Knowledge Graphs for Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) is a cutting-edge technique that combines information retrieval with language generation to provide more accurate and contextually relevant responses. This approach is particularly useful in applications such as digital assistants, chatbots, and other AI-driven systems that require a deep understanding of both structured and unstructured data. A key component of RAG is the use of knowledge graphs, which organize data as nodes and relationships, enabling more sophisticated reasoning and information retrieval.

In this article, we will explore how to implement knowledge graphs for RAG applications, providing detailed coding snippets and practical examples. We will cover the following topics:

1. Introduction to Knowledge Graphs and RAG
2. Setting Up the Environment
3. Creating and Managing Knowledge Graphs
4. Integrating Knowledge Graphs with RAG
5. Practical Examples and Coding Snippets
6. Conclusion

1. Introduction to Knowledge Graphs and RAG

What is a Knowledge Graph?

A knowledge graph is a structured representation of knowledge where entities (nodes) are connected by relationships (edges). This setup not only catalogs information but also the context and interrelation among data points. For instance, in a medical knowledge graph, nodes could represent symptoms, diseases, and treatments, with edges defining relationships like “symptom of” or “treated by” [Pandit, 2023].

What is Retrieval-Augmented Generation (RAG)?

RAG models merge the best of retrieval-based techniques with advanced generative models. They fetch relevant information from vast datasets and then craft responses that are not only accurate but contextually rich. This approach addresses the limitations of traditional search and retrieval methods by leveraging the structured nature of knowledge graphs [DataCamp, 2023].

2. Setting Up the Environment

Before we begin, ensure you have the following installed:

• Python 3.8 or higher
• Neo4j (a graph database)
• LangChain (a framework for integrating LLMs with knowledge graphs)
• Necessary Python libraries: neo4j, langchain, transformers, pandas

You can install the required libraries using pip:

pip install neo4j langchain transformers pandas

3. Creating and Managing Knowledge Graphs

Step 1: Load and Preprocess Text Data

The first step is to load and preprocess the text data from which we’ll extract the knowledge graph. In this example, we’ll use a text snippet describing a technology company called PrismaticAI, its employees, and their roles.

import pandas as pd

# Sample data
data = {
    "text": [
        "PrismaticAI is a technology company. John Doe is the CEO. Jane Smith is the CTO."
    ]
}

df = pd.DataFrame(data)

Step 2: Extract Entities and Relationships

Next, we use LangChain’s LLMGraphTransformer to extract entities and relationships from the text data.

from langchain import LLMGraphTransformer

# Initialize the transformer
transformer = LLMGraphTransformer()

# Extract entities and relationships
entities, relationships = transformer.extract(df['text'][0])

print("Entities:", entities)
print("Relationships:", relationships)

Step 3: Create the Knowledge Graph in Neo4j

We will now create the knowledge graph in Neo4j using the extracted entities and relationships.

from neo4j import GraphDatabase

# Connect to Neo4j
uri = "bolt://localhost:7687"
driver = GraphDatabase.driver(uri, auth=("neo4j", "password"))

def create_knowledge_graph(entities, relationships):
    with driver.session() as session:
        for entity in entities:
            session.run("CREATE (n:Entity {name: $name})", name=entity)
        for relationship in relationships:
            session.run(
                """
                MATCH (a:Entity {name: $start}), (b:Entity {name: $end})
                CREATE (a)-[:RELATIONSHIP {type: $type}]->(b)
                """,
                start=relationship['start'],
                end=relationship['end'],
                type=relationship['type']
            )

create_knowledge_graph(entities, relationships)

4. Integrating Knowledge Graphs with RAG

Step 4: Querying the Knowledge Graph

To integrate the knowledge graph with RAG, we need to query the graph to retrieve relevant information based on the user’s input.

def query_knowledge_graph(query):
    with driver.session() as session:
        result = session.run(
            """
            MATCH (n:Entity)-[r:RELATIONSHIP]->(m:Entity)
            WHERE n.name CONTAINS $query OR m.name CONTAINS $query
            RETURN n.name, type(r), m.name
            """,
            query=query
        )
        return result.data()

query = "PrismaticAI"
results = query_knowledge_graph(query)
print("Query Results:", results)

Step 5: Integrating with LangChain

LangChain provides abstractions for integrating knowledge retrieval systems, making it easy to plug in your knowledge graph and perform these queries.

from langchain import RetrievalQA

# Initialize the RetrievalQA module
retrieval_qa = RetrievalQA(knowledge_graph=query_knowledge_graph)

# Example query
user_query = "Who is the CEO of PrismaticAI?"
response = retrieval_qa.query(user_query)
print("Response:", response)

5. Practical Examples and Coding Snippets

Example 1: Medical Knowledge Graph

Consider a simplified medical knowledge graph where “Cough” and “Cold” are symptoms connected to “Flu,” a disease, by the relationship “symptom of.” Treatments like “Rest” and “Hydration” are connected to “Flu” with “treated by” relationships.

medical_data = {
    "text": [
        "Cough and Cold are symptoms of Flu. Rest and Hydration are treatments for Flu."
    ]
}

df_medical = pd.DataFrame(medical_data)
entities_medical, relationships_medical = transformer.extract(df_medical['text'][0])
create_knowledge_graph(entities_medical, relationships_medical)

# Query the medical knowledge graph
medical_query = "Flu"
medical_results = query_knowledge_graph(medical_query)
print("Medical Query Results:", medical_results)

Example 2: Enhancing Search with Embeddings

Embeddings transform text into high-dimensional vectors, capturing semantic meaning. In knowledge graphs, embeddings can improve search by allowing semantic matching, ensuring that retrieved information closely aligns with the query’s context.

from transformers import AutoTokenizer, AutoModel

# Load pre-trained model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
model = AutoModel.from_pretrained("bert-base-uncased")

# Generate embeddings for entities
def generate_embeddings(entities):
    embeddings = {}
    for entity in entities:
        inputs = tokenizer(entity, return_tensors="pt")
        outputs = model(**inputs)
        embeddings[entity] = outputs.last_hidden_state.mean(dim=1).detach().numpy()
    return embeddings

entity_embeddings = generate_embeddings(entities)
print("Entity Embeddings:", entity_embeddings)

6. Conclusion

Knowledge graphs offer significant advantages for RAG applications, particularly in terms of representing structured knowledge, enabling complex reasoning, and providing explainable and transparent results. By integrating knowledge graphs with RAG, we can build more intelligent and context-aware language generation systems.

In this article, we have explored the steps to create and manage knowledge graphs, integrate them with RAG, and provided practical examples and coding snippets. By following these steps and leveraging the power of open-source tools like LangChain and Neo4j, you can build robust Graph RAG systems that enhance the retrieval and generation of information.

References

• DataCamp. (2023). Using a Knowledge Graph to Implement a RAG Application. Retrieved from DataCamp
• Pandit, V. (2023). Retrieval Augmented Generation (RAG) with Knowledge Graphs. Retrieved from Medium
• Bratanic, T. (2023). Enhancing the Accuracy of RAG Applications With Knowledge Graphs. Retrieved from Neo4j Developer Blog
• Chambers, B. (2023). Knowledge Graphs for RAG without a GraphDB. Retrieved from DataStax
• Richards, D. (2023). Building a Graph RAG System with Open Source Tools: A Comprehensive Guide. Retrieved from Rag About It