Getting Started with Microsoft's GraphRAG

What is GraphRAG?

GraphRAG builds on traditional RAG by organizing information into structured knowledge graphs rather than simple vector embeddings. This enables multi-hop reasoning, relationship tracking, and hierarchical queries that traditional RAG systems struggle with.

Key Advantages

• Better reasoning: Explicit relationships enable complex multi-step queries
• Reduced hallucination: Structured knowledge reduces false connections
• Hierarchical search: Query at different levels of abstraction
• Efficient retrieval: Graph traversal finds precisely relevant information

Getting Started

Install the Microsoft GraphRAG library:

pip install graphrag

Building Your First Knowledge Graph

Start by indexing your documents:

from graphrag.index import index_docs

documents = load_documents("path/to/documents")

config = {
    "llm": {
        "api_key": "your_openai_key",
        "model": "gpt-4"
    }
}

graph = index_docs(
    documents=documents,
    config=config
)

Querying the Graph

Query your knowledge graph with context-aware search:

from graphrag.query import query_with_local_context

result = query_with_local_context(
    graph=graph,
    query="What are the key relationships?",
    query_type="global"
)

print(result.response)

When to Use GraphRAG

GraphRAG excels when:

• Documents contain complex, interconnected information
• Multi-hop reasoning is required
• Reducing hallucinations is critical
• You need to maintain semantic relationships explicitly

Best Practices

1. Start with domain expert review of extracted entities
2. Implement versioning for your knowledge graph
3. Monitor extraction quality continuously
4. Consider hybrid approaches combining GraphRAG with traditional RAG
5. Customize entity types for your specific domain

Next Steps

Explore the official Microsoft GraphRAG documentation for advanced features like community detection, hierarchical queries, and custom entity extraction patterns.