Knowledge Graphs in RAG: Enhancing AI with Structured Information

Retrieval-Augmented Generation (RAG) has been use to enhance the foundation LLM models by providing context and hence reducing hallucination. By incorporating Knowledge Graphs (KGs) into RAG systems, we can further enhance their capabilities, creating more intelligent and context-aware applications. In the context of RAG, a Knowledge Graph is a structured representation of information that captures entities, their attributes, and relationships between them. Unlike traditional databases, KGs model data in a way that mimics human understanding, allowing for complex queries and inference.

import networkx as nx
from sentence_transformers import SentenceTransformer

# Create a simple knowledge graph
G = nx.Graph()
G.add_edge("Shakespeare", "Hamlet", relation="wrote")
G.add_edge("Hamlet", "Tragedy", relation="genre")
G.add_edge("Shakespeare", "England", relation="born_in")

# Initialize sentence transformer for embedding
model = SentenceTransformer('all-MiniLM-L6-v2')

# Function to retrieve relevant information from KG
def retrieve_from_kg(query, G, model):
    query_embedding = model.encode(query)
    
    relevant_nodes = []
    for node in G.nodes():
        node_embedding = model.encode(node)
        similarity = cosine_similarity([query_embedding], [node_embedding])[0][0]
        if similarity > 0.5:  # Threshold for relevance
            relevant_nodes.append(node)
    
    subgraph = G.subgraph(relevant_nodes)
    return subgraph

# Example usage
query = "Tell me about Shakespeare's plays"
relevant_info = retrieve_from_kg(query, G, model)

# The relevant_info subgraph can now be used to augment the RAG model's input        

The integration of Knowledge Graphs into RAG systems offers numerous advantages. They provide rich context, enabling RAG systems to understand relationships between concepts. This contextual understanding leads to improved retrieval, as graph traversal algorithms can find more relevant information than simple keyword-based searches. Moreover, KGs support logical inference, allowing RAG systems to derive new knowledge from existing facts, thus enhancing their reasoning capabilities.

Implementing a Knowledge Graph-enhanced RAG system involves several key components. In our example, we'll use Amazon Neptune as our graph database to store and query our Knowledge Graph, and Amazon Bedrock to access powerful language models for generating responses. This combination allows us to leverage the structured data in the KG to provide context for the language model, resulting in more informed and relevant outputs.

Let's consider a practical implementation of such a system using a movie recommendation application. We'll use the MovieLens dataset, a popular public dataset containing information about movies, users, and ratings. This dataset provides a rich source of interconnected data perfect for demonstrating the power of Knowledge Graphs in RAG systems.

The process of building this system involves following steps:

1. Setting up the Amazon Neptune instance:

2. Loading data into the Knowledge Graph: We download the MovieLens dataset, which includes information about movies, their genres, and user ratings. This data is then loaded into Neptune, creating a graph structure where movies and users are vertices, connected by 'rated' edges representing user ratings.

3. Querying the Knowledge Graph: When a user requests a movie recommendation, we query Neptune to find similar movies. This query traverses the graph, finding movies that have been rated by users who also rated the input movie. We also calculate average ratings for these movies, providing additional context for our recommendation.

4. Generating recommendations: We use Amazon Bedrock to access a large language model (in this case, Claude). We provide the model with context from our Knowledge Graph query, including similar movies, their genres, and average ratings. The model then generates a personalized recommendation based on this rich context.

Here's a simplified version of how we might query the Knowledge Graph for movie recommendations:

def get_movie_recommendations(movie_title):
    query = f"""
    g.V().has('movie', 'title', containing('{movie_title}')).as('m')
    .in('rated').out('rated').where(neq('m')).dedup()
    .project('title', 'genres', 'avgRating')
    .by('title')
    .by('genres')
    .by(__.inE('rated').values('rating').mean())
    .order().by('avgRating', decr)
    .limit(5)
    """
    results = query_neptune(query)
    
    context = "Similar movies:\n" + "\n".join([f"{r['title']} (Genres: {r['genres']}, Avg Rating: {r['avgRating']:.2f})" for r in results])
    
    prompt = f"Based on the movie '{movie_title}' and the similar movies provided, give me a personalized movie recommendation with a brief explanation."
    recommendation = generate_bedrock_response(prompt, context)
    
    return recommendation        

This query finds movies similar to the input movie based on user ratings, retrieves their genres and average ratings, and orders them by rating. We then use this information to provide context for our language model when generating a recommendation.

The use of a Knowledge Graph in this RAG system is particularly beneficial because it captures complex relationships between movies, genres, and user preferences. It allows for efficient querying of related movies based on user behavior, providing rich context for the language model to generate personalized recommendations.

This approach is especially suitable for domains with complex, interconnected data where relationships between entities are as important as the entities themselves. In the case of movie recommendations, understanding the connections between movies through shared viewers, genres, or other attributes is crucial for making relevant suggestions.

However, it's important to note that Knowledge Graphs aren't always the best choice for every RAG application. For simpler tasks involving straightforward fact retrieval or when dealing with largely unstructured data without clear entity relationships, other approaches might be more suitable. Traditional databases or vector stores might be more efficient for straightforward lookups, while techniques like semantic search or text embedding might be more appropriate for unstructured text analysis.

The decision to use a Knowledge Graph in a RAG system should be based on factors such as the complexity of your data structure, the types of queries you need to support, your reasoning requirements, and the resources you have available for implementation and maintenance. In some cases, a hybrid approach combining KGs with other techniques might provide the best results.

The integration of Knowledge Graphs with powerful language models, as demonstrated in our movie recommendation system, represents a significant step forward in creating more intelligent and context-aware AI applications. By leveraging the structured information in KGs and the natural language understanding capabilities of large language models, we can create systems that not only retrieve relevant information but also reason about it in ways that more closely mimic human cognition.