I am finding that my agents fail when an answer requires connecting facts across different files. It feels like most RAG systems are badly designed because they only look at isolated snippets. Is anyone successfully using Knowledge Graphs to solve this specific structural problem?
3 answers
The reason most RAG systems are badly designed for multi-hop reasoning is the "siloed" nature of vector embeddings. Vectors are great at finding "points," but bad at following "paths." To solve this, the industry is moving toward GraphRAG. By mapping entities and their relationships into a graph database like Neo4j, your system can traverse nodes to find connected insights that aren't physically near each other in the text. This architectural shift ensures that your LLM receives a structured map of information rather than a random collection of high-similarity sentences.
Deborah, while GraphRAG sounds promising, isn't the latency of graph traversals a major concern? Does the overhead of building the graph justify the performance gain for standard customer support bots?
Most systems fail because they don't use "Agentic RAG" to break down complex queries into smaller, searchable sub-questions.
That is a great point, Michelle. Using a query decomposition layer makes a huge difference in how the system handles those tricky multi-part questions.
Joshua, for a simple FAQ bot, a graph is overkill. But for legal or medical research where a single missing link can lead to a wrong conclusion, it is vital. The latency can be mitigated by caching common traversal paths or using a "Graph-Light" approach where you only extract key entity relationships during the indexing phase.