The implementation of the "From Local to Global" GraphRAG approach combines text extraction, network analysis, and LLM prompting and summarization for improved RAG accuracy. The pipeline begins with input text from documents, which are processed to generate a graph. The graph is then converted back into natural language text, where the generated text contains condensed information about specific entities or graph communities previously spread across multiple documents. A knowledge graph data representation allows quickly combining information from multiple documents or data sources about particular entities. After constructing the knowledge graph, a combination of graph algorithms and LLM prompting generates natural language summaries of communities of entities found in the knowledge graph. These summaries contain condensed information spreading across multiple data sources and documents for particular entities and communities. The approach involves using Neo4j as the underlying graph store and LangChain for implementing the GraphRAG pipeline. The code repository is available, and the project page can be accessed. Entity resolution is crucial when constructing a knowledge graph to ensure that each entity is uniquely represented. An LLM-based entity resolution process is implemented to identify potential duplicates and decide which entities should be merged. Element summarization involves generating natural language summaries of nodes and relationships in the graph, while community summarization generates summaries for communities of entities found in the knowledge graph. The final step involves storing the community summaries back to the database. Overall, this implementation demonstrates a new approach to GraphRAG that combines text extraction, network analysis, and LLM prompting and summarization for improved RAG accuracy.