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Understanding Large Language Models Large Language Models (LLMs) represent a significant advancement in artificial intelligence, characterized by their massive parameter counts and extensive training on diverse datasets. These models excel at understanding and generating human language, making them ideal for tasks requiring natural language com ...

Introduction to Dify Dify is an open-source platform for developing applications powered by Large Language Models (LLMs). It combines Backend-as-a-Service (BaaS) principles with LLMOps concepts, enabling developers to quickly build production-ready generative AI applications. The platform is designed to be accessible to both technical and non-t ...

This article explores the prompt template structure for ChatGLM3 within the LangChain framework, focusing on how the model handles tool execution and knowledge-based responses. Prompt Template Definition The following configuration defines the instruction set for the ChatGLM3 agent when processing user requests: PROMPT_TEMPLATES["agent_cha ...

1. Prerequisites 1.1 Understanding MCP (Model Context Protocol) Overview MCP (Model Context Protocol) is an open protocol that standardizes how applications provide context information to Large Language Models (LLMs). Think of MCP as the USB-C interface for AI applications. Just as USB-C provides a standardized way for devices to connect variou ...

🌟 AI-Driven Text Adventure Game Development A Two-Week Journey of Building an AI-Native Game with Java Backend When ancient cultivation realms meet large language models, and moral choices collide with card battles— I built an AI-native text game from scratch in two weeks. This is not just about coding, but a test of "design" and &q ...

Generative AI systems are increasingly deployed across industries and research institutions. Developers and users interact with these systems through prompts or prompt engineering. Despite being widely discussed, prompt terminology remains inconsistent due to the field's nascent nature. This analysis presents a structured taxonomy of prompt tec ...

Modeling prompts as executable code structures enables precise, modular, and reusable interactions with large language models (LLMs). This approach treats the LLM as a software package with well-defined interfaces and capabilities. Core Capabilities input(): Accept user input output(): Return processed results Functional Modules Text Processi ...

Prerequisites and Environment Configuration Before deploying any model, ensure the Python environment is stable. Update package managers and configure mirror sources to improve download stability if network constraints exist. Dependency Installation Update pip first, then install core libraries required for Hugging Face or ModelScope models. Us ...

Retrieval-Augmented Generation Architecture Large Language Models (LLMs) often struggle with static training data limitations, leading to issues with outdated information and hallucinations. Retrieval-Augmented Generation (RAG) addresses this by grounding model responses in external, up-to-date knowledge bases. This technique operates in three ...

Setting Up the Development Environment on InternLM Studio Begin by launching a develompent instance and configuring the runtime environment. Once inside, replicate the base environment for running InternLM by creating a custom conda environment named InternLM2_Huixiangdou: studio-conda -o internlm-base -t InternLM2_Huixiangdou After cloning th ...