Overview
This article is the second part of a series on vector search using ClickHouse, focusing on practical implementations and use cases. It covers the setup of vector search, including data preparation, storage, querying, and advanced features like Approximate Nearest Neighbour (ANN) and User Defined Functions (UDFs).
What You'll Learn
1
How to set up a ClickHouse cluster for vector search
2
How to generate embeddings using the CLIP model
3
How to perform vector search with SQL queries in ClickHouse
4
When to use Approximate Nearest Neighbour (ANN) for vector search
Prerequisites & Requirements
- Understanding of vector embeddings and their applications
- Familiarity with ClickHouse and SQL
Key Questions Answered
When should I use ClickHouse for vector search?
ClickHouse is suitable for vector search when you need to combine vector matching with metadata filtering, handle large datasets, and leverage SQL capabilities. It excels in scenarios requiring fast parallelized exact matching and when existing data is already in ClickHouse.
How do I generate embeddings using the CLIP model?
Embeddings can be generated using the CLIP model by passing images or text through the model, which outputs a vector representation. This process requires a setup with the necessary libraries and a compatible environment, such as a CUDA-enabled GPU for efficiency.
What is the schema for storing vectors in ClickHouse?
The schema for storing vectors in ClickHouse includes fields for metadata and embeddings, with embeddings stored as Array(Float32). The schema allows for efficient querying and filtering based on various metadata attributes.
What are the benefits of using SQL with vector search?
Using SQL with vector search allows for powerful filtering and aggregation capabilities on metadata, enabling complex queries that combine traditional SQL operations with vector distance calculations, thus enhancing the analytical capabilities of the dataset.
Key Statistics & Figures
Number of cores in ClickHouse Cloud cluster
60 cores
This setup is used for the examples in the article, showcasing the processing capabilities of ClickHouse.
Total rows in the LAION dataset used
2.2 billion rows
This dataset consists of embeddings for images and their associated captions, demonstrating the scale at which ClickHouse can operate.
Disk space consumed by final Parquet files
5.9TB
This is the total size of the Parquet dataset generated from the LAION dataset for use in ClickHouse.
Technologies & Tools
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Database
Clickhouse
Used for storing and querying vector embeddings alongside metadata.
Machine Learning Model
Clip
Used for generating embeddings from images and text.
Programming Language
Python
Used for scripting the embedding generation and querying processes.
Key Actionable Insights
1Utilize ClickHouse for real-time analytics on large vector datasets to leverage its performance capabilities.ClickHouse's architecture allows for efficient processing of large datasets, making it ideal for applications that require real-time insights from vector data.
2Implement Approximate Nearest Neighbour (ANN) indexing to improve search performance on high-dimensional data.ANN can significantly reduce search times for large datasets by providing faster, albeit approximate, results, which is beneficial for applications needing quick responses.
3Combine vector search with SQL filtering to refine search results based on metadata attributes.This approach enhances the relevance of search results by allowing users to apply additional criteria, such as image dimensions or copyright status, thus improving the overall user experience.
Common Pitfalls
1
Overestimating the capabilities of ClickHouse for small datasets.
ClickHouse is designed for large-scale data processing, and using it for small datasets may lead to unnecessary complexity and overhead.
2
Neglecting to optimize the schema for vector storage.
Improper schema design can lead to inefficient queries and poor performance, especially with high-dimensional data.
Related Concepts
Vector Embeddings
Approximate Nearest Neighbour (ann)
User Defined Functions (udfs)
Data Compression Techniques