RAG・知識ベース
pgvector
Vector similarity search inside Postgres - store embeddings next to your data
★ 23.3k スターNOASSERTION
ガイドを見る →deeplakeは、activeloopaiが公開しているオープンソースのRAG・知識ベースプロジェクトです（GitHub: activeloopai/deeplake）。GitHubスターは9,250、フォークは726で、Apache-2.0ライセンスの下で公開されています。GitHubでの最終更新は2026年5月21日です。
Deep Lake — a multimodal datalake and vector store for AI, with version control and training streaming.
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.
Vector similarity search inside Postgres - store embeddings next to your data
Lightweight, lightning-fast in-process vector database from Alibaba that embeds in your app.
The smallest vector index in the world: private RAG over everything on your laptop with 97% less storage.
Vespa — the AI search platform: vector, text and tensor search at production scale.
A blazing-fast vector index in Rust, built on TurboQuant - with Python bindings
DeepSearcher — open-source deep research over your private data, with reasoning models and Milvus retrieval.
LibreAIカタログに掲載されたRAG・知識ベースプロジェクトの中で最もスターの多いもの — deeplakeが合わなかった場合に検討したい、現実的な候補リストです。
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Lightweight OCR toolkit turning PDFs and images into structured, LLM-ready data in 100+ languages
MinerU — turns PDFs and Office documents into LLM-ready Markdown and JSON for agents and RAG pipelines, Apache-2.0 plus terms
Stop renting your intelligence. Own it with AnythingLLM. Everything you need for a powerful local-first agent experience
カタログの中でdeeplakeとスター数が近いRAG・知識ベースプロジェクト — 前後の棚もそのまま見て回れます。
PaperQA2 — high-accuracy RAG for answering questions from scientific papers, with citations you can check.
Utopia — an open-source enterprise world model: a bitemporal knowledge substrate that learns from your documents and gives agents a governed, auditable memory
Polyglot document intelligence in Rust — extract text, tables and structured data from 106 formats.
DeepSearcher — open-source deep research over your private data, with reasoning models and Milvus retrieval.
Incremental engine that keeps RAG indexes and agent context fresh by processing only the delta.
Unstract — LLM-driven extraction of unstructured data, built for API deployments and ETL pipeline workflows.