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.)
Ultimate Guide to LangChain & Deep Lake: Build ChatGPT to Answer Questions on Your Financial Data
Here is whitepaper and academic paper for Deep Lake
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