Dense, sparse, and hybrid indexes
Choose dense indexes for semantic search, sparse indexes for full-text or keyword search, or hybrid indexes when both search approaches are needed.
Upstash Vector
Freemium
Upstash Vector is a serverless vector database for storing and querying embeddings and metadata in AI and machine-learning applications. It supports dense, sparse, and hybrid indexes through a REST API and SDKs for Python, TypeScript, Go, and PHP.
什么是 Upstash Vector？
Upstash Vector is a serverless vector database for similarity search over embeddings and metadata. It lets developers create an index, insert vectors, and query for similar results through a REST API or SDKs.
Indexes can be dense, sparse, or hybrid: dense is suited to semantic search, sparse to full-text or keyword search, and hybrid to a combination of both. The service also provides metadata filtering, namespaces, live index updates, console-based usage charts, and a query interface. It is designed for AI and machine-learning applications that need retrieval from application data.
Upstash Vector 能做什么？
Vector and metadata operations
Insert vectors with an ID and metadata, then query by vector similarity. Query responses can include stored metadata, and queries support metadata filtering.
REST API and SDK access
Access indexes through REST or the documented Python, TypeScript/JavaScript, Go, and PHP clients. Connection details and sample code are available from the console.
Namespaces and live updates
The Vector service includes namespaces and live index updates, allowing applications to organize vector data and update indexes as application data changes.
Operational visibility in the console
The Upstash console reports daily requests, throughput, mean and P99 latency, vector count, and data size, and provides a simple interface for querying an index.
Usage-based and fixed plans
Available plans include Free, Pay as You Go, Fixed, Pro, and Enterprise. The documented free plan is intended for small projects, while other plans address variable traffic, predictable loads, or larger deployments.
使用场景
“Semantic retrieval for AI applications”
Store embedding vectors for application content and query them with a vector to retrieve the most similar records for an AI or machine-learning workflow.
“Keyword-oriented document search”
Use a sparse index for full-text or keyword search when exact terms and lexical matching are more important than embedding similarity.
“Combined semantic and keyword search”
Use a hybrid index when an application needs to combine semantic similarity with full-text search in the same retrieval workflow.
“Multi-tenant or segmented vector data”
Use namespaces to organize vectors within an index when an application needs separate logical data groups, such as different collections or application partitions.
常见问题
How do I create an Upstash Vector index?
Log in to the Upstash Console or create a free account, open the Vector tab, and select Create Index. Choose a name, region, index type, and— for dense or hybrid indexes—dimensions and a distance metric.
How do I access an index from an application?
Use the REST API or an SDK/client for Python, TypeScript/JavaScript, Go, or PHP. The console provides connection details and sample code.
What search types does Upstash Vector support?
Dense indexes are intended for semantic search, sparse indexes for full-text or keyword search, and hybrid indexes for combining the two.
Are updates immediately searchable?
Not necessarily. Upstash documents Vector as eventually consistent, so there may be a delay before newly inserted or updated vectors are ready for querying.
What is the default top-k limit for queries?
The Vector pricing FAQ states that 1,000 is the default limit for top-k.
快速信息
- Category
- Vector database
- Primary use
- Similarity search over vectors and metadata
- Index types
- Dense, sparse, and hybrid
- API and SDKs
- REST, Python, TypeScript/JavaScript, Go, and PHP
- Free plan
- 10,000 queries and 10,000 updates per day
- Consistency model
- Eventually consistent
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