Learn to create LLM applications in your system using Ollama and LangChain in Python | Completely private and secure
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Covers essential patterns including Retrieval Augmented Generation (RAG), tool calling, and agent-based applications using Llama 3.1.
Editorial course preview
These 4 complementary views highlight concrete, legible examples from the course presentation.
This course content slide outlines the technical curriculum, covering setting up Python, using Ollama models, and installing the LangChain library for local LLM applications.
This course content flowchart outlines demonstrations of advanced generative AI applications, specifically focusing on Retrieval Augmented Generation and agentic tools.
This course content slide outlines the initial setup steps, specifically installing Ollama in the system and downloading an LLM model using the tool.
This course overview highlights key features including a two-hour duration, the goal of building a local LLM application, and no specific prerequisites required for enrollment.
Lectures are sized for efficient consumption, utilizing practical examples that avoid external API dependencies to clarify code construction.
One signal indicates issues with deprecated packages and API errors within the provided Jupyter notebooks.
A single signal from early 2026 suggests a missing instruction regarding starting the Ollama engine via command line; however, the displayed update date does not prove this was corrected.
Aligns with the goal of local development for developers and data scientists.