main categories, covering distinct yet interconnected efficient LLMs topics from **model-centric** , **data-centric** , and **framework-centric** perspective, respectively. We hope our survey and this GitHub repository can serve as valuable resources to help researchers and practitioners gain a systematic understanding of the research developments in efficient LLMs and inspire them to contribute to this important and exciting field.

SurfSense is a tool designed to help users save and organize content from the internet into a personal Knowledge Graph. It allows users to capture web browsing sessions and webpage content using a Chrome extension, enabling easy retrieval and recall of saved information. SurfSense offers features like powerful search capabilities, natural language interaction with saved content, self-hosting options, and integration with GraphRAG for meaningful content relations. The tool eliminates the need for web scraping by directly reading data from the DOM, making it a convenient solution for managing online information.

Commonplace Bot is a modern representation of the commonplace book, leveraging modern technological advancements in computation, data storage, machine learning, and networking. It aims to capture, engage, and share knowledge by providing a platform for users to collect ideas, quotes, and information, organize them efficiently, engage with the data through various strategies and triggers, and transform the data into new mediums for sharing. The tool utilizes embeddings and cached transformations for efficient data storage and retrieval, flips traditional engagement rules by engaging with the user, and enables users to alchemize raw data into new forms like art prompts. Commonplace Bot offers a unique approach to knowledge management and creative expression.

Connect and interact with your Google accounts. Organize, retrieve, and manipulate data with A.I

Analyzes websites, compares value elements, and organizes data into a table.

Presents Excel sheets in ordered parts as requested.

Experto en transcribir borradores de notas de texto

Hello I'm Bandoleer Straightener-Stamper Assistant! What would you like help with today?

Sorts lists into alphabetical order.

Assists in structuring a project report on unlawful conduct and FDCPA violations, focusing on clarity and factuality.

Process Documentation for serving GPTs.

Analyzes and categorizes perfume data from Excel, or lists. Upload a file with your perfume names or just the names of your perfumes and this GPT will help you organize the information.

Analyzes and organizes SERP data.

Write Excellent Medical Notes

Specializes in organizing business card data into various formats

Solve math and logic with Smart GPT. Use Tree and Chain of thoughts to organize and find answers.

Experto en organizar información en formato de texto combinado.

Comprehensive categorization and organization for AI Prompts

An Operations Department Assistant aids the operations team by handling administrative tasks, process documentation, and data analysis, helping to streamline and optimize various operational processes within an organization.

Helps with sorting data on Excel sheets

Kodlama projeleri için yapılacaklar listesi oluşturur.

Extracts, categorizes, and organizes receipt data.

Expert in sourcing and organizing knowledge databases for GPTs.