Martijn Verbove@verboveEngineering · #8

Here’s your explanation rewritten into a tighter GraphRAG pipeline: 1. Split the conversation or documents into text units such as messages or chunks. 2. Use an LLM to extract entities from each text unit, including type and usually a short description. 3. Use the LLM to extract relationships between entity pairs in that same text unit. 4. Save the supporting source text / text-unit ID as provenance for each extracted fact. 5. Convert the extracted facts into triples: subject, predicate, object. 6. Merge duplicates and normalize names into canonical entities like one node for Cypher instead of many slightly different spellings. 7. Build a nodes table for entities and an edges table for relationships between node IDs. 8. Store that graph so it can later be traversed, filtered, embedded, or queried with something like Cypher

Teaches 8-step GraphRAG pipeline implementation methodology