# Memory Management

In the **Vector Memory Management** interface, you can create, edit, and test your own vector memory sets. Each "Memory Set" contains embedded text data as numerical vectors, which allows for fast and precise searches based on meaning. You can create new memory sets, add memory items, and test your memory search queries. The interface also allows you to manage memory sets dedicated for production or testing purposes, ensuring efficient retrieval of stored data.

<figure><img src="/files/2TosZ2rhG7UvjJbR3wVn" alt=""><figcaption></figcaption></figure>

### Create a memory set

In the **Create New Memory Set** dialog, you can create a new vector memory set by selecting an embedding provider. This determines how your text data will be converted into vectors for efficient searching. After choosing an embedding provider, you must name the memory set. Once the provider is selected, it cannot be changed later, and the size of the embedding impacts the amount of information that can be encoded. When ready, click "Create New Memory" to generate your new memory set, which will be used for storing and retrieving data based on semantic meaning.

The following embedding providers are available:\
\- OpenAI (Small). Vector size: 1536. Max symbols in Embedded Description field: 28668. \
\- OpenAI (Large). Vector size: 3072. Max symbols in Embedded Description field: 28868.\
\- ThenIper GTE (Large). Vector size: 1024. Max symbols in Embedded Descripton field: 1792.

<figure><img src="/files/yQyKmbfTdXwBDqeWJRUW" alt=""><figcaption></figcaption></figure>

***

We’d love to hear from you! Reach out to **<documentation@integrail.ai>**


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://help.integrail.ai/agent-studio/memory-management.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.