Microsoft’s GraphRAG implementation has been effectively used to construct knowledge graphs from source documents processed through a Large Language Model (LLM). The output is structured information about entities and their relationships, which is further processed using graph algorithms like the Leiden community detection algorithm to generate summaries. This post discusses how to store GraphRAG output in Neo4j and implement local and global retrievers using LangChain or LlamaIndex. The dataset used is "A Christmas Carol" by Charles Dickens. Although the graph extraction process can be skipped, some important configurations include selecting entity types (organizations, people, events, geo) and setting the maximum gleanings to perform multiple extraction passes for comprehensive information capture. The output from the graph extraction pipeline is stored as parquet files, which are then imported into Neo4j. The import process can be done using a free cloud Aura instance or a local Neo4j environment, with the import code provided as a Jupyter notebook on GitHub. After importing the data into Neo4j, a simple graph analysis is performed using Cypher queries to understand the structure and content of the extracted data. For instance, the distribution of extracted entities and node degrees can be analyzed to gain insights into the data. The extracted entities and their relationships are visualized and validated using the Neo4j Browser. For implementing retrievers, the local retriever utilizes vector search to identify relevant nodes and then traverses linked information to inject into the LLM prompt. The vector index is configured, and the retrieval query is defined to collect relevant text data such as text chunks, community reports, and relationship descriptions. This is implemented using both LangChain and LlamaIndex, with the retrieval query adapted for each framework. The global retriever, on the other hand, iterates over community summaries at a specified hierarchical level, generating intermediate summaries and a final response. This approach simplifies the retrieval process but requires selecting the appropriate hierarchical level for the best results. In summary, the integration of GraphRAG with Neo4j and retriever frameworks like LangChain and LlamaIndex enables sophisticated data retrieval from structured knowledge graphs, leveraging both local and global retrieval strategies. This implementation showcases the practical applications of GraphRAG in analyzing and querying structured information extracted from textual sources.