Entity Analysis and Modeling for Graph Databases

Introduction

Vector databases like Qdrant are amazing for “similarity search,” but they struggle with structured relationships. If you ask, “Who are all the vendors connected to Project Alpha?”, a vector search might fail. This is where a Graph Database (FalkorDB) shines.

Why Graph Modeling Matters:

• Structured Knowledge: Maps real-world relationships (Person WORKS_ON Project).
• Multi-Hop Reasoning: Allows queries that traverse connections (Friends of Friends).
• Entity Disambiguation: Distinguishes between “Apple” (the fruit) and “Apple” (the company).

What We’ll Build

We will build a pipeline that extracts entities from our documents and inserts them into FalkorDB.

1. Perform NER (Named Entity Recognition): Use an LLM to find People, Organizations, and Locations.
2. Model the Schema: Define distinct Nodes and Relationships.
3. Ingest into FalkorDB: Use Cypher queries to build the graph.

Architecture Overview

The flow runs in parallel with our vector embedding pipeline.

flowchart LR
    Doc[Document Text] -->|Prompt| LLM[LLM NER]
    LLM -->|JSON| Entities[Entity List]
    Entities -->|Map| GraphModel[Graph Logic]
    GraphModel -->|Cypher| FalkorDB[(FalkorDB)]

    classDef primary fill:#7c3aed,color:#fff
    classDef secondary fill:#06b6d4,color:#fff
    classDef db fill:#f43f5e,color:#fff

    class GraphModel,LLM primary
    class Doc,Entities secondary
    class FalkorDB db

Section 1: Extracting Entities with LLMs

Traditional NLP libraries (like spaCy) are fast but often struggle with custom domain contexts. LLMs represent a leap forward in zero-shot NER.

We design a prompt that asks the LLM to return a structured JSON response.

var systemPrompt = @"
You are an expert data analyst. Extract the following entities from the text:
- Person
- Organization
- Project
- Contract

Return ONLY JSON format:
{
  ""entities"": [
    { ""type"": ""Person"", ""name"": ""Alice Smith"", ""role"": ""Manager"" },
    { ""type"": ""Organization"", ""name"": ""BlueRobin Inc"" }
  ],
  ""relationships"": [
    { ""from"": ""Alice Smith"", ""to"": ""BlueRobin Inc"", ""type"": ""WORKS_FOR"" }
  ]
}";

Section 2: Modeling for FalkorDB

In FalkorDB (which uses the Redis protocol and Cypher query language), we need to be careful about node merging. We don’t want five “Alice Smith” nodes; we want one node with multiple connections.

Cypher Merge Strategy

Use the MERGE keyword to ensure idempotency.

MERGE (p:Person {name: 'Alice Smith'})
MERGE (o:Organization {name: 'BlueRobin Inc'})
MERGE (p)-[:WORKS_FOR]->(o)

In C#, using the NRedisStack or a Redis client:

public async Task IngestGraphAsync(GraphData data)
{
    var db = _redis.GetDatabase();
    // Construct Cypher query dynamically or use parameters
    foreach (var rel in data.Relationships)
    {
        string query = "MERGE (a {name: $from}) MERGE (b {name: $to}) MERGE (a)-[:" + rel.Type + "]->(b)";
        await db.GraphQueryAsync("files", query, new { from = rel.From, to = rel.To });
    }
}

Section 3: Querying the Graph

Now that the data is in, we can perform powerful queries that RAG alone cannot handle.

Question: “Find all contracts signed by anyone who works for BlueRobin.”

MATCH (p:Person)-[:WORKS_FOR]->(o:Organization {name: 'BlueRobin'})
MATCH (p)-[:SIGNED]->(c:Contract)
RETURN c.title, p.name

Conclusion

By combining the fuzzy search capabilities of Qdrant with the structured relationship modeling of FalkorDB, we create a “GraphRAG” system. This gives our AI two brains: one for “vibe” (vectors) and one for “facts” (graph).

Next Steps:

• See how we Benchmark Microservices to handle graph ingestion load.
• specific optimization techniques in Kubernetes Image Optimization.