Master Upstash Vector with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Create a free Upstash account at console.upstash.com and provision a Vector index. Choose your embedding dimension (e.g., 1536 for OpenAI, 768 for BGE) and distance metric (cosine, euclidean, or dot product). Install the SDK: pip install upstash
vector (Python) or npm install @upstash/vector (TypeScript). Upsert vectors with metadata using the SDK or REST API, then run similarity queries. Optionally enable built
in embedding generation to skip managing a separate embedding service.
💡 Quick Start: Follow these 3 steps in order to get up and running with Upstash Vector quickly.
Explore the key features that make Upstash Vector powerful for ai memory & search workflows.
Stateless HTTP-based API that requires no persistent connections or native drivers. Works from any environment that can make HTTP requests, including edge runtimes where TCP-based database clients fail.
A Cloudflare Worker serving a RAG chatbot queries Upstash Vector on every user message without needing connection pools or WebSocket workarounds.
Send raw text instead of pre-computed vectors. Upstash generates embeddings server-side using models like BGE-base or multilingual E5, removing the need for a separate embedding pipeline.
A small development team building a docs search tool skips setting up an OpenAI embedding endpoint and lets Upstash handle text-to-vector conversion directly.
Attach JSON metadata to vectors and filter search results using equality, range, IN, and NOT IN operators. Combine semantic similarity with structured attribute filters in a single query.
An e-commerce recommendation engine searches for semantically similar products while filtering by price range, category, and availability status.
Isolate vectors into namespaces within a single index. Each namespace operates independently for queries and upserts, enabling tenant separation without provisioning separate indexes.
A SaaS platform stores each customer's document embeddings in separate namespaces, ensuring data isolation while sharing one Upstash Vector index.
No minimum fees, no idle costs. Free tier covers 10K queries/day and 10K vectors. Pay-as-you-go charges $0.40 per 100K requests. A price cap guarantees you never exceed the fixed plan cost.
An AI agent that handles sporadic queries pays near-zero during quiet periods and scales costs linearly during burst activity without capacity planning.
Native connectors for LangChain, LlamaIndex, and Vercel AI SDK. The @upstash/rag-chat package combines vector search, LLM calls, and conversation history into a single high-level API.
A developer builds a conversational RAG agent using LangChain with Upstash Vector as the retriever, adding persistent chat history through rag-chat in under 50 lines of code.
Now that you know how to use Upstash Vector, it's time to put this knowledge into practice.
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Tutorial updated March 2026