> ## Documentation index
> Fetch the complete documentation index at: https://upstash.com/docs/llms.txt
> Search it with GET https://upstash.com/docs/search?q=<query>.
> Use these to discover all available pages before exploring further.

# Upstash Vector

> Serverless vector database for similarity search, RAG, and recommendations.

Upstash Vector is a serverless vector database for high-performance vector search. Use it for retrieval augmented generation (RAG), semantic search, recommendations, and classification without managing infrastructure.

## Start here

- [Getting Started](/vector/overall/getstarted): create an index and run your first query.
- [What is Upstash Vector?](/vector/overall/whatisvector)
- [Pricing & Limits](/vector/overall/pricing)

## Sections

- [Features](/vector/features/algorithm): algorithm, similarity functions, hybrid and sparse indexes, metadata, filtering, embedding models, and namespaces.
- [REST API](/vector/api/get-started)
- [SDKs](/vector/sdks/ts/getting-started): TypeScript, Python, PHP, Go, and Semantic Cache.
- [Integrations](/vector/integrations/ai-sdk): Vercel AI SDK, LangChain, LlamaIndex, and more.
- [Tutorials](/vector/tutorials/semantic_search)
- [FAQ](/vector/help/faq)

## For AI agents

- [/docs/vector/llms.txt](https://upstash.com/docs/vector/llms.txt) lists every Vector page.
- Append `.md` to any page URL for its Markdown, for example [/docs/vector/overall/getstarted.md](https://upstash.com/docs/vector/overall/getstarted.md).