Agentic RAG: Mastering Document Retrieval with CrewAI, DeepSeek, and Streamlit 0 like February 25, 2025Share this postLast Updated on February 25, 2025 by Editorial TeamAuthor(s): Vikram Bhat Originally published on Towards AI. This member-only story is on us. Upgrade to access all of Medium.Image generated using napkin.aiIn my previous blog, I explored building a Retrieval-Augmented Generation (RAG) chatbot using DeepSeek and Ollama for privacy-focused document interactions on a local machine here. Now, Im elevating that concept with an Agentic RAG approach powered by CrewAI. This project harnesses CrewAIs multi-agent framework to process documents locally, delivering precise retrieval and concise responses without compromising data security. Running entirely on your local machine with Ollama and DeepSeek for robust language processing, it integrates seamlessly with CrewAI and features an intuitive Streamlit UI. Lets dive into building this secure, agent-driven RAG chatbot step-by-step.Building a RAG Chatbot with Context-Aware Responses Locally using DeepSeek, LangChain, and Streamlitgenerativeai.pubGitHub Repo: The complete code is available on GitHub. Follow this blog for a detailed implementation guide.To set up and run this Agentic RAG locally, ensure you have the following setup:Python 3.10 or Higher Install Python from python.org. Version 3.10 or newer is required for compatibility with the libraries used in this project.Required Libraries Install the necessary dependencies using the following command:pip install streamlit crewai langchain pypdf faiss-cpu sentence-transformersThese include Streamlit for creating an interactive user interface, CrewAI for orchestrating the multi-agent Read the full blog for free on Medium.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post