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Activeloop 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.)

Activeloop Deep Lake supports SelfQuery Retrieval: Activeloop Deep Lake Self Query Retrieval

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. Code Understanding
  4. Here is whitepaper and academic paper for Deep Lake
  5. Here is a set of additional resources available for review: Deep Lake, Get started andΒ 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

API Reference:

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