Vespa vs Neo4j Choosing the Right Vector Database for Your AI Apps
What is a Vector Database?
Before we compare Vespa and Neo4j, let's first explore the concept of vector databases.
A vector database is specifically designed to store and query high-dimensional vectors, which are numerical representations of unstructured data. These vectors encode complex information, such as the semantic meaning of text, the visual features of images, or product attributes. By enabling efficient similarity searches, vector databases play a pivotal role in AI applications, allowing for more advanced data analysis and retrieval.
Common use cases for vector databases include e-commerce product recommendations, content discovery platforms, anomaly detection in cybersecurity, medical image analysis, and natural language processing (NLP) tasks. They also play a crucial role in Retrieval Augmented Generation (RAG), a technique that enhances the performance of large language models (LLMs) by providing external knowledge to reduce issues like AI hallucinations.
There are many types of vector databases available in the market, including:
- Purpose-built vector databases such as Milvus, Zilliz Cloud (fully managed Milvus)
- Vector search libraries such as Faiss and Annoy.
- Lightweight vector databases such as Chroma and Milvus Lite.
- Traditional databases with vector search add-ons capable of performing small-scale vector searches.
Vespa is a purpose-built vector database. Neo4j is a graph database with vector search capabilities as an add-on. This post compares their vector search capabilities.
Vespa: Overview and Core Technology
Vespa is a powerful search engine and vector database that can handle multiple types of searches all at once. It's great at vector search, text search, and searching through structured data. This means you can use it to find similar items (like images or products), search for specific words in text, and filter results based on things like dates or numbers - all in one go. Vespa is flexible and can work with different types of data, from simple numbers to complex structures.
One of Vespa's standout features is its ability to do vector search. You can add any number of vector fields to your documents, and Vespa will search through them quickly. It can even handle special types of vectors called tensors, which are useful for representing things like multi-part document embeddings. Vespa is smart about how it stores and searches these vectors, so it can handle really large amounts of data without slowing down.
Vespa is built to be super fast and efficient. It uses its own special engine written in C++ to manage memory and do searches, which helps it perform well even when dealing with complex queries and lots of data. It's designed to keep working smoothly even when you're adding new data or handling a lot of searches at the same time. This makes it great for big, real-world applications that need to handle a lot of traffic and data.
Another cool thing about Vespa is that it can automatically scale up to handle more data or traffic. You can add more computers to your Vespa setup, and it will automatically spread the work across them. This means your search system can grow as your needs grow, without you having to do a lot of complicated setup. Vespa can even adjust itself automatically to handle changes in how much data or traffic you have, which can help save on costs. This makes it a great choice for businesses that need a search system that can grow with them over time.
Neo4J: The Basics
Neo4j’s vector search allows developers to create vector indexes to search for similar data across their graph. These indexes work with node properties that contain vector embeddings - numerical representations of data like text, images or audio that capture the meaning of the data. The system supports vectors up to 4096 dimensions and cosine and Euclidean similarity functions.
The implementation uses Hierarchical Navigable Small World (HNSW) graphs to do fast approximate k-nearest neighbor searches. When querying a vector index, you specify how many neighbors you want to retrieve and the system returns matching nodes ordered by similarity score. These scores are 0-1 with higher being more similar. The HNSW approach works well by keeping connections between similar vectors and allowing the system to quickly jump to different parts of the vector space.
Creating and using vector indexes is done through the query language. You can create indexes with the CREATE VECTOR INDEX command and specify parameters like vector dimensions and similarity function. The system will validate that only vectors of the configured dimensions are indexed. Querying these indexes is done with the db.index.vector.queryNodes procedure which takes an index name, number of results and query vector as input.
Neo4j’s vector indexing has performance optimizations like quantization which reduces memory usage by compressing the vector representations. You can tune the index behavior with parameters like max connections per node (M) and number of nearest neighbors tracked during insertion (ef_construction). While these parameters allow you to balance between accuracy and performance, the defaults work well for most use cases. The system also supports relationship vector indexes from version 5.18, so you can search for similar data on relationship properties.
This allows developers to build AI powered applications. By combining graph queries with vector similarity search applications can find related data based on semantic meaning not exact matches. For example a movie recommendation system could use plot embedding vectors to find similar movies, while using the graph structure to ensure the recommendations come from the same genre or era as the user prefers.
Key Differences
When choosing a vector search tool like Vespa or Neo4j, you need to consider how each fits your use case. This will cover the main differences between them across various dimensions to help you decide.
Search Methodology
Vespa: Vespa supports multiple search methods: vector search, full-text search, filtering structured data - all in one query. It can handle tensor-based search and is great for multi-modal use cases. Vespa is designed for many search scenarios and can execute complex queries without sacrificing speed.
Neo4j: Neo4j’s vector search uses Hierarchical Navigable Small World (HNSW) graphs for approximate k-nearest neighbor (k-NN) searches. This is designed for graph data and allows for similarity searches that integrate with graph relationships. It’s great when vector search needs to be combined with graph traversal.
Data
Vespa: Vespa can handle many data types: structured (e.g. tables) to unstructured (e.g. text, embeddings). It can handle tensors and multiple vector fields in documents for advanced configurations for use cases like recommendation systems and multi-modal search.
Neo4j: Neo4j is designed for graph data where relationships are as important as the nodes themselves. Its vector indexes are used to search node or relationship properties that contain embeddings. This is great for use cases like knowledge graphs where semantic connections are key.
Scalability and Performance
Vespa: Built for large-scale, real-time applications, Vespa scales horizontally by distributing workloads across multiple nodes. In-memory processing and C++-based engine means low latency even for complex queries on big data.
Neo4j: Neo4j provides scalability within graph data. HNSW indexing is optimized for speed and memory efficiency, but performance is mainly suited for graph-centric workloads. Large scale will require tuning and thoughtful architecture to perform.
Flexibility and Customization
Vespa: Vespa has lots of customization in data modeling and query design. You can define schemas, integrate multiple vector fields and customize search behavior. This makes it suitable for many use cases beyond just vector or graph search.
Neo4j: Neo4j’s customization is focused on graph use cases. You can tweak vector index parameters (e.g. max connections, ef_construction) for better accuracy or performance. But it’s less adaptable to use cases outside of graph-based applications.
Integration and Ecosystem
Vespa: Vespa is relatively ecosystem-agnostic, has APIs for custom applications, machine learning pipelines and other data sources. It’s a tool that fits into many workflows.
Neo4j: Neo4j has a strong ecosystem around graph analytics and visualization tools. Its integration is great for graph-based AI applications but might feel limited if your use case doesn’t heavily rely on graph data.
Usability
Vespa: Flexibility comes with a steeper learning curve. Configuring schemas and handling advanced tensor operations requires expertise, but the documentation and community resources are good.
Neo4j: Neo4j benefits from its simple query language (Cypher) and easy setup, especially for developers familiar with graphs. Creating and querying vector indexes is relatively simple so it’s more accessible for beginners.
Cost
Vespa: Can be cost effective for large scale deployments since it’s resource efficient and can optimize workloads on the fly. But cost depends on your infrastructure.
Neo4j: Neo4j’s pricing for enterprise features like vector search can be a issue for some users. Managed services and licensing adds to the cost, but its optimizations can reduce resource consumption in graph-heavy applications.
Security
Vespa: Has basic security features like authentication, role-based access control and encryption options. Suitable for environments that require strong data protection.
Neo4j: Has strong security features, especially for graph-centric deployments. Fine grained access control over nodes and relationships means sensitive data is well protected.
When to use Vespa
Vespa is great for big data and distributed data handling with advanced vector search. It can integrate vector search with full text and structured data search. Good for e-commerce recommendations, multi-modal search engines and real-time analytics platforms. Horizontal scalability and high performance so can handle large datasets and complex queries without performance hit. Good for businesses that expect high growth or high traffic.
When to use Neo4j
Neo4j is great for graph centric use cases where the relationships between data points are as important as the data itself. Good for building knowledge graphs, social network analysis tools and AI driven applications where semantic connections improve search results. Its vector search capabilities combined with graph traversal makes it a great option for developers who want to add semantic similarity matching to traditional graph queries. Neo4j’s query language and graph native design makes it easier for teams working on graph projects.
Summary
Vespa and Neo4j are good for different domains, each with their own strengths. Vespa is good for multi-modal search and big applications, Neo4j is good for graph driven use cases where relationships and semantic search matters most. Choose between them based on your use case requirements, your data and performance requirements of your application. By aligning your decision with these factors, you will get the most out of it.
Read this to get an overview of Vespa and Neo4j but to evaluate these you need to evaluate based on your use case. One tool that can help with that is VectorDBBench, an open-source benchmarking tool for vector database comparison. In the end, thorough benchmarking with your own datasets and query patterns will be key to making a decision between these two powerful but different approaches to vector search in distributed database systems.
Using Open-source VectorDBBench to Evaluate and Compare Vector Databases on Your Own
VectorDBBench is an open-source benchmarking tool for users who need high-performance data storage and retrieval systems, especially vector databases. This tool allows users to test and compare different vector database systems like Milvus and Zilliz Cloud (the managed Milvus) using their own datasets and find the one that fits their use cases. With VectorDBBench, users can make decisions based on actual vector database performance rather than marketing claims or hearsay.
VectorDBBench is written in Python and licensed under the MIT open-source license, meaning anyone can freely use, modify, and distribute it. The tool is actively maintained by a community of developers committed to improving its features and performance.
Download VectorDBBench from its GitHub repository to reproduce our benchmark results or obtain performance results on your own datasets.
Take a quick look at the performance of mainstream vector databases on the VectorDBBench Leaderboard.
Read the following blogs to learn more about vector database evaluation.
Further Resources about VectorDB, GenAI, and ML
Chloe WilliamsChloe Williams is a technical writer at Zilliz.
- What is a Vector Database?
- Vespa: Overview and Core Technology
- Neo4J: The Basics
- Key Differences
- Using Open-source VectorDBBench to Evaluate and Compare Vector Databases on Your Own
- Further Resources about VectorDB, GenAI, and ML
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