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Writer's pictureGad Benram

Knowledge Graph RAG vs. Vector DB RAG: Is It Time for GraphDBs to Shine?

The emergence of AI has revolutionized the way we interact with data—or even knowledge itself. Among the buzzwords circulating in the tech community is an engineering design pattern called Retrieval-Augmented Generation (RAG). RAG has many reference implementations, and the choice of retrieval technology behind your RAG application can significantly impact the quality of your system.


In this post, we'll review the differences when knowledge graphs versus vector databases are used as the backbone of RAG and ask: Is it really the time for GraphDBs to shine? Let's dive deep into the technicalities to set things straight.



What is RAG?

At its core, RAG is a combination of two components:

  1. Retrieval: Fetching relevant data or information from a database or knowledge source.

  2. Generation: Using an AI model, typically a language model like GPT-4, to generate responses based on the retrieved information.


It's important to note that the term "RAG" is used in this post more for marketing or SEO purposes. As engineers, when talking about Graph vs. Vector search, we should focus on the technologies that best support our retrieval tasks, rather than getting caught up in the hype.


The Role of Databases in Retrieval

The retrieval component of RAG relies heavily on the underlying database technology. Different databases offer various retrieval capabilities:

  • Key-Value Stores (e.g., Redis): Ideal for scenarios where you know exactly what you're looking for. Retrieval is extremely fast (1-2 ms) because it directly accesses data based on keys.

  • Relational Databases: Allow for selecting and filtering data using SQL queries. They support aggregations and can handle structured data efficiently.

  • Lexical Search Engines (e.g., Elasticsearch): Index items based on keywords, enabling full-text search capabilities. They are excellent for scenarios where you need to search unstructured text data.

  • Vector Databases (Vector Search): Retrieve information based on semantic similarity using vector embeddings. This is useful when you want to find objects where their meaning matters more than their metadata or keys.


A Note on Vector Databases

It's worth mentioning that vector databases are not necessarily a distinct type of database but rather a function that can be integrated into existing databases, as we've seen with OpenSearch's case. Vector search calculates the similarity between high-dimensional vectors (embeddings), which can be computationally expensive. Therefore, it's often used as a secondary retrieval method after narrowing down data using less costly techniques like BM25 (a ranking function used by search engines).


 

Introduction to Knowledge Graphs

A knowledge graph represents data in terms of entities and their relationships. This structure is particularly useful for modeling complex, interconnected data, such as social networks or organizational hierarchies.


Why Use Knowledge Graphs?

Consider the example of mapping a criminal organization. Imagine you're an FBI investigator tasked with understanding the intricate web of relationships within a crime syndicate. A traditional table-based database may not effectively capture these complex relationships. Members may have multiple aliases, operate in different cells, or have indirect connections through intermediaries.

  • Tables: You might have rows representing individuals with columns for known attributes. But how do you represent the relationships between them? Foreign keys can only go so far.

  • Knowledge Graphs: Here, each node represents an individual, and edges represent relationships (e.g., "associates with," "reports to," "finances"). This allows you to model and visualize the organization more naturally.

By using a knowledge graph, you can traverse the network, identify key players, and uncover hidden connections that might be missed in a traditional database.


When Does a GraphDB Become a Knowledge Graph?

A GraphDB stores nodes and edges—essentially forming a connection graph. However, it becomes a knowledge graph when the connections are meaningful and enriched with context. This transformation is crucial because:

  • Meaningful Connections: Instead of just knowing that two individuals are connected, you understand how and why they are connected.

  • Semantic Relationships: Edges carry semantic information (e.g., "is the mentor of," "collaborated on project"), adding depth to the data.


The Role of AI in Enriching Connections

This is where AI shines. Before AI advancements, establishing meaningful connections required manual input or rigid rule-based systems. With AI, we can enhance the construction of knowledge graphs by:

  • Automated Relationship Extraction: AI models can parse unstructured data—like intercepted communications or surveillance reports—to identify and establish new relationships between entities.

  • Entity Recognition and Disambiguation: AI can identify entities within unstructured data and resolve ambiguities (e.g., distinguishing between "Jordan" the person vs. "Jordan" the country).

  • Data Cleansing and Normalization: AI helps standardize data, resolving issues like differently spelled names or inconsistent formats (e.g., "John Smith" vs. "Jon Smyth").

  • Relationship Inference: AI can infer relationships based on patterns in data. For instance, if two individuals frequently appear in the same financial transactions, AI might infer a business relationship.

  • Semantic Enrichment: AI can deduce relationships that aren't explicitly stated but are implied through context, adding a layer of knowledge to the graph.

  • Dynamic Updates: AI enables the knowledge graph to evolve over time as new data comes in, maintaining up-to-date and relevant connections.


Returning to our crime organization example, AI helps transform a mere connection graph into a knowledge graph by adding layers of meaning and context to the relationships. For example, if you capture documents associated with the organization, you can parse them with large language models (LLMs) to generate the right connections and enrichments in the graph.


AI can analyze vast amounts of data—emails, phone records, financial transactions—to uncover hidden relationships and build a richer, more informative knowledge graph. Without these enriched connections, the graph is just a network of nodes and edges without deeper insights.


Retrieval Challenges with Knowledge Graphs

Retrieving data from knowledge graphs is computationally intensive due to the complexity of graph structures. Some common retrieval methods include:

  • Traversal Algorithms: Depth-First Search (DFS), Breadth-First Search (BFS), and bidirectional search to navigate through nodes.

  • Query Languages: Cypher, Gremlin, and SPARQL enable complex queries over graph data.

  • Graph Algorithms: Algorithms for finding the shortest path, calculating centrality measures, and detecting communities within the graph.

  • Subgraph Matching: Algorithms like VF2 and Ullmann’s for pattern matching within graphs.

  • Indexing: Techniques such as vertex-centric, edge-based, and property-based indexing to speed up retrieval.

  • Graph Embeddings: Methods like Node2Vec, GraphSAGE, and Graph Neural Networks (GNNs) transform graph data into vector space for similarity calculations.


The Computational Expense

Most of these techniques require significant computational resources, especially when dealing with large-scale graphs like a nationwide crime network. This limitation makes real-time retrieval challenging and costly, similar to the issues faced with vector searches.

In fact, the question of GraphDB for retrieval purposes has little to do with AI (except maybe graph embeddings, but large-scale implementations are still rare). The retrieval challenges are inherent to the graph structures themselves.



Knowledge Graph RAG vs. Vector DB RAG

Given the above, let's compare how knowledge graphs and vector databases function within the RAG framework.


Is It Time for GraphDBs to Shine?

While AI enhances the construction of knowledge graphs, the retrieval challenges remain a significant hurdle. Until breakthroughs occur in more efficient retrieval algorithms, GraphDBs may not yet be the optimal choice for RAG applications focused on speed and scalability.


Conclusion

In the rapidly evolving landscape of AI and data interaction, it's essential to look beyond the buzzwords. While RAG provides a framework for combining retrieval and generation, the choice of underlying technology for retrieval should be driven by the specific requirements of your application.

  • If your primary need is semantic search over textual data, vector search integrated into your database might be the way to go.

  • If you're dealing with complex, interconnected entities, a knowledge graph enriched by AI can offer powerful insights, but be prepared to tackle the computational challenges in retrieval.

Ultimately, understanding the strengths and limitations of each approach will enable you to make informed decisions, leveraging AI where it adds the most value—particularly in building and enriching your data structures.


Additional Resources to Explore

  • Traversal Algorithms: Implement DFS and BFS to navigate your graphs efficiently.

  • Query Languages: Learn Cypher for Neo4j or SPARQL for RDF data to write effective graph queries.

  • Graph Algorithms: Explore algorithms for network analysis, like calculating centrality or community detection.

  • Graph Embeddings: Experiment with Node2Vec or GraphSAGE to transform your graph data for machine learning tasks.


By focusing on the right tools and understanding their appropriate use cases, you'll be better equipped to harness the full potential of AI in your data-driven applications.

Feel free to share your experiences with large-scale graph embedding implementations or any insights into efficient graph retrieval methods in the comments below. Together, we can navigate these challenges and push the boundaries of what's possible with AI and data retrieval.

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