Knowledge Graphs and GenAI Workflows

The current bottleneck in LLM applications isn't model size; it's contextual reliability. Standard Retrieval-Augmented Generation (Vector RAG) treats your data as a flat list of text chunks. GraphRAG treats it as a web of entities and relationships.

This page covers the architectural shift from "finding similar text" to "traversing structural truth."

Vector RAG vs. GraphRAG: The Structural Gap

Vector RAG relies on semantic proximity (cosine similarity in embedding space). If the query is "What is the battery life of the X1 Carbon?", vector search finds chunks containing those terms.

GraphRAG relies on structural traversal. It solves the three "hard" problems of vector-only systems:

Multi-hop Reasoning: If a query requires connecting Entity A to Entity C via Entity B, vector search often fails. It retrieves $A$ and $C$ , but misses the crucial link $B$ because $B$ might not be semantically "similar" to the query.
Global Aggregation: Vector RAG is "local" — it finds specific snippets. It cannot answer "What are the three most common themes across all 5,000 incident reports?" without reading every chunk. GraphRAG can summarize communities of nodes.
Ambiguity Resolution: In a vector store, "Apple" (fruit) and "Apple" (company) occupy similar spaces if the chunks are short. In a graph, they are distinct nodes with entirely different neighbor sets (one has is-a: Fruit, the other is-a: Corporation).

The "Semantic ER" Ingestion Pipeline

Building a Knowledge Graph (KG) with an LLM isn't just about calling extract_triples(). You need a robust Entity Resolution (ER) pipeline to prevent your graph from becoming a "synonym soup."

The 2025 standard pipeline follows a three-stage generative process:

1. Semantic Blocking (Clustering)

Instead of $O(n^2)$ pairwise comparisons, use dense embeddings to group similar candidates into "blocks."

Goal: Narrow down the search space.
Implementation: HNSW or FAISS index over entity names and summaries.

2. LLM-Based Matching

Inside each block, use a small, fast model (e.g., Llama-3.1-8B or Gemini-1.5-Flash) to perform Reasoning-based Matching.

The Prompt: "Given Entity A and Entity B, are they the same real-world entity? Reason step-by-step focusing on attributes like tax_id, headquarters, and founding_date."

3. Generative Merging

Take the matched set and generate a single Golden Record.

Resolution Strategy: If Source A says "Founded 2020" and Source B says "Founded 2021", the LLM checks the source provenance or selects the value present in more high-trust documents.

Retrieval Patterns: Global vs. Local

GraphRAG retrieval isn't a single algorithm. You pick based on the query type.

Local Search (The "Seed and Expand" Pattern)

Best for: "Who is the lead engineer for Project Icarus and what is their clearance?"

Seed: Perform a vector search to find the Project Icarus node.
Traverse: Follow the lead_engineer edge to the Person node.
Fetch: Retrieve the clearance attribute of the Person.
Context: Pass the specific path Project -> Person -> Clearance to the LLM.

Global Search (The "Community Summary" Pattern)

Best for: "What are the major risks identified in the Q3 audit?"

Cluster: Partition the graph into "communities" using Leiden or Louvain algorithms.
Summarize: Pre-generate summaries for each community (e.g., "This subgraph describes IT security risks").
Retrieve: Search across the summaries, not the raw nodes.
Synthesize: Use the LLM to combine the top $N$ community summaries into a global answer.

Implementation: The "Triple Extraction" Loop

Do not use a single prompt to extract an entire graph from a PDF. It will miss ~60% of relationships. Use a Sliding Window + Deduplication loop.

def extract_and_merge(text_stream, graph_db):
    for window in sliding_window(text_stream, size=2000, overlap=500):
        # 1. Extraction: High-temperature for creativity
        raw_triples = llm.extract(window, schema=ProjectOntology)
        
        # 2. Local De-duplication: Compare triples within the window
        clean_triples = local_dedupe(raw_triples)
        
        # 3. Global Upsert: Merge into the KG using Entity Resolution
        for s, p, o in clean_triples:
            graph_db.upsert_semantic_edge(s, p, o)

Failure Modes to Watch

The "Giant Component" Problem: If your extraction is too fuzzy, every node connects to every other node through a common neighbor like United States.
- Fix: Prune high-degree "hub" nodes during retrieval. They provide zero discriminatory power.
Hallucinated Relationships: LLMs love to invent relations.
- Fix: Use Typed Constraints. If your schema says Person can only manage a Project, reject a triple where a Person manages a Document.
Traversal Explosion: A 3-hop traversal can retrieve 10,000 nodes.
- Fix: Use Pruned Breadth-First Search (BFS). Rank neighbors by semantic similarity to the query and only follow the top $N$ edges per hop.

A Concrete Reference Architecture

[Unstructured Data] ──▶ [LLM Extraction] ──▶ [Entity Resolution] ──▶ [Graph DB]
                                                                        │
                                                                        ▼
[User Query] ──▶ [Hybrid Retrieval] ◀───────────────────────────────────┘
                    │ (Vector + Graph)
                    ▼
[Reasoning Engine] ──▶ [Final Answer]

This architecture ensures that the LLM isn't "guessing" based on training data, but navigating your private enterprise facts. For the next step in implementation, see EntityResolutionTechniques for the matching logic or GraphRAG for specific traversal algorithms.