搜索结果：llms.txt

Abso is a lightweight, OpenAI-compatible JavaScript library that provides a unified interface for calling multiple LLM providers (OpenAI, Anthropic, Groq, Ollama, etc.) with full type safety. It suppo

This handbook provides comprehensive guidance for developers on ensuring structured outputs from Large Language Models (LLMs), covering tools, techniques, and best practices for reliable programmatic

This survey provides a comprehensive overview of recent progress in LLM-empowered knowledge graph construction, systematically analyzing how LLMs reshape the classical three-layered pipeline of ontology engineering, knowledge extraction, and knowledge fusion. 原文翻译： 本综述全面概述了LLM赋能知识图谱构建的最新进展，系统分析了LLM如何重塑本体工程、知识抽取和知识融合的经典三层流程。

This article explores how large language models (LLMs) function as document completion engines and demonstrates how to build practical applications by mapping between user and document domains, using GitHub Copilot's prompt engineering pipeline as a detailed case study. 原文翻译： 本文探讨了大型语言模型（LLM）如何作为文档完成引擎工作，并通过在用户领域和文档领域之间建立映射来构建实际应用，以GitHub Copilot的提示工程流程作为详细案例研究。

This article introduces the 're!think' protocol, a ~1,300-token prompt that embeds seven core reasoning mechanics directly within an LLM's context window. It contrasts this approach with traditional, code-heavy enterprise scaffolding, arguing for more efficient, in-context logic that teaches models to reason mathematically rather than generate text. 原文翻译： 本文介绍了“re!think”协议，一个约1300词元的提示词，将七种核心推理机制直接嵌入LLM的上下文窗口。它对比了这种方法与传统的、代码繁重的企业级框架，主张更高效、上下文内的逻辑，教导模型进行数学推理而非文本生成。

BlaBlaBlAI is an open-source chat system that enables multiple LLMs and humans to interact in the same room with full awareness of all participants and complete chat history, featuring cost attribution and flexible chat creation. 原文翻译： BlaBlaBlAI是一个开源聊天系统，允许多个LLM和人类在同一个房间内互动，所有参与者都能完全感知彼此并查看完整的聊天历史，具有成本归因和灵活的聊天创建功能。

Structured generation in LLMs consistently improves performance on the GSM8K benchmark, with up to 70%+ gains, and offers additional benefits like prompt consistency and thought-control. 原文翻译： LLM中的结构化生成在GSM8K基准测试中持续提升性能，最高可达70%以上的增益，并带来提示一致性和思维控制等额外优势。

Generative AI models process text through tokenization, breaking it into tokens (words, syllables, or characters) to fit transformer architectures. This method introduces biases, especially in non-English languages, affecting performance and cost. Tokenization also explains models' struggles with math and anagrams. Emerging byte-level models like MambaByte may offer solutions by eliminating tokenization. 原文翻译： 生成式AI模型通过分词处理文本，将其分解为标记（单词、音节或字符）以适应Transformer架构。这种方法引入了偏见，尤其是在非英语语言中，影响性能和成本。分词也解释了模型在数学和字谜问题上的困难。新兴的字节级模型如MambaByte可能通过消除分词提供解决方案。

This article explores an alternative approach to the Model Context Protocol (MCP) for integrating tools with Large Language Models (LLMs) by leveraging existing OpenAPI servers. It proposes a simpler, more intuitive method that uses structured HTTP API definitions as tool inputs, requiring only minimal authentication flow additions. The implementation is demonstrated through a concise Scala script, focusing on core tool integration while omitting MCP's broader features like prompts and resources. 原文翻译： 本文探讨了一种替代模型上下文协议（MCP）的方法，通过利用现有的OpenAPI服务器为大型语言模型（LLM）集成工具。它提出了一种更简单、更直观的方法，使用结构化的HTTP API定义作为工具输入，仅需添加最小的身份验证流程。通过一个简洁的Scala脚本演示了实现，专注于核心工具集成，同时省略了MCP更广泛的功能，如提示和资源。

Microsoft Research's GraphRAG enhances LLM capabilities by generating knowledge graphs from private datasets, significantly improving question-answering performance and enabling whole-dataset reasoning through graph machine learning. 原文翻译： 微软研究院的GraphRAG通过从私有数据集中生成知识图谱，显著提升大型语言模型的能力，通过图机器学习大幅改善问答性能并实现全数据集推理。