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, summarizing communities of entities using Large Language Models (LLMs), and enhancing Retrieval-...
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。
Functional solution architecture:The development of a state-of-the-art, privacy-aware, explainable, and trustworthy conversational AI architecture. This design uniquely integrates LLMs, knowledge graphs, and RBAC, and is empirically tested on the curated AI news dataset. ...
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,用于构建 PDF 本地知识库,支持多种大语言模型(...
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...
MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning: This paper first suggested the core mechanism for using tools with language models to execute complex tasks. ...
llmware provides a unified framework for building LLM-based applications (e.g., RAG, Agents), using small, specialized models that can be deployed privately, integrated with enterprise knowledge sources safely and securely, and cost-effectively tuned and adapted for any business process....
And to be candid, unless your LLM system is studying for a school exam, using MMLU as an eval doesn’t quite make sense. Thus, instead of using off-the-shelf benchmarks, we can start by collecting a set of task-specific evals (i.e., prompt, context, expected outputs as references)...