Vector Data

Store vector embeddings directly in CrateDB using your preferred machine learning or embedding models. Keep vectors and metadata perfectly aligned within the same row, without additional services, ETL jobs, or complex sync workflows. This unified approach removes the operational burden of managing separate databases for semantics, text, search, and analytics.

CrateDB supports high-dimensional float vector types (max 2,048 dimensions) and stores vectors alongside time series, JSON, geospatial, and full-text data for truly unified analytics. Integrate seamlessly with Python pipelines, LangChain, LlamaIndex, and HuggingFace embedding models using standard PostgreSQL or HTTP interfaces.

CrateDB supports high-performance vector similarity search, enabling:

• Fast nearest-neighbor lookups
• Efficient exploration of high-dimensional datasets
• Real-time matching for AI-powered applications

Whether it’s product search, semantic search, anomaly detection, or RAG workloads, CrateDB makes vector search immediate and scalable. Vector queries return results in milliseconds, even across millions of rows, thanks to CrateDB’s distributed execution engine.

SELECT id, title
FROM documents
ORDER BY KNN_MATCH(embedding, $query_vector)
LIMIT 5;

CrateDB natively combines:

• Vector similarity (KNN_MATCH)
• Full-text search (MATCH)
• Keyword/structured filters
• External semantic enrichment via your embedding models

This hybrid retrieval approach improves precision and relevance in every search workflow, by querying vectors, text, and structured attributes in a single SQL statement.

Hybrid queries make CrateDB ideal for semantic search, product search, knowledge retrieval, and multi-attribute ranking scenarios.

SELECT id, title
FROM documents
WHERE MATCH(text, 'energy storage')
ORDER BY KNN_MATCH(embedding, $query_vector)
LIMIT 10;

CrateDB integrates cleanly with LLMs, vectorizers, ML pipelines, and AI platforms. Use it as:

• A vector store
• A real-time feature store
• A RAG backend
• A context store for conversational memory

All while benefiting from CrateDB’s distributed SQL engine, automatic indexing, and interoperability.

CrateDB fits naturally into Retrieval-Augmented Generation (RAG) pipelines by storing embeddings, running semantic search, retrieving context, and feeding external LLMs such as OpenAI or Llama. No proprietary SDKs required, just SQL.

Thanks to its shared-nothing, distributed architecture, CrateDB scales with your dataset and vector dimensionality, without requiring hand-tuned infrastructure or specialized vector-only systems. Eliminate the cost and complexity of maintaining a separate vector database and focus on building your AI applications.

Automatic rebalancing ensures vector and non-vector data scale uniformly across cluster nodes. Indexing occurs within seconds, enabling near real-time use cases.

With native vector types, SQL queries, and multi-model functionality in one platform, teams avoid the integration, maintenance, and learning curve associated with external vector solutions. Development becomes faster, operations become simpler, and maintenance costs decrease.

No need to manage separate vector stores, sync pipelines, or duplicated data models, CrateDB keeps everything consistent by design. This consolidation significantly reduces total cost of ownership (TCO).