LangChain + Deep Lake = 🤍 Start building! Building with LangChain? Start for free with our Vector DB!

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Activeloop Database for AI structuring the computer vision data using a simple dataset format for AI based on tensors for easier dataset streaming, querying, version control and visualization
Deep Learning? Use Deep LakeDeep Learning? Use Deep Lake

Vector Database for
  • Videos
  • Audio
  • Text
  • Images
  • PDFs
  • All AI Data

Store anything. Deploy anywhere. Fine-tune your own LLM models.

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  • Loved by devs, trusted by enterprises

    • Trended #1 in Python

      Trended #1 in Python

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    90+

    Contributors

    • +31%

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    1.2K+

    Community members

WHAT IS DEEP LAKE?

Yet another vector database?

Kinda. But so much more.

Generative AI may be new, but we've been building for this day for the past 5 years. Deep Lake thus combines the power of both Data Lakes and Vector Databases to build and fine-tune enterprise-grade, LLM-based solutions, and iteratively improve them over time.

> pip install deeplakegihubDive into Deep Lake
    • Serverless Tensor Query Engine

      Serverless Tensor Query Engine

      Vector search does not resolve retrieval. To solve it, you need a serverless query for multi-modal data, including embeddings or metadata. Filter, search, & more from the cloud or your laptop
    • Visualize & Version Data

      Visualize & Version Data

      Visualize and understand your data, as well as the embeddings. Track & compare versions over time to improve your data & your model
    • Stream Data to Training

      Stream Data to Training

      Competitive businesses are not built on OpenAI APIs. Fine-tune your LLMs on your data. Efficiently stream data from remote storage to the GPUs as models are trained

How Deep Lake fits into your Large Language Model-based stack?

deep lake architecture

How Deep Lake compares to Pinecone, ChromaDB, or Weaviate?

FEATURES

checkboxMulti-modal

checkboxFine-tuning

checkboxDeployment

checkboxVisualization

checkboxVersion control

checkboxOpen-source

Deep Lake

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Serverless

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Pinecone

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Managed Service

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Chroma

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Self-Hosted

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Weaviate

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Managed
Self-Hosted

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Loved by 100+ data teams and counting

“As the datasets enlarge and become multi-modal, next-gen solutions built specifically to address those use cases, like Deep Lake, will help AI teams deliver models to production faster, and more efficiently.”

Arijit Bandyopadhyay

CTO – Enterprise Analytics & AI, Head of Strategy – Enterprise & Cloud Group

Intel

“Downloading data every time you run an experiment is bound to break you and the training process. Deep Lake's on-the-fly streaming was an excellent choice for us: it was really easy to set up, and it started to bring the value from day one.”

Arsenii Gorin

Lead ML Engineer

Ubenwa AI

“Just needed to deploy a solution that works - and Activeloop made it simpler to ship our AI app quickly!”

Margaux Masson-Forsythe

Director, Machine Learning

SDSC

“They started out with a vector store integration, so it's flown under the radar, but... @activeloopai's Deep Lake is an intriguing fully-fledged serverless data lake that supports attribute based filtering, multiple distance functions, MMR search.”

Harrison Chase

CEO & Founder

LangChainAI

“Awesome!”

Louis Bouchard

Researcher

MILA Quebec

Incredible tool! One of our researchers at National Center for Supercomputing Applications had great success using Deep Lake for multimodal pipelining for self supervised video embeddings. We are now trying to move away from HDF5's as they are too slow, annoying to work with, and just don't have the features we need to pipe efficiently into PyTorch. Exciting!

Priyam Mazumdar

Researcher

NCSA

“A 100x speedup of Tensor Query execution for semantic search and question answering on legal documents. Deep Lake’s minimalistic architecture provided flexibility and light touch installation for our customers without introducing complexity such as adding a microservice. With Deep Lake’s ultrafast data loader, PyTorch was able to natively access the data and distribute it automatically across MPI workers, allowing for highly parallel embedding search.”

Gevorg Karapetyan

CTO

Zero

“Davit & team are super responsive & hands-on with onboarding. Highly recommend the tool for managing large & complex datasets.”

Tony Francis

Co-Founder

Dream 3D

“New models deployed in a matter of days instead of weeks.”

Jennifer Hobbs

Director, Machine Learning

IntelinAir

Your ML projects will never be dead in the water(If you use Deep Lake)

1import deeplake
2from PIL import Image
3
4ds = deeplake.load('hub://activeloop/mnist_train')
5
6# Display an image
7Image.fromarray(ds.images[0].numpy())

Visualize, query, version, & stream datasets

Deep Lake datasets are visualized right in your browser or Jupyter notebook. Instantly retrieve different versions of your data, materialize new datasets via queries on the fly, and stream them to PyTorch or TensorFlow.

COCO dataset visualization on Activeloop Platform
  • Rapidly visualize different versions of your data
  • Understand your data and improve its quality
  • Query, train, & edit datasets with data lineage
  • Evaluate model performance

Deep Lake Integrations

langchain

LangChain

Deep Lake acts as a VectorStore for LangChain. From chatting with your docs to code understanding, we've got you covered.

Get Started
llama

LlamaIndex

Deep Lake is integrated into Llamaverse in two main ways: as a Vector Index and as a loader.

Get Started
gpt

OpenAI

Store the embeddings you compute with OpenAI APIs with Deep Lake. Deep Lake also integrates with GPT-4 to provide the Text to Tensor Query Language feature.

Get Started
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  • Deep Lake is revolutionizing Deep Learning. Dive into it.

    Drive revenue growth by shipping AI products faster, saving money by saving on GPUs, increasing data scientists’ focus on core business problems, & eliminating failed ML project risk due to the lack of a solid data foundation.

  • > pip install deeplake
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    Deep Lake. Vector Database for All AI Data.

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