Best AI tools for< Querying Metrics >

SQLyze is an AI SQL generator that revolutionizes the way users interact with SQL queries. By leveraging artificial intelligence, SQLyze simplifies the process of crafting complex SQL queries into a user-friendly three-step approach. Users can effortlessly describe their data needs, add database schema details, and receive custom AI-generated SQL queries tailored to their specific requirements. With SQLyze, users can streamline their SQL querying process and enhance query accuracy, ultimately saving time and improving productivity.

DataLang is a tool that allows you to chat with your databases, expose a specific set of data (using SQL) to train GPT, and then chat with it in natural language. You can also use DataLang to automatically make your SQL views available via API, share it with your privately users, or make it public.

Echobase is an AI tool designed to easily integrate AI into businesses by allowing teams to query, create, and analyze data from their files. It offers advanced AI models tailored to specific business needs, enabling the creation of AI agents capable of tasks like basic Q&A, data analysis, and content creation. Echobase provides a centralized workspace for uploading and querying organizational knowledge bases in real-time, along with features for collaboration and role management. The tool prioritizes data security through robust encryption, API usage, and user-controlled data access.

PrivacyDoc is an AI-powered portal that allows users to analyze and query PDF and ebooks effortlessly. By leveraging advanced NLP technology, PrivacyDoc enables users to uncover insights and conduct thorough document analysis. The platform offers features such as easy file upload, query functionality, enhanced security measures, and free access to powerful PDF analysis tools. With PrivacyDoc, users can experience the convenience of logging in with their Google account, submitting queries for prompt AI-driven responses, and ensuring data privacy with secure file handling.

FARSPEAK.AI is an AI application that offers RESTful AI for databases, allowing users to query databases using natural language and deploy AI agents to enhance data processing. The application supports MongoDB Atlas, provides up-to-date embeddings, and offers both structured and unstructured data support. FARSPEAK simplifies work for AI engineers, app & web developers, and product designers by enabling faster AI feature development, natural language querying, and insights generation from data.

TableTalk is an AI-powered tool that allows users to interact with their database using natural language. It provides a user-friendly interface for querying data and joining tables, making database management more intuitive and efficient. The tool leverages artificial intelligence to understand user queries and provide relevant responses, mimicking a conversation with a human. TableTalk is currently in beta and aims to revolutionize the way users interact with databases.

ThoughtSpot is an AI-powered analytics platform that enables users to deliver insights 10x faster for their employees. It offers AI-powered search capabilities, natural language search, live querying of data, building search data models, balancing self-service with enterprise-scale control, visualizing business data, operationalizing data sync to business apps, and mobile access. The platform also provides features for creating visualizations from spreadsheets, staying up to date with product news, embedding analytics into apps, building ThoughtSpot apps and API services, and generating more revenue with embedded analytics. ThoughtSpot is designed to provide fast, actionable insights with a focus on user experience and self-service analytics.

ThoughtSpot is an AI-powered analytics platform that enables users to deliver insights 10x faster for their employees. It offers AI-powered search capabilities, natural language search, live-querying of data, building search data models, balancing self-service with enterprise-scale control, visualizing business data with Liveboards, surfacing actionable insights with augmented analytics, operationalizing cloud data sync, and more. ThoughtSpot aims to provide fast, actionable insights for all users, eliminating reporting backlogs and developer headaches.

Frame AI is a premier Streaming AI Platform powered by STAG, designed to provide proactive insights and tools for every team by continuously querying customer data to detect traits, track trends, and trigger workflows. The platform turns unstructured data into actionable insights, helping teams stay ahead of risks and opportunities. Frame AI's architecture autonomously queries customer data based on user objectives, activating inside existing business tools to provide real-time customer data. With features like enrichments, triggers, alerts, and insights, Frame AI enables better decisions faster by combining predictive signals in customer text into task-specific scores. The platform is suitable for marketing, CX, support, and product teams, offering real-time usability feedback, demographic and psychographic trait detection, and secure data handling. Frame AI is SOC 2 Type II certified and HIPAA compliant, with a team of AI experts leading the development of AI solutions for various organizations.

Robin AI is a legal AI application that accelerates contract review and analysis processes, providing precision edits, quick data querying, and fast contract turnaround times. The platform offers AI-native software solutions to empower legal teams, combining machine learning models and expert professionals to enhance contract management efficiency. With a focus on security and privacy, Robin AI ensures compliance with GDPR and industry standards, making it suitable for businesses handling sensitive data. The application is designed to streamline legal operations, improve contract processes, and deliver high-quality services to global enterprises.

Onnix AI is a personalized AI co-pilot designed specifically for bankers, aiming to save teams time by providing accurate answers and deliverables quickly. It brings AI and powerful data science tools to the banking sector, offering features such as creating personalized slide decks, conducting Excel analysis, and querying data sources. Onnix AI caters to both senior and junior teams, enabling them to generate deeper insights and streamline their workflow efficiently.

Resolvd is an AI-powered incident resolution platform that creates a knowledge base of logs, data sources, and apps to autonomously diagnose and resolve incidents. It helps reduce time to response, correlates events across data sources, and provides automated insights for faster issue resolution. With features like simple data querying, automated anomaly detection, and in-workflow integration with existing systems, Resolvd aims to streamline incident response processes and empower engineers with actionable insights.

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.

Keep is an open-source AIOps platform designed for managing alerts and events at scale. It offers features such as enrichment, workflows, a single pane of glass view, and over 90 integrations. Keep leverages AI technology to help IT operations professionals deal with alerts in complex environments. It provides high-quality integrations with monitoring systems, ticketing, source control, and more. The platform also includes advanced querying capabilities, workflow automation, and AI-driven alert correlation for enterprise users. Keep is a versatile tool suitable for SREs, operators, engineers, startups, and global enterprises.

EchoQuery is an AI-powered user research tool that helps businesses gain valuable insights into their customers' needs. It offers a comprehensive analysis tool that guides users through four simple steps: creating analysis, sharing surveys, analyzing results using AI, and digging deeper for more insights. With features like unlimited analysis, AI-powered interviews, AI findings and themes, and an AI querying tool, EchoQuery empowers businesses to make data-driven decisions. The tool also provides email and phone support to assist users along their research journey.

The TensorRT-LLM Backend is a Triton backend designed to serve TensorRT-LLM models with Triton Inference Server. It supports features like inflight batching, paged attention, and more. Users can access the backend through pre-built Docker containers or build it using scripts provided in the repository. The backend can be used to create models for tasks like tokenizing, inferencing, de-tokenizing, ensemble modeling, and more. Users can interact with the backend using provided client scripts and query the server for metrics related to request handling, memory usage, KV cache blocks, and more. Testing for the backend can be done following the instructions in the 'ci/README.md' file.

The RAG Experiment Accelerator is a versatile tool that helps you conduct experiments and evaluations using Azure AI Search and RAG pattern. It offers a rich set of features, including experiment setup, integration with Azure AI Search, Azure Machine Learning, MLFlow, and Azure OpenAI, multiple document chunking strategies, query generation, multiple search types, sub-querying, re-ranking, metrics and evaluation, report generation, and multi-lingual support. The tool is designed to make it easier and faster to run experiments and evaluations of search queries and quality of response from OpenAI, and is useful for researchers, data scientists, and developers who want to test the performance of different search and OpenAI related hyperparameters, compare the effectiveness of various search strategies, fine-tune and optimize parameters, find the best combination of hyperparameters, and generate detailed reports and visualizations from experiment results.

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.

Synmetrix (prev. MLCraft) is an open source data engineering platform and semantic layer for centralized metrics management. It provides a complete framework for modeling, integrating, transforming, aggregating, and distributing metrics data at scale. Key features include data modeling and transformations, semantic layer for unified data model, scheduled reports and alerts, versioning, role-based access control, data exploration, caching, and collaboration on metrics modeling. Synmetrix leverages Cube (Cube.js) for flexible data models that consolidate metrics from various sources, enabling downstream distribution via a SQL API for integration into BI tools, reporting, dashboards, and data science. Use cases include data democratization, business intelligence, embedded analytics, and enhancing accuracy in data handling and queries. The tool speeds up data-driven workflows from metrics definition to consumption by combining data engineering best practices with self-service analytics capabilities.

Synmetrix is an open source data engineering platform and semantic layer for centralized metrics management. It provides a complete framework for modeling, integrating, transforming, aggregating, and distributing metrics data at scale. Key features include data modeling and transformations, semantic layer for unified data model, scheduled reports and alerts, versioning, role-based access control, data exploration, caching, and collaboration on metrics modeling. Synmetrix leverages Cube.js to consolidate metrics from various sources and distribute them downstream via a SQL API. Use cases include data democratization, business intelligence and reporting, embedded analytics, and enhancing accuracy in data handling and queries. The tool speeds up data-driven workflows from metrics definition to consumption by combining data engineering best practices with self-service analytics capabilities.

This repository contains materials for the paper 'How Far Have We Gone in Vulnerability Detection Using Large Language Model'. It provides a tool for evaluating vulnerability detection models using datasets such as d2a, ctf, magma, big-vul, and devign. Users can query the model 'Llama-2-7b-chat-hf' and store results in a SQLite database for analysis. The tool supports binary and multiple classification tasks with concurrency settings. Additionally, users can evaluate the results and generate a CSV file with metrics for each dataset and prompt type.

DevQualityEval is an evaluation benchmark and framework designed to compare and improve the quality of code generation of Language Model Models (LLMs). It provides developers with a standardized benchmark to enhance real-world usage in software development and offers users metrics and comparisons to assess the usefulness of LLMs for their tasks. The tool evaluates LLMs' performance in solving software development tasks and measures the quality of their results through a point-based system. Users can run specific tasks, such as test generation, across different programming languages to evaluate LLMs' language understanding and code generation capabilities.

Knowledge Graph Builder App is a tool designed to convert PDF documents into a structured knowledge graph stored in Neo4j. It utilizes OpenAI's GPT/Diffbot LLM to extract nodes, relationships, and properties from PDF text content. Users can upload files from local machine or S3 bucket, choose LLM model, and create a knowledge graph. The app integrates with Neo4j for easy visualization and querying of extracted information.

Awesome Text2SQL is a curated repository containing tutorials and resources for Large Language Models, Text2SQL, Text2DSL, Text2API, Text2Vis, and more. It provides guidelines on converting natural language questions into structured SQL queries, with a focus on NL2SQL. The repository includes information on various models, datasets, evaluation metrics, fine-tuning methods, libraries, and practice projects related to Text2SQL. It serves as a comprehensive resource for individuals interested in working with Text2SQL and related technologies.

The Generative AI Application Builder on AWS (GAAB) is a solution that provides a web-based management dashboard for deploying customizable Generative AI (Gen AI) use cases. Users can experiment with and compare different combinations of Large Language Model (LLM) use cases, configure and optimize their use cases, and integrate them into their applications for production. The solution is targeted at novice to experienced users who want to experiment and productionize different Gen AI use cases. It uses LangChain open-source software to configure connections to Large Language Models (LLMs) for various use cases, with the ability to deploy chat use cases that allow querying over users' enterprise data in a chatbot-style User Interface (UI) and support custom end-user implementations through an API.

This repository serves as a comprehensive survey on the application of Large Language Models (LLMs) on tabular data, focusing on tasks such as prediction, data generation, and table understanding. It aims to consolidate recent progress in this field by summarizing key techniques, metrics, datasets, models, and optimization approaches. The survey identifies strengths, limitations, unexplored territories, and gaps in the existing literature, providing insights for future research directions. It also offers code and dataset references to empower readers with the necessary tools and knowledge to address challenges in this rapidly evolving domain.

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.

RAG Me Up is a generic framework that enables users to perform Retrieve and Generate (RAG) on their own dataset easily. It consists of a small server and UIs for communication. Best run on GPU with 16GB vRAM. Users can combine RAG with fine-tuning using LLaMa2Lang repository. The tool allows configuration for LLM, data, LLM parameters, prompt, and document splitting. Funding is sought to democratize AI and advance its applications.

Neptune is a scalable experiment tracker for teams training foundation models. Log millions of runs, effortlessly monitor and visualize model training, and deploy on your infrastructure. Track 100% of metadata to accelerate AI breakthroughs. Log and display any framework and metadata type from any ML pipeline. Organize experiments with nested structures and custom dashboards. Compare results, visualize training, and optimize models quicker. Version models, review stages, and access production-ready models. Share results, manage users, and projects. Integrate with 25+ frameworks. Trusted by great companies to improve workflow.

The 'END TO END GENERATIVE AI PROJECTS' repository is a collection of awesome industry projects utilizing Large Language Models (LLM) for various tasks such as chat applications with PDFs, image to speech generation, video transcribing and summarizing, resume tracking, text to SQL conversion, invoice extraction, medical chatbot, financial stock analysis, and more. The projects showcase the deployment of LLM models like Google Gemini Pro, HuggingFace Models, OpenAI GPT, and technologies such as Langchain, Streamlit, LLaMA2, LLaMAindex, and more. The repository aims to provide end-to-end solutions for different AI applications.

A curated list of awesome Model Context Protocol (MCP) servers that enable AI models to securely interact with local and remote resources through standardized server implementations. The list focuses on production-ready and experimental servers extending AI capabilities through file access, database connections, API integrations, and other contextual services.

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.

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.

ChatGPT is a large language model that can generate human-like text, translate languages, write different kinds of creative content, and answer your questions in a conversational way. It is trained on a massive amount of text data, and it is able to understand and respond to a wide range of natural language prompts. Here are 5 jobs suitable for this tool, in lowercase letters: 1. content writer 2. chatbot assistant 3. language translator 4. creative writer 5. researcher

Assists in exploring the LUKSO Ecosystem and querying Universal Profiles.