MongoDB vs Neo4j: Selecting the Right Database for GenAI Applications
As AI-driven applications evolve, the importance of vector search capabilities in supporting these advancements cannot be overstated. This blog post will discuss two prominent databases with vector search capabilities: MongoDB and Neo4j. Each provides robust capabilities for handling vector search, an essential feature for applications such as recommendation engines, image retrieval, and semantic search. Our goal is to provide developers and engineers with a clear comparison, aiding in the decision of which database best aligns with their specific requirements.
What is a Vector Database?
Before we compare MongoDB vs 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.
MongoDB is a NoSQL database and Neo4j is a graph database. Both have vector search as an add-on. This post compares their vector search capabilities.
MongoDB: The Basics
MongoDB Atlas Vector Search is a feature that allows you to do vector similarity searches on data stored in MongoDB Atlas. You can index and query high-dimensional vector embeddings along with your document data and do AI and machine learning right in the database.
At its core, Atlas Vector Search uses the Hierarchical Navigable Small World (HNSW) algorithm for indexing and searching vector data. This creates a multi-level graph of the vector space so you can do Approximate Nearest Neighbor (ANN) searches. It’s a balance of speed and accuracy for large scale vector search. Atlas Vector Search also supports Exact Nearest Neighbors (ENN) searches which prioritizes accuracy over performance for queries of up to 10,000 documents.
One of the big advantages of Atlas Vector Search is its integration with MongoDB’s flexible document model. You can store vector embeddings along with other document data so you can search more contextually and precisely. You can query any kind of data that can be embedded up to 4096 dimensions. Atlas Vector Search allows you to combine vector similarity searches with traditional document filtering. For example, a semantic search for products could be filtered by category, price range or availability.
Atlas Vector Search also supports hybrid search, combining vector search with full text search for more granular results. This is different from Atlas Search which is focused on keyword based search. The platform integrates with popular AI services and tools so you can use it with embedding models from providers like OpenAI, VoyageAI and many others listed on Hugging Face. It also supports open-source frameworks like LangChain and LlamaIndex for building applications that use Large Language Models (LLMs).
To ensure scalability and performance, MongoDB Atlas provides Search Nodes, which provides dedicated infrastructure for Atlas Search and Vector Search workloads. This allows you to have optimized compute resources and independent scaling of search needs so you get better performance at scale.
By having these capabilities in the MongoDB ecosystem, Atlas Vector Search is a full solution for developers building AI powered applications, recommendation systems or advanced search features. No need for a separate vector database, you can use MongoDB’s scalability and rich features along with vector search.
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
Architecture and Search Approach
MongoDB Atlas Vector Search has vector search built in to its document based architecture so you can store vectors along with other document data. Neo4j has vector search built into its graph structure, so you can search for vectors on node and relationship properties. Both use the HNSW algorithm for approximate nearest neighbor searches and support up to 4096 dimensions.
Data Model and Query Flexibility
MongoDB’s approach is great when you need to combine vector searches with document based filtering. For example you can search for similar products while filtering by price range or availability. Neo4j’s strength is in its ability to traverse relationships - you can use vector similarity to find related content while using graph relationships to add context and constraints to your searches. Both support cosine and Euclidean similarity functions.
Integration and Ecosystem
MongoDB Atlas Vector Search has built in integration with popular AI services like OpenAI and VoyageAI, plus frameworks like LangChain and LlamaIndex. It also supports hybrid search, combining vector and full text search. Neo4j focuses more on graph specific integrations and lets you use any embedding model you like.
Scalability and Performance
MongoDB Atlas has dedicated Search Nodes for vector search workloads so you can scale search independently. Neo4j has performance optimisations like vector quantization and tunable parameters to balance accuracy and speed. Both can handle large scale vector operations but MongoDB’s dedicated infrastructure might give it an edge for pure search workloads.
When to use MongoDB Atlas Vector Search
Use MongoDB Atlas Vector Search when your application needs to handle large amounts of document based data with vector search. It’s great when you need to combine traditional document queries with semantic search, like e-commerce platforms that need product similarity search with filtering by category, price or availability. It’s particularly good when you have heavy AI services and LLM integrations as it has built in connections with OpenAI, VoyageAI, LangChain and LlamaIndex. The Search Nodes infrastructure is good for applications that need to scale search workloads independently.
When to use Neo4j Vector Search
Neo4j’s vector search is great when you need to understand relationships between data points. It’s the best choice for recommendation engines that need to consider both content similarity and complex relationships between items, users and categories. You can apply vector search to both nodes and relationships so it’s good for applications like knowledge graphs, fraud detection systems or social networks where the connections between entities are as important as the entities themselves. Neo4j’s approach is particularly good when you need to combine graph algorithms with vector similarity searches.
Conclusion
Your choice between MongoDB Atlas and Neo4j for vector search depends on your data model and application requirements. MongoDB Atlas is a more integrated solution with strong document based filtering and built in AI service connections so it’s great for applications that need flexible document storage with semantic search. Neo4j has unique strengths in relationship based vector search and graph analytics so it’s the better choice when your data’s relationships are key to your application’s functionality. Consider your specific needs around data structure, scaling and integration when making your decision as both have robust vector search but excel in different areas.
Read this to get an overview of MongoDB 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?
- MongoDB: The Basics
- Neo4J: The Basics
- Key Differences
- When to use MongoDB Atlas Vector Search
- When to use Neo4j Vector Search
- Conclusion
- Using Open-source VectorDBBench to Evaluate and Compare Vector Databases on Your Own
- Further Resources about VectorDB, GenAI, and ML
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