Agentic RAG: Mastering Document Retrieval with CrewAI, DeepSeek, and Streamlit
Last Updated on February 25, 2025 by Editorial Team
Author(s): Vikram Bhat
Originally published on Towards AI.
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In 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, I’m elevating that concept with an Agentic RAG approach powered by CrewAI. This project harnesses CrewAI’s 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. Let’s 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 Streamlit
generativeai.pub
GitHub 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-transformers
These include Streamlit for creating an interactive user interface, CrewAI for orchestrating the multi-agent… Read the full blog for free on Medium.
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