Best AI tools for< Build Chatbots >

Promptly is a generative AI platform designed for enterprises, offering a no-code AI app builder sheets platform solution. It enables users to create tailor-made generative AI agents, applications, and chatbots to meet unique user needs. The platform allows seamless integration of own data and GPT-powered models without any coding experience. Promptly supports various data sources and major model providers, making it developer-friendly and suitable for a wide range of applications. With features like model chaining, developer-friendly tools, and collaborative app building, Promptly empowers organizations to scale their AI capabilities securely and efficiently.

BotX is a no-code AI platform that enables users to automate and deploy generative AI workflows, chatbots, RAGs, and multi-agent solutions. With production-ready AI systems, users can increase productivity, build AI agents and chatbots, automate workflows, create or process documents, and connect models effortlessly. The platform offers a range of models and fine-tuning options, seamless integration with advanced models like ChatGPT, and enterprise-grade results with grounded responses. Users can protect their data with various deployment options and benefit from dedicated support, integrations-ready solutions, and tailor-made solutions for enterprises and SMEs.

Rooftops AI is an AI application that provides unlimited roofing reports and property data to help businesses scale. It offers features like generating AI content, building chatbots, managing tasks, and creating legal documents. The application aims to streamline processes and improve efficiency in various business operations.

Dify.AI is a generative AI application development platform that allows users to create AI agents, chatbots, and other AI-powered applications. It provides a variety of tools and services to help developers build, deploy, and manage their AI applications. Dify.AI is designed to be easy to use, even for those with no prior experience in AI development.

Flowise is an open-source, low-code tool that enables developers to build customized LLM orchestration flows and AI agents. It provides a drag-and-drop interface, pre-built app templates, conversational agents with memory, and seamless deployment on cloud platforms. Flowise is backed by Combinator and trusted by teams around the globe.

Prisms is a no-code platform for building AI-powered apps. It allows users to harness the power of AI without having to write any code. Prisms is built on top of Large Language models including GPT3, DALL-E, and Stable Diffusion. Users can connect the pieces in Prisms to stack together data sources, user inputs, and off-the-shelf building blocks to create their own AI-powered apps. Prisms also makes it easy to deploy AI-powered apps directly from the platform with its pre-built UI. Alternatively, users can build their own frontend and use Prisms as a backend for their AI logic.

re:tune is a no-code AI app solution that provides everything you need to transform your business with AI, from custom chatbots to autonomous agents. With re:tune, you can build chatbots for any use case, connect any data source, and integrate with all your favorite tools and platforms. re:tune is the missing platform to build your AI apps.

Iconi Ai is an all-in-one platform that provides a suite of AI-powered tools to help businesses and individuals create and manage content, generate code, and automate tasks. With Iconi Ai, users can generate text, images, code, chatbots, and more, all with just a few clicks. The platform also includes a range of features to help users track their progress, manage their team, and get support. Iconi Ai is a powerful tool that can help businesses and individuals save time, money, and effort while creating high-quality content and code.

AI Tools Masters is a comprehensive platform that empowers users to discover and evaluate the latest and most exceptional AI tools. Catering to diverse needs, from education to personal advancement, AI Tools Masters offers a curated collection of top-notch solutions tailored to specific requirements. With a user-friendly interface and extensive filtering options, users can effortlessly navigate through a wide range of AI tools, ensuring they find the perfect fit for their projects and goals.

AgentLabs is a frontend-as-a-service platform that allows developers to build and share AI-powered chat-based applications in minutes, without any front-end experience. It provides a range of features such as real-time and asynchronous communication, background task management, backend agnosticism, and support for Markdown, files, and more.

Dialogflow is a natural language processing platform that allows developers to build conversational interfaces for applications. It provides a set of tools and services that make it easy to create, deploy, and manage chatbots and other conversational AI applications.

AI Experts is an AI agency that helps businesses integrate AI into their operations. They offer a range of AI-driven tools for content creation, data analysis, and more. Their mission is to bridge the gap between groundbreaking AI advancements and business goals.

LlamaIndex is a framework for building context-augmented Large Language Model (LLM) applications. It provides tools to ingest and process data, implement complex query workflows, and build applications like question-answering chatbots, document understanding systems, and autonomous agents. LlamaIndex enables context augmentation by combining LLMs with private or domain-specific data, offering tools for data connectors, data indexes, engines for natural language access, chat engines, agents, and observability/evaluation integrations. It caters to users of all levels, from beginners to advanced developers, and is available in Python and Typescript.

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.

AI Tool Center is a comprehensive platform that showcases a wide array of cutting-edge AI tools across various domains. From organizing browsing tabs to language learning and chatbot building, the platform caters to diverse user needs. It offers solutions for data analysis, customer feedback analysis, e-commerce marketing, and more. With a focus on innovation and efficiency, AI Tool Center aims to simplify tasks and enhance user experiences through AI-driven technologies.

AITurbos is an AI-powered platform that offers a suite of tools designed to revolutionize content creation and marketing strategies. With a focus on boosting engagement, saving time, and enhancing productivity, AITurbos provides advanced AI models for generating text, images, code, chatbots, and more. Users can access features like AI text generation, image generation, code generation, chatbot creation, and speech-to-text conversion. The platform supports multiple languages, custom templates, and data-driven customization to meet diverse content creation needs.

Watermelon is an AI customer service tool that integrates with OpenAI's latest model, GPT-4o. It allows users to build chatbots powered by GPT-4o to automate customer interactions, handle frequently asked questions, and collaborate seamlessly between chatbots and human agents. Watermelon offers features such as chatbot building, customizable chat widgets, statistics tracking, inbox collaboration, and various integrations with APIs and webhooks. The application caters to industries like e-commerce, education, healthcare, and financial services, providing solutions for sales support, lead generation, marketing, HR, and customer service.

Cerebras is a leading AI tool and application provider that offers cutting-edge AI supercomputers, model services, and cloud solutions for various industries. The platform specializes in high-performance computing, large language models, and AI model training, catering to sectors such as health, energy, government, and financial services. Cerebras empowers developers and researchers with access to advanced AI models, open-source resources, and innovative hardware and software development kits.

AgentCloud is an open-source platform that enables companies to build and deploy private LLM chat apps, empowering teams to securely interact with their data. It comprises three main components: Agent Backend, Webapp, and Vector Proxy. To run this project locally, clone the repository, install Docker, and start the services. The project is licensed under the GNU Affero General Public License, version 3 only. Contributions and feedback are welcome from the community.

Zep is an open-source platform for building and deploying large language model (LLM) applications. It provides a suite of tools and services that make it easy to integrate LLMs into your applications, including chat history memory, embedding, vector search, and data enrichment. Zep is designed to be scalable, reliable, and easy to use, making it a great choice for developers who want to build LLM-powered applications quickly and easily.

LoLLMs Server is a text generation server based on large language models. It provides a Flask-based API for generating text using various pre-trained language models. This server is designed to be easy to install and use, allowing developers to integrate powerful text generation capabilities into their applications.

LlamaIndex.TS is a data framework for your LLM application. Use your own data with large language models (LLMs, OpenAI ChatGPT and others) in Typescript and Javascript.

Semantic Kernel is an SDK that integrates Large Language Models (LLMs) like OpenAI, Azure OpenAI, and Hugging Face with conventional programming languages like C#, Python, and Java. Semantic Kernel achieves this by allowing you to define plugins that can be chained together in just a few lines of code. What makes Semantic Kernel _special_ , however, is its ability to _automatically_ orchestrate plugins with AI. With Semantic Kernel planners, you can ask an LLM to generate a plan that achieves a user's unique goal. Afterwards, Semantic Kernel will execute the plan for the user.

Botpress is a platform for building next-generation chatbots and assistants powered by OpenAI. It provides a range of tools and integrations to help developers quickly and easily create and deploy chatbots for various use cases.

BotSharp is an open-source machine learning framework for building AI bot platforms. It provides a comprehensive set of tools and components for developing and deploying intelligent virtual assistants. BotSharp is designed to be modular and extensible, allowing developers to easily integrate it with their existing systems and applications. With BotSharp, you can quickly and easily create AI-powered chatbots, virtual assistants, and other conversational AI applications.

Qdrant is a vector similarity search engine and vector database. It is written in Rust, which makes it fast and reliable even under high load. Qdrant can be used for a variety of applications, including: * Semantic search * Image search * Product recommendations * Chatbots * Anomaly detection Qdrant offers a variety of features, including: * Payload storage and filtering * Hybrid search with sparse vectors * Vector quantization and on-disk storage * Distributed deployment * Highlighted features such as query planning, payload indexes, SIMD hardware acceleration, async I/O, and write-ahead logging Qdrant is available as a fully managed cloud service or as an open-source software that can be deployed on-premises.

Milvus is an open-source vector database built to power embedding similarity search and AI applications. Milvus makes unstructured data search more accessible, and provides a consistent user experience regardless of the deployment environment. Milvus 2.0 is a cloud-native vector database with storage and computation separated by design. All components in this refactored version of Milvus are stateless to enhance elasticity and flexibility. For more architecture details, see Milvus Architecture Overview. Milvus was released under the open-source Apache License 2.0 in October 2019. It is currently a graduate project under LF AI & Data Foundation.

Resonance is a framework designed to facilitate interoperability and messaging between services in your infrastructure and beyond. It provides AI capabilities and takes full advantage of asynchronous PHP, built on top of Swoole. With Resonance, you can: * Chat with Open-Source LLMs: Create prompt controllers to directly answer user's prompts. LLM takes care of determining user's intention, so you can focus on taking appropriate action. * Asynchronous Where it Matters: Respond asynchronously to incoming RPC or WebSocket messages (or both combined) with little overhead. You can set up all the asynchronous features using attributes. No elaborate configuration is needed. * Simple Things Remain Simple: Writing HTTP controllers is similar to how it's done in the synchronous code. Controllers have new exciting features that take advantage of the asynchronous environment. * Consistency is Key: You can keep the same approach to writing software no matter the size of your project. There are no growing central configuration files or service dependencies registries. Every relation between code modules is local to those modules. * Promises in PHP: Resonance provides a partial implementation of Promise/A+ spec to handle various asynchronous tasks. * GraphQL Out of the Box: You can build elaborate GraphQL schemas by using just the PHP attributes. Resonance takes care of reusing SQL queries and optimizing the resources' usage. All fields can be resolved asynchronously.

PPL LLM Serving is a serving based on ppl.nn for various Large Language Models (LLMs). It provides inference support for LLaMA. Key features include: * **High Performance:** Optimized for fast and efficient inference on LLM models. * **Scalability:** Supports distributed deployment across multiple GPUs or machines. * **Flexibility:** Allows for customization of model configurations and inference pipelines. * **Ease of Use:** Provides a user-friendly interface for deploying and managing LLM models. This tool is suitable for various tasks, including: * **Text Generation:** Generating text, stories, or code from scratch or based on a given prompt. * **Text Summarization:** Condensing long pieces of text into concise summaries. * **Question Answering:** Answering questions based on a given context or knowledge base. * **Language Translation:** Translating text between different languages. * **Chatbot Development:** Building conversational AI systems that can engage in natural language interactions. Keywords: llm, large language model, natural language processing, text generation, question answering, language translation, chatbot development

LangChain Rust is a library for building applications with Large Language Models (LLMs) through composability. It provides a set of tools and components that can be used to create conversational agents, document loaders, and other applications that leverage LLMs. LangChain Rust supports a variety of LLMs, including OpenAI, Azure OpenAI, Ollama, and Anthropic Claude. It also supports a variety of embeddings, vector stores, and document loaders. LangChain Rust is designed to be easy to use and extensible, making it a great choice for developers who want to build applications with LLMs.

Lagent is a lightweight open-source framework that allows users to efficiently build large language model(LLM)-based agents. It also provides some typical tools to augment LLM. The overview of our framework is shown below:

The Google AI client SDK for Android enables developers to use Google's state-of-the-art generative AI models (like Gemini) to build AI-powered features and applications. This SDK supports use cases like: - Generate text from text-only input - Generate text from text-and-images input (multimodal) - Build multi-turn conversations (chat)

The Google AI SDK for Swift enables developers to use Google's state-of-the-art generative AI models (like Gemini) to build AI-powered features and applications. This SDK supports use cases like: - Generate text from text-only input - Generate text from text-and-images input (multimodal) - Build multi-turn conversations (chat)

This repository is a curated list of open-source, actively maintained web applications that leverage large language models (LLMs) for various use cases, including chatbots, natural language interfaces, assistants, and question answering systems. The projects are evaluated based on key criteria such as licensing, maintenance status, complexity, and features, to help users select the most suitable starting point for their LLM-based applications. The repository welcomes contributions and encourages users to submit projects that meet the criteria or suggest improvements to the existing list.

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 |

Dexter is a set of mature LLM tools used in production at Dexa, with a focus on real-world RAG (Retrieval Augmented Generation). It is a production-quality RAG that is extremely fast and minimal, and handles caching, throttling, and batching for ingesting large datasets. It also supports optional hybrid search with SPLADE embeddings, and is a minimal TS package with full typing that uses `fetch` everywhere and supports Node.js 18+, Deno, Cloudflare Workers, Vercel edge functions, etc. Dexter has full docs and includes examples for basic usage, caching, Redis caching, AI function, AI runner, and chatbot.

WebLLM is a modular and customizable javascript package that directly brings language model chats directly onto web browsers with hardware acceleration. Everything runs inside the browser with no server support and is accelerated with WebGPU. WebLLM is fully compatible with OpenAI API. That is, you can use the same OpenAI API on any open source models locally, with functionalities including json-mode, function-calling, streaming, etc. We can bring a lot of fun opportunities to build AI assistants for everyone and enable privacy while enjoying GPU acceleration.

DAMO-ConvAI is the official repository for Alibaba DAMO Conversational AI. It contains the codebase for various conversational AI models and tools developed by Alibaba Research. These models and tools cover a wide range of tasks, including natural language understanding, natural language generation, dialogue management, and knowledge graph construction. DAMO-ConvAI is released under the MIT license and is available for use by researchers and developers in the field of conversational AI.

Awesome Ruby AI is a curated list of awesome AI projects built in Ruby. It includes open source API libraries, vector database clients, bot platforms, composability frameworks, and i18n tools. These tools can be used for a variety of tasks, such as natural language processing, computer vision, and machine learning.

Open LLMs is a repository containing various Large Language Models licensed for commercial use. It includes models like T5, GPT-NeoX, UL2, Bloom, Cerebras-GPT, Pythia, Dolly, and more. These models are designed for tasks such as transfer learning, language understanding, chatbot development, code generation, and more. The repository provides information on release dates, checkpoints, papers/blogs, parameters, context length, and licenses for each model. Contributions to the repository are welcome, and it serves as a resource for exploring the capabilities of different language models.

This repository serves as a comprehensive guide for technical teams interested in developing conversational AI solutions using Retrieval-Augmented Generation (RAG) techniques. It covers theoretical knowledge and practical code implementations, making it suitable for individuals with a basic technical background. The content includes information on large language models (LLMs), transformers, prompt engineering, embeddings, vector stores, and various other key concepts related to conversational AI. The repository also provides hands-on examples for two different use cases, along with implementation details and performance analysis.

LangDrive is an open-source AI library that simplifies training, deploying, and querying open-source large language models (LLMs) using private data. It supports data ingestion, fine-tuning, and deployment via a command-line interface, YAML file, or API, with a quick, easy setup. Users can build AI applications such as question/answering systems, chatbots, AI agents, and content generators. The library provides features like data connectors for ingestion, fine-tuning of LLMs, deployment to Hugging Face hub, inference querying, data utilities for CRUD operations, and APIs for model access. LangDrive is designed to streamline the process of working with LLMs and making AI development more accessible.

Generative AI with LangChain is a code repository for building large language model (LLM) apps with Python, ChatGPT, and other LLMs. The repository provides code examples, instructions, and configurations for creating generative AI applications using the LangChain framework. It covers topics such as setting up the development environment, installing dependencies with Conda or Pip, using Docker for environment setup, and setting API keys securely. The repository also emphasizes stability, code updates, and user engagement through issue reporting and feedback. It aims to empower users to leverage generative AI technologies for tasks like building chatbots, question-answering systems, software development aids, and data analysis applications.

Practical LangChain tutorials for developing LLM applications, including prompt templates, output parsing, chatbots memory, chains, evaluating applications, building agents using LangChain & OpenAI API, retrieval augmented generation with LangChain, documents loading, splitting, vector database & text embeddings, information retrieval, answering questions from documents, chat with files, and introduction to Open AI function calling.

LangChain Decoded is an open-source framework designed to facilitate the development of applications utilizing large language models (LLMs). It can be applied to tasks such as chatbots, text summarization, data generation, code understanding, question answering, and evaluation. The framework consists of various modules like Models, Embeddings, Prompts, Indexes, Memory, Chains, Agents, and Callbacks, each explored in separate Python notebooks. Users can follow the blog post series to understand and utilize LangChain for their projects.

A comprehensive repository of resources for Chinese large language models (LLMs), including data processing tools, fine-tuning frameworks, inference libraries, evaluation platforms, RAG engines, agent frameworks, books, courses, tutorials, and tips. The repository covers a wide range of tools and resources for working with LLMs, from data labeling and processing to model fine-tuning, inference, evaluation, and application development. It also includes resources for learning about LLMs through books, courses, and tutorials, as well as insights and strategies from building with LLMs.

Rubra is a collection of open-weight large language models enhanced with tool-calling capability. It allows users to call user-defined external tools in a deterministic manner while reasoning and chatting, making it ideal for agentic use cases. The models are further post-trained to teach instruct-tuned models new skills and mitigate catastrophic forgetting. Rubra extends popular inferencing projects for easy use, enabling users to run the models easily.

Awesome AI is a curated list of artificial intelligence resources including courses, tools, apps, and open-source projects. It covers a wide range of topics such as machine learning, deep learning, natural language processing, robotics, conversational interfaces, data science, and more. The repository serves as a comprehensive guide for individuals interested in exploring the field of artificial intelligence and its applications across various domains.

OpenVoiceChat is an open-source tool designed for having natural voice conversations with an LLM model. It supports various speech-to-text (STT), text-to-speech (TTS), and large language model (LLM) models. The tool aims to provide an alternative to closed commercial implementations, with well-abstracted APIs that are easy to use and extend. Users can install base and functionality-specific packages using pip, and the tool supports interruptions during conversations. The project encourages contributions through bounties and has a detailed roadmap available for reference.

Awesome Generative AI & LLM APIs is a curated list of useful APIs that allow developers to integrate generative models into their applications without building the models from scratch. These APIs provide an interface for generating text, images, or other content, and include pre-trained language models for various tasks. The goal of this project is to create a hub for developers to create innovative applications, enhance user experiences, and drive progress in the AI field.

DeepPavlov is an open-source conversational AI library built on PyTorch. It is designed for the development of production-ready chatbots and complex conversational systems, as well as for research in the area of NLP and dialog systems. The library offers a wide range of models for tasks such as Named Entity Recognition, Intent/Sentence Classification, Question Answering, Sentence Similarity/Ranking, Syntactic Parsing, and more. DeepPavlov also provides embeddings like BERT, ELMo, and FastText for various languages, along with AutoML capabilities and integrations with REST API, Socket API, and Amazon AWS.

SUQL (Structured and Unstructured Query Language) is a tool that augments SQL with free text primitives for building chatbots that can interact with relational data sources containing both structured and unstructured information. It seamlessly integrates retrieval models, large language models (LLMs), and traditional SQL to provide a clean interface for hybrid data access. SUQL supports optimizations to minimize expensive LLM calls, scalability to large databases with PostgreSQL, and general SQL operations like JOINs and GROUP BYs.

TigerGraph CoPilot is an AI assistant that combines graph databases and generative AI to enhance productivity across various business functions. It includes three core component services: InquiryAI for natural language assistance, SupportAI for knowledge Q&A, and QueryAI for GSQL code generation. Users can interact with CoPilot through a chat interface on TigerGraph Cloud and APIs. CoPilot requires LLM services for beta but will support TigerGraph's LLM in future releases. It aims to improve contextual relevance and accuracy of answers to natural-language questions by building knowledge graphs and using RAG. CoPilot is extensible and can be configured with different LLM providers, graph schemas, and LangChain tools.

IntelliQ is an open-source project aimed at providing a multi-turn question-answering system based on a large language model (LLM). The system combines advanced intent recognition and slot filling technology to enhance the depth of understanding and accuracy of responses in conversation systems. It offers a flexible and efficient solution for developers to build and optimize various conversational applications. The system features multi-turn dialogue management, intent recognition, slot filling, interface slot technology for real-time data retrieval and processing, adaptive learning for improving response accuracy and speed, and easy integration with detailed API documentation supporting multiple programming languages and platforms.

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.

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

ai-collective-tools is an open-source community dedicated to creating a comprehensive collection of AI tools for developers, researchers, and enthusiasts. The repository provides a curated selection of AI tools and resources across various categories such as 3D, Agriculture, Art, Audio Editing, Avatars, Chatbots, Code Assistant, Cooking, Copywriting, Crypto, Customer Support, Dating, Design Assistant, Design Generator, Developer, E-Commerce, Education, Email Assistant, Experiments, Fashion, Finance, Fitness, Fun Tools, Gaming, General Writing, Gift Ideas, HealthCare, Human Resources, Image Classification, Image Editing, Image Generator, Interior Designing, Legal Assistant, Logo Generator, Low Code, Models, Music, Paraphraser, Personal Assistant, Presentations, Productivity, Prompt Generator, Psychology, Real Estate, Religion, Research, Resume, Sales, Search Engine, SEO, Shopping, Social Media, Spreadsheets, SQL, Startup Tools, Story Teller, Summarizer, Testing, Text to Speech, Text to Image, Transcriber, Travel, Video Editing, Video Generator, Weather, Writing Generator, and Other Resources.

BlendSQL is a superset of SQLite designed for problem decomposition and hybrid question-answering with Large Language Models (LLMs). It allows users to blend operations over heterogeneous data sources like tables, text, and images, combining the structured and interpretable reasoning of SQL with the generalizable reasoning of LLMs. Users can oversee all calls (LLM + SQL) within a unified query language, enabling tasks such as building LLM chatbots for travel planning and answering complex questions by injecting 'ingredients' as callable functions.

Flock is a workflow-based low-code platform that enables rapid development of chatbots, RAG applications, and coordination of multi-agent teams. It offers a flexible, low-code solution for orchestrating collaborative agents, supporting various node types for specific tasks, such as input processing, text generation, knowledge retrieval, tool execution, intent recognition, answer generation, and more. Flock integrates LangChain and LangGraph to provide offline operation capabilities and supports future nodes like Conditional Branch, File Upload, and Parameter Extraction for creating complex workflows. Inspired by StreetLamb, Lobe-chat, Dify, and fastgpt projects, Flock introduces new features and directions while leveraging open-source models and multi-tenancy support.

Sarvadnya is a repository focused on interfacing custom data using Large Language Models (LLMs) through Proof-of-Concepts (PoCs) like Retrieval Augmented Generation (RAG) and Fine-Tuning. It aims to enable domain adaptation for LLMs to answer on user-specific corpora. The repository also covers topics such as Indic-languages models, 3D World Simulations, Knowledge Graphs Generation, Signal Processing, Drones, UAV Image Processing, and Floor Plan Segmentation. It provides insights into building chatbots of various modalities, preparing videos, and creating content for different platforms like Medium, LinkedIn, and YouTube. The tech stacks involved range from enterprise solutions like Google Doc AI and Microsoft Azure Language AI Services to open-source tools like Langchain and HuggingFace.

Curating the best Large Language Model (LLM) Web User Interfaces that facilitate interaction with powerful AI models. Explore and catalogue intuitive, feature-rich, and innovative web interfaces for interacting with LLMs, ranging from simple chatbots to comprehensive platforms equipped with functionalities like PDF generation and web search.

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