Too Long; Didn't ReadThis article explores the implementation of a "From Local to Global" GraphRAG pipeline using Neo4j and LangChain. It covers the process of constructing knowledge graphs from text, summarizin
We demonstrate through extensive experiments that using LLMs a zero-shot process produces a wide range of errors. To remedy them, we propose two different model-driven prompting strategies by which LLMs can be used to improve the accuracy of knowledge graph construction. We demonstrate that a ...
在2024年12月20日发布的这篇文章《构建高效智能体(Building Effective Agents)》中,Anthropic公司分享了他们在过去一年中与多个行业团队合作开发大型语言模型(Large Language Model, LLM)智能体的经验。文章的核心观点令人深思:最成功的智能体实现并非依赖于复杂的框架或专门的库,而是通过简单、可组合的模式构建而成。
Building on this foundation, we propose a novel functional architecture that seamlessly integrates the structured dynamics of knowledge graphs with the linguistic capabilities of LLMs. Validated using real-world AI news data, our architecture adeptly blends linguistic sophistication with factual rigor and ...
A learning project for building local knowledge bases from PDFs using LangChain, supporting multiple LLMs (DeepSeek, OpenAI). Features include PDF processing, knowledge graph construction, and natural language Q&A interface.一个基于 LangChain 的学习项目
We have moved past making large language models (LLMs) better and are now focused on using them to create AI applications that help businesses. This is where large language model operations (LLMOps) tools come in, simplifying the process of creating fully automated systems for building and depl...
Agents are emerging in production as LLMs mature in key capabilities—understanding complex inputs, engaging in reasoning and planning, using tools reliably, and recovering from errors. Agents begin their work with either a command from, or interactive discussion with, the human user. Once the tas...
This project demonstrates a fullstack application using a React frontend and a LangGraph-powered backend agent. The agent is designed to perform comprehensive research on a user's query by dynamically generating search terms, querying the web using Google Search, reflecting on the results to identif...
LangGraph: From Basics to Advanced AI Agents with LLMs 总共1.5 小时更新日期 2025年5月 评分:4.5,满分 5 分4.5928 当前价格US$13.99 原价US$29.99 Generative AI : LLM, Fine-tuning, RAG & Prompt engineering 总共4 小时更新日期 2024年11月 评分:4.5,满分 5 分4.5432 当前价格US$13.99 原价US$39.99...
Course1 - Building and Scaling Text Summarization Service using Langchain, OpenAI and Amazon Web Services 评分:4.5,满分 5 分4.5(57 个评分) 633 个学生 创建者LLM Developer 上次更新时间:10/2023 英语 英语[自动], 土耳其语 [自动], 您将会学到 ...