pytorch-metric-learning
qdrant
| pytorch-metric-learning | qdrant | |
|---|---|---|
| 3 | 141 | |
| 5,770 | 17,943 | |
| - | 3.4% | |
| 7.9 | 9.9 | |
| about 1 month ago | 6 days ago | |
| Python | Rust | |
| MIT License | 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.
pytorch-metric-learning
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Similarity Learning lacks a framework. So we built one
Not a full featured framework, but pytorch-metric-learning has data loaders, lossess, etc. to facilitate similarity learning: https://github.com/KevinMusgrave/pytorch-metric-learning
Disclaimer: I've made some contributions to it.
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[R][D] VAE Embedding Space - Can we force it to learn a metric?
You can use the triplet loss together with the Gaussian prior. It will be zero centered though and the clusters are not as separated when you use the triplet loss only.There are many alternative to the triplet loss, in case it needs to be a metric: https://github.com/KevinMusgrave/pytorch-metric-learning
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[D] Similar Image Retrieval
This repo provides the tools and examples needed to build such a model: https://github.com/KevinMusgrave/pytorch-metric-learning
qdrant
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Hindi-Language AI Chatbot for Enterprises Using Qdrant, MLFlow, and LangChain
Great. Now that we have the embeddings, we need to store them in a vector database. We will be using Qdrant for this purpose. Qdrant is an open-source vector database that allows you to store and query high-dimensional vectors. The easiest way to get started with the Qdrant database is using the docker.
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Boost Your Code's Efficiency: Introducing Semantic Cache with Qdrant
I took Qdrant for this project. The reason was that Qdrant stands for high-performance vector search, the best choice against use cases like finding similar function calls based on semantic similarity. Qdrant is not only powerful but also scalable to support a variety of advanced search features that are greatly useful to nuanced caching mechanisms like ours.
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Ask HN: Has Anyone Trained a personal LLM using their personal notes?
I'm currently looking to implement locally, using QDrant [1] for instance.
I'm just playing around, but it makes sense to have a runnable example for our users at work too :) [2].
[1]. https://qdrant.tech/
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Show HN: A fast HNSW implementation in Rust
Also compare with qdrant's Rust implementation; they tout their performance. https://github.com/qdrant/qdrant/tree/master/lib/segment/src...
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pgvecto.rs alternatives - qdrant and Weaviate
3 projects | 13 Mar 2024
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Open-source Rust-based RAG
There are much better known examples, such as https://qdrant.tech/ and https://github.com/lancedb/lancedb
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Qdrant 1.8.0 - Major Performance Enhancements
For more information, see our release notes. Qdrant is an open source project. We welcome your contributions; raise issues, or contribute via pull requests!
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Perform Image-Driven Reverse Image Search on E-Commerce Sites with ImageBind and Qdrant
Initialize the Qdrant Client with in-memory storage. The collection name will be “imagebind_data” and we will be using cosine distance.
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7 Vector Databases Every Developer Should Know!
Qdrant is an open-source vector search engine optimized for performance and flexibility. It supports both exact and approximate nearest neighbor search, providing a balance between accuracy and speed for various AI and ML applications.
- Ask HN: Who is hiring? (February 2024)
What are some alternatives?
dino - PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO
Milvus - A cloud-native vector database, storage for next generation AI applications
lightly - A python library for self-supervised learning on images.
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.
EasyOCR - Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.
faiss - A library for efficient similarity search and clustering of dense vectors.
byol-pytorch - Usable Implementation of "Bootstrap Your Own Latent" self-supervised learning, from Deepmind, in Pytorch
pgvector - Open-source vector similarity search for Postgres
autogluon - Fast and Accurate ML in 3 Lines of Code
Elasticsearch - Free and Open, Distributed, RESTful Search Engine
simsiam-cifar10 - Code to train the SimSiam model on cifar10 using PyTorch
towhee - Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.