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кастомизация подсказок

  • Agent API by HackerGCLASS: a Python RESTful framework for deploying AI agents with custom tools, memory, and workflows.
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    What is HackerGCLASS Agent API?
    HackerGCLASS Agent API is an open-source Python framework that exposes RESTful endpoints to run AI agents. Developers can define custom tool integrations, configure prompt templates, and maintain agent state and memory across sessions. The framework supports orchestrating multiple agents in parallel, handling complex conversational flows, and integrating external services. It simplifies deployment via Uvicorn or other ASGI servers and offers extensibility with plugin modules, enabling rapid creation of domain-specific AI agents for diverse use cases.
  • Framework for building retrieval-augmented AI agents using LlamaIndex for document ingestion, vector indexing, and QA.
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    What is Custom Agent with LlamaIndex?
    This project demonstrates a comprehensive framework for creating retrieval-augmented AI agents using LlamaIndex. It guides developers through the entire workflow, starting with document ingestion and vector store creation, followed by defining a custom agent loop for contextual question-answering. Leveraging LlamaIndex's powerful indexing and retrieval capabilities, users can integrate any OpenAI-compatible language model, customize prompt templates, and manage conversation flows via a CLI interface. The modular architecture supports various data connectors, plugin extensions, and dynamic response customization, enabling rapid prototyping of enterprise-grade knowledge assistants, interactive chatbots, and research tools. This solution streamlines building domain-specific AI agents in Python, ensuring scalability, flexibility, and ease of integration.
  • A framework to run local large language models with function calling support for offline AI agent development.
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    What is Local LLM with Function Calling?
    Local LLM with Function Calling allows developers to create AI agents that run entirely on local hardware, eliminating data privacy concerns and cloud dependencies. The framework includes sample code for integrating local LLMs such as LLaMA, GPT4All, or other open-weight models, and demonstrates how to configure function schemas that the model can invoke to perform tasks like fetching data, executing shell commands, or interacting with APIs. Users can extend the design by defining custom function endpoints, customizing prompts, and handling function responses. This lightweight solution simplifies the process of building offline AI assistants, chatbots, and automation tools for a wide range of applications.
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