腾讯网

构建分层的 Agentic RAG 系统：具备自主纠错的多模态推理

作者 ｜ Abhijit Ubale译者 ｜ 张卫滨引 言企业 AI 团队长期面临着一个难题，那就是大多数检索增强生成 (RAG) 系统要么擅长结构化的数据查询，要么擅长文档检索，但当两者需要同时使用时就会无能为力。比如财务分析师提出“为什么欧洲业务表现不佳？”这类问题时，既需要 SQL 数据库中的结构化数据 ...

Architectural patterns for graph-enhanced RAG: Moving beyond vector search in production

The standard architecture — chunking documents, embedding them into a vector database, and retrieving top-k results via ...
VentureBeat

Vector database company Qdrant wants RAG to be more cost-effective

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More More companies are looking to include retrieval augmented generation (RAG ...
dbta

Bringing Your Own Data to Generative AI with RAG

However, when it comes to adding generative AI capabilities to enterprise applications, we usually find that something is missing—the generative AI programs simply don't have the context to interact ...
dbta

RESEARCH@DBTA: Survey: RAG Emerges as the Connective Tissue of Enterprise AI

While the generative AI (GenAI) revolution is rolling forward at full steam, it’s not without its share of fear, uncertainty, and doubt. The great promises that can be delivered through large language ...
CIO

Vector Database vs. Knowledge Graph: Making the Right Choice When Implementing RAG

Whether IT leaders opt for the precision of a Knowledge Graph or the efficiency of a Vector DB, the goal remains clear—to harness the power of RAG systems and drive innovation, productivity, and ...