Best AI tools for< Search Databases >

VecRank is an AI-powered Vector Search and Reranking API service that leverages cutting-edge GenAI technologies to enhance natural language understanding and contextual relevance. It offers a scalable, AI-driven search solution for software developers and business owners. With VecRank, users can revolutionize their search capabilities with the power of AI, enabling seamless integration and powerful tools that scale with their business needs. The service allows for bulk data upload, incremental data updates, and easy integration into various programming languages and platforms, all without the hassle of setting up infrastructure for embeddings and vector search databases.

ClearAI is an AI-powered platform that offers instant extraction of insights, effortless document navigation, and natural language interaction. It enables users to upload PDFs securely, ask questions, and receive accurate responses in seconds. With features like structured results, intelligent search, and lifetime access offers, ClearAI simplifies tasks such as analyzing company reports, risk assessment, audit support, contract review, legal research, and due diligence. The platform is designed to streamline document analysis and provide relevant data efficiently.

Chessvision.ai is an AI-powered eBook reader designed to enhance the study experience of chess enthusiasts. The application utilizes Artificial Intelligence and Computer Vision to make chess eBooks interactive, allowing users to analyze positions, add comments, search online databases, watch YouTube videos, and analyze with the engine. It is a highly recommended tool for players of all levels looking to improve their chess skills.

BugFree.ai is an AI-powered platform designed to help users practice system design and behavior interviews, similar to Leetcode. The platform offers a range of features to assist users in preparing for technical interviews, including mock interviews, real-time feedback, and personalized study plans. With BugFree.ai, users can improve their problem-solving skills and gain confidence in tackling complex interview questions.

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.

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.

Mixpeek is a flexible search infrastructure designed to simplify multimodal search across various media types. It allows users to search using natural language, images, or video clips, providing insights and recommendations with just one line of code. The platform offers features like semantic search, visual query, hybrid search, fine-tuning & reranking, custom entities, performance analytics, and advanced aggregations. Mixpeek is suitable for a wide range of vision use cases, from basic image search to complex video understanding systems, without the need for reengineering. It addresses common challenges like tedious annotations, limited transcriptions, and basic object detection, offering integrations with various databases, cloud apps, content systems, and more.

TextMine is an AI-powered knowledge base that helps businesses analyze, manage, and search thousands of documents. It uses AI to analyze unstructured textual data and document databases, automatically retrieving key terms to help users make informed decisions. TextMine's features include a document vault for storing and managing documents, a categorization system for organizing documents, and a data extraction tool for extracting insights from documents. TextMine can help businesses save time, money, and improve efficiency by automating manual data entry and information retrieval tasks.

Skima is a recruitment AI and talent intelligence platform that aims to revolutionize the recruitment process by leveraging AI technology to help recruiters find the perfect candidates quickly and efficiently. The platform offers features such as AI-powered candidate assessment, seamless ATS integration, and smart job description generation to streamline the hiring process and improve recruitment ROI. Skima enables recruiters to unlock valuable insights from candidate databases, diversify client reach, and identify talent based on client demands. With Skima, recruiters can save time, increase productivity, and make data-driven hiring decisions.

LangSearch is an AI tool that offers a free Web Search API and Rerank API, serving as the World Engine for AGI. It allows users to connect their LLM applications to access clean, accurate, high-quality context from billions of web documents, including news, images, videos, and more. The tool supports natural language search and provides enhanced search details for various content types.

GoSearch is an AI-powered Enterprise Search and Resource Discovery platform that enables users to search all internal apps and resources in seconds with the help of AI technology. It offers features like AI workplace assistant, unified knowledge hub, multimodal AI, custom GPTs, and a no-code AI chatbot builder. GoSearch aims to streamline knowledge management and boost productivity by providing instant answers and information discovery through advanced search innovations.

Law.co is an advanced AI platform designed specifically for lawyers and law firms to streamline legal operations and enhance efficiency. The platform offers a semantic database search with access to over 1 million historical legal cases and 40,000 legal contracts, enabling users to perform detailed legal research, contract drafting, document review, and more. Law.co leverages custom-trained artificial intelligence and semantic search tools to deliver measurable results, revolutionizing legal research and document preparation processes for legal professionals.

Census GPT is an AI tool that provides data analysis services based on census information in the USA. The tool allows users to access data related to crime, demographics, income, education levels, and population. Users can ask specific questions to retrieve detailed information and insights from the available datasets.

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.

Find My Remote is an AI-powered job search platform that streamlines the job hunting process by leveraging artificial intelligence to find and structure job postings from various ATS platforms. Users can set their job preferences, receive personalized job matches, and save time by applying to curated job listings. The platform offers exclusive job opportunities not typically found on popular job search websites like LinkedIn. With features such as job discovery, application tracking, and faster application process, Find My Remote aims to revolutionize the way job seekers find and apply for jobs.

Hirebase is an AI-powered job search engine that provides ultra-fresh job market data directly from company pages. It uses AI to scan 100,000 jobs in real-time, ensuring that every job listed is actively hiring on the internet. Users can receive email alerts for new job listings based on their preferences for job title, keywords, location, experience level, date posted, salary range, and more. Hirebase aims to 'unsuckify' the job search process by leveraging AI technology to streamline and enhance the job hunting experience.

Papertalk.io is an AI-powered platform that revolutionizes research by providing users with access to over 215 million papers, AI-generated explanations, and actionable insights. The platform offers precision search tools, AI-powered understanding of research papers, and personalized guidance on applying insights practically. Papertalk.io aims to make research more accessible and approachable for users from diverse backgrounds, transforming complex data into easy-to-digest formats to foster innovation and expertise.

Slot Scatter Hitam is an online platform that offers a high Return to Player (RTP) experience, especially in the Mahjong Ways game. It provides players with the opportunity to achieve big wins by obtaining the rare black scatter symbol. The platform collaborates with major providers to enhance gameplay and increase jackpot chances. Players can benefit from game pattern hints, bonuses, and promotions, making their gaming sessions more enjoyable and rewarding. Slot Scatter Hitam aims to deliver a satisfying winning experience through strategic play and a bit of luck.

MajorGen is an AI-powered resume creator that helps you create professional and tailored resumes in minutes. With MajorGen, you can easily create a resume that highlights your skills and experience, and that is tailored to the specific job you are applying for. MajorGen offers a variety of features to help you create the perfect resume, including a library of professional resume templates, a resume builder that helps you write your resume in a clear and concise way, and a resume checker that checks your resume for errors.

AI Search is a comprehensive AI tools database that helps users discover and explore a wide range of AI tools and applications. With over 13000 AI tools listed and updated daily, AI Search provides a valuable resource for individuals and businesses seeking to leverage AI technologies. The platform allows users to search for AI tools based on specific functions or keywords, making it easy to find the right tool for their needs. AI Search also offers a newsletter service that delivers top updates in AI directly to users' inboxes every weekend.

Langchain.rb is a Ruby library that makes it easy to build LLM-powered applications. It provides a unified interface to a variety of LLMs, vector search databases, and other tools, making it easy to build and deploy RAG (Retrieval Augmented Generation) systems and assistants. Langchain.rb is open source and available under the MIT License.

The 'vector-search-class-notes' repository contains class materials for a course on Long Term Memory in AI, focusing on vector search and databases. The course covers theoretical foundations and practical implementation of vector search applications, algorithms, and systems. It explores the intersection of Artificial Intelligence and Database Management Systems, with topics including text embeddings, image embeddings, low dimensional vector search, dimensionality reduction, approximate nearest neighbor search, clustering, quantization, and graph-based indexes. The repository also includes information on the course syllabus, project details, selected literature, and contributions from industry experts in the field.

AI Samples for .NET is a repository containing various samples demonstrating how to use AI in .NET applications. It provides quickstarts using Semantic Kernel and Azure OpenAI SDK, covers LLM Core Concepts, End to End Examples, Local Models, Local Embedding Models, Tokenizers, Vector Databases, and Reference Examples. The repository showcases different AI-related projects and tools for developers to explore and learn from.

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. |  |

ChatDev IDE is a tool for building your AI agent, Whether it's NPCs in games or powerful agent tools, you can design what you want for this platform. It accelerates prompt engineering through **JavaScript Support** that allows implementing complex prompting techniques.

The RAG ChatBot project combines Lama.cpp, Chroma, and Streamlit to build a Conversation-aware Chatbot and a Retrieval-augmented generation (RAG) ChatBot. The RAG Chatbot works by taking a collection of Markdown files as input and provides answers based on the context provided by those files. It utilizes a Memory Builder component to load Markdown pages, divide them into sections, calculate embeddings, and save them in an embedding database. The chatbot retrieves relevant sections from the database, rewrites questions for optimal retrieval, and generates answers using a local language model. It also remembers previous interactions for more accurate responses. Various strategies are implemented to deal with context overflows, including creating and refining context, hierarchical summarization, and async hierarchical summarization.

Swirl is an open-source software that allows users to simultaneously search multiple content sources and receive AI-ranked results. It connects to various data sources, including databases, public data services, and enterprise sources, and utilizes AI and LLMs to generate insights and answers based on the user's data. Swirl is easy to use, requiring only the download of a YML file, starting in Docker, and searching with Swirl. Users can add credentials to preloaded SearchProviders to access more sources. Swirl also offers integration with ChatGPT as a configured AI model. It adapts and distributes user queries to anything with a search API, re-ranking the unified results using Large Language Models without extracting or indexing anything. Swirl includes five Google Programmable Search Engines (PSEs) to get users up and running quickly. Key features of Swirl include Microsoft 365 integration, SearchProvider configurations, query adaptation, synchronous or asynchronous search federation, optional subscribe feature, pipelining of Processor stages, results stored in SQLite3 or PostgreSQL, built-in Query Transformation support, matching on word stems and handling of stopwords, duplicate detection, re-ranking of unified results using Cosine Vector Similarity, result mixers, page through all results requested, sample data sets, optional spell correction, optional search/result expiration service, easily extensible Connector and Mixer objects, and a welcoming community for collaboration and support.

Build your own conversation-based search with AI, a simple implementation with Node.js & Vue3. Live Demo Features: * Built-in support for LLM: OpenAI, Google, Lepton, Ollama(Free) * Built-in support for search engine: Bing, Sogou, Google, SearXNG(Free) * Customizable pretty UI interface * Support dark mode * Support mobile display * Support local LLM with Ollama * Support i18n * Support Continue Q&A with contexts.

Azure Functions OpenAI Extension is a project that adds support for OpenAI LLM (GPT-3.5-turbo, GPT-4) bindings in Azure Functions. It provides NuGet packages for various functionalities like text completions, chat completions, assistants, embeddings generators, and semantic search. The project requires .NET 6 SDK or greater, Azure Functions Core Tools v4.x, and specific settings in Azure Function or local settings for development. It offers features like text completions, chat completion, assistants with custom skills, embeddings generators for text relatedness, and semantic search using vector databases. The project also includes examples in C# and Python for different functionalities.

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 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 |

Txtai is an all-in-one embeddings database for semantic search, LLM orchestration, and language model workflows. It combines vector indexes, graph networks, and relational databases to enable vector search with SQL, topic modeling, retrieval augmented generation, and more. Txtai can stand alone or serve as a knowledge source for large language models (LLMs). Key features include vector search with SQL, object storage, topic modeling, graph analysis, multimodal indexing, embedding creation for various data types, pipelines powered by language models, workflows to connect pipelines, and support for Python, JavaScript, Java, Rust, and Go. Txtai is open-source under the Apache 2.0 license.

Denser Retriever is an enterprise-grade AI retriever designed to streamline AI integration into applications, combining keyword-based searches, vector databases, and machine learning rerankers using xgboost. It provides state-of-the-art accuracy on MTEB Retrieval benchmarking and supports various heterogeneous retrievers for end-to-end applications like chatbots and semantic search.

Airweave is an open-core tool that simplifies the process of making data searchable by unifying apps, APIs, and databases into a vector database with minimal configuration. It offers over 120 integrations, simplicity in syncing data from diverse sources, extensibility through 'sources', 'destinations', and 'embedders', and an async-first approach for large-scale data synchronization. With features like no-code setup, white-labeled multi-tenant support, chunk generators, automated sync, versioning & hashing, multi-source support, and scalability, Airweave provides a comprehensive solution for building applications that require semantic search.

AI-Powered Search provides code examples for the book 'AI-Powered Search' by Trey Grainger, Doug Turnbull, and Max Irwin. The book teaches modern machine learning techniques for building search engines that continuously learn from users and content to deliver more intelligent and domain-aware search experiences. It covers semantic search, retrieval augmented generation, question answering, summarization, fine-tuning transformer-based models, personalized search, machine-learned ranking, click models, and more. The code examples are in Python, leveraging PySpark for data processing and Apache Solr as the default search engine. The repository is open source under the Apache License, Version 2.0.

This repository showcases a project that indexes millions of Wikipedia articles using Upstash Vector. It includes a semantic search engine and a RAG chatbot SDK. The project involves preparing and embedding Wikipedia articles, indexing vectors, building a semantic search engine, and implementing a RAG chatbot. Key features include indexing over 144 million vectors, multilingual support, cross-lingual semantic search, and a RAG chatbot. Technologies used include Upstash Vector, Upstash Redis, Upstash RAG Chat SDK, SentenceTransformers, and Meta-Llama-3-8B-Instruct for LLM provider.

VectorETL is a lightweight ETL framework designed to assist Data & AI engineers in processing data for AI applications quickly. It streamlines the conversion of diverse data sources into vector embeddings and storage in various vector databases. The framework supports multiple data sources, embedding models, and vector database targets, simplifying the creation and management of vector search systems for semantic search, recommendation systems, and other vector-based operations.

cuVS is a library that contains state-of-the-art implementations of several algorithms for running approximate nearest neighbors and clustering on the GPU. It can be used directly or through the various databases and other libraries that have integrated it. The primary goal of cuVS is to simplify the use of GPUs for vector similarity search and clustering.

SuperDuperDB is a Python framework for integrating AI models, APIs, and vector search engines directly with your existing databases, including hosting of your own models, streaming inference and scalable model training/fine-tuning. Build, deploy and manage any AI application without the need for complex pipelines, infrastructure as well as specialized vector databases, and moving our data there, by integrating AI at your data's source: - Generative AI, LLMs, RAG, vector search - Standard machine learning use-cases (classification, segmentation, regression, forecasting recommendation etc.) - Custom AI use-cases involving specialized models - Even the most complex applications/workflows in which different models work together SuperDuperDB is **not** a database. Think `db = superduper(db)`: SuperDuperDB transforms your databases into an intelligent platform that allows you to leverage the full AI and Python ecosystem. A single development and deployment environment for all your AI applications in one place, fully scalable and easy to manage.

superduper.io is a Python framework that integrates AI models, APIs, and vector search engines directly with existing databases. It allows hosting of models, streaming inference, and scalable model training/fine-tuning. Key features include integration of AI with data infrastructure, inference via change-data-capture, scalable model training, model chaining, simple Python interface, Python-first approach, working with difficult data types, feature storing, and vector search capabilities. The tool enables users to turn their existing databases into centralized repositories for managing AI model inputs and outputs, as well as conducting vector searches without the need for specialized databases.

Comprehensive LibGuide Assistant

Create the most effective database or search engine queries using keywords, truncation, and Boolean operators!

This GPT has access to both PubMed and the UnPaywall database, allowing conversational exploration of the literature and direct access to full-text articles

AI Search Engine - About us & for Advertising at DigitalMediaMarketing.Agency

I conjure advanced Google search queries.

Analyzes GSC data and highlights the impact of search updates.

Creates Google Ads headlines in bulk based on direct response copy principles.

Search data on the impact of AI on jobs, productivity and operations published by Synthetic Work (https://synthetic.work)

Analyse content through the official Google Search Quality Rater Guidelines.

Refines search queries with specific terms and includes Google links

Your go-to expert for web searches.

Proustian Chatbot with Full French & English Volumes

GPT search. Click ["] to start working.

Browse the web for the search term and summarize the results from sources

Optimized with advanced search operators for refined results. Specializing in finding and linking top custom GPTs from builders around the world. Version 0.3.0

Expert en Islam offrant des conseils détaillés sur la base du Saint Coran et des Hadiths

This GPT answers all the questions related to Google Search Console

OnlyFans Search Engine, provided by FansIQ.com

Expert in tailored accelerator search for startups.

I'm Quora Search, specialized in finding Quora answers and insights.