# Docker

This product is a web-based AI research tool designed to help users conduct in-depth topic research quickly and efficiently. By integrating multiple search engines, web crawler technology, and large language models, it enables iterative in-depth research and displays the research process in an intuitive tree structure. The tool supports multilingual search and features real-time feedback, search visualization, and report export functions, greatly improving research efficiency. It is suitable for users who need to collect and analyze large amounts of information; students, researchers, and professionals alike can benefit from it. Currently, this product is offered for free, providing high cost-effectiveness and practical value.

Minima is an open-source, fully localized Retrieval-Augmented Generation (RAG) model, capable of integrating with ChatGPT and the Model Context Protocol (MCP). It supports three modes: full local installation, querying local documents through ChatGPT, and querying local files using Anthropic Claude. The primary advantages of Minima include localized data processing, privacy protection, and the ability to leverage powerful language models to enhance retrieval and generation tasks. Background information indicates that Minima supports multiple file formats and allows users to customize configurations to fit different usage scenarios. Minima is free and open-source, targeting developers and enterprises seeking localized AI solutions.

Learning to Fly (L2F) is an open-source project designed to train end-to-end control policies using deep reinforcement learning, capable of completing training quickly on consumer-grade laptops. The project's main advantages include fast training speed, achievable within seconds, and robust generalization capabilities that allow trained policies to be directly deployed to real quadcopters. The L2F project relies on the RLtools deep reinforcement learning library and provides comprehensive installation and deployment guides, enabling researchers and developers to quickly get started and experiment.

AIKit is an open-source tool designed to simplify the process of hosting, deploying, building, and fine-tuning large language models (LLMs). It offers a REST API compatible with the OpenAI API, supporting various inference capabilities and formats, allowing users to send requests using any compatible client. Furthermore, AIKit provides an extensible fine-tuning interface with Unsloth support, offering users a fast, memory-efficient, and user-friendly fine-tuning experience.

DeployFast is an ML code template using FastAPI, Streamlit, Docker, ElevenLabs API, and OpenAI API to help users quickly deploy and demonstrate AI applications. It can be easily containerized in the cloud and is suitable for users from AI beginners to busy professionals.

The Docker GenAI Stack is a solution for AI application development tailored for developers. It integrates leading AI technologies, allowing for the deployment of a complete AI application stack with just a few clicks, achieving AI integration at the code level. The GenAI Stack features pre-configured large language models, provides Ollama management, uses Neo4j as the default database, and enables knowledge graph and vector search. It comes with the LangChain framework for orchestration and debugging, as well as comprehensive technical support and community resources. The GenAI Stack simplifies and streamlines AI application development, enabling developers to rapidly build practical AI solutions.