# Vector Store Tutorials

## How to use Deep Lake as a Vector Store for LLM applications

Deep Lake can be used as a Vector Storing for storing embeddings and their metadata including text, jsons, images, audio, video, and more. Its serverless architecture can be self-hosted, and it is also available via fully managed service. 

### Vector Store Tutorials:

{% content-ref url="/pages/2D910tVqHjkGdkvb0RFs" %}
[Deep Lake Vector Store in LangChain](/v3.6.0/tutorials/vector-store/deep-lake-vector-store-in-langchain.md)
{% endcontent-ref %}

{% content-ref url="/pages/eQzEpavdtAhnl8hoVJWh" %}
[Vector Search Options](/v3.6.0/tutorials/vector-store/vector-search-options.md)
{% endcontent-ref %}


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# 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://docs-v3.activeloop.ai/v3.6.0/tutorials/vector-store.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.