Best AI tools for< Build Rag Applications >

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.

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.

Langflow is a low-code app builder for RAG and multi-agent AI applications. It is Python-based and agnostic to any model, API, or database. Langflow offers a visual IDE for building and testing workflows, multi-agent orchestration, free cloud service, observability features, and ecosystem integrations. Users can customize workflows using Python and publish them as APIs or export as Python applications.

Helix AI is a private GenAI platform that enables users to build AI applications using open source models. The platform offers tools for RAG (Retrieval-Augmented Generation) and fine-tuning, allowing deployment on-premises or in a Virtual Private Cloud (VPC). Users can access curated models, utilize Helix API tools to connect internal and external APIs, embed Helix Assistants into websites/apps for chatbot functionality, write AI application logic in natural language, and benefit from the innovative RAG system for Q&A generation. Additionally, users can fine-tune models for domain-specific needs and deploy securely on Kubernetes or Docker in any cloud environment. Helix Cloud offers free and premium tiers with GPU priority, catering to individuals, students, educators, and companies of varying sizes.

FutureSmart AI is a platform that provides custom Natural Language Processing (NLP) solutions. The platform focuses on integrating Mem0 with LangChain to enhance AI Assistants with Intelligent Memory. It offers tutorials, guides, and practical tips for building applications with large language models (LLMs) to create sophisticated and interactive systems. FutureSmart AI also features internship journeys and practical guides for mastering RAG with LangChain, catering to developers and enthusiasts in the realm of NLP and AI.

Langtrace AI is an open-source observability tool powered by Scale3 Labs that helps monitor, evaluate, and improve LLM (Large Language Model) applications. It collects and analyzes traces and metrics to provide insights into the ML pipeline, ensuring security through SOC 2 Type II certification. Langtrace supports popular LLMs, frameworks, and vector databases, offering end-to-end observability and the ability to build and deploy AI applications with confidence.

Dify is an open-source platform for building AI applications that combines Backend-as-a-Service and LLMOps to streamline the development of generative AI solutions. It integrates support for mainstream LLMs, an intuitive Prompt orchestration interface, high-quality RAG engines, a flexible AI Agent framework, and easy-to-use interfaces and APIs. Dify allows users to skip complexity and focus on creating innovative AI applications that solve real-world problems. It offers a comprehensive, production-ready solution with a user-friendly interface.

Lyzr AI is a full-stack agent framework designed to build GenAI applications faster. It offers a range of AI agents for various tasks such as chatbots, knowledge search, summarization, content generation, and data analysis. The platform provides features like memory management, human-in-loop interaction, toxicity control, reinforcement learning, and custom RAG prompts. Lyzr AI ensures data privacy by running data locally on cloud servers. Enterprises and developers can easily configure, deploy, and manage AI agents using Lyzr's platform.

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 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.

StartKit.AI is a boilerplate code for AI products that helps users build their AI startups 100x faster. It includes pre-built REST API routes for all common AI functionality, a pre-configured Pinecone for text embeddings and Retrieval-Augmented Generation (RAG) for chat endpoints, and five React demo apps to help users get started quickly. StartKit.AI also provides a license key and magic link authentication, user & API limit management, and full documentation for all its code. Additionally, users get access to guides to help them get set up and one year of updates.

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.

Allapi.ai is an advanced AI API platform designed to simplify AI integration for developers and startup founders. It offers a powerful ecosystem of models, plugins, and APIs to help users build and deploy AI-powered applications quickly and efficiently. With features like dynamic data capabilities, advanced RAG system, streamlined development process, and intelligent code assistant, Allapi.ai aims to accelerate innovation and reduce development costs. The platform provides access to cutting-edge AI models like Claude3, GPT-4, Gemini 1.5 Pro, and LLaMA 3, along with a wide range of plugins and tools to supercharge AI-driven applications.

Tonic.ai is a platform that allows users to build AI models on their unstructured data. It offers various products for software development and LLM development, including tools for de-identifying and subsetting structured data, scaling down data, handling semi-structured data, and managing ephemeral data environments. Tonic.ai focuses on standardizing, enriching, and protecting unstructured data, as well as validating RAG systems. The platform also provides integrations with relational databases, data lakes, NoSQL databases, flat files, and SaaS applications, ensuring secure data transformation for software and AI developers.

Trieve is an AI-first infrastructure API that offers a modern solution for search, recommendations, and RAG (Retrieve and Generate) tasks. It combines language models with tools for fine-tuning ranking and relevance, providing production-ready capabilities for building search, discovery, and RAG experiences. Trieve supports semantic vector search, full-text search using BM25 & SPLADE models, custom embedding models, hybrid search, and sub-sentence highlighting. With features like merchandising, relevance tuning, and self-hostable options, Trieve empowers companies to enhance their search capabilities and user experiences.

Motific.ai is a responsible GenAI tool powered by data at scale. It offers a fully managed service with natural language compliance and security guardrails, an intelligence service, and an enterprise data-powered, end-to-end retrieval augmented generation (RAG) service. Users can rapidly deliver trustworthy GenAI assistants and API endpoints, configure assistants with organization's data, optimize performance, and connect with top GenAI model providers. Motific.ai enables users to create custom knowledge bases, connect to various data sources, and ensure responsible AI practices. It supports English language only and offers insights on usage, time savings, and model optimization.

Clarifai is an AI Workflow Orchestration Platform that helps businesses establish an AI Operating Model and transition from prototype to production efficiently. It offers end-to-end solutions for operationalizing AI, including Retrieval Augmented Generation (RAG), Generative AI, Digital Asset Management, Visual Inspection, Automated Data Labeling, and Content Moderation. Clarifai's platform enables users to build and deploy AI faster, reduce development costs, ensure oversight and security, and unlock AI capabilities across the organization. The platform simplifies data labeling, content moderation, intelligence & surveillance, generative AI, content organization & personalization, and visual inspection. Trusted by top enterprises, Clarifai helps companies overcome challenges in hiring AI talent and misuse of data, ultimately leading to AI success at scale.

TrainMyAI is a comprehensive solution for creating AI chatbots using retrieval augmented generation (RAG) technology. It allows users to build custom AI chatbots on their servers, enabling interactions over WhatsApp, web, and private APIs. The platform offers deep customization options, fine-grained user management, usage history tracking, content optimization, and linked citations. With TrainMyAI, users can maintain full control over their AI models and data, either on-premise or in the cloud.

AI21 Labs is a reliable generative AI tool designed for enterprise products. It offers accurate, scalable, and tailored generative AI solutions to power critical workflows. The tool is human-centered, practical, and easily scalable to fit enterprise needs. Leading companies trust AI21 for its production-grade AI systems that amplify human potential and provide valuable assistance in various use cases.

This is a unified DevOps platform to build AI applications. It provides a comprehensive set of tools and services to help developers build, deploy, and manage AI applications. The platform includes a variety of features such as a code editor, a debugger, a profiler, and a deployment manager. It also provides access to a variety of AI services, such as natural language processing, machine learning, and computer vision.

RAGHack is a hackathon focused on building AI applications using the power of RAG (Retrieval Augmented Generation). RAG combines large language models with search engine knowledge to provide contextually relevant answers. Participants can learn to build RAG apps on Azure AI using various languages and retrievers, explore frameworks like LangChain and Semantic Kernel, and leverage technologies such as agents and vision models. The hackathon features live streams, hack submissions, and prizes for innovative projects.

RAGGENIE is a low-code RAG builder tool designed to simplify the creation of conversational AI applications. It offers out-of-the-box plugins for connecting to various data sources and building conversational AI on top of them, including integration with pre-built agents for actions. The tool is open-source under the MIT license, with a current focus on making it easy to build RAG applications and future plans for maintenance, monitoring, and transitioning applications from pilots to production.

This repository is a comprehensive guide for individuals looking to start and improve their skills in Large Language Models (LLMs) without an advanced background in the field. It provides free resources, online courses, books, articles, and practical tips to become an expert in machine learning. The guide covers topics such as terminology, transformers, prompting, retrieval augmented generation (RAG), and more. It also includes recommendations for podcasts, YouTube videos, and communities to stay updated with the latest news in AI and LLMs.

This repository provides resources, demos, and recipes for working with Large Language Models (LLMs) on OpenShift using OpenShift AI or Open Data Hub. It includes instructions for deploying inference servers for LLMs, such as vLLM, Hugging Face TGI, Caikit-TGIS-Serving, and Ollama. Additionally, it offers guidance on deploying serving runtimes, such as vLLM Serving Runtime and Hugging Face Text Generation Inference, in the Single-Model Serving stack of Open Data Hub or OpenShift AI. The repository also covers vector databases that can be used as a Vector Store for Retrieval Augmented Generation (RAG) applications, including Milvus, PostgreSQL+pgvector, and Redis. Furthermore, it provides examples of inference and application usage, such as Caikit, Langchain, Langflow, and UI examples.

Data Prep Kit is a community project aimed at democratizing and speeding up unstructured data preparation for LLM app developers. It provides high-level APIs and modules for transforming data (code, language, speech, visual) to optimize LLM performance across different use cases. The toolkit supports Python, Ray, Spark, and Kubeflow Pipelines runtimes, offering scalability from laptop to datacenter-scale processing. Developers can contribute new custom modules and leverage the data processing library for building data pipelines. Automation features include workflow automation with Kubeflow Pipelines for transform execution.

Awesome-LLM-RAG-Application is a repository that provides resources and information about applications based on Large Language Models (LLM) with Retrieval-Augmented Generation (RAG) pattern. It includes a survey paper, GitHub repo, and guides on advanced RAG techniques. The repository covers various aspects of RAG, including academic papers, evaluation benchmarks, downstream tasks, tools, and technologies. It also explores different frameworks, preprocessing tools, routing mechanisms, evaluation frameworks, embeddings, security guardrails, prompting tools, SQL enhancements, LLM deployment, observability tools, and more. The repository aims to offer comprehensive knowledge on RAG for readers interested in exploring and implementing LLM-based systems and products.

The Generative AI Use Cases Repository showcases a wide range of applications in generative AI, including Retrieval-Augmented Generation (RAG), AI Agents, and industry-specific use cases. It provides practical notebooks and guidance on utilizing frameworks such as LlamaIndex and LangChain, and demonstrates how to integrate models from leading AI research companies like Anthropic and OpenAI.

A comprehensive guide to building Retrieval Augmented Generation (RAG)-based LLM applications for production. This guide covers developing a RAG-based LLM application from scratch, scaling the major components, evaluating different configurations, implementing LLM hybrid routing, serving the application in a highly scalable and available manner, and sharing the impacts LLM applications have had on products.

RAGHub is a community-driven project focused on cataloging new and emerging frameworks, projects, and resources in the Retrieval-Augmented Generation (RAG) ecosystem. It aims to help users stay ahead of changes in the field by providing a platform for the latest innovations in RAG. The repository includes information on RAG frameworks, evaluation frameworks, optimization frameworks, citation frameworks, engines, search reranker frameworks, projects, resources, and real-world use cases across industries and professions.

Pathway is a Python data processing framework for analytics and AI pipelines over data streams. It's the ideal solution for real-time processing use cases like streaming ETL or RAG pipelines for unstructured data. Pathway comes with an **easy-to-use Python API** , allowing you to seamlessly integrate your favorite Python ML libraries. Pathway code is versatile and robust: **you can use it in both development and production environments, handling both batch and streaming data effectively**. The same code can be used for local development, CI/CD tests, running batch jobs, handling stream replays, and processing data streams. Pathway is powered by a **scalable Rust engine** based on Differential Dataflow and performs incremental computation. Your Pathway code, despite being written in Python, is run by the Rust engine, enabling multithreading, multiprocessing, and distributed computations. All the pipeline is kept in memory and can be easily deployed with **Docker and Kubernetes**. You can install Pathway with pip: `pip install -U pathway` For any questions, you will find the community and team behind the project on Discord.

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.

AiTreasureBox is a versatile AI tool that provides a collection of pre-trained models and algorithms for various machine learning tasks. It simplifies the process of implementing AI solutions by offering ready-to-use components that can be easily integrated into projects. With AiTreasureBox, users can quickly prototype and deploy AI applications without the need for extensive knowledge in machine learning or deep learning. The tool covers a wide range of tasks such as image classification, text generation, sentiment analysis, object detection, and more. It is designed to be user-friendly and accessible to both beginners and experienced developers, making AI development more efficient and accessible to a wider audience.

Haystack is an end-to-end LLM framework that allows you to build applications powered by LLMs, Transformer models, vector search and more. Whether you want to perform retrieval-augmented generation (RAG), document search, question answering or answer generation, Haystack can orchestrate state-of-the-art embedding models and LLMs into pipelines to build end-to-end NLP applications and solve your use case.

This project is a tutorial on developing large model applications for novice developers. It aims to provide a comprehensive introduction to large model development, focusing on Alibaba Cloud servers and integrating personal knowledge assistant projects. The tutorial covers the following topics: 1. **Introduction to Large Models**: A simplified introduction for novice developers on what large models are, their characteristics, what LangChain is, and how to develop an LLM application. 2. **How to Call Large Model APIs**: This section introduces various methods for calling APIs of well-known domestic and foreign large model products, including calling native APIs, encapsulating them as LangChain LLMs, and encapsulating them as Fastapi calls. It also provides a unified encapsulation for various large model APIs, such as Baidu Wenxin, Xunfei Xinghuo, and Zh譜AI. 3. **Knowledge Base Construction**: Loading, processing, and vector database construction of different types of knowledge base documents. 4. **Building RAG Applications**: Integrating LLM into LangChain to build a retrieval question and answer chain, and deploying applications using Streamlit. 5. **Verification and Iteration**: How to implement verification and iteration in large model development, and common evaluation methods. The project consists of three main parts: 1. **Introduction to LLM Development**: A simplified version of V1 aims to help beginners get started with LLM development quickly and conveniently, understand the general process of LLM development, and build a simple demo. 2. **LLM Development Techniques**: More advanced LLM development techniques, including but not limited to: Prompt Engineering, processing of multiple types of source data, optimizing retrieval, recall ranking, Agent framework, etc. 3. **LLM Application Examples**: Introduce some successful open source cases, analyze the ideas, core concepts, and implementation frameworks of these application examples from the perspective of this course, and help beginners understand what kind of applications they can develop through LLM. Currently, the first part has been completed, and everyone is welcome to read and learn; the second and third parts are under creation. **Directory Structure Description**: requirements.txt: Installation dependencies in the official environment notebook: Notebook source code file docs: Markdown documentation file figures: Pictures data_base: Knowledge base source file used

fastRAG is a research framework designed to build and explore efficient retrieval-augmented generative models. It incorporates state-of-the-art Large Language Models (LLMs) and Information Retrieval to empower researchers and developers with a comprehensive tool-set for advancing retrieval augmented generation. The framework is optimized for Intel hardware, customizable, and includes key features such as optimized RAG pipelines, efficient components, and RAG-efficient components like ColBERT and Fusion-in-Decoder (FiD). fastRAG supports various unique components and backends for running LLMs, making it a versatile tool for research and development in the field of retrieval-augmented generation.

Tonic Validate is a framework for the evaluation of LLM outputs, such as Retrieval Augmented Generation (RAG) pipelines. Validate makes it easy to evaluate, track, and monitor your LLM and RAG applications. Validate allows you to evaluate your LLM outputs through the use of our provided metrics which measure everything from answer correctness to LLM hallucination. Additionally, Validate has an optional UI to visualize your evaluation results for easy tracking and monitoring.

Giskard is an open-source Python library that automatically detects performance, bias & security issues in AI applications. The library covers LLM-based applications such as RAG agents, all the way to traditional ML models for tabular data.

A curated repository of code recipes, demos, and resources for basic and advanced Redis use cases in the AI ecosystem. It includes demos for ArxivChatGuru, Redis VSS, Vertex AI & Redis, Agentic RAG, ArXiv Search, and Product Search. Recipes cover topics like Getting started with RAG, Semantic Cache, Advanced RAG, and Recommendation systems. The repository also provides integrations/tools like RedisVL, AWS Bedrock, LangChain Python, LangChain JS, LlamaIndex, Semantic Kernel, RelevanceAI, and DocArray. Additional content includes blog posts, talks, reviews, and documentation related to Vector Similarity Search, AI-Powered Document Search, Vector Databases, Real-Time Product Recommendations, and more. Benchmarks compare Redis against other Vector Databases and ANN benchmarks. Documentation includes QuickStart guides, official literature for Vector Similarity Search, Redis-py client library docs, Redis Stack documentation, and Redis client list.

Hands-On-LLM-Applications-Development is a repository focused on developing applications using Large Language Models (LLMs). The repository provides hands-on tutorials, guides, and resources for building various applications such as LangChain for LLM applications, Retrieval Augmented Generation (RAG) with LangChain, building LLM agents with LangGraph, and advanced LangChain with OpenAI. It covers topics like prompt engineering for LLMs, building applications using HuggingFace open-source models, LLM fine-tuning, and advanced RAG applications.

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