Using ClickHouse® for Vector Search and AI Workloads
Artificial Intelligence (AI) applications are changing how organizations search, analyze, and interact with data. Instead of relying only on exact keyword matches, modern AI systems understand the meaning behind text, images, audio, and other content using vector embeddings and semantic search.
Traditionally, implementing vector search required deploying a dedicated vector database alongside your operational systems. While effective, this approach increases infrastructure complexity, data synchronization overhead, and operational costs.
Recent versions of ClickHouse® introduce native support for vector similarity search, allowing you to store embeddings alongside structured data and perform high-performance semantic searches on millions or even billions of vectors. This enables AI applications such as Retrieval-Augmented Generation (RAG), recommendation engines, semantic document search, image similarity search, and intelligent chatbots—all within the same analytical database.
In this article, we'll explore what vector search is, how ClickHouse stores vector embeddings, and how you can build scalable AI workloads using ClickHouse.
What Is Vector Search?
Traditional search engines rely on keyword matching.
Suppose your database contains the following product description: ""
"Lightweight running shoes for marathon training."
A user searches for:
"Best sneakers for long-distance running"
Although both sentences describe the same concept, a keyword search may miss the result because the exact words don't match.
Vector search solves this problem.
Instead of comparing words, it compares semantic meaning.
Both pieces of text are converted into numerical vectors by an embedding model.
"Lightweight running shoes for marathon training."
↓
[0.21, -0.67, 0.44, ...]
"Best sneakers for long-distance running"
↓
[0.18, -0.71, 0.40, ...]
Since these vectors are close together in vector space, ClickHouse recognizes that they represent similar concepts and returns the relevant product.
What Are Vector Embeddings?
A vector embedding is a numerical representation of the meaning of an object.
Embeddings can represent:
- Text
- Images
- Audio
- Videos
- Documents
- Products
Example:
"ClickHouse is a fast analytics database"
↓
[0.13, -0.52, 0.78, 0.41, ...]
Modern embedding models commonly generate vectors with dimensions such as:
- 384
- 768
- 1024
- 1536
- 3072
Each value captures part of the semantic meaning learned during model training. Similar objects generate vectors that are positioned close together within the embedding space.
Why AI Applications Need Vector Search
Modern AI systems rarely depend solely on keyword matching. Instead, they retrieve information based on semantic similarity.
Common applications include:
| Use Case | Example |
|---|---|
| Semantic Search | Find documents by meaning |
| Recommendation Systems | Recommend similar products or movies |
| Retrieval-Augmented Generation (RAG) | Retrieve context for LLMs |
| Image Search | Find visually similar images |
| AI Chatbots | Retrieve relevant knowledge before responding |
| Fraud Detection | Detect similar behavioral patterns |
Without vector search, these systems become slower and less accurate.
Why Use ClickHouse for Vector Search?
Rather than maintaining separate databases for analytics and AI workloads, ClickHouse allows both to coexist within a single platform.
Key benefits include:
- Store structured data and embeddings together
- High-performance analytical SQL
- Excellent compression
- Horizontal scalability
- Real-time ingestion
- Familiar SQL interface
- Support for billions of vectors
This unified architecture eliminates synchronization challenges between analytical databases and dedicated vector stores.
How ClickHouse Stores Vector Embeddings
Embeddings are typically stored using the Array(Float32) data type.
CREATE TABLE documents
(
id UInt64,
title String,
content String,
embedding Array(Float32)
)
ENGINE = MergeTree
ORDER BY id;
Example record:
| id | title | embedding |
|---|---|---|
| 1 | AI Basics | [0.24, -0.11, 0.56, ...] |
Each embedding can contain hundreds or even thousands of floating-point values.
How Vector Similarity Search Works
A typical workflow looks like this:
User Query
│
▼
Embedding Model
│
▼
Query Vector
│
▼
ClickHouse Vector Search
│
▼
Most Similar Records
For example:
A user asks:
"How does ClickHouse replication work?"
The embedding model converts the question into a vector.
ClickHouse compares that vector with stored document embeddings and returns the most semantically relevant results.
Distance Metrics Used in Vector Search
Vector similarity is calculated using mathematical distance metrics.
Cosine Similarity
Measures the angle between vectors.
Best suited for:
- Text embeddings
- Semantic search
- LLM applications
Euclidean Distance (L2)
Measures straight-line distance between vectors.
Commonly used for:
- Numerical features
- Image embeddings
Dot Product
Measures similarity based on vector magnitude and direction.
Frequently used by transformer-based embedding models.
Approximate Nearest Neighbor (ANN) Search
Comparing a query against every stored vector becomes increasingly expensive as datasets grow.
Approximate Nearest Neighbor (ANN) algorithms dramatically improve performance by searching only the most promising candidates instead of performing exhaustive comparisons.
Benefits include:
- Millisecond-level search latency
- High recall
- Efficient CPU utilization
- Scalability to billions of vectors
ClickHouse supports ANN indexing to accelerate similarity searches while maintaining excellent accuracy.
Example Workflow
Consider an AI-powered documentation assistant.
Documents
│
▼
Embedding Model
│
▼
ClickHouse
(Store Documents + Embeddings)
│
▼
User Query
│
▼
Embedding Model
│
▼
Vector Search
│
▼
Relevant Documents
│
▼
Large Language Model
│
▼
Final Answer
This architecture forms the foundation of Retrieval-Augmented Generation (RAG).
Example Table
CREATE TABLE knowledge_base
(
id UInt64,
title String,
content String,
embedding Array(Float32)
)
ENGINE = MergeTree
ORDER BY id;
After generating embeddings using models such as OpenAI Embeddings, Sentence Transformers, or other embedding models, store the vectors inside the embedding column for semantic retrieval.
AI Workloads That Benefit from ClickHouse
Semantic Search
Retrieve documents based on meaning instead of exact keywords.
Recommendation Systems
Recommend products, movies, music, or articles using vector similarity.
RAG Pipelines
Retrieve relevant context before sending prompts to a Large Language Model.
AI Chatbots
Fetch accurate knowledge base articles before generating responses.
Image Similarity Search
Search for visually similar images using image embeddings.
Log Analysis
Perform semantic searches across operational logs instead of exact-text matching.
Performance Best Practices
For optimal performance:
- Store embeddings as
Float32 - Maintain consistent vector dimensions
- Use ANN indexes for large datasets
- Partition data appropriately
- Separate metadata from embedding storage
- Batch insert vectors
- Eliminate duplicate embeddings
- Continuously monitor query latency
Advantages of Using ClickHouse for AI
| Feature | Benefit |
|---|---|
| SQL Interface | Easy integration with existing applications |
| High Compression | Lower storage costs |
| Fast Analytics | Combine AI retrieval with analytical queries |
| Real-Time Ingestion | Continuously ingest new embeddings |
| Horizontal Scaling | Handle billions of vectors |
| Unified Platform | Analytics and AI in one database |
When Should You Use ClickHouse for Vector Search?
ClickHouse is an excellent choice when you need to:
- Combine analytics and AI workloads in a single platform
- Build semantic search applications
- Implement Retrieval-Augmented Generation (RAG)
- Store millions or billions of embeddings
- Analyze structured data alongside vector data
- Support real-time ingestion with fast similarity searches
If your application requires both analytical processing and semantic retrieval, ClickHouse provides a unified architecture that reduces operational complexity while delivering exceptional performance.
Conclusion
Vector search enables applications to understand the meaning behind data instead of relying solely on exact keyword matches. By storing vector embeddings alongside structured data, ClickHouse makes it possible to build semantic search engines, recommendation systems, AI chatbots, image similarity search, and Retrieval-Augmented Generation (RAG) pipelines without introducing a separate vector database.
With efficient storage, SQL-based querying, real-time ingestion, and support for Approximate Nearest Neighbor (ANN) indexing, ClickHouse provides a scalable foundation for AI-powered applications that combine large-scale analytics with semantic retrieval. As AI workloads continue to grow, ClickHouse offers a practical and high-performance platform for unifying analytical and vector search capabilities within a single database.
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