#vue #accessibility #components #javascript #semantic #typescript #ui_components #ui_kit #vue_components #vue3 #vuejs #vuejs3 #vuestic #vuestic_admin #vuestic_ui

https://github.com/epicmaxco/vuestic-ui

#python #cross_modal #data_structures #dataclass #deep_learning #docarray #elasticsearch #graphql #multi_modal #multimodal #nearest_neighbor_search #nested_data #neural_search #protobuf #qdrant #semantic_search #sqlite #unstructured_data #vector_search #weaviate

https://github.com/docarray/docarray

#python #chatgpt #clip #deep_learning #gpt #hacktoberfest #hnsw #information_retrieval #knn #large_language_models #machine_learning #machinelearning #multi_modal #natural_language_processing #search_engine #semantic_search #tensor_search #transformers #vector_search #vision_language #visual_search

https://github.com/marqo-ai/marqo

#jupyter_notebook #computer_vision #deep_learning #image_classification #imagenet #neural_network #object_detection #pretrained_models #pretrained_weights #pytorch #semantic_segmentation #transfer_learning

https://github.com/Deci-AI/super-gradients

#python #deeplab_v3_plus #deeplabv3 #fpn #hacktoberfest #image_processing #image_segmentation #imagenet #linknet #models #pretrained_backbones #pretrained_models #pretrained_weights #pspnet #pytorch #segmentation #segmentation_models #semantic_segmentation #unet #unet_pytorch #unetplusplus

https://github.com/qubvel/segmentation_models.pytorch

#python #ai #chat #chatgpt #emacs #llm #markdown #obsidian_md #org_mode #personal_assistant #productivity #search_engine #semantic_search #sentence_transformer

https://github.com/khoj-ai/khoj

#python #embeddings #information_retrieval #language_model #large_language_models #llm #machine_learning #nearest_neighbor_search #neural_search #nlp #search #search_engine #semantic_search #sentence_embeddings #similarity_search #transformers #txtai #vector_database #vector_search #vector_search_engine

https://github.com/neuml/txtai

#python #3d_perception #camera #lidar #object_detection #pytorch #semantic_segmentation #sensor_fusion

https://github.com/mit-han-lab/bevfusion

#go #approximate_nearest_neighbor_search #generative_search #grpc #hnsw #hybrid_search #image_search #information_retrieval #mlops #nearest_neighbor_search #neural_search #recommender_system #search_engine #semantic_search #semantic_search_engine #similarity_search #vector_database #vector_search #vector_search_engine #vectors #weaviate

Weaviate is a powerful, open-source vector database that uses machine learning to make your data searchable. It's fast, scalable, and flexible, allowing you to vectorize your data at import or upload your own vectors. Weaviate supports various modules for integrating with popular AI services like OpenAI, Cohere, and Hugging Face. It's designed for production use with features like scaling, replication, and security. You can use Weaviate for tasks beyond search, such as recommendations, summarization, and integration with neural search frameworks. It offers APIs in GraphQL, REST, and gRPC and has client libraries for several programming languages. This makes it easy to build applications like chatbots, recommendation systems, and image search tools quickly and efficiently. Joining the Weaviate community provides access to tutorials, demos, blogs, and forums to help you get started and stay updated.

https://github.com/weaviate/weaviate

#jupyter_notebook #ai #azure #chatgpt #dall_e #generative_ai #generativeai #gpt #language_model #llms #openai #prompt_engineering #semantic_search #transformers

This course teaches you how to build Generative AI applications with 21 comprehensive lessons from Microsoft Cloud Advocates. You'll learn about Generative AI, Large Language Models (LLMs), prompt engineering, and how to build various applications like text generation, chat apps, and image generation using Python and TypeScript. The course includes videos, written lessons, code samples, and additional learning resources. You can start anywhere and even join a Discord server for support and networking with other learners. This helps you gain practical skills in building and deploying Generative AI applications responsibly and effectively.

https://github.com/microsoft/generative-ai-for-beginners

#javascript #annotation #annotation_tool #annotations #boundingbox #computer_vision #data_labeling #dataset #datasets #deep_learning #image_annotation #image_classification #image_labeling #image_labelling_tool #label_studio #labeling #labeling_tool #mlops #semantic_segmentation #text_annotation #yolo

Label Studio is a free, open-source tool that helps you label different types of data like images, audio, text, videos, and more. It has a simple and user-friendly interface that makes it easy to prepare or improve your data for machine learning models. You can customize it to fit your needs and export labeled data in various formats. It supports multi-user labeling, multiple projects, and integration with machine learning models for pre-labeling and active learning. You can install it locally using Docker, pip, or other methods, or deploy it in cloud services like Heroku or Google Cloud Platform. This tool streamlines your data labeling process and helps you create more accurate ML models.

https://github.com/HumanSignal/label-studio

#python #deeplabv3 #image_segmentation #medical_image_segmentation #pspnet #pytorch #realtime_segmentation #retinal_vessel_segmentation #semantic_segmentation #swin_transformer #transformer #vessel_segmentation

MMSegmentation is an open-source semantic segmentation toolbox based on PyTorch, part of the OpenMMLab project. It offers a unified benchmark for various semantic segmentation methods, a modular design allowing easy customization, and support for multiple popular segmentation frameworks like PSPNet and DeepLabV3+. The toolbox is highly efficient and provides detailed tutorials, advanced guides, and support for numerous datasets and backbones. This makes it a powerful tool for researchers and developers to implement and develop new semantic segmentation methods efficiently. By using MMSegmentation, you can leverage its flexibility, extensive features, and community support to enhance your projects in image segmentation tasks.

https://github.com/open-mmlab/mmsegmentation

#python #ade20k #image_classification #imagenet #mask_rcnn #mscoco #object_detection #semantic_segmentation #swin_transformer

The Swin Transformer is a powerful tool for computer vision tasks like image classification, object detection, semantic segmentation, and video recognition. It uses a hierarchical structure with shifted windows to efficiently process images, making it more efficient than other models. Here are the key benefits Swin Transformer achieves state-of-the-art results in various tasks such as COCO object detection, ADE20K semantic segmentation, and ImageNet classification.
- **Efficiency** The model supports multiple tasks including image classification, object detection, instance segmentation, semantic segmentation, and video action recognition.
- **Improved Speed** The model is integrated into popular frameworks like Hugging Face Spaces and PaddleClas, making it easy to use and deploy.

Overall, the Swin Transformer offers high accuracy, efficiency, and versatility, making it a valuable tool for various computer vision applications.

https://github.com/microsoft/Swin-Transformer

#rust #app_search #database #enterprise_search #faceting #full_text_search #fuzzy_search #geosearch #hybrid_search #instantsearch #rest #rust #search #search_as_you_type #search_engine #semantic_search #site_search #synonyms #typo_tolerance #vector_database #vectors

Meilisearch is a fast and powerful search engine that you can easily integrate into your apps, websites, and workflow. It offers features like hybrid search, search-as-you-type, typo tolerance, filtering, and sorting to enhance the user experience. You can customize it to fit your needs with support for multiple languages and advanced security management. It's easy to install, deploy, and maintain, and you can use their cloud service for added convenience. Meilisearch also provides extensive documentation, SDKs for various programming languages, and a supportive community through Discord and other channels. This makes it a great tool to supercharge your search capabilities quickly and efficiently.

https://github.com/meilisearch/meilisearch

#python #annotation #annotation_tool #annotations #boundingbox #computer_vision #computer_vision_annotation #dataset #deep_learning #image_annotation #image_classification #image_labeling #image_labelling_tool #imagenet #labeling #labeling_tool #object_detection #pytorch #semantic_segmentation #tensorflow #video_annotation

CVAT is a powerful tool for annotating videos and images, especially useful for computer vision projects. It helps developers and companies annotate data quickly and efficiently. You can use CVAT online for free or subscribe for more features like unlimited data and integrations with other tools. It also offers a self-hosted option with enterprise support. CVAT supports many annotation formats and has automatic labeling options to speed up your work. It's widely used by many teams worldwide, making it a reliable choice for your data annotation needs.

https://github.com/cvat-ai/cvat

#jupyter_notebook #agentic_ai #agentic_framework #agentic_rag #ai_agents #ai_agents_framework #autogen #generative_ai #semantic_kernel

This course helps you learn about AI Agents from the basics to advanced levels. AI Agents are systems that use large language models to perform tasks by accessing tools and knowledge. The course includes 10 lessons covering topics like agent fundamentals, frameworks, and use cases. It provides code examples and supports multiple languages. By completing this course, you can build your own AI Agents and apply them in various applications, such as customer support or event planning, making complex tasks easier and more efficient.

https://github.com/microsoft/ai-agents-for-beginners

#typescript #ai #chatgpt #docsgpt #hacktoberfest #information_retrieval #language_model #llm #machine_learning #natural_language_processing #python #pytorch #rag #react #semantic_search #transformers #web_app

DocsGPT is an open-source AI tool that helps you quickly find accurate answers from many types of documents and web sources without errors. It supports formats like PDF, DOCX, images, and integrates with websites, APIs, and chat platforms like Discord and Telegram. You can deploy it privately for security, customize it to fit your brand, and connect it to tools for advanced actions. This means you save time searching for information, get reliable answers with sources, and improve productivity whether you’re a developer, support team, or business user. It’s easy to set up and scales well for many users[2][3][4].

https://github.com/arc53/DocsGPT

#python #ai #context #embedded #faiss #knowledge_base #knowledge_graph #llm #machine_learning #memory #nlp #offline_first #opencv #python #rag #retrieval_augmented_generation #semantic_search #vector_database #video_processing

Memvid lets you store millions of text pieces inside a single MP4 video file using QR codes, making your data 50-100 times smaller than usual databases. You can search this video instantly in under 100 milliseconds without needing servers or internet after setup. It works offline, is easy to use with simple Python code, and supports PDFs and chat with your data. The upcoming version 2 will add features like continuous memory updates, shareable capsules, fast local caching, and better video compression, making your AI memory smarter, faster, and more flexible. This means you get a powerful, portable, and efficient way to manage and search huge knowledge bases quickly and easily.

https://github.com/Olow304/memvid

#go #agent #agentic #ai #chatbot #chatbots #embeddings #evaluation #generative_ai #golang #knowledge_base #llm #multi_tenant #multimodel #ollama #openai #question_answering #rag #reranking #semantic_search #vector_search

WeKnora is a powerful tool that helps you understand and find answers in complex documents like PDFs and Word files. It uses advanced AI to read documents, understand what they mean, and answer your questions in a simple way. This tool is useful for businesses and researchers because it can quickly find information from many documents, making it easier to manage knowledge and make decisions. It also supports multiple languages and can be used privately, ensuring your data stays safe.

https://github.com/Tencent/WeKnora

#rust #ai #change_data_capture #context_engineering #data #data_engineering #data_indexing #data_infrastructure #data_processing #etl #hacktoberfest #help_wanted #indexing #knowledge_graph #llm #pipeline #python #rag #real_time #rust #semantic_search

**CocoIndex** is a fast, open-source Python tool (Rust core) for transforming data into AI formats like vector indexes or knowledge graphs. Define simple data flows in ~100 lines of code using plug-and-play blocks for sources, embeddings, and targets—install via `pip install cocoindex`, add Postgres, and run. It auto-syncs fresh data with minimal recompute on changes, tracking lineage. **You save time building scalable RAG/semantic search pipelines effortlessly, avoiding complex ETL and stale data issues for production-ready AI apps.**

https://github.com/cocoindex-io/cocoindex