Avoiding Dirty RAGs: Retrieval-Augmented Generation with Ollama and LangChain

This blog post provides a hands-on tutorial for implementing a Retrieval-Augmented Generation (RAG) pipeline using Ollama (for models), a specialized embedding model, LangChain/LangGraph for orchestration, and LangSmith for telemetry; it walks through environment setup, code to ingest web and local documents, and examples showing how retrieved context augments LLM responses. The author also highlights a demonstrative security risk where granting RAG access to sensitive files can inadvertently expose credentials, underscoring the need for careful data access controls when deploying RAG systems.