similarity
qdrant
similarity | qdrant | |
---|---|---|
7 | 141 | |
996 | 18,036 | |
0.2% | 3.4% | |
6.5 | 9.9 | |
about 1 month ago | about 9 hours ago | |
Python | Rust | |
Apache 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.
similarity
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New free tool that uses fine-tuned BERT model to surface answers from research papers
Tensorflow Ranking and Tensorflow similarity (maybe relevant/irrelevant contrastive learning?) look like they could be useful.
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Non-Machine Learning Image Matching with a Vector DB
There is the metric learning problem to learn a hash for similarity https://github.com/tensorflow/similarity
That said, I don't see many good models available for download on tfhub or huggingface optimized for it, but you can always programmatically modify your images (if you truly mean identical to humans) - change white balance, crop, rotate, select adjacent frames from videos, etc. and optimize a network that is small enough for you to be satisfied and see if that works, as a possible alternative.
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Face Detection for 520 People
Metric learning has great implementations inside Tensorflow Similarity library: https://github.com/tensorflow/similarity Although the documentation is quite bad, but the jupyter notebooks are great.
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[P] TensorFlow Similarity 0.16 is out
Just a quick note that TensorFlow Similarity 0.16 is out -- this release beside adding the XMB loss is mostly focus on refactoring and optimizing the core components to ensure everything works smoothly and accurately. Details are in the changelog as usual and a simple pip install -U tensorflow_similarity should just work.
- Self-supervised learning added to TensorFlow Similarity
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[P] TensorFlow Similarity now self-supervised training
Very happy to announce that as part of the 0.15 release, TensorFlow Similarity now support self-supervised learning using STOA algorithms. To help you get started we included in the release a detailed getting started notebook that you can run in Colab. This notebook shows you how to use SimSiam self-supervised pre-training to almost double the accuracy compared to a model trained from scratch on CIFAR 10.
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TensorFlow Introduces ‘TensorFlow Similarity’, An Easy And Fast Python Package To Train Similarity Models Using TensorFlow
Github: https://github.com/tensorflow/similarity
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?
pytorch-metric-learning - The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.
Milvus - A cloud-native vector database, storage for next generation AI applications
pgANN - Fast Approximate Nearest Neighbor (ANN) searches with a PostgreSQL database.
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.
quaterion - Blazing fast framework for fine-tuning similarity learning models
faiss - A library for efficient similarity search and clustering of dense vectors.
ContraD - Code for the paper "Training GANs with Stronger Augmentations via Contrastive Discriminator" (ICLR 2021)
pgvector - Open-source vector similarity search for Postgres
Real-Time-Voice-Cloning - Clone a voice in 5 seconds to generate arbitrary speech in real-time
Elasticsearch - Free and Open, Distributed, RESTful Search Engine
sparse_dot_topn - Python package to accelerate the sparse matrix multiplication and top-n similarity selection
towhee - Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.