Entity Extraction Pipeline

In the previous lesson, you learned why a graph outperforms a pure vector store for long-term memory — multi-hop traversalFollowing relationships from one node to the next to reach other parts of a graph., temporal validity, and combined vector-plus-graph search are all things a vector database cannot do. When you call add_message(), the library does more than store the message text — it automatically runs an entity extraction pipeline that connects short-term and long-term memory.

In this lesson, you will learn how the three-stage pipeline works and how to configure the merge strategy. The three stages are spaCy, GLiNER2, and LLMA model trained on text to predict the next token, and so to generate language. fallback — each increasing in accuracy and cost.