Best AI tools for< Accelerate Search Process >

ModernQuery is an AI-powered search solution that enhances website search functionality with ChatGPT's conversational search technology. It offers a no-code solution for easy integration, allowing users to improve their on-site search experience without the need for technical expertise. With features like plug-and-play setup, manual search result adjustments, and autocomplete functionality, ModernQuery aims to provide a seamless and efficient search experience for website visitors. The application supports popular CMS platforms like WordPress and Drupal, as well as custom website integrations through JavaScript embedding.

ApplyFox.ai is an AI-powered job application tool that automates the job search process by allowing users to upload their resume and preferences, and then auto-applies to hundreds of job listings on their behalf. The tool helps users save time, increase their chances of landing interviews, and ensures professional and tailored applications across various industries and skill levels.

EarnBetter is an AI-powered platform that offers assistance in creating professional resumes, cover letters, and job search support. The platform utilizes artificial intelligence to rewrite and reformat resumes, generate tailored cover letters, provide personalized job matches, and offer interview support. Users can upload their current resume to get started and access a range of features to enhance their job search process. EarnBetter aims to streamline the job search experience by providing free, unlimited, and professional document creation services.

Jobsolv is an all-in-one job search platform that simplifies the application process for high-paying remote and hybrid roles. It offers a personalized AI-smart job board, automated resume tailoring, and one-click job applications. With Jobsolv, job seekers can efficiently apply to multiple jobs, increasing their chances of securing interviews and landing their dream jobs.

Swiftly AI is an AI-powered recruiting tool designed to streamline the candidate selection process for businesses. By leveraging artificial intelligence, Swiftly AI helps recruiters quickly identify and rank qualified candidates based on specified role requirements. The platform enables users to efficiently filter through a large pool of applicants, saving time and resources in the recruitment process. With Swiftly AI, companies can accelerate their talent search and make data-driven hiring decisions to recruit the most suitable candidates at scale.

ScholarAI is an AI-powered scientific research tool that offers a wide range of features to help users navigate and extract insights from scientific literature. With access to over 200 million peer-reviewed articles, ScholarAI allows users to conduct abstract searches, literature mapping, PDF reading, literature reviews, gap analysis, direct Q&A, table and figure extraction, citation management, and project management. The tool is designed to accelerate the research process and provide tailored scientific insights to users.

Kira Systems is a machine learning contract search, review, and analysis software that helps businesses identify, extract, and analyze content in their contracts and documents. It uses patented machine learning technology to extract concepts and data points with high efficiency and accuracy. Kira also has built-in intelligence that streamlines the contract review process with out-of-the-box smart fields. Businesses can also create their own smart fields to find specific data points using Kira's no-code machine learning tool. Kira's adaptive workflows allow businesses to organize, track, and export results. Kira has a partner ecosystem that allows businesses to transform how teams work with their contracts.

Toloka AI is a data labeling platform that empowers AI development by combining human insight with machine learning models. It offers adaptive AutoML, human-in-the-loop workflows, large language models, and automated data labeling. The platform supports various AI solutions with human input, such as e-commerce services, content moderation, computer vision, and NLP. Toloka AI aims to accelerate machine learning processes by providing high-quality human-labeled data and leveraging the power of the crowd.

QueryHub is an academic platform that combines collaborative learning with AI-powered solutions. It offers an interactive forum, AI chatbot assistance, and a smart search capability to enhance the academic experience of students. Users can engage in discussions, get instant answers to their queries, and access a pool of shared resources to accelerate their learning. QueryHub's user-driven innovation and personalized learning experience make it a valuable tool for students seeking academic success.

Transcript.LOL is a transcription tool designed to save time and enhance productivity for creators and small to medium-sized businesses. It offers a platform to transcribe audio, video, and meeting recordings, supporting over 1500 platforms. The tool provides summaries, categorizes key themes, and offers contextual Q&A based on the transcriptions. With speaker identification and readable transcripts, users can easily navigate and understand the content. Transcript.LOL aims to streamline the transcription process and provide valuable insights faster than ever before.

AutoSEO is an AI-powered tool designed to transform the content creation process on WordPress websites. By utilizing advanced artificial intelligence technology, AutoSEO automates and accelerates the production of SEO-optimized web content, enabling users to generate a large quantity of high-quality articles efficiently. The tool integrates seamlessly with WordPress and leverages OpenAI's technology to create content in multiple languages, making it a versatile solution for global content creation.

Wrangle is an AI-powered job search and resume builder platform designed to help job seekers find jobs faster and more efficiently. The platform offers personalized AI copilot assistance, extensive job information, resume creation tools, job tracking features, and a learning algorithm to improve job matches. Wrangle provides a user-friendly experience with a focus on data security and no ads or subscriptions.

Objective is an AI-native search platform designed for developers to build modern search experiences for web and mobile applications. It offers a multimodal search API that understands human language, images, and text relationships. The platform integrates various search techniques to provide natural and relevant search results, even with inconsistent data. Objective is trusted by great companies and accelerates data science roadmaps through its efficient search capabilities.

Tellius is an AI Augmented Analytics Software and Decision Intelligence platform that empowers users to get faster insights from data, break silos between Business Intelligence (BI) and AI, and accelerate complex data analysis with AI-driven automation. The platform offers guided insights, data preparation, natural language search, automated machine learning, and self-service analytics & reporting. Tellius is loved by analytics and business teams for providing instant ad hoc answers, simplifying complex analysis, and surfacing hidden key drivers and anomalies through best-in-class automated insights.

TextSmith.ai is an AI-powered SEO article and keyword generation tool designed to help users effortlessly create SEO-optimized content for their websites. With advanced AI capabilities, TextSmith.ai maximizes productivity by handling the heavy lifting of SEO tasks, such as keyword research and content creation. Users can generate keyword-rich articles quickly, outpacing competitors and climbing search rankings. The tool also offers seamless integration with WordPress, allowing users to publish high-quality content directly to their websites. TextSmith.ai stands out in the SEO tool market by providing tailored, data-driven insights and automated content generation, making it a must-have for businesses serious about SEO.

Epsilon is an AI search engine designed for scientific research solutions. It helps researchers find evidence, citations, and relevant information from over 200 million academic papers. Epsilon can summarize passages, group search results, extract key information from multiple papers, and provide comprehensive summaries. Trusted by over 30,000 researchers worldwide, Epsilon is a reliable tool for conducting literature reviews, drafting proposals, and executing research projects.

Climate Policy Radar is an AI-powered application that serves as a live, searchable database containing over 5,000 national climate laws, policies, and UN submissions. The app aims to organize, analyze, and democratize climate data by providing open data, code, and machine learning models. It promotes a responsible approach to AI, fosters a climate NLP community, and offers an API for organizations to utilize the data. The tool addresses the challenge of sparse and siloed climate-related information, empowering decision-makers with evidence-based policies to accelerate climate action.

Viinyx AI is an all-in-one AI browser assistant powered by leading AI technologies like ChatGPT-4, GPT-4o, Gemini 1.5, Claude 3+, DALL·E, and more. It offers features such as AI chatbox, writing assistant, prompt toolbar, document analysis, and text enhancement. Users can summarize pages, videos, search results, draft emails, articles, and interact with PDF documents and images. Viinyx aims to boost online productivity and creativity by providing a suite of AI tools accessible through a Chrome extension.

Lilac is an AI tool designed to enhance data quality and exploration for AI applications. It offers features such as data search, quantification, editing, clustering, semantic search, field comparison, and fuzzy-concept search. Lilac enables users to accelerate dataset computations and transformations, making it a valuable asset for data scientists and AI practitioners. The tool is trusted by Alignment Lab and is recommended for working with LLM datasets.

CoFinance is an AI-driven legal intelligence and collaboration hub that revolutionizes legal and compliance research workflows. It combines semantic search, multi-faceted document analysis, and intelligent organization tools to provide precise and efficient research solutions. The platform leverages cutting-edge Regulatory Artificial Intelligence (RAI) technology to ensure that answers are sourced from real, authoritative data. CoFinance prioritizes simplifying regulatory complexity, mitigating compliance risks, accelerating research efficiency, and providing reliable partnership for long-term compliance success. It caters to organizations navigating complex regulatory landscapes, offering quick adaptation to changes and seamless compliance across various industries and jurisdictions.

This repository contains a comprehensive collection of 63 must-know Large Language Models (LLMs) interview questions. It covers topics such as the architecture of LLMs, transformer models, attention mechanisms, training processes, encoder-decoder frameworks, differences between LLMs and traditional statistical language models, handling context and long-term dependencies, transformers for parallelization, applications of LLMs, sentiment analysis, language translation, conversation AI, chatbots, and more. The readme provides detailed explanations, code examples, and insights into utilizing LLMs for various tasks.

This paper list is about Large Language Model-enhanced Recommender System. It also contains some related works. Keywords: recommendation system, large language models

The LLM RAG Workshop repository provides a workshop on using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) to generate and understand text in a human-like manner. It includes instructions on setting up the environment, indexing Zoomcamp FAQ documents, creating a Q&A system, and using OpenAI for generation based on retrieved information. The repository focuses on enhancing language model responses with retrieved information from external sources, such as document databases or search engines, to improve factual accuracy and relevance of generated text.

This repository is dedicated to the pruning of large language models (LLMs). It aims to serve as a comprehensive resource for researchers and practitioners interested in the efficient reduction of model size while maintaining or enhancing performance. The repository contains various papers, summaries, and links related to different pruning approaches for LLMs, along with author information and publication details. It covers a wide range of topics such as structured pruning, unstructured pruning, semi-structured pruning, and benchmarking methods. Researchers and practitioners can explore different pruning techniques, understand their implications, and access relevant resources for further study and implementation.

This repository contains examples, applications, starter code, & tutorials to help you kickstart your GenAI projects. * These are built using LanceDB, a free, open-source, serverless vectorDB that **requires no setup**. * It **integrates into python data ecosystem** so you can simply start using these in your existing data pipelines in pandas, arrow, pydantic etc. * LanceDB has **native Typescript SDK** using which you can **run vector search** in serverless functions! This repository is divided into 3 sections: - Examples - Get right into the code with minimal introduction, aimed at getting you from an idea to PoC within minutes! - Applications - Ready to use Python and web apps using applied LLMs, VectorDB and GenAI tools - Tutorials - A curated list of tutorials, blogs, Colabs and courses to get you started with GenAI in greater depth.

Data-to-paper is an AI-driven framework designed to guide users through the process of conducting end-to-end scientific research, starting from raw data to the creation of comprehensive and human-verifiable research papers. The framework leverages a combination of LLM and rule-based agents to assist in tasks such as hypothesis generation, literature search, data analysis, result interpretation, and paper writing. It aims to accelerate research while maintaining key scientific values like transparency, traceability, and verifiability. The framework is field-agnostic, supports both open-goal and fixed-goal research, creates data-chained manuscripts, involves human-in-the-loop interaction, and allows for transparent replay of the research process.

This repository is a curated collection of powerful AI tools that accelerate and enhance game development. It provides tools for asset, texture, image, code generation, animation video mocap, voice generation, speech recognition, conversational models, game design, search engine, AI NPC, Python libraries, and C# libraries. These tools streamline the creation process, save time, automate tasks, and unlock creative possibilities for game developers, whether indie or part of a studio. The repository aims to speed up development and enable the creation of immersive games by leveraging cutting-edge AI technologies.

Build-your-own-AI-Assistant-Solution-Accelerator is a pre-release and preview solution that helps users create their own AI assistants. It leverages Azure Open AI Service, Azure AI Search, and Microsoft Fabric to identify, summarize, and categorize unstructured information. Users can easily find relevant articles and grants, generate grant applications, and export them as PDF or Word documents. The solution accelerator provides reusable architecture and code snippets for building AI assistants with enterprise data. It is designed for researchers looking to explore flu vaccine studies and grants to accelerate grant proposal submissions.

Open AI is a powerful tool for artificial intelligence research and development. It provides a wide range of machine learning models and algorithms, making it easier for developers to create innovative AI applications. With Open AI, users can explore cutting-edge technologies such as natural language processing, computer vision, and reinforcement learning. The platform offers a user-friendly interface and comprehensive documentation to support users in building and deploying AI solutions. Whether you are a beginner or an experienced AI practitioner, Open AI offers the tools and resources you need to accelerate your AI projects and stay ahead in the rapidly evolving field of artificial intelligence.

Reinforcement Learning from AI Feedback (RLAIF) is a concept that describes a type of machine learning approach where **an AI agent learns by receiving feedback or guidance from another AI system**. This concept is closely related to the field of Reinforcement Learning (RL), which is a type of machine learning where an agent learns to make a sequence of decisions in an environment to maximize a cumulative reward. In traditional RL, an agent interacts with an environment and receives feedback in the form of rewards or penalties based on the actions it takes. It learns to improve its decision-making over time to achieve its goals. In the context of Reinforcement Learning from AI Feedback, the AI agent still aims to learn optimal behavior through interactions, but **the feedback comes from another AI system rather than from the environment or human evaluators**. This can be **particularly useful in situations where it may be challenging to define clear reward functions or when it is more efficient to use another AI system to provide guidance**. The feedback from the AI system can take various forms, such as: - **Demonstrations** : The AI system provides demonstrations of desired behavior, and the learning agent tries to imitate these demonstrations. - **Comparison Data** : The AI system ranks or compares different actions taken by the learning agent, helping it to understand which actions are better or worse. - **Reward Shaping** : The AI system provides additional reward signals to guide the learning agent's behavior, supplementing the rewards from the environment. This approach is often used in scenarios where the RL agent needs to learn from **limited human or expert feedback or when the reward signal from the environment is sparse or unclear**. It can also be used to **accelerate the learning process and make RL more sample-efficient**. Reinforcement Learning from AI Feedback is an area of ongoing research and has applications in various domains, including robotics, autonomous vehicles, and game playing, among others.

Chat with your data using OpenAI and AI Search. This solution accelerator uses an Azure OpenAI GPT model and an Azure AI Search index generated from your data, which is integrated into a web application to provide a natural language interface, including speech-to-text functionality, for search queries. Users can drag and drop files, point to storage, and take care of technical setup to transform documents. There is a web app that users can create in their own subscription with security and authentication.

This repository contains a hand-curated list of great machine (deep) learning resources for Natural Language Processing (NLP) with a focus on Generative Pre-trained Transformer (GPT), Bidirectional Encoder Representations from Transformers (BERT), attention mechanism, Transformer architectures/networks, Chatbot, and transfer learning in NLP.

Awesome-AI-Coding is a curated list of AI coding topics, projects, datasets, LLM models, embedding models, papers, blogs, products, startups, and peer awesome lists related to artificial intelligence in coding. It includes tools for code completion, code generation, code documentation, and code search, as well as AI models and techniques for improving developer productivity. The repository also features information on various AI-powered developer tools, copilots, and related resources in the AI coding domain.

Easy-Translate is a script designed for translating large text files with a single command. It supports various models like M2M100, NLLB200, SeamlessM4T, LLaMA, and Bloom. The tool is beginner-friendly and offers seamless and customizable features for advanced users. It allows acceleration on CPU, multi-CPU, GPU, multi-GPU, and TPU, with support for different precisions and decoding strategies. Easy-Translate also provides an evaluation script for translations. Built on HuggingFace's Transformers and Accelerate library, it supports prompt usage and loading huge models efficiently.

This repository contains a collection of papers and resources related to Large Language Models (LLMs), including their applications in various domains such as text generation, translation, question answering, and dialogue systems. The repository also includes discussions on the ethical and societal implications of LLMs. **Description** This repository is a collection of papers and resources related to Large Language Models (LLMs). LLMs are a type of artificial intelligence (AI) that can understand and generate human-like text. They have a wide range of applications, including text generation, translation, question answering, and dialogue systems. **For Jobs** - **Content Writer** - **Copywriter** - **Editor** - **Journalist** - **Marketer** **AI Keywords** - **Large Language Models** - **Natural Language Processing** - **Machine Learning** - **Artificial Intelligence** - **Deep Learning** **For Tasks** - **Generate text** - **Translate text** - **Answer questions** - **Engage in dialogue** - **Summarize text**

Awesome LLM compression research papers and tools to accelerate LLM training and inference.

Co-LLM (Collaborative Language Models) is a tool for learning to decode collaboratively with multiple language models. It provides a method for data processing, training, and inference using a collaborative approach. The tool involves steps such as formatting/tokenization, scoring logits, initializing Z vector, deferral training, and generating results using multiple models. Co-LLM supports training with different collaboration pairs and provides baseline training scripts for various models. In inference, it uses 'vllm' services to orchestrate models and generate results through API-like services. The tool is inspired by allenai/open-instruct and aims to improve decoding performance through collaborative learning.

Renumics RAG is a retrieval-augmented generation assistant demo that utilizes LangChain and Streamlit. It provides a tool for indexing documents and answering questions based on the indexed data. Users can explore and visualize RAG data, configure OpenAI and Hugging Face models, and interactively explore questions and document snippets. The tool supports GPU and CPU setups, offers a command-line interface for retrieving and answering questions, and includes a web application for easy access. It also allows users to customize retrieval settings, embeddings models, and database creation. Renumics RAG is designed to enhance the question-answering process by leveraging indexed documents and providing detailed answers with sources.

NoLabs is an open-source biolab that provides easy access to state-of-the-art models for bio research. It supports various tasks, including drug discovery, protein analysis, and small molecule design. NoLabs aims to accelerate bio research by making inference models accessible to everyone.

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Accelerates the learning of key phrases and cultural norms for travelers in various languages.

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