Chroma vs Neo4j: Choosing the Right Vector Database for Your Needs
As AI and data-driven technologies advance, selecting an appropriate vector database for your application is becoming increasingly important. Chroma and Neo4j are two options in this space. This article compares these technologies to help you make an informed decision for your project.What is a Vector Database?
Before we compare Chroma 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 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 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.
Chroma is a vector database and Neo4j is a graph database with vector search as an add-on. This post compares their vector search capabilities.
What is Chroma? An Overview
Chroma is an open-source, AI-native vector database that simplifies the process of building AI applications. It acts as a bridge between large language models (LLMs) and the data they require to function effectively. Chroma's main objective is to make knowledge, facts, and skills easily accessible to LLMs, thereby streamlining the development of AI-powered applications. At its core, Chroma provides tools for managing vector data, allowing developers to store embeddings (vector representations of data) along with their associated metadata. This capability is crucial for many AI applications, as it enables efficient similarity searches and data retrieval based on vector relationships.
One of Chroma's key strengths is its focus on simplicity and developer productivity. The team behind Chroma has prioritized creating an intuitive interface that allows developers to quickly integrate vector search capabilities into their applications. This emphasis on ease of use doesn't come at the cost of performance. Chroma is designed to be fast and efficient, making it suitable for a wide range of applications. It operates as a server and offers first-party client SDKs for both Python and JavaScript/TypeScript, providing flexibility for developers to work in their preferred programming environment.
Chroma's functionality revolves around the concept of collections, which are groups of related embeddings. When adding documents to a Chroma collection, the system can automatically tokenize and embed them using a specified embedding function, or a default one if not provided. This process transforms raw data into vector representations that can be efficiently searched. Along with the embeddings, Chroma allows storage of metadata for each document, which can include additional information useful for filtering or organizing data. Chroma provides flexible querying options, allowing searches for similar documents using either vector embeddings or text queries, returning the closest matches based on vector similarity.
Chroma stands out in several ways. Its API is designed to be intuitive and easy to use, reducing the learning curve for developers new to vector databases. It supports various types of data and can work with different embedding models, allowing users to choose the best approach for their specific use case. Chroma is built to integrate seamlessly with other AI tools and frameworks, making it a good fit for complex AI pipelines. Additionally, Chroma's open-source nature (licensed under Apache 2.0) provides transparency and the potential for community-driven improvements and customizations. The Chroma team is actively working on enhancements, including plans for a managed service (Hosted Chroma) and various tooling improvements, indicating a commitment to ongoing development and support.
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
To choose between Chroma and Neo4j as a vector search tool you need to understand their strengths and use cases. Here’s a breakdown of their features, methodology and practical implications to help you make a decision.
Search Methodology
- Chroma: Chroma is all about simplicity in vector search. It uses vector embeddings to do similarity searches efficiently, making it easy for developers. Querying options are flexible, you can search via vector embeddings or text queries. Chroma’s methodology is straightforward, perfect for developers who want minimal setup.
- Neo4j: Neo4j uses Hierarchical Navigable Small World (HNSW) graphs for approximate nearest neighbor (ANN) searches. This complex algorithm allows for fast queries in large datasets by maintaining a graph structure that connects similar vectors. The system supports cosine and Euclidean similarity, but you’ll need to have some knowledge of ANN to tune parameters like max connections (M) and nearest neighbors tracked (ef_construction).
Data
- Chroma: For unstructured data like text, images and other embeddings Chroma simplifies managing vector data and metadata. It’s designed for embedding centric workflows and is perfect for AI applications that rely heavily on semantic similarity. While Chroma supports structured metadata for filtering, its strength lies in handling unstructured and semi-structured data.
- Neo4j: Neo4j is great at combining structured, semi-structured and unstructured data. Its graph database model is very flexible, you can create relationships between nodes and use vector embeddings for similarity searches. So it’s a good choice for applications that need rich relationship modeling and vector search.
Scalability and Performance
- Chroma: Chroma is for high speed vector search, focused on developer productivity. It scales well for most AI and machine learning workloads but is better suited for smaller, embedding focused datasets vs massive, interconnected datasets.
- Neo4j: Neo4j’s scalability is tied to its graph architecture. With quantization and configurable parameters for HNSW indexing Neo4j is optimized for large datasets. Its scalability is best in graph heavy use cases where relationships between data points are as important as the data itself.
Flexibility and Customization
- Chroma: Simple APIs and SDKs (Python, JavaScript/TypeScript) to reduce complexity for developers. Customization is mainly around embedding functions and metadata management. Perfect for users who want ease of integration over feature tuning.
- Neo4j: Very flexible with many customization options for indexing, querying and graph modeling. Developers can tune the vector index and combine graph queries with vector search for hybrid applications. This flexibility comes with a steeper learning curve.
Integration and Ecosystem
- Chroma: Integrates with many AI tools and frameworks. Open source so it’s compatible with custom workflows and upcoming features like Hosted Chroma mean a growing ecosystem.
- Neo4j: Part of a mature graph database ecosystem, Neo4j integrates with many enterprise tools and frameworks. Relationship vector indexing (from version 5.18 onward) adds a new dimension to AI applications by combining graph insights with semantic similarity.
Ease of Use
- Chroma: Simple. The API and first-party SDKs reduce the learning curve, perfect for developers who want a plug-and-play vector search solution.
- Neo4j: Requires knowledge of graph databases and HNSW. While the query language is powerful, getting started with vector indexing might be complex for developers new to Neo4j or graph based approaches.
Cost
- Chroma: Open source, minimal operational costs if self hosted. Hosted Chroma (coming soon) may add costs but will simplify maintenance.
- Neo4j: Enterprise features including vector indexing may come with higher licensing and operational costs especially for large scale deployments. But the graph capabilities are worth the investment for complex applications.
Security
- Chroma: Basic security in the open source version. Upcoming managed offerings will add more.
- Neo4j: Advanced security options including encryption, authentication and role-based access control. Good for enterprise deployments.
When to use Chroma
Chroma is good for developers building AI applications that rely heavily on embedding based similarity search. It’s lightweight, developer friendly and open source so good for smaller projects or projects that are just managing and querying vector data with metadata. If you’re working with unstructured or semi structured data like text or images and you care more about simplicity and speed of integration over graph relationships Chroma is a good fit. Upcoming features like Hosted Chroma will make it even easier for teams that want a managed solution.
When to use Neo4j
Neo4j is good for scenarios where the relationships between data points are as important as the data itself. Its graph database and vector indexing capabilities make it great for use cases like recommendation systems, knowledge graphs or applications that blend semantic search with relational insights. If your application needs to combine structured data with graph queries or leverage advanced features like relationship vector indexing for hybrid AI workflows Neo4j is unmatched. But its more complex setup and tuning requirements are for projects with deep technical expertise.
Summary
Both Chroma and Neo4j are good for vector search. Chroma is good for simplicity and embedding centric workflows and Neo4j is good for graph modeling and semantic search. The choice should match your use case, data types and performance requirements. For embedding focused, AI native applications Chroma is the obvious choice. For graph heavy projects that need advanced relationship modeling and vector search Neo4j is a better fit. Think about your project goals and the type of data you’ll be working with to make the right decision.
While this article provides an overview of Chroma and Neo4j, it's key to evaluate these databases based on your specific use case. One tool that can assist in this process is VectorDBBench, an open-source benchmarking tool designed for comparing vector database performance. Ultimately, thorough benchmarking with specific datasets and query patterns will be essential in making an informed decision between these two powerful, yet distinct, 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 designed for users who require high-performance data storage and retrieval systems, particularly vector databases. This tool allows users to test and compare the performance of different vector database systems such as Milvus and Zilliz Cloud (the managed Milvus) using their own datasets and determine the most suitable one for their use cases. Using VectorDBBench, users can make informed decisions based on the actual vector database performance rather than relying on marketing claims or anecdotal evidence.
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 Chroma**? An Overview**
- 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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