deeplake
Milvus
deeplake | Milvus | |
---|---|---|
13 | 105 | |
7,729 | 26,979 | |
1.1% | 2.5% | |
9.8 | 10.0 | |
about 11 hours ago | 4 days ago | |
Python | Go | |
Mozilla Public License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
deeplake
- FLaNK AI Weekly 25 March 2025
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Qdrant, the Vector Search Database, raised $28M in a Series A round
I think Activeloop(YC) is too: https://github.com/activeloopai/deeplake/
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[P] I built a Chatbot to talk with any Github Repo. 🪄
This repository contains two Python scripts that demonstrate how to create a chatbot using Streamlit, OpenAI GPT-3.5-turbo, and Activeloop's Deep Lake. The chatbot searches a dataset stored in Deep Lake to find relevant information and generates responses based on the user's input.
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[P] Chat With Any GitHub Repo - Code Understanding with @LangChainAI & @activeloopai
Deep Lake GitHub
- [P] A 'ChatGPT Interface' to Explore Your ML Datasets -> app.activeloop.ai
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Build ChatGPT for Financial Documents with LangChain + Deep Lake
As the world is increasingly generating vast amounts of financial data, the need for advanced tools to analyze and make sense of it has never been greater. This is where LangChain and Deep Lake come in, offering a powerful combination of technology to help build a question-answering tool based on financial data. After participating in a LangChain hackathon last week, I created a way to use Deep Lake, the data lake for deep learning (a package my team and I are building) with LangChain. I decided to put together a guide of sorts on how you can approach building your own question-answering tools with LangChain and Deep Lake as the data store.
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Launch HN: Activeloop (YC S18) – Data lake for deep learning
Re: HF - we know them and admire their work (primarily, until very recently, focused on NLP, while we focus mostly on CV). As mentioned in the post, a large part of Deep Lake, including the Python-based dataloader and dataset format, is open source as well - https://github.com/activeloopai/deeplake.
Likewise, we curate a list of large open source datasets here -> https://datasets.activeloop.ai/docs/ml/, but our main thing isn't aggregating datasets (focus for HF datasets), but rather providing people with a way to manage their data efficiently. That being said, all of the 125+ public datasets we have are available in seconds with one line of code. :)
We haven't benchmarked against HF datasets in a while, but Deep Lake's dataloader is much, much faster in third-party benchmarks (see this https://arxiv.org/pdf/2209.13705 and here for an older version, that was much slower than what we have now, see this: https://pasteboard.co/la3DmCUR2iFb.png). HF under the hood uses Git-LFS (to the best of my knowledge) and is not opinionated on formats, so LAION just dumps Parquet files on their storage.
While your setup would work for a few TBs, scaling to PB would be tricky including maintaining your own infrastructure. And yep, as you said NAS/NFS would neither be able to handle the scale (especially writes with 1k workers). I am also slightly curious about your use of mmap files with image/video compressed data (as zero-copy won’t happen) unless you decompress inside the GPU ;), but would love to learn more from you! Re: pricing thanks for the feedback, storage is one component and customly priced for PB-scale workloads.
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[P] Launching Deep Lake: the data lake for deep learning applications - https://activeloop.ai/
Deep Lake is fresh off the "press", so we would really appreciate your feedback here or in our community, a star on GitHub. If you're interested to learn more, you can read the Deep Lake academic paper or the whitepaper (that talks more about our vision!).
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Researchers at Activeloop AI Introduce ‘Deep Lake,’ an Open-Source Lakehouse for Deep Learning Applications
Continue reading | heck out the paper and github
GIthub: https://github.com/activeloopai/deeplake
Milvus
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Computer Vision Meetup: Develop a Legal Search Application from Scratch using Milvus and DSPy!
Legal practitioners often need to find specific cases and clauses across thousands of dense documents. While traditional keyword-based search techniques are useful, they fail to fully capture semantic content of queries and case files. Vector search engines and large language models provide an intriguing alternative. In this talk, I will show you how to build a legal search application using the DSPy framework and the Milvus vector search engine.
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Ask HN: Who is hiring? (April 2024)
Zilliz (zilliz.com) | Hybrid/ONSITE (SF, NYC) | Full-time
I am part of the hiring team for DevRel
NYC - https://boards.greenhouse.io/zilliz/jobs/4307910005
SF - https://boards.greenhouse.io/zilliz/jobs/4317590005
Zilliz is the company behind Milvus (https://github.com/milvus-io/milvus), the most starred vector database on GitHub. Milvus is a distributed vector database that shines in 1B+ vector use cases. Examples include autonomous driving, e-commerce, and drug discovery. (and, of course, RAG)
We are also hiring for other roles that I am not personally involved in the hiring process for such as product managers, software engineers, and recruiters.
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Unlock Advanced Search Capabilities with Milvus and Read about RAG
Get started with Milvus on GitHub.
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Milvus VS pgvecto.rs - a user suggested alternative
2 projects | 13 Mar 2024
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How to choose the right type of database
Milvus: An open-source vector database designed for AI and ML applications. It excels in handling large-scale vector similarity searches, making it suitable for recommendation systems, image and video retrieval, and natural language processing tasks.
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Simplifying the Milvus Selection Process
Selecting the right version of open-source Milvus is important to the success of any project leveraging vector search technology. With Milvus offering different versions of its vector database tailored to varying requirements, understanding the significance of selecting the correct version is key for achieving desired outcomes.
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7 Vector Databases Every Developer Should Know!
Milvus is an open-source vector database designed to handle large-scale similarity search and vector indexing. It supports multiple index types and offers highly efficient search capabilities, making it suitable for a wide range of AI and ML applications, including image and video recognition, natural language processing, and recommendation systems.
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Ask HN: Who is hiring? (February 2024)
Zilliz is hiring! We're looking for REMOTE and/or HYBRID roles in SF
Zilliz is the company behind Milvus (https://github.com/milvus-io/milvus), the most widely adopted vector database. Vector databases are a crucial piece of any technology stack looking to take advantage of unstructured data. Most recently and notably, Retrieval Augmented Generation (RAG). For RAG, vector databases like Milvus are used as the tool to inject customized data. In other words, vector databases make things like customized chat bots, personalized product recommendations, and more possible.
We are hiring for Developer Advocates, Senior+ Level Engineers and Product people, and Talent Acquisition. Check out all the roles here: https://zilliz.com/careers
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Qdrant, the Vector Search Database, raised $28M in a Series A round
Good on them, I know the crustaceans are out here happy about this raise for a Rust based Vector DB!
(now I'm gonna plug what I work on)
If you're interested in a more scalable vector database written in Go, check out Milvus (https://github.com/milvus-io/milvus)
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Open Source Advent Fun Wraps Up!
But before we do, I do want to say that 🤩 all these lovely Open-Source projects would love a little 🎉💕 love by getting a GitHub star ⭐ for their efforts. Including Open Source Milvus 🥰
What are some alternatives?
lance - Modern columnar data format for ML and LLMs implemented in Rust. Convert from parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow, with more integrations coming..
pgvector - Open-source vector similarity search for Postgres
auto-maple - Artificial intelligence software for MapleStory that uses various machine learning and computer vision techniques to navigate challenging in-game environments
faiss - A library for efficient similarity search and clustering of dense vectors.
tensorstore - Library for reading and writing large multi-dimensional arrays.
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
Weaviate - Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database.
barfi - Python Flow Based Programming environment that provides a graphical programming environment.
Elasticsearch - Free and Open, Distributed, RESTful Search Engine
super-image - Image super resolution models for PyTorch.
Face Recognition - The world's simplest facial recognition api for Python and the command line