Agentic RAG is an advanced framework designed to handle complex information retrieval tasks using a network of intelligent agents. These agents collaborate to perform nuanced tasks such as synthesizing information from multiple documents, summarizing content, and comparing data points across various sources. Agentic RAG infuses autonomy and intelligence into traditional retrieval systems, enabling them to act as proactive entities that understand context, evaluate data quality, and make informed decisions.
Install necessary packages:
!pip install langchain-community tiktoken langchain-openai langchainhub lancedb langchain langgraph langchain-text-splitters langchain_openai gradio
For a detailed, interactive walkthrough of this implementation, you can explore the Google Colab notebook provided below. This notebook includes support for Gradio, making it easier to create UIs for your machine-learning models, ensuring a more interactive and user-friendly experience.
For a detailed explanation of agentic rag, check out blog post on Medium.