Best AI tools for< Build A Database >

Interview.study is an AI-powered interview preparation platform that helps candidates practice real interview questions asked by top companies. The platform provides users with instant feedback on their responses, helping them identify areas for improvement and develop stronger answers. Interview.study also offers a variety of features to help candidates prepare for their interviews, including a database of interview questions, a mock interview tool, and a resume builder.

No Code Camp is an online learning platform that teaches people how to use artificial intelligence (AI) and no-code tools to automate their work and build applications. The platform offers a live, 5-week cohort-based course that covers the essentials of no-code development, including data architecture, interface design, AI scaling, and no-code automation. The course is designed for people with no prior coding experience and is taught by experienced instructors who have built and scaled digital products using no-code tools.

Hanabi.rest is an AI-based API building platform that allows users to create REST APIs from natural language and screenshots using AI technology. Users can deploy the APIs on Cloudflare Workers and roll them out globally. The platform offers a live editor for testing database access and API endpoints, generates code compatible with various runtimes, and provides features like sharing APIs via URL, npm package integration, and CLI dump functionality. Hanabi.rest simplifies API design and deployment by leveraging natural language processing, image recognition, and v0.dev components.

Spreadsheet.com is an online spreadsheet that allows users to build no-code solutions for project management, CRM, and other tasks. It offers features such as sheet views, Gantt charts, Kanban boards, form views, calendar views, attachments, related rows, charts, reports, dashboards, automations, integrations, and Spreadsheet AI. Spreadsheet.com also provides a variety of templates for specific use cases, such as project management, sales and CRM, product development, finance and accounting, lists and checklists, HR and recruiting, small business, personal, team collaboration, marketing, IT and support, construction, education, real estate, startups, and nonprofits.

Marblism is a platform that allows developers to quickly and easily launch React and Node.js applications. With Marblism, developers can generate the database schema, all the endpoints in the API, the design system, and even a few pages in the front-end. This can save developers a significant amount of time and effort, allowing them to focus on adding their unique touch to their applications.

Activeloop is an AI tool that offers Deep Lake, a database for AI solutions across various industries such as agriculture, audio processing, autonomous vehicles, robotics, biomedical and healthcare, generative AI, multimedia, safety, and security. The platform provides features like fast AI search, faster data preparation, serverless DB for code assistant, and more. Activeloop aims to streamline data processing and enhance AI development for businesses and researchers.

RquestR is an AI-powered knowledge management platform designed specifically for procurement professionals. It streamlines projects, enables instant answers retrieval, and facilitates informed decision-making. The platform offers features such as intelligent document querying, automated Q&A generation, and knowledge base building. RquestR helps in reducing response time by up to 40% and enhancing decision-making accuracy by 30%. It provides a centralized knowledge hub for managing RFPs, security questionnaires, and Q&As, all while ensuring enterprise-grade security. The platform revolutionizes the procurement process by leveraging advanced AI for lightning-fast information retrieval, accurate responses, and adaptive learning.

Pinecone is a vector database that helps power AI for the world's best companies. It is a serverless database that lets you deliver remarkable GenAI applications faster, at up to 50x lower cost. Pinecone is easy to use and can be integrated with your favorite cloud provider, data sources, models, frameworks, and more.

EasySBC is a web-based application that provides solutions for Squad Building Challenges (SBCs) in the popular video game FIFA 23. It features an AI-powered squad builder that helps users create optimal squads for SBCs, taking into account player ratings, chemistry, and other factors. The application also includes a comprehensive database of players and their attributes, as well as meta ratings that indicate the effectiveness of players in different positions and formations.

MyHeritage is an online family history website that allows users to build family trees, search historical records, and take DNA tests. The website has a large database of historical records, including birth, marriage, and death certificates, as well as census records and immigration records. MyHeritage also offers a variety of tools to help users build their family trees, including a search engine, a record matching tool, and a collaboration tool. The website also offers a variety of DNA tests, including a basic ancestry test, a health and ancestry test, and a mitochondrial DNA test. MyHeritage is a valuable resource for anyone who is interested in learning more about their family history.

Pinecone is a vector database designed to help power AI applications for various companies. It offers a serverless platform that enables users to build knowledgeable AI applications quickly and cost-effectively. With Pinecone, users can perform low-latency vector searches for tasks such as search, recommendation, detection, and more. The platform is scalable, secure, and cloud-native, making it suitable for a wide range of AI projects.

Press Ranger is an AI-powered PR tool that makes building media lists and pitching journalists painless. It offers a massive database of over 500,000 journalists and publishers, and uses AI tools to create media lists and run outreach campaigns. Press Ranger also provides real-time analytics to track and optimize PR efforts.

SOMA is a Research Automation Platform that accelerates medical innovation by providing up to 100x speedup through process automation. The platform collates and analyzes medical research articles, extracting important concepts and identifying 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 build pipelines for tasks like article search, concept analysis, drug repurposing, and target discovery. SOMA enhances literature review by finding relevant articles based on causal chains and keywords, enabling users to uncover hidden connections efficiently. The platform is freemium, offering basic functionality for free with the option to subscribe for advanced features.

Pinecone is a vector database designed to build knowledgeable AI applications. It offers a serverless platform with high capacity and low cost, enabling users to perform low-latency vector search for various AI tasks. Pinecone is easy to start and scale, allowing users to create an account, upload vector embeddings, and retrieve relevant data quickly. The platform combines vector search with metadata filters and keyword boosting for better application performance. Pinecone is secure, reliable, and cloud-native, making it suitable for powering mission-critical AI applications.

Sheety is a spreadsheet-like database that lets you build powerful apps without writing any code. It's perfect for teams who need to track data, manage projects, and collaborate on documents.

Rowy is a low-code backend platform that allows users to manage their database on a spreadsheet-like interface and build powerful backend cloud functions without leaving their browser. It offers a variety of features such as derivative fields, action fields, extensions, webhooks, and integrations with popular tools like Google Vision, GPT-3, Figma, and Webflow. Rowy is designed to be accessible to both developers and non-technical users, making it a versatile tool for building and managing backend applications.

GlobeNewswire is a press release distribution service that offers a variety of features to help businesses get their news in front of the right audience. These features include targeted distribution options, media monitoring, a media contacts database, and PR measurement. GlobeNewswire also offers an AI press release generator that can help businesses create press releases quickly and easily.

PromptChainer is a powerful AI flow generation tool that allows users to create complex AI-driven flows with ease using a visual flow builder. It seamlessly integrates AI and traditional programming, enabling users to chain prompts and models and manage AI-generated insights on large-scale data effortlessly. With pre-built templates, a user-friendly database, and versatile logic nodes, PromptChainer empowers users to build custom flows or applications with infinite possibilities.

SingleStore is a real-time data platform designed for apps, analytics, and gen AI. It offers faster hybrid vector + full-text search, fast-scaling integrations, and a free tier. SingleStore can read, write, and reason on petabyte-scale data in milliseconds. It supports streaming ingestion, high concurrency, first-class vector support, record lookups, and more.

Neurelo is a cloud API platform that offers services for PostgreSQL, MongoDB, and MySQL. It provides features such as auto-generated APIs, custom query APIs with AI assistance, query observability, schema as code, and the ability to build full-stack applications in minutes. Neurelo aims to empower developers by simplifying database programming complexities and enhancing productivity. The platform leverages the power of cloud technology, APIs, and AI to offer a seamless and efficient way to build and run applications.

Buildware is a tool designed to help developers accelerate their code shipping process by leveraging AI technology. Users can build a code instruction system, submit an issue, and receive an AI-generated pull request. The tool is created by Mckay Wrigley and Tyler Bruno at Takeoff AI. Buildware offers a simple setup process involving cloning the repository, installing dependencies, setting up environment variables, configuring a database, and obtaining a GitHub Personal Access Token (PAT). The tool is currently being updated to include advanced features such as Linear integration, local codebase mode, and team support.

This sample shows how to build a serverless chat experience with Retrieval-Augmented Generation using LangChain.js and Azure. The application is hosted on Azure Static Web Apps and Azure Functions, with Azure Cosmos DB for MongoDB vCore as the vector database. You can use it as a starting point for building more complex AI applications.

LLM-PowerHouse is a comprehensive and curated guide designed to empower developers, researchers, and enthusiasts to harness the true capabilities of Large Language Models (LLMs) and build intelligent applications that push the boundaries of natural language understanding. This GitHub repository provides in-depth articles, codebase mastery, LLM PlayLab, and resources for cost analysis and network visualization. It covers various aspects of LLMs, including NLP, models, training, evaluation metrics, open LLMs, and more. The repository also includes a collection of code examples and tutorials to help users build and deploy LLM-based applications.

LLMWare is a framework for quickly developing LLM-based applications including Retrieval Augmented Generation (RAG) and Multi-Step Orchestration of Agent Workflows. This project provides a comprehensive set of tools that anyone can use - from a beginner to the most sophisticated AI developer - to rapidly build industrial-grade, knowledge-based enterprise LLM applications. Our specific focus is on making it easy to integrate open source small specialized models and connecting enterprise knowledge safely and securely.

A massive list including a huge amount of products and services that are completely free! ⭐ Star on GitHub • 🤝 Contribute # Table of Contents * APIs, Data & ML * Artificial Intelligence * BaaS * Code Editors * Code Generation * DNS * Databases * Design & UI * Domains * Email * Font * For Students * Forms * Linux Distributions * Messaging & Streaming * PaaS * Payments & Billing * SSL

Local Multimodal AI Chat is a hands-on project that teaches you how to build a multimodal chat application. It integrates different AI models to handle audio, images, and PDFs in a single chat interface. This project is perfect for anyone interested in AI and software development who wants to gain practical experience with these technologies.

Comflowy is a community dedicated to providing comprehensive tutorials, fostering discussions, and building a database of workflows and models for ComfyUI and Stable Diffusion. Our mission is to lower the entry barrier for ComfyUI users, promote its mainstream adoption, and contribute to the growth of the AI generative graphics community.

Open Data QnA is a Python library that allows users to interact with their PostgreSQL or BigQuery databases in a conversational manner, without needing to write SQL queries. The library leverages Large Language Models (LLMs) to bridge the gap between human language and database queries, enabling users to ask questions in natural language and receive informative responses. It offers features such as conversational querying with multiturn support, table grouping, multi schema/dataset support, SQL generation, query refinement, natural language responses, visualizations, and extensibility. The library is built on a modular design and supports various components like Database Connectors, Vector Stores, and Agents for SQL generation, validation, debugging, descriptions, embeddings, responses, and visualizations.

This repository contains samples to help design AI applications using data from an Azure SQL Database. It showcases technical concepts and workflows integrating Azure SQL data with popular AI components both within and outside Azure. The samples cover various AI features such as Azure Cognitive Services, Promptflow, OpenAI, Vanna.AI, Content Moderation, LangChain, and more. Additionally, there are end-to-end samples like Similar Content Finder, Session Conference Assistant, Chatbots, Vectorization, SQL Server Database Development, Redis Vector Search, and Similarity Search with FAISS.

The LLM course is divided into three parts: 1. 🧩 **LLM Fundamentals** covers essential knowledge about mathematics, Python, and neural networks. 2. 🧑‍🔬 **The LLM Scientist** focuses on building the best possible LLMs using the latest techniques. 3. 👷 **The LLM Engineer** focuses on creating LLM-based applications and deploying them. For an interactive version of this course, I created two **LLM assistants** that will answer questions and test your knowledge in a personalized way: * 🤗 **HuggingChat Assistant**: Free version using Mixtral-8x7B. * 🤖 **ChatGPT Assistant**: Requires a premium account. ## 📝 Notebooks A list of notebooks and articles related to large language models. ### Tools | Notebook | Description | Notebook | |----------|-------------|----------| | 🧐 LLM AutoEval | Automatically evaluate your LLMs using RunPod |  | | 🥱 LazyMergekit | Easily merge models using MergeKit in one click. |  | | 🦎 LazyAxolotl | Fine-tune models in the cloud using Axolotl in one click. |  | | ⚡ AutoQuant | Quantize LLMs in GGUF, GPTQ, EXL2, AWQ, and HQQ formats in one click. |  | | 🌳 Model Family Tree | Visualize the family tree of merged models. |  | | 🚀 ZeroSpace | Automatically create a Gradio chat interface using a free ZeroGPU. |  |

Aimeos Laravel is a professional, full-featured, and ultra-fast Laravel ecommerce package that can be easily integrated into existing Laravel applications. It offers a wide range of features including multi-vendor, multi-channel, and multi-warehouse support, fast performance, support for various product types, subscriptions with recurring payments, multiple payment gateways, full RTL support, flexible pricing options, admin backend, REST and GraphQL APIs, modular structure, SEO optimization, multi-language support, AI-based text translation, mobile optimization, and high-quality source code. The package is highly configurable and extensible, making it suitable for e-commerce SaaS solutions, marketplaces, and online shops with millions of vendors.

A curated list of Generative AI projects, tools, artworks, and models

This repository does not provide a tool, but rather a collection of resources and strategies for academics in the field of artificial intelligence who are feeling depressed or overwhelmed by the rapid advancements in the field. The resources include articles, blog posts, and other materials that offer advice on how to cope with the challenges of working in a fast-paced and competitive environment.

Awesome-LLM-Large-Language-Models-Notes is a repository that provides a comprehensive collection of information on various Large Language Models (LLMs) classified by year, size, and name. It includes details on known LLM models, their papers, implementations, and specific characteristics. The repository also covers LLM models classified by architecture, must-read papers, blog articles, tutorials, and implementations from scratch. It serves as a valuable resource for individuals interested in understanding and working with LLMs in the field of Natural Language Processing (NLP).

KDB.AI is a time-based vector database that allows developers to build scalable, reliable, and real-time applications by providing advanced search, recommendation, and personalization for Generative AI applications. It supports multiple index types, distance metrics, top-N and metadata filtered retrieval, as well as Python and REST interfaces. The repository contains samples demonstrating various use-cases such as temporal similarity search, document search, image search, recommendation systems, sentiment analysis, and more. KDB.AI integrates with platforms like ChatGPT, Langchain, and LlamaIndex. The setup steps require Unix terminal, Python 3.8+, and pip installed. Users can install necessary Python packages and run Jupyter notebooks to interact with the samples.

TinyLLM is a project that helps build a small locally hosted language model with a web interface using consumer-grade hardware. It supports multiple language models, builds a local OpenAI API web service, and serves a Chatbot web interface with customizable prompts. The project requires specific hardware and software configurations for optimal performance. Users can run a local language model using inference servers like vLLM, llama-cpp-python, and Ollama. The Chatbot feature allows users to interact with the language model through a web-based interface, supporting features like summarizing websites, displaying news headlines, stock prices, weather conditions, and using vector databases for queries.

LLMFlex is a python package designed for developing AI applications with local Large Language Models (LLMs). It provides classes to load LLM models, embedding models, and vector databases to create AI-powered solutions with prompt engineering and RAG techniques. The package supports multiple LLMs with different generation configurations, embedding toolkits, vector databases, chat memories, prompt templates, custom tools, and a chatbot frontend interface. Users can easily create LLMs, load embeddings toolkit, use tools, chat with models in a Streamlit web app, and serve an OpenAI API with a GGUF model. LLMFlex aims to offer a simple interface for developers to work with LLMs and build private AI solutions using local resources.

This course has 18 lessons. Each lesson covers its own topic so start wherever you like! Lessons are labeled either "Learn" lessons explaining a Generative AI concept or "Build" lessons that explain a concept and code examples in both **Python** and **TypeScript** when possible. Each lesson also includes a "Keep Learning" section with additional learning tools. **What You Need** * Access to the Azure OpenAI Service **OR** OpenAI API - _Only required to complete coding lessons_ * Basic knowledge of Python or Typescript is helpful - *For absolute beginners check out these Python and TypeScript courses. * A Github account to fork this entire repo to your own GitHub account We have created a **Course Setup** lesson to help you with setting up your development environment. Don't forget to star (🌟) this repo to find it easier later. ## 🧠 Ready to Deploy? If you are looking for more advanced code samples, check out our collection of Generative AI Code Samples in both **Python** and **TypeScript**. ## 🗣️ Meet Other Learners, Get Support Join our official AI Discord server to meet and network with other learners taking this course and get support. ## 🚀 Building a Startup? Sign up for Microsoft for Startups Founders Hub to receive **free OpenAI credits** and up to **$150k towards Azure credits to access OpenAI models through Azure OpenAI Services**. ## 🙏 Want to help? Do you have suggestions or found spelling or code errors? Raise an issue or Create a pull request ## 📂 Each lesson includes: * A short video introduction to the topic * A written lesson located in the README * Python and TypeScript code samples supporting Azure OpenAI and OpenAI API * Links to extra resources to continue your learning ## 🗃️ Lessons | | Lesson Link | Description | Additional Learning | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | | 00 | Course Setup | **Learn:** How to Setup Your Development Environment | Learn More | | 01 | Introduction to Generative AI and LLMs | **Learn:** Understanding what Generative AI is and how Large Language Models (LLMs) work. | Learn More | | 02 | Exploring and comparing different LLMs | **Learn:** How to select the right model for your use case | Learn More | | 03 | Using Generative AI Responsibly | **Learn:** How to build Generative AI Applications responsibly | Learn More | | 04 | Understanding Prompt Engineering Fundamentals | **Learn:** Hands-on Prompt Engineering Best Practices | Learn More | | 05 | Creating Advanced Prompts | **Learn:** How to apply prompt engineering techniques that improve the outcome of your prompts. | Learn More | | 06 | Building Text Generation Applications | **Build:** A text generation app using Azure OpenAI | Learn More | | 07 | Building Chat Applications | **Build:** Techniques for efficiently building and integrating chat applications. | Learn More | | 08 | Building Search Apps Vector Databases | **Build:** A search application that uses Embeddings to search for data. | Learn More | | 09 | Building Image Generation Applications | **Build:** A image generation application | Learn More | | 10 | Building Low Code AI Applications | **Build:** A Generative AI application using Low Code tools | Learn More | | 11 | Integrating External Applications with Function Calling | **Build:** What is function calling and its use cases for applications | Learn More | | 12 | Designing UX for AI Applications | **Learn:** How to apply UX design principles when developing Generative AI Applications | Learn More | | 13 | Securing Your Generative AI Applications | **Learn:** The threats and risks to AI systems and methods to secure these systems. | Learn More | | 14 | The Generative AI Application Lifecycle | **Learn:** The tools and metrics to manage the LLM Lifecycle and LLMOps | Learn More | | 15 | Retrieval Augmented Generation (RAG) and Vector Databases | **Build:** An application using a RAG Framework to retrieve embeddings from a Vector Databases | Learn More | | 16 | Open Source Models and Hugging Face | **Build:** An application using open source models available on Hugging Face | Learn More | | 17 | AI Agents | **Build:** An application using an AI Agent Framework | Learn More | | 18 | Fine-Tuning LLMs | **Learn:** The what, why and how of fine-tuning LLMs | Learn More |

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