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, CircleCI should be more popular than Qdrant. It has been mentiond 62 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.
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 / 3 days 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 / 14 days 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 / 21 days 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 / about 1 month ago
There are much better known examples, such as https://qdrant.tech/ and https://github.com/lancedb/lancedb. - Source: Hacker News / 2 months ago
It will give you a possibility to find and solve problems faster, release more stable and higher quality products. Here we will use CircleCI, but you can use whatever you need (Jenkins, Travis CI, GitLab CI). - Source: dev.to / 2 days ago
CircleCI is a leading cloud-native CI/CD platform that empowers developers to rapidly build, test, and deploy their applications at scale. It is highly configurable and has rich integrations and performance optimization tools. These features have made it a favorite among modern development teams seeking agility and speed. - Source: dev.to / about 1 month ago
For instance, IDPs can automatically trigger a deployment process in Jenkins or CircleCI when a developer pushes code to a Git repository. - Source: dev.to / 2 months ago
CircleCI — Comprehensive free plan with all features included in a hosted CI/CD service for GitHub, GitLab, and BitBucket repositories. Multiple resource classes, Docker, Windows, Mac OS, ARM executors, local runners, test splitting, Docker Layer Caching, and other advanced CI/CD features. Free for up to 6000 minutes/month execution time, unlimited collaborators, 30 parallel jobs in private projects, and up to... - Source: dev.to / 3 months ago
CircleCI: Another popular CI/CD platform that offers powerful features and ease of use. - Source: dev.to / 4 months ago
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