GraphRAG: Combining Graph Databases and LLMs for High-Precision RAG

Traditional Retrieval-Augmented Generation (RAG) based solely on vector embeddings often lacks global context and semantic relations between entities. GraphRAG bridges this gap by integrating graph databases like Neo4j with LLMs.

### 1. Entity-Based Semantic Indexing Unlike plain text chunking, GraphRAG extracts entities (nodes) and relations (edges) from the dataset via asynchronous LLM calls, building a structured knowledge graph. This enables highly precise, multidimensional relational queries.

### 2. Dynamic Cypher Query Generation Using Few-Shot prompting techniques, the backend translates the user's natural language into structured Cypher queries. This allows accurate retrieval of interconnected attributes across multiple levels of network depth.

### 3. Hybrid Search (Vector + Graph) Combining cosine similarity from a traditional vector store with the structural depth of a Neo4j query delivers optimal context to the LLM. This drops model hallucinations to virtually zero when auditing complex data.