Qdrant
What is Qdrant?
Qdrant is an Open-Source Vector Database and Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API.
How to use Qdrant?
To use Qdrant, you can pull the Qdrant image from Docker and run it. Alternatively, you can follow the Quick Start Guide or the step-by-step Tutorial to build your own neural search.
Qdrant's Core Features
Fast and scalable vector similarity search
Support for vector embeddings and neural network encoders
Easy-to-use API with OpenAPI v3 specification
Fast and accurate search with a custom modification of the HNSW algorithm
Filterable results based on payload values
Support for rich data types and query conditions
Distributed and cloud-native architecture
Efficient utilization of computational resources
Qdrant's Use Cases
Similar Image Search
Semantic Text Search
Recommendations
FAQ from Qdrant
What is Qdrant?
How can I use Qdrant?
What are the core features of Qdrant?
Qdrant Support Email & Customer service contact & Refund contact etc.
More Contact, visit the contact us page(https://qdrant.to/contact-us)
Qdrant Pricing
Qdrant Pricing Link: https://qdrant.tech/pricing/
Qdrant Youtube
Qdrant Youtube Link: https://www.youtube.com/channel/UC6ftm8PwH1RU_LM1jwG0LQA
Qdrant Linkedin
Qdrant Linkedin Link: https://qdrant.to/linkedin
Qdrant Twitter
Qdrant Twitter Link: https://qdrant.to/twitter
Qdrant Github
Qdrant Github Link: https://github.com/qdrant/qdrant
Qdrant Reviews (0)
Analytic of Qdrant
Qdrant Website Traffic Analysis
Geography
Top 5 Regions
Qdrant Launch embeds
Use website badges to drive support from your community for your Toolify Launch. They're easy to embed on your homepage or footer.
Alternative of Qdrant
Qdrant Comparisons
More Content About Qdrant
9 Reasons Vector Databases are Revolutionizing Data Storage
By Eira on May 16 2024
Unlock Data's Future: 9 Groundbreaking Reasons Vector Databases Transform Storage!
Social Listening
Question Answering with LangChain and Qdrant - Kacper Łukawski | Munich NLP Hands-on 012
Kacper Łukawski is a developer advocate at Qdrant as well as the founder of embassy.ai. He previously worked as a freelance software developer and in data engineering and machine learning. The Qdrant engine is an open-source vector search database. It is deployed as an API service providing a search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into fully-fledged applications for matching, searching, recommending, and much more. LangChain is an open-source library that assists in the development of applications built around complex large language models (LLMs). But using these LLMs in isolation is often not enough to create a truly powerful app - the real power comes when you are able to combine them with other sources of computation or knowledge. The LangChain library enables, for instance, question aswering systems like Qdrant, chatbots, and end-to-end agents like WolframAlpha. Slides from the talk can be found here: https://github.com/qdrant/qdrant-langchain-qa/tree/master About Kacper: LinkedIn: https://www.linkedin.com/in/kacperlukawski/ Twitter: https://twitter.com/lukawskikacper Embassy.ai: https://www.embassy.ai/en About Qdrant: Homepage: https://qdrant.tech/ Cloud: https://cloud.qdrant.io/ Github: https://github.com/qdrant/qdrant Twitter: https://qdrant.to/twitter LinkedIn: https://www.linkedin.com/company/qdrant/ About LangChain: Github: https://github.com/hwchase17/langchain Twitter: https://twitter.com/LangChainAI Documentation: https://langchain.readthedocs.io/en/latest/index.html About Munich NLP: Munich🥨NLP is a community newly founded in May 2022 by LMU and TUM students focusing on NLP topics. Within just six months, the community has already grown to over 1000 members consisting not only of current students, but also including PhD students, professors, and industry practitioners. We host weekly workshops and/or paper-reading events, both to learn from guests and to gather inspiration for our own (research) projects, as well as to establish and keep going an active student NLP community in the Munich area. The goal is to promote NLP-related exchange between students, researchers, and practitioners inside and outside the university and to showcase paths and possibilities during and after university. https://munich-nlp.github.io/ https://www.linkedin.com/company/muni... https://twitter.com/MunichNlp/ #qdrant #questionanswering #questionanswer #ai #deeplearning #machinelearning #nlp #learning #teaching #opensource #bert #artificialintelligence #transformers #chatgpt #gpt3 #gpt4 #languagemodel #langchain #naturallanguageprocessing #nlp
Build a Semantic Search App with Qdrant Cloud
In this tutorial, you will learn how to use Qdrant Cloud and its Python SDK to power a Semantic Search App, built from scratch, to search over thousands of songs from different genres. The repo to follow along with the video can be found at: https://github.com/ramonpzg/music-semantic-search To learn more about how to create embeddings from audio data, check out our previous tutorial here: https://t.ly/OmcTJ To get started with Qdrant Cloud visit: cloud.qdrant.io All other tutorials can be found on Qdrant's main Documentation page at: https://qdrant.tech/documentation/tutorials/
Visualizing Vector Embeddings: Qdrant’s WebUI Graph Tool
Access the dashboard by creating a free account: https://cloud.qdrant.io Quickstart: https://qdrant.tech/documentation/quickstart-cloud/
Unlock to view 10 social media results.