Vector database vs graph database for LLM applications
In recent years, the advancement of Large Language Models (LLMs) has led to an increasing interest in designing efficient data structures for handling and querying large amounts of vector data. Two popular options for this task are Vector Databases (VDBs) and Graph Databases (GDBs). In this blog post, we will discuss the key differences, advantages, and applications of VDBs and GDBs in the context of LLM systems.
First, let us briefly discuss both concepts and their underlying data structures. A vector database (VDB) is a type of database management system optimized for the storage, indexing, and retrieval of dense or sparse vectors. Vectors can be thought of as mathematical objects with a fixed number of dimensions, which in machine learning applications represent features or embeddings. The primary goal of a VDB is to efficiently support various vector-based operations like nearest neighbor search (NN), vector quantization, and indexing.
On the other hand, a graph database (GDB) is a NoSQL database that stores data in the form of graphs — nodes and edges that represent entities and relationships between them, respectively. Graph databases are particularly suitable for representing complex relationships and networks with nodes having multiple connections to other nodes. In machine learning applications, graphs can be used as input structures for graph neural networks (GNNs), or they can represent knowledge graphs, where nodes correspond to concepts and edges denote relations between them.
Now that we have a basic understanding of both VDBs and GDBs, let us discuss their application in LLM systems. One common application of VDBs in LLMs is indexing word embeddings or text representations for efficient search in large text collections. For instance, Microsoft’s Faiss library implements various vector indexing methods such as IVF-Clustering and HNSW tree algorithms to support efficient similarity search in high-dimensional spaces. Another application is in the context of recommendation systems where user/item embeddings are stored in VDBs for fast retrieval of nearest neighbors.
In graph databases, LLMs can be used as part of GNNs. GNNs have shown great success in various applications like recommendation systems, social network analysis, and molecular chemistry. They learn node representations by propagating information through the graph and updating them iteratively based on local neighborhood information. Neo4j, a popular graph database system, provides native support for running graph-based machine learning algorithms that include LLMs as part of their models.
To compare the performance of VDBs and GDBs in different contexts, let us look at some theoretical aspects and practical examples: From a theoretical perspective, VDBs are optimized for vector operations like NN search and indexing. In contrast, graph databases support graph traversal and subgraph pattern matching. The former is more suitable for large-scale similarity search tasks, while the latter excels at handling complex relationships and networks.
Practically, VDBs can efficiently handle millions to billions of vectors in high-dimensional spaces with sublinear query time complexity (O(n+kd), where n is the number of indexed vectors and kd is the dimensionality). However, they may not scale well when dealing with complex relationships. Graph databases are better suited for handling complex relationships, making them a popular choice in recommendation systems or social network analysis. However, they require graph traversal and pattern matching, which can increase query time.
An example application of these databases in LLM systems is in recommender systems. A common task is to find items similar to a given user’s profile based on their past interactions. In this scenario, we can use either a VDB or a GDB. If the interaction data consists primarily of item embeddings and user profiles as vectors, then a VDB like Faiss would be more suitable due to its efficiency in handling vector operations and large-scale similarity search. However, if we have more complex relationships between users, items, and their interactions, such as friendships or collaborations, then a graph database like Neo4j would be a better choice as it supports efficient graph traversal and can handle complex relationships and patterns in the data.
In conclusion, both vector databases and graph databases have their strengths and weaknesses when it comes to handling data structures for LLM systems. Vector databases excel at handling large-scale similarity search tasks, while graph databases are better suited for handling complex relationships and networks. The choice between VDBs and GDBs ultimately depends on the nature of the application domain and the specific requirements of the machine learning model.
