Best AI tools for< Helm Developer >

HelixML is a private GenAI platform that allows users to deploy the best of open AI in their own data center or VPC while retaining complete data security and control. It includes support for fine-tuning models with drag-and-drop functionality. HelixML brings the best of open source AI to businesses in an ergonomic and scalable way, optimizing the tradeoff between GPU memory and latency.

Airbroke is an open-source error catcher tool designed for modern web applications. It provides a PostgreSQL-based backend with an Airbrake-compatible HTTP collector endpoint and a React-based frontend for error management. The tool focuses on simplicity, maintaining a small database footprint even under heavy data ingestion. Users can ask AI about issues, replay HTTP exceptions, and save/manage bookmarks for important occurrences. Airbroke supports multiple OAuth providers for secure user authentication and offers occurrence charts for better insights into error occurrences. The tool can be deployed in various ways, including building from source, using Docker images, deploying on Vercel, Render.com, Kubernetes with Helm, or Docker Compose. It requires Node.js, PostgreSQL, and specific system resources for deployment.

AI DIAL Core is an HTTP Proxy that provides a unified API to different chat completion and embedding models, assistants, and applications. It is written in Java 17 and built on Eclipse Vert.x. The core functionality includes handling static and dynamic settings, deployment on Kubernetes using Helm charts, and storing user data in Blob Storage and Redis. It supports various identity providers, storage providers like AWS S3, Google Cloud Storage, and Azure Blob Store, and features like AI DIAL Addons, Interceptors, Assistants, Applications, and Models with customizable parameters and configurations.

AI DIAL is an open-source project that provides a platform for developing and deploying conversational AI applications. It includes components such as DIAL Core for API exposure, DIAL SDK for development, and DIAL Chat for default UI. The project offers tutorials for launching AI DIAL Chat with different models and applications, along with a user manual and configuration guide. Additionally, there are various open-source repositories related to DIAL, including DIAL Helm for helm chart, DIAL Assistant for model agnostic assistant implementation, and DIAL Analytics Realtime for usage analytics. The project aims to simplify the development and deployment of AI-powered chat applications.

Xorbits Inference (Xinference) is a powerful and versatile library designed to serve language, speech recognition, and multimodal models. With Xorbits Inference, you can effortlessly deploy and serve your or state-of-the-art built-in models using just a single command. Whether you are a researcher, developer, or data scientist, Xorbits Inference empowers you to unleash the full potential of cutting-edge AI models.

The 'llms-tools' repository is a comprehensive collection of AI tools, open-source projects, and research related to Large Language Models (LLMs) and Chatbots. It covers a wide range of topics such as AI in various domains, open-source models, chats & assistants, visual language models, evaluation tools, libraries, devices, income models, text-to-image, computer vision, audio & speech, code & math, games, robotics, typography, bio & med, military, climate, finance, and presentation. The repository provides valuable resources for researchers, developers, and enthusiasts interested in exploring the capabilities of LLMs and related technologies.

Spice is a portable runtime written in Rust that offers developers a unified SQL interface to materialize, accelerate, and query data from any database, data warehouse, or data lake. It connects, fuses, and delivers data to applications, machine-learning models, and AI-backends, functioning as an application-specific, tier-optimized Database CDN. Built with industry-leading technologies such as Apache DataFusion, Apache Arrow, Apache Arrow Flight, SQLite, and DuckDB. Spice makes it fast and easy to query data from one or more sources using SQL, co-locating a managed dataset with applications or machine learning models, and accelerating it with Arrow in-memory, SQLite/DuckDB, or attached PostgreSQL for fast, high-concurrency, low-latency queries.

Vearch is a cloud-native distributed vector database designed for efficient similarity search of embedding vectors in AI applications. It supports hybrid search with vector search and scalar filtering, offers fast vector retrieval from millions of objects in milliseconds, and ensures scalability and reliability through replication and elastic scaling out. Users can deploy Vearch cluster on Kubernetes, add charts from the repository or locally, start with Docker-compose, or compile from source code. The tool includes components like Master for schema management, Router for RESTful API, and PartitionServer for hosting document partitions with raft-based replication. Vearch can be used for building visual search systems for indexing images and offers a Python SDK for easy installation and usage. The tool is suitable for AI developers and researchers looking for efficient vector search capabilities in their applications.

LangStream is a tool for natural language processing tasks, providing a CLI for easy installation and usage. Users can try sample applications like Chat Completions and create their own applications using the developer documentation. It supports running on Kubernetes for production-ready deployment, with support for various Kubernetes distributions and external components like Apache Kafka or Apache Pulsar cluster. Users can deploy LangStream locally using minikube and manage the cluster with mini-langstream. Development requirements include Docker, Java 17, Git, Python 3.11+, and PIP, with the option to test local code changes using mini-langstream.

Starwhale is an MLOps/LLMOps platform that brings efficiency and standardization to machine learning operations. It streamlines the model development lifecycle, enabling teams to optimize workflows around key areas like model building, evaluation, release, and fine-tuning. Starwhale abstracts Model, Runtime, and Dataset as first-class citizens, providing tailored capabilities for common workflow scenarios including Models Evaluation, Live Demo, and LLM Fine-tuning. It is an open-source platform designed for clarity and ease of use, empowering developers to build customized MLOps features tailored to their needs.

KubeSphere is a distributed operating system for cloud-native application management, using Kubernetes as its kernel. It provides a plug-and-play architecture, allowing third-party applications to be seamlessly integrated into its ecosystem. KubeSphere is also a multi-tenant container platform with full-stack automated IT operation and streamlined DevOps workflows. It provides developer-friendly wizard web UI, helping enterprises to build out a more robust and feature-rich platform, which includes most common functionalities needed for enterprise Kubernetes strategy.

ChatOpsLLM is a project designed to empower chatbots with effortless DevOps capabilities. It provides an intuitive interface and streamlined workflows for managing and scaling language models. The project incorporates robust MLOps practices, including CI/CD pipelines with Jenkins and Ansible, monitoring with Prometheus and Grafana, and centralized logging with the ELK stack. Developers can find detailed documentation and instructions on the project's website.

This repository codifies the Airflow cluster that is deployed at workflow.telemetry.mozilla.org (behind SSO) and commonly referred to as "WTMO" or simply "Airflow". Some links relevant to users and developers of WTMO: * The `dags` directory in this repository contains some custom DAG definitions * Many of the DAGs registered with WTMO don't live in this repository, but are instead generated from ETL task definitions in bigquery-etl * The Data SRE team maintains a WTMO Developer Guide (behind SSO)

Ollama is a lightweight, extensible framework for building and running language models on the local machine. It provides a simple API for creating, running, and managing models, as well as a library of pre-built models that can be easily used in a variety of applications. Ollama is designed to be easy to use and accessible to developers of all levels. It is open source and available for free on GitHub.

The NVIDIA AI Blueprint for Vulnerability Analysis for Container Security showcases accelerated analysis on common vulnerabilities and exposures (CVE) at an enterprise scale, reducing mitigation time from days to seconds. It enables security analysts to determine software package vulnerabilities using large language models (LLMs) and retrieval-augmented generation (RAG). The blueprint is designed for security analysts, IT engineers, and AI practitioners in cybersecurity. It requires NVAIE developer license and API keys for vulnerability databases, search engines, and LLM model services. Hardware requirements include L40 GPU for pipeline operation and optional LLM NIM and Embedding NIM. The workflow involves LLM pipeline for CVE impact analysis, utilizing LLM planner, agent, and summarization nodes. The blueprint uses NVIDIA NIM microservices and Morpheus Cybersecurity AI SDK for vulnerability analysis.

k8m is an AI-driven Mini Kubernetes AI Dashboard lightweight console tool designed to simplify cluster management. It is built on AMIS and uses 'kom' as the Kubernetes API client. k8m has built-in Qwen2.5-Coder-7B model interaction capabilities and supports integration with your own private large models. Its key features include miniaturized design for easy deployment, user-friendly interface for intuitive operation, efficient performance with backend in Golang and frontend based on Baidu AMIS, pod file management for browsing, editing, uploading, downloading, and deleting files, pod runtime management for real-time log viewing, log downloading, and executing shell commands within pods, CRD management for automatic discovery and management of CRD resources, and intelligent translation and diagnosis based on ChatGPT for YAML property translation, Describe information interpretation, AI log diagnosis, and command recommendations, providing intelligent support for managing k8s. It is cross-platform compatible with Linux, macOS, and Windows, supporting multiple architectures like x86 and ARM for seamless operation. k8m's design philosophy is 'AI-driven, lightweight and efficient, simplifying complexity,' helping developers and operators quickly get started and easily manage Kubernetes clusters.

HAMi is a Heterogeneous AI Computing Virtualization Middleware designed to manage Heterogeneous AI Computing Devices in a Kubernetes cluster. It allows for device sharing, device memory control, device type specification, and device UUID specification. The tool is easy to use and does not require modifying task YAML files. It includes features like hard limits on device memory, partial device allocation, streaming multiprocessor limits, and core usage specification. HAMi consists of components like a mutating webhook, scheduler extender, device plugins, and in-container virtualization techniques. It is suitable for scenarios requiring device sharing, specific device memory allocation, GPU balancing, low utilization optimization, and scenarios needing multiple small GPUs. The tool requires prerequisites like NVIDIA drivers, CUDA version, nvidia-docker, Kubernetes version, glibc version, and helm. Users can install, upgrade, and uninstall HAMi, submit tasks, and monitor cluster information. The tool's roadmap includes supporting additional AI computing devices, video codec processing, and Multi-Instance GPUs (MIG).

This project provides a collective list of Generative AI (GenAI) and Retrieval-Augmented Generation (RAG) examples such as chatbot with question and answering (ChatQnA), code generation (CodeGen), document summary (DocSum), etc.

aider.el is an AI pair programming tool for Emacs that provides an interactive interface to communicate with Aider. It offers features such as pop-up menu for commands, Git repository-specific sessions, batch file adding from dired buffer, region-based refactor support, and the ability to add custom Elisp functions. Users can install aider.el and dependencies to enhance their pair programming experience within Emacs.

Tiledesk is an Open Source Live Chat platform with integrated Chatbots written in NodeJs and Express. It provides a multi-channel platform for Web, Android, and iOS, offering out-of-the-box chatbots that work alongside humans. Users can automate conversations using native chatbot technology powered by AI, connect applications via APIs or Webhooks, deploy visual applications within conversations, and enable applications to interact with chatbots or end-users. Tiledesk is multichannel, allowing chatbot scripts with images and buttons to run on various channels like Whatsapp, Facebook Messenger, and Telegram. The project includes Tiledesk Server, Dashboard, Design Studio, Chat21 ionic, Web Widget, Server, Http Server, MongoDB, and a proxy. It offers Helm charts for Kubernetes deployment, but customization is recommended for production environments, such as integrating with external MongoDB or monitoring/logging tools. Enterprise customers can request private Docker images by contacting info@tiledesk.com.

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