openplayground
Hugging Face
LangChain
Haystack NLP Framework
Vercel AI SDK
StableLM
MiniGPT-4
GPT4All
Qdrant
Milvus
Weaviate
pgvecto.rs
Vespa.ai
txtai
Zilliz
ElasticSearch
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.
openplayground QdrantNo features have been listed yet.
Qdrant's answer:
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 seems to be a lot more popular than openplayground. While we know about 40 links to Qdrant, we've tracked only 1 mention of openplayground. 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.
From there you have lots of other models: One of the best places to easily start using multiple models is using a multiple model UI program lik GPT4All, there are also some programs that provide access to more models or use different ways of interfacing with them, here are some of what I've found are the best / most popular programs to play around with lots of different models and compare them: LocalAI, ... Source: about 1 year ago
Vector Databases: Qdrant for efficient data storage and retrieval. - Source: dev.to / 4 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 1 month 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 / 2 months ago
Hugging Face - The Tamagotchi powered by Artificial Intelligence 🤗
Milvus - Vector database built for scalable similarity search Open-source, highly scalable, and blazing fast.
LangChain - Framework for building applications with LLMs through composability
Weaviate - Welcome to Weaviate
Haystack NLP Framework - Haystack is an open source NLP framework to build applications with Transformer models and LLMs.
pgvecto.rs - Scalable, Low-latency and Hybrid-enabled Vector Search in Postgres. Revolutionize Vector Search, not Database. - tensorchord/pgvecto.rs
