RAG & Knowledge
pgvector
Vector similarity search inside Postgres - store embeddings next to your data
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View guide →Deep Lake — a multimodal datalake and vector store for AI, with version control and training streaming.
deeplake is an open-source rag & knowledge project by activeloopai (activeloopai/deeplake on GitHub). It has 9,250 GitHub stars and 726 forks, is released under the Apache-2.0 license, and was last updated on GitHub on 21 May 2026.
Decide whether you need the vector store for retrieval, dataset storage and streaming for training, or both — and whether you will use the hosted app or fully local storage.
Clone activeloopai/deeplake from GitHub (or use the install path in the run steps below) and skim its README for the parts relevant to your goal.
Install the deeplake Python package; register in the Deep Lake app only if you want the managed features.
Create a small dataset or vector store with a few samples, query it for similarity, and open it in the visualiser to check the data.
Store images, video, audio, text and embeddings together in a columnar format designed for ML, with per-column compression control.
Use Deep Lake as a vector store for retrieval applications, with LangChain and LlamaIndex integrations and image similarity search.
Native dataset versioning lets you track, diff and roll back dataset states like code.
Inspect datasets visually in the browser instead of guessing from file listings.
Stream data efficiently into deep-learning training loops, with tutorials for training models on stored datasets.
Positioned as an AI data runtime for agents, combining serverless Postgres with the multimodal datalake for retrieval and training.
Get the source from the official GitHub repository for deeplake.
git clone https://github.com/activeloopai/deeplake.git
cd deeplakeInstall the Python package exactly as the README's install section shows.
pip install deeplakeFollow the README's vector-store quickstart pattern to create a dataset and run a similarity query.
# In Python: import deeplake; ds = deeplake.empty('mem://example') # see README quickstart for the full exampleActiveloop (activeloopai), through the activeloopai/deeplake repository, with the hosted product at deeplake.ai.
Yes — the project is open source under the Apache-2.0 license as recorded for this listing. See the license terms for any conditions on redistribution.
The open-source library is free; the managed Deep Lake app and some hosted features require an Activeloop account, and the README directs you to register for full feature access.
A plain vector database stores embeddings for search. Deep Lake also stores the underlying multimodal data with versioning, visualisation and training streaming — closer to a data lake for AI than a search-only store.
Clone the repository (or use the documented installer), follow the install and configure steps on this page, and run the smallest real task first — create a small dataset or vector store with a few samples, query it for similarity, and open it in the visualiser to check the data.
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