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リアルタイム推論

  • Provides a FastAPI backend for visual graph-based orchestration and execution of language model workflows in LangGraph GUI.
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    What is LangGraph-GUI Backend?
    The LangGraph-GUI Backend is an open-source FastAPI service that powers the LangGraph graphical interface. It handles CRUD operations on graph nodes and edges, manages workflow execution against various language models, and returns real-time inference results. The backend supports authentication, logging, and extensibility for custom plugins, enabling users to prototype, test, and deploy complex natural language processing workflows through a visual programming paradigm while maintaining full control over execution pipelines.
    LangGraph-GUI Backend Core Features
    • Graph node and edge management APIs
    • Visual workflow execution orchestration
    • Multiple LLM model integrations
    • Authentication and logging support
    • Extensible plugin architecture
  • SuperDuperDB integrates AI with databases for seamless real-time inference and training.
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    What is SuperDuperDB?
    SuperDuperDB is a platform that enhances the capabilities of integrating AI with databases. It allows developers to deploy, manage, and process AI models directly within their data environment using simple Python commands. SuperDuperDB facilitates real-time inference and model training without the need for additional data ingestion or pre-processing. Additionally, it integrates AI APIs effortlessly, providing a seamless experience to scale and move AI projects across different environments.
  • OpenNARS is an open-source reasoning engine enabling real-time inference, belief revision, and learning under uncertain and resource-limited conditions.
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    What is OpenNARS?
    OpenNARS is built upon the principles of Non-Axiomatic Logic, enabling the system to perform deduction, induction, and abduction using truth-value pairs that reflect uncertainty. It maintains an experience-based memory of statements and dynamically recruits inference rules based on available resources, ensuring robust performance in real-time environments. The engine’s belief revision mechanism updates confidences as new information arrives, improving decision accuracy. Developers can integrate OpenNARS via provided SDKs in Java, C++, Python, JavaScript, Dart, or Go, and deploy it on desktops, servers, mobile devices, or embedded systems. Typical applications include cognitive robotics, autonomous agents, and complex problem-solving tasks where adaptive learning and efficient knowledge management are essential.
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