Demystify Knowledge RAG frameworks

In this writing, let’s take a dive into knowledge graph concepts, how it solves the problem of finding a needle in a haystack. GraphRAG frameworks visualize the result, cost, and how it works, including running locally using Ollama and LM-Studio. plus some nice tools like GraphRAG-Ollama-UI and GraphRAG4OpenWebUI both integrated with graphrag framework under a simple UI which worth to take a quick spin.

How to find a needle in a haystack?

When dealing with a large amount of data, finding the right answer can be challenging. Even with large language models (LLMs) increasing their context window size to over 1 million tokens, they still struggle to provide accurate responses due to hallucination issues and a lack of domain-specific content. This is because general pre-trained LLM models require fine-tuning to align with the specific domain context and terminologies of a business. Fine-tuning allows businesses to control the data the model is exposed to, ensuring that the generated content is highly relevant and accurate for their needs.

Traditional Retrieval Augmented Generation (RAG) is effective for answering short questions when the answer is contained within a single chunk of text outside the Large Language Model (LLM). However, it falls short in providing a comprehensive view of a topic or field by integrating information from multiple sources, particularly due to the lack of domain-specific context. This limitation can be addressed by incorporating knowledge graphs, which offer an alternative…