# Use Cases

> Tutorials for hybrid search, adaptive retrieval, image search, multi-tenancy, sparse vectors, and RAG QA.

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LLMS index: [llms.txt](/llms.txt)

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Section pages:

- [Hybrid Search](/pgvecto-rs/use-case/hybrid-search/): We will show you how to use pgvecto.rs to implement hybrid search in this section.
- [Adaptive retrieval and shortening embeddings](/pgvecto-rs/use-case/adaptive-retrieval/): OpenAI latest embedding models support shortening embeddings, where developers could simply “remove some numbers from the end of a sequence” and still maintain
- [Image Search](/pgvecto-rs/use-case/image-search/): Image intelligent search, also known as content-based image retrieval, goes beyond traditional keyword-based search and enables users to search for images based
- [Multi Tenancy](/pgvecto-rs/use-case/multi-tenancy/): Multi-tenancy is essential for SaaS applications, allowing a single instance to serve multiple users or groups while ensuring data privacy and security. This bl
- [Sparse Vector Search](/pgvecto-rs/use-case/sparse-vector/): In the past, hybrid search combined two search methods: traditional keyword-based search and vector-based similarity search. However, sparse vector can act as a
- [Generative Question-Answering](/pgvecto-rs/use-case/question-answering/): Vector search can also be used to provide context to generative models such as OpenAI's GPT series, improving the quality of the outputs.