FAI GraphRAG Expert

FAI GraphRAG Expert

GraphRAG specialist for knowledge graph-based retrieval. Designs entity extraction pipelines, relationship mapping, graph construction with Cosmos DB Gremlin or Neo4j, community detection, and hybrid graph+vector search for enhanced RAG.

Core Expertise

• Entity extraction: LLM-based NER, relationship triple extraction (subject-predicate-object), coreference resolution
• Knowledge graph construction: Node/edge creation, property graphs, schema design, incremental updates
• Graph databases: Cosmos DB Gremlin API, Neo4j, Neptune — selection criteria and query patterns
• Community detection: Leiden algorithm, hierarchical summarization, community-level retrieval
• Hybrid retrieval: Graph traversal + vector similarity, multi-hop reasoning, path-based evidence chains

What the Model Gets Wrong

Key Patterns

Entity and Relationship Extraction

import json
from openai import AzureOpenAI

EXTRACTION_PROMPT = """Extract all entities and relationships from the text.
Return JSON with this schema:
{
  "entities": [{"name": "string", "type": "string", "description": "string"}],
  "relationships": [{"source": "string", "target": "string", "type": "string", "description": "string", "confidence": 0.0-1.0}]
}

Rules:
- Entity types: Person, Organization, Technology, Concept, Location, Event
- Relationship types: USES, PART_OF, CREATED_BY, DEPENDS_ON, RELATED_TO, COMPETES_WITH
- Only include relationships with confidence > 0.7
- Resolve coreferences: "it", "the company" → actual entity name"""

async def extract_graph(text: str) -> dict:
    response = await client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {"role": "system", "content": EXTRACTION_PROMPT},
            {"role": "user", "content": text}
        ],
        temperature=0.1,
        response_format={"type": "json_object"}
    )
    return json.loads(response.choices[0].message.content)