AI tools for RAG
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Ragie
Ragie is a fully managed RAG-as-a-Service platform designed for developers. It offers easy-to-use APIs and SDKs to help developers get started quickly, with advanced features like LLM re-ranking, summary index, entity extraction, flexible filtering, and hybrid semantic and keyword search. Ragie allows users to connect directly to popular data sources like Google Drive, Notion, Confluence, and more, ensuring accurate and reliable information delivery. The platform is led by Craft Ventures and offers seamless data connectivity through connectors. Ragie simplifies the process of data ingestion, chunking, indexing, and retrieval, making it a valuable tool for AI applications.
RAGnexus
RAGnexus is a company that specializes in creating personalized AI assistants using RAG (Retriever-Augmented Generation) technology. Their assistants are designed to provide highly personalized and contextually relevant responses to clients' individual needs. RAGnexus uses private information provided by customers to ensure that responses are accurate and tailored to each specific use case. Retriever-Augmented Generation (RAG) technology uses a two-step approach for generating responses: first, it retrieves relevant information from a database, and then it uses that information to generate accurate and context-specific answers.
Ragobble
Ragobble is an audio to LLM data tool that allows you to easily convert audio files into text data that can be used to train large language models (LLMs). With Ragobble, you can quickly and easily create high-quality training data for your LLM projects.
RAGNA Desktop
RAGNA Desktop is a private AI multitool that runs locally on your desktop PC or laptop without the need for an internet connection. It is designed to automate repetitive tasks, increase efficiency, and free up capacity for more important matters. The application ensures data privacy and security by processing all AI, calculations, and analyses on your device, keeping sensitive information protected. RAGNA Desktop offers tools for AI automation, flexibility, and security, helping users enhance productivity and optimize work processes while adhering to the latest data protection regulations.
RagaAI Catalyst
RagaAI Catalyst is a sophisticated AI observability, monitoring, and evaluation platform designed to help users observe, evaluate, and debug AI agents at all stages of Agentic AI workflows. It offers features like visualizing trace data, instrumenting and monitoring tools and agents, enhancing AI performance, agentic testing, comprehensive trace logging, evaluation for each step of the agent, enterprise-grade experiment management, secure and reliable LLM outputs, finetuning with human feedback integration, defining custom evaluation logic, generating synthetic data, and optimizing LLM testing with speed and precision. The platform is trusted by AI leaders globally and provides a comprehensive suite of tools for AI developers and enterprises.
MaxKB
MaxKB is a ready-to-use, flexible RAG Chatbot that is based on Large Language Models (LLM) and Retrieval-Augmented Generation (RAG). It is widely applied in scenarios such as intelligent customer service, corporate internal knowledge bases, academic research, and education. MaxKB supports direct uploading of documents, automatic crawling of online documents, automatic text splitting, vectorization, and RAG for smart Q&A interactions. It also offers flexible orchestration, seamless integration into third-party systems, and supports various large models for enhanced user satisfaction.
Activeloop
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.
Pongo
Pongo is an AI-powered tool that helps reduce hallucinations in Large Language Models (LLMs) by up to 80%. It utilizes multiple state-of-the-art semantic similarity models and a proprietary ranking algorithm to ensure accurate and relevant search results. Pongo integrates seamlessly with existing pipelines, whether using a vector database or Elasticsearch, and processes top search results to deliver refined and reliable information. Its distributed architecture ensures consistent latency, handling a wide range of requests without compromising speed. Pongo prioritizes data security, operating at runtime with zero data retention and no data leaving its secure AWS VPC.
Cohere
Cohere is the leading AI platform for enterprise, offering products optimized for generative AI, search and discovery, and advanced retrieval. Their models are designed to enhance the global workforce, enabling businesses to thrive in the AI era. Cohere provides Command R+, Cohere Command, Cohere Embed, and Cohere Rerank for building efficient AI-powered applications. The platform also offers deployment options for enterprise-grade AI on any cloud or on-premises, along with developer resources like Playground, LLM University, and Developer Docs.
Cohere
Cohere is the leading AI platform for enterprise, offering generative AI, search and discovery, and advanced retrieval solutions. Their models are designed to enhance the global workforce, empowering businesses to thrive in the AI era. With features like Cohere Command, Cohere Embed, and Cohere Rerank, the platform enables the development of scalable and efficient AI-powered applications. Cohere focuses on optimizing enterprise data through language-based models, supporting over 100 languages for enhanced accuracy and efficiency.
Graphlogic.ai
Graphlogic.ai is an AI-powered platform that offers Conversational AI solutions through text and voice bots. It provides partner-enabled services for various industries, including HR, customer support, marketing, and internal task management. The platform features AI-powered chatbots with goal-oriented NLU and rule-based bots, seamless integrations with CRM systems, and 24/7 omnichannel availability. Graphlogic.ai aims to transform and speed up customer service and FAQ conversations by providing instant replies in a human-like manner. It also offers dedicated HR manager bots, hiring assistants for mass recruitment, responsible managers for internal tasks, and outbound marketing coordinators.
Trieve
Trieve is an AI-first infrastructure API that offers search, recommendations, and RAG capabilities by combining language models with tools for fine-tuning ranking and relevance. It provides features such as semantic vector search, BM25 & SPLADE full-text search, hybrid search, merchandising & relevance tuning, and sub-sentence highlighting. Trieve helps companies build unfair competitive advantages through their search, discovery, and RAG experiences. The platform is built on the best foundations, offering private open-source models, self-hostable options, and easy integration with existing data. With Trieve, users can set up industry-leading search in just 30 minutes and take control of their discovery process.
Krux AI
Krux AI is an advanced artificial intelligence tool designed to streamline and optimize various business processes. It leverages cutting-edge machine learning algorithms to provide actionable insights and predictions for data-driven decision-making. With its user-friendly interface and powerful capabilities, Krux AI empowers users to enhance efficiency, productivity, and profitability across different industries.
Glean
Glean is an AI-powered work assistant and enterprise search platform that enables teams to harness generative AI to make better decisions faster. It connects all company data, provides advanced personalization, and ensures retrieval of the most relevant information. Glean offers responsible AI solutions that scale to businesses, respecting permissions and providing secure, private, and fully referenceable answers. With turnkey deployment and a variety of platform tools, Glean helps teams move faster and be more productive.
Myple
Myple is an AI application that enables users to build, scale, and secure AI applications with ease. It provides production-ready AI solutions tailored to individual needs, offering a seamless user experience. With support for multiple languages and frameworks, Myple simplifies the integration of AI through open-source SDKs. The platform features a clean interface, keyboard shortcuts for efficient navigation, and templates to kickstart AI projects. Additionally, Myple offers AI-powered tools like RAG chatbot for documentation, Gmail agent for email notifications, and AskFeynman for physics-related queries. Users can connect their favorite tools and services effortlessly, without any coding. Joining the beta program grants early access to new features and issue resolution prioritization.
FinetuneFast
FinetuneFast is an AI tool designed to help developers, indie makers, and businesses to efficiently finetune machine learning models, process data, and deploy AI solutions at lightning speed. With pre-configured training scripts, efficient data loading pipelines, and one-click model deployment, FinetuneFast streamlines the process of building and deploying AI models, saving users valuable time and effort. The tool is user-friendly, accessible for ML beginners, and offers lifetime updates for continuous improvement.
AI Builders Summit
AI Builders Summit is a 4-week virtual training event designed to equip data scientists, ML and AI engineers, and innovators with the latest advancements in large language models (LLMs), AI agents, and Retrieval-Augmented Generation (RAG). The summit emphasizes hands-on learning and real-world applications, with interactive workshops, platform credits, and direct exposure to industry-leading tools. Attendees can learn progressively over four weeks, building practical skills through expert-led sessions, cutting-edge tools, and industry insights.
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Allganize
Allganize Inc. is a leading provider of enterprise AI solutions. Their platform enables businesses to build and deploy custom AI applications without the need for coding. Allganize's solutions are used by a variety of industries, including financial services, healthcare, and manufacturing.
Vellum AI
Vellum AI is an AI platform that supports using Microsoft Azure hosted OpenAI models. It offers tools for prompt engineering, semantic search, prompt chaining, evaluations, and monitoring. Vellum enables users to build AI systems with features like workflow automation, document analysis, fine-tuning, Q&A over documents, intent classification, summarization, vector search, chatbots, blog generation, sentiment analysis, and more. The platform is backed by top VCs and founders of well-known companies, providing a complete solution for building LLM-powered applications.
Overleaf GPT
Overleaf GPT is an interactive assistant for writing detailed Overleaf documents. Overleaf GPT writes complete LaTeX reports, tailored to the user’s requirements. This GPT starts with conceptualizing the structure to iteratively developing the content and providing best-practice formatting in LaTeX.
Automated Knowledge Distillation
For strategic knowledge distillation, upload the document you need to analyze and use !start. ENSURE the uploaded file shows DOCUMENT and NOT PDF. This workflow requires leveraging RAG to operate. Only a small amount of PDFs are supported, convert to txt or doc. For timeout, refresh & !continue
Rage Debater
Go to the Brain Gym. Argue better or Get Wrecked. Practice persuading your friends, coworkers, everyone.
rag
RAG with txtai is a Retrieval Augmented Generation (RAG) Streamlit application that helps generate factually correct content by limiting the context in which a Large Language Model (LLM) can generate answers. It supports two categories of RAG: Vector RAG, where context is supplied via a vector search query, and Graph RAG, where context is supplied via a graph path traversal query. The application allows users to run queries, add data to the index, and configure various parameters to control its behavior.
rag-experiment-accelerator
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.
ragstack-ai
RAGStack is an out-of-the-box solution simplifying Retrieval Augmented Generation (RAG) in GenAI apps. RAGStack includes the best open-source for implementing RAG, giving developers a comprehensive Gen AI Stack leveraging LangChain, CassIO, and more. RAGStack leverages the LangChain ecosystem and is fully compatible with LangSmith for monitoring your AI deployments.
ragflow
RAGFlow is an open-source Retrieval-Augmented Generation (RAG) engine that combines deep document understanding with Large Language Models (LLMs) to provide accurate question-answering capabilities. It offers a streamlined RAG workflow for businesses of all sizes, enabling them to extract knowledge from unstructured data in various formats, including Word documents, slides, Excel files, images, and more. RAGFlow's key features include deep document understanding, template-based chunking, grounded citations with reduced hallucinations, compatibility with heterogeneous data sources, and an automated and effortless RAG workflow. It supports multiple recall paired with fused re-ranking, configurable LLMs and embedding models, and intuitive APIs for seamless integration with business applications.
ragas
Ragas is a framework that helps you evaluate your Retrieval Augmented Generation (RAG) pipelines. RAG denotes a class of LLM applications that use external data to augment the LLM’s context. There are existing tools and frameworks that help you build these pipelines but evaluating it and quantifying your pipeline performance can be hard. This is where Ragas (RAG Assessment) comes in. Ragas provides you with the tools based on the latest research for evaluating LLM-generated text to give you insights about your RAG pipeline. Ragas can be integrated with your CI/CD to provide continuous checks to ensure performance.
ragna
Ragna is a RAG orchestration framework designed for managing workflows and orchestrating tasks. It provides a comprehensive set of features for users to streamline their processes and automate repetitive tasks. With Ragna, users can easily create, schedule, and monitor workflows, making it an ideal tool for teams and individuals looking to improve their productivity and efficiency. The framework offers extensive documentation, community support, and a user-friendly interface, making it accessible to users of all skill levels. Whether you are a developer, data scientist, or project manager, Ragna can help you simplify your workflow management and boost your overall performance.
local-rag
Local RAG is an offline, open-source tool that allows users to ingest files for retrieval augmented generation (RAG) using large language models (LLMs) without relying on third parties or exposing sensitive data. It supports offline embeddings and LLMs, multiple sources including local files, GitHub repos, and websites, streaming responses, conversational memory, and chat export. Users can set up and deploy the app, learn how to use Local RAG, explore the RAG pipeline, check planned features, known bugs and issues, access additional resources, and contribute to the project.
rag-chatbot
rag-chatbot is a tool that allows users to chat with multiple PDFs using Ollama and LlamaIndex. It provides an easy setup for running on local machines or Kaggle notebooks. Users can leverage models from Huggingface and Ollama, process multiple PDF inputs, and chat in multiple languages. The tool offers a simple UI with Gradio, supporting chat with history and QA modes. Setup instructions are provided for both Kaggle and local environments, including installation steps for Docker, Ollama, Ngrok, and the rag_chatbot package. Users can run the tool locally and access it via a web interface. Future enhancements include adding evaluation, better embedding models, knowledge graph support, improved document processing, MLX model integration, and Corrective RAG.
rag-gpt
RAG-GPT is a tool that allows users to quickly launch an intelligent customer service system with Flask, LLM, and RAG. It includes frontend, backend, and admin console components. The tool supports cloud-based and local LLMs, enables deployment of conversational service robots in minutes, integrates diverse knowledge bases, offers flexible configuration options, and features an attractive user interface.
ragapp
RAGapp is a tool designed for easy deployment of Agentic RAG in any enterprise. It allows users to configure and deploy RAG in their own cloud infrastructure using Docker. The tool is built using LlamaIndex and supports hosted AI models from OpenAI or Gemini, as well as local models using Ollama. RAGapp provides endpoints for Admin UI, Chat UI, and API, with the option to specify the model and Ollama host. The tool does not come with an authentication layer, requiring users to secure the '/admin' path in their cloud environment. Deployment can be done using Docker Compose with customizable model and Ollama host settings, or in Kubernetes for cloud infrastructure deployment. Development setup involves using Poetry for installation and building frontends.
ragtacts
Ragtacts is a Clojure library that allows users to easily interact with Large Language Models (LLMs) such as OpenAI's GPT-4. Users can ask questions to LLMs, create question templates, call Clojure functions in natural language, and utilize vector databases for more accurate answers. Ragtacts also supports RAG (Retrieval-Augmented Generation) method for enhancing LLM output by incorporating external data. Users can use Ragtacts as a CLI tool, API server, or through a RAG Playground for interactive querying.
rag-gpt
RAG-GPT is a tool that allows users to quickly launch an intelligent customer service system with Flask, LLM, and RAG. It includes frontend, backend, and admin console components. The tool supports cloud-based and local LLMs, offers quick setup for conversational service robots, integrates diverse knowledge bases, provides flexible configuration options, and features an attractive user interface.
RAGMeUp
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.
rageval
Rageval is an evaluation tool for Retrieval-augmented Generation (RAG) methods. It helps evaluate RAG systems by performing tasks such as query rewriting, document ranking, information compression, evidence verification, answer generation, and result validation. The tool provides metrics for answer correctness and answer groundedness, along with benchmark results for ASQA and ALCE datasets. Users can install and use Rageval to assess the performance of RAG models in question-answering tasks.
RAG-Retrieval
RAG-Retrieval provides full-chain RAG retrieval fine-tuning and inference code. It supports fine-tuning any open-source RAG retrieval models, including vector (embedding, graph a), delayed interactive models (ColBERT, graph d), interactive models (cross encoder, graph c). For inference, RAG-Retrieval focuses on ranking (reranker) and has developed a lightweight Python library rag-retrieval, providing a unified way to call any different RAG ranking models.
aws-bedrock-with-rag-and-react
This solution provides a low-code ReactJS application to prototype and vet business use cases for GenAI using Retrieval Augmented Generation (RAG). It includes a backend Flask application that uses LangChain to provide PDF data as embeddings to a text-gen model via Amazon Bedrock and a vector database with FAISS or Kendra Index. The solution utilizes Amazon Bedrock as the only cost-generating AWS service.
RAGFoundry
RAG Foundry is a library designed to enhance Large Language Models (LLMs) by fine-tuning models on RAG-augmented datasets. It helps create training data, train models using parameter-efficient finetuning (PEFT), and measure performance using RAG-specific metrics. The library is modular, customizable using configuration files, and facilitates prototyping with various RAG settings and configurations for tasks like data processing, retrieval, training, inference, and evaluation.
Controllable-RAG-Agent
This repository contains a sophisticated deterministic graph-based solution for answering complex questions using a controllable autonomous agent. The solution is designed to ensure that answers are solely based on the provided data, avoiding hallucinations. It involves various steps such as PDF loading, text preprocessing, summarization, database creation, encoding, and utilizing large language models. The algorithm follows a detailed workflow involving planning, retrieval, answering, replanning, content distillation, and performance evaluation. Heuristics and techniques implemented focus on content encoding, anonymizing questions, task breakdown, content distillation, chain of thought answering, verification, and model performance evaluation.
rag-chat
The `@upstash/rag-chat` package simplifies the development of retrieval-augmented generation (RAG) chat applications by providing Next.js compatibility with streaming support, built-in vector store, optional Redis compatibility for fast chat history management, rate limiting, and disableRag option. Users can easily set up the environment variables and initialize RAGChat to interact with AI models, manage knowledge base, chat history, and enable debugging features. Advanced configuration options allow customization of RAGChat instance with built-in rate limiting, observability via Helicone, and integration with Next.js route handlers and Vercel AI SDK. The package supports OpenAI models, Upstash-hosted models, and custom providers like TogetherAi and Replicate.
RAG-Survey
This repository is dedicated to collecting and categorizing papers related to Retrieval-Augmented Generation (RAG) for AI-generated content. It serves as a survey repository based on the paper 'Retrieval-Augmented Generation for AI-Generated Content: A Survey'. The repository is continuously updated to keep up with the rapid growth in the field of RAG.