Qdrant
Milvus
Weaviate
pgvecto.rs
Vespa.ai
txtai
Zilliz
ElasticSearch
Django
Laravel
Ruby on Rails
Flask
ASP.NET
Node.js
ExpressJS
CodeIgniter
Qdrant is a leading open-source high-performance Vector Database written in Rust with extended metadata filtering support and advanced features. It deploys as an API service providing a search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications. Powering vector similarity search solutions of any scale due to a flexible architecture and low-level optimization. Qdrant is trusted and high-rated by Machine Learning and Data Science teams of top-tier companies worldwide.
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Advanced Features, Performance, Scalability, Developer Experience, and Resources Saving.
Qdrant's answer
Highest performance https://qdrant.tech/benchmarks/, scalability and ease of use.
Qdrant's answer
Qdrant is written completely in Rust. SDKs available for all popular languages Python, Go, Rust, Java, .NET, etc.
Based on our record, Qdrant should be more popular than Django. It has been mentiond 40 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Vector Databases: Qdrant for efficient data storage and retrieval. - Source: dev.to / 9 days ago
AgentCloud uses Qdrant as the vector store to efficiently store and manage large sets of vector embeddings. For a given user query the RAG application fetches relevant documents from vector store by analyzing how similar their vector representation is compared to the query vector. - Source: dev.to / about 1 month ago
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. - Source: dev.to / about 2 months ago
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. - Source: dev.to / about 2 months ago
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/. - Source: Hacker News / 3 months ago
Django is a high-level Python web framework. It is an Model-View-Template(MVT)-based, open-source web application development framework. It was released in 2005. It comes with batteries included. Some popular websites using Django are Instagram, Mozilla, Disqus, Bitbucket, Nextdoor and Clubhouse. - Source: dev.to / over 1 year ago
This seems like a job for Django. MDN offers a really good tutorial here. To be honest, it would be a massive undertaking so I’d recommend going for a prebuilt solution like PowerSchool and the like. Source: almost 2 years ago
The first party docs are second to none. Start out with the official tutorial on https://djangoproject.com . Source: almost 2 years ago
Im teaching myself to build a backend SaaS. Can you build it just as fast as with RoR and gems? Is it all on the documentation on djangoproject.com? Just learning how to use it atm, any good tutorials as well? Source: almost 2 years ago
I have worked with Django before and have recently seen the Play framework. Source: about 2 years ago
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