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# Deep Lake

This page covers how to use the Deep Lake ecosystem within LangChain.

## Why Deep Lake?

- More than just a (multi-modal) vector store. You can later use the dataset to fine-tune your own LLM models.

- Not only stores embeddings, but also the original data with automatic version control.

- Truly serverless. Doesn’t require another service and can be used with major cloud providers (AWS S3, GCS, etc.)

## More Resources

1. Ultimate Guide to LangChain & Deep Lake: Build ChatGPT to Answer Questions on Your Financial Data

2. Twitter the-algorithm codebase analysis with Deep Lake

3. Here is whitepaper and academic paper for Deep Lake

4. Here is a set of additional resources available for review: Deep Lake, Getting Started and Tutorials

## Installation and Setup

- Install the Python package with `pip install deeplake`

## Wrappers

### VectorStore

There exists a wrapper around Deep Lake, a data lake for Deep Learning applications, allowing you to use it as a vector store (for now), whether for semantic search or example selection.

To import this vectorstore:

    from langchain.vectorstores import DeepLake

For a more detailed walkthrough of the Deep Lake wrapper, see this notebook