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Динамическое создание подсказок

  • Prepare AI tools for generating creative and effective prompts.
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    What is Style Transfer AI Prepare?
    Prepare is an AI-driven platform that assists users in creating impactful prompts for a variety of applications including writing, content creation, and educational purposes. The tool leverages advanced algorithms to suggest ideas, structures, and keywords, facilitating a dynamic and efficient prompt creation process. Users can refine their inputs to ensure that the prompts align with their specific needs, thus maximizing productivity and creativity.
  • Open-source agent framework bridging ZhipuAI API with OpenAI-compatible function calling, tool orchestration, and multi-step workflows.
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    What is ZhipuAI Agent to OpenAI?
    ZhipuAI Agent to OpenAI is a specialized agent framework designed to bridge ZhipuAI’s chat completion services with OpenAI-style agent interfaces. It provides a Python SDK that mirrors OpenAI’s function calling paradigm and supports third-party tool integrations, enabling developers to define custom tools, call external APIs, and maintain conversation context across turns. The framework handles request orchestration, dynamic prompt construction, and response parsing, returning structured outputs compatible with OpenAI’s ChatCompletion format. By abstracting API differences, it allows seamless leveraging of ZhipuAI’s Chinese-language models within existing OpenAI-oriented workflows. Ideal for building chatbots, virtual assistants, and automated workflows that require Chinese LLM capabilities without changing established OpenAI-based codebases.
  • A Python sample demonstrating LLM-based AI agents with integrated tools like search, code execution, and QA.
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    What is LLM Agents Example?
    LLM Agents Example provides a hands-on codebase for building AI agents in Python. It demonstrates registering custom tools (web search, math solver via WolframAlpha, CSV analyzer, Python REPL), creating chat and retrieval-based agents, and connecting to vector stores for document question answering. The repo illustrates patterns for maintaining conversational memory, dispatching tool calls dynamically, and chaining multiple LLM prompts to solve complex tasks. Users learn how to integrate third-party APIs, structure agent workflows, and extend the framework with new capabilities—serving as a practical guide for developer experimentation and prototyping.
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