The GPT-RAG Data Ingestion service automates processing of diverse document types for indexing in Azure AI Search. It uses intelligent chunking strategies tailored to each format, generates text and image embeddings, and enables rich, multimodal retrieval experiences for agent-based RAG applications. Supported data sources include Blob Storage, NL2SQL Metadata, and SharePoint. The service selects chunkers based on file extension, such as DocAnalysisChunker for PDF files, OCR for image files, LangChainChunker for text-based files, TranscriptionChunker for video transcripts, and SpreadsheetChunker for spreadsheets. Deployment requires provisioning infrastructure and assigning specific roles to the user or service principal.

WeKnora is a document understanding and semantic retrieval framework based on large language models (LLM), designed specifically for scenarios with complex structures and heterogeneous content. The framework adopts a modular architecture, integrating multimodal preprocessing, semantic vector indexing, intelligent recall, and large model generation reasoning to build an efficient and controllable document question-answering process. The core retrieval process is based on the RAG (Retrieval-Augmented Generation) mechanism, combining context-relevant segments with language models to achieve higher-quality semantic answers. It supports various document formats, intelligent inference, flexible extension, efficient retrieval, ease of use, and security and control. Suitable for enterprise knowledge management, scientific literature analysis, product technical support, legal compliance review, and medical knowledge assistance.

The FuzzyAI Fuzzer is a powerful tool for automated LLM fuzzing, designed to help developers and security researchers identify jailbreaks and mitigate potential security vulnerabilities in their LLM APIs. It supports various fuzzing techniques, provides input generation capabilities, can be easily integrated into existing workflows, and offers an extensible architecture for customization and extension. The tool includes attacks like ArtPrompt, Taxonomy-based paraphrasing, Many-shot jailbreaking, Genetic algorithm, Hallucinations, DAN (Do Anything Now), WordGame, Crescendo, ActorAttack, Back To The Past, Please, Thought Experiment, and Default. It supports models from providers like Anthropic, OpenAI, Gemini, Azure, Bedrock, AI21, and Ollama, with the ability to add support for newer models. The tool also supports various cloud APIs and datasets for testing and experimentation.

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

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.

Vision Parse is a tool that leverages Vision Language Models to parse PDF documents into beautifully formatted markdown content. It offers smart content extraction, content formatting, multi-LLM support, PDF document support, and local model hosting using Ollama. Users can easily convert PDFs to markdown with high precision and preserve document hierarchy and styling. The tool supports multiple Vision LLM providers like OpenAI, LLama, and Gemini for accuracy and speed, making document processing efficient and effortless.

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

Srt-AI-Voice-Assistant is a convenient tool that generates audio from uploaded .srt subtitle files by calling APIs such as Bert-VITS2 (HiyoriUI), GPT-SoVITS, and Microsoft TTS (online). The code is currently not perfect, and feedback on bugs or suggestions can be provided at https://github.com/YYuX-1145/Srt-AI-Voice-Assistant/issues. Recent updates include adding custom API functionality with a focus on security, support for Microsoft online TTS (requires key configuration), error handling improvements, automatic project path detection, compatibility with API-v1 for limited functionality, and significant feature updates supporting card synthesis.

Instill Core is an open-source orchestrator comprising a collection of source-available projects designed to streamline every aspect of building versatile AI features with unstructured data. It includes Instill VDP (Versatile Data Pipeline) for unstructured data, AI, and pipeline orchestration, Instill Model for scalable MLOps and LLMOps for open-source or custom AI models, and Instill Artifact for unified unstructured data management. Instill Core can be used for tasks such as building, testing, and sharing pipelines, importing, serving, fine-tuning, and monitoring ML models, and transforming documents, images, audio, and video into a unified AI-ready format.

llmcord.py is a tool that allows users to chat with Language Model Models (LLMs) directly in Discord. It supports various LLM providers, both remote and locally hosted, and offers features like reply-based chat system, choosing any LLM, support for image and text file attachments, customizable system prompt, private access via DM, user identity awareness, streamed responses, warning messages, efficient message data caching, and asynchronous operation. The tool is designed to facilitate seamless conversations with LLMs and enhance user experience on Discord.

Clai is a command line context-feeder for AI tasks, supporting MCP client, vendor agnosticism, conversations, rate limit circumvention, profiles, and Unix-like functionality. Users can easily combine and tweak features for diverse use cases. Supported vendors include OpenAI, Anthropic, Mistral, Deepseek, Novita AI, Ollama, and Inception. Users need API keys for model access. Installation via 'go install' or setup script. 'clai help' provides guidance on usage. Glow can be installed for formatted markdown output.

Next generation artificial intelligent ERP system. On the basis of ERP business, we have expanded GPT-3.5. Individually or company can fine-tune your model through our system. You can provide fully automated business form submission operations through your simple description, and you can chat, interact, and consult information with GPT. You can deploy through Docker to quickly start and use. Completely free project. Enginsh / 简体中文.

xFasterTransformer is an optimized solution for Large Language Models (LLMs) on the X86 platform, providing high performance and scalability for inference on mainstream LLM models. It offers C++ and Python APIs for easy integration, along with example codes and benchmark scripts. Users can prepare models in a different format, convert them, and use the APIs for tasks like encoding input prompts, generating token ids, and serving inference requests. The tool supports various data types and models, and can run in single or multi-rank modes using MPI. A web demo based on Gradio is available for popular LLM models like ChatGLM and Llama2. Benchmark scripts help evaluate model inference performance quickly, and MLServer enables serving with REST and gRPC interfaces.

DataFlow is a data preparation and training system designed to parse, generate, process, and evaluate high-quality data from noisy sources, improving the performance of large language models in specific domains. It constructs diverse operators and pipelines, validated to enhance domain-oriented LLM's performance in fields like healthcare, finance, and law. DataFlow also features an intelligent DataFlow-agent capable of dynamically assembling new pipelines by recombining existing operators on demand.

Midscene.js is an AI-powered automation SDK that allows users to control web pages, perform assertions, and extract data in JSON format using natural language. It offers features such as natural language interaction, understanding UI and providing responses in JSON, intuitive assertion based on AI understanding, compatibility with public multimodal LLMs like GPT-4o, visualization tool for easy debugging, and a brand new experience in automation development.

This repository is part of Intelligent Document Processing with AWS AI Services workshop. It aims to automate the extraction of information from complex content in various document formats such as insurance claims, mortgages, healthcare claims, contracts, and legal contracts using AWS Machine Learning services like Amazon Textract and Amazon Comprehend. The repository provides hands-on labs to familiarize users with these AI services and build solutions to automate business processes that rely on manual inputs and intervention across different file types and formats.

The GPT-RAG Data Ingestion service automates processing of diverse document types for indexing in Azure AI Search. It uses intelligent chunking strategies tailored to each format, generates text and image embeddings, and enables rich, multimodal retrieval experiences for agent-based RAG applications. Supported data sources include Blob Storage, NL2SQL Metadata, and SharePoint. The service selects chunkers based on file extension, such as DocAnalysisChunker for PDF files, OCR for image files, LangChainChunker for text-based files, TranscriptionChunker for video transcripts, and SpreadsheetChunker for spreadsheets. Deployment requires provisioning infrastructure and assigning specific roles to the user or service principal.

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.