기본 RAG에서 Agentic RAG로 | RAG 파이프라인을 다중 에이전트 파이프라인의 도구로 사용하세요

Agentic RAG (Retrieval-Augmented Generation) is when agents are used to improve retrieval quality, make query and context decisions, and reason over or refine retrieval results for more intelligent workflows.

In this video, Misbah Syed, AI Builder and Educator, shows you how to use Langflow to implement Agentic RAG (Retrieval-Augmented Generation) from a basic RAG workflow.

In this example, Langflow Tool Mode is used to convert a basic RAG pipeline to an agent tool. The agent then can make decisions about the need to retrieve or refine data from a vector database, or whether to use other tools to such as a Search API to answer the user's query.

RESOURCES:

Langflow GitHub: bit.ly/langflow_repo

Langflow Discord: https://bit.ly/langflow-discord

Try Langflow: http://Langflow.org

Learn how to:

• Use agents with memory and multi-tool capabilities.

• Transform traditional RAG pipelines into agent-powered workflows.

• Combine vector databases, search APIs, and more into a seamless agentic flow.

• Get started with Langflow templates for quick and scalable AI apps.

🌟 Highlights:

• Agentic RAG explained: What it is and why it’s powerful.

• Live walkthrough of Langflow 1.1, featuring Tool Mode and multi-agent collaboration.

• Hands-on demo of creating a retrieval + internet search agent app.

Ready to revolutionize your AI projects? Watch now and level up your workflows!

📥 Try Langflow for free: https://langflow.datastax.com