# RAG Tutorials

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

Deep Lake can be used as a Vector Store 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](/examples/rag/managed-database.md).&#x20;

### RAG Tutorials:

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[Vector Store Basics](/examples/rag/tutorials/vector-store-basics.md)
{% endcontent-ref %}

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[LangChain Integration](/examples/rag/langchain-integration.md)
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[LlamaIndex Integration](/examples/rag/llamaindex-integration.md)
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{% content-ref url="/pages/eQzEpavdtAhnl8hoVJWh" %}
[Vector Search Options](/examples/rag/tutorials/vector-search-options.md)
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{% content-ref url="/pages/M1sEhXV3Ro44vZFR2G2I" %}
[Image Similarity Search](/examples/rag/tutorials/image-similarity-search.md)
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{% content-ref url="/pages/K9qimPNhP6GEAAQ41OSr" %}
[Managed Tensor Database](/examples/rag/managed-database.md)
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{% content-ref url="/pages/sxfUxu5goK0DO1jF9FDD" %}
[Improving Search Accuracy using Deep Memory](/examples/rag/tutorials/deepmemory.md)
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