The GraphRAG pipeline follows the core RAG architecture, consisting of three stages: (1) Retrieval: The system identifies relevant content from external sources—documents, databases, or knowledge graphs—using techniques like vector similarity, structured queries, or hybrid approaches, which are then ranked and filtered. (2) Augmentation: The retrieved information is combined with the original query and task-specific instructions to form an augmented prompt that grounds the language model's response in authoritative data. (3) Generation: The language model generates an answer based on the augmented prompt, ensuring the output is accurate, aligned to source material, and potentially includes references to original sources or metadata.

Authors

• Person: Not available Organization: Neo4j
  How to Improve Multi-Hop Reasoning With Knowledge Graphs and ...

Sources

• How to Improve Multi-Hop Reasoning With Knowledge Graphs and ... neo4j.com Neo4j via serper

Referenced by nodes (1)

• Language Model concept