Learn how to build a local Retrieval-Augmented Generation (RAG) system using DeepSeek-R1, LangChain, and Ollama. This guide details the installation, setup, and deployment of a RAG pipeline that processes PDFs locally, ensuring data privacy, cost efficiency, and customizability. The solution utilizes ChromaDB for document retrieval and Streamlit for a user-friendly interface.
Table of contents
How to Build a Local RAG Using DeepSeek-R1, LangChain, and OllamaWhy Choose a Private RAG Solution?Tools and Technologies: LangChain, DeepSeek-R1, Ollama, ChromaDB and StreamlitBuilding the RAG Pipeline: Step-by-Step GuideBuilding a RAG Pipeline with DeepSeek-R1, Ollama, LangChain and ChromaDBCustomizing Retrieval Settings for Optimal ResultsUse Cases and Testing Your RAG ApplicationConclusionReferences:3 Comments
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