Creating advanced vector search technology
131
Publiczne repozytoria
43 665
Łączna liczba gwiazdek
1718
Obserwujący
Qdrant to organizacja zajmująca się tworzeniem zaawansowanej technologii wyszukiwania wektorowego. Na swoim koncie GitHub posiada szeroki wachlarz publicznych repozytoriów, w tym projekty takie jak qdrant, fastembed oraz qdrant-client, wykorzystujące głównie języki Python, Rust i TypeScript.
Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
Fast, Accurate, Lightweight Python library to make State of the Art Embedding
An official Qdrant Model Context Protocol (MCP) server implementation
Python client for Qdrant vector search engine
Blazing fast framework for fine-tuning similarity learning models
😎 A curated list of awesome practical Metric Learning and its applications
JavaScript/Typescript SDK for Qdrant Vector Database
Self-hosted web UI for Qdrant
Rust client for Qdrant vector search engine
Framework for benchmarking vector search engines
Go client for Qdrant vector search engine
Qdrant .Net SDK
A collection of examples and tutorials for Qdrant vector search engine
Demo of the neural semantic search built with Qdrant
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Agent skills for Qdrant vector search: scaling, performance optimization, search quality, monitoring, deployment, model migration, version upgrades, and SDK usage across Python, TypeScript, Rust, Go, .NET, Java
Neural search for web-sites, docs, articles - online!
Official Java client for Qdrant
This repo is the central repo for all the RAG Evaluation reference material and partner workshop
Tool to migrate data into Qdrant
Official n8n node for interfacing with Qdrant
Evaluation of bm42 sparse indexing algorithm
Collection of datasets for benchmarking filtered vector similarity retrieval
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Landing page for qdrant.tech
POC visual search with smart glasses and Qdrant Edge.
Source code of the food discovery demo built on top of Qdrant
Qdrant's Apache Spark connector
Materials for the Ultimate Hybrid Search Workshop
An integration of Qdrant ANN vector database backend with Haystack
Write Ahead Logging for Rust
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Contextualized per-token embeddings
The collection of bulding blocks building fine-tunable metric learning models
Demo example of consumer goods categorization
An implementation of a CrewAI agent acting as a personal assistant. The agent can draft emails based on the knowledge base built in Obsidian.
Trainable embedding transformation for confidence estimation, feature extraction, explainability and conversion from dense to sparse.
An integration of Qdrant ANN vector database backend with txtai
Notebooks for RAG optimization workshop, using HackerNews data
*high-load* benchmarking tool
The Command Line Interface for Qdrant Cloud
An attempt to live code a working Retrieval Augmented Generation app with AI coding tools
Real-time video anomaly detection from edge to cloud using Qdrant, Twelve Labs, and NVIDIA VSS
Upload and benchmark of LAION 400M dataset - large scale image similarity search
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Fine-tune SPLADE sparse embedding models for your product catalog. CLI, web dashboard, and Python API.
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Notebooks for RAG improving workshop, using HackerNews data
Kafka Sink connector for Qdrant. Stream vector data into a Qdrant collection. Supports named/unnamed dense/sparse vectors.
Simple pipeline to index markdown files into Qdrant using OpenAI embeddings
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MCP server for accessing documentation and code snippets of dev tools. Based on `mcp-server-qdrant`
Antipsychotic therapy for qdrant cluster
Exploration of Midjourney Library styles using Qdrant and NextJS
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A simple script to solve contexto.me using word embeddings
The primary goal of Qdrant Stars is to recognize, reward, and support our most active and helpful users making significant contributions to the vector search community. The Qdrant Stars Handbook is your go-to resource for navigating the Stars program and how to take the most advantage of it.
Repository for Qdrant's API Reference Documentation
A simple tool to monitor process resource usage as frequent as possible
Stream records from Fluvio topics to a Qdrant collection.
Extracts semantics from rust code
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Autocomplete queries using OpenAPI spec
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API definitions for the Qdrant Kubernetes operator
A Terraform plugin that allows to manage and configure Qdrant Cloud resources
Qdrant powered retriever module for DSPy
An example of setting up the distributed deployment of Qdrant with docker-compose
Scripts for benchmarking Qdrant with Cohere Wiki dataset
Crashing the party
A template for a FastHTML app implementing hybrid search with Qdrant
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This is a benchmarking tool for Qdrant's sparse vector implementation
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Web demo for miniCOIL model
Coach running drills to train qdrant deployments
Common helper packages to use across Go based Qdrant projects.
Tools to help Qdrant Cloud users troubleshoot issues and help Qdrant Support Engineers diagnose problems
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A rust implementation of some popular snowball stemming algorithms
LocalStack Extension for developing Qdrant-based applications locally.
The GitOps Toolkit Helm reconciler, for declarative Helming
An example of integrating Qdrant with FiftyOne
Collection of Buf plugins used by Qdrant Cloud APIs
A native gRPC client & server implementation with async/await support.
Qdrant's Homebrew Tap
NVIDIA Metropolis VSS + Twelve Labs integration for video understanding
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Simple example of monitoring Qdrant cluster with Prometheus and Grafana
Benchmarks of approximate nearest neighbor libraries in Python
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This repository contains a challenge task for Core Rust Engineer position
Exporter parses kernel log for oomkilled pods
Protocol Buffers for ECMAScript. The only JavaScript Protobuf library that is fully-compliant with Protobuf conformance tests.
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Tar file reading/writing for Rust
Sophisticated form to create qdrant collections simple
An HTTP library for Rust
A rust implementation of murmur3
Qdrant rozwija różnorodne projekty związane z wyszukiwaniem wektorowym, w tym bazę danych qdrant oraz bibliotekę fastembed. Te projekty koncentrują się na użyciu technologii AI i efektywnym przetwarzaniu danych.
Qdrant używa kilku języków programowania, w tym Python, Rust i TypeScript. Te języki są kluczowe dla ich projektów, które obejmują zarówno backend, jak i interfejsy użytkownika.
Tak, wszystkie repozytoria qdrant są publiczne. Użytkownicy mogą przeglądać i korzystać z ich kodu, co wspiera otwartą współpracę oraz innowacje w dziedzinie wyszukiwania wektorowego.
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