How To Do GraphRAG with DeepSeek

FI Consulting, a data solutions firm, has developed a Retrieval-Augmented Generation (RAG) system that integrates Memgraph and DeepSeek to enhance the accuracy and context of responses generated by large language models (LLMs). This system addresses the challenge of LLMs producing incomplete or inaccurate answers when working with extensive, unstructured datasets by using graphs to incorporate structure, relationships, and domain-specific knowledge. The approach allows for the filtering of data and prioritization of internal language and terms, ensuring that responses are accurate and grounded in the company's unique context. During a demonstration, FI Consulting showcased how they break down large documents into manageable chunks, use vector embeddings, and employ Named Entity Recognition to build a comprehensive context within their graph. The system also includes smart retrieval and fallback logic to maintain focused and relevant responses. Hosted on Azure with Memgraph running in a Docker container, the setup is designed to be cost-effective and scalable, providing real-time graph analytics without requiring extensive GPU resources. The RAG system is not only precise and context-aware but is also affordable and capable of supporting various industry applications, from healthcare to industrial automation.