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Comprehensive 可擴展的AI應用 Tools for Every Need

Get access to 可擴展的AI應用 solutions that address multiple requirements. One-stop resources for streamlined workflows.

可擴展的AI應用

  • Controllable RAG Agent

    An open-source Python framework to build Retrieval-Augmented Generation agents with customizable control over retrieval and response generation.
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    What is Controllable RAG Agent?
    The Controllable RAG Agent framework provides a modular approach to building Retrieval-Augmented Generation systems. It allows you to configure and chain retrieval components, memory modules, and generation strategies. Developers can plug in different LLMs, vector databases, and policy controllers to adjust how documents are fetched and processed before generation. Built on Python, it includes utilities for indexing, querying, conversation history tracking, and action-based control flows, making it ideal for chatbots, knowledge assistants, and research tools.
    Controllable RAG Agent Core Features
    • Modular RAG pipeline with retriever, memory, and generator components
    • Support for FAISS, Pinecone, and custom vector stores
    • Customizable policy controllers for retrieval and generation
    • Conversation history and memory management
    • Plugin system for extending behaviors and actions

  • Role Model AI

    Role Model AI helps users create custom AI agents for various tasks.
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    What is Role Model AI?
    Role Model AI is a powerful platform that enables users to create AI agents tailored to their needs. It offers functionalities for custom workflows, task automation, and integration with various applications. Users can design their agents to excel in specific areas like customer service, marketing automation, or data analysis, making it a versatile tool for professionals across different industries.

  • Chatbot-Grok

    An extensible Python-based AI Agent for multi-turn conversation, memory, custom prompts, and Grok integration.
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    What is Chatbot-Grok?
    Chatbot-Grok provides a modular AI Agent framework written in Python, designed to simplify development of conversational bots. It supports multi-turn dialogue management, retains chat memory across sessions, and allows users to define custom prompt templates. The architecture is extensible, letting developers integrate various LLMs including Grok, and connect to platforms such as Telegram or Slack. With clear code organization and plugin-friendly structure, Chatbot-Grok accelerates prototyping and deployment of production-ready chat assistants.
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