5 Ways Graph RAG Boosts AI with Knowledge Graphs

Graph RAG Key Takeaways: AI Retrieval with Knowledge Graphs and Cypher

• Graph RAG Overview: Graph Retrieval Augmented Generation (Graph RAG) enhances AI by using knowledge graphs to store and retrieve data, offering richer context than traditional vector-based methods.
• Knowledge Graphs: These are structured databases where entities (nodes) and their relationships (edges) are stored, enabling complex queries about interconnected data.
• Cypher Queries: Cypher is a query language for graph databases like Neo4j, used to extract information based on relationships.
• Ease of Use: With large language models (LLMs), users can ask questions in plain language, and the system translates them into Cypher queries for precise answers.
• Applications: Graph RAG is useful in fields like scientific research, legal tech, and enterprise knowledge management, where relationships between data points are key.

Introduction to Graph RAG

Retrieval Augmented Generation (RAG) is a technique that enhances large language models (LLMs) by connecting them to external data sources, allowing them to provide more accurate and context-specific answers. Traditional RAG uses vector databases to retrieve text based on semantic similarity, but this approach can miss deeper connections between data points. Graph RAG addresses this by using a knowledge graph stored in a graph database, such as Neo4j, to capture and utilize relationships between entities.

Graph RAG is particularly effective for complex queries that involve interconnected data, such as organizational hierarchies, scientific research Tyre, or legal precedents. By leveraging the structured nature of knowledge graphs, it provides richer context and more precise answers. Research suggests that Graph RAG can outperform vector-based RAG in scenarios requiring relationship-based insights, such as enterprise knowledge management or research analysis.

Benefits of Graph RAG

• Contextual Depth: Captures relationships between entities for more comprehensive answers.
• Multi-Source Integration: Combines data from various sources into a unified graph.
• Explainability: Offers transparent reasoning by showing how answers are derived from graph connections.
• Complex Query Handling: Excels at answering questions involving multiple entities and relationships.

For example, in a company setting, Graph RAG can answer “Who reports to the CEO?” by traversing the organizational graph, something vector RAG might struggle with.

Understanding Knowledge Graphs

A knowledge graph is a structured database that represents information as a network of entities (nodes) and their relationships (edges). Nodes might represent people, places, or concepts, while edges represent connections like “works for” or “is located in.” Properties add details, such as a person’s job title or a relationship’s strength.

Real-World Analogy

Think of a knowledge graph as a city map. Landmarks (nodes) like schools or parks are connected by roads (edges) like highways or paths. Each landmark has details (properties), like its name or type. Querying the graph is like asking, “What’s the fastest route from the park to the school?” or “Which landmarks are near the library?” This structure makes it easy to navigate complex relationships.

Real-World Applications

• Google Knowledge Graph: Enhances search results by linking related entities, like connecting an actor to their movies.
• DBpedia: Extracts structured data from Wikipedia for research and analysis.
• Enterprise Knowledge Graphs: Used by companies to manage customer relationships, supply chains, or internal data.

Setting Up the Environment

To implement Graph RAG, you need a graph database and the right software tools. The open-source Neo4j Community Edition is a popular choice due to its robust features and support for the Cypher query language.

Setting Up Neo4j

1️⃣Install a Containerization Tool: Use Docker or Podman to run Neo4j in an isolated environment.

2️⃣Pull Neo4j Image: Run docker pull neo4j to download Neo4j.

3️⃣Start Neo4j Container: Use a command like:

docker run --name neo4j -p7474:7474 -p7687:7687 -d -v $HOME/neo4j/data:/data -v $HOME/neo4j/logs:/logs -v $HOME/neo4j/import:/var/lib/neo4j/import -v $HOME/neo4j/plugins:/plugins --env NEO4J_AUTH=neo4j/password neo4j

4️⃣Access Neo4j: Open the Neo4j browser at http://localhost:7474 and log in with the credentials (e.g., username: neo4j, password: password).

Required Python Libraries

Install these Python libraries using pip:

pip install langchain langchain-neo4j langchain-ibm langchain-watsonx neo4j

Populating the Knowledge Graph

Creating a knowledge graph involves extracting entities and relationships from unstructured text and inserting them into the graph database.

Using an LLM for Extraction

An LLM, such as IBM Watsonx, can analyze text to identify entities (e.g., people, organizations) and relationships (e.g., “collaborates with”). The Langchain LLMGraphTransformer tool allows you to specify allowed nodes (e.g., Person, Group) and relationships (e.g., COLLABORATES, WORKS_IN) to ensure accurate extraction.

Transforming and Inserting Data

The extracted data is converted into a graph format (nodes and edges) and inserted into the database using methods like add_graph_documents.

Example Python Code

from langchain.llms import IBMWatsonx
from langchain.graphs import Neo4jGraph
from langchain.graph_transformers import LLMGraphTransformer
from langchain.documents import Document

# Initialize LLM
llm = IBMWatsonx(api_key="your_api_key", project_id="your_project_id")

# Configure graph transformer
transformer = LLMGraphTransformer(
    llm=llm,
    allowed_nodes=["Person", "Title", "Group"],
    allowed_relationships=["TITLE", "COLLABORATES", "GROUP"]
)

# Sample text
text = "John is the director of the digital marketing group. He collaborates with Jane, who is in the executive group."
doc = Document(page_content=text)

# Convert to graph documents
graph_docs = transformer.convert_to_graph_documents([doc])

# Connect to Neo4j
graph = Neo4jGraph(url="bolt://localhost:7687", username="neo4j", password="password")

# Add to graph
graph.add_graph_documents(graph_docs)

This code transforms text about employees into a knowledge graph, creating nodes for John and Jane, their titles, groups, and relationships.

Visualizing the Graph

You can visualize the graph in the Neo4j browser by running a Cypher query like:

MATCH (n)-[r]->(m) RETURN n, r, m

This displays all nodes and relationships, helping verify the graph’s structure.

Querying the Knowledge Graph

Querying involves translating natural language questions into Cypher queries, executing them, and converting the results back into natural language.

Introduction to Cypher

Cypher is a declarative query language for graph databases, similar to SQL but designed for graphs. It uses patterns to match nodes and relationships.

Example Cypher Queries

• Find a person’s title: MATCH (p:Person {name: 'John'})-[:TITLE]->(t:Title) RETURN t.name
• Find collaborators: MATCH (p:Person {name: 'John'})-[:COLLABORATES]->(q:Person) RETURN q.name

Generating Cypher Queries with an LLM

The LLM translates user questions into Cypher queries using prompt engineering. Langchain’s prompt templates provide examples to guide the LLM, ensuring accurate query generation. For example:

Prompt Example

Given a question, generate a Cypher query:
Question: What is John's title?
Cypher: MATCH (p:Person {name: 'John'})-[:TITLE]->(t:Title) RETURN t.name

A second prompt translates query results into natural language answers.

Example Interaction

• Question: “What is John’s title?”
• Cypher Query: MATCH (p:Person {name: 'John'})-[:TITLE]->(t:Title) RETURN t.name
• Result: “director”
• LLM Response: “John’s title is director.”

Practical Example: Employee Knowledge Graph

Consider a dataset about a company’s employees:

Text Input

“John is the director of the digital marketing group. He collaborates with Jane, who is in the executive group. Jane collaborates with Sharon.”

Graph Structure

• Nodes: John (Person), Director (Title), Digital Marketing (Group), Jane (Person), Executive (Group), Sharon (Person)
• Edges: John-[:TITLE]->Director, John-[:GROUP]->Digital Marketing, John-[:COLLABORATES]->Jane, Jane-[:GROUP]->Executive, Jane-[:COLLABORATES]->Sharon

Sample Queries

Additional Example: Scientific Research

Graph RAG is valuable in scientific research, where relationships between studies are complex.

Text Input

“The American Revolution began in 1775 and ended in 1783. It led to the independence of the United States from Britain. Key figures include George Washington and Thomas Jefferson.”

Graph Structure

• Nodes: American Revolution (Event), 1775 (Year), 1783 (Year), United States (Country), Britain (Country), George Washington (Person), Thomas Jefferson (Person)
• Edges: American Revolution-[:BEGAN_IN]->1775, American Revolution-[:ENDED_IN]->1783, American Revolution-[:LED_TO]->Independence, George Washington-[:KEY_FIGURE_IN]->American Revolution

Sample Query

• Question: “When did the American Revolution start?”
• Cypher Query: MATCH (e:Event {name: 'American Revolution'})-[:BEGAN_IN]->(y:Year) RETURN y.name
• Answer: 1775

This example shows how Graph RAG can handle historical data, making it easier to explore connections like key figures or outcomes.

Advantages and Limitations

Advantages Over Vector-Based RAG

• Relationship Awareness: Graph RAG excels at queries involving connections, unlike vector RAG’s focus on semantic similarity.
• Comprehensive Context: It can summarize entire datasets by leveraging graph structures.
• Explainability: The graph’s structure makes it clear how answers are derived.
• Multi-Source Integration: Combines diverse data into a single graph.

Limitations

• Complexity: Building and maintaining knowledge graphs is more complex than vector databases.
• Scalability: Large graphs can slow down queries, requiring optimization.
• Data Quality: Accuracy depends on the quality of entity and relationship extraction.

Use Cases

Graph RAG shines in scenarios requiring deep relationship understanding:

• Scientific Discovery: Mapping connections between studies, like climate change and biodiversity.
• Legal Tech: Linking legal precedents, statutes, and cases for efficient research.
• Enterprise Knowledge Management: Organizing employee, customer, or product data.

Conclusion

Graph RAG combines the power of knowledge graphs and Cypher queries to enhance AI’s ability to answer complex questions with rich context. By structuring data as interconnected nodes and edges, it provides a more nuanced understanding than traditional vector-based RAG. Recent developments, like Microsoft’s LazyGraphRAG, suggest even simpler approaches that don’t require prior data summarization, indicating a bright future for this technology. Whether you’re exploring scientific connections or managing enterprise data, Graph RAG offers a powerful, explainable, and flexible solution.

FAQs