# Vector Database (VDB) and Embedding

> Sisense employs a VDB to power its similarity service and facilitate RAG. Information undergoes an embedding process, where it is stored alongside its vector embedding representation. Sisense hosts the embedding model internally, eliminating the need for an external cloud service.

*Source: https://docs.sisense.com/main/SisenseLinux/vdb-embedding.htm*

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Last updated: June 11, 2026

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| [Tier](https://www.sisense.com/pricing/#pricing) | [Deployment](https://docs.sisense.com/main/SisenseLinux/introduction-to-sisense-cloud-managed-services.md#ComparisonofManagedCloudandSelfHosted) |
| Launch     Grow    Enterprise | Cloud     On-Prem |

Sisense employs a Vector Database (VDB) to power its similarity service and facilitate [Retrieval-Augmented Generation (RAG)](https://en.wikipedia.org/wiki/Retrieval-augmented_generation). Information undergoes an embedding process, where it is stored alongside its vector embedding representation. Notably, Sisense hosts the embedding model internally, eliminating the need for an external cloud service.

## Retrieval-Augmented Generation (RAG)

A RAG architecture is utilized to enhance the performance of natural language query translation. Sisense has uploaded a set of sample question-query pairs that function as [few-shot examples](https://www.promptingguide.ai/techniques/fewshot). This process does not use customer data; instead, a pre-curated synthetic sample is employed. Only natural language queries (NLQ) processed through the assistant or via API require the use of the Vector DB. Other prompt types and Generative AI features do not rely on vector retrieval.

## Smart Value Matching

This feature indexes column values to support fuzzy and natural language filtering. Administrators have control over which fields are included.

## Vector Database Data Storage

The vector database may store the following:

- Few-shot question-query examples: Customer data is never utilized for this purpose.
- Column values from smart value matching enabled columns: Data designers explicitly select which columns will be supported.

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| --- | --- | --- |
| Feature | Few Shot Examples | Selected Column Values |
| Explanations | - | - |
| Forecast | - | - |
| Trend | - | - |
| Exploration Paths | - | - |
| Simply Ask (Legacy NLQ) | - | - |
| Assistant (NLQ only) | ✅ | - |
| Narrative | - | - |
| Semantic Enrichment | - | - |
| Smart value matching | - | ✅ |