Entity Extraction Pipeline

In the previous lesson, you learned why a graph outperforms a pure vector store for long-term memory — multi-hop traversaliFull definition for traversal (opens in a new tab)Following 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 LLMiGo to glossary for large language model (opens in a new tab)A model trained on text to predict the next token, and so to generate language. fallback — each increasing in accuracy and cost.