https://ai-blog.noorshomelab.dev/tags/llm-e.g.-openai/Recent content in LLM (E.g., OpenAI) on AI VOIDHugoenSun, 10 May 2026 00:00:00 +0000https://ai-blog.noorshomelab.dev/tutorials/get-started-falkordb-graphrag-sdk-1-0/Sun, 10 May 2026 00:00:00 +0000https://ai-blog.noorshomelab.dev/tutorials/get-started-falkordb-graphrag-sdk-1-0/<p><strong>What you&rsquo;ll build:</strong> A basic GraphRAG application that leverages FalkorDB and an LLM to answer natural language queries from ingested data. <strong>Time needed:</strong> ~45 minutes <strong>Prerequisites:</strong> Python 3.10+, Running FalkorDB instance, LLM API Key (e.g., OpenAI, Anthropic) <strong>Version used:</strong> 1.0.0</p> <hr> <h3 id="introduction-to-falkordb-graphrag-sdk-10">Introduction to FalkorDB GraphRAG SDK 1.0</h3> <p>In the exciting world of Large Language Models (LLMs), one of the biggest challenges is ensuring they provide accurate, up-to-date, and contextually relevant information, rather than &ldquo;hallucinating&rdquo; or relying on outdated training data. This is where Retrieval Augmented Generation (RAG) comes into play. RAG empowers LLMs to retrieve information from an external knowledge base before generating a response, drastically improving accuracy and trustworthiness.</p>