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:

1. **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.

2. **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.

3. **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?"

1. **Seed:** Perform a vector search to find the `Project Icarus` node.

2. **Traverse:** Follow the `lead_engineer` edge to the `Person` node.

3. **Fetch:** Retrieve the `clearance` attribute of the `Person`.

4. **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?"

1. **Cluster:** Partition the graph into "communities" using Leiden or Louvain algorithms.

2. **Summarize:** Pre-generate summaries for each community (e.g., "This subgraph describes IT security risks").

3. **Retrieve:** Search across the *summaries*, not the raw nodes.

4. **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.

```python

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

1. **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.

2. **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`.

3. **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.