搜索结果：结构化数据

This article explains the critical importance of structured outputs in LLM workflows, detailing how to implement them from scratch using JSON and Pydantic, and through the Gemini SDK, to build reliabl

AI and machine learning technologies now enable organizations to efficiently analyze unstructured data (80-90% of global content), uncovering valuable insights for fraud detection, risk management, an

OpenLIT simplifies AI development with one-line OpenTelemetry-native observability, supporting LLM, vector DB, and GPU monitoring, plus cost tracking and evaluation. 原文翻译：OpenLIT通过一行代码提供OpenTelemetry原生可观测性，简化AI开发，支持LLM、向量数据库和GPU监控，以及成本追踪和评估。

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。

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

Unstract is an open-source platform that uses LLMs to extract structured JSON data from various document formats (PDFs, images, scans) through natural language prompts, deployable as APIs or ETL pipelines for finance, insurance, healthcare, and compliance teams. 原文翻译： Unstract是一个开源平台，利用大语言模型通过自然语言提示从各种文档格式（PDF、图像、扫描件）中提取结构化JSON数据，可作为API或ETL管道部署，适用于金融、保险、医疗和合规团队。

RΞASON is an open-source TypeScript backend framework designed for building LLM applications, featuring structured output using TypeScript interfaces, functional agents, native streaming support, and zero-setup observability. 原文翻译： RΞASON是一个开源的TypeScript后端框架，专为构建LLM应用而设计，具有使用TypeScript接口的结构化输出、函数式代理、原生流式支持以及零配置可观测性。

This paper introduces Generative Engine Optimization (GEO), a black-box optimization framework to help content creators improve their visibility in generative engine responses. It proposes GEO-bench f

Open Deep Research is an open-source implementation of OpenAI's Deep Research experiment that uses Firecrawl for web search/extraction with reasoning models for structured data analysis. 原文翻译： Open Deep Research 是 OpenAI Deep Research 实验的开源实现，使用 Firecrawl 进行网络搜索/提取，并结合推理模型进行结构化数据分析。

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。