Qdrant AI technology page Top Builders

Explore the top contributors showcasing the highest number of Qdrant AI technology page app submissions within our community.

Qdrant

Qdrant is a high-performance search engine and database designed specifically for vector similarity. Written in Rust, Qdrant offers fast and reliable performance even under high load, making it an ideal choice for applications that demand speed and scalability. With Qdrant, you can turn your embeddings or neural network encoders into powerful, full-fledged applications for a wide range of use cases. Whether you need to match, search, recommend, or perform other complex operations on large datasets, Qdrant provides a convenient and user-friendly API that simplifies the process. Qdrant's extended filtering support also makes it well-suited for a variety of applications, including faceted search and semantic-based matching. Plus, with its managed solution available on the Qdrant Cloud, you can easily deploy and manage your applications with minimal setup and maintenance.

General
Relese date	October 29, 2021
Author	Qdrant
Type	Search engine, database

Tutorials

Great tutorials on how to build with Qdrant

👉 Discover more Qdrant Tutorials on lablab.ai

Qdrant - General sources

Learn even more about Qdrant!

Qdrant Cloud Qdrant Cloud is a managed solution for Qdrant. It provides a production-ready service for storing, managing, and searching vectors
Qdrant Repository Qdrant is an open-source search engine and database designed for vector similarity
Qdrant Documentation Qdrant Documentation
Qdrant Demo Qdrant Demo Project. This project contains a set of examples of using Qdrant

Qdrant x Cohere resources

Use Qdrant and Cohere to make powerful applications!

Basic example of the Qdrant and Cohere integration This example shows how to use Qdrant and Cohere to start with this stack
Q&A application How to build a Q&A application with Qdrant and Cohere

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Qdrant AI technology page Hackathon projects

Discover innovative solutions crafted with Qdrant AI technology page, developed by our community members during our engaging hackathons.

CoPilot for Education

- Top quality education is very inaccessible, private tutors are unaffordable and it become very difficult for students to choose correct path, understand complex concepts, visualization of concept, evaluate themselves etc. - With LLMs and GenAI we can bridge that gap between top quality education and students coming from different background. - JAMS tries to solve top-quality education for all, by providing students and teacher with tool or assitant which will be available with them 24/hrs to solve any query, explain them concepts, curating personalised content because one teacher can not do this for all of their students. - JAMS CoPilot can act as a companion for students for Explaining concepts in details. Visualizing any concepts they feel is difficult to understand. Help them better prepare, by creating mock test and testing their understanding. Rewriting the concept in configured analogies. eg: Explain Photosynthesis using football terms. - JAMS can also act as a companion for teachers for creating mock tests, evaluating students, generating the concepts in visualized format and also to create customized teaching materials for each student. It is always better to add a few visual representations (drawings/sketches/illustrations etc.) to your presentation, it adds to the power through which it reaches the audience.

LoopX

Problem: Businesses in the Arabian market face a unique set of challenges: they're swamped with tedious, repetitive tasks like customer service, data entry, and record management. These jobs consume a lot of time and resources, dragging down efficiency and keeping staff from focusing on more strategic work. Plus, most tech solutions out there don’t fully grasp the Arabic language or culture, making them less effective and sometimes even out of touch with local needs. Solutions: LoopX brings a game-changing solution to Arabian businesses bogged down by routine, manual tasks. We offer all of Stratups, SMEs and even Governments an easy, fast, and affordable way to run their specialized AI Agents through our platform and our support team of experts. Giving every Arabian business the easy way to build and run their own AI Agents within hours, making it possible for +23 Million Startups & SMEs to get the power of AI Agents in Fast, Smooth, and Cheap way without the need to build their AI Agents from scratch taking months of development and paying thousands of dollars. Our AI agents, crafted with a deep understanding of local culture and language, automate operations like customer support and data processing with unmatched precision. This tech leap not only streamlines workflows but also aligns perfectly with regional nuances, offering a tech ally that boosts productivity while respecting cultural identity. With LoopX, companies leap into efficient, culturally coherent automation, transforming how they operate in the digital era. Products and Services we offer: 1. AI Agents Marketplace: A dynamic platform offering specialized AI agents for business process automation. 2. Custom AI Agents Building: Tailored AI automation services for unique business needs. 3. AI Consultation Service: Expert guidance on AI adoption and strategic implementation. LoopX's journey is marked by gaining traction with 8 customers and 2 major projects, driving us close to 3K USD in revenue.

Cohere-Powered Exam RAG Bot

The "Cohere-Powered Exam RAG Bot" is a cutting-edge educational platform for O Level Physics, leveraging Cohere's language models to aid learning. Cohere Embed transforms course notes into vector embeddings, creating a mathematical representation for quick information retrieval, with Qdrant ensuring a scalable vector database. Cohere Generate, using the command-nightly model, responds to queries with contextually aware answers, simulating a tutor's assistance. Cohere ReRank refines this process, selecting the most pertinent documents to bolster the bot's response quality and speed. At the core, Langchain weaves together document retrieval and natural language processing to form an intelligent RAG system, while Streamlit's interactive interface makes the AI accessible and user-friendly. The app's market value stems from its innovative approach to personalized, tech-based learning. It stands out for its on-demand educational support, meeting the increasing need for accessible learning tools. Future expansions include multimodal capabilities, enabling the bot to interpret diagrams and images, and multilingual support, broadening its user base. Plans to enrich the knowledge base and branch into subjects like Chemistry and Math will transform the app into a comprehensive academic resource. This growth trajectory positions the "Cohere-Powered Exam RAG Bot" to reshape e-learning globally.

GENIS Voice and Visual Chat Messaging Application

Get visual explanation and text/number extraction by sending an image to our LINE Official Account And use the image as well as the explanation from the image to turn it into an office documentation including word powerpoint and excel file format. You can do this either using the LINE messaging application @genis or through our web app chat ui chat.genis.ai Feel free to connect your data and email to our RAG solution and get the visual and export it with our solution. Read any youtube video with our whisper distil transcription and use your voice to generate image or talk and command our chat bot through our LINE Official account.

DoctorsMate

DoctorsMate incorporates advanced technological components like RAG (Retriever-Answer Generator) architecture, Cohere embeddings, Cohere Generate, and Weaviate as a vector database, each contributing to its functionality in unique ways. You as an acting doctor have to put together the symptoms, patient details, and the previous history(if any). Here the RAG architecture is central to the tool's ability to process and interpret medical information. It combines a retriever component by using the Cohere embeddings and the Cohere Generate endpoint to finalize the output at the end. Weaviate, as a vector database, plays a critical role in organizing and retrieving medical data efficiently. I have also used the Qdrant just for the log data collections (not related to the application use cases). Currently, It supports around 600 diseases.

AddArguflow to vectara-ingest

This is the Arguflow team’s submission to the “best contribution to vectara-ingest” part of the hackathon. We really admire the work that has gone into this repository, and want to start a trend of making it compatible with more varied services. This PR adds support for Arguflow to the crawler such that users are able to add documents/chunks to Arguflow, Vectara, or both. Internally, we were motivated to add this support so that we can stand up more Arguflow demos by using the crawlers, however, we are PR’ing it because we think it can also offer Vectara users value. Arguflow has support for a few things that Vectara does not which users may desire: self-host option for E2E data ownership support for arbitrary LLM’s and text-embedding models RAG options beyond summarization (both fully managed and search-before-generate) enhanced duplicate detection beyond UPSERT Arguflow also ships with a default search and chat UI in addition to its OpenAPI spec which makes it a bit easier to get going with a deployment you can share and reverse-engineer to build your own applications. This repository is great, and we really admire @ofermend’s work on it especially. Excited to enhance it and bring it more into the lens of the open source AI world! We also edited the documentation in all the right places, let us know if may have missed a few spots. Happy to address any review comments or change requests in a timely manner.