graphrag-patterns

GraphRAG Patterns

Combine knowledge graphs with RAG for relationship-aware retrieval and reasoning.

When to Use

• Data has rich entity relationships
• Questions involve connections ("How is X related to Y?")
• Need multi-hop reasoning across documents
• Building over structured + unstructured data
• Want explainable retrieval paths

GraphRAG Architecture

┌──────────────────────────────────────────────────────────┐
│                    Documents                              │
└─────────────────────────┬────────────────────────────────┘
                          │
          ┌───────────────┼───────────────┐
          │               │               │
          ▼               ▼               ▼
   ┌────────────┐  ┌────────────┐  ┌────────────┐
   │   Entity   │  │   Vector   │  │    Text    │
   │ Extraction │  │ Embeddings │  │   Chunks   │
   └─────┬──────┘  └─────┬──────┘  └─────┬──────┘
         │               │               │
         ▼               │               │
   ┌────────────┐        │               │
   │  Knowledge │        │               │
   │    Graph   │        │               │
   └─────┬──────┘        │               │
         │               │               │
         └───────────────┼───────────────┘
                         │
                         ▼
              ┌─────────────────────┐
              │    Hybrid Index     │
              │ (Graph + Vectors)   │
              └──────────┬──────────┘
                         │
                         ▼
              ┌─────────────────────┐
              │   Graph-Aware RAG   │
              └─────────────────────┘

Building the Knowledge Graph

Entity & Relationship Extraction

from langchain_openai import ChatOpenAI
from langchain.prompts import ChatPromptTemplate

EXTRACTION_PROMPT = """Extract entities and relationships from the text.

Text: {text}

Return JSON:
{{
  "entities": [
    {{"name": "...", "type": "PERSON|ORG|PRODUCT|CONCEPT|...", "description": "..."}}
  ],
  "relationships": [
    {{"source": "...", "target": "...", "type": "WORKS_FOR|USES|RELATED_TO|...", "description": "..."}}
  ]
}}
"""

def extract_graph_elements(text: str) -> dict:
    llm = ChatOpenAI(model="gpt-4", temperature=0)
    prompt = ChatPromptTemplate.from_template(EXTRACTION_PROMPT)
    chain = prompt | llm
    result = chain.invoke({"text": text})
    return json.loads(result.content)