Best AI tools for< Graph Functions >

Math Sniper is an AI-powered application designed to provide precise math solutions, exam preparation assistance, and exploration of mathematical concepts. The app offers step-by-step solutions to math challenges at all levels, connects users with math tutors for personalized help, and covers a wide range of subjects beyond mathematics, such as biology, chemistry, physics, history, economics, and language tasks. With features like Snap & Ask for instant answers, step-by-step explanations, and a user-friendly interface, Math Sniper aims to enhance users' understanding of complex concepts and facilitate learning in various disciplines.

Math.bot is an advanced AI math solver powered by GPT-4o, offering fast and accurate solutions to a wide range of math problems. The platform integrates leading technologies like Microsoft Math Solver, providing free and user-friendly services accessible via web and mobile apps. Users can solve math equations and word problems with detailed step-by-step guidance, upload math problems for instant AI-assisted solutions, and engage with an interactive ChatGPT math solver for dynamic learning experiences.

VIVA is an AI-powered creative visual design platform that aims to bring every moment to life. It provides users with tools and features to create visually appealing designs effortlessly. With VIVA, users can unleash their creativity and design stunning visuals for various purposes such as social media posts, presentations, and marketing materials. The platform leverages artificial intelligence to streamline the design process and help users achieve professional-looking results without the need for advanced design skills.

Salieri is a multi-agent LLM home multiverse platform that offers an efficient, trustworthy, and automated AI workflow. The innovative Multiverse Factory allows developers to elevate their projects by generating personalized AI applications through an intuitive interface. The platform aims to optimize user queries via LLM API calls, reduce expenses, and enhance the cognitive functions of AI agents. Salieri's team comprises experts from top AI institutes like MIT and Google, focusing on generative AI, neural knowledge graph, and composite AI models.

Floneum is a versatile AI-powered tool designed for language-related tasks. It allows users to build workflows using large language models through a user-friendly drag-and-drop interface. Additionally, Floneum supports the secure extension of functionalities with WebAssembly plugins, enabling users to write plugins in various languages like Rust, C, Java, or Go. With 41 built-in plugins, Floneum offers a range of features to enhance text processing, search engine operations, file handling, Python script execution, browser automation, and more.

SOMA is a Research Automation Platform that accelerates medical innovation by providing up to 100x speedup through process automation. The platform analyzes medical research articles, extracts important concepts, and identifies causal and associative relationships between them. It organizes this information into a specialized database forming a knowledge graph. Researchers can retrieve causal chains, access specific research articles, and perform tasks like concept analysis, drug repurposing, and target discovery. SOMA enhances literature review efficiency by finding relevant articles based on causal chains and keywords specified by the user. It empowers researchers to focus on their research by saving up to 95% of the time spent on pre-processing documents. The platform offers freemium access with extended functionality for 14 days and advanced features available through subscription.

AuthorAI is a platform that uses artificial intelligence to enhance the creative authoring process. It offers a variety of tools and services to help writers, bloggers, and other content creators produce high-quality content more efficiently. AuthorAI's features include: - A natural language processing engine that can generate text, translate languages, and answer questions. - A machine learning algorithm that can identify patterns and trends in data. - A knowledge graph that stores information about a wide range of topics. - A set of APIs that allow developers to integrate AuthorAI's functionality into their own applications.

Magick is a cutting-edge Artificial Intelligence Development Environment (AIDE) that empowers users to rapidly prototype and deploy advanced AI agents and applications without coding. It provides a full-stack solution for building, deploying, maintaining, and scaling AI creations. Magick's open-source, platform-agnostic nature allows for full control and flexibility, making it suitable for users of all skill levels. With its visual node-graph editors, users can code visually and create intuitively. Magick also offers powerful document processing capabilities, enabling effortless embedding and access to complex data. Its real-time and event-driven agents respond to events right in the AIDE, ensuring prompt and efficient handling of tasks. Magick's scalable deployment feature allows agents to handle any number of users, making it suitable for large-scale applications. Additionally, its multi-platform integrations with tools like Discord, Unreal Blueprints, and Google AI provide seamless connectivity and enhanced functionality.

Electe is an AI-powered platform that empowers businesses to leverage the potential of artificial intelligence for data analysis and insights. With its intuitive interface and advanced AI algorithms, Electe enables users to extract valuable insights from their data, visualize data through intuitive graphs and customizable dashboards, generate personalized notes based on customer order analysis, monitor and compare competitor performance, and automate data extraction and classification using machine learning techniques. The platform also offers features like Q&A Document interaction, advanced presentations generation, daily email reports, and mobile app access. Electe is designed to cater to businesses of all sizes, providing scalable plans with essential functionalities, advanced analysis tools, and premium support.

The website is an AI tool designed to generate a knowledge graph based on input text. It uses advanced algorithms and machine learning capabilities to streamline operations, deliver personalized experiences, and unlock new possibilities. Users can input text related to various topics, and the tool processes the information to create a structured knowledge graph.

Quanty is an AI-driven financial knowledge graph application that provides market insights on crypto and stocks news through advanced algorithms and knowledge graphs. It offers a GraphQL API for deep market understanding, smart data classification, current market insights, entity and relationship extraction, and dynamic GraphQL access. Users can access a wide range of financial news, insights, and analytics seamlessly through Quanty's robust GraphQL API.

UPTO3 is a decentralized event knowledge graph protocol that aims to provide consensus verification for Web3 events by turning them into NFTs. Users can mint and verify events on the platform, with rewards based on the outcomes. The platform promotes transparency, open data access, and unbiased analysis through economic incentives. UPTO3 will be built on Blast(L2) and offers features such as event minting as NFTs, decentralized verification tasks, and gas rewards for validators. The project launch is scheduled to coincide with Blast's mainnet launch in February, following the testnet launch in January.

Derwen is an open-source integration platform for production machine learning in enterprise, specializing in natural language processing, graph technologies, and decision support. It offers expertise in developing knowledge graph applications and domain-specific authoring. Derwen collaborates closely with Hugging Face and provides strong data privacy guarantees, low carbon footprint, and no cloud vendor involvement. The platform aims to empower AI engineers and domain experts with quality, time-to-value, and ownership since 2017.

ChartFast is an AI Data Analyzer tool that automates data visualization and analysis tasks, powered by GPT-4 technology. It allows users to generate precise and sleek graphs in seconds, process vast amounts of data, and provide interactive data queries and quick exports. With features like specialized internal libraries for complex graph generation, customizable visualization code, and instant data export, ChartFast aims to streamline data work and enhance data analysis efficiency.

Capital Companion is an AI-powered trading and investing platform designed to provide users with a competitive edge in the markets. The platform offers a range of features including 24/7 AI assistant support, intelligent trading recommendations, risk analysis tools, real-time stock analytics, market sentiment analysis, and pattern recognition for technical analysis. By leveraging artificial intelligence, Capital Companion aims to help traders make well-informed decisions and protect their investments in a dynamic market environment.

Metabob is an AI-powered code review tool that helps developers detect, explain, and fix coding problems. It utilizes proprietary graph neural networks to detect problems and LLMs to explain and resolve them, combining the best of both worlds. Metabob's AI is trained on millions of bug fixes performed by experienced developers, enabling it to detect complex problems that span across codebases and automatically generate fixes for them. It integrates with popular code hosting platforms such as GitHub, Bitbucket, Gitlab, and VS Code, and supports various programming languages including Python, Javascript, Typescript, Java, C++, and C.

Zelma is an AI-powered research assistant that enables users to find, graph, and understand U.S. school testing data using plain English queries. It allows users to search student test data by school district, demographics, grade, and more, and presents the results with graphs, tables, and descriptions. Zelma aims to make education data accessible and understandable for everyone.

Factori is a data intelligence platform designed for an AI-first world, offering a wide range of products and datasets to empower businesses with advanced data insights. It enables users to uncover movement trends, reach customers effectively, drive data-driven insights, personalize experiences, and target the right consumers with unique segments. Factori is trusted by global businesses for all their data requirements, providing high-quality data to improve predictive and causal models.

NOLEJ is an AI-powered platform that helps instructional designers and teachers rapidly generate interactive eLearning material. It can automatically generate interactive content from existing learning materials, such as textbooks, videos, and online media resources. NOLEJ also offers a variety of interactive formats, including interactive videos, flashcards, glossaries, crosswords, drag-and-drop activities, find-the-word puzzles, and interactive books.

Goover is a personalized AI research agent that streamlines the process of acquiring knowledge by providing self-driving experiences. It offers users the ability to dive deeper into various topics through curated briefings, reports, and insights. Goover utilizes advanced AI technology to deliver tailored answers, identify key information, and facilitate meaningful discussions. Users can access knowledge anytime, anywhere through the mobile app, ensuring they stay informed and engaged with their passions. With Goover, users can track specific topics, receive automatic updates, and explore diverse perspectives effortlessly.

GPTSwarm is a graph-based framework for LLM-based agents that enables the creation of LLM-based agents from graphs and facilitates the customized and automatic self-organization of agent swarms with self-improvement capabilities. The library includes components for domain-specific operations, graph-related functions, LLM backend selection, memory management, and optimization algorithms to enhance agent performance and swarm efficiency. Users can quickly run predefined swarms or utilize tools like the file analyzer. GPTSwarm supports local LM inference via LM Studio, allowing users to run with a local LLM model. The framework has been accepted by ICML2024 and offers advanced features for experimentation and customization.

Basalt is a lightweight and flexible CSS framework designed to help developers quickly build responsive and modern websites. It provides a set of pre-designed components and utilities that can be easily customized to create unique and visually appealing web interfaces. With Basalt, developers can save time and effort by leveraging its modular structure and responsive design principles to create professional-looking websites with ease.

TigerGraph CoPilot is an AI assistant that combines graph databases and generative AI to enhance productivity across various business functions. It includes three core component services: InquiryAI for natural language assistance, SupportAI for knowledge Q&A, and QueryAI for GSQL code generation. Users can interact with CoPilot through a chat interface on TigerGraph Cloud and APIs. CoPilot requires LLM services for beta but will support TigerGraph's LLM in future releases. It aims to improve contextual relevance and accuracy of answers to natural-language questions by building knowledge graphs and using RAG. CoPilot is extensible and can be configured with different LLM providers, graph schemas, and LangChain tools.

GOLEM is an open-source AI framework focused on optimization and learning of structured graph-based models using meta-heuristic methods. It emphasizes the potential of meta-heuristics in complex problem spaces where gradient-based methods are not suitable, and the importance of structured models in various problem domains. The framework offers features like structured model optimization, metaheuristic methods, multi-objective optimization, constrained optimization, extensibility, interpretability, and reproducibility. It can be applied to optimization problems represented as directed graphs with defined fitness functions. GOLEM has applications in areas like AutoML, Bayesian network structure search, differential equation discovery, geometric design, and neural architecture search. The project structure includes packages for core functionalities, adapters, graph representation, optimizers, genetic algorithms, utilities, serialization, visualization, examples, and testing. Contributions are welcome, and the project is supported by ITMO University's Research Center Strong Artificial Intelligence in Industry.

Torch-Pruning (TP) is a library for structural pruning that enables pruning for a wide range of deep neural networks. It uses an algorithm called DepGraph to physically remove parameters. The library supports pruning off-the-shelf models from various frameworks and provides benchmarks for reproducing results. It offers high-level pruners, dependency graph for automatic pruning, low-level pruning functions, and supports various importance criteria and modules. Torch-Pruning is compatible with both PyTorch 1.x and 2.x versions.

Lantern is an open-source PostgreSQL database extension designed to store vector data, generate embeddings, and handle vector search operations efficiently. It introduces a new index type called 'lantern_hnsw' for vector columns, which speeds up 'ORDER BY ... LIMIT' queries. Lantern utilizes the state-of-the-art HNSW implementation called usearch. Users can easily install Lantern using Docker, Homebrew, or precompiled binaries. The tool supports various distance functions, index construction parameters, and operator classes for efficient querying. Lantern offers features like embedding generation, interoperability with pgvector, parallel index creation, and external index graph generation. It aims to provide superior performance metrics compared to other similar tools and has a roadmap for future enhancements such as cloud-hosted version, hardware-accelerated distance metrics, industry-specific application templates, and support for version control and A/B testing of embeddings.

The Neo4j GenAI Workshop repository contains notebooks for a workshop focusing on building a Neo4j Graph, text embedding, and providing demos for content generation. The workshop includes data staging, loading, and exploration using Cypher queries. It also covers improvements in LLM response quality, GPT-4 usage, and vector search speed. The repository has undergone multiple updates to enhance course quality, simplify content, and provide better explainers and examples.

The NaLLM project repository explores the synergies between Neo4j and Large Language Models (LLMs) through three primary use cases: Natural Language Interface to a Knowledge Graph, Creating a Knowledge Graph from Unstructured Data, and Generating a Report using static and LLM data. The repository contains backend and frontend code organized for easy navigation. It includes blog posts, a demo database, instructions for running demos, and guidelines for contributing. The project aims to showcase the potential of Neo4j and LLMs in various applications.

TrustGraph is a tool that deploys private GraphRAG pipelines to build a RDF style knowledge graph from data, enabling accurate and secure `RAG` requests compatible with cloud LLMs and open-source SLMs. It showcases the reliability and efficiencies of GraphRAG algorithms, capturing contextual language flags missed in conventional RAG approaches. The tool offers features like PDF decoding, text chunking, inference of various LMs, RDF-aligned Knowledge Graph extraction, and more. TrustGraph is designed to be modular, supporting multiple Language Models and environments, with a plug'n'play architecture for easy customization.

RAFT (Reusable Accelerated Functions and Tools) is a C++ header-only template library with an optional shared library that contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications.

nano-GraphRAG is a simple, easy-to-hack implementation of GraphRAG that provides a smaller, faster, and cleaner version of the official implementation. It is about 800 lines of code, small yet scalable, asynchronous, and fully typed. The tool supports incremental insert, async methods, and various parameters for customization. Users can replace storage components and LLM functions as needed. It also allows for embedding function replacement and comes with pre-defined prompts for entity extraction and community reports. However, some features like covariates and global search implementation differ from the original GraphRAG. Future versions aim to address issues related to data source ID, community description truncation, and add new components.

Guardrails is a Python framework that helps build reliable AI applications by performing two key functions: 1. Guardrails runs Input/Output Guards in your application that detect, quantify and mitigate the presence of specific types of risks. To look at the full suite of risks, check out Guardrails Hub. 2. Guardrails help you generate structured data from LLMs.

Endia is a dynamic Array library for Scientific Computing, offering automatic differentiation of arbitrary order, complex number support, dual API with PyTorch-like imperative or JAX-like functional interface, and JIT Compilation for speeding up training and inference. It can handle complex valued functions, perform both forward and reverse-mode automatic differentiation, and has a builtin JIT compiler. Endia aims to advance AI & Scientific Computing by pushing boundaries with clear algorithms, providing high-performance open-source code that remains readable and pythonic, and prioritizing clarity and educational value over exhaustive features.

This repository contains a sophisticated deterministic graph-based solution for answering complex questions using a controllable autonomous agent. The solution is designed to ensure that answers are solely based on the provided data, avoiding hallucinations. It involves various steps such as PDF loading, text preprocessing, summarization, database creation, encoding, and utilizing large language models. The algorithm follows a detailed workflow involving planning, retrieval, answering, replanning, content distillation, and performance evaluation. Heuristics and techniques implemented focus on content encoding, anonymizing questions, task breakdown, content distillation, chain of thought answering, verification, and model performance evaluation.

Aiocache is an asyncio cache library that supports multiple backends such as memory, redis, and memcached. It provides a simple interface for functions like add, get, set, multi_get, multi_set, exists, increment, delete, clear, and raw. Users can easily install and use the library for caching data in Python applications. Aiocache allows for easy instantiation of caches and setup of cache aliases for reusing configurations. It also provides support for backends, serializers, and plugins to customize cache operations. The library offers detailed documentation and examples for different use cases and configurations.

ChatLab is a Python package that simplifies experimenting with OpenAI's chat models. It provides an interactive interface for chatting with the models and registering custom functions. Users can easily create chat experiments, visualize color palettes, work with function registry, create knowledge graphs, and perform direct parallel function calling. The tool enables users to interact with chat models and customize functionalities for various tasks.

Langfun is a Python library that aims to make language models (LM) fun to work with. It enables a programming model that flows naturally, resembling the human thought process. Langfun emphasizes the reuse and combination of language pieces to form prompts, thereby accelerating innovation. Unlike other LM frameworks, which feed program-generated data into the LM, langfun takes a distinct approach: It starts with natural language, allowing for seamless interactions between language and program logic, and concludes with natural language and optional structured output. Consequently, langfun can aptly be described as Language as functions, capturing the core of its methodology.

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.

AiScript is a lightweight scripting language that runs on JavaScript. It supports arrays, objects, and functions as first-class citizens, and is easy to write without the need for semicolons or commas. AiScript runs in a secure sandbox environment, preventing infinite loops from freezing the host. It also allows for easy provision of variables and functions from the host.

PyTensor is a Python library that allows one to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays. It provides the computational backend for `PyMC `__. PyTensor is a fork of `Aesara `__, which is a fork of `Theano `__. PyTensor maintains a static graph which can be modified in-place to allow for advanced optimizations. Contrary to PyTorch and TensorFlow, PyTensor maintains a static graph which can be modified in-place to allow for advanced optimizations.

Educational guide for understanding algebra principles and equations.

IntegriPal: Your friendly integral calculus guide!

I craft PowerShell scripts for Microsoft Graph.

Converts graph triples to clear text, no assumptions.

Specialist in creating diagrams and graphs based on user requests

Japanese patent data graph generator with user engagement

Detailed technical diagram and graph assistant

Meet Tech Guru, your go-to AI for data engineering, coding expertise, and graph databases. Combining humor, reliability, and approachability to simplify tech with a personal touch.

Expert in generating and debugging Neo4j code, with explanations on graph database principles.

Maps PM skills to competencies, plots on radar graph.

Diagram creation: flowcharts, mindmaps, UML, chart, PlotUML, workflow, sequence, ERD, database & architecture visualization for code, presentations and documentation. [New] Add a logo or any image to graph diagrams. Easy Download & Edit

Expert in Micro Economics, interprets graphs, explains concepts, avoids direct exam answers.

Creates schedules, charts, or graphs based on user inputs.

Expert in schema.org, guiding precise use of 'additionalType'.

Provide a hand-drawing or screenshot of your desired plot along with the data and I'll make the plot.

Obsidian wine log assistant with code block outputs

💎 The worlds greatest data analysis tool 💎

Propagation Analysis over Graphs

Ask: "What graphs can you create from MY pee times data file (.xlsx)?" Show to your Urologist.

Crafts Powerpoint PPT presentations with visuals and graphs