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



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